

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


__A    	=			logging.get_logger(__name__)

__A    	=			{
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
}

__A    	=			{
    '''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'''},
}

__A    	=			{
    '''openai-gpt''': 5_12,
}



def    snake_case_(_UpperCamelCase  )							->			Union[str, Any]:



  """simple docstring"""






  _snake_case		=							text.strip()
  if not text:
    return []
  _snake_case		=							text.split()
  return tokens



class 						lowercase_					(						__lowercase  ):



      def __init__(    self   :       Any    ,      A__   :       Any=True    ,      A__   :       Optional[int]=None    ,      A__   :       Optional[Any]=True    ,      A__   :       Dict=None    ,      A__   :       Tuple=True    ,      )					->  int:
        if never_split is None:
          _snake_case		=							[]
        _snake_case		=							do_lower_case
        _snake_case		=							set(A__	)
        _snake_case		=							tokenize_chinese_chars
        _snake_case		=							strip_accents
        _snake_case		=							do_split_on_punc



      def 		UpperCamelCase_	(    self   :       Any    ,      A__   :       int    ,      A__   :       int=None	)					->  List[str]:
        _snake_case		=							self.never_split.union(set(A__	)	) if never_split else self.never_split
        _snake_case		=							self._clean_text(A__	)

        # 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:
          _snake_case		=							self._tokenize_chinese_chars(A__	)
        # prevents treating the same character with different unicode codepoints as different characters
        _snake_case		=							unicodedata.normalize('''NFC'''    ,      A__	)
        _snake_case		=							whitespace_tokenize(A__	)
        _snake_case		=							[]
        for token in orig_tokens:
          if token not in never_split:
            if self.do_lower_case:
              _snake_case		=							token.lower()
              if self.strip_accents is not False:
                _snake_case		=							self._run_strip_accents(A__	)
            elif self.strip_accents:
              _snake_case		=							self._run_strip_accents(A__	)
          split_tokens.extend(self._run_split_on_punc(A__    ,      A__	)	)

        _snake_case		=							whitespace_tokenize(''' '''.join(A__	)	)
        return output_tokens



      def 		UpperCamelCase_	(    self   :       Tuple    ,      A__   :       List[str]	)					->  Union[str, Any]:
        _snake_case		=							unicodedata.normalize('''NFD'''    ,      A__	)
        _snake_case		=							[]
        for char in text:
          _snake_case		=							unicodedata.category(A__	)
          if cat == "Mn":
            continue
          output.append(A__	)
        return "".join(A__	)



      def 		UpperCamelCase_	(    self   :       str    ,      A__   :       List[str]    ,      A__   :       Dict=None	)					->  str:
        if not self.do_split_on_punc or (never_split is not None and text in never_split):
          return [text]
        _snake_case		=							list(A__	)
        _snake_case		=							0
        _snake_case		=							True
        _snake_case		=							[]
        while i < len(A__	):
          _snake_case		=							chars[i]
          if _is_punctuation(A__	):
            output.append([char]	)
            _snake_case		=							True
          else:
            if start_new_word:
              output.append([]	)
            _snake_case		=							False
            output[-1].append(A__	)
          i += 1

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



      def 		UpperCamelCase_	(    self   :       List[Any]    ,      A__   :       Union[str, Any]	)					->  Optional[int]:
        _snake_case		=							[]
        for char in text:
          _snake_case		=							ord(A__	)
          if self._is_chinese_char(A__	):
            output.append(''' '''	)
            output.append(A__	)
            output.append(''' '''	)
          else:
            output.append(A__	)
        return "".join(A__	)



      def 		UpperCamelCase_	(    self   :       List[Any]    ,      A__   :       List[Any]	)					->  str:
        # 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 >= 0X4_E_0_0 and cp <= 0X9_F_F_F)
            or (cp >= 0X3_4_0_0 and cp <= 0X4_D_B_F)  #
            or (cp >= 0X2_0_0_0_0 and cp <= 0X2_A_6_D_F)  #
            or (cp >= 0X2_A_7_0_0 and cp <= 0X2_B_7_3_F)  #
            or (cp >= 0X2_B_7_4_0 and cp <= 0X2_B_8_1_F)  #
            or (cp >= 0X2_B_8_2_0 and cp <= 0X2_C_E_A_F)  #
            or (cp >= 0XF_9_0_0 and cp <= 0XF_A_F_F)
            or (cp >= 0X2_F_8_0_0 and cp <= 0X2_F_A_1_F)  #
        ):  #
          return True

        return False







      def 		UpperCamelCase_	(    self   :       Any    ,      A__   :       Optional[int]	)					->  Optional[Any]:
        _snake_case		=							[]
        for char in text:
          _snake_case		=							ord(A__	)
          if cp == 0 or cp == 0XF_F_F_D or _is_control(A__	):
            continue
          if _is_whitespace(A__	):
            output.append(''' '''	)
          else:
            output.append(A__	)
        return "".join(A__	)



def    snake_case_(_UpperCamelCase  )							->			Optional[int]:



  """simple docstring"""






  _snake_case		=							set()
  _snake_case		=							word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)  )
    _snake_case		=							char
  return pairs



def    snake_case_(_UpperCamelCase  )							->			Optional[Any]:



  """simple docstring"""






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



class 						lowercase_					(						__lowercase  ):
      UpperCamelCase_    :	str		   =       VOCAB_FILES_NAMES
      UpperCamelCase_    :	Optional[Any]		   =       PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase_    :	Dict		   =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase_    :	Optional[int]		   =       ["input_ids", "attention_mask"]



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

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

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

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



      @property
      def 		UpperCamelCase_	(    self   :       Optional[int]	)					->  Union[str, Any]:
        return True



      @property
      def 		UpperCamelCase_	(    self   :       Optional[int]	)					->  str:
        return len(self.encoder	)



      def 		UpperCamelCase_	(    self   :       Any	)					->  Any:
        return dict(self.encoder    ,      **self.added_tokens_encoder	)



      def 		UpperCamelCase_	(    self   :       List[Any]    ,      A__   :       List[str]	)					->  Dict:
        _snake_case		=							tuple(token[:-1]	) + (token[-1] + '''</w>''',)
        if token in self.cache:
          return self.cache[token]
        _snake_case		=							get_pairs(A__	)

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

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

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



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



      def 		UpperCamelCase_	(    self   :       Any    ,      A__   :       str	)					->  Optional[Any]:
        return self.encoder.get(A__    ,      self.encoder.get(self.unk_token	)	)



      def 		UpperCamelCase_	(    self   :       Optional[int]    ,      A__   :       Union[str, Any]	)					->  Union[str, Any]:
        return self.decoder.get(A__    ,      self.unk_token	)



      def 		UpperCamelCase_	(    self   :       str    ,      A__   :       Dict	)					->  List[Any]:
        _snake_case		=							''''''.join(A__	).replace('''</w>'''    ,      ''' '''	).strip()
        return out_string







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

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

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

        return vocab_file, merge_file




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DeformableDetrConfig, DeformableDetrForObjectDetection, DeformableDetrImageProcessor
from transformers.utils import logging


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



def    snake_case_(_UpperCamelCase  )							->			Tuple:



  """simple docstring"""






  if "backbone.0.body" in orig_key:
    _snake_case		=							orig_key.replace('''backbone.0.body'''				, '''backbone.conv_encoder.model'''  )
  if "transformer" in orig_key:
    _snake_case		=							orig_key.replace('''transformer.'''				, ''''''  )
  if "norm1" in orig_key:
    if "encoder" in orig_key:
      _snake_case		=							orig_key.replace('''norm1'''				, '''self_attn_layer_norm'''  )
    else:
      _snake_case		=							orig_key.replace('''norm1'''				, '''encoder_attn_layer_norm'''  )
  if "norm2" in orig_key:
    if "encoder" in orig_key:
      _snake_case		=							orig_key.replace('''norm2'''				, '''final_layer_norm'''  )
    else:
      _snake_case		=							orig_key.replace('''norm2'''				, '''self_attn_layer_norm'''  )
  if "norm3" in orig_key:
    _snake_case		=							orig_key.replace('''norm3'''				, '''final_layer_norm'''  )
  if "linear1" in orig_key:
    _snake_case		=							orig_key.replace('''linear1'''				, '''fc1'''  )
  if "linear2" in orig_key:
    _snake_case		=							orig_key.replace('''linear2'''				, '''fc2'''  )
  if "query_embed" in orig_key:
    _snake_case		=							orig_key.replace('''query_embed'''				, '''query_position_embeddings'''  )
  if "cross_attn" in orig_key:
    _snake_case		=							orig_key.replace('''cross_attn'''				, '''encoder_attn'''  )

  return orig_key



def    snake_case_(_UpperCamelCase  )							->			Any:



  """simple docstring"""






  for i in range(6  ):
    # read in weights + bias of input projection layer of self-attention
    _snake_case		=							state_dict.pop(F"""decoder.layers.{i}.self_attn.in_proj_weight"""  )
    _snake_case		=							state_dict.pop(F"""decoder.layers.{i}.self_attn.in_proj_bias"""  )
    # next, add query, keys and values (in that order) to the state dict
    _snake_case		=							in_proj_weight[:256, :]
    _snake_case		=							in_proj_bias[:256]
    _snake_case		=							in_proj_weight[256:512, :]
    _snake_case		=							in_proj_bias[256:512]
    _snake_case		=							in_proj_weight[-256:, :]
    _snake_case		=							in_proj_bias[-256:]



def    snake_case_()							->			int:



  """simple docstring"""






  _snake_case		=							'''http://images.cocodataset.org/val2017/000000039769.jpg'''
  _snake_case		=							Image.open(requests.get(_UpperCamelCase				, stream=_UpperCamelCase  ).raw  )

  return im



@torch.no_grad()
def    snake_case_(_UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase				, )							->			Dict:



  """simple docstring"""






  _snake_case		=							DeformableDetrConfig()
  # set config attributes
  if single_scale:
    _snake_case		=							1
  _snake_case		=							dilation
  _snake_case		=							with_box_refine
  _snake_case		=							two_stage
  # set labels
  _snake_case		=							91
  _snake_case		=							'''huggingface/label-files'''
  _snake_case		=							'''coco-detection-id2label.json'''
  _snake_case		=							json.load(open(cached_download(hf_hub_url(_UpperCamelCase				, _UpperCamelCase				, repo_type='''dataset'''  )  )				, '''r'''  )  )
  _snake_case		=							{int(_UpperCamelCase  ): v for k, v in idalabel.items()}
  _snake_case		=							idalabel
  _snake_case		=							{v: k for k, v in idalabel.items()}

  # load image processor
  _snake_case		=							DeformableDetrImageProcessor(format='''coco_detection'''  )

  # prepare image
  _snake_case		=							prepare_img()
  _snake_case		=							image_processor(images=_UpperCamelCase				, return_tensors='''pt'''  )
  _snake_case		=							encoding['''pixel_values''']

  logger.info('''Converting model...'''  )

  # load original state dict
  _snake_case		=							torch.load(_UpperCamelCase				, map_location='''cpu'''  )['''model''']
  # rename keys
  for key in state_dict.copy().keys():
    _snake_case		=							state_dict.pop(_UpperCamelCase  )
    _snake_case		=							val
  # query, key and value matrices need special treatment
  read_in_q_k_v(_UpperCamelCase  )
  # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
  _snake_case		=							'''model.'''
  for key in state_dict.copy().keys():
    if not key.startswith('''class_embed'''  ) and not key.startswith('''bbox_embed'''  ):
      _snake_case		=							state_dict.pop(_UpperCamelCase  )
      _snake_case		=							val
    # finally, create HuggingFace model and load state dict
  _snake_case		=							DeformableDetrForObjectDetection(_UpperCamelCase  )
  model.load_state_dict(_UpperCamelCase  )
  model.eval()

  _snake_case		=							'''cuda''' if torch.cuda.is_available() else '''cpu'''
  model.to(_UpperCamelCase  )
  # verify our conversion
  _snake_case		=							model(pixel_values.to(_UpperCamelCase  )  )

  _snake_case		=							torch.tensor(
      [[-9.6645, -4.3449, -5.8705], [-9.7035, -3.8504, -5.0724], [-10.5634, -5.3379, -7.5116]]  )
  _snake_case		=							torch.tensor([[0.8693, 0.2289, 0.2492], [0.3150, 0.5489, 0.5845], [0.5563, 0.7580, 0.8518]]  )

  if single_scale:
    _snake_case		=							torch.tensor(
        [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]]  )
    _snake_case		=							torch.tensor([[0.7292, 0.4991, 0.5532], [0.7959, 0.2426, 0.4236], [0.7582, 0.3518, 0.4451]]  )

  if single_scale and dilation:
    _snake_case		=							torch.tensor(
        [[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]]  )
    _snake_case		=							torch.tensor([[0.7665, 0.4130, 0.4769], [0.8364, 0.1841, 0.3391], [0.6261, 0.3895, 0.7978]]  )

  if with_box_refine:
    _snake_case		=							torch.tensor(
        [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]]  )
    _snake_case		=							torch.tensor([[0.7828, 0.2208, 0.4323], [0.0892, 0.5996, 0.1319], [0.5524, 0.6389, 0.8914]]  )

  if with_box_refine and two_stage:
    _snake_case		=							torch.tensor(
        [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]  )
    _snake_case		=							torch.tensor([[0.2583, 0.5499, 0.4683], [0.7652, 0.9068, 0.4882], [0.5490, 0.2763, 0.0564]]  )

  print('''Logits:'''				, outputs.logits[0, :3, :3]  )

  assert torch.allclose(outputs.logits[0, :3, :3]				, expected_logits.to(_UpperCamelCase  )				, atol=1E-4  )
  assert torch.allclose(outputs.pred_boxes[0, :3, :3]				, expected_boxes.to(_UpperCamelCase  )				, atol=1E-4  )

  print('''Everything ok!'''  )

  # 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  )
  image_processor.save_pretrained(_UpperCamelCase  )

  # Push to hub
  if push_to_hub:
    _snake_case		=							'''deformable-detr'''
    model_name += "-single-scale" if single_scale else ""
    model_name += "-dc5" if dilation else ""
    model_name += "-with-box-refine" if with_box_refine else ""
    model_name += "-two-stage" if two_stage else ""
    print('''Pushing model to hub...'''  )
    model.push_to_hub(repo_path_or_name=_UpperCamelCase				, organization='''nielsr'''				, commit_message='''Add model'''  )


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

 parser.add_argument(
     '''--checkpoint_path''',
     type=str,
     default='''/home/niels/checkpoints/deformable_detr/r50_deformable_detr-checkpoint.pth''',
     help='''Path to Pytorch checkpoint (.pth file) you\'d like to convert.''',
 )
 parser.add_argument('''--single_scale''', action='''store_true''', help='''Whether to set config.num_features_levels = 1.''')
 parser.add_argument('''--dilation''', action='''store_true''', help='''Whether to set config.dilation=True.''')
 parser.add_argument('''--with_box_refine''', action='''store_true''', help='''Whether to set config.with_box_refine=True.''')
 parser.add_argument('''--two_stage''', action='''store_true''', help='''Whether to set config.two_stage=True.''')
 parser.add_argument(
     '''--pytorch_dump_folder_path''',
     default=None,
     type=str,
     required=True,
     help='''Path to the folder to output PyTorch model.''',
 )
 parser.add_argument(
     '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
 )
 __A    	=			parser.parse_args()
 convert_deformable_detr_checkpoint(
     args.checkpoint_path,
     args.single_scale,
     args.dilation,
     args.with_box_refine,
     args.two_stage,
     args.pytorch_dump_folder_path,
     args.push_to_hub,
 )

