




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      ):

						@slow
						def      A__    (		self :Tuple			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=FlaxMTaForConditionalGeneration.from_pretrained("""google/mt5-small"""			)
										__magic_name__						:						Optional[Any]										=AutoTokenizer.from_pretrained("""google/mt5-small"""			)

										__magic_name__						:						List[str]										=tokenizer("""Hello there"""     ,			return_tensors="""np"""			).input_ids
										__magic_name__						:						Tuple										=tokenizer("""Hi I am"""     ,			return_tensors="""np"""			).input_ids

										__magic_name__						:						List[str]										=shift_tokens_right(__snake_case     ,			model.config.pad_token_id     ,			model.config.decoder_start_token_id			)

										__magic_name__						:						Tuple										=model(__snake_case     ,			decoder_input_ids=__snake_case			).logits
										__magic_name__						:						Union[str, Any]										=optax.softmax_cross_entropy(__snake_case     ,			onehot(__snake_case     ,			logits.shape[-1]			)			).mean()

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

										__magic_name__						:						List[Any]										=-84.9127
										self.assertTrue(abs(mtf_score - EXPECTED_SCORE			) < 1E-4			)






"""simple docstring"""


from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase				 =					{
    '''configuration_mctct''': ['''MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MCTCTConfig'''],
    '''feature_extraction_mctct''': ['''MCTCTFeatureExtractor'''],
    '''processing_mctct''': ['''MCTCTProcessor'''],
}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				 =					[
		    '''MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''MCTCTForCTC''',
		    '''MCTCTModel''',
		    '''MCTCTPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
		from .feature_extraction_mctct import MCTCTFeatureExtractor
		from .processing_mctct import MCTCTProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
		import sys

		_lowercase				 =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)