






"""simple docstring"""



import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
A_    =							logging.get_logger(__name__)




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Any      )		->Union[str, Any]:
						# initialize config
						if "resnet-50" in model_name:
												A__						:  List[str]		  =  ResNetConfig.from_pretrained("""microsoft/resnet-50"""      )
						elif "resnet-101" in model_name:
												A__						:  Union[str, Any]		  =  ResNetConfig.from_pretrained("""microsoft/resnet-101"""      )
						else:
												raise ValueError("""Model name should include either resnet50 or resnet101"""      )

						A__						:  Any		  =  DetrConfig(use_timm_backbone=UpperCAmelCase__,						backbone_config=UpperCAmelCase__      )

						# set label attributes
						A__						:  int		  =  """panoptic""" in model_name
						if is_panoptic:
												A__						:  Optional[Any]		  =  2_5_0
						else:
												A__						:  Dict		  =  9_1
												A__						:  int		  =  """huggingface/label-files"""
												A__						:  List[str]		  =  """coco-detection-id2label.json"""
												A__						:  Dict		  =  json.load(open(hf_hub_download(UpperCAmelCase__,						UpperCAmelCase__,						repo_type="""dataset"""      ),						"""r"""      )      )
												A__						:  Dict		  =  {int(UpperCAmelCase__      ): v for k, v in idalabel.items()}
												A__						:  Union[str, Any]		  =  idalabel
												A__						:  Any		  =  {v: k for k, v in idalabel.items()}

						return config, is_panoptic




def      _lowerCAmelCase		(							UpperCAmelCase__       :					List[str]      )		->int:
						# here we list all keys to be renamed (original name on the left, our name on the right)
						A__						:  Optional[int]		  =  []

						# stem
						# fmt: off
						rename_keys.append(("""backbone.0.body.conv1.weight""", """backbone.conv_encoder.model.embedder.embedder.convolution.weight""")      )
						rename_keys.append(("""backbone.0.body.bn1.weight""", """backbone.conv_encoder.model.embedder.embedder.normalization.weight""")      )
						rename_keys.append(("""backbone.0.body.bn1.bias""", """backbone.conv_encoder.model.embedder.embedder.normalization.bias""")      )
						rename_keys.append(("""backbone.0.body.bn1.running_mean""", """backbone.conv_encoder.model.embedder.embedder.normalization.running_mean""")      )
						rename_keys.append(("""backbone.0.body.bn1.running_var""", """backbone.conv_encoder.model.embedder.embedder.normalization.running_var""")      )
						# stages
						for stage_idx in range(len(config.backbone_config.depths      )      ):
												for layer_idx in range(config.backbone_config.depths[stage_idx]      ):
																		# shortcut
																		if layer_idx == 0:
																								rename_keys.append(
																								    (
																								        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight',
																								        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight',
																								    )      )
																								rename_keys.append(
																								    (
																								        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight',
																								        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight',
																								    )      )
																								rename_keys.append(
																								    (
																								        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias',
																								        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias',
																								    )      )
																								rename_keys.append(
																								    (
																								        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean',
																								        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean',
																								    )      )
																								rename_keys.append(
																								    (
																								        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var',
																								        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var',
																								    )      )
																		# 3 convs
																		for i in range(3      ):
																								rename_keys.append(
																								    (
																								        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight',
																								        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight',
																								    )      )
																								rename_keys.append(
																								    (
																								        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight',
																								        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight',
																								    )      )
																								rename_keys.append(
																								    (
																								        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias',
																								        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias',
																								    )      )
																								rename_keys.append(
																								    (
																								        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean',
																								        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean',
																								    )      )
																								rename_keys.append(
																								    (
																								        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var',
																								        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var',
																								    )      )
    # fmt: on

						for i in range(config.encoder_layers      ):
												# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
												rename_keys.append(
												    (
												        f'transformer.encoder.layers.{i}.self_attn.out_proj.weight',
												        f'encoder.layers.{i}.self_attn.out_proj.weight',
												    )      )
												rename_keys.append(
												    (f'transformer.encoder.layers.{i}.self_attn.out_proj.bias', f'encoder.layers.{i}.self_attn.out_proj.bias')      )
												rename_keys.append((f'transformer.encoder.layers.{i}.linear1.weight', f'encoder.layers.{i}.fc1.weight')      )
												rename_keys.append((f'transformer.encoder.layers.{i}.linear1.bias', f'encoder.layers.{i}.fc1.bias')      )
												rename_keys.append((f'transformer.encoder.layers.{i}.linear2.weight', f'encoder.layers.{i}.fc2.weight')      )
												rename_keys.append((f'transformer.encoder.layers.{i}.linear2.bias', f'encoder.layers.{i}.fc2.bias')      )
												rename_keys.append(
												    (f'transformer.encoder.layers.{i}.norm1.weight', f'encoder.layers.{i}.self_attn_layer_norm.weight')      )
												rename_keys.append(
												    (f'transformer.encoder.layers.{i}.norm1.bias', f'encoder.layers.{i}.self_attn_layer_norm.bias')      )
												rename_keys.append(
												    (f'transformer.encoder.layers.{i}.norm2.weight', f'encoder.layers.{i}.final_layer_norm.weight')      )
												rename_keys.append((f'transformer.encoder.layers.{i}.norm2.bias', f'encoder.layers.{i}.final_layer_norm.bias')      )
												# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
												rename_keys.append(
												    (
												        f'transformer.decoder.layers.{i}.self_attn.out_proj.weight',
												        f'decoder.layers.{i}.self_attn.out_proj.weight',
												    )      )
												rename_keys.append(
												    (f'transformer.decoder.layers.{i}.self_attn.out_proj.bias', f'decoder.layers.{i}.self_attn.out_proj.bias')      )
												rename_keys.append(
												    (
												        f'transformer.decoder.layers.{i}.multihead_attn.out_proj.weight',
												        f'decoder.layers.{i}.encoder_attn.out_proj.weight',
												    )      )
												rename_keys.append(
												    (
												        f'transformer.decoder.layers.{i}.multihead_attn.out_proj.bias',
												        f'decoder.layers.{i}.encoder_attn.out_proj.bias',
												    )      )
												rename_keys.append((f'transformer.decoder.layers.{i}.linear1.weight', f'decoder.layers.{i}.fc1.weight')      )
												rename_keys.append((f'transformer.decoder.layers.{i}.linear1.bias', f'decoder.layers.{i}.fc1.bias')      )
												rename_keys.append((f'transformer.decoder.layers.{i}.linear2.weight', f'decoder.layers.{i}.fc2.weight')      )
												rename_keys.append((f'transformer.decoder.layers.{i}.linear2.bias', f'decoder.layers.{i}.fc2.bias')      )
												rename_keys.append(
												    (f'transformer.decoder.layers.{i}.norm1.weight', f'decoder.layers.{i}.self_attn_layer_norm.weight')      )
												rename_keys.append(
												    (f'transformer.decoder.layers.{i}.norm1.bias', f'decoder.layers.{i}.self_attn_layer_norm.bias')      )
												rename_keys.append(
												    (f'transformer.decoder.layers.{i}.norm2.weight', f'decoder.layers.{i}.encoder_attn_layer_norm.weight')      )
												rename_keys.append(
												    (f'transformer.decoder.layers.{i}.norm2.bias', f'decoder.layers.{i}.encoder_attn_layer_norm.bias')      )
												rename_keys.append(
												    (f'transformer.decoder.layers.{i}.norm3.weight', f'decoder.layers.{i}.final_layer_norm.weight')      )
												rename_keys.append((f'transformer.decoder.layers.{i}.norm3.bias', f'decoder.layers.{i}.final_layer_norm.bias')      )

						# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
						rename_keys.extend(
						    [
						        ("""input_proj.weight""", """input_projection.weight"""),
						        ("""input_proj.bias""", """input_projection.bias"""),
						        ("""query_embed.weight""", """query_position_embeddings.weight"""),
						        ("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
						        ("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
						        ("""class_embed.weight""", """class_labels_classifier.weight"""),
						        ("""class_embed.bias""", """class_labels_classifier.bias"""),
						        ("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
						        ("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
						        ("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
						        ("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
						        ("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
						        ("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
						    ]      )

						return rename_keys




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Dict,						UpperCAmelCase__       :					Tuple,						UpperCAmelCase__       :					Any      )		->Dict:
						A__						:  Tuple		  =  state_dict.pop(UpperCAmelCase__      )
						A__						:  Union[str, Any]		  =  val




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Any,						UpperCAmelCase__       :					Union[str, Any]=False      )		->Any:
						A__						:  Optional[Any]		  =  """"""
						if is_panoptic:
												A__						:  Union[str, Any]		  =  """detr."""

						# first: transformer encoder
						for i in range(6      ):
												# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
												A__						:  List[str]		  =  state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'      )
												A__						:  Any		  =  state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'      )
												# next, add query, keys and values (in that order) to the state dict
												A__						:  List[Any]		  =  in_proj_weight[:2_5_6, :]
												A__						:  Dict		  =  in_proj_bias[:2_5_6]
												A__						:  Union[str, Any]		  =  in_proj_weight[2_5_6:5_1_2, :]
												A__						:  Union[str, Any]		  =  in_proj_bias[2_5_6:5_1_2]
												A__						:  List[str]		  =  in_proj_weight[-2_5_6:, :]
												A__						:  int		  =  in_proj_bias[-2_5_6:]
						# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
						for i in range(6      ):
												# read in weights + bias of input projection layer of self-attention
												A__						:  Optional[int]		  =  state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'      )
												A__						:  Dict		  =  state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias'      )
												# next, add query, keys and values (in that order) to the state dict
												A__						:  Optional[int]		  =  in_proj_weight[:2_5_6, :]
												A__						:  Any		  =  in_proj_bias[:2_5_6]
												A__						:  Tuple		  =  in_proj_weight[2_5_6:5_1_2, :]
												A__						:  List[Any]		  =  in_proj_bias[2_5_6:5_1_2]
												A__						:  int		  =  in_proj_weight[-2_5_6:, :]
												A__						:  List[Any]		  =  in_proj_bias[-2_5_6:]
												# read in weights + bias of input projection layer of cross-attention
												A__						:  str		  =  state_dict.pop(
												    f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'      )
												A__						:  Dict		  =  state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias'      )
												# next, add query, keys and values (in that order) of cross-attention to the state dict
												A__						:  Optional[int]		  =  in_proj_weight_cross_attn[:2_5_6, :]
												A__						:  Dict		  =  in_proj_bias_cross_attn[:2_5_6]
												A__						:  Optional[Any]		  =  in_proj_weight_cross_attn[2_5_6:5_1_2, :]
												A__						:  List[str]		  =  in_proj_bias_cross_attn[2_5_6:5_1_2]
												A__						:  List[Any]		  =  in_proj_weight_cross_attn[-2_5_6:, :]
												A__						:  Union[str, Any]		  =  in_proj_bias_cross_attn[-2_5_6:]




def      _lowerCAmelCase		(							)		->Dict:
						A__						:  Any		  =  """http://images.cocodataset.org/val2017/000000039769.jpg"""
						A__						:  Tuple		  =  Image.open(requests.get(UpperCAmelCase__,						stream=UpperCAmelCase__      ).raw      )

						return im




@torch.no_grad()
def      _lowerCAmelCase		(							UpperCAmelCase__       :					Tuple,						UpperCAmelCase__       :					List[Any]=None,						UpperCAmelCase__       :					Dict=False      )		->List[Any]:
						A__  ,  A__						:  List[str]		  =  get_detr_config(UpperCAmelCase__      )

						# load original model from torch hub
						A__						:  Dict		  =  {
						    """detr-resnet-50""": """detr_resnet50""",
						    """detr-resnet-101""": """detr_resnet101""",
						}
						logger.info(f'Converting model {model_name}...'      )
						A__						:  Union[str, Any]		  =  torch.hub.load("""facebookresearch/detr""",						model_name_to_original_name[model_name],						pretrained=UpperCAmelCase__      ).eval()
						A__						:  Any		  =  detr.state_dict()
						# rename keys
						for src, dest in create_rename_keys(UpperCAmelCase__      ):
												if is_panoptic:
																		A__						:  Optional[Any]		  =  """detr.""" + src
												rename_key(UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__      )
						# query, key and value matrices need special treatment
						read_in_q_k_v(UpperCAmelCase__,						is_panoptic=UpperCAmelCase__      )
						# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
						A__						:  Optional[int]		  =  """detr.model.""" if is_panoptic else """model."""
						for key in state_dict.copy().keys():
												if is_panoptic:
																		if (
																		    key.startswith("""detr"""      )
																		    and not key.startswith("""class_labels_classifier"""      )
																		    and not key.startswith("""bbox_predictor"""      )
																		):
																								A__						:  Optional[Any]		  =  state_dict.pop(UpperCAmelCase__      )
																								A__						:  str		  =  val
																		elif "class_labels_classifier" in key or "bbox_predictor" in key:
																								A__						:  List[Any]		  =  state_dict.pop(UpperCAmelCase__      )
																								A__						:  Union[str, Any]		  =  val
																		elif key.startswith("""bbox_attention"""      ) or key.startswith("""mask_head"""      ):
																								continue
																		else:
																								A__						:  Any		  =  state_dict.pop(UpperCAmelCase__      )
																								A__						:  List[str]		  =  val
												else:
																		if not key.startswith("""class_labels_classifier"""      ) and not key.startswith("""bbox_predictor"""      ):
																								A__						:  Union[str, Any]		  =  state_dict.pop(UpperCAmelCase__      )
																								A__						:  Dict		  =  val

    # finally, create HuggingFace model and load state dict
						A__						:  int		  =  DetrForSegmentation(UpperCAmelCase__      ) if is_panoptic else DetrForObjectDetection(UpperCAmelCase__      )
						model.load_state_dict(UpperCAmelCase__      )
						model.eval()

						# verify our conversion on an image
						A__						:  Union[str, Any]		  =  """coco_panoptic""" if is_panoptic else """coco_detection"""
						A__						:  str		  =  DetrImageProcessor(format=UpperCAmelCase__      )

						A__						:  int		  =  processor(images=prepare_img(),						return_tensors="""pt"""      )
						A__						:  Optional[int]		  =  encoding["""pixel_values"""]

						A__						:  Optional[Any]		  =  detr(UpperCAmelCase__      )
						A__						:  List[str]		  =  model(UpperCAmelCase__      )

						assert torch.allclose(outputs.logits,						original_outputs["""pred_logits"""],						atol=1e-3      )
						assert torch.allclose(outputs.pred_boxes,						original_outputs["""pred_boxes"""],						atol=1e-3      )
						if is_panoptic:
												assert torch.allclose(outputs.pred_masks,						original_outputs["""pred_masks"""],						atol=1e-4      )
						print("""Looks ok!"""      )

						if pytorch_dump_folder_path is not None:
												# Save model and image processor
												logger.info(f'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'      )
												Path(UpperCAmelCase__      ).mkdir(exist_ok=UpperCAmelCase__      )
												model.save_pretrained(UpperCAmelCase__      )
												processor.save_pretrained(UpperCAmelCase__      )

						if push_to_hub:
												# Upload model and image processor to the hub
												logger.info("""Uploading PyTorch model and image processor to the hub..."""      )
												model.push_to_hub(f'nielsr/{model_name}'      )
												processor.push_to_hub(f'nielsr/{model_name}'      )


if __name__ == "__main__":
						A_    =							argparse.ArgumentParser()

						parser.add_argument(
						    '''--model_name''',
						    default='''detr-resnet-50''',
						    type=str,
						    choices=['''detr-resnet-50''', '''detr-resnet-101'''],
						    help='''Name of the DETR model you\'d like to convert.''',
						)
						parser.add_argument(
						    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
						)
						parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Whether to push the model to the hub or not.''')
						A_    =							parser.parse_args()
						convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)














"""simple docstring"""



import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation

A_    =							logging.get_logger(__name__)


A_    =							{
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

A_    =							{
    '''vocab_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'''},
    '''merges_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'''},
    '''tokenizer_config_file''': {
        '''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'''
    },
}

A_    =							{'''facebook/blenderbot-3B''': 128}




@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def      _lowerCAmelCase		(							)		->Any:
						A__						:  Any		  =  (
						    list(range(ord("""!"""      ),						ord("""~"""      ) + 1      )      ) + list(range(ord("""¡"""      ),						ord("""¬"""      ) + 1      )      ) + list(range(ord("""®"""      ),						ord("""ÿ"""      ) + 1      )      )
						)
						A__						:  Optional[int]		  =  bs[:]
						A__						:  Union[str, Any]		  =  0
						for b in range(2**8      ):
												if b not in bs:
																		bs.append(UpperCAmelCase__      )
																		cs.append(2**8 + n      )
																		n += 1
						A__						:  Optional[int]		  =  [chr(UpperCAmelCase__      ) for n in cs]
						return dict(zip(UpperCAmelCase__,						UpperCAmelCase__      )      )




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Dict      )		->Tuple:
						A__						:  Any		  =  set()
						A__						:  Optional[int]		  =  word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)      )
												A__						:  Dict		  =  char
						return pairs






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							VOCAB_FILES_NAMES
							snake_case_     =							PRETRAINED_VOCAB_FILES_MAP
							snake_case_     =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							snake_case_     =							['input_ids', 'attention_mask']


							def __init__(			self      :						int			,    snake_case      :						str			,    snake_case      :						Optional[Any]			,    snake_case      :						str="replace"			,    snake_case      :						Tuple="<s>"			,    snake_case      :						Dict="</s>"			,    snake_case      :						Optional[Any]="</s>"			,    snake_case      :						int="<s>"			,    snake_case      :						Dict="<unk>"			,    snake_case      :						List[Any]="<pad>"			,    snake_case      :						Union[str, Any]="<mask>"			,    snake_case      :						Optional[Any]=False			,    **snake_case      :						Union[str, Any]			,    ):
													'''simple docstring'''





													A__						:  Optional[Any]		  =  AddedToken(snake_case			,    lstrip=snake_case			,    rstrip=snake_case							) if isinstance(snake_case			,    snake_case							) else bos_token
													A__						:  Dict		  =  AddedToken(snake_case			,    lstrip=snake_case			,    rstrip=snake_case							) if isinstance(snake_case			,    snake_case							) else eos_token
													A__						:  List[str]		  =  AddedToken(snake_case			,    lstrip=snake_case			,    rstrip=snake_case							) if isinstance(snake_case			,    snake_case							) else sep_token
													A__						:  Tuple		  =  AddedToken(snake_case			,    lstrip=snake_case			,    rstrip=snake_case							) if isinstance(snake_case			,    snake_case							) else cls_token
													A__						:  Dict		  =  AddedToken(snake_case			,    lstrip=snake_case			,    rstrip=snake_case							) if isinstance(snake_case			,    snake_case							) else unk_token
													A__						:  int		  =  AddedToken(snake_case			,    lstrip=snake_case			,    rstrip=snake_case							) if isinstance(snake_case			,    snake_case							) else pad_token

													# Mask token behave like a normal word, i.e. include the space before it
													A__						:  str		  =  AddedToken(snake_case			,    lstrip=snake_case			,    rstrip=snake_case							) if isinstance(snake_case			,    snake_case							) else mask_token

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

													with open(snake_case			,    encoding="""utf-8"""							) as vocab_handle:
																			A__						:  Dict		  =  json.load(snake_case							)
													A__						:  Tuple		  =  {v: k for k, v in self.encoder.items()}
													A__						:  Union[str, Any]		  =  errors  # how to handle errors in decoding
													A__						:  int		  =  bytes_to_unicode()
													A__						:  str		  =  {v: k for k, v in self.byte_encoder.items()}
													with open(snake_case			,    encoding="""utf-8"""							) as merges_handle:
																			A__						:  Tuple		  =  merges_handle.read().split("""\n"""							)[1:-1]
													A__						:  Optional[Any]		  =  [tuple(merge.split()							) for merge in bpe_merges]
													A__						:  List[str]		  =  dict(zip(snake_case			,    range(len(snake_case							)							)							)							)
													A__						:  Union[str, Any]		  =  {}
													A__						:  int		  =  add_prefix_space

													# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
													A__						:  Optional[Any]		  =  re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""							)


							@property
							# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
							def 						_UpperCamelCase       (			self      :						Any							):
													'''simple docstring'''





													return len(self.encoder							)


							def 						_UpperCamelCase       (			self      :						int							):
													'''simple docstring'''





													return dict(self.encoder			,    **self.added_tokens_encoder							)


							def 						_UpperCamelCase       (			self      :						Union[str, Any]			,    snake_case      :						Optional[Any]							):
													'''simple docstring'''





													if token in self.cache:
																			return self.cache[token]
													A__						:  Tuple		  =  tuple(snake_case							)
													A__						:  Union[str, Any]		  =  get_pairs(snake_case							)

													if not pairs:
																			return token

													while True:
																			A__						:  Any		  =  min(snake_case			,    key=lambda snake_case							: self.bpe_ranks.get(snake_case			,    float("""inf"""							)							)							)
																			if bigram not in self.bpe_ranks:
																									break
																			A__  ,  A__						:  Optional[int]		  =  bigram
																			A__						:  Tuple		  =  []
																			A__						:  Dict		  =  0
																			while i < len(snake_case							):
																									try:
																															A__						:  int		  =  word.index(snake_case			,    snake_case							)
																									except ValueError:
																															new_word.extend(word[i:]							)
																															break
																									else:
																															new_word.extend(word[i:j]							)
																															A__						:  Optional[Any]		  =  j

																									if word[i] == first and i < len(snake_case							) - 1 and word[i + 1] == second:
																															new_word.append(first + second							)
																															i += 2
																									else:
																															new_word.append(word[i]							)
																															i += 1
																			A__						:  Optional[int]		  =  tuple(snake_case							)
																			A__						:  str		  =  new_word
																			if len(snake_case							) == 1:
																									break
																			else:
																									A__						:  List[str]		  =  get_pairs(snake_case							)
													A__						:  int		  =  """ """.join(snake_case							)
													A__						:  Optional[int]		  =  word
													return word


							def 						_UpperCamelCase       (			self      :						Dict			,    snake_case      :						int							):
													'''simple docstring'''





													A__						:  Optional[Any]		  =  []
													for token in re.findall(self.pat			,    snake_case							):
																			A__						:  Optional[Any]		  =  """""".join(
																			    self.byte_encoder[b] for b in token.encode("""utf-8"""							)							)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
																			bpe_tokens.extend(bpe_token for bpe_token in self.bpe(snake_case							).split(""" """							)							)
													return bpe_tokens


							def 						_UpperCamelCase       (			self      :						str			,    snake_case      :						Union[str, Any]							):
													'''simple docstring'''





													return self.encoder.get(snake_case			,    self.encoder.get(self.unk_token							)							)


							def 						_UpperCamelCase       (			self      :						Union[str, Any]			,    snake_case      :						int							):
													'''simple docstring'''





													return self.decoder.get(snake_case							)


							def 						_UpperCamelCase       (			self      :						str			,    snake_case      :						int							):
													'''simple docstring'''





													A__						:  Optional[Any]		  =  """""".join(snake_case							)
													A__						:  Dict		  =  bytearray([self.byte_decoder[c] for c in text]							).decode("""utf-8"""			,    errors=self.errors							)
													return text


							def 						_UpperCamelCase       (			self      :						str			,    snake_case      :						str			,    snake_case      :						Optional[str] = None							):
													'''simple docstring'''





													if not os.path.isdir(snake_case							):
																			logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
																			return
													A__						:  Any		  =  os.path.join(
													    snake_case			,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)
													A__						:  List[Any]		  =  os.path.join(
													    snake_case			,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]							)

													with open(snake_case			,    """w"""			,    encoding="""utf-8"""							) as f:
																			f.write(json.dumps(self.encoder			,    indent=2			,    sort_keys=snake_case			,    ensure_ascii=snake_case							) + """\n"""							)

													A__						:  str		  =  0
													with open(snake_case			,    """w"""			,    encoding="""utf-8"""							) as writer:
																			writer.write("""#version: 0.2\n"""							)
																			for bpe_tokens, token_index in sorted(self.bpe_ranks.items()			,    key=lambda snake_case							: kv[1]							):
																									if index != token_index:
																															logger.warning(
																															    F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
																															    """ Please check that the tokenizer is not corrupted!"""							)
																															A__						:  List[str]		  =  token_index
																									writer.write(""" """.join(snake_case							) + """\n"""							)
																									index += 1

													return vocab_file, merge_file


							def 						_UpperCamelCase       (			self      :						Any			,    snake_case      :						List[int]			,    snake_case      :						Optional[List[int]] = None			,    snake_case      :						bool = False							):
													'''simple docstring'''





													if already_has_special_tokens:
																			return super().get_special_tokens_mask(
																			    token_ids_a=snake_case			,    token_ids_a=snake_case			,    already_has_special_tokens=snake_case							)

													if token_ids_a is None:
																			return [1] + ([0] * len(snake_case							)) + [1]
													return [1] + ([0] * len(snake_case							)) + [1, 1] + ([0] * len(snake_case							)) + [1]


							def 						_UpperCamelCase       (			self      :						List[str]			,    snake_case      :						List[int]			,    snake_case      :						Optional[List[int]] = None							):
													'''simple docstring'''





													A__						:  Union[str, Any]		  =  [self.sep_token_id]
													A__						:  Optional[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 						_UpperCamelCase       (			self      :						List[Any]			,    snake_case      :						Optional[int]			,    snake_case      :						List[Any]=False			,    **snake_case      :						Tuple							):
													'''simple docstring'''





													A__						:  Optional[Any]		  =  kwargs.pop("""add_prefix_space"""			,    self.add_prefix_space							)
													if (is_split_into_words or add_prefix_space) and (len(snake_case							) > 0 and not text[0].isspace()):
																			A__						:  int		  =  """ """ + text
													return (text, kwargs)


							def 						_UpperCamelCase       (			self      :						Dict			,    snake_case      :						List[int]			,    snake_case      :						Optional[List[int]] = None							):
													'''simple docstring'''





													return token_ids_a + [self.eos_token_id]


							def 						_UpperCamelCase       (			self      :						int			,    snake_case      :						"Conversation"							):
													'''simple docstring'''





													A__						:  str		  =  []
													for is_user, text in conversation.iter_texts():
																			if is_user:
																									# We need to space prefix as it's being done within blenderbot
																									inputs.append(""" """ + text							)
																			else:
																									# Generated responses should contain them already.
																									inputs.append(snake_case							)

													A__						:  Tuple		  =  """  """.join(snake_case							)
													A__						:  Dict		  =  self.encode(snake_case							)
													if len(snake_case							) > self.model_max_length:
																			A__						:  Optional[int]		  =  input_ids[-self.model_max_length :]
																			logger.warning(F'Trimmed input from conversation as it was longer than {self.model_max_length} tokens.'							)
													return input_ids






