

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_     :    int     		=   logging.get_logger(__name__)

# General docstring
a_     :    str     		=   "RegNetConfig"

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

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

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




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

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

     """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			:   Optional[Any]				,     snake_case__			:   RegNetConfig				,     **snake_case__			:   int     ):

     """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			:   Optional[Any]				,     snake_case__			:   Dict     ):

     """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			:   List[Any]				,     snake_case__			:   int				,     snake_case__			:   int = 2				,     **snake_case__			:   List[Any]     ):

     """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			:   str				,     snake_case__			:   int				,     snake_case__			:   int				,     **snake_case__			:   int     ):

     """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			:   List[str]				,     snake_case__			:   Any     ):

     """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[Any]				,     snake_case__			:   RegNetConfig				,     snake_case__			:   int				,     snake_case__			:   int				,     snake_case__			:   int = 1				,     **snake_case__			:   int     ):

     """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			:   List[Any]				,     snake_case__			:   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			:   Tuple				,     snake_case__			:   RegNetConfig				,     snake_case__			:   int				,     snake_case__			:   int				,     snake_case__			:   int = 1				,     **snake_case__			:   List[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			:   int				,     snake_case__			:   List[Any]     ):

     """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			:   Tuple				,     snake_case__			:   RegNetConfig				,     snake_case__			:   int				,     snake_case__			:   int				,     snake_case__			:   int = 2				,     snake_case__			:   int = 2				,     **snake_case__			:   Optional[int]     ):

     """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			:   Any				,     snake_case__			:   int     ):

     """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			:   Any				,     snake_case__			:   RegNetConfig				,     **snake_case__			:   Dict     ):

     """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			:   Tuple				,     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			:   Dict				,     snake_case__			:   str				,     **snake_case__			:   List[Any]     ):

     """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			:   List[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			:   Union[str, Any]     ):

     """simple docstring"""






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


a_     :    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"

a_     :    int     		=   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			:   List[str]				,     snake_case__			:   RegNetConfig				,     *snake_case__			:   Optional[int]				,     **snake_case__			:   str     ):

     """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			:   Tuple				,     snake_case__			:   tf.Tensor				,     snake_case__			:   Optional[bool] = None				,     snake_case__			:   Optional[bool] = None				,     snake_case__			:   int=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			:   Any				,     snake_case__			:   RegNetConfig				,     *snake_case__			:   Union[str, Any]				,     **snake_case__			:   Optional[int]     ):

     """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__			:   Optional[int]=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 os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_rembert import RemBertTokenizer
else:
      a_     :    int     		=   None

a_     :    List[Any]     		=   logging.get_logger(__name__)
a_     :    List[Any]     		=   {"vocab_file": "sentencepiece.model", "tokenizer_file": "tokenizer.json"}

a_     :    Optional[int]     		=   {
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
    "tokenizer_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/tokenizer.json",
    },
}

a_     :    Union[str, Any]     		=   {
    "google/rembert": 256,
}

a_     :    List[Any]     		=   "▁"




class   UpperCamelCase			(  SCREAMING_SNAKE_CASE ):
    __UpperCamelCase    =VOCAB_FILES_NAMES
    __UpperCamelCase    =PRETRAINED_VOCAB_FILES_MAP
    __UpperCamelCase    =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __UpperCamelCase    =RemBertTokenizer

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

     """simple docstring"""






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

     super().__init__(
         snake_case__				,     tokenizer_file=snake_case__				,     do_lower_case=snake_case__				,     remove_space=snake_case__				,     keep_accents=snake_case__				,     bos_token=snake_case__				,     eos_token=snake_case__				,     unk_token=snake_case__				,     sep_token=snake_case__				,     pad_token=snake_case__				,     cls_token=snake_case__				,     mask_token=snake_case__				,     **snake_case__				,     )

     SCREAMING_SNAKE_CASE		    =      do_lower_case
     SCREAMING_SNAKE_CASE		    =      remove_space
     SCREAMING_SNAKE_CASE		    =      keep_accents
     SCREAMING_SNAKE_CASE		    =      vocab_file
     SCREAMING_SNAKE_CASE		    =      False if not self.vocab_file else True

    def UpperCamelCase      (      self			:   Tuple				,     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 cls + token_ids_a + sep
     return cls + token_ids_a + sep + token_ids_a + sep

    def UpperCamelCase      (      self			:   Tuple				,     snake_case__			:   List[int]				,     snake_case__			:   Optional[List[int]] = None				,     snake_case__			:   bool = False     ):

     """simple docstring"""






     if already_has_special_tokens:
      if token_ids_a is not None:
       raise ValueError(
           'You should not supply a second sequence if the provided sequence of '
           'ids is already formatted with special tokens for the model.'     )
      return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

     if token_ids_a is not None:
      return [1] + ([0] * len(snake_case__     )) + [1] + ([0] * len(snake_case__     )) + [1]
     return [1] + ([0] * len(snake_case__     )) + [1]

    def UpperCamelCase      (      self			:   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     ) * [0] + len(token_ids_a + sep     ) * [1]




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

     """simple docstring"""






     if not os.path.isdir(snake_case__     ):
      logger.error('Vocabulary path ({}) should be a directory'.format(snake_case__     )     )
      return
     SCREAMING_SNAKE_CASE		    =      os.path.join(
         snake_case__				,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']     )

     if os.path.abspath(self.vocab_file     ) != os.path.abspath(snake_case__     ):
      copyfile(self.vocab_file				,     snake_case__     )

     return (out_vocab_file,)

