
"""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 					_snake_case (						snake_case__      :   Dict   ):
	# initialize config
	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=snake_case__		,					backbone_config=snake_case__   )

	# 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(snake_case__		,					snake_case__		,					repo_type='dataset'   )		,					'r'   )   )
		A        =      {int(snake_case__   ): 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 (						snake_case__      :   int   ):
	# here we list all keys to be renamed (original name on the left, our name on the right)
	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 (						snake_case__      :   List[Any]		,					snake_case__      :   Optional[Any]		,					snake_case__      :   List[Any]   ):
	A        =      state_dict.pop(snake_case__   )
	A        =      val






def 					_snake_case (						snake_case__      :   int		,					snake_case__      :   Optional[int]=False   ):
	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 (						):
	A        =      'http://images.cocodataset.org/val2017/000000039769.jpg'
	A        =      Image.open(requests.get(snake_case__		,					stream=snake_case__   ).raw   )

	return im






@torch.no_grad()
def 					_snake_case (						snake_case__      :   Any		,					snake_case__      :   List[Any]=None		,					snake_case__      :   int=False   ):
	A      ,					A        =      get_detr_config(snake_case__   )

	# 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=snake_case__   ).eval()
	A        =      detr.state_dict()
	# rename keys
	for src, dest in create_rename_keys(snake_case__   ):
		if is_panoptic:
			A        =      'detr.' + src
		rename_key(snake_case__		,					snake_case__		,					snake_case__   )
	# query, key and value matrices need special treatment
	read_in_q_k_v(snake_case__		,					is_panoptic=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
	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(snake_case__   )
				A        =      val
			elif "class_labels_classifier" in key or "bbox_predictor" in key:
				A        =      state_dict.pop(snake_case__   )
				A        =      val
			elif key.startswith('bbox_attention'   ) or key.startswith('mask_head'   ):
				continue
			else:
				A        =      state_dict.pop(snake_case__   )
				A        =      val
		else:
			if not key.startswith('class_labels_classifier'   ) and not key.startswith('bbox_predictor'   ):
				A        =      state_dict.pop(snake_case__   )
				A        =      val

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

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

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

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


from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						['image_processor', 'tokenizer']
			SCREAMING_SNAKE_CASE							   =						'BridgeTowerImageProcessor'
			SCREAMING_SNAKE_CASE							   =						('RobertaTokenizer', 'RobertaTokenizerFast')





			def __init__(		self					,  __snake_case					,  __snake_case					)   -> Optional[int]:




						'''simple docstring'''
						super().__init__(__snake_case					,  __snake_case					)





			def __call__(		self					,  __snake_case					,  __snake_case = None					,  __snake_case = True					,  __snake_case = False					,  __snake_case = None					,  __snake_case = None					,  __snake_case = 0					,  __snake_case = None					,  __snake_case = None					,  __snake_case = None					,  __snake_case = False					,  __snake_case = False					,  __snake_case = False					,  __snake_case = False					,  __snake_case = True					,  __snake_case = None					,  **__snake_case					,  )   -> BatchEncoding:




						'''simple docstring'''
						__a        =self.tokenizer(
						    text=__snake_case					,  add_special_tokens=__snake_case					,  padding=__snake_case					,  truncation=__snake_case					,  max_length=__snake_case					,  stride=__snake_case					,  pad_to_multiple_of=__snake_case					,  return_token_type_ids=__snake_case					,  return_attention_mask=__snake_case					,  return_overflowing_tokens=__snake_case					,  return_special_tokens_mask=__snake_case					,  return_offsets_mapping=__snake_case					,  return_length=__snake_case					,  verbose=__snake_case					,  return_tensors=__snake_case					,  **__snake_case					,  )
						# add pixel_values + pixel_mask
						__a        =self.image_processor(
						    __snake_case					,  return_tensors=__snake_case					,  do_normalize=__snake_case					,  do_center_crop=__snake_case					,  **__snake_case					)
						encoding.update(__snake_case					)

						return encoding





			def 		__magic_name__    (		self					,  *__snake_case					,  **__snake_case					)   -> str:




						'''simple docstring'''
						return self.tokenizer.batch_decode(*__snake_case					,  **__snake_case					)





			def 		__magic_name__    (		self					,  *__snake_case					,  **__snake_case					)   -> Optional[Any]:




						'''simple docstring'''
						return self.tokenizer.decode(*__snake_case					,  **__snake_case					)





			@property
			def 		__magic_name__    (		self					)   -> Union[str, Any]:




						'''simple docstring'''
						__a        =self.tokenizer.model_input_names
						__a        =self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)






