



'''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	(     __SCREAMING_SNAKE_CASE     ):
		_SCREAMING_SNAKE_CASE       :     str       =      "reformer"
		_SCREAMING_SNAKE_CASE       :     Union[str, Any]       =      ["past_buckets_states"]
		_SCREAMING_SNAKE_CASE       :     Union[str, Any]       =      {}





		def __init__(  self			,							__UpperCAmelCase=64			,							__UpperCAmelCase=["local", "lsh", "local", "lsh", "local", "lsh"]			,							__UpperCAmelCase=1.0			,							__UpperCAmelCase=True			,							__UpperCAmelCase=[64, 64]			,							__UpperCAmelCase=[64, 192]			,							__UpperCAmelCase=0			,							__UpperCAmelCase=2			,							__UpperCAmelCase=512			,							__UpperCAmelCase=None			,							__UpperCAmelCase="relu"			,							__UpperCAmelCase=0.05			,							__UpperCAmelCase=256			,							__UpperCAmelCase=0.02			,							__UpperCAmelCase=False			,							__UpperCAmelCase=1E-12			,							__UpperCAmelCase=1			,							__UpperCAmelCase=0			,							__UpperCAmelCase=0.05			,							__UpperCAmelCase=64			,							__UpperCAmelCase=64			,							__UpperCAmelCase=0.0			,							__UpperCAmelCase=1			,							__UpperCAmelCase=0			,							__UpperCAmelCase=4_096			,							__UpperCAmelCase=12			,							__UpperCAmelCase=None			,							__UpperCAmelCase=1			,							__UpperCAmelCase=0			,							__UpperCAmelCase=320			,							__UpperCAmelCase=False			,							__UpperCAmelCase=True			,							__UpperCAmelCase=None			,							**__UpperCAmelCase			,							)							->  Tuple:

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









'''simple docstring'''
from typing import TYPE_CHECKING

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


_UpperCamelCase									=      {'''configuration_t5''': ['''T5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''T5Config''', '''T5OnnxConfig''']}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCamelCase									=      ['''T5Tokenizer''']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCamelCase									=      ['''T5TokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCamelCase									=      [
					    '''T5_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''T5EncoderModel''',
					    '''T5ForConditionalGeneration''',
					    '''T5Model''',
					    '''T5PreTrainedModel''',
					    '''load_tf_weights_in_t5''',
					    '''T5ForQuestionAnswering''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCamelCase									=      [
					    '''TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFT5EncoderModel''',
					    '''TFT5ForConditionalGeneration''',
					    '''TFT5Model''',
					    '''TFT5PreTrainedModel''',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCamelCase									=      [
					    '''FlaxT5EncoderModel''',
					    '''FlaxT5ForConditionalGeneration''',
					    '''FlaxT5Model''',
					    '''FlaxT5PreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_ta import TaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_ta_fast import TaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_ta import (
										    T5_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TaEncoderModel,
										    TaForConditionalGeneration,
										    TaForQuestionAnswering,
										    TaModel,
										    TaPreTrainedModel,
										    load_tf_weights_in_ta,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_ta import (
										    TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFTaEncoderModel,
										    TFTaForConditionalGeneration,
										    TFTaModel,
										    TFTaPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_ta import (
										    FlaxTaEncoderModel,
										    FlaxTaForConditionalGeneration,
										    FlaxTaModel,
										    FlaxTaPreTrainedModel,
										)


else:
					import sys

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




