




'''simple docstring'''






from typing import Mapping

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


lowercase						:   Tuple      						=							logging.get_logger(__name__)

lowercase						:   Dict      						=							{
    '''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 	SCREAMING_SNAKE_CASE_		(							__SCREAMING_SNAKE_CASE					):
 __snake_case						:  Optional[Any]       =   "longt5"
 __snake_case						:  List[Any]       =   ["past_key_values"]
 __snake_case						:  Optional[int]       =   {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}



 def __init__(	self ,		A=32128 ,		A=512 ,		A=64 ,		A=2048 ,		A=6 ,		A=None ,		A=8 ,		A=127 ,		A=16 ,		A=32 ,		A=128 ,		A=0.1 ,		A=1E-6 ,		A=1.0 ,		A="relu" ,		A=True ,		A="local" ,		A=True ,		A=0 ,		A=1 ,		**A ,		):






        '''simple docstring'''




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

        lowercase	:		int    =   self.feed_forward_proj.split('''-'''			)
        lowercase	:		List[str]    =   act_info[-1]
        lowercase	:		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":
               lowercase	:		Optional[Any]    =   """gelu_new"""

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






class 	SCREAMING_SNAKE_CASE_		(							__SCREAMING_SNAKE_CASE					):



 @property
 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        lowercase	:		List[Any]    =   {
            """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
            """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
        }
        if self.use_past:
               lowercase	:		List[Any]    =   """past_encoder_sequence + sequence"""
               lowercase	:		str    =   {0: """batch"""}
               lowercase	:		List[Any]    =   {0: """batch""", 1: """past_decoder_sequence + sequence"""}
        else:
               lowercase	:		List[str]    =   {0: """batch""", 1: """decoder_sequence"""}
               lowercase	:		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  SCREAMING_SNAKE_CASE(	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,
)


_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__)




