





from typing import Mapping

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


lowercase_		:				Union[str, Any]				=						logging.get_logger(__name__)

lowercase_		:				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  :	Dict								=	"t5"
						_UpperCamelCase  :	Optional[int]								=	["past_key_values"]
						_UpperCamelCase  :	List[str]								=	{"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}







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





										'''simple docstring'''







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

										_snake_case :			Any  						=   self.feed_forward_proj.split('-'					)
										_snake_case :			Dict  						=   act_info[-1]
										_snake_case :			Dict  						=   act_info[0] == 'gated'

										if len(lowerCamelCase_					) > 1 and act_info[0] != "gated" or len(lowerCamelCase_					) > 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":
														_snake_case :			Union[str, Any]  						=   'gelu_new'

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



class 			lowercase				(							a_     ):



						"""simple docstring"""

						@property
						def 		__UpperCAmelCase       (    self			:		Optional[Any]					):





										'''simple docstring'''







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

										if self.use_past:
														self.fill_with_past_key_values_(lowerCamelCase_ ,				direction='inputs'					)

										return common_inputs







						@property
						def 		__UpperCAmelCase       (    self			:		str					):





										'''simple docstring'''







										return 13













from typing import TYPE_CHECKING

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


lowercase_		:				Optional[Any]				=						{
    '''configuration_roc_bert''': ['''ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoCBertConfig'''],
    '''tokenization_roc_bert''': ['''RoCBertTokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		pass

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_		:				Tuple				=						[
		    '''ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''RoCBertForCausalLM''',
		    '''RoCBertForMaskedLM''',
		    '''RoCBertForMultipleChoice''',
		    '''RoCBertForPreTraining''',
		    '''RoCBertForQuestionAnswering''',
		    '''RoCBertForSequenceClassification''',
		    '''RoCBertForTokenClassification''',
		    '''RoCBertLayer''',
		    '''RoCBertModel''',
		    '''RoCBertPreTrainedModel''',
		    '''load_tf_weights_in_roc_bert''',
		]

if TYPE_CHECKING:
		from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
		from .tokenization_roc_bert import RoCBertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				raise OptionalDependencyNotAvailable()

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roc_bert import (
				    ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RoCBertForCausalLM,
				    RoCBertForMaskedLM,
				    RoCBertForMultipleChoice,
				    RoCBertForPreTraining,
				    RoCBertForQuestionAnswering,
				    RoCBertForSequenceClassification,
				    RoCBertForTokenClassification,
				    RoCBertLayer,
				    RoCBertModel,
				    RoCBertPreTrainedModel,
				    load_tf_weights_in_roc_bert,
				)


else:
		import sys

		lowercase_		:				Tuple				=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






