
'''simple docstring'''
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple

import numpy as np

from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation


lowercase__						       =      logging.get_logger(__name__)

lowercase__						       =      {"vocab_file": "vocab.txt", "emoji_file": "emoji.json"}

lowercase__						       =      {
    "vocab_file": {
        "abeja/gpt-neox-japanese-2.7b": "https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt",
    },
    "emoji_file": {
        "abeja/gpt-neox-japanese-2.7b": "https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json",
    },
}

lowercase__						       =      {
    "abeja/gpt-neox-japanese-2.7b": 2_0_4_8,
}




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							List[str]:


		'''simple docstring'''


		with open(SCREAMING_SNAKE_CASE__ ,       '''r''' ,       encoding='''utf-8'''			) as f:
				snake_case  :       List[str]     			=						json.loads(f.read()			)

		snake_case  :       Union[str, Any]     			=						collections.OrderedDict()
		snake_case  :       Tuple     			=						collections.OrderedDict()
		snake_case  :       List[str]     			=						collections.OrderedDict()
		with open(SCREAMING_SNAKE_CASE__ ,       '''r''' ,       encoding='''utf-8'''			) as f:
				snake_case  :       Any     			=						f.readlines()
		snake_case  :       Optional[Any]     			=						[[t.rstrip('''\n'''			)] if (t == ''',''' or ''',''' not in t) else t.rstrip('''\n'''			).split(''','''			) for t in token]
		for idx, b in enumerate(SCREAMING_SNAKE_CASE__			):
				snake_case  :       str     			=						b
				snake_case  :       List[str]     			=						idx
				for wd in b:
						snake_case  :       Tuple     			=						idx

		return vocab, raw_vocab, ids_to_tokens, emoji






class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						VOCAB_FILES_NAMES
		lowerCamelCase     							=						PRETRAINED_VOCAB_FILES_MAP
		lowerCamelCase     							=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowerCamelCase     							=						["""input_ids""", """attention_mask"""]



		def __init__(      self				:			List[str]  ,	UpperCamelCase__				:			Optional[int]  ,	UpperCamelCase__				:			Any  ,	UpperCamelCase__				:			Optional[int]="<|endoftext|>"  ,	UpperCamelCase__				:			Optional[Any]="<|endoftext|>"  ,	UpperCamelCase__				:			int="<|startoftext|>"  ,	UpperCamelCase__				:			Optional[int]="<|endoftext|>"  ,	UpperCamelCase__				:			List[Any]=False  ,	**UpperCamelCase__				:			Any  ,	)						->			Tuple:






				"""simple docstring"""







				super().__init__(
				    unk_token=UpperCamelCase__  ,	pad_token=UpperCamelCase__  ,	bos_token=UpperCamelCase__  ,	eos_token=UpperCamelCase__  ,	do_clean_text=UpperCamelCase__  ,	**UpperCamelCase__  ,	)
				if not os.path.isfile(UpperCamelCase__      ):
						raise ValueError(
						    f'Can\'t find a vocabulary file at path \'{vocab_file}\'. To load the vocabulary from a Google pretrained'
						    ''' model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`'''      )
				if not os.path.isfile(UpperCamelCase__      ):
						raise ValueError(
						    f'Can\'t find a emoji file at path \'{emoji_file}\'. To load the emoji information from a Google'
						    ''' pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`'''      )
				snake_case  :       Dict     			=						do_clean_text
				snake_case			,snake_case			,snake_case			,snake_case  :       Dict     			=						load_vocab_and_emoji(UpperCamelCase__  ,	UpperCamelCase__      )
				snake_case  :       Dict     			=						SubWordJapaneseTokenizer(
				    vocab=self.vocab  ,	ids_to_tokens=self.ids_to_tokens  ,	emoji=self.emoji      )



		@property
		def       lowerCAmelCase				(      self				:			Optional[int]      )						->			Any:






				"""simple docstring"""







				return len(self.raw_vocab      )



		def       lowerCAmelCase				(      self				:			Optional[int]      )						->			List[Any]:






				"""simple docstring"""







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



		def       lowerCAmelCase				(      self				:			int  ,	UpperCamelCase__				:			List[Any]      )						->			str:






				"""simple docstring"""







				return self.subword_tokenizer.tokenize(UpperCamelCase__  ,	clean=self.do_clean_text      )



		def       lowerCAmelCase				(      self				:			Optional[int]  ,	UpperCamelCase__				:			Optional[int]      )						->			str:






				"""simple docstring"""







				return self.vocab.get(UpperCamelCase__  ,	self.vocab.get(self.unk_token      )      )



		def       lowerCAmelCase				(      self				:			Optional[Any]  ,	UpperCamelCase__				:			Tuple      )						->			int:






				"""simple docstring"""







				return self.subword_tokenizer.convert_id_to_token(UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			Optional[Any]  ,	UpperCamelCase__				:			Tuple      )						->			Union[str, Any]:






				"""simple docstring"""







				snake_case  :       List[str]     			=						''''''.join(UpperCamelCase__      ).strip()
				return out_string



		def       lowerCAmelCase				(      self				:			int  ,	UpperCamelCase__				:			"Conversation"      )						->			List[int]:






				"""simple docstring"""







				snake_case  :       Tuple     			=						[]
				for is_user, text in conversation.iter_texts():
						input_ids.extend(self.encode(UpperCamelCase__  ,	add_special_tokens=UpperCamelCase__      ) + [self.eos_token_id]      )

				if len(UpperCamelCase__      ) > self.model_max_length:
						snake_case  :       List[str]     			=						input_ids[-self.model_max_length :]
				return input_ids



		def       lowerCAmelCase				(      self				:			Optional[Any]  ,	UpperCamelCase__				:			str  ,	UpperCamelCase__				:			Optional[str] = None      )						->			Tuple[str]:






				"""simple docstring"""







				snake_case  :       str     			=						0
				if os.path.isdir(UpperCamelCase__      ):
						snake_case  :       Optional[Any]     			=						os.path.join(
						    UpperCamelCase__  ,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )
						snake_case  :       Optional[Any]     			=						os.path.join(
						    UpperCamelCase__  ,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''emoji_file''']      )
				else:
						snake_case  :       Optional[Any]     			=						(
						    (filename_prefix + '''-''' if filename_prefix else '''''') + save_directory + VOCAB_FILES_NAMES['''vocab_file''']
						)
						snake_case  :       str     			=						(
						    (filename_prefix + '''-''' if filename_prefix else '''''') + save_directory + VOCAB_FILES_NAMES['''emoji_file''']
						)
				with open(UpperCamelCase__  ,	'''w'''  ,	encoding='''utf-8'''      ) as writer:
						for token_index, token in self.ids_to_tokens.items():
								if index != token_index:
										logger.warning(
										    f'Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'
										    ''' Please check that the vocabulary is not corrupted!'''      )
										snake_case  :       Any     			=						token_index
								writer.write(''','''.join(UpperCamelCase__      ) + '''\n'''      )
								index += 1
				with open(UpperCamelCase__  ,	'''w'''  ,	encoding='''utf-8'''      ) as writer:
						json.dump(self.emoji  ,	UpperCamelCase__      )
				return vocab_file, emoji_file







class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		def __init__(      self				:			List[str]  ,	UpperCamelCase__				:			List[str]  ,	UpperCamelCase__				:			int  ,	UpperCamelCase__				:			List[str]      )						->			Dict:






				"""simple docstring"""







				snake_case  :       Any     			=						vocab  # same as swe
				snake_case  :       Tuple     			=						ids_to_tokens  # same as bpe
				snake_case  :       List[str]     			=						emoji
				snake_case  :       Union[str, Any]     			=						np.max([len(UpperCamelCase__      ) for w in self.vocab.keys()]      )
				snake_case  :       Optional[int]     			=						re.compile(R'''(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)'''      )
				snake_case  :       List[str]     			=						re.compile(R'''[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*'''      )
				snake_case  :       Union[str, Any]     			=						re.compile(R'''[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}'''      )
				snake_case  :       Dict     			=						re.compile(
				    R'''([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*'''      )
				snake_case  :       List[Any]     			=						re.compile(
				    R'''(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*'''      )
				snake_case  :       str     			=						re.compile(
				    R'''((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*'''      )
				snake_case  :       Any     			=						'''─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿'''
				snake_case  :       List[str]     			=						'''▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟'''
				snake_case  :       Dict     			=						str.maketrans({k: '''<BLOCK>''' for k in keisen + blocks}      )



		def __len__(      self				:			Tuple      )						->			Tuple:






				"""simple docstring"""







				return len(self.ids_to_tokens      )



		def       lowerCAmelCase				(      self				:			Optional[int]  ,	UpperCamelCase__				:			List[Any]      )						->			List[str]:






				"""simple docstring"""







				snake_case  :       Dict     			=						self.content_repattera.sub('''<URL>'''  ,	UpperCamelCase__      )
				snake_case  :       int     			=						self.content_repattera.sub('''<EMAIL>'''  ,	UpperCamelCase__      )
				snake_case  :       Union[str, Any]     			=						self.content_repattera.sub('''<TEL>'''  ,	UpperCamelCase__      )
				snake_case  :       List[str]     			=						self.content_repattera.sub('''<DATE>'''  ,	UpperCamelCase__      )
				snake_case  :       Union[str, Any]     			=						self.content_repattera.sub('''<DATE>'''  ,	UpperCamelCase__      )
				snake_case  :       List[Any]     			=						self.content_repattera.sub('''<PRICE>'''  ,	UpperCamelCase__      )
				snake_case  :       Any     			=						content.translate(self.content_transa      )
				while "<BLOCK><BLOCK>" in content:
						snake_case  :       str     			=						content.replace('''<BLOCK><BLOCK>'''  ,	'''<BLOCK>'''      )
				return content



		def       lowerCAmelCase				(      self				:			List[Any]  ,	UpperCamelCase__				:			Optional[Any]  ,	UpperCamelCase__				:			str=False      )						->			List[str]:






				"""simple docstring"""







				snake_case  :       Union[str, Any]     			=						text.replace(''' '''  ,	'''<SP>'''      )
				snake_case  :       Optional[int]     			=						text.replace('''　'''  ,	'''<SP>'''      )
				snake_case  :       List[Any]     			=						text.replace('''\r\n'''  ,	'''<BR>'''      )
				snake_case  :       Optional[int]     			=						text.replace('''\n'''  ,	'''<BR>'''      )
				snake_case  :       Dict     			=						text.replace('''\r'''  ,	'''<BR>'''      )
				snake_case  :       int     			=						text.replace('''\t'''  ,	'''<TAB>'''      )
				snake_case  :       Optional[int]     			=						text.replace('''—'''  ,	'''ー'''      )
				snake_case  :       List[str]     			=						text.replace('''−'''  ,	'''ー'''      )
				for k, v in self.emoji["emoji"].items():
						if k in text:
								snake_case  :       str     			=						text.replace(UpperCamelCase__  ,	UpperCamelCase__      )
				if clean:
						snake_case  :       int     			=						self.clean_text(UpperCamelCase__      )

				def check_simbol(UpperCamelCase__				:			List[str]      ):
						snake_case  :       Optional[int]     			=						x.encode()
						if len(UpperCamelCase__      ) == 1 and len(UpperCamelCase__      ) == 2:
								snake_case  :       List[str]     			=						(int(e[0]      ) << 8) + int(e[1]      )
								if (
								    (c >= 0xC_2_A_1 and c <= 0xC_2_B_F)
								    or (c >= 0xC_7_8_0 and c <= 0xC_7_8_3)
								    or (c >= 0xC_A_B_9 and c <= 0xC_B_B_F)
								    or (c >= 0xC_C_8_0 and c <= 0xC_D_A_2)
								):
										return True
						return False

				def checkuae(UpperCamelCase__				:			str      ):
						snake_case  :       Any     			=						x.encode()
						if len(UpperCamelCase__      ) == 1 and len(UpperCamelCase__      ) == 3:
								snake_case  :       Tuple     			=						(int(e[0]      ) << 16) + (int(e[1]      ) << 8) + int(e[2]      )
								if c >= 0xE_2_8_0_8_0 and c <= 0xE_2_B_0_7_F:
										return True
						return False

				snake_case  :       List[Any]     			=						0
				snake_case  :       Dict     			=						[]
				while pos < len(UpperCamelCase__      ):
						snake_case  :       List[str]     			=						min(len(UpperCamelCase__      )  ,	pos + self.maxlen + 1      ) if text[pos] == '''<''' else pos + 3
						snake_case  :       Tuple     			=						[]  # (token_id, token, pos)
						for e in range(UpperCamelCase__  ,	UpperCamelCase__  ,	-1      ):
								snake_case  :       Dict     			=						text[pos:e]
								if wd in self.vocab:
										if wd[0] == "<" and len(UpperCamelCase__      ) > 2:
												snake_case  :       Any     			=						[(self.vocab[wd], wd, e)]
												break
										else:
												candidates.append((self.vocab[wd], wd, e)      )
						if len(UpperCamelCase__      ) > 0:
								# the smallest token_id is adopted
								snake_case			,snake_case			,snake_case  :       Optional[int]     			=						sorted(UpperCamelCase__  ,	key=lambda UpperCamelCase__      : x[0]      )[0]
								result.append(UpperCamelCase__      )
								snake_case  :       Tuple     			=						e
						else:
								snake_case  :       List[Any]     			=						pos + 1
								snake_case  :       Optional[Any]     			=						text[pos:end]
								if check_simbol(UpperCamelCase__      ):
										result.append('''<KIGOU>'''      )
								elif checkuae(UpperCamelCase__      ):
										result.append('''<U2000U2BFF>'''      )
								else:
										for i in wd.encode('''utf-8'''      ):
												result.append('''<|byte%d|>''' % i      )
								snake_case  :       Any     			=						end
				return result



		def       lowerCAmelCase				(      self				:			int  ,	UpperCamelCase__				:			Any  ,	UpperCamelCase__				:			Optional[Any]="\n"      )						->			List[str]:






				"""simple docstring"""







				snake_case  :       str     			=						[]
				snake_case  :       Union[str, Any]     			=						[]
				snake_case  :       int     			=						self.ids_to_tokens[index][0]
				if word[:6] == "<|byte" and word[-2:] == "|>":
						byte_tokens.append(int(word[6:-2]      )      )
				else:
						if len(UpperCamelCase__      ) > 0:
								words.append(bytearray(UpperCamelCase__      ).decode('''utf-8'''  ,	errors='''replace'''      )      )
								snake_case  :       Dict     			=						[]
						if word[:7] == "<|emoji" and word[-2:] == "|>":
								words.append(self.emoji['''emoji_inv'''][word]      )
						elif word == "<SP>":
								words.append(''' '''      )
						elif word == "<BR>":
								words.append(UpperCamelCase__      )
						elif word == "<TAB>":
								words.append('''\t'''      )
						elif word == "<BLOCK>":
								words.append('''▀'''      )
						elif word == "<KIGOU>":
								words.append('''ǀ'''      )
						elif word == "<U2000U2BFF>":
								words.append('''‖'''      )
						else:
								words.append(UpperCamelCase__      )
				if len(UpperCamelCase__      ) > 0:
						words.append(bytearray(UpperCamelCase__      ).decode('''utf-8'''  ,	errors='''replace'''      )      )
				snake_case  :       str     			=						''''''.join(UpperCamelCase__      )
				return text




'''simple docstring'''
import argparse
import json
from pathlib import Path

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

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


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




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							Tuple:


		'''simple docstring'''


		if "resnet-50" in model_name:
				snake_case  :       int     			=						ResNetConfig.from_pretrained('''microsoft/resnet-50'''			)
		elif "resnet-101" in model_name:
				snake_case  :       Any     			=						ResNetConfig.from_pretrained('''microsoft/resnet-101'''			)
		else:
				raise ValueError('''Model name should include either resnet50 or resnet101'''			)

		snake_case  :       Optional[Any]     			=						DetrConfig(use_timm_backbone=SCREAMING_SNAKE_CASE__ ,       backbone_config=SCREAMING_SNAKE_CASE__			)

		# set label attributes
		snake_case  :       Tuple     			=						'''panoptic''' in model_name
		if is_panoptic:
				snake_case  :       Tuple     			=						250
		else:
				snake_case  :       Any     			=						91
				snake_case  :       str     			=						'''huggingface/label-files'''
				snake_case  :       Optional[Any]     			=						'''coco-detection-id2label.json'''
				snake_case  :       List[str]     			=						json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       repo_type='''dataset'''			) ,       '''r'''			)			)
				snake_case  :       Optional[Any]     			=						{int(SCREAMING_SNAKE_CASE__			): v for k, v in idalabel.items()}
				snake_case  :       str     			=						idalabel
				snake_case  :       List[Any]     			=						{v: k for k, v in idalabel.items()}

		return config, is_panoptic




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


		'''simple docstring'''


		snake_case  :       str     			=						[]

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

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

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

		return rename_keys




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							List[str]:


		'''simple docstring'''


		snake_case  :       List[str]     			=						state_dict.pop(SCREAMING_SNAKE_CASE__			)
		snake_case  :       List[Any]     			=						val




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__=False			)  ->							List[Any]:


		'''simple docstring'''


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

		# first: transformer encoder
		for i in range(6			):
				# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
				snake_case  :       Dict     			=						state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'			)
				snake_case  :       Optional[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
				snake_case  :       Union[str, Any]     			=						in_proj_weight[:256, :]
				snake_case  :       Any     			=						in_proj_bias[:256]
				snake_case  :       Dict     			=						in_proj_weight[256:512, :]
				snake_case  :       Tuple     			=						in_proj_bias[256:512]
				snake_case  :       Optional[int]     			=						in_proj_weight[-256:, :]
				snake_case  :       Optional[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
				snake_case  :       Union[str, Any]     			=						state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'			)
				snake_case  :       List[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
				snake_case  :       Union[str, Any]     			=						in_proj_weight[:256, :]
				snake_case  :       str     			=						in_proj_bias[:256]
				snake_case  :       Optional[int]     			=						in_proj_weight[256:512, :]
				snake_case  :       Optional[int]     			=						in_proj_bias[256:512]
				snake_case  :       Optional[Any]     			=						in_proj_weight[-256:, :]
				snake_case  :       Tuple     			=						in_proj_bias[-256:]
				# read in weights + bias of input projection layer of cross-attention
				snake_case  :       Union[str, Any]     			=						state_dict.pop(
				    F'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'			)
				snake_case  :       str     			=						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
				snake_case  :       Optional[Any]     			=						in_proj_weight_cross_attn[:256, :]
				snake_case  :       List[Any]     			=						in_proj_bias_cross_attn[:256]
				snake_case  :       Union[str, Any]     			=						in_proj_weight_cross_attn[256:512, :]
				snake_case  :       Any     			=						in_proj_bias_cross_attn[256:512]
				snake_case  :       List[str]     			=						in_proj_weight_cross_attn[-256:, :]
				snake_case  :       Tuple     			=						in_proj_bias_cross_attn[-256:]




def    _UpperCamelCase						(       )  ->							Dict:


		'''simple docstring'''


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

		return im




@torch.no_grad()
def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__=None ,       SCREAMING_SNAKE_CASE__=False			)  ->							Dict:


		'''simple docstring'''


		snake_case			,snake_case  :       Tuple     			=						get_detr_config(SCREAMING_SNAKE_CASE__			)

		# load original model from torch hub
		snake_case  :       Union[str, Any]     			=						{
		    '''detr-resnet-50''': '''detr_resnet50''',
		    '''detr-resnet-101''': '''detr_resnet101''',
		}
		logger.info(F'Converting model {model_name}...'			)
		snake_case  :       Union[str, Any]     			=						torch.hub.load('''facebookresearch/detr''' ,       model_name_to_original_name[model_name] ,       pretrained=SCREAMING_SNAKE_CASE__			).eval()
		snake_case  :       Optional[int]     			=						detr.state_dict()
		# rename keys
		for src, dest in create_rename_keys(SCREAMING_SNAKE_CASE__			):
				if is_panoptic:
						snake_case  :       Union[str, Any]     			=						'''detr.''' + src
				rename_key(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)
		# query, key and value matrices need special treatment
		read_in_q_k_v(SCREAMING_SNAKE_CASE__ ,       is_panoptic=SCREAMING_SNAKE_CASE__			)
		# 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  :       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'''			)
						):
								snake_case  :       Optional[Any]     			=						state_dict.pop(SCREAMING_SNAKE_CASE__			)
								snake_case  :       Optional[int]     			=						val
						elif "class_labels_classifier" in key or "bbox_predictor" in key:
								snake_case  :       Dict     			=						state_dict.pop(SCREAMING_SNAKE_CASE__			)
								snake_case  :       List[Any]     			=						val
						elif key.startswith('''bbox_attention'''			) or key.startswith('''mask_head'''			):
								continue
						else:
								snake_case  :       int     			=						state_dict.pop(SCREAMING_SNAKE_CASE__			)
								snake_case  :       Optional[int]     			=						val
				else:
						if not key.startswith('''class_labels_classifier'''			) and not key.startswith('''bbox_predictor'''			):
								snake_case  :       Dict     			=						state_dict.pop(SCREAMING_SNAKE_CASE__			)
								snake_case  :       Any     			=						val

    # finally, create HuggingFace model and load state dict
		snake_case  :       Optional[Any]     			=						DetrForSegmentation(SCREAMING_SNAKE_CASE__			) if is_panoptic else DetrForObjectDetection(SCREAMING_SNAKE_CASE__			)
		model.load_state_dict(SCREAMING_SNAKE_CASE__			)
		model.eval()

		# verify our conversion on an image
		snake_case  :       List[str]     			=						'''coco_panoptic''' if is_panoptic else '''coco_detection'''
		snake_case  :       Optional[int]     			=						DetrImageProcessor(format=SCREAMING_SNAKE_CASE__			)

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

		snake_case  :       str     			=						detr(SCREAMING_SNAKE_CASE__			)
		snake_case  :       Optional[int]     			=						model(SCREAMING_SNAKE_CASE__			)

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

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

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


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

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


