

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


a_    :						Dict    		=      logging.get_logger(__name__)

# General docstring
a_    :						int    		=      "RegNetConfig"

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

# Image classification docstring
a_    :						Optional[Any]    		=      "facebook/regnet-y-040"
a_    :						int    		=      "tabby, tabby cat"

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




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

    def __init__(			self :      Tuple							,    snake_case__ :      int							,    snake_case__ :      int = 3							,    snake_case__ :      int = 1							,    snake_case__ :      int = 1							,    snake_case__ :      Optional[str] = "relu"							,    **snake_case__ :      Optional[int]							,    ):

     """simple docstring"""






     super().__init__(**snake_case__    )
     # The padding and conv has been verified in
     # https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
     SCREAMING_SNAKE_CASE								=			tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2    )
     SCREAMING_SNAKE_CASE								=			tf.keras.layers.ConvaD(
         filters=snake_case__							,    kernel_size=snake_case__							,    strides=snake_case__							,    padding='VALID'							,    groups=snake_case__							,    use_bias=snake_case__							,    name='convolution'							,    )
     SCREAMING_SNAKE_CASE								=			tf.keras.layers.BatchNormalization(epsilon=1E-5							,    momentum=0.9							,    name='normalization'    )
     SCREAMING_SNAKE_CASE								=			ACTaFN[activation] if activation is not None else tf.identity




    def UpperCamelCase			(			self :      int							,    snake_case__ :      List[str]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.convolution(self.padding(snake_case__    )    )
     SCREAMING_SNAKE_CASE								=			self.normalization(snake_case__    )
     SCREAMING_SNAKE_CASE								=			self.activation(snake_case__    )
     return hidden_state




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

    def __init__(			self :      Dict							,    snake_case__ :      RegNetConfig							,    **snake_case__ :      Union[str, Any]    ):

     """simple docstring"""






     super().__init__(**snake_case__    )
     SCREAMING_SNAKE_CASE								=			config.num_channels
     SCREAMING_SNAKE_CASE								=			TFRegNetConvLayer(
         out_channels=config.embedding_size							,    kernel_size=3							,    stride=2							,    activation=config.hidden_act							,    name='embedder'							,    )




    def UpperCamelCase			(			self :      Tuple							,    snake_case__ :      List[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			shape_list(snake_case__    )[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)
     SCREAMING_SNAKE_CASE								=			tf.transpose(snake_case__							,    perm=(0, 2, 3, 1)    )
     SCREAMING_SNAKE_CASE								=			self.embedder(snake_case__    )
     return hidden_state




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

    def __init__(			self :      str							,    snake_case__ :      int							,    snake_case__ :      int = 2							,    **snake_case__ :      Tuple    ):

     """simple docstring"""






     super().__init__(**snake_case__    )
     SCREAMING_SNAKE_CASE								=			tf.keras.layers.ConvaD(
         filters=snake_case__							,    kernel_size=1							,    strides=snake_case__							,    use_bias=snake_case__							,    name='convolution'    )
     SCREAMING_SNAKE_CASE								=			tf.keras.layers.BatchNormalization(epsilon=1E-5							,    momentum=0.9							,    name='normalization'    )




    def UpperCamelCase			(			self :      List[Any]							,    snake_case__ :      tf.Tensor							,    snake_case__ :      bool = False    ):

     """simple docstring"""






     return self.normalization(self.convolution(snake_case__    )							,    training=snake_case__    )




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

    def __init__(			self :      Tuple							,    snake_case__ :      int							,    snake_case__ :      int							,    **snake_case__ :      List[Any]    ):

     """simple docstring"""






     super().__init__(**snake_case__    )
     SCREAMING_SNAKE_CASE								=			tf.keras.layers.GlobalAveragePoolingaD(keepdims=snake_case__							,    name='pooler'    )
     SCREAMING_SNAKE_CASE								=			[
         tf.keras.layers.ConvaD(filters=snake_case__							,    kernel_size=1							,    activation='relu'							,    name='attention.0'    ),
         tf.keras.layers.ConvaD(filters=snake_case__							,    kernel_size=1							,    activation='sigmoid'							,    name='attention.2'    ),
     ]




    def UpperCamelCase			(			self :      Optional[Any]							,    snake_case__ :      List[str]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.pooler(snake_case__    )
     for layer_module in self.attention:
      SCREAMING_SNAKE_CASE								=			layer_module(snake_case__    )
     SCREAMING_SNAKE_CASE								=			hidden_state * pooled
     return hidden_state




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

    def __init__(			self :      Optional[int]							,    snake_case__ :      RegNetConfig							,    snake_case__ :      int							,    snake_case__ :      int							,    snake_case__ :      int = 1							,    **snake_case__ :      Dict    ):

     """simple docstring"""






     super().__init__(**snake_case__    )
     SCREAMING_SNAKE_CASE								=			in_channels != out_channels or stride != 1
     SCREAMING_SNAKE_CASE								=			max(1							,    out_channels // config.groups_width    )
     SCREAMING_SNAKE_CASE								=			(
         TFRegNetShortCut(snake_case__							,    stride=snake_case__							,    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.
     SCREAMING_SNAKE_CASE								=			[
         TFRegNetConvLayer(snake_case__							,    kernel_size=1							,    activation=config.hidden_act							,    name='layer.0'    ),
         TFRegNetConvLayer(
             snake_case__							,    stride=snake_case__							,    groups=snake_case__							,    activation=config.hidden_act							,    name='layer.1'    ),
         TFRegNetConvLayer(snake_case__							,    kernel_size=1							,    activation=snake_case__							,    name='layer.2'    ),
     ]
     SCREAMING_SNAKE_CASE								=			ACTaFN[config.hidden_act]




    def UpperCamelCase			(			self :      Optional[int]							,    snake_case__ :      Optional[int]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			hidden_state
     for layer_module in self.layers:
      SCREAMING_SNAKE_CASE								=			layer_module(snake_case__    )
     SCREAMING_SNAKE_CASE								=			self.shortcut(snake_case__    )
     hidden_state += residual
     SCREAMING_SNAKE_CASE								=			self.activation(snake_case__    )
     return hidden_state




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

    def __init__(			self :      int							,    snake_case__ :      RegNetConfig							,    snake_case__ :      int							,    snake_case__ :      int							,    snake_case__ :      int = 1							,    **snake_case__ :      Any    ):

     """simple docstring"""






     super().__init__(**snake_case__    )
     SCREAMING_SNAKE_CASE								=			in_channels != out_channels or stride != 1
     SCREAMING_SNAKE_CASE								=			max(1							,    out_channels // config.groups_width    )
     SCREAMING_SNAKE_CASE								=			(
         TFRegNetShortCut(snake_case__							,    stride=snake_case__							,    name='shortcut'    )
         if should_apply_shortcut
         else tf.keras.layers.Activation('linear'							,    name='shortcut'    )
     )
     SCREAMING_SNAKE_CASE								=			[
         TFRegNetConvLayer(snake_case__							,    kernel_size=1							,    activation=config.hidden_act							,    name='layer.0'    ),
         TFRegNetConvLayer(
             snake_case__							,    stride=snake_case__							,    groups=snake_case__							,    activation=config.hidden_act							,    name='layer.1'    ),
         TFRegNetSELayer(snake_case__							,    reduced_channels=int(round(in_channels / 4    )    )							,    name='layer.2'    ),
         TFRegNetConvLayer(snake_case__							,    kernel_size=1							,    activation=snake_case__							,    name='layer.3'    ),
     ]
     SCREAMING_SNAKE_CASE								=			ACTaFN[config.hidden_act]




    def UpperCamelCase			(			self :      Optional[int]							,    snake_case__ :      Optional[int]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			hidden_state
     for layer_module in self.layers:
      SCREAMING_SNAKE_CASE								=			layer_module(snake_case__    )
     SCREAMING_SNAKE_CASE								=			self.shortcut(snake_case__    )
     hidden_state += residual
     SCREAMING_SNAKE_CASE								=			self.activation(snake_case__    )
     return hidden_state




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

    def __init__(			self :      Optional[Any]							,    snake_case__ :      RegNetConfig							,    snake_case__ :      int							,    snake_case__ :      int							,    snake_case__ :      int = 2							,    snake_case__ :      int = 2							,    **snake_case__ :      Optional[Any]    ):

     """simple docstring"""






     super().__init__(**snake_case__    )

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




    def UpperCamelCase			(			self :      List[str]							,    snake_case__ :      Tuple    ):

     """simple docstring"""






     for layer_module in self.layers:
      SCREAMING_SNAKE_CASE								=			layer_module(snake_case__    )
     return hidden_state




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

    def __init__(			self :      List[Any]							,    snake_case__ :      RegNetConfig							,    **snake_case__ :      Union[str, Any]    ):

     """simple docstring"""






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




    def UpperCamelCase			(			self :      Optional[int]							,    snake_case__ :      tf.Tensor							,    snake_case__ :      bool = False							,    snake_case__ :      bool = True    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			() if output_hidden_states else None

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

      SCREAMING_SNAKE_CASE								=			stage_module(snake_case__    )

     if output_hidden_states:
      SCREAMING_SNAKE_CASE								=			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=snake_case__							,    hidden_states=snake_case__    )




@keras_serializable
class      UpperCamelCase (     tf.keras.layers.Layer   ):
    __UpperCamelCase         =RegNetConfig

    def __init__(			self :      Optional[int]							,    snake_case__ :      Dict							,    **snake_case__ :      List[str]    ):

     """simple docstring"""






     super().__init__(**snake_case__    )
     SCREAMING_SNAKE_CASE								=			config
     SCREAMING_SNAKE_CASE								=			TFRegNetEmbeddings(snake_case__							,    name='embedder'    )
     SCREAMING_SNAKE_CASE								=			TFRegNetEncoder(snake_case__							,    name='encoder'    )
     SCREAMING_SNAKE_CASE								=			tf.keras.layers.GlobalAveragePoolingaD(keepdims=snake_case__							,    name='pooler'    )




    @unpack_inputs
    def UpperCamelCase			(			self :      Optional[Any]							,    snake_case__ :      tf.Tensor							,    snake_case__ :      Optional[bool] = None							,    snake_case__ :      Optional[bool] = None							,    snake_case__ :      bool = False							,    ):

     """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

     SCREAMING_SNAKE_CASE								=			self.embedder(snake_case__							,    training=snake_case__    )

     SCREAMING_SNAKE_CASE								=			self.encoder(
         snake_case__							,    output_hidden_states=snake_case__							,    return_dict=snake_case__							,    training=snake_case__    )

     SCREAMING_SNAKE_CASE								=			encoder_outputs[0]
     SCREAMING_SNAKE_CASE								=			self.pooler(snake_case__    )

     # Change to NCHW output format have uniformity in the modules
     SCREAMING_SNAKE_CASE								=			tf.transpose(snake_case__							,    perm=(0, 3, 1, 2)    )
     SCREAMING_SNAKE_CASE								=			tf.transpose(snake_case__							,    perm=(0, 3, 1, 2)    )

     # Change the other hidden state outputs to NCHW as well
     if output_hidden_states:
      SCREAMING_SNAKE_CASE								=			tuple([tf.transpose(snake_case__							,    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=snake_case__							,    pooler_output=snake_case__							,    hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states							,    )




class      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):
    __UpperCamelCase         =RegNetConfig
    __UpperCamelCase         ="regnet"
    __UpperCamelCase         ="pixel_values"

    @property
    def UpperCamelCase			(			self :      Dict    ):

     """simple docstring"""






     return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_2_4, 2_2_4)							,    dtype=tf.floataa    )}


a_    :						Optional[Any]    		=      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"

a_    :						Dict    		=      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      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):

    def __init__(			self :      Union[str, Any]							,    snake_case__ :      RegNetConfig							,    *snake_case__ :      Any							,    **snake_case__ :      Any    ):

     """simple docstring"""






     super().__init__(snake_case__							,    *snake_case__							,    **snake_case__    )
     SCREAMING_SNAKE_CASE								=			TFRegNetMainLayer(snake_case__							,    name='regnet'    )




    @unpack_inputs
    @add_start_docstrings_to_model_forward(snake_case__    )
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC							,    output_type=snake_case__							,    config_class=_CONFIG_FOR_DOC							,    modality='vision'							,    expected_output=_EXPECTED_OUTPUT_SHAPE							,    )
    def UpperCamelCase			(			self :      Dict							,    snake_case__ :      tf.Tensor							,    snake_case__ :      Optional[bool] = None							,    snake_case__ :      Optional[bool] = None							,    snake_case__ :      Any=False							,    ):

     """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

     SCREAMING_SNAKE_CASE								=			self.regnet(
         pixel_values=snake_case__							,    output_hidden_states=snake_case__							,    return_dict=snake_case__							,    training=snake_case__							,    )
     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      UpperCamelCase (     SCREAMING_SNAKE_CASE	,			SCREAMING_SNAKE_CASE   ):

    def __init__(			self :      str							,    snake_case__ :      RegNetConfig							,    *snake_case__ :      Optional[Any]							,    **snake_case__ :      Tuple    ):

     """simple docstring"""






     super().__init__(snake_case__							,    *snake_case__							,    **snake_case__    )
     SCREAMING_SNAKE_CASE								=			config.num_labels
     SCREAMING_SNAKE_CASE								=			TFRegNetMainLayer(snake_case__							,    name='regnet'    )
     # classification head
     SCREAMING_SNAKE_CASE								=			[
         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(snake_case__    )
    @add_code_sample_docstrings(
        checkpoint=_IMAGE_CLASS_CHECKPOINT							,    output_type=snake_case__							,    config_class=_CONFIG_FOR_DOC							,    expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT							,    )
    def UpperCamelCase			(			self :      int							,    snake_case__ :      tf.Tensor = None							,    snake_case__ :      tf.Tensor = None							,    snake_case__ :      bool = None							,    snake_case__ :      bool = None							,    snake_case__ :      Dict=False							,    ):

     """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

     SCREAMING_SNAKE_CASE								=			self.regnet(
         snake_case__							,    output_hidden_states=snake_case__							,    return_dict=snake_case__							,    training=snake_case__    )

     SCREAMING_SNAKE_CASE								=			outputs.pooler_output if return_dict else outputs[1]

     SCREAMING_SNAKE_CASE								=			self.classifier[0](snake_case__    )
     SCREAMING_SNAKE_CASE								=			self.classifier[1](snake_case__    )

     SCREAMING_SNAKE_CASE								=			None if labels is None else self.hf_compute_loss(labels=snake_case__							,    logits=snake_case__    )

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

     return TFSequenceClassifierOutput(loss=snake_case__							,    logits=snake_case__							,    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


a_    :						Optional[int]    		=      logging.get_logger(__name__)


a_    :						Union[str, Any]    		=      {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

a_    :						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",
    },
}

a_    :						List[Any]    		=      {
    "allenai/led-base-16384": 1_6384,
}




class      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):
    __UpperCamelCase         =VOCAB_FILES_NAMES
    __UpperCamelCase         =PRETRAINED_VOCAB_FILES_MAP
    __UpperCamelCase         =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __UpperCamelCase         =LEDTokenizer
    __UpperCamelCase         =["input_ids", "attention_mask"]

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

     """simple docstring"""






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

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

     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							,    snake_case__							,    snake_case__    )
     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'							,    snake_case__    ) != add_prefix_space:
       SCREAMING_SNAKE_CASE								=			add_prefix_space
       SCREAMING_SNAKE_CASE								=			True

      if state.get('trim_offsets'							,    snake_case__    ) != trim_offsets:
       SCREAMING_SNAKE_CASE								=			trim_offsets
       SCREAMING_SNAKE_CASE								=			True

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

    @property
    # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
    def UpperCamelCase			(			self :      Optional[Any]    ):

     """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 UpperCamelCase			(			self :      List[Any]							,    snake_case__ :      Any    ):

     """simple docstring"""






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

    def UpperCamelCase			(			self :      Dict							,    *snake_case__ :      Optional[Any]							,    **snake_case__ :      Tuple    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			kwargs.get('is_split_into_words'							,    snake_case__    )

     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(*snake_case__							,    **snake_case__    )

    def UpperCamelCase			(			self :      List[str]							,    *snake_case__ :      List[Any]							,    **snake_case__ :      Optional[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			kwargs.get('is_split_into_words'							,    snake_case__    )

     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(*snake_case__							,    **snake_case__    )

    def UpperCamelCase			(			self :      Union[str, Any]							,    snake_case__ :      str							,    snake_case__ :      Optional[str] = None    ):

     """simple docstring"""






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

    def UpperCamelCase			(			self :      List[str]							,    snake_case__ :      int							,    snake_case__ :      Tuple=None    ):

     """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 UpperCamelCase			(			self :      Optional[int]							,    snake_case__ :      List[int]							,    snake_case__ :      Optional[List[int]] = None    ):

     """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 UpperCamelCase			(			self :      Optional[Any]							,    snake_case__ :      Union[Dict[str, EncodedInput], BatchEncoding]							,    snake_case__ :      Optional[int] = None							,    snake_case__ :      PaddingStrategy = PaddingStrategy.DO_NOT_PAD							,    snake_case__ :      Optional[int] = None							,    snake_case__ :      Optional[bool] = None							,    ):

     """simple docstring"""






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

     # 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(snake_case__    )

      if needs_to_be_padded:
       SCREAMING_SNAKE_CASE								=			len(snake_case__    ) - 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

