


"""simple docstring"""






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
  from transformers import AutoModelForSeqaSeqLM, AutoTokenizer




@require_torch
@require_sentencepiece
@require_tokenizers
class 							A_      (       unittest.TestCase ):


  @slow
  def 							_lowercase					(			self:						Dict    ):







     '''simple docstring'''







     _lowerCamelCase   :				Dict							=     AutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"							,return_dict=_lowerCamelCase    ).to(_lowerCamelCase    )
     _lowerCamelCase   :				Optional[Any]							=     AutoTokenizer.from_pretrained("google/mt5-small"    )

     _lowerCamelCase   :				Any							=     tokenizer("Hello there"							,return_tensors="pt"    ).input_ids
     _lowerCamelCase   :				str							=     tokenizer("Hi I am"							,return_tensors="pt"    ).input_ids

     _lowerCamelCase   :				Tuple							=     model(input_ids.to(_lowerCamelCase    )							,labels=labels.to(_lowerCamelCase    )    ).loss
     _lowerCamelCase   :				Union[str, Any]							=     -(labels.shape[-1] * loss.item())

     _lowerCamelCase   :				List[str]							=     -84.91_27
     self.assertTrue(abs(mtf_score - EXPECTED_SCORE    ) < 1e-4    )


"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A							=  {"""configuration_sew""": ["""SEW_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWConfig"""]}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _A							=  [
        """SEW_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """SEWForCTC""",
        """SEWForSequenceClassification""",
        """SEWModel""",
        """SEWPreTrainedModel""",
    ]

if TYPE_CHECKING:
    from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_sew import (
            SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
            SEWForCTC,
            SEWForSequenceClassification,
            SEWModel,
            SEWPreTrainedModel,
        )


else:
    import sys

    _A							=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

