



"""simple docstring"""


import argparse
import json

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.models import create_model

from transformers import (
    BeitImageProcessor,
    DataaVecVisionConfig,
    DataaVecVisionForImageClassification,
    DataaVecVisionModel,
)

def 				_lowerCAmelCase   (     lowercase_	,			lowercase_=False	,			lowercase_=False	,			lowercase_="data2vec."		):
      UpperCAmelCase    =				'backbone.' if is_semantic else ''

      UpperCAmelCase    =				[]
      for i in range(config.num_hidden_layers		):
            # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
            rename_keys.append(
                (F"""{prefix}blocks.{i}.norm1.weight""", F"""{hf_prefix}encoder.layer.{i}.layernorm_before.weight""")		)
            rename_keys.append((F"""{prefix}blocks.{i}.norm1.bias""", F"""{hf_prefix}encoder.layer.{i}.layernorm_before.bias""")		)
            rename_keys.append(
                (F"""{prefix}blocks.{i}.attn.proj.weight""", F"""{hf_prefix}encoder.layer.{i}.attention.output.dense.weight""")		)
            rename_keys.append(
                (F"""{prefix}blocks.{i}.attn.proj.bias""", F"""{hf_prefix}encoder.layer.{i}.attention.output.dense.bias""")		)
            rename_keys.append(
                (F"""{prefix}blocks.{i}.norm2.weight""", F"""{hf_prefix}encoder.layer.{i}.layernorm_after.weight""")		)
            rename_keys.append((F"""{prefix}blocks.{i}.norm2.bias""", F"""{hf_prefix}encoder.layer.{i}.layernorm_after.bias""")		)
            rename_keys.append(
                (F"""{prefix}blocks.{i}.mlp.fc1.weight""", F"""{hf_prefix}encoder.layer.{i}.intermediate.dense.weight""")		)
            rename_keys.append(
                (F"""{prefix}blocks.{i}.mlp.fc1.bias""", F"""{hf_prefix}encoder.layer.{i}.intermediate.dense.bias""")		)
            rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc2.weight""", F"""{hf_prefix}encoder.layer.{i}.output.dense.weight""")		)
            rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc2.bias""", F"""{hf_prefix}encoder.layer.{i}.output.dense.bias""")		)

      # projection layer + position embeddings
      rename_keys.extend(
          [
              (F"""{prefix}cls_token""", F"""{hf_prefix}embeddings.cls_token"""),
              (F"""{prefix}patch_embed.proj.weight""", F"""{hf_prefix}embeddings.patch_embeddings.projection.weight"""),
              (F"""{prefix}patch_embed.proj.bias""", F"""{hf_prefix}embeddings.patch_embeddings.projection.bias"""),
          ]		)

      if has_lm_head:
            # mask token + shared relative position bias + layernorm
            rename_keys.extend(
                [
                    ('mask_token', F"""{hf_prefix}embeddings.mask_token"""),
                    (
                        'rel_pos_bias.relative_position_bias_table',
                        F"""{hf_prefix}encoder.relative_position_bias.relative_position_bias_table""",
                    ),
                    (
                        'rel_pos_bias.relative_position_index',
                        F"""{hf_prefix}encoder.relative_position_bias.relative_position_index""",
                    ),
                    ('norm.weight', 'layernorm.weight'),
                    ('norm.bias', 'layernorm.bias'),
                ]		)
      elif is_semantic:
            # semantic segmentation classification heads
            rename_keys.extend(
                [
                    ('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
                    ('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
                    ('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
                    ('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
                ]		)
      else:
            # layernorm + classification head
            rename_keys.extend(
                [
                    ('fc_norm.weight', F"""{hf_prefix}pooler.layernorm.weight"""),
                    ('fc_norm.bias', F"""{hf_prefix}pooler.layernorm.bias"""),
                    ('head.weight', 'classifier.weight'),
                    ('head.bias', 'classifier.bias'),
                ]		)

      return rename_keys

def 				_lowerCAmelCase   (     lowercase_	,			lowercase_	,			lowercase_=False	,			lowercase_=False	,			lowercase_="data2vec_vision."		):
      for i in range(config.num_hidden_layers		):
            UpperCAmelCase    =				'backbone.' if is_semantic else ''
            # queries, keys and values
            UpperCAmelCase    =				state_dict.pop(F"""{prefix}blocks.{i}.attn.qkv.weight"""		)
            UpperCAmelCase    =				state_dict.pop(F"""{prefix}blocks.{i}.attn.q_bias"""		)
            UpperCAmelCase    =				state_dict.pop(F"""{prefix}blocks.{i}.attn.v_bias"""		)

            UpperCAmelCase    =				in_proj_weight[
                : config.hidden_size, :
            ]
            UpperCAmelCase    =				q_bias
            UpperCAmelCase    =				in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            UpperCAmelCase    =				in_proj_weight[
                -config.hidden_size :, :
            ]
            UpperCAmelCase    =				v_bias

            # gamma_1 and gamma_2
            # we call them lambda because otherwise they are renamed when using .from_pretrained
            UpperCAmelCase    =				state_dict.pop(F"""{prefix}blocks.{i}.gamma_1"""		)
            UpperCAmelCase    =				state_dict.pop(F"""{prefix}blocks.{i}.gamma_2"""		)

            UpperCAmelCase    =				gamma_a
            UpperCAmelCase    =				gamma_a

            # relative_position bias table + index
            if not has_lm_head:
                  # each layer has its own relative position bias
                  UpperCAmelCase    =				state_dict.pop(F"""{prefix}blocks.{i}.attn.relative_position_bias_table"""		)
                  UpperCAmelCase    =				state_dict.pop(F"""{prefix}blocks.{i}.attn.relative_position_index"""		)

                  UpperCAmelCase    =				table
                  UpperCAmelCase    =				index

def 				_lowerCAmelCase   (     ):
      UpperCAmelCase    =				argparse.ArgumentParser(
          'Convert Data2VecVision to HF for image classification and pretraining'	,			add_help=lowercase__		)
      parser.add_argument('--hf_checkpoint_name'	,			type=lowercase__		)
      parser.add_argument('--input_size'	,			default=224	,			type=lowercase__	,			help='images input size'		)
      parser.add_argument('--beit_checkpoint'	,			default=''	,			help='beit checkpoint'		)

      return parser.parse_args()

def 				_lowerCAmelCase   (     lowercase_	,			lowercase_	,			lowercase_		):
      def load_state_dict(lowercase_	,			lowercase_	,			lowercase_=""	,			lowercase_="relative_position_index"		):
            UpperCAmelCase    =				[]
            UpperCAmelCase    =				[]
            UpperCAmelCase    =				[]
            # copy state_dict so _load_from_state_dict can modify it
            UpperCAmelCase    =				getattr(lowercase__	,			'_metadata'	,			lowercase__		)
            UpperCAmelCase    =				state_dict.copy()
            if metadata is not None:
                  UpperCAmelCase    =				metadata

            def load(lowercase_	,			lowercase_=""		):
                  UpperCAmelCase    =				{} if metadata is None else metadata.get(prefix[:-1]	,			{}		)
                  module._load_from_state_dict(
                      lowercase__	,			lowercase__	,			lowercase__	,			lowercase__	,			lowercase__	,			lowercase__	,			lowercase__		)
                  for name, child in module._modules.items():
                        if child is not None:
                              load(lowercase__	,			prefix + name + '.'		)

            load(lowercase__	,			prefix=lowercase__		)

            UpperCAmelCase    =				[]
            UpperCAmelCase    =				[]
            for key in missing_keys:
                  UpperCAmelCase    =				True
                  for ignore_key in ignore_missing.split('|'		):
                        if ignore_key in key:
                              UpperCAmelCase    =				False
                              break
                  if keep_flag:
                        warn_missing_keys.append(lowercase__		)
                  else:
                        ignore_missing_keys.append(lowercase__		)

            UpperCAmelCase    =				warn_missing_keys

            if len(lowercase__		) > 0:
                  print(
                      'Weights of {} not initialized from pretrained model: {}'.format(
                          model.__class__.__name__	,			lowercase__		)		)
            if len(lowercase__		) > 0:
                  print('Weights from pretrained model not used in {}: {}'.format(model.__class__.__name__	,			lowercase__		)		)
            if len(lowercase__		) > 0:
                  print(
                      'Ignored weights of {} not initialized from pretrained model: {}'.format(
                          model.__class__.__name__	,			lowercase__		)		)
            if len(lowercase__		) > 0:
                  print('\n'.join(lowercase__		)		)

      UpperCAmelCase    =				{
          'pretrained': False,
          'use_shared_rel_pos_bias': True,
          'use_abs_pos_emb': False,
          'init_values': 0.1,
      }

      if is_finetuned:
            model_kwargs.update(
                {
                    'num_classes': 1000,
                    'use_mean_pooling': True,
                    'init_scale': 0.0_0_1,
                    'use_rel_pos_bias': True,
                }		)

      UpperCAmelCase    =				create_model(
          'beit_large_patch16_224' if is_large else 'beit_base_patch16_224'	,			**lowercase__	,			)
      UpperCAmelCase    =				model.patch_embed.patch_size
      UpperCAmelCase    =				(args.input_size // patch_size[0], args.input_size // patch_size[1])
      UpperCAmelCase    =				torch.load(args.beit_checkpoint	,			map_location='cpu'		)

      print(F"""Load ckpt from {args.beit_checkpoint}"""		)
      UpperCAmelCase    =				None
      for model_key in ("model", "module"):
            if model_key in checkpoint:
                  UpperCAmelCase    =				checkpoint[model_key]
                  print(F"""Load state_dict by model_key = {model_key}"""		)
                  break

      UpperCAmelCase    =				list(checkpoint_model.keys()		)
      for key in all_keys:
            if "relative_position_index" in key:
                  checkpoint_model.pop(lowercase__		)

            if "relative_position_bias_table" in key:
                  UpperCAmelCase    =				checkpoint_model[key]
                  UpperCAmelCase   ,					UpperCAmelCase    =				rel_pos_bias.size()
                  UpperCAmelCase   ,					UpperCAmelCase    =				model.state_dict()[key].size()
                  UpperCAmelCase    =				model.patch_embed.patch_shape
                  if dst_patch_shape[0] != dst_patch_shape[1]:
                        raise NotImplementedError()

      load_state_dict(lowercase__	,			lowercase__	,			prefix=''		)

      return model

def 				_lowerCAmelCase   (     ):
      UpperCAmelCase    =				get_args()

      UpperCAmelCase    =				'ft1k' in args.hf_checkpoint_name
      UpperCAmelCase    =				'large' in args.hf_checkpoint_name

      if is_finetuned:
            # To convert Beit's data2vec_vision to HF you need to copy
            # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
            # into this folder.
            import modeling_finetune  # noqa: F401
      else:
            # To convert Beit's data2vec_vision to HF you need to copy
            # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
            # into this folder
            # IMPORTANT: Note that for now we've only converted the down-stream
            # model and not the full pretrained model. This means for the integration
            # test you need to add a `return x` after the following line:
            # https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
            # to make the integration test pass.
            import modeling_cyclical  # noqa: F401

      # 1. Create model config
      UpperCAmelCase    =				DataaVecVisionConfig()
      if is_finetuned:
            UpperCAmelCase    =				True
            UpperCAmelCase    =				False
            UpperCAmelCase    =				True
            UpperCAmelCase    =				1000

            UpperCAmelCase    =				'huggingface/label-files'
            UpperCAmelCase    =				'imagenet-1k-id2label.json'
            UpperCAmelCase    =				json.load(open(hf_hub_download(lowercase__	,			lowercase__	,			repo_type='dataset'		)	,			'r'		)		)
            UpperCAmelCase    =				{int(lowercase__		): v for k, v in idalabel.items()}
            UpperCAmelCase    =				idalabel
            UpperCAmelCase    =				{v: k for k, v in idalabel.items()}
      else:
            UpperCAmelCase    =				False
            UpperCAmelCase    =				True
            UpperCAmelCase    =				False

      if is_large:
            UpperCAmelCase    =				1024
            UpperCAmelCase    =				4096
            UpperCAmelCase    =				24
            UpperCAmelCase    =				16

      # 2. Load Beit model
      UpperCAmelCase    =				load_beit_model(lowercase__	,			lowercase__	,			lowercase__		)
      orig_model.eval()

      # 3. Forward Beit model
      UpperCAmelCase    =				BeitImageProcessor(size=config.image_size	,			do_center_crop=lowercase__		)
      UpperCAmelCase    =				Image.open('../../../../tests/fixtures/tests_samples/COCO/000000039769.png'		)
      UpperCAmelCase    =				image_processor(images=lowercase__	,			return_tensors='pt'		)
      UpperCAmelCase    =				encoding['pixel_values']

      UpperCAmelCase    =				(pixel_values,) if is_finetuned else (pixel_values, None)
      with torch.no_grad():
            UpperCAmelCase    =				orig_model(*lowercase__		)

      # 4. Load HF Data2VecVision model
      if is_finetuned:
            UpperCAmelCase    =				DataaVecVisionForImageClassification(lowercase__		)
            hf_model.eval()
            UpperCAmelCase    =				False
            UpperCAmelCase    =				'data2vec_vision.'
      else:
            UpperCAmelCase    =				DataaVecVisionModel(lowercase__		)
            hf_model.eval()
            UpperCAmelCase    =				True
            UpperCAmelCase    =				''

      UpperCAmelCase    =				create_rename_keys(lowercase__	,			hf_prefix=lowercase__	,			has_lm_head=lowercase__		)
      UpperCAmelCase    =				orig_model.state_dict()
      for src, dest in rename_keys:
            UpperCAmelCase    =				state_dict.pop(lowercase__		)
            UpperCAmelCase    =				val

      read_in_q_k_v(lowercase__	,			lowercase__	,			hf_prefix=lowercase__	,			has_lm_head=lowercase__		)
      UpperCAmelCase   ,					UpperCAmelCase    =				hf_model.load_state_dict(lowercase__	,			strict=lowercase__		)
      print('HF missing'	,			lowercase__		)
      print('HF unexpected_keys'	,			lowercase__		)

      # 5. Forward HF Data2VecVision model
      with torch.no_grad():
            UpperCAmelCase    =				hf_model(lowercase__		)

      UpperCAmelCase    =				hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

      # 6. Compare
      UpperCAmelCase    =				torch.max(torch.abs(hf_output - orig_model_output		)		).item()

      print(F"""max_absolute_diff = {max_absolute_diff}"""		)
      UpperCAmelCase    =				torch.allclose(lowercase__	,			lowercase__	,			atol=1e-3		)
      print('Do both models output the same tensors?'	,			'🔥' if success else '💩'		)
      if not success:
            raise Exception('Something went wRoNg'		)

      # 7. Save
      print(F"""Saving to {args.hf_checkpoint_name}"""		)
      hf_model.save_pretrained(args.hf_checkpoint_name		)
      image_processor.save_pretrained(args.hf_checkpoint_name		)


if __name__ == "__main__":
    main()
    # Run the following to convert checkpoints
    #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
    #          --beit_checkpoint ./pretrained_base.pt \
    #          --hf_checkpoint_name "./data2vec-vision-base"
    #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
    #          --beit_checkpoint ./finetuned_base.pt \
    #          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
    #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
    #          --beit_checkpoint ./pretrained_large.pt \
    #          --hf_checkpoint_name "./data2vec-vision-large"
    #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
    #          --beit_checkpoint ./finetuned_large.pt \
    #          --hf_checkpoint_name "./data2vec-vision-large-ft1k"










"""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





