

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 	A_		(	unittest.TestCase    ):
       @slow
       def    lowerCAmelCase					(		self		:      Optional[int]):
        __lowerCamelCase    :    Tuple 	=			TFCamembertModel.from_pretrained('jplu/tf-camembert-base')

        __lowerCamelCase    :    str 	=			tf.convert_to_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]]       ,dtype=tf.intaa       ,)  # J'aime le camembert !"

        __lowerCamelCase    :    int 	=			model(SCREAMING_SNAKE_CASE__)['last_hidden_state']
        __lowerCamelCase    :    Dict 	=			tf.TensorShape((1, 1_0, 7_6_8))
        self.assertEqual(output.shape       ,SCREAMING_SNAKE_CASE__)
        # compare the actual values for a slice.
        __lowerCamelCase    :    Any 	=			tf.convert_to_tensor(
            [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]       ,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))







from typing import TYPE_CHECKING

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


a		={
    """configuration_roformer""": ["""ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RoFormerConfig""", """RoFormerOnnxConfig"""],
    """tokenization_roformer""": ["""RoFormerTokenizer"""],
}

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

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """RoFormerForCausalLM""",
					    """RoFormerForMaskedLM""",
					    """RoFormerForMultipleChoice""",
					    """RoFormerForQuestionAnswering""",
					    """RoFormerForSequenceClassification""",
					    """RoFormerForTokenClassification""",
					    """RoFormerLayer""",
					    """RoFormerModel""",
					    """RoFormerPreTrainedModel""",
					    """load_tf_weights_in_roformer""",
					]


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFRoFormerForCausalLM""",
					    """TFRoFormerForMaskedLM""",
					    """TFRoFormerForMultipleChoice""",
					    """TFRoFormerForQuestionAnswering""",
					    """TFRoFormerForSequenceClassification""",
					    """TFRoFormerForTokenClassification""",
					    """TFRoFormerLayer""",
					    """TFRoFormerModel""",
					    """TFRoFormerPreTrainedModel""",
					]


try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """FlaxRoFormerForMaskedLM""",
					    """FlaxRoFormerForMultipleChoice""",
					    """FlaxRoFormerForQuestionAnswering""",
					    """FlaxRoFormerForSequenceClassification""",
					    """FlaxRoFormerForTokenClassification""",
					    """FlaxRoFormerModel""",
					    """FlaxRoFormerPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
					from .tokenization_roformer import RoFormerTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_roformer_fast import RoFormerTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_roformer import (
										    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    RoFormerForCausalLM,
										    RoFormerForMaskedLM,
										    RoFormerForMultipleChoice,
										    RoFormerForQuestionAnswering,
										    RoFormerForSequenceClassification,
										    RoFormerForTokenClassification,
										    RoFormerLayer,
										    RoFormerModel,
										    RoFormerPreTrainedModel,
										    load_tf_weights_in_roformer,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_roformer import (
										    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFRoFormerForCausalLM,
										    TFRoFormerForMaskedLM,
										    TFRoFormerForMultipleChoice,
										    TFRoFormerForQuestionAnswering,
										    TFRoFormerForSequenceClassification,
										    TFRoFormerForTokenClassification,
										    TFRoFormerLayer,
										    TFRoFormerModel,
										    TFRoFormerPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_roformer import (
										    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    FlaxRoFormerForMaskedLM,
										    FlaxRoFormerForMultipleChoice,
										    FlaxRoFormerForQuestionAnswering,
										    FlaxRoFormerForSequenceClassification,
										    FlaxRoFormerForTokenClassification,
										    FlaxRoFormerModel,
										    FlaxRoFormerPreTrainedModel,
										)


else:
					import sys

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




