




'''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()
_snake_case : List[Any]  				=						logging.get_logger(__name__)
def 						snake_case_       (UpperCamelCase			:    Dict ):





							'''simple docstring'''


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

							_a      	=							DetrConfig(use_timm_backbone=UpperCamelCase					,		backbone_config=UpperCamelCase )

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

							return config, is_panoptic
def 						snake_case_       (UpperCamelCase			:    Optional[int] ):





							'''simple docstring'''


							_a      	=							[]

							# 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 						snake_case_       (UpperCamelCase			:    str					,		UpperCamelCase			:    Optional[int]					,		UpperCamelCase			:    List[Any] ):





							'''simple docstring'''


							_a      	=							state_dict.pop(UpperCamelCase )
							_a      	=							val
def 						snake_case_       (UpperCamelCase			:    List[str]					,		UpperCamelCase			:    Union[str, Any]=False ):





							'''simple docstring'''


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





							'''simple docstring'''


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

							return im
@torch.no_grad()
def 						snake_case_       (UpperCamelCase			:    int					,		UpperCamelCase			:    Tuple=None					,		UpperCamelCase			:    int=False ):





							'''simple docstring'''


							_a	,				_a      	=							get_detr_config(UpperCamelCase )

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

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

							# verify our conversion on an image
							_a      	=							'''coco_panoptic''' if is_panoptic else '''coco_detection'''
							_a      	=							DetrImageProcessor(format=UpperCamelCase )

							_a      	=							processor(images=prepare_img()					,		return_tensors='''pt''' )
							_a      	=							encoding['''pixel_values''']

							_a      	=							detr(UpperCamelCase )
							_a      	=							model(UpperCamelCase )

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

							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__":
				_snake_case : Optional[int]  				=						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.')
				_snake_case : Optional[int]  				=						parser.parse_args()
				convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_lowercase		:  List[str]			       =5_0000
_lowercase		:  str			       =5000

_lowercase       , _lowercase		:  List[str]			       =os.path.split(__file__)
_lowercase		:  Union[str, Any]			       =os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))


@get_duration
def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__						):
		for i in range(lowerCAmelCase__						):
				lowerCamelCase_	:     Tuple 						=      dataset[i]


@get_duration
def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		for i in range(0							,len(lowerCAmelCase__						)							,lowerCAmelCase__						):
				lowerCamelCase_	:     Tuple 						=      dataset[i : i + batch_size]


@get_duration
def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		with dataset.formatted_as(type=lowerCAmelCase__						):
				for i in range(lowerCAmelCase__						):
						lowerCamelCase_	:     Tuple 						=      dataset[i]


@get_duration
def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		with dataset.formatted_as(type=lowerCAmelCase__						):
				for i in range(0							,lowerCAmelCase__							,lowerCAmelCase__						):
						lowerCamelCase_	:     Optional[int] 						=      dataset[i : i + batch_size]


def 		_SCREAMING_SNAKE_CASE     ( ):
		lowerCamelCase_	:     Any 						=      {'num examples': SPEED_TEST_N_EXAMPLES}
		lowerCamelCase_	:     Optional[Any] 						=      [
		    (read, {'length': SMALL_TEST}),
		    (read, {'length': SPEED_TEST_N_EXAMPLES}),
		    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
		    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_00}),
		    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10_00}),
		    (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
		    (read_formatted, {'type': 'pandas', 'length': SMALL_TEST}),
		    (read_formatted, {'type': 'torch', 'length': SMALL_TEST}),
		    (read_formatted, {'type': 'tensorflow', 'length': SMALL_TEST}),
		    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
		    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10_00}),
		]

		lowerCamelCase_	:     Optional[Any] 						=      [
		    (read, {'length': SMALL_TEST}),
		    (read, {'length': SPEED_TEST_N_EXAMPLES}),
		    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
		    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_00}),
		    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10_00}),
		    (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
		    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
		    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10_00}),
		]
		with tempfile.TemporaryDirectory() as tmp_dir:
				print('generating dataset'						)
				lowerCamelCase_	:     List[Any] 						=      datasets.Features(
				    {'list': datasets.Sequence(datasets.Value('float32'						)						), 'numbers': datasets.Value('float32'						)}						)
				lowerCamelCase_	:     Union[str, Any] 						=      generate_example_dataset(
				    os.path.join(lowerCAmelCase__							,'dataset.arrow'						)							,lowerCAmelCase__							,num_examples=lowerCAmelCase__							,seq_shapes={'list': (1_00,)}							,)
				print('first set of iterations'						)
				for func, kwargs in functions:
						print(func.__name__							,str(lowerCAmelCase__						)						)
						lowerCamelCase_	:     Optional[int] 						=      func(lowerCAmelCase__							,**lowerCAmelCase__						)

				print('shuffling dataset'						)
				lowerCamelCase_	:     int 						=      dataset.shuffle()
				print('Second set of iterations (after shuffling'						)
				for func, kwargs in functions_shuffled:
						print('shuffled '							,func.__name__							,str(lowerCAmelCase__						)						)
						lowerCamelCase_	:     Optional[Any] 						=      func(
						    lowerCAmelCase__							,**lowerCAmelCase__						)

		with open(lowerCAmelCase__							,'wb'						) as f:
				f.write(json.dumps(lowerCAmelCase__						).encode('utf-8'						)						)


if __name__ == "__main__":  # useful to run the profiler
		benchmark_iterating()
