






from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig


__lowerCamelCase :      Optional[Any]										=				logging.get_logger(__name__)

# General docstring
__lowerCamelCase :      Dict										=				"RegNetConfig"

# Base docstring
__lowerCamelCase :      List[str]										=				"facebook/regnet-y-040"
__lowerCamelCase :      Optional[Any]										=				[1, 1088, 7, 7]

# Image classification docstring
__lowerCamelCase :      int										=				"facebook/regnet-y-040"
__lowerCamelCase :      Tuple										=				"tabby, tabby cat"

__lowerCamelCase :      Dict										=				[
    "facebook/regnet-y-040",
    # See all regnet models at https://huggingface.co/models?filter=regnet
]



class 							SCREAMING_SNAKE_CASE__						(    tf.keras.layers.Layer						):





					"""simple docstring"""






					def __init__( self					:  Union[str, Any]							,		__A					:  Dict							,		__A					:  int = 3							,		__A					:  Optional[int] = 1							,		__A					:  Union[str, Any] = 1							,		__A					:  int = "relu"							,		**__A					:  Optional[Any]							,		):
						super().__init__(**A__	)
						# The padding and conv has been verified in
						# https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
						snake_case__			:				Union[str, Any]       =  tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2	)
						snake_case__			:				List[str]       =  tf.keras.layers.ConvaD(
						    filters=A__							,		kernel_size=A__							,		strides=A__							,		padding="VALID"							,		groups=A__							,		use_bias=A__							,		name="convolution"							,		)
						snake_case__			:				int       =  tf.keras.layers.BatchNormalization(epsilon=1e-5							,		momentum=0.9							,		name="normalization"	)
						snake_case__			:				List[Any]       =  ACTaFN[activation] if activation is not None else tf.identity






					def 	_lowercase					( self					:  str							,		__A					:  int	):
						snake_case__			:				Optional[Any]       =  self.convolution(self.padding(A__	)	)
						snake_case__			:				Optional[Any]       =  self.normalization(A__	)
						snake_case__			:				str       =  self.activation(A__	)
						return hidden_state



class 							SCREAMING_SNAKE_CASE__						(    tf.keras.layers.Layer						):





					"""simple docstring"""






					def __init__( self					:  List[str]							,		__A					:  Any							,		**__A					:  Dict	):
						super().__init__(**A__	)
						snake_case__			:				List[str]       =  config.num_channels
						snake_case__			:				List[Any]       =  TFRegNetConvLayer(
						    out_channels=config.embedding_size							,		kernel_size=3							,		stride=2							,		activation=config.hidden_act							,		name="embedder"							,		)






					def 	_lowercase					( self					:  Any							,		__A					:  Optional[int]	):
						snake_case__			:				List[str]       =  shape_list(A__	)[1]
						if tf.executing_eagerly() and num_channels != self.num_channels:
							raise ValueError(
							    "Make sure that the channel dimension of the pixel values match with the one set in the configuration."	)

						# When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
						# So change the input format from `NCHW` to `NHWC`.
						# shape = (batch_size, in_height, in_width, in_channels=num_channels)
						snake_case__			:				Tuple       =  tf.transpose(A__							,		perm=(0, 2, 3, 1)	)
						snake_case__			:				List[str]       =  self.embedder(A__	)
						return hidden_state



class 							SCREAMING_SNAKE_CASE__						(    tf.keras.layers.Layer						):





					"""simple docstring"""






					def __init__( self					:  List[Any]							,		__A					:  Any							,		__A					:  Any = 2							,		**__A					:  Union[str, Any]	):
						super().__init__(**A__	)
						snake_case__			:				int       =  tf.keras.layers.ConvaD(
						    filters=A__							,		kernel_size=1							,		strides=A__							,		use_bias=A__							,		name="convolution"	)
						snake_case__			:				Dict       =  tf.keras.layers.BatchNormalization(epsilon=1e-5							,		momentum=0.9							,		name="normalization"	)






					def 	_lowercase					( self					:  Dict							,		__A					:  int							,		__A					:  Tuple = False	):
						return self.normalization(self.convolution(A__	)							,		training=A__	)



class 							SCREAMING_SNAKE_CASE__						(    tf.keras.layers.Layer						):





					"""simple docstring"""






					def __init__( self					:  Dict							,		__A					:  str							,		__A					:  List[str]							,		**__A					:  List[Any]	):
						super().__init__(**A__	)
						snake_case__			:				int       =  tf.keras.layers.GlobalAveragePoolingaD(keepdims=A__							,		name="pooler"	)
						snake_case__			:				Tuple       =  [
						    tf.keras.layers.ConvaD(filters=A__							,		kernel_size=1							,		activation="relu"							,		name="attention.0"	),
						    tf.keras.layers.ConvaD(filters=A__							,		kernel_size=1							,		activation="sigmoid"							,		name="attention.2"	),
						]






					def 	_lowercase					( self					:  List[Any]							,		__A					:  List[Any]	):
						# [batch_size, h, w, num_channels] -> [batch_size, 1, 1, num_channels]
						snake_case__			:				Dict       =  self.pooler(A__	)
						for layer_module in self.attention:
							snake_case__			:				Dict       =  layer_module(A__	)
						snake_case__			:				List[Any]       =  hidden_state * pooled
						return hidden_state



class 							SCREAMING_SNAKE_CASE__						(    tf.keras.layers.Layer						):





					"""simple docstring"""






					def __init__( self					:  Optional[Any]							,		__A					:  int							,		__A					:  Union[str, Any]							,		__A					:  Any							,		__A					:  Dict = 1							,		**__A					:  int	):
						super().__init__(**A__	)
						snake_case__			:				Optional[Any]       =  in_channels != out_channels or stride != 1
						snake_case__			:				List[str]       =  max(1							,		out_channels // config.groups_width	)
						snake_case__			:				List[str]       =  (
						    TFRegNetShortCut(A__							,		stride=A__							,		name="shortcut"	)
						    if should_apply_shortcut
						    else tf.keras.layers.Activation("linear"							,		name="shortcut"	)
						)
						# `self.layers` instead of `self.layer` because that is a reserved argument.
						snake_case__			:				Optional[Any]       =  [
						    TFRegNetConvLayer(A__							,		kernel_size=1							,		activation=config.hidden_act							,		name="layer.0"	),
						    TFRegNetConvLayer(
						        A__							,		stride=A__							,		groups=A__							,		activation=config.hidden_act							,		name="layer.1"	),
						    TFRegNetConvLayer(A__							,		kernel_size=1							,		activation=A__							,		name="layer.2"	),
						]
						snake_case__			:				int       =  ACTaFN[config.hidden_act]






					def 	_lowercase					( self					:  Dict							,		__A					:  Dict	):
						snake_case__			:				Optional[Any]       =  hidden_state
						for layer_module in self.layers:
							snake_case__			:				List[str]       =  layer_module(A__	)
						snake_case__			:				int       =  self.shortcut(A__	)
						hidden_state += residual
						snake_case__			:				List[str]       =  self.activation(A__	)
						return hidden_state



class 							SCREAMING_SNAKE_CASE__						(    tf.keras.layers.Layer						):





					"""simple docstring"""






					def __init__( self					:  Optional[Any]							,		__A					:  Optional[Any]							,		__A					:  Union[str, Any]							,		__A					:  Optional[Any]							,		__A					:  int = 1							,		**__A					:  List[Any]	):
						super().__init__(**A__	)
						snake_case__			:				List[Any]       =  in_channels != out_channels or stride != 1
						snake_case__			:				Union[str, Any]       =  max(1							,		out_channels // config.groups_width	)
						snake_case__			:				Any       =  (
						    TFRegNetShortCut(A__							,		stride=A__							,		name="shortcut"	)
						    if should_apply_shortcut
						    else tf.keras.layers.Activation("linear"							,		name="shortcut"	)
						)
						snake_case__			:				Dict       =  [
						    TFRegNetConvLayer(A__							,		kernel_size=1							,		activation=config.hidden_act							,		name="layer.0"	),
						    TFRegNetConvLayer(
						        A__							,		stride=A__							,		groups=A__							,		activation=config.hidden_act							,		name="layer.1"	),
						    TFRegNetSELayer(A__							,		reduced_channels=int(round(in_channels / 4	)	)							,		name="layer.2"	),
						    TFRegNetConvLayer(A__							,		kernel_size=1							,		activation=A__							,		name="layer.3"	),
						]
						snake_case__			:				str       =  ACTaFN[config.hidden_act]






					def 	_lowercase					( self					:  int							,		__A					:  List[str]	):
						snake_case__			:				str       =  hidden_state
						for layer_module in self.layers:
							snake_case__			:				List[Any]       =  layer_module(A__	)
						snake_case__			:				Optional[Any]       =  self.shortcut(A__	)
						hidden_state += residual
						snake_case__			:				Any       =  self.activation(A__	)
						return hidden_state



class 							SCREAMING_SNAKE_CASE__						(    tf.keras.layers.Layer						):





					"""simple docstring"""






					def __init__( self					:  List[str]							,		__A					:  Dict							,		__A					:  List[str]							,		__A					:  int							,		__A					:  Tuple = 2							,		__A					:  int = 2							,		**__A					:  Optional[Any]	):
						super().__init__(**A__	)

						snake_case__			:				Dict       =  TFRegNetXLayer if config.layer_type == "x" else TFRegNetYLayer
						snake_case__			:				str       =  [
						    # downsampling is done in the first layer with stride of 2
						    layer(A__							,		A__							,		A__							,		stride=A__							,		name="layers.0"	),
						    *[layer(A__							,		A__							,		A__							,		name=f'''layers.{i+1}'''	) for i in range(depth - 1	)],
						]






					def 	_lowercase					( self					:  List[str]							,		__A					:  List[str]	):
						for layer_module in self.layers:
							snake_case__			:				List[str]       =  layer_module(A__	)
						return hidden_state



class 							SCREAMING_SNAKE_CASE__						(    tf.keras.layers.Layer						):





					"""simple docstring"""






					def __init__( self					:  List[Any]							,		__A					:  Dict							,		**__A					:  Optional[Any]	):
						super().__init__(**A__	)
						snake_case__			:				Dict       =  []
						# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
						self.stages.append(
						    TFRegNetStage(
						        A__							,		config.embedding_size							,		config.hidden_sizes[0]							,		stride=2 if config.downsample_in_first_stage else 1							,		depth=config.depths[0]							,		name="stages.0"							,		)	)
						snake_case__			:				Any       =  zip(config.hidden_sizes							,		config.hidden_sizes[1:]	)
						for i, ((in_channels, out_channels), depth) in enumerate(zip(A__							,		config.depths[1:]	)	):
							self.stages.append(TFRegNetStage(A__							,		A__							,		A__							,		depth=A__							,		name=f'''stages.{i+1}'''	)	)






					def 	_lowercase					( self					:  List[Any]							,		__A					:  List[Any]							,		__A					:  Any = False							,		__A					:  Any = True	):
						snake_case__			:				List[Any]       =  () if output_hidden_states else None

						for stage_module in self.stages:
							if output_hidden_states:
								snake_case__			:				Any       =  hidden_states + (hidden_state,)

							snake_case__			:				str       =  stage_module(A__	)

						if output_hidden_states:
							snake_case__			:				List[str]       =  hidden_states + (hidden_state,)

						if not return_dict:
							return tuple(v for v in [hidden_state, hidden_states] if v is not None	)

						return TFBaseModelOutputWithNoAttention(last_hidden_state=A__							,		hidden_states=A__	)



@keras_serializable
class 							SCREAMING_SNAKE_CASE__						(    tf.keras.layers.Layer						):





					"""simple docstring"""






					a_     =		RegNetConfig



					def __init__( self					:  int							,		__A					:  Optional[Any]							,		**__A					:  Any	):
						super().__init__(**A__	)
						snake_case__			:				Union[str, Any]       =  config
						snake_case__			:				Optional[Any]       =  TFRegNetEmbeddings(A__							,		name="embedder"	)
						snake_case__			:				Optional[int]       =  TFRegNetEncoder(A__							,		name="encoder"	)
						snake_case__			:				Optional[Any]       =  tf.keras.layers.GlobalAveragePoolingaD(keepdims=A__							,		name="pooler"	)






					@unpack_inputs
					def 	_lowercase					( self					:  int							,		__A					:  Union[str, Any]							,		__A					:  Dict = None							,		__A					:  List[Any] = None							,		__A					:  Dict = False							,		):
						snake_case__			:				Any       =  (
						    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
						)
						snake_case__			:				Optional[Any]       =  return_dict if return_dict is not None else self.config.use_return_dict

						snake_case__			:				Optional[int]       =  self.embedder(A__							,		training=A__	)

						snake_case__			:				Optional[Any]       =  self.encoder(
						    A__							,		output_hidden_states=A__							,		return_dict=A__							,		training=A__	)

						snake_case__			:				Union[str, Any]       =  encoder_outputs[0]
						snake_case__			:				Dict       =  self.pooler(A__	)

						# Change to NCHW output format have uniformity in the modules
						snake_case__			:				Any       =  tf.transpose(A__							,		perm=(0, 3, 1, 2)	)
						snake_case__			:				Union[str, Any]       =  tf.transpose(A__							,		perm=(0, 3, 1, 2)	)

						# Change the other hidden state outputs to NCHW as well
						if output_hidden_states:
							snake_case__			:				Optional[Any]       =  tuple([tf.transpose(A__							,		perm=(0, 3, 1, 2)	) for h in encoder_outputs[1]]	)

						if not return_dict:
							return (last_hidden_state, pooled_output) + encoder_outputs[1:]

						return TFBaseModelOutputWithPoolingAndNoAttention(
						    last_hidden_state=A__							,		pooler_output=A__							,		hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states							,		)



class 							SCREAMING_SNAKE_CASE__						(    SCREAMING_SNAKE_CASE__						):





					"""simple docstring"""






					a_     =		RegNetConfig
					a_     =		'''regnet'''
					a_     =		'''pixel_values'''



					@property
					def 	_lowercase					( self					:  Optional[int]	):
						return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_2_4, 2_2_4)							,		dtype=tf.floataa	)}
__lowerCamelCase :      List[str]										=				r"\n    Parameters:\n    This model is a Tensorflow\n    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a\n    regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and\n    behavior.\n        config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n"

__lowerCamelCase :      List[Any]										=				r"\n    Args:\n        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConveNextImageProcessor.__call__`] for details.\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n"



@add_start_docstrings(
    "The bare RegNet model outputting raw features without any specific head on top."			,       SCREAMING_SNAKE_CASE__			,       )
class 							SCREAMING_SNAKE_CASE__						(    SCREAMING_SNAKE_CASE__						):





					"""simple docstring"""






					def __init__( self					:  int							,		__A					:  List[str]							,		*__A					:  Optional[Any]							,		**__A					:  Dict	):
						super().__init__(A__							,		*A__							,		**A__	)
						snake_case__			:				int       =  TFRegNetMainLayer(A__							,		name="regnet"	)






					@unpack_inputs
					@add_start_docstrings_to_model_forward(A__	)
					@add_code_sample_docstrings(
					    checkpoint=_CHECKPOINT_FOR_DOC							,		output_type=A__							,		config_class=_CONFIG_FOR_DOC							,		modality="vision"							,		expected_output=_EXPECTED_OUTPUT_SHAPE							,		)
					def 	_lowercase					( self					:  Optional[int]							,		__A					:  Tuple							,		__A					:  Any = None							,		__A					:  Union[str, Any] = None							,		__A					:  int=False							,		):
						snake_case__			:				Optional[Any]       =  (
						    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
						)
						snake_case__			:				Dict       =  return_dict if return_dict is not None else self.config.use_return_dict

						snake_case__			:				Any       =  self.regnet(
						    pixel_values=A__							,		output_hidden_states=A__							,		return_dict=A__							,		training=A__							,		)
						if not return_dict:
							return (outputs[0],) + outputs[1:]

						return TFBaseModelOutputWithPoolingAndNoAttention(
						    last_hidden_state=outputs.last_hidden_state							,		pooler_output=outputs.pooler_output							,		hidden_states=outputs.hidden_states							,		)



@add_start_docstrings(
    "\n    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    "			,       SCREAMING_SNAKE_CASE__			,       )
class 							SCREAMING_SNAKE_CASE__						(    SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__						):





					"""simple docstring"""






					def __init__( self					:  Dict							,		__A					:  Tuple							,		*__A					:  Union[str, Any]							,		**__A					:  Any	):
						super().__init__(A__							,		*A__							,		**A__	)
						snake_case__			:				Any       =  config.num_labels
						snake_case__			:				Dict       =  TFRegNetMainLayer(A__							,		name="regnet"	)
						# classification head
						snake_case__			:				Dict       =  [
						    tf.keras.layers.Flatten(),
						    tf.keras.layers.Dense(config.num_labels							,		name="classifier.1"	) if config.num_labels > 0 else tf.identity,
						]






					@unpack_inputs
					@add_start_docstrings_to_model_forward(A__	)
					@add_code_sample_docstrings(
					    checkpoint=_IMAGE_CLASS_CHECKPOINT							,		output_type=A__							,		config_class=_CONFIG_FOR_DOC							,		expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT							,		)
					def 	_lowercase					( self					:  int							,		__A					:  int = None							,		__A					:  Any = None							,		__A					:  Dict = None							,		__A					:  List[Any] = None							,		__A					:  Union[str, Any]=False							,		):
						snake_case__			:				int       =  (
						    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
						)
						snake_case__			:				Dict       =  return_dict if return_dict is not None else self.config.use_return_dict

						snake_case__			:				Tuple       =  self.regnet(
						    A__							,		output_hidden_states=A__							,		return_dict=A__							,		training=A__	)

						snake_case__			:				Optional[Any]       =  outputs.pooler_output if return_dict else outputs[1]

						snake_case__			:				Dict       =  self.classifier[0](A__	)
						snake_case__			:				List[Any]       =  self.classifier[1](A__	)

						snake_case__			:				Dict       =  None if labels is None else self.hf_compute_loss(labels=A__							,		logits=A__	)

						if not return_dict:
							snake_case__			:				Optional[Any]       =  (logits,) + outputs[2:]
							return ((loss,) + output) if loss is not None else output

						return TFSequenceClassifierOutput(loss=A__							,		logits=A__							,		hidden_states=outputs.hidden_states	)











import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


__lowerCamelCase :      int										=				logging.get_logger(__name__)


__lowerCamelCase :      List[str]										=				{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

__lowerCamelCase :      Any										=				{
    """vocab_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
    },
}

__lowerCamelCase :      Optional[int]										=				{
    """allenai/led-base-16384""": 1_6384,
}



class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					a_     =		VOCAB_FILES_NAMES
					a_     =		PRETRAINED_VOCAB_FILES_MAP
					a_     =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					a_     =		LEDTokenizer
					a_     =		["input_ids", "attention_mask"]



					def __init__( self					:  int							,		__A					:  Optional[int]=None							,		__A					:  Any=None							,		__A					:  Any=None							,		__A					:  int="replace"							,		__A					:  Optional[Any]="<s>"							,		__A					:  List[Any]="</s>"							,		__A					:  Dict="</s>"							,		__A					:  Union[str, Any]="<s>"							,		__A					:  List[Any]="<unk>"							,		__A					:  Optional[Any]="<pad>"							,		__A					:  Optional[int]="<mask>"							,		__A					:  str=False							,		__A					:  Optional[Any]=True							,		**__A					:  List[Any]							,		):
						super().__init__(
						    __A							,		__A							,		tokenizer_file=__A							,		errors=__A							,		bos_token=__A							,		eos_token=__A							,		sep_token=__A							,		cls_token=__A							,		unk_token=__A							,		pad_token=__A							,		mask_token=__A							,		add_prefix_space=__A							,		trim_offsets=__A							,		**__A							,		)

						snake_case__			:				List[Any]       =  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
						if pre_tok_state.get("add_prefix_space"							,		__A	) != add_prefix_space:
							snake_case__			:				List[str]       =  getattr(__A							,		pre_tok_state.pop("type"	)	)
							snake_case__			:				Tuple       =  add_prefix_space
							snake_case__			:				Any       =  pre_tok_class(**__A	)

						snake_case__			:				List[Any]       =  add_prefix_space

						# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
						snake_case__			:				str       =  "post_processor"
						snake_case__			:				Union[str, Any]       =  getattr(self.backend_tokenizer							,		__A							,		__A	)
						if tokenizer_component_instance:
							snake_case__			:				Any       =  json.loads(tokenizer_component_instance.__getstate__()	)

							# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
							if "sep" in state:
								snake_case__			:				int       =  tuple(state["sep"]	)
							if "cls" in state:
								snake_case__			:				Optional[int]       =  tuple(state["cls"]	)

							snake_case__			:				Union[str, Any]       =  False

							if state.get("add_prefix_space"							,		__A	) != add_prefix_space:
								snake_case__			:				Optional[Any]       =  add_prefix_space
								snake_case__			:				List[str]       =  True

							if state.get("trim_offsets"							,		__A	) != trim_offsets:
								snake_case__			:				str       =  trim_offsets
								snake_case__			:				Tuple       =  True

							if changes_to_apply:
								snake_case__			:				Any       =  getattr(__A							,		state.pop("type"	)	)
								snake_case__			:				Optional[Any]       =  component_class(**__A	)
								setattr(self.backend_tokenizer							,		__A							,		__A	)



					@property
					# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
					def 	_lowercase					( self					:  Union[str, Any]	):
						if self._mask_token is None:
							if self.verbose:
								logger.error("Using mask_token, but it is not set yet."	)
							return None
						return str(self._mask_token	)



					@mask_token.setter
					def 	_lowercase					( self					:  str							,		__A					:  int	):
						snake_case__			:				str       =  AddedToken(__A							,		lstrip=__A							,		rstrip=__A	) if isinstance(__A							,		__A	) else value
						snake_case__			:				Optional[int]       =  value



					def 	_lowercase					( self					:  List[str]							,		*__A					:  Union[str, Any]							,		**__A					:  int	):
						snake_case__			:				Dict       =  kwargs.get("is_split_into_words"							,		__A	)

						if is_split_into_words and not self.add_prefix_space:
							raise ValueError(
							    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
							    "to use it with pretokenized inputs."	)

						return super()._batch_encode_plus(*__A							,		**__A	)



					def 	_lowercase					( self					:  Union[str, Any]							,		*__A					:  Tuple							,		**__A					:  Union[str, Any]	):
						snake_case__			:				List[str]       =  kwargs.get("is_split_into_words"							,		__A	)

						if is_split_into_words and not self.add_prefix_space:
							raise ValueError(
							    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
							    "to use it with pretokenized inputs."	)

						return super()._encode_plus(*__A							,		**__A	)



					def 	_lowercase					( self					:  Union[str, Any]							,		__A					:  str							,		__A					:  Optional[str] = None	):
						snake_case__			:				str       =  self._tokenizer.model.save(__A							,		name=__A	)
						return tuple(__A	)



					def 	_lowercase					( self					:  Union[str, Any]							,		__A					:  int							,		__A					:  str=None	):
						snake_case__			:				int       =  [self.bos_token_id] + token_ids_a + [self.eos_token_id]
						if token_ids_a is None:
							return output

						return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]



					def 	_lowercase					( self					:  Any							,		__A					:  List[int]							,		__A					:  Optional[List[int]] = None	):
						snake_case__			:				Optional[Any]       =  [self.sep_token_id]
						snake_case__			:				Any       =  [self.cls_token_id]

						if token_ids_a is None:
							return len(cls + token_ids_a + sep	) * [0]
						return len(cls + token_ids_a + sep + sep + token_ids_a + sep	) * [0]






					def 	_lowercase					( self					:  str							,		__A					:  Union[Dict[str, EncodedInput], BatchEncoding]							,		__A					:  Optional[int] = None							,		__A					:  PaddingStrategy = PaddingStrategy.DO_NOT_PAD							,		__A					:  Optional[int] = None							,		__A					:  Optional[bool] = None							,		):
						snake_case__			:				Dict       =  super()._pad(
						    encoded_inputs=__A							,		max_length=__A							,		padding_strategy=__A							,		pad_to_multiple_of=__A							,		return_attention_mask=__A							,		)

						# Load from model defaults
						if return_attention_mask is None:
							snake_case__			:				Optional[Any]       =  "attention_mask" in self.model_input_names

						if return_attention_mask and "global_attention_mask" in encoded_inputs:
							snake_case__			:				List[Any]       =  encoded_inputs[self.model_input_names[0]]
							# `global_attention_mask` need to have the same length as other (sequential) inputs.
							snake_case__			:				Union[str, Any]       =  len(encoded_inputs["global_attention_mask"]	) != len(__A	)

							if needs_to_be_padded:
								snake_case__			:				Dict       =  len(__A	) - len(encoded_inputs["global_attention_mask"]	)

								if self.padding_side == "right":
									# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
									snake_case__			:				Optional[Any]       =  (
									    encoded_inputs["global_attention_mask"] + [-1] * difference
									)
								elif self.padding_side == "left":
									snake_case__			:				Optional[Any]       =  [-1] * difference + encoded_inputs[
									    "global_attention_mask"
									]
								else:
									raise ValueError("Invalid padding strategy:" + str(self.padding_side	)	)

						return encoded_inputs



