


from typing import TYPE_CHECKING

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


A   :					Any	=      {
    "configuration_m2m_100": ["M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP", "M2M100Config", "M2M100OnnxConfig"],
    "tokenization_m2m_100": ["M2M100Tokenizer"],
}


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A   :					List[Any]	=      [
        "M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST",
        "M2M100ForConditionalGeneration",
        "M2M100Model",
        "M2M100PreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
    from .tokenization_mam_aaa import MaMaaaTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mam_aaa import (
            M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
            MaMaaaForConditionalGeneration,
            MaMaaaModel,
            MaMaaaPreTrainedModel,
        )


else:
    import sys

    A   :					Optional[Any]	=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






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     _lowercase				(							unittest.TestCase):


       """simple docstring"""




       @slow
       def 		lowerCAmelCase  (     self							:    Any ):


             '''simple docstring'''



             lowerCamelCase__	:       Dict					=						TFCamembertModel.from_pretrained("jplu/tf-camembert-base" )

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

             lowerCamelCase__	:       Any					=						model(__lowerCamelCase )["last_hidden_state"]
             lowerCamelCase__	:       List[str]					=						tf.TensorShape((1, 10, 768) )
             self.assertEqual(output.shape      ,   __lowerCamelCase )
             # compare the actual values for a slice.
             lowerCamelCase__	:       str					=						tf.convert_to_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]]]      ,   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 ) )


