
"""simple docstring"""


from typing import Mapping

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


__A 		= logging.get_logger(__name__)

__A 		= {
    '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 	lowerCamelCase__      (      __magic_name__       ):





			'''simple docstring'''
			lowerCamelCase						      =	'''longt5'''
			lowerCamelCase						      =	['''past_key_values''']
			lowerCamelCase						      =	{'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}



			def __init__(							self  ,  __UpperCAmelCase=3_21_28  ,  __UpperCAmelCase=5_12  ,  __UpperCAmelCase=64  ,  __UpperCAmelCase=20_48  ,  __UpperCAmelCase=6  ,  __UpperCAmelCase=None  ,  __UpperCAmelCase=8  ,  __UpperCAmelCase=1_27  ,  __UpperCAmelCase=16  ,  __UpperCAmelCase=32  ,  __UpperCAmelCase=1_28  ,  __UpperCAmelCase=0.1  ,  __UpperCAmelCase=1e-6  ,  __UpperCAmelCase=1.0  ,  __UpperCAmelCase="relu"  ,  __UpperCAmelCase=True  ,  __UpperCAmelCase="local"  ,  __UpperCAmelCase=True  ,  __UpperCAmelCase=0  ,  __UpperCAmelCase=1  ,  **__UpperCAmelCase  ,  )				->  Any:
				_lowerCAmelCase			=vocab_size
				_lowerCAmelCase			=d_model
				_lowerCAmelCase			=d_kv
				_lowerCAmelCase			=d_ff
				_lowerCAmelCase			=num_layers
				# default = symmetry
				_lowerCAmelCase			=num_decoder_layers if num_decoder_layers is not None else self.num_layers
				_lowerCAmelCase			=num_heads
				_lowerCAmelCase			=local_radius
				_lowerCAmelCase			=global_block_size
				_lowerCAmelCase			=relative_attention_num_buckets
				_lowerCAmelCase			=relative_attention_max_distance
				_lowerCAmelCase			=dropout_rate
				_lowerCAmelCase			=layer_norm_epsilon
				_lowerCAmelCase			=initializer_factor
				_lowerCAmelCase			=feed_forward_proj
				_lowerCAmelCase			=encoder_attention_type
				_lowerCAmelCase			=use_cache

				_lowerCAmelCase			=self.feed_forward_proj.split("""-"""						)
				_lowerCAmelCase			=act_info[-1]
				_lowerCAmelCase			=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":
					_lowerCAmelCase			="""gelu_new"""

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







class 	lowerCamelCase__      (      __magic_name__       ):





			'''simple docstring'''



			@property
			def       _lowerCAmelCase	(							self						)				->  Mapping[str, Mapping[int, str]]:
				_lowerCAmelCase			={
				    """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
				    """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
				}
				if self.use_past:
					_lowerCAmelCase			="""past_encoder_sequence + sequence"""
					_lowerCAmelCase			={0: """batch"""}
					_lowerCAmelCase			={0: """batch""", 1: """past_decoder_sequence + sequence"""}
				else:
					_lowerCAmelCase			={0: """batch""", 1: """decoder_sequence"""}
					_lowerCAmelCase			={0: """batch""", 1: """decoder_sequence"""}

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

				return common_inputs



			@property
			def       _lowerCAmelCase	(							self						)				->  int:
				return 13





"""simple docstring"""


from typing import TYPE_CHECKING

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


__A 		= {
    'configuration_led': ['LED_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LEDConfig'],
    'tokenization_led': ['LEDTokenizer'],
}

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

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


try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A 		= ['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

			__A 		= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



