



"""simple docstring"""






import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
	import optax
	from flax.training.common_utils import onehot

	from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
	from transformers.models.ta.modeling_flax_ta import shift_tokens_right



@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 			A__   (  unittest.TestCase							):




	'''simple docstring'''





	@slow
	def 							_SCREAMING_SNAKE_CASE   (     self:		Union[str, Any])				->       str:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small")
				__lowerCAmelCase      :  Optional[Any]												=       AutoTokenizer.from_pretrained("google/mt5-small")

				__lowerCAmelCase      :  Tuple												=       tokenizer("Hello there"       ,			return_tensors="np").input_ids
				__lowerCAmelCase      :  Dict												=       tokenizer("Hi I am"       ,			return_tensors="np").input_ids

				__lowerCAmelCase      :  str												=       shift_tokens_right(_SCREAMING_SNAKE_CASE       ,			model.config.pad_token_id       ,			model.config.decoder_start_token_id)

				__lowerCAmelCase      :  Optional[int]												=       model(_SCREAMING_SNAKE_CASE       ,			decoder_input_ids=_SCREAMING_SNAKE_CASE).logits
				__lowerCAmelCase      :  int												=       optax.softmax_cross_entropy(_SCREAMING_SNAKE_CASE       ,			onehot(_SCREAMING_SNAKE_CASE       ,			logits.shape[-1])).mean()

				__lowerCAmelCase      :  List[str]												=       -(labels.shape[-1] * loss.item())

				__lowerCAmelCase      :  str												=       -84.9127
				self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)




"""simple docstring"""






import re
from pathlib import Path
from unittest import TestCase

import pytest



@pytest.mark.integration
class 			A__   (  __SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[int]       ,			_SCREAMING_SNAKE_CASE:		str)				->       Dict:

				"""simple docstring"""

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

				return match

	def 							_SCREAMING_SNAKE_CASE   (     self:		List[Any]       ,			_SCREAMING_SNAKE_CASE:		str)				->       Optional[Any]:

				"""simple docstring"""

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

				__lowerCAmelCase      :  Union[str, 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:		str)				->       List[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       Path("./datasets")
				__lowerCAmelCase      :  Optional[int]												=       list(dataset_paths.absolute().glob("**/*.py"))

				for dataset in dataset_files:
							if self._no_encoding_on_file_open(str(_SCREAMING_SNAKE_CASE)):
										raise AssertionError(F"""open(...) must use utf-8 encoding in {dataset}""")







	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[Any])				->       Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase      :  Dict												=       Path("./datasets")
				__lowerCAmelCase      :  Union[str, Any]												=       list(dataset_paths.absolute().glob("**/*.py"))

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