

'''simple docstring'''

from typing import Mapping

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


lowerCAmelCase_						   =			logging.get_logger(__name__)







class lowerCamelCase      (						__lowerCAmelCase							):
				snake_case_           =   '''mt5'''
				snake_case_           =   ['''past_key_values''']

				def __init__(					self,	lowercase_=250112,	lowercase_=512,	lowercase_=64,	lowercase_=1024,	lowercase_=8,	lowercase_=None,	lowercase_=6,	lowercase_=32,	lowercase_=128,	lowercase_=0.1,	lowercase_=1E-6,	lowercase_=1.0,	lowercase_="gated-gelu",	lowercase_=True,	lowercase_=True,	lowercase_="T5Tokenizer",	lowercase_=False,	lowercase_=0,	lowercase_=1,	lowercase_=0,	**lowercase_,	)						->       Optional[int]:
					super().__init__(
					    is_encoder_decoder=lowercase_,	tokenizer_class=lowercase_,	tie_word_embeddings=lowercase_,	pad_token_id=lowercase_,	eos_token_id=lowercase_,	decoder_start_token_id=lowercase_,	**lowercase_,	)
					snake_case							 =				vocab_size
					snake_case							 =				d_model
					snake_case							 =				d_kv
					snake_case							 =				d_ff
					snake_case							 =				num_layers
					snake_case							 =				(
					    num_decoder_layers if num_decoder_layers is not None else self.num_layers
					)  # default = symmetry
					snake_case							 =				num_heads
					snake_case							 =				relative_attention_num_buckets
					snake_case							 =				relative_attention_max_distance
					snake_case							 =				dropout_rate
					snake_case							 =				layer_norm_epsilon
					snake_case							 =				initializer_factor
					snake_case							 =				feed_forward_proj
					snake_case							 =				use_cache

					snake_case							 =				self.feed_forward_proj.split('-'   )
					snake_case							 =				act_info[-1]
					snake_case							 =				act_info[0] == 'gated'

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

				@property
				def 				_lowerCamelCase						(					self   )						->       int:
					return self.d_model

				@property
				def 				_lowerCamelCase						(					self   )						->       Dict:
					return self.num_heads






				@property
				def 				_lowerCamelCase						(					self   )						->       Any:
					return self.num_layers







class lowerCamelCase      (						__lowerCAmelCase							):

				@property
				# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
				def 				_lowerCamelCase						(					self   )						->       Mapping[str, Mapping[int, str]]:
					snake_case							 =				{
					    'input_ids': {0: 'batch', 1: 'encoder_sequence'},
					    'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
					}
					if self.use_past:
						snake_case							 =				'past_encoder_sequence + sequence'
						snake_case							 =				{0: 'batch'}
						snake_case							 =				{0: 'batch', 1: 'past_decoder_sequence + sequence'}
					else:
						snake_case							 =				{0: 'batch', 1: 'decoder_sequence'}
						snake_case							 =				{0: 'batch', 1: 'decoder_sequence'}

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

					return common_inputs

				@property
				# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
				def 				_lowerCamelCase						(					self   )						->       int:
					return 13






				@property
				def 				_lowerCamelCase						(					self   )						->       float:
					return 5E-4








'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase_						   =			{
    "configuration_led": ["LED_PRETRAINED_CONFIG_ARCHIVE_MAP", "LEDConfig"],
    "tokenization_led": ["LEDTokenizer"],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase_						   =			["LEDTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase_						   =			[
						    "LED_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "LEDForConditionalGeneration",
						    "LEDForQuestionAnswering",
						    "LEDForSequenceClassification",
						    "LEDModel",
						    "LEDPreTrainedModel",
						]


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase_						   =			["TFLEDForConditionalGeneration", "TFLEDModel", "TFLEDPreTrainedModel"]


if TYPE_CHECKING:
						from .configuration_led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig
						from .tokenization_led import LEDTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_led_fast import LEDTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_led import (
												    LED_PRETRAINED_MODEL_ARCHIVE_LIST,
												    LEDForConditionalGeneration,
												    LEDForQuestionAnswering,
												    LEDForSequenceClassification,
												    LEDModel,
												    LEDPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel

else:
						import sys

						lowerCAmelCase_						   =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





