





from typing import TYPE_CHECKING

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


lowercase :			List[str]     		=    {"""configuration_encoder_decoder""": ["""EncoderDecoderConfig"""]}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase :			str     		=    ["""EncoderDecoderModel"""]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase :			Dict     		=    ["""TFEncoderDecoderModel"""]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase :			Union[str, Any]     		=    ["""FlaxEncoderDecoderModel"""]

if TYPE_CHECKING:
    from .configuration_encoder_decoder import EncoderDecoderConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_encoder_decoder import EncoderDecoderModel

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_encoder_decoder import TFEncoderDecoderModel

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel

else:
    import sys

    lowercase :			int     		=    _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 			snake_case  (				unittest.TestCase ):

							@slow
							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			TFCamembertModel.from_pretrained('''jplu/tf-camembert-base'''     )

									SCREAMING_SNAKE_CASE_    			=			tf.convert_to_tensor(
									    [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]]							,							dtype=tf.intaa							,							)  # J'aime le camembert !"

									SCREAMING_SNAKE_CASE_    			=			model(SCREAMING_SNAKE_CASE_     )['''last_hidden_state''']
									SCREAMING_SNAKE_CASE_    			=			tf.TensorShape((1, 10, 7_68)     )
									self.assertEqual(output.shape							,							SCREAMING_SNAKE_CASE_     )
									# compare the actual values for a slice.
									SCREAMING_SNAKE_CASE_    			=			tf.convert_to_tensor(
									    [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]							,							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     )     )