
"""simple docstring"""






from typing import Mapping

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


lowerCamelCase__ :       List[str]						     =			logging.get_logger(__name__)

lowerCamelCase__ :       List[str]						     =			{
    """t5-small""": """https://huggingface.co/t5-small/resolve/main/config.json""",
    """t5-base""": """https://huggingface.co/t5-base/resolve/main/config.json""",
    """t5-large""": """https://huggingface.co/t5-large/resolve/main/config.json""",
    """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/config.json""",
    """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/config.json""",
}




class 				lowercase__(				_a   ):
	'''simple docstring'''

	UpperCamelCase				=  """t5"""
	UpperCamelCase				=  ["""past_key_values"""]
	UpperCamelCase				=  {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}





	def __init__(						self    :Tuple		, lowerCamelCase_    :Tuple=3_21_28		, lowerCamelCase_    :Union[str, Any]=5_12		, lowerCamelCase_    :Optional[int]=64		, lowerCamelCase_    :Union[str, Any]=20_48		, lowerCamelCase_    :Any=6		, lowerCamelCase_    :str=None		, lowerCamelCase_    :Dict=8		, lowerCamelCase_    :Optional[Any]=32		, lowerCamelCase_    :List[str]=1_28		, lowerCamelCase_    :Tuple=0.1		, lowerCamelCase_    :List[str]=1E-6		, lowerCamelCase_    :Any=1.0		, lowerCamelCase_    :Tuple="relu"		, lowerCamelCase_    :List[Any]=True		, lowerCamelCase_    :List[Any]=True		, lowerCamelCase_    :Dict=0		, lowerCamelCase_    :Tuple=1		, **lowerCamelCase_    :Any		, )							->			List[str]:







			'''simple docstring'''




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

			SCREAMING_SNAKE_CASE       :     int				= self.feed_forward_proj.split('''-'''					)
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= act_info[-1]
			SCREAMING_SNAKE_CASE       :     Tuple				= 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":
					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= '''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    :Tuple					)							->			Mapping[str, Mapping[int, str]]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= {
			    '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
			    '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
			}
			if self.use_past:
					SCREAMING_SNAKE_CASE       :     Dict				= '''past_encoder_sequence + sequence'''
					SCREAMING_SNAKE_CASE       :     List[str]				= {0: '''batch'''}
					SCREAMING_SNAKE_CASE       :     Optional[int]				= {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
			else:
					SCREAMING_SNAKE_CASE       :     int				= {0: '''batch''', 1: '''decoder_sequence'''}
					SCREAMING_SNAKE_CASE       :     List[Any]				= {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    :Any					)							->			int:







			'''simple docstring'''




			return 13







"""simple docstring"""






from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase__ :       Tuple						     =			{
    "configuration_mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig"],
    "feature_extraction_mctct": ["MCTCTFeatureExtractor"],
    "processing_mctct": ["MCTCTProcessor"],
}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase__ :       Union[str, Any]						     =			[
					    "MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "MCTCTForCTC",
					    "MCTCTModel",
					    "MCTCTPreTrainedModel",
					]


if TYPE_CHECKING:
					from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
					from .feature_extraction_mctct import MCTCTFeatureExtractor
					from .processing_mctct import MCTCTProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
					import sys

					lowerCamelCase__ :       Tuple						     =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





