
"""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 				lowerCAmelCase_				(      unittest.TestCase    ):



      '''simple docstring'''


      @slow
      def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple      )     ->   Tuple:
       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						   =   -84.91_27
       self.assertTrue(abs(mtf_score - EXPECTED_SCORE      ) < 1e-4      )

"""simple docstring"""


from argparse import ArgumentParser

from . import BaseTransformersCLICommand






def       _snake_case				(		snake_case__   :     Optional[int]     ):
	return DownloadCommand(args.model	,      args.cache_dir	,      args.force	,      args.trust_remote_code     )







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						@staticmethod
						def 						_SCREAMING_SNAKE_CASE      ( A_     :      ArgumentParser      )     ->   Any:
							A						   =   parser.add_parser('download'      )
							download_parser.add_argument(
							    '--cache-dir'  ,type=A_  ,default=A_  ,help='Path to location to store the models'      )
							download_parser.add_argument(
							    '--force'  ,action='store_true'  ,help='Force the model to be download even if already in cache-dir'      )
							download_parser.add_argument(
							    '--trust-remote-code'  ,action='store_true'  ,help='Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you\'ve reviewed the code as it will execute on your local machine'  ,)
							download_parser.add_argument('model'  ,type=A_  ,help='Name of the model to download'      )
							download_parser.set_defaults(func=A_      )


						def __init__( self     :      Dict  ,A_     :      str  ,A_     :      str  ,A_     :      bool  ,A_     :      bool      )     ->   Union[str, Any]:
							A						   =   model
							A						   =   cache
							A						   =   force
							A						   =   trust_remote_code




						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict      )     ->   Optional[int]:
							from ..models.auto import AutoModel, AutoTokenizer

							AutoModel.from_pretrained(
							    self._model  ,cache_dir=self._cache  ,force_download=self._force  ,trust_remote_code=self._trust_remote_code      )
							AutoTokenizer.from_pretrained(
							    self._model  ,cache_dir=self._cache  ,force_download=self._force  ,trust_remote_code=self._trust_remote_code      )