






"""simple docstring"""



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


A_     =				logging.get_logger(__name__)

A_     =				{
    '''google/reformer-crime-and-punishment''': (
        '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/config.json'''
    ),
    '''google/reformer-enwik8''': '''https://huggingface.co/google/reformer-enwik8/resolve/main/config.json''',
}






class 				__SCREAMING_SNAKE_CASE    (					UpperCamelCase		):
							snake_case_  =				'reformer'
							snake_case_  =				['past_buckets_states']
							snake_case_  =				{}


							def __init__(      self   :    List[str]      ,     snake_case   :    str=64      ,     snake_case   :    Any=["local", "lsh", "local", "lsh", "local", "lsh"]      ,     snake_case   :    Dict=1.0      ,     snake_case   :    Optional[Any]=True      ,     snake_case   :    Tuple=[64, 64]      ,     snake_case   :    Any=[64, 192]      ,     snake_case   :    List[str]=0      ,     snake_case   :    Optional[Any]=2      ,     snake_case   :    Optional[Any]=512      ,     snake_case   :    Union[str, Any]=None      ,     snake_case   :    Any="relu"      ,     snake_case   :    List[Any]=0.05      ,     snake_case   :    List[Any]=256      ,     snake_case   :    Optional[int]=0.02      ,     snake_case   :    Union[str, Any]=False      ,     snake_case   :    Optional[int]=1e-12      ,     snake_case   :    Union[str, Any]=1      ,     snake_case   :    Union[str, Any]=0      ,     snake_case   :    Union[str, Any]=0.05      ,     snake_case   :    str=64      ,     snake_case   :    Optional[int]=64      ,     snake_case   :    str=0.0      ,     snake_case   :    List[str]=1      ,     snake_case   :    str=0      ,     snake_case   :    Optional[int]=4096      ,     snake_case   :    Optional[int]=12      ,     snake_case   :    int=None      ,     snake_case   :    str=1      ,     snake_case   :    Dict=0      ,     snake_case   :    int=320      ,     snake_case   :    Union[str, Any]=False      ,     snake_case   :    Tuple=True      ,     snake_case   :    Tuple=None      ,     **snake_case   :    List[Any]      ,     ):
													'''simple docstring'''





													A__    :	Any			=	hash_seed
													A__    :	Union[str, Any]			=	vocab_size
													A__    :	Optional[int]			=	attention_head_size
													A__    :	int			=	hidden_size
													A__    :	List[Any]			=	num_attention_heads
													A__    :	Tuple			=	num_hashes
													A__    :	int			=	len(snake_case				)
													A__    :	Optional[int]			=	tuple(snake_case				) if isinstance(snake_case      ,     snake_case				) else num_buckets
													A__    :	Tuple			=	lsh_attn_chunk_length
													A__    :	Tuple			=	local_attn_chunk_length
													A__    :	int			=	lsh_num_chunks_after
													A__    :	int			=	lsh_num_chunks_before
													A__    :	Any			=	local_num_chunks_after
													A__    :	Union[str, Any]			=	local_num_chunks_before
													A__    :	int			=	hidden_act
													A__    :	int			=	feed_forward_size
													A__    :	Optional[Any]			=	hidden_dropout_prob
													A__    :	Dict			=	lsh_attention_probs_dropout_prob
													A__    :	str			=	local_attention_probs_dropout_prob
													A__    :	List[Any]			=	max_position_embeddings
													A__    :	Optional[int]			=	initializer_range
													A__    :	Union[str, Any]			=	layer_norm_eps
													A__    :	Tuple			=	axial_pos_embds
													A__    :	Any			=	tuple(snake_case				)
													A__    :	Tuple			=	tuple(snake_case				)
													A__    :	int			=	axial_norm_std
													A__    :	List[str]			=	chunk_size_lm_head
													A__    :	Any			=	attn_layers
													A__    :	List[Any]			=	use_cache
													A__    :	str			=	classifier_dropout
													super().__init__(
													    pad_token_id=snake_case      ,     eos_token_id=snake_case      ,     is_decoder=snake_case      ,     tie_word_embeddings=snake_case      ,     **snake_case      ,     )














"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A_     =				{
    '''configuration_deformable_detr''': ['''DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DeformableDetrConfig'''],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_     =				['''DeformableDetrFeatureExtractor''']
						A_     =				['''DeformableDetrImageProcessor''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_     =				[
						    '''DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''DeformableDetrForObjectDetection''',
						    '''DeformableDetrModel''',
						    '''DeformableDetrPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor
												from .image_processing_deformable_detr import DeformableDetrImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_deformable_detr import (
												    DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DeformableDetrForObjectDetection,
												    DeformableDetrModel,
												    DeformableDetrPreTrainedModel,
												)

else:
						import sys

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






