


"""simple docstring"""


import unittest

import numpy as np

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
				from transformers.models.bert.modeling_flax_bert import (
				    FlaxBertForMaskedLM,
				    FlaxBertForMultipleChoice,
				    FlaxBertForNextSentencePrediction,
				    FlaxBertForPreTraining,
				    FlaxBertForQuestionAnswering,
				    FlaxBertForSequenceClassification,
				    FlaxBertForTokenClassification,
				    FlaxBertModel,
				)


class 			A__	(						unittest.TestCase):


					"""simple docstring"""

					def __init__(	self:						int     ,		__a:						Any     ,		__a:						int=13     ,		__a:						Any=7     ,		__a:						Union[str, Any]=True     ,		__a:						List[Any]=True     ,		__a:						List[str]=True     ,		__a:						List[Any]=True     ,		__a:						Tuple=99     ,		__a:						str=32     ,		__a:						Union[str, Any]=5     ,		__a:						List[str]=4     ,		__a:						List[Any]=37     ,		__a:						Optional[Any]="gelu"     ,		__a:						Dict=0.1     ,		__a:						List[str]=0.1     ,		__a:						Tuple=512     ,		__a:						Union[str, Any]=16     ,		__a:						int=2     ,		__a:						List[str]=0.02     ,		__a:						List[str]=4     ,		)->    Optional[int]:
											lowerCamelCase					:					Optional[Any]        =	parent
											lowerCamelCase					:					Dict        =	batch_size
											lowerCamelCase					:					Dict        =	seq_length
											lowerCamelCase					:					Any        =	is_training
											lowerCamelCase					:					List[str]        =	use_attention_mask
											lowerCamelCase					:					int        =	use_token_type_ids
											lowerCamelCase					:					Union[str, Any]        =	use_labels
											lowerCamelCase					:					Optional[int]        =	vocab_size
											lowerCamelCase					:					Tuple        =	hidden_size
											lowerCamelCase					:					Tuple        =	num_hidden_layers
											lowerCamelCase					:					Optional[int]        =	num_attention_heads
											lowerCamelCase					:					Tuple        =	intermediate_size
											lowerCamelCase					:					Any        =	hidden_act
											lowerCamelCase					:					Optional[Any]        =	hidden_dropout_prob
											lowerCamelCase					:					int        =	attention_probs_dropout_prob
											lowerCamelCase					:					Dict        =	max_position_embeddings
											lowerCamelCase					:					List[str]        =	type_vocab_size
											lowerCamelCase					:					Union[str, Any]        =	type_sequence_label_size
											lowerCamelCase					:					Any        =	initializer_range
											lowerCamelCase					:					List[Any]        =	num_choices

					def 		a__						(	self:						str	)->    int:
											lowerCamelCase					:					Union[str, Any]        =	ids_tensor([self.batch_size, self.seq_length]     ,		self.vocab_size	)

											lowerCamelCase					:					List[Any]        =	None
											if self.use_attention_mask:
																	lowerCamelCase					:					Any        =	random_attention_mask([self.batch_size, self.seq_length]	)

											lowerCamelCase					:					Union[str, Any]        =	None
											if self.use_token_type_ids:
																	lowerCamelCase					:					Optional[Any]        =	ids_tensor([self.batch_size, self.seq_length]     ,		self.type_vocab_size	)

											lowerCamelCase					:					Union[str, Any]        =	BertConfig(
											    vocab_size=self.vocab_size     ,		hidden_size=self.hidden_size     ,		num_hidden_layers=self.num_hidden_layers     ,		num_attention_heads=self.num_attention_heads     ,		intermediate_size=self.intermediate_size     ,		hidden_act=self.hidden_act     ,		hidden_dropout_prob=self.hidden_dropout_prob     ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,		max_position_embeddings=self.max_position_embeddings     ,		type_vocab_size=self.type_vocab_size     ,		is_decoder=__lowerCamelCase     ,		initializer_range=self.initializer_range     ,		)

											return config, input_ids, token_type_ids, attention_mask

					def 		a__						(	self:						List[str]	)->    List[Any]:
											lowerCamelCase					:					Optional[int]        =	self.prepare_config_and_inputs()
											lowerCamelCase					:					List[str]        =	config_and_inputs
											lowerCamelCase					:					Tuple        =	{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
											return config, inputs_dict







					def 		a__						(	self:						Union[str, Any]	)->    Optional[Any]:
											lowerCamelCase					:					str        =	self.prepare_config_and_inputs()
											lowerCamelCase					:					Tuple        =	config_and_inputs

											lowerCamelCase					:					Optional[Any]        =	True
											lowerCamelCase					:					Dict        =	floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
											lowerCamelCase					:					Dict        =	ids_tensor([self.batch_size, self.seq_length]     ,		vocab_size=2	)

											return (
											    config,
											    input_ids,
											    attention_mask,
											    encoder_hidden_states,
											    encoder_attention_mask,
											)


@require_flax
class 			A__	(						lowerCamelCase__ ,     unittest.TestCase):


					"""simple docstring"""
					snake_case__	:					str													=True

					snake_case__	:					Dict													=(
					    (
					        FlaxBertModel,
					        FlaxBertForPreTraining,
					        FlaxBertForMaskedLM,
					        FlaxBertForMultipleChoice,
					        FlaxBertForQuestionAnswering,
					        FlaxBertForNextSentencePrediction,
					        FlaxBertForSequenceClassification,
					        FlaxBertForTokenClassification,
					        FlaxBertForQuestionAnswering,
					    )
					    if is_flax_available()
					    else ()
					)

					def 		a__						(	self:						Dict	)->    Dict:
											lowerCamelCase					:					Optional[int]        =	FlaxBertModelTester(self	)







					@slow
					def 		a__						(	self:						List[Any]	)->    str:
											lowerCamelCase					:					Tuple        =	FlaxBertModel.from_pretrained("""bert-base-cased"""	)
											lowerCamelCase					:					int        =	model(np.ones((1, 1)	)	)
											self.assertIsNotNone(__lowerCamelCase	)








"""simple docstring"""


from typing import TYPE_CHECKING

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


__lowerCamelCase      :List[str]	    =		{
    'configuration_owlvit': [
        'OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'OwlViTConfig',
        'OwlViTOnnxConfig',
        'OwlViTTextConfig',
        'OwlViTVisionConfig',
    ],
    'processing_owlvit': ['OwlViTProcessor'],
}


try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase      :Optional[int]	    =		['OwlViTFeatureExtractor']
    __lowerCamelCase      :List[str]	    =		['OwlViTImageProcessor']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase      :Optional[Any]	    =		[
        'OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'OwlViTModel',
        'OwlViTPreTrainedModel',
        'OwlViTTextModel',
        'OwlViTVisionModel',
        'OwlViTForObjectDetection',
    ]

if TYPE_CHECKING:
    from .configuration_owlvit import (
        OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
        OwlViTConfig,
        OwlViTOnnxConfig,
        OwlViTTextConfig,
        OwlViTVisionConfig,
    )
    from .processing_owlvit import OwlViTProcessor

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_owlvit import OwlViTFeatureExtractor
        from .image_processing_owlvit import OwlViTImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_owlvit import (
            OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            OwlViTForObjectDetection,
            OwlViTModel,
            OwlViTPreTrainedModel,
            OwlViTTextModel,
            OwlViTVisionModel,
        )

else:
    import sys

    __lowerCamelCase      :Dict	    =		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




