



from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__						:	List[str]					=logging.get_logger(__name__)

lowerCAmelCase__						:	str					={
    '''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/config.json''',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}

class   UpperCAmelCase_    (  UpperCamelCase_			):

						'''simple docstring'''




						UpperCamelCase__     :					Tuple							    =    """biogpt"""






						def __init__(       self ,				_A=42_384 ,				_A=1_024 ,				_A=24 ,				_A=16 ,				_A=4_096 ,				_A="gelu" ,				_A=0.1 ,				_A=0.1 ,				_A=1_024 ,				_A=0.0_2 ,				_A=1e-12 ,				_A=True ,				_A=True ,				_A=0.0 ,				_A=0.0 ,				_A=1 ,				_A=0 ,				_A=2 ,				**_A ,				):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   vocab_size
													__SCREAMING_SNAKE_CASE										=   max_position_embeddings
													__SCREAMING_SNAKE_CASE										=   hidden_size
													__SCREAMING_SNAKE_CASE										=   num_hidden_layers
													__SCREAMING_SNAKE_CASE										=   num_attention_heads
													__SCREAMING_SNAKE_CASE										=   intermediate_size
													__SCREAMING_SNAKE_CASE										=   hidden_act
													__SCREAMING_SNAKE_CASE										=   hidden_dropout_prob
													__SCREAMING_SNAKE_CASE										=   attention_probs_dropout_prob
													__SCREAMING_SNAKE_CASE										=   initializer_range
													__SCREAMING_SNAKE_CASE										=   layer_norm_eps
													__SCREAMING_SNAKE_CASE										=   scale_embedding
													__SCREAMING_SNAKE_CASE										=   use_cache
													__SCREAMING_SNAKE_CASE										=   layerdrop
													__SCREAMING_SNAKE_CASE										=   activation_dropout
													super().__init__(pad_token_id=a_ ,				bos_token_id=a_ ,				eos_token_id=a_ ,				**a_						)











'''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,
)


a							   =				{
    '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:
	a							   =				['OwlViTFeatureExtractor']
	a							   =				['OwlViTImageProcessor']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a							   =				[
	    '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

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




