



import numpy as np

from transformers import Pipeline


def  __lowerCamelCase (       lowerCamelCase__						):

				"""simple docstring"""


				lowercase__					:   Dict													=					np.max(UpperCamelCase__					,			axis=-1					,			keepdims=UpperCamelCase__						)
				lowercase__					:   List[Any]													=					np.exp(outputs - maxes						)
				return shifted_exp / shifted_exp.sum(axis=-1					,			keepdims=UpperCamelCase__						)

class 				snake_case__(_UpperCamelCase      ):

					"""simple docstring"""






					def 			snake_case					(							self :     List[Any]				,       **SCREAMING_SNAKE_CASE :     int					):
									lowercase__					:   List[Any]													=					{}
									if "second_text" in kwargs:
													lowercase__					:   Optional[Any]													=					kwargs["second_text"]
									return preprocess_kwargs, {}, {}







					def 			snake_case					(							self :     int				,       SCREAMING_SNAKE_CASE :     Optional[int]				,       SCREAMING_SNAKE_CASE :     List[Any]=None					):
									return self.tokenizer(_a				,       text_pair=_a				,       return_tensors=self.framework					)







					def 			snake_case					(							self :     Union[str, Any]				,       SCREAMING_SNAKE_CASE :     Optional[int]					):
									return self.model(**_a					)







					def 			snake_case					(							self :     Optional[int]				,       SCREAMING_SNAKE_CASE :     Optional[int]					):
									lowercase__					:   int													=					model_outputs.logits[0].numpy()
									lowercase__					:   str													=					softmax(_a					)

									lowercase__					:   List[str]													=					np.argmax(_a					)
									lowercase__					:   List[str]													=					self.model.config.idalabel[best_class]
									lowercase__					:   Optional[Any]													=					probabilities[best_class].item()
									lowercase__					:   str													=					logits.tolist()
									return {"label": label, "score": score, "logits": logits}









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 				snake_case__(unittest.TestCase      ):

					"""simple docstring"""






					@slow
					def 			snake_case					(							self :     Optional[int]					):
									lowercase__					:   Dict													=					FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small"					)
									lowercase__					:   Dict													=					AutoTokenizer.from_pretrained("google/mt5-small"					)

									lowercase__					:   Optional[Any]													=					tokenizer("Hello there"				,       return_tensors="np"					).input_ids
									lowercase__					:   Optional[Any]													=					tokenizer("Hi I am"				,       return_tensors="np"					).input_ids

									lowercase__					:   int													=					shift_tokens_right(SCREAMING_SNAKE_CASE				,       model.config.pad_token_id				,       model.config.decoder_start_token_id					)

									lowercase__					:   Tuple													=					model(SCREAMING_SNAKE_CASE				,       decoder_input_ids=SCREAMING_SNAKE_CASE					).logits
									lowercase__					:   Dict													=					optax.softmax_cross_entropy(SCREAMING_SNAKE_CASE				,       onehot(SCREAMING_SNAKE_CASE				,       logits.shape[-1]					)					).mean()

									lowercase__					:   Union[str, Any]													=					-(labels.shape[-1] * loss.item())

									lowercase__					:   Union[str, Any]													=					-84.9_127
									self.assertTrue(abs(mtf_score - EXPECTED_SCORE					) < 1E-4					)




