

import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


_a:    Any 							=			logging.get_logger(__name__)

_a:    Optional[Any] 							=			"""▁"""

_a:    Optional[int] 							=			{"""vocab_file""": """sentencepiece.bpe.model"""}

_a:    Any 							=			{
    """vocab_file""": {
        """facebook/mbart-large-50-one-to-many-mmt""": (
            """https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model"""
        ),
    }
}

_a:    List[Any] 							=			{
    """facebook/mbart-large-50-one-to-many-mmt""": 1024,
}

# fmt: off
_a:    Optional[int] 							=			["""ar_AR""", """cs_CZ""", """de_DE""", """en_XX""", """es_XX""", """et_EE""", """fi_FI""", """fr_XX""", """gu_IN""", """hi_IN""", """it_IT""", """ja_XX""", """kk_KZ""", """ko_KR""", """lt_LT""", """lv_LV""", """my_MM""", """ne_NP""", """nl_XX""", """ro_RO""", """ru_RU""", """si_LK""", """tr_TR""", """vi_VN""", """zh_CN""", """af_ZA""", """az_AZ""", """bn_IN""", """fa_IR""", """he_IL""", """hr_HR""", """id_ID""", """ka_GE""", """km_KH""", """mk_MK""", """ml_IN""", """mn_MN""", """mr_IN""", """pl_PL""", """ps_AF""", """pt_XX""", """sv_SE""", """sw_KE""", """ta_IN""", """te_IN""", """th_TH""", """tl_XX""", """uk_UA""", """ur_PK""", """xh_ZA""", """gl_ES""", """sl_SI"""]

class 						__UpperCamelCase    (					__lowerCAmelCase						):
	SCREAMING_SNAKE_CASE__    				=     VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE__    				=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE__    				=     PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE__    				=     ['''input_ids''', '''attention_mask''']

	SCREAMING_SNAKE_CASE__    				=     []
	SCREAMING_SNAKE_CASE__    				=     []




	def __init__(		self	:						Any		,	lowerCAmelCase	:						Optional[Any]		,	lowerCAmelCase	:						Dict=None		,	lowerCAmelCase	:						str=None		,	lowerCAmelCase	:						int="</s>"		,	lowerCAmelCase	:						Any="</s>"		,	lowerCAmelCase	:						Tuple="<s>"		,	lowerCAmelCase	:						str="<unk>"		,	lowerCAmelCase	:						Tuple="<pad>"		,	lowerCAmelCase	:						Union[str, Any]="<mask>"		,	lowerCAmelCase	:						Any = None		,	**lowerCAmelCase	:						List[str]		,	):


		'''simple docstring'''







		UpperCAmelCase_       				=			AddedToken(lowerCAmelCase_		,	lstrip=lowerCAmelCase_		,	rstrip=lowerCAmelCase_							) if isinstance(lowerCAmelCase_		,	lowerCAmelCase_							) else mask_token

		UpperCAmelCase_       				=			{} if sp_model_kwargs is None else sp_model_kwargs

		UpperCAmelCase_       				=			kwargs.get("additional_special_tokens"		,	[]							)
		kwargs["additional_special_tokens"] += [
		    code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
		]

		super().__init__(
		    src_lang=lowerCAmelCase_		,	tgt_lang=lowerCAmelCase_		,	eos_token=lowerCAmelCase_		,	unk_token=lowerCAmelCase_		,	sep_token=lowerCAmelCase_		,	cls_token=lowerCAmelCase_		,	pad_token=lowerCAmelCase_		,	mask_token=lowerCAmelCase_		,	sp_model_kwargs=self.sp_model_kwargs		,	**lowerCAmelCase_		,	)

		UpperCAmelCase_       				=			spm.SentencePieceProcessor(**self.sp_model_kwargs							)
		self.sp_model.Load(str(lowerCAmelCase_							)							)
		UpperCAmelCase_       				=			vocab_file

		# Original fairseq vocab and spm vocab must be "aligned":
		# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
		# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
		# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
		# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

		# Mimic fairseq token-to-id alignment for the first 4 token
		UpperCAmelCase_       				=			{"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

		# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
		UpperCAmelCase_       				=			1

		UpperCAmelCase_       				=			len(self.sp_model							)
		UpperCAmelCase_       				=			{
		    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(lowerCAmelCase_							)
		}
		UpperCAmelCase_       				=			{v: k for k, v in self.lang_code_to_id.items()}
		UpperCAmelCase_       				=			len(self.sp_model							) + len(self.lang_code_to_id							) + self.fairseq_offset

		self.fairseq_tokens_to_ids.update(self.lang_code_to_id							)
		UpperCAmelCase_       				=			{v: k for k, v in self.fairseq_tokens_to_ids.items()}

		UpperCAmelCase_       				=			src_lang if src_lang is not None else "en_XX"
		UpperCAmelCase_       				=			self.lang_code_to_id[self._src_lang]
		UpperCAmelCase_       				=			tgt_lang
		self.set_src_lang_special_tokens(self._src_lang							)




	@property
	def       __A					(		self	:						Dict							):


		'''simple docstring'''







		return len(self.sp_model							) + len(self.lang_code_to_id							) + self.fairseq_offset + 1  # Plus 1 for the mask token




	@property
	def       __A					(		self	:						Dict							):


		'''simple docstring'''







		return self._src_lang




	@src_lang.setter
	def       __A					(		self	:						Optional[int]		,	lowerCAmelCase	:						int							):


		'''simple docstring'''







		UpperCAmelCase_       				=			new_src_lang
		self.set_src_lang_special_tokens(self._src_lang							)




	def __getstate__(		self	:						Tuple							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.__dict__.copy()
		UpperCAmelCase_       				=			None
		return state




	def __setstate__(		self	:						Dict		,	lowerCAmelCase	:						Any							):


		'''simple docstring'''







		UpperCAmelCase_       				=			d

		# for backward compatibility
		if not hasattr(self		,	"sp_model_kwargs"							):
			UpperCAmelCase_       				=			{}

		UpperCAmelCase_       				=			spm.SentencePieceProcessor(**self.sp_model_kwargs							)
		self.sp_model.Load(self.vocab_file							)




	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			{self.convert_ids_to_tokens(lowerCAmelCase_							): i for i in range(self.vocab_size							)}
		vocab.update(self.added_tokens_encoder							)
		return vocab




	def       __A					(		self	:						Tuple		,	lowerCAmelCase	:						Union[str, Any]							):


		'''simple docstring'''







		return self.sp_model.encode(lowerCAmelCase_		,	out_type=lowerCAmelCase_							)




	def       __A					(		self	:						List[Any]		,	lowerCAmelCase	:						List[str]							):


		'''simple docstring'''







		if token in self.fairseq_tokens_to_ids:
			return self.fairseq_tokens_to_ids[token]
		UpperCAmelCase_       				=			self.sp_model.PieceToId(lowerCAmelCase_							)

		# Need to return unknown token if the SP model returned 0
		return spm_id + self.fairseq_offset if spm_id else self.unk_token_id




	def       __A					(		self	:						Optional[int]		,	lowerCAmelCase	:						Optional[int]							):


		'''simple docstring'''







		if index in self.fairseq_ids_to_tokens:
			return self.fairseq_ids_to_tokens[index]
		return self.sp_model.IdToPiece(index - self.fairseq_offset							)




	def       __A					(		self	:						Any		,	lowerCAmelCase	:						Optional[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			[]
		UpperCAmelCase_       				=			""
		UpperCAmelCase_       				=			False
		for token in tokens:
			# make sure that special tokens are not decoded using sentencepiece model
			if token in self.all_special_tokens:
				if not prev_is_special:
					out_string += " "
				out_string += self.sp_model.decode(lowerCAmelCase_							) + token
				UpperCAmelCase_       				=			True
				UpperCAmelCase_       				=			[]
			else:
				current_sub_tokens.append(lowerCAmelCase_							)
				UpperCAmelCase_       				=			False
		out_string += self.sp_model.decode(lowerCAmelCase_							)
		return out_string.strip()




	def       __A					(		self	:						Union[str, Any]		,	lowerCAmelCase	:						Tuple		,	lowerCAmelCase	:						Optional[int] = None							):


		'''simple docstring'''







		if not os.path.isdir(lowerCAmelCase_							):
			logger.error(F"Vocabulary path ({save_directory}) should be a directory"							)
			return
		UpperCAmelCase_       				=			os.path.join(
		    lowerCAmelCase_		,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]							)

		if os.path.abspath(self.vocab_file							) != os.path.abspath(lowerCAmelCase_							) and os.path.isfile(self.vocab_file							):
			copyfile(self.vocab_file		,	lowerCAmelCase_							)
		elif not os.path.isfile(self.vocab_file							):
			with open(lowerCAmelCase_		,	"wb"							) as fi:
				UpperCAmelCase_       				=			self.sp_model.serialized_model_proto()
				fi.write(lowerCAmelCase_							)

		return (out_vocab_file,)




	def       __A					(		self	:						Tuple		,	lowerCAmelCase	:						Dict		,	lowerCAmelCase	:						Tuple = None		,	lowerCAmelCase	:						Tuple = False							):


		'''simple docstring'''







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

		UpperCAmelCase_       				=			[1] * len(self.prefix_tokens							)
		UpperCAmelCase_       				=			[1] * len(self.suffix_tokens							)
		if token_ids_a is None:
			return prefix_ones + ([0] * len(lowerCAmelCase_							)) + suffix_ones
		return prefix_ones + ([0] * len(lowerCAmelCase_							)) + ([0] * len(lowerCAmelCase_							)) + suffix_ones




	def       __A					(		self	:						List[str]		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						Tuple = None							):


		'''simple docstring'''







		if token_ids_a is None:
			return self.prefix_tokens + token_ids_a + self.suffix_tokens
		# We don't expect to process pairs, but leave the pair logic for API consistency
		return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens




	def       __A					(		self	:						Union[str, Any]		,	lowerCAmelCase	:						List[str]		,	lowerCAmelCase	:						List[Any]		,	lowerCAmelCase	:						List[Any]		,	lowerCAmelCase	:						Any		,	**lowerCAmelCase	:						Optional[int]							):


		'''simple docstring'''







		if src_lang is None or tgt_lang is None:
			raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"							)
		UpperCAmelCase_       				=			src_lang
		UpperCAmelCase_       				=			self(lowerCAmelCase_		,	add_special_tokens=lowerCAmelCase_		,	return_tensors=lowerCAmelCase_		,	**lowerCAmelCase_							)
		UpperCAmelCase_       				=			self.convert_tokens_to_ids(lowerCAmelCase_							)
		UpperCAmelCase_       				=			tgt_lang_id
		return inputs




	def       __A					(		self	:						Tuple		,	lowerCAmelCase	:						Tuple		,	lowerCAmelCase	:						List[str] = "en_XX"		,	lowerCAmelCase	:						int = None		,	lowerCAmelCase	:						Union[str, Any] = "ro_RO"		,	**lowerCAmelCase	:						Optional[Any]		,	):


		'''simple docstring'''







		UpperCAmelCase_       				=			src_lang
		UpperCAmelCase_       				=			tgt_lang
		return super().prepare_seqaseq_batch(lowerCAmelCase_		,	lowerCAmelCase_		,	**lowerCAmelCase_							)




	def       __A					(		self	:						Optional[Any]							):


		'''simple docstring'''







		return self.set_src_lang_special_tokens(self.src_lang							)




	def       __A					(		self	:						str							):


		'''simple docstring'''







		return self.set_tgt_lang_special_tokens(self.tgt_lang							)




	def       __A					(		self	:						int		,	lowerCAmelCase	:						Union[str, Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.lang_code_to_id[src_lang]
		UpperCAmelCase_       				=			[self.cur_lang_code_id]
		UpperCAmelCase_       				=			[self.eos_token_id]




	def       __A					(		self	:						Union[str, Any]		,	lowerCAmelCase	:						int							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.lang_code_to_id[tgt_lang]
		UpperCAmelCase_       				=			[self.cur_lang_code_id]
		UpperCAmelCase_       				=			[self.eos_token_id]


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()
_a:    Optional[Any] 							=			logging.get_logger(__name__)



def       __lowerCAmelCase      (						A    ):
	# initialize config
	if "resnet-50" in model_name:
		UpperCAmelCase_       				=			ResNetConfig.from_pretrained("microsoft/resnet-50"    )
	elif "resnet-101" in model_name:
		UpperCAmelCase_       				=			ResNetConfig.from_pretrained("microsoft/resnet-101"    )
	else:
		raise ValueError("Model name should include either resnet50 or resnet101"    )

	UpperCAmelCase_       				=			DetrConfig(use_timm_backbone=A       ,     backbone_config=A    )

	# set label attributes
	UpperCAmelCase_       				=			"panoptic" in model_name
	if is_panoptic:
		UpperCAmelCase_       				=			250
	else:
		UpperCAmelCase_       				=			91
		UpperCAmelCase_       				=			"huggingface/label-files"
		UpperCAmelCase_       				=			"coco-detection-id2label.json"
		UpperCAmelCase_       				=			json.load(open(hf_hub_download(A       ,     A       ,     repo_type="dataset"    )       ,     "r"    )    )
		UpperCAmelCase_       				=			{int(A    ): v for k, v in idalabel.items()}
		UpperCAmelCase_       				=			idalabel
		UpperCAmelCase_       				=			{v: k for k, v in idalabel.items()}

	return config, is_panoptic



def       __lowerCAmelCase      (						A    ):
	# here we list all keys to be renamed (original name on the left, our name on the right)
	UpperCAmelCase_       				=			[]

	# 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       __lowerCAmelCase      (						A       ,     A       ,     A    ):
	UpperCAmelCase_       				=			state_dict.pop(A    )
	UpperCAmelCase_       				=			val



def       __lowerCAmelCase      (						A       ,     A=False    ):
	UpperCAmelCase_       				=			""
	if is_panoptic:
		UpperCAmelCase_       				=			"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)
		UpperCAmelCase_       				=			state_dict.pop(F"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"    )
		UpperCAmelCase_       				=			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
		UpperCAmelCase_       				=			in_proj_weight[:256, :]
		UpperCAmelCase_       				=			in_proj_bias[:256]
		UpperCAmelCase_       				=			in_proj_weight[256:512, :]
		UpperCAmelCase_       				=			in_proj_bias[256:512]
		UpperCAmelCase_       				=			in_proj_weight[-256:, :]
		UpperCAmelCase_       				=			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
		UpperCAmelCase_       				=			state_dict.pop(F"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"    )
		UpperCAmelCase_       				=			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
		UpperCAmelCase_       				=			in_proj_weight[:256, :]
		UpperCAmelCase_       				=			in_proj_bias[:256]
		UpperCAmelCase_       				=			in_proj_weight[256:512, :]
		UpperCAmelCase_       				=			in_proj_bias[256:512]
		UpperCAmelCase_       				=			in_proj_weight[-256:, :]
		UpperCAmelCase_       				=			in_proj_bias[-256:]
		# read in weights + bias of input projection layer of cross-attention
		UpperCAmelCase_       				=			state_dict.pop(
		    F"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"    )
		UpperCAmelCase_       				=			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
		UpperCAmelCase_       				=			in_proj_weight_cross_attn[:256, :]
		UpperCAmelCase_       				=			in_proj_bias_cross_attn[:256]
		UpperCAmelCase_       				=			in_proj_weight_cross_attn[256:512, :]
		UpperCAmelCase_       				=			in_proj_bias_cross_attn[256:512]
		UpperCAmelCase_       				=			in_proj_weight_cross_attn[-256:, :]
		UpperCAmelCase_       				=			in_proj_bias_cross_attn[-256:]



def       __lowerCAmelCase      (						):
	UpperCAmelCase_       				=			"http://images.cocodataset.org/val2017/000000039769.jpg"
	UpperCAmelCase_       				=			Image.open(requests.get(A       ,     stream=A    ).raw    )

	return im



@torch.no_grad()
def       __lowerCAmelCase      (						A       ,     A=None       ,     A=False    ):
	UpperCAmelCase_				,   UpperCAmelCase_       				=			get_detr_config(A    )

	# load original model from torch hub
	UpperCAmelCase_       				=			{
	    "detr-resnet-50": "detr_resnet50",
	    "detr-resnet-101": "detr_resnet101",
	}
	logger.info(F"Converting model {model_name}..."    )
	UpperCAmelCase_       				=			torch.hub.load("facebookresearch/detr"       ,     model_name_to_original_name[model_name]       ,     pretrained=A    ).eval()
	UpperCAmelCase_       				=			detr.state_dict()
	# rename keys
	for src, dest in create_rename_keys(A    ):
		if is_panoptic:
			UpperCAmelCase_       				=			"detr." + src
		rename_key(A       ,     A       ,     A    )
	# query, key and value matrices need special treatment
	read_in_q_k_v(A       ,     is_panoptic=A    )
	# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
	UpperCAmelCase_       				=			"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"    )
			):
				UpperCAmelCase_       				=			state_dict.pop(A    )
				UpperCAmelCase_       				=			val
			elif "class_labels_classifier" in key or "bbox_predictor" in key:
				UpperCAmelCase_       				=			state_dict.pop(A    )
				UpperCAmelCase_       				=			val
			elif key.startswith("bbox_attention"    ) or key.startswith("mask_head"    ):
				continue
			else:
				UpperCAmelCase_       				=			state_dict.pop(A    )
				UpperCAmelCase_       				=			val
		else:
			if not key.startswith("class_labels_classifier"    ) and not key.startswith("bbox_predictor"    ):
				UpperCAmelCase_       				=			state_dict.pop(A    )
				UpperCAmelCase_       				=			val

    # finally, create HuggingFace model and load state dict
	UpperCAmelCase_       				=			DetrForSegmentation(A    ) if is_panoptic else DetrForObjectDetection(A    )
	model.load_state_dict(A    )
	model.eval()

	# verify our conversion on an image
	UpperCAmelCase_       				=			"coco_panoptic" if is_panoptic else "coco_detection"
	UpperCAmelCase_       				=			DetrImageProcessor(format=A    )

	UpperCAmelCase_       				=			processor(images=prepare_img()       ,     return_tensors="pt"    )
	UpperCAmelCase_       				=			encoding["pixel_values"]

	UpperCAmelCase_       				=			detr(A    )
	UpperCAmelCase_       				=			model(A    )

	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(A    ).mkdir(exist_ok=A    )
		model.save_pretrained(A    )
		processor.save_pretrained(A    )

	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__":
				_a:    List[str] 							=			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.""")
				_a:    str 							=			parser.parse_args()
				convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)