



from typing import Mapping

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


UpperCAmelCase :				List[Any]							=logging.get_logger(__name__)

UpperCAmelCase :				List[str]							={
    """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    _lowercase (a_   ):
				'''simple docstring'''







				lowercase__     =    """longt5"""
				lowercase__     =    ["""past_key_values"""]
				lowercase__     =    {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}

				def __init__(			self  ,			snake_case__=3_2128  ,			snake_case__=512  ,			snake_case__=64  ,			snake_case__=2048  ,			snake_case__=6  ,			snake_case__=None  ,			snake_case__=8  ,			snake_case__=127  ,			snake_case__=16  ,			snake_case__=32  ,			snake_case__=128  ,			snake_case__=0.1  ,			snake_case__=1e-6  ,			snake_case__=1.0  ,			snake_case__="relu"  ,			snake_case__=True  ,			snake_case__="local"  ,			snake_case__=True  ,			snake_case__=0  ,			snake_case__=1  ,			**snake_case__  ,			):

									'''simple docstring'''


									UpperCamelCase_			= vocab_size
									UpperCamelCase_			= d_model
									UpperCamelCase_			= d_kv
									UpperCamelCase_			= d_ff
									UpperCamelCase_			= num_layers
									# default = symmetry
									UpperCamelCase_			= num_decoder_layers if num_decoder_layers is not None else self.num_layers
									UpperCamelCase_			= num_heads
									UpperCamelCase_			= local_radius
									UpperCamelCase_			= global_block_size
									UpperCamelCase_			= relative_attention_num_buckets
									UpperCamelCase_			= relative_attention_max_distance
									UpperCamelCase_			= dropout_rate
									UpperCamelCase_			= layer_norm_epsilon
									UpperCamelCase_			= initializer_factor
									UpperCamelCase_			= feed_forward_proj
									UpperCamelCase_			= encoder_attention_type
									UpperCamelCase_			= use_cache

									UpperCamelCase_			= self.feed_forward_proj.split("-"     )
									UpperCamelCase_			= act_info[-1]
									UpperCamelCase_			= act_info[0] == "gated"

									if len(snake_case__     ) > 1 and act_info[0] != "gated" or len(snake_case__     ) > 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_			= "gelu_new"

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




class    _lowercase (a_   ):
				'''simple docstring'''







				@property
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= {
									    "input_ids": {0: "batch", 1: "encoder_sequence"},
									    "attention_mask": {0: "batch", 1: "encoder_sequence"},
									}
									if self.use_past:
														UpperCamelCase_			= "past_encoder_sequence + sequence"
														UpperCamelCase_			= {0: "batch"}
														UpperCamelCase_			= {0: "batch", 1: "past_decoder_sequence + sequence"}
									else:
														UpperCamelCase_			= {0: "batch", 1: "decoder_sequence"}
														UpperCamelCase_			= {0: "batch", 1: "decoder_sequence"}

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

									return common_inputs

				@property
				def    _lowerCamelCase       (			self     ):

									'''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,
)


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

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

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A__ :		Union[str, Any]					=['T5TokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A__ :		Union[str, Any]					=[
         '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:
     A__ :		List[str]					=[
         'TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TFT5EncoderModel',
         'TFT5ForConditionalGeneration',
         'TFT5Model',
         'TFT5PreTrainedModel',
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A__ :		Union[str, Any]					=[
         '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

     A__ :		Dict					=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



