





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 	lowerCAmelCase__    (	self:						List[str]       )						->    Dict:
         '''simple docstring'''

         UpperCAmelCase_											=FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small"       )
         UpperCAmelCase_											=AutoTokenizer.from_pretrained("google/mt5-small"       )

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

         UpperCAmelCase_											=shift_tokens_right(_lowerCAmelCase		,  model.config.pad_token_id		,  model.config.decoder_start_token_id       )

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

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

         UpperCAmelCase_											=-84.91_27
         self.assertTrue(abs(mtf_score - EXPECTED_SCORE       ) < 1e-4       )











from typing import TYPE_CHECKING

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


__lowercase  :   Optional[Any]						      ={
    """configuration_groupvit""": [
        """GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """GroupViTConfig""",
        """GroupViTOnnxConfig""",
        """GroupViTTextConfig""",
        """GroupViTVisionConfig""",
    ],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __lowercase  :   List[Any]						      =[
         """GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """GroupViTModel""",
         """GroupViTPreTrainedModel""",
         """GroupViTTextModel""",
         """GroupViTVisionModel""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __lowercase  :   List[str]						      =[
         """TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFGroupViTModel""",
         """TFGroupViTPreTrainedModel""",
         """TFGroupViTTextModel""",
         """TFGroupViTVisionModel""",
     ]

if TYPE_CHECKING:
     from .configuration_groupvit import (
         GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         GroupViTConfig,
         GroupViTOnnxConfig,
         GroupViTTextConfig,
         GroupViTVisionConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_groupvit import (
              GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
              GroupViTModel,
              GroupViTPreTrainedModel,
              GroupViTTextModel,
              GroupViTVisionModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_groupvit import (
              TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFGroupViTModel,
              TFGroupViTPreTrainedModel,
              TFGroupViTTextModel,
              TFGroupViTVisionModel,
          )

else:
     import sys

     __lowercase  :   str						      =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




