Spaces:
Running
Running
Handled the edge cases and added better error message
Browse files
semncg.py
CHANGED
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@@ -308,29 +308,31 @@ def _validate_input_format(
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>>> _validate_input_format(tokenize_sentences, predictions, references, documents)
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"""
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if not (len(predictions) == len(references) == len(documents)):
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raise ValueError(
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if len(predictions) == 0:
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raise ValueError("Can't have empty inputs")
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def
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raise ValueError("Predictions, References and Documents are not valid input format. Refer to documentation.")
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@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
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>>> _validate_input_format(tokenize_sentences, predictions, references, documents)
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"""
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if not (len(predictions) == len(references) == len(documents)):
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raise ValueError(
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f"Predictions, References and Documents must have the same length. "
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f"Got {len(predictions)} predictions, {len(references)} references and {len(documents)} documents."
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)
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if len(predictions) == 0:
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raise ValueError("Can't have empty inputs")
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def check_format(lst_obj, expected_depth: int, name: str):
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is_valid, error_message = is_nested_list_of_type(lst_obj, element_type=str, depth=expected_depth)
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if not is_valid:
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raise ValueError(f"{name} are not in the expected format.\n"
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f"Error: {error_message}.")
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try:
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if tokenize_sentences:
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check_format(predictions, expected_depth=1, name="predictions")
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check_format(references, expected_depth=1, name="references")
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check_format(documents, expected_depth=1, name="documents")
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else:
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check_format(predictions, expected_depth=2, name="predictions")
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check_format(references, expected_depth=2, name="references")
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check_format(documents, expected_depth=2, name="documents")
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except ValueError as ve:
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raise ValueError(f"Input validation error: {ve}")
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@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
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tests.py
CHANGED
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@@ -139,29 +139,35 @@ class TestUtils(unittest.TestCase):
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def test_is_nested_list_of_type(self):
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# Test case: Depth 0, single element matching element_type
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self.
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# Test case: Depth 0, single element not matching element_type
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# Test case: Depth 1, list of elements matching element_type
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self.
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# Test case: Depth 1, list of elements not matching element_type
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-
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# Test case: Depth 0 (Wrong), list of elements matching element_type
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-
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# Depth 2
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self.
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self.
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# Depth 3
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self.
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with self.assertRaises(ValueError):
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is_nested_list_of_type([1, 2], int, -1)
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@@ -358,7 +364,7 @@ class TestValidateInputFormat(unittest.TestCase):
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_validate_input_format(tokenize_sentences, predictions, references, documents_invalid)
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class
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def setUp(self):
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self.model_name = "stsb-distilbert-base"
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self.metric = SemNCG(self.model_name)
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@@ -424,6 +430,48 @@ class TestSemnCG(unittest.TestCase):
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with self.assertRaises(ValueError):
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self.metric.compute(predictions=predictions, references=references, documents=documents)
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if __name__ == '__main__':
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unittest.main(verbosity=2)
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def test_is_nested_list_of_type(self):
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# Test case: Depth 0, single element matching element_type
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self.assertEqual(is_nested_list_of_type("test", str, 0), (True, ""))
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# Test case: Depth 0, single element not matching element_type
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is_valid, err_msg = is_nested_list_of_type("test", int, 0)
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self.assertEqual(is_valid, False)
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# Test case: Depth 1, list of elements matching element_type
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self.assertEqual(is_nested_list_of_type(["apple", "banana"], str, 1), (True, ""))
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# Test case: Depth 1, list of elements not matching element_type
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is_valid, err_msg = is_nested_list_of_type([1, 2, 3], str, 1)
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self.assertEqual(is_valid, False)
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# Test case: Depth 0 (Wrong), list of elements matching element_type
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is_valid, err_msg = is_nested_list_of_type([1, 2, 3], str, 0)
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self.assertEqual(is_valid, False)
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# Depth 2
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self.assertEqual(is_nested_list_of_type([[1, 2], [3, 4]], int, 2), (True, ""))
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self.assertEqual(is_nested_list_of_type([['1', '2'], ['3', '4']], str, 2), (True, ""))
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is_valid, err_msg = is_nested_list_of_type([[1, 2], ["a", "b"]], int, 2)
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self.assertEqual(is_valid, False)
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# Depth 3
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is_valid, err_msg = is_nested_list_of_type([[[1], [2]], [[3], [4]]], list, 3)
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self.assertEqual(is_valid, False)
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self.assertEqual(is_nested_list_of_type([[[1], [2]], [[3], [4]]], int, 3), (True, ""))
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# Test case: Depth is negative, expecting ValueError
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with self.assertRaises(ValueError):
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is_nested_list_of_type([1, 2], int, -1)
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_validate_input_format(tokenize_sentences, predictions, references, documents_invalid)
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class TestSemNCG(unittest.TestCase):
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def setUp(self):
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self.model_name = "stsb-distilbert-base"
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self.metric = SemNCG(self.model_name)
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with self.assertRaises(ValueError):
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self.metric.compute(predictions=predictions, references=references, documents=documents)
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def test_bad_inputs(self):
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def _call_metric(preds, refs, docs, tok):
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with self.assertRaises(Exception) as ctx:
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_ = self.metric.compute(
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predictions=preds,
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references=refs,
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documents=docs,
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tokenize_sentences=tok,
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pre_compute_embeddings=True,
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)
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print(f"Raised Exception with message: {ctx.exception}")
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return ""
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# None Inputs
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# Case I
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tokenize_sentences = True
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predictions = [None]
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references = ["A cat was sitting on a mat."]
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documents = ["There was a cat on a mat."]
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print(f"Case I\n{_call_metric(predictions, references, documents, tokenize_sentences)}\n")
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# Case II
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tokenize_sentences = False
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predictions = [["A cat was sitting on a mat.", None]]
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references = [["A cat was sitting on a mat.", "A cat was sitting on a mat."]]
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documents = [["There was a cat on a mat.", "There was a cat on a mat."]]
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print(f"Case II\n{_call_metric(predictions, references, documents, tokenize_sentences)}\n")
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# Empty Input
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tokenize_sentences = True
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predictions = []
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references = ["A cat was sitting on a mat."]
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documents = ["There was a cat on a mat."]
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print(f"Case: Empty Input\n{_call_metric(predictions, references, documents, tokenize_sentences)}\n")
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# Empty String Input
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tokenize_sentences = True
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predictions = [""]
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references = ["A cat was sitting on a mat."]
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documents = ["There was a cat on a mat."]
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print(f"Case: Empty String Input\n{_call_metric(predictions, references, documents, tokenize_sentences)}\n")
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if __name__ == '__main__':
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unittest.main(verbosity=2)
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utils.py
CHANGED
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@@ -1,5 +1,5 @@
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import string
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from typing import List, Union
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import nltk
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import torch
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@@ -167,45 +167,66 @@ def flatten_list(nested_list: list) -> list:
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return flat_list
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def is_nested_list_of_type(lst_obj, element_type, depth: int) -> bool:
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"""
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Returns:
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- bool: True if lst_obj is a nested list of the specified type up to the given depth, False otherwise.
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# Test cases
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is_nested_list_of_type("test", str, 0) # Returns True
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is_nested_list_of_type([1, 2, 3], str, 0) # Returns False
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is_nested_list_of_type(["apple", "banana"], str, 1) # Returns True
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is_nested_list_of_type([[1, 2], [3, 4]], int, 2) # Returns True
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is_nested_list_of_type([[1, 2], ["a", "b"]], int, 2) # Returns False
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is_nested_list_of_type([[[1], [2]], [[3], [4]]], int, 3) # Returns True
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```
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"""
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def slice_embeddings(embeddings: NDArray, num_sentences: NumSentencesType) -> EmbeddingSlicesType:
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import string
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from typing import List, Union, Tuple
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import nltk
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import torch
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return flat_list
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def is_nested_list_of_type(lst_obj, element_type, depth: int) -> Tuple[bool, str]:
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"""
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Check if the given object is a nested list of a specific type up to a specified depth.
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Args:
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- lst_obj: The object to check, expected to be a list or a single element.
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- element_type: The type that each element in the nested list should match.
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- depth (int): The depth of nesting to check. Must be non-negative.
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Returns:
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- Tuple[bool, str]: A tuple containing:
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- A boolean indicating if lst_obj is a nested list of the specified type up to the given depth.
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- A string containing an error message if the check fails, or an empty string if the check passes.
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Raises:
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- ValueError: If depth is negative.
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Example:
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```python
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# Test cases
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is_nested_list_of_type("test", str, 0) # Returns (True, "")
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is_nested_list_of_type([1, 2, 3], str, 0) # Returns (False, "Element is of type int, expected type str.")
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is_nested_list_of_type(["apple", "banana"], str, 1) # Returns (True, "")
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is_nested_list_of_type([[1, 2], [3, 4]], int, 2) # Returns (True, "")
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is_nested_list_of_type([[1, 2], ["a", "b"]], int, 2) # Returns (False, "Element at index 1 is of incorrect type.")
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is_nested_list_of_type([[[1], [2]], [[3], [4]]], int, 3) # Returns (True, "")
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```
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Explanation:
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- The function checks if `lst_obj` is a nested list of elements of type `element_type` up to `depth` levels deep.
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- If `depth` is 0, it checks if `lst_obj` itself is of type `element_type`.
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- If `depth` is greater than 0, it recursively checks each level of nesting to ensure all elements match
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`element_type`.
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- Returns a tuple containing a boolean and an error message. The boolean is `True` if `lst_obj` matches the
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criteria, `False` otherwise. The error message provides details if the check fails.
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- Raises a `ValueError` if `depth` is negative, as depth must be a non-negative integer.
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"""
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orig_depth = depth
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def _is_nested_list_of_type(lst_o, e_type, d) -> Tuple[bool, str]:
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if d == 0:
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if isinstance(lst_o, e_type):
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return True, ""
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else:
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return False, f"Element is of type {type(lst_o).__name__}, expected type {e_type.__name__}."
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elif d > 0:
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if isinstance(lst_o, list):
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for i, item in enumerate(lst_o):
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is_valid, err = _is_nested_list_of_type(item, e_type, d - 1)
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if not is_valid:
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msg = (f"Element at index {i} has incorrect type.\nGiven Element at index {i}: {lst_o[i]}"
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f"\n{err}") if d == orig_depth else err
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return False, msg
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return True, ""
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else:
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return False, f"Object is not a list but {type(lst_o)}."
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else:
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raise ValueError("Depth can't be negative")
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return _is_nested_list_of_type(lst_obj, element_type, depth)
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def slice_embeddings(embeddings: NDArray, num_sentences: NumSentencesType) -> EmbeddingSlicesType:
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