



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():
      import torch

      from transformers import CamembertModel

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





     @slow
     def 						__snake_case     (							self			:				Optional[Any]					)->Optional[int]:
      __SCREAMING_SNAKE_CASE							:     str							=						CamembertModel.from_pretrained("camembert-base"					)
      model.to(UpperCamelCase					)

      __SCREAMING_SNAKE_CASE							:     str							=						torch.tensor(
          [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]  ,	device=UpperCamelCase  ,	dtype=torch.long  ,	)  # J'aime le camembert !
      with torch.no_grad():
       __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						model(UpperCamelCase					)["last_hidden_state"]
      __SCREAMING_SNAKE_CASE							:     str							=						torch.Size((1, 1_0, 7_6_8)					)
      self.assertEqual(output.shape  ,	UpperCamelCase					)
      # compare the actual values for a slice.
      __SCREAMING_SNAKE_CASE							:     int							=						torch.tensor(
          [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]  ,	device=UpperCamelCase  ,	dtype=torch.float  ,	)
      # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
      # camembert.eval()
      # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

      self.assertTrue(torch.allclose(output[:, :3, :3]  ,	UpperCamelCase  ,	atol=1E-4					)					)














"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase											=     {"""configuration_pegasus""": ["""PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusConfig"""]}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase											=     ["""PegasusTokenizer"""]

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase											=     ["""PegasusTokenizerFast"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase											=     [
         """PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """PegasusForCausalLM""",
         """PegasusForConditionalGeneration""",
         """PegasusModel""",
         """PegasusPreTrainedModel""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase											=     [
         """TFPegasusForConditionalGeneration""",
         """TFPegasusModel""",
         """TFPegasusPreTrainedModel""",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase											=     [
         """FlaxPegasusForConditionalGeneration""",
         """FlaxPegasusModel""",
         """FlaxPegasusPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_pegasus import PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_pegasus import PegasusTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_pegasus_fast import PegasusTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_pegasus import (
              PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
              PegasusForCausalLM,
              PegasusForConditionalGeneration,
              PegasusModel,
              PegasusPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_pegasus import TFPegasusForConditionalGeneration, TFPegasusModel, TFPegasusPreTrainedModel

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_pegasus import (
              FlaxPegasusForConditionalGeneration,
              FlaxPegasusModel,
              FlaxPegasusPreTrainedModel,
          )

else:
     import sys

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


