
'''simple docstring'''
from collections import defaultdict
from pathlib import Path

import pandas as pd
from rouge_cli import calculate_rouge_path

from utils import calculate_rouge


lowercase__						       =      [
    "Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of the"
    " final seconds on board Flight 9525. The Germanwings co-pilot says he had a \"previous episode of severe"
    " depression\" German airline confirms it knew of Andreas Lubitz's depression years before he took control.",
    "The Palestinian Authority officially becomes the 123rd member of the International Criminal Court. The formal"
    " accession was marked with a ceremony at The Hague, in the Netherlands. The Palestinians signed the ICC's"
    " founding Rome Statute in January. Israel and the United States opposed the Palestinians' efforts to join the"
    " body.",
    "Amnesty International releases its annual report on the death penalty. The report catalogs the use of"
    " state-sanctioned killing as a punitive measure across the globe. At least 607 people were executed around the"
    " world in 2014, compared to 778 in 2013. The U.S. remains one of the worst offenders for imposing capital"
    " punishment.",
]

lowercase__						       =      [
    "Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports ."
    " Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz"
    " had informed his Lufthansa training school of an episode of severe depression, airline says .",
    "Membership gives the ICC jurisdiction over alleged crimes committed in Palestinian territories since last June ."
    " Israel and the United States opposed the move, which could open the door to war crimes investigations against"
    " Israelis .",
    "Amnesty's annual death penalty report catalogs encouraging signs, but setbacks in numbers of those sentenced to"
    " death . Organization claims that governments around the world are using the threat of terrorism to advance"
    " executions . The number of executions worldwide has gone down by almost 22% compared with 2013, but death"
    " sentences up by 28% .",
]




def    _UpperCamelCase						(       )  ->							Optional[Any]:


		'''simple docstring'''


		snake_case  :       str     			=						calculate_rouge(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       bootstrap_aggregation=SCREAMING_SNAKE_CASE__ ,       rouge_keys=['''rouge2''', '''rougeL''']			)
		assert isinstance(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)
		snake_case  :       Union[str, Any]     			=						calculate_rouge(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       bootstrap_aggregation=SCREAMING_SNAKE_CASE__ ,       rouge_keys=['''rouge2''']			)
		assert (
		    pd.DataFrame(no_aggregation['''rouge2''']			).fmeasure.mean()
		    == pd.DataFrame(no_aggregation_just_ra['''rouge2''']			).fmeasure.mean()
		)




def    _UpperCamelCase						(       )  ->							List[str]:


		'''simple docstring'''


		snake_case  :       Optional[int]     			=						'''rougeLsum'''
		snake_case  :       Optional[Any]     			=						calculate_rouge(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       newline_sep=SCREAMING_SNAKE_CASE__ ,       rouge_keys=[k]			)[k]
		snake_case  :       Any     			=						calculate_rouge(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       newline_sep=SCREAMING_SNAKE_CASE__ ,       rouge_keys=[k]			)[k]
		assert score > score_no_sep




def    _UpperCamelCase						(       )  ->							List[str]:


		'''simple docstring'''


		snake_case  :       Any     			=						['''rouge1''', '''rouge2''', '''rougeL''']
		snake_case  :       Tuple     			=						calculate_rouge(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       newline_sep=SCREAMING_SNAKE_CASE__ ,       rouge_keys=SCREAMING_SNAKE_CASE__			)
		snake_case  :       Optional[Any]     			=						calculate_rouge(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       newline_sep=SCREAMING_SNAKE_CASE__ ,       rouge_keys=SCREAMING_SNAKE_CASE__			)
		assert score_sep == score_no_sep




def    _UpperCamelCase						(       )  ->							List[str]:


		'''simple docstring'''


		snake_case  :       List[str]     			=						[
		    '''Her older sister, Margot Frank, died in 1945, a month earlier than previously thought.''',
		    '''Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports .''',
		]
		snake_case  :       List[Any]     			=						[
		    '''Margot Frank, died in 1945, a month earlier than previously thought.''',
		    '''Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of'''
		    ''' the final seconds on board Flight 9525.''',
		]
		assert calculate_rouge(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       newline_sep=SCREAMING_SNAKE_CASE__			) == calculate_rouge(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       newline_sep=SCREAMING_SNAKE_CASE__			)




def    _UpperCamelCase						(       )  ->							str:


		'''simple docstring'''


		snake_case  :       str     			=						[
		    '''" "a person who has such a video needs to immediately give it to the investigators," prosecutor says .<n> "it is a very disturbing scene," editor-in-chief of bild online tells "erin burnett: outfront" '''
		]
		snake_case  :       List[Any]     			=						[
		    ''' Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports . Journalists at Bild and Paris Match are "very confident" the video clip is real, an editor says . Andreas Lubitz had informed his Lufthansa training school of an episode of severe depression, airline says .'''
		]

		snake_case  :       Dict     			=						calculate_rouge(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       rouge_keys=['''rougeLsum'''] ,       newline_sep=SCREAMING_SNAKE_CASE__			)['''rougeLsum''']
		snake_case  :       int     			=						calculate_rouge(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       rouge_keys=['''rougeLsum''']			)['''rougeLsum''']
		assert new_score > prev_score




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


		'''simple docstring'''


		snake_case  :       int     			=						Path('''examples/seq2seq/test_data/wmt_en_ro'''			)
		snake_case  :       Optional[int]     			=						calculate_rouge_path(data_dir.joinpath('''test.source'''			) ,       data_dir.joinpath('''test.target'''			)			)
		assert isinstance(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)
		snake_case  :       Optional[Any]     			=						calculate_rouge_path(
		    data_dir.joinpath('''test.source'''			) ,       data_dir.joinpath('''test.target'''			) ,       bootstrap_aggregation=SCREAMING_SNAKE_CASE__			)
		assert isinstance(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)




'''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)


