



'''simple docstring'''
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


_UpperCamelCase									=      logging.get_logger(__name__)

_UpperCamelCase									=      {
    '''google/long-t5-local-base''': '''https://huggingface.co/google/long-t5-local-base/blob/main/config.json''',
    '''google/long-t5-local-large''': '''https://huggingface.co/google/long-t5-local-large/blob/main/config.json''',
    '''google/long-t5-tglobal-base''': '''https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json''',
    '''google/long-t5-tglobal-large''': '''https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json''',
}



class _A	(     __SCREAMING_SNAKE_CASE     ):
		_SCREAMING_SNAKE_CASE       :     Optional[Any]       =      "longt5"
		_SCREAMING_SNAKE_CASE       :     List[Any]       =      ["past_key_values"]
		_SCREAMING_SNAKE_CASE       :     Optional[int]       =      {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}





		def __init__(  self			,							__UpperCAmelCase=32_128			,							__UpperCAmelCase=512			,							__UpperCAmelCase=64			,							__UpperCAmelCase=2_048			,							__UpperCAmelCase=6			,							__UpperCAmelCase=None			,							__UpperCAmelCase=8			,							__UpperCAmelCase=127			,							__UpperCAmelCase=16			,							__UpperCAmelCase=32			,							__UpperCAmelCase=128			,							__UpperCAmelCase=0.1			,							__UpperCAmelCase=1E-6			,							__UpperCAmelCase=1.0			,							__UpperCAmelCase="relu"			,							__UpperCAmelCase=True			,							__UpperCAmelCase="local"			,							__UpperCAmelCase=True			,							__UpperCAmelCase=0			,							__UpperCAmelCase=1			,							**__UpperCAmelCase			,							)							->  Optional[Any]:

					'''simple docstring'''
					__UpperCAmelCase :   Union[str, Any]    					=  vocab_size
					__UpperCAmelCase :   str    					=  d_model
					__UpperCAmelCase :   Optional[Any]    					=  d_kv
					__UpperCAmelCase :   int    					=  d_ff
					__UpperCAmelCase :   Optional[Any]    					=  num_layers
					# default = symmetry
					__UpperCAmelCase :   Dict    					=  num_decoder_layers if num_decoder_layers is not None else self.num_layers
					__UpperCAmelCase :   int    					=  num_heads
					__UpperCAmelCase :   Optional[int]    					=  local_radius
					__UpperCAmelCase :   Tuple    					=  global_block_size
					__UpperCAmelCase :   int    					=  relative_attention_num_buckets
					__UpperCAmelCase :   Optional[int]    					=  relative_attention_max_distance
					__UpperCAmelCase :   Optional[int]    					=  dropout_rate
					__UpperCAmelCase :   int    					=  layer_norm_epsilon
					__UpperCAmelCase :   Optional[Any]    					=  initializer_factor
					__UpperCAmelCase :   Optional[int]    					=  feed_forward_proj
					__UpperCAmelCase :   Optional[int]    					=  encoder_attention_type
					__UpperCAmelCase :   Union[str, Any]    					=  use_cache

					__UpperCAmelCase :   int    					=  self.feed_forward_proj.split("""-"""					)
					__UpperCAmelCase :   List[str]    					=  act_info[-1]
					__UpperCAmelCase :   Optional[Any]    					=  act_info[0] == """gated"""

					if len(__UpperCAmelCase					) > 1 and act_info[0] != "gated" or len(__UpperCAmelCase					) > 2:
								raise ValueError(
								    f'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
								    """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
								    """'gated-gelu' or 'relu'"""					)

					# for backwards compatibility
					if feed_forward_proj == "gated-gelu":
								__UpperCAmelCase :   Optional[Any]    					=  """gelu_new"""

					super().__init__(
					    pad_token_id=__UpperCAmelCase			,							eos_token_id=__UpperCAmelCase			,							is_encoder_decoder=__UpperCAmelCase			,							**__UpperCAmelCase			,							)



class _A	(     __SCREAMING_SNAKE_CASE     ):





		@property
		def 				__A							(  self					)							->  Mapping[str, Mapping[int, str]]:

					'''simple docstring'''
					__UpperCAmelCase :   List[Any]    					=  {
					    """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
					    """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
					}
					if self.use_past:
								__UpperCAmelCase :   List[Any]    					=  """past_encoder_sequence + sequence"""
								__UpperCAmelCase :   str    					=  {0: """batch"""}
								__UpperCAmelCase :   List[Any]    					=  {0: """batch""", 1: """past_decoder_sequence + sequence"""}
					else:
								__UpperCAmelCase :   List[str]    					=  {0: """batch""", 1: """decoder_sequence"""}
								__UpperCAmelCase :   Union[str, Any]    					=  {0: """batch""", 1: """decoder_sequence"""}

					if self.use_past:
								self.fill_with_past_key_values_(__UpperCAmelCase			,							direction="""inputs"""					)

					return common_inputs





		@property
		def 				__A							(  self					)							->  int:

					'''simple docstring'''
					return 13









'''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__)




