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

import regex as re

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


if TYPE_CHECKING:
	from transformers.pipelines.conversational import Conversation

snake_case_      :					Tuple				      =     logging.get_logger(__name__)

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

snake_case_      :					List[str]				      =     {
    """vocab_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/vocab.json""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/vocab.json""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/vocab.json""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/vocab.json""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/merges.txt""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/merges.txt""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/merges.txt""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/merges.txt""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/merges.txt""",
    },
}

snake_case_      :					int				      =     {
    """gpt2""": 1_0_2_4,
    """gpt2-medium""": 1_0_2_4,
    """gpt2-large""": 1_0_2_4,
    """gpt2-xl""": 1_0_2_4,
    """distilgpt2""": 1_0_2_4,
}



@lru_cache()
def      __UpperCAmelCase    (  ):



	'''simple docstring'''



	UpperCAmelCase:					List[str] 						=     (
	    list(range(ord("!"				)				,					ord("~"				) + 1				)				) + list(range(ord("¡"				)				,					ord("¬"				) + 1				)				) + list(range(ord("®"				)				,					ord("ÿ"				) + 1				)				)
	)
	UpperCAmelCase:					Dict 						=     bs[:]
	UpperCAmelCase:					List[Any] 						=     0
	for b in range(2**8				):
		if b not in bs:
			bs.append(lowerCAmelCase__				)
			cs.append(2**8 + n				)
			n += 1
	UpperCAmelCase:					Any 						=     [chr(lowerCAmelCase__				) for n in cs]
	return dict(zip(lowerCAmelCase__				,					lowerCAmelCase__				)				)



def      __UpperCAmelCase    (  snake_case_							:		Optional[int]				):



	'''simple docstring'''



	UpperCAmelCase:					Optional[Any] 						=     set()
	UpperCAmelCase:					Optional[int] 						=     word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)				)
		UpperCAmelCase:					Union[str, Any] 						=     char
	return pairs





class 		__lowerCamelCase     (   lowercase_		):
							lowerCamelCase__:				Union[str, Any]      =							VOCAB_FILES_NAMES
							lowerCamelCase__:				List[Any]      =							PRETRAINED_VOCAB_FILES_MAP
							lowerCamelCase__:				Optional[Any]      =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCamelCase__:				Optional[int]      =							['''input_ids''', '''attention_mask''']



							def __init__(     self	,				__snake_case	,				__snake_case	,				__snake_case="replace"	,				__snake_case="<|endoftext|>"	,				__snake_case="<|endoftext|>"	,				__snake_case="<|endoftext|>"	,				__snake_case=None	,				__snake_case=False	,				__snake_case=False	,				**__snake_case	,				)						->     Dict:






								"""simple docstring"""



								UpperCAmelCase:					int 						=     AddedToken(SCREAMING_SNAKE_CASE_	,				lstrip=SCREAMING_SNAKE_CASE_	,				rstrip=SCREAMING_SNAKE_CASE_							) if isinstance(SCREAMING_SNAKE_CASE_	,				SCREAMING_SNAKE_CASE_							) else bos_token
								UpperCAmelCase:					List[str] 						=     AddedToken(SCREAMING_SNAKE_CASE_	,				lstrip=SCREAMING_SNAKE_CASE_	,				rstrip=SCREAMING_SNAKE_CASE_							) if isinstance(SCREAMING_SNAKE_CASE_	,				SCREAMING_SNAKE_CASE_							) else eos_token
								UpperCAmelCase:					Any 						=     AddedToken(SCREAMING_SNAKE_CASE_	,				lstrip=SCREAMING_SNAKE_CASE_	,				rstrip=SCREAMING_SNAKE_CASE_							) if isinstance(SCREAMING_SNAKE_CASE_	,				SCREAMING_SNAKE_CASE_							) else unk_token
								UpperCAmelCase:					Dict 						=     AddedToken(SCREAMING_SNAKE_CASE_	,				lstrip=SCREAMING_SNAKE_CASE_	,				rstrip=SCREAMING_SNAKE_CASE_							) if isinstance(SCREAMING_SNAKE_CASE_	,				SCREAMING_SNAKE_CASE_							) else pad_token
								super().__init__(
								    errors=SCREAMING_SNAKE_CASE_	,				unk_token=SCREAMING_SNAKE_CASE_	,				bos_token=SCREAMING_SNAKE_CASE_	,				eos_token=SCREAMING_SNAKE_CASE_	,				pad_token=SCREAMING_SNAKE_CASE_	,				add_prefix_space=SCREAMING_SNAKE_CASE_	,				add_bos_token=SCREAMING_SNAKE_CASE_	,				**SCREAMING_SNAKE_CASE_	,				)
								UpperCAmelCase:					Tuple 						=     add_bos_token

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

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



							@property
							def     A__ (     self							)						->     Any:






								"""simple docstring"""



								return len(self.encoder							)



							def     A__ (     self							)						->     List[str]:






								"""simple docstring"""



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



							def     A__ (     self	,				__snake_case							)						->     Tuple:






								"""simple docstring"""



								if token in self.cache:
									return self.cache[token]
								UpperCAmelCase:					List[str] 						=     tuple(SCREAMING_SNAKE_CASE_							)
								UpperCAmelCase:					str 						=     get_pairs(SCREAMING_SNAKE_CASE_							)

								if not pairs:
									return token

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

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



							def     A__ (     self	,				__snake_case	,				__snake_case=None							)						->     Union[str, Any]:






								"""simple docstring"""



								if self.add_bos_token:
									UpperCAmelCase:					Union[str, Any] 						=     [self.bos_token_id]
								else:
									UpperCAmelCase:					Optional[int] 						=     []

								UpperCAmelCase:					List[Any] 						=     bos_token_ids + token_ids_a

								if token_ids_a is None:
									return output

								return output + bos_token_ids + token_ids_a



							def     A__ (     self	,				__snake_case	,				__snake_case = None	,				__snake_case = False							)						->     int:






								"""simple docstring"""



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

								if not self.add_bos_token:
									return super().get_special_tokens_mask(
									    token_ids_a=SCREAMING_SNAKE_CASE_	,				token_ids_a=SCREAMING_SNAKE_CASE_	,				already_has_special_tokens=SCREAMING_SNAKE_CASE_							)

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



							def     A__ (     self	,				__snake_case							)						->     int:






								"""simple docstring"""



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



							def     A__ (     self	,				__snake_case							)						->     Union[str, Any]:






								"""simple docstring"""



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



							def     A__ (     self	,				__snake_case							)						->     Any:






								"""simple docstring"""



								return self.decoder.get(SCREAMING_SNAKE_CASE_							)



							def     A__ (     self	,				__snake_case							)						->     Tuple:






								"""simple docstring"""



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



							def     A__ (     self	,				__snake_case	,				__snake_case = None							)						->     Optional[int]:






								"""simple docstring"""



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

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

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

								return vocab_file, merge_file



							def     A__ (     self	,				__snake_case	,				__snake_case=False	,				**__snake_case							)						->     Tuple:






								"""simple docstring"""



								UpperCAmelCase:					Tuple 						=     kwargs.pop("add_prefix_space"	,				self.add_prefix_space							)
								if is_split_into_words or add_prefix_space:
									UpperCAmelCase:					Tuple 						=     ' ' + text
								return (text, kwargs)




							def     A__ (     self	,				__snake_case							)						->     Tuple:






								"""simple docstring"""



								UpperCAmelCase:					Any 						=     []
								for is_user, text in conversation.iter_texts():
									input_ids.extend(self.encode(SCREAMING_SNAKE_CASE_	,				add_special_tokens=SCREAMING_SNAKE_CASE_							) + [self.eos_token_id]							)
								if len(SCREAMING_SNAKE_CASE_							) > self.model_max_length:
									UpperCAmelCase:					List[Any] 						=     input_ids[-self.model_max_length :]
								return input_ids





"""simple docstring"""







import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

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


logging.set_verbosity_info()
lowercase__						:							Dict        =      logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowercase__						:							Union[str, Any]        =      []
for i in range(6):
     # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
     rename_keys.append(
         (f'transformer.encoder.layers.{i}.self_attn.out_proj.weight', f'encoder.layers.{i}.self_attn.out_proj.weight')
     )
     rename_keys.append(
         (f'transformer.encoder.layers.{i}.self_attn.out_proj.bias', f'encoder.layers.{i}.self_attn.out_proj.bias')
     )
     rename_keys.append((f'transformer.encoder.layers.{i}.linear1.weight', f'encoder.layers.{i}.fc1.weight'))
     rename_keys.append((f'transformer.encoder.layers.{i}.linear1.bias', f'encoder.layers.{i}.fc1.bias'))
     rename_keys.append((f'transformer.encoder.layers.{i}.linear2.weight', f'encoder.layers.{i}.fc2.weight'))
     rename_keys.append((f'transformer.encoder.layers.{i}.linear2.bias', f'encoder.layers.{i}.fc2.bias'))
     rename_keys.append(
         (f'transformer.encoder.layers.{i}.norm1.weight', f'encoder.layers.{i}.self_attn_layer_norm.weight')
     )
     rename_keys.append((f'transformer.encoder.layers.{i}.norm1.bias', f'encoder.layers.{i}.self_attn_layer_norm.bias'))
     rename_keys.append((f'transformer.encoder.layers.{i}.norm2.weight', f'encoder.layers.{i}.final_layer_norm.weight'))
     rename_keys.append((f'transformer.encoder.layers.{i}.norm2.bias', f'encoder.layers.{i}.final_layer_norm.bias'))
     # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
     rename_keys.append(
         (f'transformer.decoder.layers.{i}.self_attn.out_proj.weight', f'decoder.layers.{i}.self_attn.out_proj.weight')
     )
     rename_keys.append(
         (f'transformer.decoder.layers.{i}.self_attn.out_proj.bias', f'decoder.layers.{i}.self_attn.out_proj.bias')
     )
     rename_keys.append(
         (
             f'transformer.decoder.layers.{i}.multihead_attn.out_proj.weight',
             f'decoder.layers.{i}.encoder_attn.out_proj.weight',
         )
     )
     rename_keys.append(
         (
             f'transformer.decoder.layers.{i}.multihead_attn.out_proj.bias',
             f'decoder.layers.{i}.encoder_attn.out_proj.bias',
         )
     )
     rename_keys.append((f'transformer.decoder.layers.{i}.linear1.weight', f'decoder.layers.{i}.fc1.weight'))
     rename_keys.append((f'transformer.decoder.layers.{i}.linear1.bias', f'decoder.layers.{i}.fc1.bias'))
     rename_keys.append((f'transformer.decoder.layers.{i}.linear2.weight', f'decoder.layers.{i}.fc2.weight'))
     rename_keys.append((f'transformer.decoder.layers.{i}.linear2.bias', f'decoder.layers.{i}.fc2.bias'))
     rename_keys.append(
         (f'transformer.decoder.layers.{i}.norm1.weight', f'decoder.layers.{i}.self_attn_layer_norm.weight')
     )
     rename_keys.append((f'transformer.decoder.layers.{i}.norm1.bias', f'decoder.layers.{i}.self_attn_layer_norm.bias'))
     rename_keys.append(
         (f'transformer.decoder.layers.{i}.norm2.weight', f'decoder.layers.{i}.encoder_attn_layer_norm.weight')
     )
     rename_keys.append(
         (f'transformer.decoder.layers.{i}.norm2.bias', f'decoder.layers.{i}.encoder_attn_layer_norm.bias')
     )
     rename_keys.append((f'transformer.decoder.layers.{i}.norm3.weight', f'decoder.layers.{i}.final_layer_norm.weight'))
     rename_keys.append((f'transformer.decoder.layers.{i}.norm3.bias', f'decoder.layers.{i}.final_layer_norm.bias'))

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

def 							UpperCamelCase_       (       lowerCAmelCase__ :							str       ,  lowerCAmelCase__ :							Dict       ,  lowerCAmelCase__ :							Optional[Any]		)		->  List[Any]:

  """simple docstring"""




  lowerCAmelCase_  :							Dict								=					state_dict.pop(lowerCAmelCase__		)
  lowerCAmelCase_  :							Union[str, Any]								=					val

def 							UpperCamelCase_       (       lowerCAmelCase__ :							Union[str, Any]		)		->  Any:

  """simple docstring"""




  lowerCAmelCase_  :							Tuple								=					OrderedDict()
  for key, value in state_dict.items():
    if "backbone.0.body" in key:
      lowerCAmelCase_  :							List[str]								=					key.replace('backbone.0.body'       ,  'backbone.conv_encoder.model'		)
      lowerCAmelCase_  :							Optional[int]								=					value
    else:
      lowerCAmelCase_  :							Optional[Any]								=					value

  return new_state_dict

def 							UpperCamelCase_       (       lowerCAmelCase__ :							Dict       ,  lowerCAmelCase__ :							List[Any]=False		)		->  Optional[int]:

  """simple docstring"""




  lowerCAmelCase_  :							Union[str, Any]								=					''
  if is_panoptic:
    lowerCAmelCase_  :							str								=					'detr.'

  # first: transformer encoder
  for i in range(6		):
    # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
    lowerCAmelCase_  :							Optional[Any]								=					state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"		)
    lowerCAmelCase_  :							Any								=					state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"		)
    # next, add query, keys and values (in that order) to the state dict
    lowerCAmelCase_  :							Optional[int]								=					in_proj_weight[:256, :]
    lowerCAmelCase_  :							int								=					in_proj_bias[:256]
    lowerCAmelCase_  :							Optional[Any]								=					in_proj_weight[256:512, :]
    lowerCAmelCase_  :							Union[str, Any]								=					in_proj_bias[256:512]
    lowerCAmelCase_  :							List[Any]								=					in_proj_weight[-256:, :]
    lowerCAmelCase_  :							int								=					in_proj_bias[-256:]
  # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
  for i in range(6		):
    # read in weights + bias of input projection layer of self-attention
    lowerCAmelCase_  :							List[str]								=					state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"		)
    lowerCAmelCase_  :							str								=					state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"		)
    # next, add query, keys and values (in that order) to the state dict
    lowerCAmelCase_  :							Any								=					in_proj_weight[:256, :]
    lowerCAmelCase_  :							List[Any]								=					in_proj_bias[:256]
    lowerCAmelCase_  :							List[Any]								=					in_proj_weight[256:512, :]
    lowerCAmelCase_  :							Tuple								=					in_proj_bias[256:512]
    lowerCAmelCase_  :							Any								=					in_proj_weight[-256:, :]
    lowerCAmelCase_  :							Tuple								=					in_proj_bias[-256:]
    # read in weights + bias of input projection layer of cross-attention
    lowerCAmelCase_  :							Optional[Any]								=					state_dict.pop(
        f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"		)
    lowerCAmelCase_  :							Optional[int]								=					state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"		)
    # next, add query, keys and values (in that order) of cross-attention to the state dict
    lowerCAmelCase_  :							int								=					in_proj_weight_cross_attn[:256, :]
    lowerCAmelCase_  :							Optional[Any]								=					in_proj_bias_cross_attn[:256]
    lowerCAmelCase_  :							Any								=					in_proj_weight_cross_attn[256:512, :]
    lowerCAmelCase_  :							int								=					in_proj_bias_cross_attn[256:512]
    lowerCAmelCase_  :							Dict								=					in_proj_weight_cross_attn[-256:, :]
    lowerCAmelCase_  :							Optional[Any]								=					in_proj_bias_cross_attn[-256:]

def 							UpperCamelCase_       (       )		->  Any:

  """simple docstring"""




  lowerCAmelCase_  :							Optional[Any]								=					'http://images.cocodataset.org/val2017/000000039769.jpg'
  lowerCAmelCase_  :							List[str]								=					Image.open(requests.get(lowerCAmelCase__       ,  stream=lowerCAmelCase__		).raw		)

  return im

@torch.no_grad()
def 							UpperCamelCase_       (       lowerCAmelCase__ :							Any       ,  lowerCAmelCase__ :							Tuple		)		->  str:

  """simple docstring"""




  lowerCAmelCase_  :							int								=					DetrConfig()
  # set backbone and dilation attributes
  if "resnet101" in model_name:
    lowerCAmelCase_  :							int								=					'resnet101'
  if "dc5" in model_name:
    lowerCAmelCase_  :							Any								=					True
  lowerCAmelCase_  :							Tuple								=					'panoptic' in model_name
  if is_panoptic:
    lowerCAmelCase_  :							int								=					250
  else:
    lowerCAmelCase_  :							Optional[int]								=					91
    lowerCAmelCase_  :							int								=					'huggingface/label-files'
    lowerCAmelCase_  :							Dict								=					'coco-detection-id2label.json'
    lowerCAmelCase_  :							List[str]								=					json.load(open(hf_hub_download(lowerCAmelCase__       ,  lowerCAmelCase__       ,  repo_type='dataset'		)       ,  'r'		)		)
    lowerCAmelCase_  :							str								=					{int(lowerCAmelCase__		): v for k, v in idalabel.items()}
    lowerCAmelCase_  :							Union[str, Any]								=					idalabel
    lowerCAmelCase_  :							int								=					{v: k for k, v in idalabel.items()}

  # load image processor
  lowerCAmelCase_  :							Optional[int]								=					'coco_panoptic' if is_panoptic else 'coco_detection'
  lowerCAmelCase_  :							List[Any]								=					DetrImageProcessor(format=lowerCAmelCase__		)

  # prepare image
  lowerCAmelCase_  :							str								=					prepare_img()
  lowerCAmelCase_  :							Tuple								=					image_processor(images=lowerCAmelCase__       ,  return_tensors='pt'		)
  lowerCAmelCase_  :							int								=					encoding['pixel_values']

  logger.info(f"Converting model {model_name}..."		)

  # load original model from torch hub
  lowerCAmelCase_  :							int								=					torch.hub.load('facebookresearch/detr'       ,  lowerCAmelCase__       ,  pretrained=lowerCAmelCase__		).eval()
  lowerCAmelCase_  :							Optional[Any]								=					detr.state_dict()
  # rename keys
  for src, dest in rename_keys:
    if is_panoptic:
      lowerCAmelCase_  :							List[Any]								=					'detr.' + src
    rename_key(lowerCAmelCase__       ,  lowerCAmelCase__       ,  lowerCAmelCase__		)
  lowerCAmelCase_  :							Optional[int]								=					rename_backbone_keys(lowerCAmelCase__		)
  # query, key and value matrices need special treatment
  read_in_q_k_v(lowerCAmelCase__       ,  is_panoptic=lowerCAmelCase__		)
  # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
  lowerCAmelCase_  :							List[str]								=					'detr.model.' if is_panoptic else 'model.'
  for key in state_dict.copy().keys():
    if is_panoptic:
      if (
          key.startswith('detr'		)
          and not key.startswith('class_labels_classifier'		)
          and not key.startswith('bbox_predictor'		)
      ):
        lowerCAmelCase_  :							Optional[int]								=					state_dict.pop(lowerCAmelCase__		)
        lowerCAmelCase_  :							Any								=					val
      elif "class_labels_classifier" in key or "bbox_predictor" in key:
        lowerCAmelCase_  :							str								=					state_dict.pop(lowerCAmelCase__		)
        lowerCAmelCase_  :							str								=					val
      elif key.startswith('bbox_attention'		) or key.startswith('mask_head'		):
        continue
      else:
        lowerCAmelCase_  :							Optional[int]								=					state_dict.pop(lowerCAmelCase__		)
        lowerCAmelCase_  :							Dict								=					val
    else:
      if not key.startswith('class_labels_classifier'		) and not key.startswith('bbox_predictor'		):
        lowerCAmelCase_  :							str								=					state_dict.pop(lowerCAmelCase__		)
        lowerCAmelCase_  :							str								=					val
    # finally, create HuggingFace model and load state dict
  lowerCAmelCase_  :							List[str]								=					DetrForSegmentation(lowerCAmelCase__		) if is_panoptic else DetrForObjectDetection(lowerCAmelCase__		)
  model.load_state_dict(lowerCAmelCase__		)
  model.eval()
  # verify our conversion
  lowerCAmelCase_  :							Tuple								=					detr(lowerCAmelCase__		)
  lowerCAmelCase_  :							List[Any]								=					model(lowerCAmelCase__		)
  assert torch.allclose(outputs.logits       ,  original_outputs['pred_logits']       ,  atol=1e-4		)
  assert torch.allclose(outputs.pred_boxes       ,  original_outputs['pred_boxes']       ,  atol=1e-4		)
  if is_panoptic:
    assert torch.allclose(outputs.pred_masks       ,  original_outputs['pred_masks']       ,  atol=1e-4		)

  # Save model and image processor
  logger.info(f"Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."		)
  Path(lowerCAmelCase__		).mkdir(exist_ok=lowerCAmelCase__		)
  model.save_pretrained(lowerCAmelCase__		)
  image_processor.save_pretrained(lowerCAmelCase__		)


if __name__ == "__main__":
     lowercase__						:							Tuple        =      argparse.ArgumentParser()

     parser.add_argument(
         """--model_name""", default="""detr_resnet50""", type=str, help="""Name of the DETR model you'd like to convert."""
     )
     parser.add_argument(
         """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
     )
     lowercase__						:							Tuple        =      parser.parse_args()
     convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)





