





"""simple docstring"""




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


UpperCamelCase_	 =logging.get_logger(__name__)

UpperCamelCase_	 ={
    """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 					_a		( _lowerCAmelCase							):
				UpperCamelCase								=       '''reformer'''
				UpperCamelCase								=       ['''past_buckets_states''']
				UpperCamelCase								=       {}






				def __init__(					self						:       Optional[Any],					lowerCAmelCase__						:       str=6_4,					lowerCAmelCase__						:       Union[str, Any]=["local", "lsh", "local", "lsh", "local", "lsh"],					lowerCAmelCase__						:       Optional[int]=1.0,					lowerCAmelCase__						:       Union[str, Any]=True,					lowerCAmelCase__						:       int=[6_4, 6_4],					lowerCAmelCase__						:       List[Any]=[6_4, 1_9_2],					lowerCAmelCase__						:       str=0,					lowerCAmelCase__						:       List[Any]=2,					lowerCAmelCase__						:       Dict=5_1_2,					lowerCAmelCase__						:       List[Any]=None,					lowerCAmelCase__						:       int="relu",					lowerCAmelCase__						:       Optional[Any]=0.05,					lowerCAmelCase__						:       Union[str, Any]=2_5_6,					lowerCAmelCase__						:       Tuple=0.02,					lowerCAmelCase__						:       str=False,					lowerCAmelCase__						:       Any=1e-1_2,					lowerCAmelCase__						:       Tuple=1,					lowerCAmelCase__						:       str=0,					lowerCAmelCase__						:       List[str]=0.05,					lowerCAmelCase__						:       Optional[int]=6_4,					lowerCAmelCase__						:       Union[str, Any]=6_4,					lowerCAmelCase__						:       List[Any]=0.0,					lowerCAmelCase__						:       Optional[Any]=1,					lowerCAmelCase__						:       str=0,					lowerCAmelCase__						:       Optional[int]=4_0_9_6,					lowerCAmelCase__						:       int=1_2,					lowerCAmelCase__						:       Optional[int]=None,					lowerCAmelCase__						:       Tuple=1,					lowerCAmelCase__						:       str=0,					lowerCAmelCase__						:       int=3_2_0,					lowerCAmelCase__						:       List[str]=False,					lowerCAmelCase__						:       Optional[int]=True,					lowerCAmelCase__						:       List[Any]=None,					**lowerCAmelCase__						:       Any,					)      -> Optional[Any]:




										'''simple docstring'''



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










"""simple docstring"""




from typing import TYPE_CHECKING

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


UpperCamelCase_	 ={
    """configuration_camembert""": ["""CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CamembertConfig""", """CamembertOnnxConfig"""],
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase_	 =["""CamembertTokenizer"""]

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

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase_	 =[
						    """CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """CamembertForCausalLM""",
						    """CamembertForMaskedLM""",
						    """CamembertForMultipleChoice""",
						    """CamembertForQuestionAnswering""",
						    """CamembertForSequenceClassification""",
						    """CamembertForTokenClassification""",
						    """CamembertModel""",
						    """CamembertPreTrainedModel""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase_	 =[
						    """TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFCamembertForCausalLM""",
						    """TFCamembertForMaskedLM""",
						    """TFCamembertForMultipleChoice""",
						    """TFCamembertForQuestionAnswering""",
						    """TFCamembertForSequenceClassification""",
						    """TFCamembertForTokenClassification""",
						    """TFCamembertModel""",
						    """TFCamembertPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_camembert import CamembertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_camembert_fast import CamembertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_camembert import (
												    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    CamembertForCausalLM,
												    CamembertForMaskedLM,
												    CamembertForMultipleChoice,
												    CamembertForQuestionAnswering,
												    CamembertForSequenceClassification,
												    CamembertForTokenClassification,
												    CamembertModel,
												    CamembertPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_camembert import (
												    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFCamembertForCausalLM,
												    TFCamembertForMaskedLM,
												    TFCamembertForMultipleChoice,
												    TFCamembertForQuestionAnswering,
												    TFCamembertForSequenceClassification,
												    TFCamembertForTokenClassification,
												    TFCamembertModel,
												    TFCamembertPreTrainedModel,
												)

else:
						import sys

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



