





'''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 				__SCREAMING_SNAKE_CASE  (unittest.TestCase      ):






				"""simple docstring"""







				@slow
				def   UpperCamelCase__   (  self     :					Union[str, Any]					):
								_a								= FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small"					)
								_a								= AutoTokenizer.from_pretrained("google/mt5-small"					)

								_a								= tokenizer("Hello there"					,				return_tensors="np"					).input_ids
								_a								= tokenizer("Hi I am"					,				return_tensors="np"					).input_ids

								_a								= shift_tokens_right(__a					,				model.config.pad_token_id					,				model.config.decoder_start_token_id					)

								_a								= model(__a					,				decoder_input_ids=__a					).logits
								_a								= optax.softmax_cross_entropy(__a					,				onehot(__a					,				logits.shape[-1]					)					).mean()

								_a								= -(labels.shape[-1] * loss.item())

								_a								= -84.9127
								self.assertTrue(abs(mtf_score - EXPECTED_SCORE					) < 1e-4					)







'''simple docstring'''







from ....utils import logging


lowerCAmelCase_					:    Union[str, Any]      	=			logging.get_logger(__name__)
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				def __init__(  self     :					Tuple					,				__a     :					int					,				__a     :					Any=None					,				__a     :					Optional[int]=20_48					):
								_a								= config.__dict__
								_a								= modal_hidden_size
								if num_labels:
												_a								= num_labels
