import collections.abc
from typing import Optional, Tuple, Union

import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_poolformer import PoolFormerConfig


_lowerCamelCase   :				int     			=						logging.get_logger(__name__)

# General docstring
_lowerCamelCase   :				List[Any]     			=						"""PoolFormerConfig"""

# Base docstring
_lowerCamelCase   :				Optional[int]     			=						"""sail/poolformer_s12"""
_lowerCamelCase   :				Union[str, Any]     			=						[1, 5_12, 7, 7]

# Image classification docstring
_lowerCamelCase   :				Tuple     			=						"""sail/poolformer_s12"""
_lowerCamelCase   :				Tuple     			=						"""tabby, tabby cat"""

_lowerCamelCase   :				str     			=						[
    """sail/poolformer_s12""",
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
]





def 		__lowerCamelCase							(UpperCAmelCase__       :							Optional[int] ,	UpperCAmelCase__       :							Dict = 0.0 ,	UpperCAmelCase__       :							Tuple = False					):
					if drop_prob == 0.0 or not training:
										return input
					SCREAMING_SNAKE_CASE           =    1 - drop_prob
					SCREAMING_SNAKE_CASE           =    (input.shape[0],) + (1,) * (input.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
					SCREAMING_SNAKE_CASE           =    keep_prob + torch.rand(UpperCAmelCase__ ,	dtype=input.dtype ,	device=input.device					)
					random_tensor.floor_()  # binarize
					SCREAMING_SNAKE_CASE           =    input.div(UpperCAmelCase__					) * random_tensor
					return output


class   lowercase      (       nn.Module       ):




							def __init__(  self				:   Tuple		,							_UpperCamelCase				:   Union[str, Any] = None )   ->			Optional[Any]:



												'''simple docstring'''

												super().__init__()
												SCREAMING_SNAKE_CASE           =    drop_prob




							def 		__snake_case(  self				:   Union[str, Any]		,							_UpperCamelCase				:   List[Any] )   ->			Union[str, Any]:



												'''simple docstring'''

												return drop_path(__lowercase		,							self.drop_prob		,							self.training )





							def 		__snake_case(  self				:   List[str] )   ->			Optional[Any]:



												'''simple docstring'''

												return "p={}".format(self.drop_prob )



class   lowercase      (       nn.Module       ):




							def __init__(  self				:   List[str]		,							_UpperCamelCase				:   str		,							_UpperCamelCase				:   Dict		,							_UpperCamelCase				:   List[Any]		,							_UpperCamelCase				:   Dict		,							_UpperCamelCase				:   Union[str, Any]		,							_UpperCamelCase				:   List[str]=None )   ->			Any:



												'''simple docstring'''

												super().__init__()
												SCREAMING_SNAKE_CASE           =    patch_size if isinstance(__lowercase		,							collections.abc.Iterable ) else (patch_size, patch_size)
												SCREAMING_SNAKE_CASE           =    stride if isinstance(__lowercase		,							collections.abc.Iterable ) else (stride, stride)
												SCREAMING_SNAKE_CASE           =    padding if isinstance(__lowercase		,							collections.abc.Iterable ) else (padding, padding)

												SCREAMING_SNAKE_CASE           =    nn.Convad(__lowercase		,							__lowercase		,							kernel_size=__lowercase		,							stride=__lowercase		,							padding=__lowercase )
												SCREAMING_SNAKE_CASE           =    norm_layer(__lowercase ) if norm_layer else nn.Identity()





							def 		__snake_case(  self				:   Optional[int]		,							_UpperCamelCase				:   List[str] )   ->			Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.projection(__lowercase )
												SCREAMING_SNAKE_CASE           =    self.norm(__lowercase )
												return embeddings



class   lowercase      (       nn.GroupNorm       ):




							def __init__(  self				:   Optional[Any]		,							_UpperCamelCase				:   int		,							**_UpperCamelCase				:   Union[str, Any] )   ->			int:



												'''simple docstring'''

												super().__init__(1		,							__lowercase		,							**__lowercase )



class   lowercase      (       nn.Module       ):




							def __init__(  self				:   Any		,							_UpperCamelCase				:   Any )   ->			str:



												'''simple docstring'''

												super().__init__()
												SCREAMING_SNAKE_CASE           =    nn.AvgPoolad(__lowercase		,							stride=1		,							padding=pool_size // 2		,							count_include_pad=__lowercase )





							def 		__snake_case(  self				:   List[Any]		,							_UpperCamelCase				:   str )   ->			Union[str, Any]:



												'''simple docstring'''

												return self.pool(__lowercase ) - hidden_states



class   lowercase      (       nn.Module       ):




							def __init__(  self				:   int		,							_UpperCamelCase				:   Dict		,							_UpperCamelCase				:   Optional[int]		,							_UpperCamelCase				:   Dict		,							_UpperCamelCase				:   Dict )   ->			Optional[Any]:



												'''simple docstring'''

												super().__init__()
												SCREAMING_SNAKE_CASE           =    nn.Convad(__lowercase		,							__lowercase		,							1 )
												SCREAMING_SNAKE_CASE           =    nn.Convad(__lowercase		,							__lowercase		,							1 )
												SCREAMING_SNAKE_CASE           =    PoolFormerDropPath(__lowercase )
												if isinstance(config.hidden_act		,							__lowercase ):
																	SCREAMING_SNAKE_CASE           =    ACTaFN[config.hidden_act]
												else:
																	SCREAMING_SNAKE_CASE           =    config.hidden_act





							def 		__snake_case(  self				:   Optional[int]		,							_UpperCamelCase				:   List[Any] )   ->			Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.conva(__lowercase )
												SCREAMING_SNAKE_CASE           =    self.act_fn(__lowercase )
												SCREAMING_SNAKE_CASE           =    self.drop(__lowercase )
												SCREAMING_SNAKE_CASE           =    self.conva(__lowercase )
												SCREAMING_SNAKE_CASE           =    self.drop(__lowercase )

												return hidden_states



class   lowercase      (       nn.Module       ):




							def __init__(  self				:   Optional[int]		,							_UpperCamelCase				:   List[str]		,							_UpperCamelCase				:   Union[str, Any]		,							_UpperCamelCase				:   Any		,							_UpperCamelCase				:   Optional[int]		,							_UpperCamelCase				:   List[Any]		,							_UpperCamelCase				:   Optional[int] )   ->			int:



												'''simple docstring'''

												super().__init__()
												SCREAMING_SNAKE_CASE           =    PoolFormerPooling(__lowercase )
												SCREAMING_SNAKE_CASE           =    PoolFormerOutput(__lowercase		,							__lowercase		,							__lowercase		,							__lowercase )
												SCREAMING_SNAKE_CASE           =    PoolFormerGroupNorm(__lowercase )
												SCREAMING_SNAKE_CASE           =    PoolFormerGroupNorm(__lowercase )

												# Useful for training neural nets
												SCREAMING_SNAKE_CASE           =    PoolFormerDropPath(__lowercase ) if drop_path > 0.0 else nn.Identity()
												SCREAMING_SNAKE_CASE           =    config.use_layer_scale
												if config.use_layer_scale:
																	SCREAMING_SNAKE_CASE           =    nn.Parameter(
																	    config.layer_scale_init_value * torch.ones((__lowercase) )		,							requires_grad=__lowercase )
																	SCREAMING_SNAKE_CASE           =    nn.Parameter(
																	    config.layer_scale_init_value * torch.ones((__lowercase) )		,							requires_grad=__lowercase )





							def 		__snake_case(  self				:   List[Any]		,							_UpperCamelCase				:   Tuple )   ->			Tuple:



												'''simple docstring'''

												if self.use_layer_scale:
																	SCREAMING_SNAKE_CASE           =    self.pooling(self.before_norm(__lowercase ) )
																	SCREAMING_SNAKE_CASE           =    self.layer_scale_a.unsqueeze(-1 ).unsqueeze(-1 ) * pooling_output
																	# First residual connection
																	SCREAMING_SNAKE_CASE           =    hidden_states + self.drop_path(__lowercase )
																	SCREAMING_SNAKE_CASE           =    ()

																	SCREAMING_SNAKE_CASE           =    self.output(self.after_norm(__lowercase ) )
																	SCREAMING_SNAKE_CASE           =    self.layer_scale_a.unsqueeze(-1 ).unsqueeze(-1 ) * layer_output
																	# Second residual connection
																	SCREAMING_SNAKE_CASE           =    hidden_states + self.drop_path(__lowercase )

																	SCREAMING_SNAKE_CASE           =    (output,) + outputs
																	return outputs

												else:
																	SCREAMING_SNAKE_CASE           =    self.drop_path(self.pooling(self.before_norm(__lowercase ) ) )
																	# First residual connection
																	SCREAMING_SNAKE_CASE           =    pooling_output + hidden_states
																	SCREAMING_SNAKE_CASE           =    ()

																	# Second residual connection inside the PoolFormerOutput block
																	SCREAMING_SNAKE_CASE           =    self.drop_path(self.output(self.after_norm(__lowercase ) ) )
																	SCREAMING_SNAKE_CASE           =    hidden_states + layer_output

																	SCREAMING_SNAKE_CASE           =    (output,) + outputs
																	return outputs



class   lowercase      (       nn.Module       ):




							def __init__(  self				:   Any		,							_UpperCamelCase				:   Any )   ->			List[str]:



												'''simple docstring'''

												super().__init__()
												SCREAMING_SNAKE_CASE           =    config
												# stochastic depth decay rule
												SCREAMING_SNAKE_CASE           =    [x.item() for x in torch.linspace(0		,							config.drop_path_rate		,							sum(config.depths ) )]

												# patch embeddings
												SCREAMING_SNAKE_CASE           =    []
												for i in range(config.num_encoder_blocks ):
																	embeddings.append(
																	    PoolFormerEmbeddings(
																	        patch_size=config.patch_sizes[i]		,							stride=config.strides[i]		,							padding=config.padding[i]		,							num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1]		,							hidden_size=config.hidden_sizes[i]		,							) )
												SCREAMING_SNAKE_CASE           =    nn.ModuleList(__lowercase )

												# Transformer blocks
												SCREAMING_SNAKE_CASE           =    []
												SCREAMING_SNAKE_CASE           =    0
												for i in range(config.num_encoder_blocks ):
																	# each block consists of layers
																	SCREAMING_SNAKE_CASE           =    []
																	if i != 0:
																						cur += config.depths[i - 1]
																	for j in range(config.depths[i] ):
																						layers.append(
																						    PoolFormerLayer(
																						        __lowercase		,							num_channels=config.hidden_sizes[i]		,							pool_size=config.pool_size		,							hidden_size=config.hidden_sizes[i]		,							intermediate_size=int(config.hidden_sizes[i] * config.mlp_ratio )		,							drop_path=dpr[cur + j]		,							) )
																	blocks.append(nn.ModuleList(__lowercase ) )

												SCREAMING_SNAKE_CASE           =    nn.ModuleList(__lowercase )





							def 		__snake_case(  self				:   str		,							_UpperCamelCase				:   str		,							_UpperCamelCase				:   Dict=False		,							_UpperCamelCase				:   str=True )   ->			str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    () if output_hidden_states else None

												SCREAMING_SNAKE_CASE           =    pixel_values
												for idx, layers in enumerate(zip(self.patch_embeddings		,							self.block ) ):
																	SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    layers
																	# Get patch embeddings from hidden_states
																	SCREAMING_SNAKE_CASE           =    embedding_layer(__lowercase )
																	# Send the embeddings through the blocks
																	for _, blk in enumerate(__lowercase ):
																						SCREAMING_SNAKE_CASE           =    blk(__lowercase )
																						SCREAMING_SNAKE_CASE           =    layer_outputs[0]

																	if output_hidden_states:
																						SCREAMING_SNAKE_CASE           =    all_hidden_states + (hidden_states,)

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

												return BaseModelOutputWithNoAttention(last_hidden_state=__lowercase		,							hidden_states=__lowercase )



class   lowercase      (       __A       ):
							lowercase__  :    int									=							PoolFormerConfig
							lowercase__  :    Union[str, Any]									=							'poolformer'
							lowercase__  :    Tuple									=							'pixel_values'
							lowercase__  :    Tuple									=							True




							def 		__snake_case(  self				:   Any		,							_UpperCamelCase				:   Any )   ->			str:



												'''simple docstring'''

												if isinstance(__lowercase		,							(nn.Linear, nn.Convad) ):
																	module.weight.data.normal_(mean=0.0		,							std=self.config.initializer_range )
																	if module.bias is not None:
																						module.bias.data.zero_()
												elif isinstance(__lowercase		,							nn.LayerNorm ):
																	module.bias.data.zero_()
																	module.weight.data.fill_(1.0 )





							def 		__snake_case(  self				:   List[str]		,							_UpperCamelCase				:   List[str]		,							_UpperCamelCase				:   Optional[int]=False )   ->			Dict:



												'''simple docstring'''

												if isinstance(__lowercase		,							__lowercase ):
																	SCREAMING_SNAKE_CASE           =    value



_lowerCamelCase   :				Any     			=						r"""
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`PoolFormerConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

_lowerCamelCase   :				List[Any]     			=						r"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`PoolFormerImageProcessor.__call__`] for details.
"""


@add_start_docstrings(
    """The bare PoolFormer Model transformer outputting raw hidden-states without any specific head on top."""			,    __A			,    )
class   lowercase      (       __A       ):




							def __init__(  self				:   List[Any]		,							_UpperCamelCase				:   Dict )   ->			int:



												'''simple docstring'''

												super().__init__(__lowercase )
												SCREAMING_SNAKE_CASE           =    config

												SCREAMING_SNAKE_CASE           =    PoolFormerEncoder(__lowercase )

												# Initialize weights and apply final processing
												self.post_init()




							def 		__snake_case(  self				:   List[str] )   ->			Any:



												'''simple docstring'''

												return self.embeddings.patch_embeddings





							@add_start_docstrings_to_model_forward(__lowercase )
							@add_code_sample_docstrings(
							    checkpoint=_CHECKPOINT_FOR_DOC		,							output_type=__lowercase		,							config_class=_CONFIG_FOR_DOC		,							modality="vision"		,							expected_output=_EXPECTED_OUTPUT_SHAPE		,							)
							def 		__snake_case(  self				:   Optional[Any]		,							_UpperCamelCase				:   Optional[int] = None		,							_UpperCamelCase				:   List[str] = None		,							_UpperCamelCase				:   Dict = None		,							)   ->			Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    (
												    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
												)
												SCREAMING_SNAKE_CASE           =    return_dict if return_dict is not None else self.config.use_return_dict

												if pixel_values is None:
																	raise ValueError("You have to specify pixel_values" )

												SCREAMING_SNAKE_CASE           =    self.encoder(
												    __lowercase		,							output_hidden_states=__lowercase		,							return_dict=__lowercase		,							)
												SCREAMING_SNAKE_CASE           =    encoder_outputs[0]

												if not return_dict:
																	return (sequence_output, None) + encoder_outputs[1:]

												return BaseModelOutputWithNoAttention(
												    last_hidden_state=__lowercase		,							hidden_states=encoder_outputs.hidden_states		,							)



class   lowercase      (       nn.Module       ):




							def __init__(  self				:   Tuple		,							_UpperCamelCase				:   Optional[Any] )   ->			List[Any]:



												'''simple docstring'''

												super().__init__()
												SCREAMING_SNAKE_CASE           =    nn.Linear(config.hidden_size		,							config.hidden_size )





							def 		__snake_case(  self				:   Union[str, Any]		,							_UpperCamelCase				:   Union[str, Any] )   ->			Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.dense(__lowercase )
												return output



@add_start_docstrings(
    """\n    PoolFormer Model transformer with an image classification head on top\n    """			,    __A			,    )
class   lowercase      (       __A       ):




							def __init__(  self				:   Tuple		,							_UpperCamelCase				:   int )   ->			Optional[int]:



												'''simple docstring'''

												super().__init__(__lowercase )
												SCREAMING_SNAKE_CASE           =    config.num_labels
												SCREAMING_SNAKE_CASE           =    PoolFormerModel(__lowercase )

												# Final norm
												SCREAMING_SNAKE_CASE           =    PoolFormerGroupNorm(config.hidden_sizes[-1] )
												# Classifier head
												SCREAMING_SNAKE_CASE           =    (
												    nn.Linear(config.hidden_sizes[-1]		,							config.num_labels ) if config.num_labels > 0 else nn.Identity()
												)

												# Initialize weights and apply final processing
												self.post_init()





							@add_start_docstrings_to_model_forward(__lowercase )
							@add_code_sample_docstrings(
							    checkpoint=_IMAGE_CLASS_CHECKPOINT		,							output_type=__lowercase		,							config_class=_CONFIG_FOR_DOC		,							expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT		,							)
							def 		__snake_case(  self				:   Optional[Any]		,							_UpperCamelCase				:   Optional[int] = None		,							_UpperCamelCase				:   Optional[Any] = None		,							_UpperCamelCase				:   Any = None		,							_UpperCamelCase				:   List[str] = None		,							)   ->			List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    return_dict if return_dict is not None else self.config.use_return_dict

												SCREAMING_SNAKE_CASE           =    self.poolformer(
												    __lowercase		,							output_hidden_states=__lowercase		,							return_dict=__lowercase		,							)

												SCREAMING_SNAKE_CASE           =    outputs[0]

												SCREAMING_SNAKE_CASE           =    self.classifier(self.norm(__lowercase ).mean([-2, -1] ) )

												SCREAMING_SNAKE_CASE           =    None
												if labels is not None:
																	if self.config.problem_type is None:
																						if self.num_labels == 1:
																											SCREAMING_SNAKE_CASE           =    "regression"
																						elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
																											SCREAMING_SNAKE_CASE           =    "single_label_classification"
																						else:
																											SCREAMING_SNAKE_CASE           =    "multi_label_classification"

																	if self.config.problem_type == "regression":
																						SCREAMING_SNAKE_CASE           =    MSELoss()
																						if self.num_labels == 1:
																											SCREAMING_SNAKE_CASE           =    loss_fct(logits.squeeze()		,							labels.squeeze() )
																						else:
																											SCREAMING_SNAKE_CASE           =    loss_fct(__lowercase		,							__lowercase )
																	elif self.config.problem_type == "single_label_classification":
																						SCREAMING_SNAKE_CASE           =    CrossEntropyLoss()
																						SCREAMING_SNAKE_CASE           =    loss_fct(logits.view(-1		,							self.num_labels )		,							labels.view(-1 ) )
																	elif self.config.problem_type == "multi_label_classification":
																						SCREAMING_SNAKE_CASE           =    BCEWithLogitsLoss()
																						SCREAMING_SNAKE_CASE           =    loss_fct(__lowercase		,							__lowercase )

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

												return ImageClassifierOutputWithNoAttention(loss=__lowercase		,							logits=__lowercase		,							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   :				Union[str, Any]     			=						logging.get_logger(__name__)


_lowerCamelCase   :				str     			=						{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_lowerCamelCase   :				int     			=						{
    '''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   :				List[Any]     			=						{
    '''allenai/led-base-16384''': 1_63_84,
}


class   lowercase      (       a       ):
							lowercase__  :    Tuple									=							VOCAB_FILES_NAMES
							lowercase__  :    Any									=							PRETRAINED_VOCAB_FILES_MAP
							lowercase__  :    Optional[Any]									=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase__  :    Optional[Any]									=							LEDTokenizer
							lowercase__  :    str									=							["""input_ids""", """attention_mask"""]




							def __init__(  self				:   Union[str, Any]		,							_UpperCamelCase				:   Tuple=None		,							_UpperCamelCase				:   str=None		,							_UpperCamelCase				:   Tuple=None		,							_UpperCamelCase				:   List[str]="replace"		,							_UpperCamelCase				:   str="<s>"		,							_UpperCamelCase				:   List[Any]="</s>"		,							_UpperCamelCase				:   List[Any]="</s>"		,							_UpperCamelCase				:   List[str]="<s>"		,							_UpperCamelCase				:   Tuple="<unk>"		,							_UpperCamelCase				:   List[Any]="<pad>"		,							_UpperCamelCase				:   Tuple="<mask>"		,							_UpperCamelCase				:   List[str]=False		,							_UpperCamelCase				:   List[Any]=True		,							**_UpperCamelCase				:   Optional[Any]		,							)   ->			Tuple:



												'''simple docstring'''

												super().__init__(
												    _UpperCamelCase		,							_UpperCamelCase		,							tokenizer_file=_UpperCamelCase		,							errors=_UpperCamelCase		,							bos_token=_UpperCamelCase		,							eos_token=_UpperCamelCase		,							sep_token=_UpperCamelCase		,							cls_token=_UpperCamelCase		,							unk_token=_UpperCamelCase		,							pad_token=_UpperCamelCase		,							mask_token=_UpperCamelCase		,							add_prefix_space=_UpperCamelCase		,							trim_offsets=_UpperCamelCase		,							**_UpperCamelCase		,							)

												SCREAMING_SNAKE_CASE           =    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
												if pre_tok_state.get("add_prefix_space"		,							_UpperCamelCase ) != add_prefix_space:
																	SCREAMING_SNAKE_CASE           =    getattr(_UpperCamelCase		,							pre_tok_state.pop("type" ) )
																	SCREAMING_SNAKE_CASE           =    add_prefix_space
																	SCREAMING_SNAKE_CASE           =    pre_tok_class(**_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    add_prefix_space

												# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
												SCREAMING_SNAKE_CASE           =    "post_processor"
												SCREAMING_SNAKE_CASE           =    getattr(self.backend_tokenizer		,							_UpperCamelCase		,							_UpperCamelCase )
												if tokenizer_component_instance:
																	SCREAMING_SNAKE_CASE           =    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:
																						SCREAMING_SNAKE_CASE           =    tuple(state["sep"] )
																	if "cls" in state:
																						SCREAMING_SNAKE_CASE           =    tuple(state["cls"] )

																	SCREAMING_SNAKE_CASE           =    False

																	if state.get("add_prefix_space"		,							_UpperCamelCase ) != add_prefix_space:
																						SCREAMING_SNAKE_CASE           =    add_prefix_space
																						SCREAMING_SNAKE_CASE           =    True

																	if state.get("trim_offsets"		,							_UpperCamelCase ) != trim_offsets:
																						SCREAMING_SNAKE_CASE           =    trim_offsets
																						SCREAMING_SNAKE_CASE           =    True

																	if changes_to_apply:
																						SCREAMING_SNAKE_CASE           =    getattr(_UpperCamelCase		,							state.pop("type" ) )
																						SCREAMING_SNAKE_CASE           =    component_class(**_UpperCamelCase )
																						setattr(self.backend_tokenizer		,							_UpperCamelCase		,							_UpperCamelCase )




							@property
							# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
							def 		__snake_case(  self				:   int )   ->			str:



												'''simple docstring'''

												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 		__snake_case(  self				:   Optional[int]		,							_UpperCamelCase				:   Optional[int] )   ->			Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    AddedToken(_UpperCamelCase		,							lstrip=_UpperCamelCase		,							rstrip=_UpperCamelCase ) if isinstance(_UpperCamelCase		,							_UpperCamelCase ) else value
												SCREAMING_SNAKE_CASE           =    value




							def 		__snake_case(  self				:   List[Any]		,							*_UpperCamelCase				:   Optional[int]		,							**_UpperCamelCase				:   Union[str, Any] )   ->			BatchEncoding:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    kwargs.get("is_split_into_words"		,							_UpperCamelCase )

												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(*_UpperCamelCase		,							**_UpperCamelCase )




							def 		__snake_case(  self				:   int		,							*_UpperCamelCase				:   Dict		,							**_UpperCamelCase				:   Tuple )   ->			BatchEncoding:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    kwargs.get("is_split_into_words"		,							_UpperCamelCase )

												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(*_UpperCamelCase		,							**_UpperCamelCase )




							def 		__snake_case(  self				:   List[Any]		,							_UpperCamelCase				:   str		,							_UpperCamelCase				:   Optional[str] = None )   ->			Tuple[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self._tokenizer.model.save(_UpperCamelCase		,							name=_UpperCamelCase )
												return tuple(_UpperCamelCase )




							def 		__snake_case(  self				:   List[Any]		,							_UpperCamelCase				:   List[Any]		,							_UpperCamelCase				:   int=None )   ->			List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    [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 		__snake_case(  self				:   Dict		,							_UpperCamelCase				:   List[int]		,							_UpperCamelCase				:   Optional[List[int]] = None )   ->			List[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    [self.sep_token_id]
												SCREAMING_SNAKE_CASE           =    [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 		__snake_case(  self				:   Optional[Any]		,							_UpperCamelCase				:   Union[Dict[str, EncodedInput], BatchEncoding]		,							_UpperCamelCase				:   Optional[int] = None		,							_UpperCamelCase				:   PaddingStrategy = PaddingStrategy.DO_NOT_PAD		,							_UpperCamelCase				:   Optional[int] = None		,							_UpperCamelCase				:   Optional[bool] = None		,							)   ->			dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    super()._pad(
												    encoded_inputs=_UpperCamelCase		,							max_length=_UpperCamelCase		,							padding_strategy=_UpperCamelCase		,							pad_to_multiple_of=_UpperCamelCase		,							return_attention_mask=_UpperCamelCase		,							)

												# Load from model defaults
												if return_attention_mask is None:
																	SCREAMING_SNAKE_CASE           =    "attention_mask" in self.model_input_names

												if return_attention_mask and "global_attention_mask" in encoded_inputs:
																	SCREAMING_SNAKE_CASE           =    encoded_inputs[self.model_input_names[0]]
																	# `global_attention_mask` need to have the same length as other (sequential) inputs.
																	SCREAMING_SNAKE_CASE           =    len(encoded_inputs["global_attention_mask"] ) != len(_UpperCamelCase )

																	if needs_to_be_padded:
																						SCREAMING_SNAKE_CASE           =    len(_UpperCamelCase ) - 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`
																											SCREAMING_SNAKE_CASE           =    (
																											    encoded_inputs["global_attention_mask"] + [-1] * difference
																											)
																						elif self.padding_side == "left":
																											SCREAMING_SNAKE_CASE           =    [-1] * difference + encoded_inputs[
																											    "global_attention_mask"
																											]
																						else:
																											raise ValueError("Invalid padding strategy:" + str(self.padding_side ) )

												return encoded_inputs




