
import argparse
from collections import defaultdict




def 							__SCREAMING_SNAKE_CASE   (		SCREAMING_SNAKE_CASE       :      Dict    ,							SCREAMING_SNAKE_CASE       :      Dict    ,							SCREAMING_SNAKE_CASE       :      Tuple    ,							SCREAMING_SNAKE_CASE       :      List[str]    ,							SCREAMING_SNAKE_CASE       :      Tuple			)     ->						Dict:
				__lowercase								=					F"""{file}_{class_name}_{test_name}"""
				done_test[_id] += 1

				with open(__snake_case    ,							'r'			) as f:
								__lowercase								=					f.readlines()

				__lowercase								=					F"""class {class_name}("""
				__lowercase								=					F"""{4 * " "}def {test_name}("""
				__lowercase								=					F"""{8 * " "}{correct_line.split()[0]}"""
				__lowercase								=					F"""{16 * " "}{correct_line.split()[0]}"""
				__lowercase								=					False
				__lowercase								=					False
				__lowercase								=					False
				__lowercase								=					False
				__lowercase								=					0
				__lowercase								=					0

				__lowercase								=					[]
				for line in lines:
								if line.startswith(__snake_case			):
												__lowercase								=					True
								elif in_class and line.startswith(__snake_case			):
												__lowercase								=					True
								elif in_class and in_func and (line.startswith(__snake_case			) or line.startswith(__snake_case			)):
												__lowercase								=					len(line.split(correct_line.split()[0]			)[0]			)
												count += 1

												if count == done_test[_id]:
																__lowercase								=					True

								if in_class and in_func and in_line:
												if ")" not in line:
																continue
												else:
																__lowercase								=					True

								if in_class and in_func and in_line and insert_line:
												new_lines.append(F"""{spaces * " "}{correct_line}"""			)
												__lowercase								=					False
								else:
												new_lines.append(__snake_case			)

				with open(__snake_case    ,							'w'			) as f:
								for line in new_lines:
												f.write(__snake_case			)




def 							__SCREAMING_SNAKE_CASE   (		SCREAMING_SNAKE_CASE       :      Any    ,							SCREAMING_SNAKE_CASE       :      Optional[int]=None			)     ->						Tuple:
				if fail is not None:
								with open(__snake_case    ,							'r'			) as f:
												__lowercase								=					{l.strip() for l in f.readlines()}
				else:
								__lowercase								=					None

				with open(__snake_case    ,							'r'			) as f:
								__lowercase								=					f.readlines()

				__lowercase								=					defaultdict(__snake_case			)
				for line in correct_lines:
								__lowercase								=					line.split(';'			)
								if test_failures is None or "::".join([file, class_name, test_name]			) in test_failures:
												overwrite_file(__snake_case    ,							__snake_case    ,							__snake_case    ,							__snake_case    ,							__snake_case			)


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__							=				argparse.ArgumentParser()
							parser.add_argument("""--correct_filename""", help="""filename of tests with expected result""")
							parser.add_argument("""--fail_filename""", help="""filename of test failures""", type=str, default=None)
							SCREAMING_SNAKE_CASE__							=				parser.parse_args()

							main(args.correct_filename, args.fail_filename)










"""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)