
"""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__   :     Optional[Any]     ):
	# 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__   :     List[str]     ):
	# 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[str]	,      snake_case__   :     List[str]	,      snake_case__   :     str     ):
	A						   =   state_dict.pop(snake_case__     )
	A						   =   val






def       _snake_case				(		snake_case__   :     int	,      snake_case__   :     Tuple=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__   :     Tuple	,      snake_case__   :     List[Any]=None	,      snake_case__   :     str=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)

"""simple docstring"""


from argparse import ArgumentParser

from . import BaseTransformersCLICommand






def       _snake_case				(		snake_case__   :     Optional[int]     ):
	return DownloadCommand(args.model	,      args.cache_dir	,      args.force	,      args.trust_remote_code     )







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						@staticmethod
						def 						_SCREAMING_SNAKE_CASE      ( A_     :      ArgumentParser      )     ->   Any:
							A						   =   parser.add_parser('download'      )
							download_parser.add_argument(
							    '--cache-dir'  ,type=A_  ,default=A_  ,help='Path to location to store the models'      )
							download_parser.add_argument(
							    '--force'  ,action='store_true'  ,help='Force the model to be download even if already in cache-dir'      )
							download_parser.add_argument(
							    '--trust-remote-code'  ,action='store_true'  ,help='Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you\'ve reviewed the code as it will execute on your local machine'  ,)
							download_parser.add_argument('model'  ,type=A_  ,help='Name of the model to download'      )
							download_parser.set_defaults(func=A_      )


						def __init__( self     :      Dict  ,A_     :      str  ,A_     :      str  ,A_     :      bool  ,A_     :      bool      )     ->   Union[str, Any]:
							A						   =   model
							A						   =   cache
							A						   =   force
							A						   =   trust_remote_code




						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict      )     ->   Optional[int]:
							from ..models.auto import AutoModel, AutoTokenizer

							AutoModel.from_pretrained(
							    self._model  ,cache_dir=self._cache  ,force_download=self._force  ,trust_remote_code=self._trust_remote_code      )
							AutoTokenizer.from_pretrained(
							    self._model  ,cache_dir=self._cache  ,force_download=self._force  ,trust_remote_code=self._trust_remote_code      )