


from typing import TYPE_CHECKING

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


a_ :str  							=			{
    'configuration_blip': [
        'BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BlipConfig',
        'BlipTextConfig',
        'BlipVisionConfig',
    ],
    'processing_blip': ['BlipProcessor'],
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    a_ :Tuple  							=			['BlipImageProcessor']


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    a_ :List[str]  							=			[
        'BLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
        'BlipModel',
        'BlipPreTrainedModel',
        'BlipForConditionalGeneration',
        'BlipForQuestionAnswering',
        'BlipVisionModel',
        'BlipTextModel',
        'BlipForImageTextRetrieval',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    a_ :Tuple  							=			[
        'TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFBlipModel',
        'TFBlipPreTrainedModel',
        'TFBlipForConditionalGeneration',
        'TFBlipForQuestionAnswering',
        'TFBlipVisionModel',
        'TFBlipTextModel',
        'TFBlipForImageTextRetrieval',
    ]

if TYPE_CHECKING:
    from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
    from .processing_blip import BlipProcessor

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .image_processing_blip import BlipImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_blip import (
            BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
            BlipForConditionalGeneration,
            BlipForImageTextRetrieval,
            BlipForQuestionAnswering,
            BlipModel,
            BlipPreTrainedModel,
            BlipTextModel,
            BlipVisionModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_blip import (
            TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFBlipForConditionalGeneration,
            TFBlipForImageTextRetrieval,
            TFBlipForQuestionAnswering,
            TFBlipModel,
            TFBlipPreTrainedModel,
            TFBlipTextModel,
            TFBlipVisionModel,
        )

else:
    import sys

    a_ :Dict  							=			_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					):







		"""simple docstring"""

		@slow
		def       lowerCAmelCase				(      self				:			Optional[Any]      )						->			Optional[Any]:






				"""simple docstring"""







				snake_case  :       Any     			=						TFCamembertModel.from_pretrained('''jplu/tf-camembert-base'''      )

				snake_case  :       List[Any]     			=						tf.convert_to_tensor(
				    [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]  ,	dtype=tf.intaa  ,	)  # J'aime le camembert !"

				snake_case  :       Any     			=						model(UpperCamelCase__      )['''last_hidden_state''']
				snake_case  :       int     			=						tf.TensorShape((1, 10, 768)      )
				self.assertEqual(output.shape  ,	UpperCamelCase__      )
				# compare the actual values for a slice.
				snake_case  :       Dict     			=						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      )      )


