



"""simple docstring"""


import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel

def 				_lowerCAmelCase   (     lowercase_	,			lowercase_=1		):
      if n_shave_prefix_segments >= 0:
            return ".".join(path.split('.'		)[n_shave_prefix_segments:]		)
      else:
            return ".".join(path.split('.'		)[:n_shave_prefix_segments]		)

def 				_lowerCAmelCase   (     lowercase_	,			lowercase_=0		):
      UpperCAmelCase    =				[]
      for old_item in old_list:
            UpperCAmelCase    =				old_item
            UpperCAmelCase    =				new_item.replace('block.'	,			'resnets.'		)
            UpperCAmelCase    =				new_item.replace('conv_shorcut'	,			'conv1'		)
            UpperCAmelCase    =				new_item.replace('in_shortcut'	,			'conv_shortcut'		)
            UpperCAmelCase    =				new_item.replace('temb_proj'	,			'time_emb_proj'		)

            UpperCAmelCase    =				shave_segments(lowercase_	,			n_shave_prefix_segments=lowercase_		)

            mapping.append({'old': old_item, 'new': new_item}		)

      return mapping

def 				_lowerCAmelCase   (     lowercase_	,			lowercase_=0	,			lowercase_=False		):
      UpperCAmelCase    =				[]
      for old_item in old_list:
            UpperCAmelCase    =				old_item

            # In `model.mid`, the layer is called `attn`.
            if not in_mid:
                  UpperCAmelCase    =				new_item.replace('attn'	,			'attentions'		)
            UpperCAmelCase    =				new_item.replace('.k.'	,			'.key.'		)
            UpperCAmelCase    =				new_item.replace('.v.'	,			'.value.'		)
            UpperCAmelCase    =				new_item.replace('.q.'	,			'.query.'		)

            UpperCAmelCase    =				new_item.replace('proj_out'	,			'proj_attn'		)
            UpperCAmelCase    =				new_item.replace('norm'	,			'group_norm'		)

            UpperCAmelCase    =				shave_segments(lowercase_	,			n_shave_prefix_segments=lowercase_		)
            mapping.append({'old': old_item, 'new': new_item}		)

      return mapping

def 				_lowerCAmelCase   (     lowercase_	,			lowercase_	,			lowercase_	,			lowercase_=None	,			lowercase_=None	,			lowercase_=None		):
      assert isinstance(lowercase_	,			lowercase_		), "Paths should be a list of dicts containing 'old' and 'new' keys."

      if attention_paths_to_split is not None:
            if config is None:
                  raise ValueError('Please specify the config if setting \'attention_paths_to_split\' to \'True\'.'		)

            for path, path_map in attention_paths_to_split.items():
                  UpperCAmelCase    =				old_checkpoint[path]
                  UpperCAmelCase    =				old_tensor.shape[0] // 3

                  UpperCAmelCase    =				(-1, channels) if len(old_tensor.shape		) == 3 else (-1)

                  UpperCAmelCase    =				old_tensor.shape[0] // config.get('num_head_channels'	,			1		) // 3

                  UpperCAmelCase    =				old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]		)
                  UpperCAmelCase   ,					UpperCAmelCase   ,					UpperCAmelCase    =				old_tensor.split(channels // num_heads	,			dim=1		)

                  UpperCAmelCase    =				query.reshape(lowercase_		).squeeze()
                  UpperCAmelCase    =				key.reshape(lowercase_		).squeeze()
                  UpperCAmelCase    =				value.reshape(lowercase_		).squeeze()

      for path in paths:
            UpperCAmelCase    =				path['new']

            if attention_paths_to_split is not None and new_path in attention_paths_to_split:
                  continue

            UpperCAmelCase    =				new_path.replace('down.'	,			'down_blocks.'		)
            UpperCAmelCase    =				new_path.replace('up.'	,			'up_blocks.'		)

            if additional_replacements is not None:
                  for replacement in additional_replacements:
                        UpperCAmelCase    =				new_path.replace(replacement['old']	,			replacement['new']		)

            if "attentions" in new_path:
                  UpperCAmelCase    =				old_checkpoint[path['old']].squeeze()
            else:
                  UpperCAmelCase    =				old_checkpoint[path['old']]

def 				_lowerCAmelCase   (     lowercase_	,			lowercase_		):
      UpperCAmelCase    =				{}

      UpperCAmelCase    =				checkpoint['temb.dense.0.weight']
      UpperCAmelCase    =				checkpoint['temb.dense.0.bias']
      UpperCAmelCase    =				checkpoint['temb.dense.1.weight']
      UpperCAmelCase    =				checkpoint['temb.dense.1.bias']

      UpperCAmelCase    =				checkpoint['norm_out.weight']
      UpperCAmelCase    =				checkpoint['norm_out.bias']

      UpperCAmelCase    =				checkpoint['conv_in.weight']
      UpperCAmelCase    =				checkpoint['conv_in.bias']
      UpperCAmelCase    =				checkpoint['conv_out.weight']
      UpperCAmelCase    =				checkpoint['conv_out.bias']

      UpperCAmelCase    =				len({'.'.join(layer.split('.'		)[:2]		) for layer in checkpoint if 'down' in layer}		)
      UpperCAmelCase    =				{
          layer_id: [key for key in checkpoint if F"""down.{layer_id}""" in key] for layer_id in range(lowercase_		)
      }

      UpperCAmelCase    =				len({'.'.join(layer.split('.'		)[:2]		) for layer in checkpoint if 'up' in layer}		)
      UpperCAmelCase    =				{layer_id: [key for key in checkpoint if F"""up.{layer_id}""" in key] for layer_id in range(lowercase_		)}

      for i in range(lowercase_		):
            UpperCAmelCase    =				(i - 1) // (config['layers_per_block'] + 1)

            if any('downsample' in layer for layer in down_blocks[i]		):
                  UpperCAmelCase    =				checkpoint[
                      F"""down.{i}.downsample.op.weight"""
                  ]
                  UpperCAmelCase    =				checkpoint[F"""down.{i}.downsample.op.bias"""]
            #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
            #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

            if any('block' in layer for layer in down_blocks[i]		):
                  UpperCAmelCase    =				len(
                      {'.'.join(shave_segments(lowercase_	,			2		).split('.'		)[:2]		) for layer in down_blocks[i] if 'block' in layer}		)
                  UpperCAmelCase    =				{
                      layer_id: [key for key in down_blocks[i] if F"""block.{layer_id}""" in key]
                      for layer_id in range(lowercase_		)
                  }

                  if num_blocks > 0:
                        for j in range(config['layers_per_block']		):
                              UpperCAmelCase    =				renew_resnet_paths(blocks[j]		)
                              assign_to_checkpoint(lowercase_	,			lowercase_	,			lowercase_		)

            if any('attn' in layer for layer in down_blocks[i]		):
                  UpperCAmelCase    =				len(
                      {'.'.join(shave_segments(lowercase_	,			2		).split('.'		)[:2]		) for layer in down_blocks[i] if 'attn' in layer}		)
                  UpperCAmelCase    =				{
                      layer_id: [key for key in down_blocks[i] if F"""attn.{layer_id}""" in key]
                      for layer_id in range(lowercase_		)
                  }

                  if num_attn > 0:
                        for j in range(config['layers_per_block']		):
                              UpperCAmelCase    =				renew_attention_paths(attns[j]		)
                              assign_to_checkpoint(lowercase_	,			lowercase_	,			lowercase_	,			config=lowercase_		)

      UpperCAmelCase    =				[key for key in checkpoint if 'mid.block_1' in key]
      UpperCAmelCase    =				[key for key in checkpoint if 'mid.block_2' in key]
      UpperCAmelCase    =				[key for key in checkpoint if 'mid.attn_1' in key]

      # Mid new 2
      UpperCAmelCase    =				renew_resnet_paths(lowercase_		)
      assign_to_checkpoint(
          lowercase_	,			lowercase_	,			lowercase_	,			additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]	,			)

      UpperCAmelCase    =				renew_resnet_paths(lowercase_		)
      assign_to_checkpoint(
          lowercase_	,			lowercase_	,			lowercase_	,			additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]	,			)

      UpperCAmelCase    =				renew_attention_paths(lowercase_	,			in_mid=lowercase_		)
      assign_to_checkpoint(
          lowercase_	,			lowercase_	,			lowercase_	,			additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]	,			)

      for i in range(lowercase_		):
            UpperCAmelCase    =				num_up_blocks - 1 - i

            if any('upsample' in layer for layer in up_blocks[i]		):
                  UpperCAmelCase    =				checkpoint[
                      F"""up.{i}.upsample.conv.weight"""
                  ]
                  UpperCAmelCase    =				checkpoint[F"""up.{i}.upsample.conv.bias"""]

            if any('block' in layer for layer in up_blocks[i]		):
                  UpperCAmelCase    =				len(
                      {'.'.join(shave_segments(lowercase_	,			2		).split('.'		)[:2]		) for layer in up_blocks[i] if 'block' in layer}		)
                  UpperCAmelCase    =				{
                      layer_id: [key for key in up_blocks[i] if F"""block.{layer_id}""" in key] for layer_id in range(lowercase_		)
                  }

                  if num_blocks > 0:
                        for j in range(config['layers_per_block'] + 1		):
                              UpperCAmelCase    =				{'old': F"""up_blocks.{i}""", 'new': F"""up_blocks.{block_id}"""}
                              UpperCAmelCase    =				renew_resnet_paths(blocks[j]		)
                              assign_to_checkpoint(lowercase_	,			lowercase_	,			lowercase_	,			additional_replacements=[replace_indices]		)

            if any('attn' in layer for layer in up_blocks[i]		):
                  UpperCAmelCase    =				len(
                      {'.'.join(shave_segments(lowercase_	,			2		).split('.'		)[:2]		) for layer in up_blocks[i] if 'attn' in layer}		)
                  UpperCAmelCase    =				{
                      layer_id: [key for key in up_blocks[i] if F"""attn.{layer_id}""" in key] for layer_id in range(lowercase_		)
                  }

                  if num_attn > 0:
                        for j in range(config['layers_per_block'] + 1		):
                              UpperCAmelCase    =				{'old': F"""up_blocks.{i}""", 'new': F"""up_blocks.{block_id}"""}
                              UpperCAmelCase    =				renew_attention_paths(attns[j]		)
                              assign_to_checkpoint(lowercase_	,			lowercase_	,			lowercase_	,			additional_replacements=[replace_indices]		)

      UpperCAmelCase    =				{k.replace('mid_new_2'	,			'mid_block'		): v for k, v in new_checkpoint.items()}
      return new_checkpoint

def 				_lowerCAmelCase   (     lowercase_	,			lowercase_		):
      UpperCAmelCase    =				{}

      UpperCAmelCase    =				checkpoint['encoder.norm_out.weight']
      UpperCAmelCase    =				checkpoint['encoder.norm_out.bias']

      UpperCAmelCase    =				checkpoint['encoder.conv_in.weight']
      UpperCAmelCase    =				checkpoint['encoder.conv_in.bias']
      UpperCAmelCase    =				checkpoint['encoder.conv_out.weight']
      UpperCAmelCase    =				checkpoint['encoder.conv_out.bias']

      UpperCAmelCase    =				checkpoint['decoder.norm_out.weight']
      UpperCAmelCase    =				checkpoint['decoder.norm_out.bias']

      UpperCAmelCase    =				checkpoint['decoder.conv_in.weight']
      UpperCAmelCase    =				checkpoint['decoder.conv_in.bias']
      UpperCAmelCase    =				checkpoint['decoder.conv_out.weight']
      UpperCAmelCase    =				checkpoint['decoder.conv_out.bias']

      UpperCAmelCase    =				len({'.'.join(layer.split('.'		)[:3]		) for layer in checkpoint if 'down' in layer}		)
      UpperCAmelCase    =				{
          layer_id: [key for key in checkpoint if F"""down.{layer_id}""" in key] for layer_id in range(lowercase_		)
      }

      UpperCAmelCase    =				len({'.'.join(layer.split('.'		)[:3]		) for layer in checkpoint if 'up' in layer}		)
      UpperCAmelCase    =				{layer_id: [key for key in checkpoint if F"""up.{layer_id}""" in key] for layer_id in range(lowercase_		)}

      for i in range(lowercase_		):
            UpperCAmelCase    =				(i - 1) // (config['layers_per_block'] + 1)

            if any('downsample' in layer for layer in down_blocks[i]		):
                  UpperCAmelCase    =				checkpoint[
                      F"""encoder.down.{i}.downsample.conv.weight"""
                  ]
                  UpperCAmelCase    =				checkpoint[
                      F"""encoder.down.{i}.downsample.conv.bias"""
                  ]

            if any('block' in layer for layer in down_blocks[i]		):
                  UpperCAmelCase    =				len(
                      {'.'.join(shave_segments(lowercase_	,			3		).split('.'		)[:3]		) for layer in down_blocks[i] if 'block' in layer}		)
                  UpperCAmelCase    =				{
                      layer_id: [key for key in down_blocks[i] if F"""block.{layer_id}""" in key]
                      for layer_id in range(lowercase_		)
                  }

                  if num_blocks > 0:
                        for j in range(config['layers_per_block']		):
                              UpperCAmelCase    =				renew_resnet_paths(blocks[j]		)
                              assign_to_checkpoint(lowercase_	,			lowercase_	,			lowercase_		)

            if any('attn' in layer for layer in down_blocks[i]		):
                  UpperCAmelCase    =				len(
                      {'.'.join(shave_segments(lowercase_	,			3		).split('.'		)[:3]		) for layer in down_blocks[i] if 'attn' in layer}		)
                  UpperCAmelCase    =				{
                      layer_id: [key for key in down_blocks[i] if F"""attn.{layer_id}""" in key]
                      for layer_id in range(lowercase_		)
                  }

                  if num_attn > 0:
                        for j in range(config['layers_per_block']		):
                              UpperCAmelCase    =				renew_attention_paths(attns[j]		)
                              assign_to_checkpoint(lowercase_	,			lowercase_	,			lowercase_	,			config=lowercase_		)

      UpperCAmelCase    =				[key for key in checkpoint if 'mid.block_1' in key]
      UpperCAmelCase    =				[key for key in checkpoint if 'mid.block_2' in key]
      UpperCAmelCase    =				[key for key in checkpoint if 'mid.attn_1' in key]

      # Mid new 2
      UpperCAmelCase    =				renew_resnet_paths(lowercase_		)
      assign_to_checkpoint(
          lowercase_	,			lowercase_	,			lowercase_	,			additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]	,			)

      UpperCAmelCase    =				renew_resnet_paths(lowercase_		)
      assign_to_checkpoint(
          lowercase_	,			lowercase_	,			lowercase_	,			additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]	,			)

      UpperCAmelCase    =				renew_attention_paths(lowercase_	,			in_mid=lowercase_		)
      assign_to_checkpoint(
          lowercase_	,			lowercase_	,			lowercase_	,			additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]	,			)

      for i in range(lowercase_		):
            UpperCAmelCase    =				num_up_blocks - 1 - i

            if any('upsample' in layer for layer in up_blocks[i]		):
                  UpperCAmelCase    =				checkpoint[
                      F"""decoder.up.{i}.upsample.conv.weight"""
                  ]
                  UpperCAmelCase    =				checkpoint[
                      F"""decoder.up.{i}.upsample.conv.bias"""
                  ]

            if any('block' in layer for layer in up_blocks[i]		):
                  UpperCAmelCase    =				len(
                      {'.'.join(shave_segments(lowercase_	,			3		).split('.'		)[:3]		) for layer in up_blocks[i] if 'block' in layer}		)
                  UpperCAmelCase    =				{
                      layer_id: [key for key in up_blocks[i] if F"""block.{layer_id}""" in key] for layer_id in range(lowercase_		)
                  }

                  if num_blocks > 0:
                        for j in range(config['layers_per_block'] + 1		):
                              UpperCAmelCase    =				{'old': F"""up_blocks.{i}""", 'new': F"""up_blocks.{block_id}"""}
                              UpperCAmelCase    =				renew_resnet_paths(blocks[j]		)
                              assign_to_checkpoint(lowercase_	,			lowercase_	,			lowercase_	,			additional_replacements=[replace_indices]		)

            if any('attn' in layer for layer in up_blocks[i]		):
                  UpperCAmelCase    =				len(
                      {'.'.join(shave_segments(lowercase_	,			3		).split('.'		)[:3]		) for layer in up_blocks[i] if 'attn' in layer}		)
                  UpperCAmelCase    =				{
                      layer_id: [key for key in up_blocks[i] if F"""attn.{layer_id}""" in key] for layer_id in range(lowercase_		)
                  }

                  if num_attn > 0:
                        for j in range(config['layers_per_block'] + 1		):
                              UpperCAmelCase    =				{'old': F"""up_blocks.{i}""", 'new': F"""up_blocks.{block_id}"""}
                              UpperCAmelCase    =				renew_attention_paths(attns[j]		)
                              assign_to_checkpoint(lowercase_	,			lowercase_	,			lowercase_	,			additional_replacements=[replace_indices]		)

      UpperCAmelCase    =				{k.replace('mid_new_2'	,			'mid_block'		): v for k, v in new_checkpoint.items()}
      UpperCAmelCase    =				checkpoint['quant_conv.weight']
      UpperCAmelCase    =				checkpoint['quant_conv.bias']
      if "quantize.embedding.weight" in checkpoint:
            UpperCAmelCase    =				checkpoint['quantize.embedding.weight']
      UpperCAmelCase    =				checkpoint['post_quant_conv.weight']
      UpperCAmelCase    =				checkpoint['post_quant_conv.bias']

      return new_checkpoint


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

    parser.add_argument(
        """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
    )

    parser.add_argument(
        """--config_file""",
        default=None,
        type=str,
        required=True,
        help="""The config json file corresponding to the architecture.""",
    )

    parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")

    snake_case_         = parser.parse_args()
    snake_case_         = torch.load(args.checkpoint_path)

    with open(args.config_file) as f:
        snake_case_         = json.loads(f.read())

    # unet case
    snake_case_         = {key.split(""".""")[0] for key in checkpoint.keys()}
    if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
        snake_case_         = convert_vq_autoenc_checkpoint(checkpoint, config)
    else:
        snake_case_         = convert_ddpm_checkpoint(checkpoint, config)

    if "ddpm" in config:
        del config["ddpm"]

    if config["_class_name"] == "VQModel":
        snake_case_         = VQModel(**config)
        model.load_state_dict(converted_checkpoint)
        model.save_pretrained(args.dump_path)
    elif config["_class_name"] == "AutoencoderKL":
        snake_case_         = AutoencoderKL(**config)
        model.load_state_dict(converted_checkpoint)
        model.save_pretrained(args.dump_path)
    else:
        snake_case_         = UNetaDModel(**config)
        model.load_state_dict(converted_checkpoint)

        snake_case_         = DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

        snake_case_         = DDPMPipeline(unet=model, scheduler=scheduler)
        pipe.save_pretrained(args.dump_path)










"""simple docstring"""


import json
import os
import re
import unicodedata
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging


snake_case_         = logging.get_logger(__name__)

snake_case_         = {
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
}

snake_case_         = {
    """vocab_file""": {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/vocab.json"""},
    """merges_file""": {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/merges.txt"""},
}

snake_case_         = {
    """openai-gpt""": 512,
}

def 				_lowerCAmelCase   (     lowercase_		):
						UpperCAmelCase    =				text.strip()
						if not text:
												return []
						UpperCAmelCase    =				text.split()
						return tokens







class    A_    (  SCREAMING_SNAKE_CASE_       ):



						"""simple docstring"""






						def __init__(      self				:Dict     ,						lowercase_				:int=True     ,						lowercase_				:Union[str, Any]=None     ,						lowercase_				:List[Any]=True     ,						lowercase_				:List[str]=None     ,						lowercase_				:Optional[int]=True     ,						)       ->    Any:
												if never_split is None:
																		UpperCAmelCase    =				[]
												UpperCAmelCase    =				do_lower_case
												UpperCAmelCase    =				set(lowercase_      )
												UpperCAmelCase    =				tokenize_chinese_chars
												UpperCAmelCase    =				strip_accents
												UpperCAmelCase    =				do_split_on_punc






						def 			UpperCAmelCase__  (      self				:List[str]     ,						lowercase_				:Optional[int]     ,						lowercase_				:int=None      )       ->    List[Any]:
												UpperCAmelCase    =				self.never_split.union(set(lowercase_      )      ) if never_split else self.never_split
												UpperCAmelCase    =				self._clean_text(lowercase_      )

												# This was added on November 1st, 2018 for the multilingual and Chinese
												# models. This is also applied to the English models now, but it doesn't
												# matter since the English models were not trained on any Chinese data
												# and generally don't have any Chinese data in them (there are Chinese
												# characters in the vocabulary because Wikipedia does have some Chinese
												# words in the English Wikipedia.).
												if self.tokenize_chinese_chars:
																		UpperCAmelCase    =				self._tokenize_chinese_chars(lowercase_      )
												# prevents treating the same character with different unicode codepoints as different characters
												UpperCAmelCase    =				unicodedata.normalize('NFC'     ,						lowercase_      )
												UpperCAmelCase    =				whitespace_tokenize(lowercase_      )
												UpperCAmelCase    =				[]
												for token in orig_tokens:
																		if token not in never_split:
																								if self.do_lower_case:
																														UpperCAmelCase    =				token.lower()
																														if self.strip_accents is not False:
																																				UpperCAmelCase    =				self._run_strip_accents(lowercase_      )
																								elif self.strip_accents:
																														UpperCAmelCase    =				self._run_strip_accents(lowercase_      )
																		split_tokens.extend(self._run_split_on_punc(lowercase_     ,						lowercase_      )      )

												UpperCAmelCase    =				whitespace_tokenize(' '.join(lowercase_      )      )
												return output_tokens






						def 			UpperCAmelCase__  (      self				:Tuple     ,						lowercase_				:int      )       ->    Tuple:
												UpperCAmelCase    =				unicodedata.normalize('NFD'     ,						lowercase_      )
												UpperCAmelCase    =				[]
												for char in text:
																		UpperCAmelCase    =				unicodedata.category(lowercase_      )
																		if cat == "Mn":
																								continue
																		output.append(lowercase_      )
												return "".join(lowercase_      )






						def 			UpperCAmelCase__  (      self				:Optional[Any]     ,						lowercase_				:Tuple     ,						lowercase_				:Optional[Any]=None      )       ->    Optional[int]:
												if not self.do_split_on_punc or (never_split is not None and text in never_split):
																		return [text]
												UpperCAmelCase    =				list(lowercase_      )
												UpperCAmelCase    =				0
												UpperCAmelCase    =				True
												UpperCAmelCase    =				[]
												while i < len(lowercase_      ):
																		UpperCAmelCase    =				chars[i]
																		if _is_punctuation(lowercase_      ):
																								output.append([char]      )
																								UpperCAmelCase    =				True
																		else:
																								if start_new_word:
																														output.append([]      )
																								UpperCAmelCase    =				False
																								output[-1].append(lowercase_      )
																		i += 1

												return ["".join(lowercase_      ) for x in output]






						def 			UpperCAmelCase__  (      self				:Optional[int]     ,						lowercase_				:str      )       ->    int:
												UpperCAmelCase    =				[]
												for char in text:
																		UpperCAmelCase    =				ord(lowercase_      )
																		if self._is_chinese_char(lowercase_      ):
																								output.append(' '      )
																								output.append(lowercase_      )
																								output.append(' '      )
																		else:
																								output.append(lowercase_      )
												return "".join(lowercase_      )






						def 			UpperCAmelCase__  (      self				:List[Any]     ,						lowercase_				:int      )       ->    Optional[Any]:
												# This defines a "chinese character" as anything in the CJK Unicode block:
												#   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
												#
												# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
												# despite its name. The modern Korean Hangul alphabet is a different block,
												# as is Japanese Hiragana and Katakana. Those alphabets are used to write
												# space-separated words, so they are not treated specially and handled
												# like the all of the other languages.
												if (
												    (cp >= 0x4E00 and cp <= 0x9FFF)
												    or (cp >= 0x3400 and cp <= 0x4DBF)  #
												    or (cp >= 0x20000 and cp <= 0x2A6DF)  #
												    or (cp >= 0x2A700 and cp <= 0x2B73F)  #
												    or (cp >= 0x2B740 and cp <= 0x2B81F)  #
												    or (cp >= 0x2B820 and cp <= 0x2CEAF)  #
												    or (cp >= 0xF900 and cp <= 0xFAFF)
												    or (cp >= 0x2F800 and cp <= 0x2FA1F)  #
												):  #
																		return True

												return False






						def 			UpperCAmelCase__  (      self				:Optional[Any]     ,						lowercase_				:int      )       ->    List[Any]:
												UpperCAmelCase    =				[]
												for char in text:
																		UpperCAmelCase    =				ord(lowercase_      )
																		if cp == 0 or cp == 0xFFFD or _is_control(lowercase_      ):
																								continue
																		if _is_whitespace(lowercase_      ):
																								output.append(' '      )
																		else:
																								output.append(lowercase_      )
												return "".join(lowercase_      )


def 				_lowerCAmelCase   (     lowercase_		):
						UpperCAmelCase    =				set()
						UpperCAmelCase    =				word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)		)
												UpperCAmelCase    =				char
						return pairs

def 				_lowerCAmelCase   (     lowercase_		):
						UpperCAmelCase    =				text.replace('—'	,			'-'		)
						UpperCAmelCase    =				text.replace('–'	,			'-'		)
						UpperCAmelCase    =				text.replace('―'	,			'-'		)
						UpperCAmelCase    =				text.replace('…'	,			'...'		)
						UpperCAmelCase    =				text.replace('´'	,			'\''		)
						UpperCAmelCase    =				re.sub(R'(-+|~+|!+|"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)'	,			R' \1 '	,			lowercase_		)
						UpperCAmelCase    =				re.sub(R'\s*\n\s*'	,			' \n '	,			lowercase_		)
						UpperCAmelCase    =				re.sub(R'[^\S\n]+'	,			' '	,			lowercase_		)
						return text.strip()







class    A_    (  SCREAMING_SNAKE_CASE_       ):



						"""simple docstring"""
						__UpperCamelCase	       =							VOCAB_FILES_NAMES
						__UpperCamelCase	       =							PRETRAINED_VOCAB_FILES_MAP
						__UpperCamelCase	       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__UpperCamelCase	       =							["""input_ids""", """attention_mask"""]






						def __init__(      self				:Dict     ,						lowercase_				:Optional[int]     ,						lowercase_				:Dict     ,						lowercase_				:List[str]="<unk>"     ,						**lowercase_				:Tuple      )       ->    Dict:
												super().__init__(unk_token=lowercase_     ,						**lowercase_      )

												try:
																		import ftfy
																		from spacy.lang.en import English

																		UpperCAmelCase    =				English()
																		UpperCAmelCase    =				_nlp.tokenizer
																		UpperCAmelCase    =				ftfy.fix_text
												except ImportError:
																		logger.warning('ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy.'      )
																		UpperCAmelCase    =				BasicTokenizer(do_lower_case=lowercase_      )
																		UpperCAmelCase    =				None

												with open(lowercase_     ,						encoding='utf-8'      ) as vocab_handle:
																		UpperCAmelCase    =				json.load(lowercase_      )
												UpperCAmelCase    =				{v: k for k, v in self.encoder.items()}
												with open(lowercase_     ,						encoding='utf-8'      ) as merges_handle:
																		UpperCAmelCase    =				merges_handle.read().split('\n'      )[1:-1]
												UpperCAmelCase    =				[tuple(merge.split()      ) for merge in merges]
												UpperCAmelCase    =				dict(zip(lowercase_     ,						range(len(lowercase_      )      )      )      )
												UpperCAmelCase    =				{}






						@property
						def 			UpperCAmelCase__  (      self				:Union[str, Any]      )       ->    List[Any]:
												return True






						@property
						def 			UpperCAmelCase__  (      self				:str      )       ->    str:
												return len(self.encoder      )






						def 			UpperCAmelCase__  (      self				:Optional[int]      )       ->    Optional[Any]:
												return dict(self.encoder     ,						**self.added_tokens_encoder      )






						def 			UpperCAmelCase__  (      self				:Union[str, Any]     ,						lowercase_				:List[Any]      )       ->    int:
												UpperCAmelCase    =				tuple(token[:-1]      ) + (token[-1] + '</w>',)
												if token in self.cache:
																		return self.cache[token]
												UpperCAmelCase    =				get_pairs(lowercase_      )

												if not pairs:
																		return token + "</w>"

												while True:
																		UpperCAmelCase    =				min(lowercase_     ,						key=lambda lowercase_      : self.bpe_ranks.get(lowercase_     ,						float('inf'      )      )      )
																		if bigram not in self.bpe_ranks:
																								break
																		UpperCAmelCase   ,					UpperCAmelCase    =				bigram
																		UpperCAmelCase    =				[]
																		UpperCAmelCase    =				0
																		while i < len(lowercase_      ):
																								try:
																														UpperCAmelCase    =				word.index(lowercase_     ,						lowercase_      )
																								except ValueError:
																														new_word.extend(word[i:]      )
																														break
																								else:
																														new_word.extend(word[i:j]      )
																														UpperCAmelCase    =				j

																								if word[i] == first and i < len(lowercase_      ) - 1 and word[i + 1] == second:
																														new_word.append(first + second      )
																														i += 2
																								else:
																														new_word.append(word[i]      )
																														i += 1
																		UpperCAmelCase    =				tuple(lowercase_      )
																		UpperCAmelCase    =				new_word
																		if len(lowercase_      ) == 1:
																								break
																		else:
																								UpperCAmelCase    =				get_pairs(lowercase_      )
												UpperCAmelCase    =				' '.join(lowercase_      )
												if word == "\n  </w>":
																		UpperCAmelCase    =				'\n</w>'
												UpperCAmelCase    =				word
												return word






						def 			UpperCAmelCase__  (      self				:List[str]     ,						lowercase_				:Union[str, Any]      )       ->    str:
												UpperCAmelCase    =				[]
												if self.fix_text is None:
																		# Using BERT's BasicTokenizer
																		UpperCAmelCase    =				self.nlp.tokenize(lowercase_      )
																		for token in text:
																								split_tokens.extend(list(self.bpe(lowercase_      ).split(' '      )      )      )
												else:
																		# Using SpaCy & ftfy (original tokenization process of OpenAI GPT)
																		UpperCAmelCase    =				self.nlp(text_standardize(self.fix_text(lowercase_      )      )      )
																		for token in text:
																								split_tokens.extend(list(self.bpe(token.text.lower()      ).split(' '      )      )      )
												return split_tokens






						def 			UpperCAmelCase__  (      self				:Tuple     ,						lowercase_				:Optional[Any]      )       ->    Tuple:
												return self.encoder.get(lowercase_     ,						self.encoder.get(self.unk_token      )      )






						def 			UpperCAmelCase__  (      self				:List[Any]     ,						lowercase_				:str      )       ->    Union[str, Any]:
												return self.decoder.get(lowercase_     ,						self.unk_token      )






						def 			UpperCAmelCase__  (      self				:Tuple     ,						lowercase_				:Optional[Any]      )       ->    Tuple:
												UpperCAmelCase    =				''.join(lowercase_      ).replace('</w>'     ,						' '      ).strip()
												return out_string






						def 			UpperCAmelCase__  (      self				:List[str]     ,						lowercase_				:str     ,						lowercase_				:Optional[str] = None      )       ->    Tuple[str]:
												if not os.path.isdir(lowercase_      ):
																		logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""      )
																		return
												UpperCAmelCase    =				os.path.join(
												    lowercase_     ,						(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']      )
												UpperCAmelCase    =				os.path.join(
												    lowercase_     ,						(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']      )

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

												UpperCAmelCase    =				0
												with open(lowercase_     ,						'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 lowercase_      : 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!'      )
																														UpperCAmelCase    =				token_index
																								writer.write(' '.join(lowercase_      ) + '\n'      )
																								index += 1

												return vocab_file, merge_file





