'''simple docstring'''




import re
from pathlib import Path
from unittest import TestCase

import pytest





@pytest.mark.integration
class 					__magic_name__		(					UpperCAmelCase__):







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]       ,    lowercase_       :						str					):






										with open(__lowerCAmelCase       ,    encoding="""utf-8"""					) as input_file:
																	lowercase_      :    Any       		= re.compile(r"""(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)"""					)
																	lowercase_      :    str       		= input_file.read()
																	lowercase_      :    List[str]       		= regexp.search(__lowerCAmelCase					)

										return match







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]       ,    lowercase_       :						str					):






										with open(__lowerCAmelCase       ,    encoding="""utf-8"""					) as input_file:
																	lowercase_      :    str       		= re.compile(r"""#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()"""       ,    re.DOTALL					)
																	lowercase_      :    Union[str, Any]       		= input_file.read()
																	# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
																	lowercase_      :    Dict       		= regexp.finditer(__lowerCAmelCase					)

										lowercase_      :    List[Any]       		= [match for match in matches if match is not None and match.group(1					) is not None]
										return matches[0] if matches else None







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]					):
										lowercase_      :    Optional[int]       		= Path("""./datasets"""					)
										lowercase_      :    Tuple       		= list(dataset_paths.absolute().glob("""**/*.py"""					)					)

										for dataset in dataset_files:
																	if self._no_encoding_on_file_open(str(__lowerCAmelCase					)					):
																								raise AssertionError(f'''open(...) must use utf-8 encoding in {dataset}'''					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple					):
										lowercase_      :    List[Any]       		= Path("""./datasets"""					)
										lowercase_      :    List[Any]       		= list(dataset_paths.absolute().glob("""**/*.py"""					)					)

										for dataset in dataset_files:
																	if self._no_print_statements(str(__lowerCAmelCase					)					):
																								raise AssertionError(f'''print statement found in {dataset}. Use datasets.logger/logging instead.'''					)






'''simple docstring'''




from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
						import tensorflow as tf

						from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM





@require_tf
@require_sentencepiece
@require_tokenizers
class 					__magic_name__		(					unittest.TestCase):







			@slow
			def 			SCREAMING_SNAKE_CASE_  (			self       :						str					):
										lowercase_      :    Any       		= TFAutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""					)
										lowercase_      :    Dict       		= AutoTokenizer.from_pretrained("""google/mt5-small"""					)

										lowercase_      :    Union[str, Any]       		= tokenizer("""Hello there"""       ,    return_tensors="""tf"""					).input_ids
										lowercase_      :    List[str]       		= tokenizer("""Hi I am"""       ,    return_tensors="""tf"""					).input_ids

										lowercase_      :    Optional[Any]       		= model(lowercase_       ,    labels=lowercase_					).loss
										lowercase_      :    Optional[int]       		= -tf.math.reduce_mean(lowercase_					).numpy()

										lowercase_      :    Optional[int]       		= -21.22_81_68
										self.assertTrue(abs(mtf_score - EXPECTED_SCORE					) < 2E-4					)





