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

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


lowercase_       :						Tuple     					=	{'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechConfig''']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowercase_       :						Optional[int]     					=	[
						    '''UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''UniSpeechForCTC''',
						    '''UniSpeechForPreTraining''',
						    '''UniSpeechForSequenceClassification''',
						    '''UniSpeechModel''',
						    '''UniSpeechPreTrainedModel''',
						]

if TYPE_CHECKING:
						from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_unispeech import (
												    UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
												    UniSpeechForCTC,
												    UniSpeechForPreTraining,
												    UniSpeechForSequenceClassification,
												    UniSpeechModel,
												    UniSpeechPreTrainedModel,
												)

else:
						import sys

						lowercase_       :						List[str]     					=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





"""simple docstring"""
from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
      import numpy as np
      import tensorflow as tf

      from transformers import TFCamembertModel
@require_tf
@require_sentencepiece
@require_tokenizers
class 			UpperCamelCase      (							unittest.TestCase  ):
     @slow
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				str							    = TFCamembertModel.from_pretrained("jplu/tf-camembert-base"				)

       _SCREAMING_SNAKE_CASE   :				str							    = tf.convert_to_tensor(
           [[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]]  ,     dtype=tf.intaa  ,     )  # J'aime le camembert !"

       _SCREAMING_SNAKE_CASE   :				str							    = model(snake_case__				)["last_hidden_state"]
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = tf.TensorShape((1, 10, 768)				)
       self.assertEqual(output.shape  ,     snake_case__				)
       # compare the actual values for a slice.
       _SCREAMING_SNAKE_CASE   :				int							    = tf.convert_to_tensor(
           [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]]  ,     dtype=tf.floataa  ,     )
       # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
       # camembert.eval()
       # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

       self.assertTrue(np.allclose(output[:, :3, :3].numpy()  ,     expected_slice.numpy()  ,     atol=1E-4				)				)



