
"""simple docstring"""


import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
_lowercase				 =					logging.get_logger(__name__)






def    _snake_case					(		snake_case__ :  int  ):
	A     = SwinConfig(
	    embed_dim=192						,   depths=(2, 2, 18, 2)						,   num_heads=(6, 12, 24, 48)						,   window_size=12						,   out_features=['stage2', 'stage3', 'stage4']						,   )

	A     = DetaConfig(
	    backbone_config=snake_case__						,   num_queries=900						,   encoder_ffn_dim=2048						,   decoder_ffn_dim=2048						,   num_feature_levels=5						,   assign_first_stage=snake_case__						,   with_box_refine=snake_case__						,   two_stage=snake_case__						,   )

	# set labels
	A     = 'huggingface/label-files'
	if "o365" in model_name:
		A     = 366
		A     = 'object365-id2label.json'
	else:
		A     = 91
		A     = 'coco-detection-id2label.json'

	A     = num_labels
	A     = json.load(open(cached_download(hf_hub_url(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






def    _snake_case					(		snake_case__ :  Any  ):
	A     = []

	# stem
	# fmt: off
	rename_keys.append(('backbone.0.body.patch_embed.proj.weight', 'model.backbone.model.embeddings.patch_embeddings.projection.weight')  )
	rename_keys.append(('backbone.0.body.patch_embed.proj.bias', 'model.backbone.model.embeddings.patch_embeddings.projection.bias')  )
	rename_keys.append(('backbone.0.body.patch_embed.norm.weight', 'model.backbone.model.embeddings.norm.weight')  )
	rename_keys.append(('backbone.0.body.patch_embed.norm.bias', 'model.backbone.model.embeddings.norm.bias')  )
	# stages
	for i in range(len(config.backbone_config.depths  )  ):
		for j in range(config.backbone_config.depths[i]  ):
			rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.norm1.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight')  )
			rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.norm1.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias')  )
			rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table')  )
			rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index')  )
			rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight')  )
			rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias')  )
			rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.norm2.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight')  )
			rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.norm2.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias')  )
			rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight')  )
			rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias')  )
			rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight')  )
			rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias')  )

		if i < 3:
			rename_keys.append((F'backbone.0.body.layers.{i}.downsample.reduction.weight', F'model.backbone.model.encoder.layers.{i}.downsample.reduction.weight')  )
			rename_keys.append((F'backbone.0.body.layers.{i}.downsample.norm.weight', F'model.backbone.model.encoder.layers.{i}.downsample.norm.weight')  )
			rename_keys.append((F'backbone.0.body.layers.{i}.downsample.norm.bias', F'model.backbone.model.encoder.layers.{i}.downsample.norm.bias')  )

	rename_keys.append(('backbone.0.body.norm1.weight', 'model.backbone.model.hidden_states_norms.stage2.weight')  )
	rename_keys.append(('backbone.0.body.norm1.bias', 'model.backbone.model.hidden_states_norms.stage2.bias')  )
	rename_keys.append(('backbone.0.body.norm2.weight', 'model.backbone.model.hidden_states_norms.stage3.weight')  )
	rename_keys.append(('backbone.0.body.norm2.bias', 'model.backbone.model.hidden_states_norms.stage3.bias')  )
	rename_keys.append(('backbone.0.body.norm3.weight', 'model.backbone.model.hidden_states_norms.stage4.weight')  )
	rename_keys.append(('backbone.0.body.norm3.bias', 'model.backbone.model.hidden_states_norms.stage4.bias')  )

	# transformer encoder
	for i in range(config.encoder_layers  ):
		rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight', F'model.encoder.layers.{i}.self_attn.sampling_offsets.weight')  )
		rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias', F'model.encoder.layers.{i}.self_attn.sampling_offsets.bias')  )
		rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.attention_weights.weight', F'model.encoder.layers.{i}.self_attn.attention_weights.weight')  )
		rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.attention_weights.bias', F'model.encoder.layers.{i}.self_attn.attention_weights.bias')  )
		rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.value_proj.weight', F'model.encoder.layers.{i}.self_attn.value_proj.weight')  )
		rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.value_proj.bias', F'model.encoder.layers.{i}.self_attn.value_proj.bias')  )
		rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.output_proj.weight', F'model.encoder.layers.{i}.self_attn.output_proj.weight')  )
		rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.output_proj.bias', F'model.encoder.layers.{i}.self_attn.output_proj.bias')  )
		rename_keys.append((F'transformer.encoder.layers.{i}.norm1.weight', F'model.encoder.layers.{i}.self_attn_layer_norm.weight')  )
		rename_keys.append((F'transformer.encoder.layers.{i}.norm1.bias', F'model.encoder.layers.{i}.self_attn_layer_norm.bias')  )
		rename_keys.append((F'transformer.encoder.layers.{i}.linear1.weight', F'model.encoder.layers.{i}.fc1.weight')  )
		rename_keys.append((F'transformer.encoder.layers.{i}.linear1.bias', F'model.encoder.layers.{i}.fc1.bias')  )
		rename_keys.append((F'transformer.encoder.layers.{i}.linear2.weight', F'model.encoder.layers.{i}.fc2.weight')  )
		rename_keys.append((F'transformer.encoder.layers.{i}.linear2.bias', F'model.encoder.layers.{i}.fc2.bias')  )
		rename_keys.append((F'transformer.encoder.layers.{i}.norm2.weight', F'model.encoder.layers.{i}.final_layer_norm.weight')  )
		rename_keys.append((F'transformer.encoder.layers.{i}.norm2.bias', F'model.encoder.layers.{i}.final_layer_norm.bias')  )

	# transformer decoder
	for i in range(config.decoder_layers  ):
		rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight', F'model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight')  )
		rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias', F'model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias')  )
		rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.attention_weights.weight', F'model.decoder.layers.{i}.encoder_attn.attention_weights.weight')  )
		rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.attention_weights.bias', F'model.decoder.layers.{i}.encoder_attn.attention_weights.bias')  )
		rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.value_proj.weight', F'model.decoder.layers.{i}.encoder_attn.value_proj.weight')  )
		rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.value_proj.bias', F'model.decoder.layers.{i}.encoder_attn.value_proj.bias')  )
		rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.output_proj.weight', F'model.decoder.layers.{i}.encoder_attn.output_proj.weight')  )
		rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.output_proj.bias', F'model.decoder.layers.{i}.encoder_attn.output_proj.bias')  )
		rename_keys.append((F'transformer.decoder.layers.{i}.norm1.weight', F'model.decoder.layers.{i}.encoder_attn_layer_norm.weight')  )
		rename_keys.append((F'transformer.decoder.layers.{i}.norm1.bias', F'model.decoder.layers.{i}.encoder_attn_layer_norm.bias')  )
		rename_keys.append((F'transformer.decoder.layers.{i}.self_attn.out_proj.weight', F'model.decoder.layers.{i}.self_attn.out_proj.weight')  )
		rename_keys.append((F'transformer.decoder.layers.{i}.self_attn.out_proj.bias', F'model.decoder.layers.{i}.self_attn.out_proj.bias')  )
		rename_keys.append((F'transformer.decoder.layers.{i}.norm2.weight', F'model.decoder.layers.{i}.self_attn_layer_norm.weight')  )
		rename_keys.append((F'transformer.decoder.layers.{i}.norm2.bias', F'model.decoder.layers.{i}.self_attn_layer_norm.bias')  )
		rename_keys.append((F'transformer.decoder.layers.{i}.linear1.weight', F'model.decoder.layers.{i}.fc1.weight')  )
		rename_keys.append((F'transformer.decoder.layers.{i}.linear1.bias', F'model.decoder.layers.{i}.fc1.bias')  )
		rename_keys.append((F'transformer.decoder.layers.{i}.linear2.weight', F'model.decoder.layers.{i}.fc2.weight')  )
		rename_keys.append((F'transformer.decoder.layers.{i}.linear2.bias', F'model.decoder.layers.{i}.fc2.bias')  )
		rename_keys.append((F'transformer.decoder.layers.{i}.norm3.weight', F'model.decoder.layers.{i}.final_layer_norm.weight')  )
		rename_keys.append((F'transformer.decoder.layers.{i}.norm3.bias', F'model.decoder.layers.{i}.final_layer_norm.bias')  )

	# fmt: on

	return rename_keys






def    _snake_case					(		snake_case__ :  Tuple						,   snake_case__ :  List[Any]						,   snake_case__ :  Any  ):
	A     = dct.pop(snake_case__  )
	A     = val






def    _snake_case					(		snake_case__ :  Any						,   snake_case__ :  Optional[int]  ):
	A     = [int(backbone_config.embed_dim * 2**i  ) for i in range(len(backbone_config.depths  )  )]
	for i in range(len(backbone_config.depths  )  ):
		A     = num_features[i]
		for j in range(backbone_config.depths[i]  ):
			# fmt: off
			# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
			A     = state_dict.pop(F'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight'  )
			A     = state_dict.pop(F'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias'  )
			# next, add query, keys and values (in that order) to the state dict
			A     = in_proj_weight[:dim, :]
			A     = in_proj_bias[: dim]
			A     = in_proj_weight[
			    dim : dim * 2, :
			]
			A     = in_proj_bias[
			    dim : dim * 2
			]
			A     = in_proj_weight[
			    -dim :, :
			]
			A     = in_proj_bias[-dim :]
			# fmt: on






def    _snake_case					(		snake_case__ :  Tuple						,   snake_case__ :  Union[str, Any]  ):
	# transformer decoder self-attention layers
	A     = config.d_model
	for i in range(config.decoder_layers  ):
		# read in weights + bias of input projection layer of self-attention
		A     = state_dict.pop(F'transformer.decoder.layers.{i}.self_attn.in_proj_weight'  )
		A     = state_dict.pop(F'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[:hidden_size, :]
		A     = in_proj_bias[:hidden_size]
		A     = in_proj_weight[
		    hidden_size : hidden_size * 2, :
		]
		A     = in_proj_bias[hidden_size : hidden_size * 2]
		A     = in_proj_weight[-hidden_size:, :]
		A     = in_proj_bias[-hidden_size:]






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__ :  Dict						,   snake_case__ :  Dict						,   snake_case__ :  List[str]  ):
	A     = get_deta_config(snake_case__  )

	# load original state dict
	if model_name == "deta-swin-large":
		A     = hf_hub_download(repo_id='nielsr/deta-checkpoints'						,   filename='adet_swin_ft.pth'  )
	elif model_name == "deta-swin-large-o365":
		A     = hf_hub_download(repo_id='jozhang97/deta-swin-l-o365'						,   filename='deta_swin_pt_o365.pth'  )
	else:
		raise ValueError(F'Model name {model_name} not supported'  )

	A     = torch.load(snake_case__						,   map_location='cpu'  )['model']

	# original state dict
	for name, param in state_dict.items():
		print(snake_case__						,   param.shape  )

	# rename keys
	A     = create_rename_keys(snake_case__  )
	for src, dest in rename_keys:
		rename_key(snake_case__						,   snake_case__						,   snake_case__  )
	read_in_swin_q_k_v(snake_case__						,   config.backbone_config  )
	read_in_decoder_q_k_v(snake_case__						,   snake_case__  )

	# fix some prefixes
	for key in state_dict.copy().keys():
		if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
			A     = state_dict.pop(snake_case__  )
			A     = val
		if "input_proj" in key:
			A     = state_dict.pop(snake_case__  )
			A     = val
		if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
			A     = state_dict.pop(snake_case__  )
			A     = val

    # finally, create HuggingFace model and load state dict
	A     = DetaForObjectDetection(snake_case__  )
	model.load_state_dict(snake_case__  )
	model.eval()

	A     = 'cuda' if torch.cuda.is_available() else 'cpu'
	model.to(snake_case__  )

	# load image processor
	A     = DetaImageProcessor(format='coco_detection'  )

	# verify our conversion on image
	A     = prepare_img()
	A     = processor(images=snake_case__						,   return_tensors='pt'  )
	A     = encoding['pixel_values']
	A     = model(pixel_values.to(snake_case__  )  )

	# verify logits
	print('Logits:'						,   outputs.logits[0, :3, :3]  )
	print('Boxes:'						,   outputs.pred_boxes[0, :3, :3]  )
	if model_name == "deta-swin-large":
		A     = torch.tensor(
		    [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]]  )
		A     = torch.tensor([[0.4987, 0.4969, 0.9999], [0.2549, 0.5498, 0.4805], [0.5498, 0.2757, 0.0569]]  )
	elif model_name == "deta-swin-large-o365":
		A     = torch.tensor(
		    [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]]  )
		A     = torch.tensor([[0.2523, 0.5549, 0.4881], [0.7715, 0.4149, 0.4601], [0.5503, 0.2753, 0.0575]]  )
	assert torch.allclose(outputs.logits[0, :3, :3]						,   expected_logits.to(snake_case__  )						,   atol=1e-4  )
	assert torch.allclose(outputs.pred_boxes[0, :3, :3]						,   expected_boxes.to(snake_case__  )						,   atol=1e-4  )
	print('Everything ok!'  )

	if pytorch_dump_folder_path:
		# Save model and processor
		logger.info(F'Saving PyTorch model and processor to {pytorch_dump_folder_path}...'  )
		Path(snake_case__  ).mkdir(exist_ok=snake_case__  )
		model.save_pretrained(snake_case__  )
		processor.save_pretrained(snake_case__  )

	# Push to hub
	if push_to_hub:
		print('Pushing model and processor to hub...'  )
		model.push_to_hub(F'jozhang97/{model_name}'  )
		processor.push_to_hub(F'jozhang97/{model_name}'  )


if __name__ == "__main__":
		_lowercase				 =					argparse.ArgumentParser()

		parser.add_argument(
		    '''--model_name''',
		    type=str,
		    default='''deta-swin-large''',
		    choices=['''deta-swin-large''', '''deta-swin-large-o365'''],
		    help='''Name of the 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 or not to push the converted model to the 🤗 hub.'''
		)
		_lowercase				 =					parser.parse_args()
		convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)

"""simple docstring"""


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 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						_lowerCamelCase:     List[str]   =     ['''image_processor''', '''tokenizer''']
						_lowerCamelCase:     Optional[int]   =     '''Pix2StructImageProcessor'''
						_lowerCamelCase:     Dict   =     ('''T5Tokenizer''', '''T5TokenizerFast''')


						def __init__(			self  :   Optional[int]	,A_  :   List[str]	,A_  :   Optional[int]   )  -> int:
							A     = False
							super().__init__(A_	,A_   )


						def __call__(			self  :   Any	,A_  :   List[str]=None	,A_  :   Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None	,A_  :   bool = True	,A_  :   Union[bool, str, PaddingStrategy] = False	,A_  :   Union[bool, str, TruncationStrategy] = None	,A_  :   Optional[int] = None	,A_  :   Optional[int] = 2048	,A_  :   int = 0	,A_  :   Optional[int] = None	,A_  :   Optional[bool] = None	,A_  :   bool = False	,A_  :   bool = False	,A_  :   bool = False	,A_  :   bool = False	,A_  :   bool = False	,A_  :   bool = True	,A_  :   Optional[Union[str, TensorType]] = None	,**A_  :   Tuple	,)  -> BatchEncoding:
							if images is None and text is None:
								raise ValueError('You have to specify either images or text.'   )

							# Get only text
							if images is None and not self.image_processor.is_vqa:
								A     = self.tokenizer
								A     = self.tokenizer(
								    text=A_	,add_special_tokens=A_	,padding=A_	,truncation=A_	,max_length=A_	,stride=A_	,pad_to_multiple_of=A_	,return_attention_mask=A_	,return_overflowing_tokens=A_	,return_special_tokens_mask=A_	,return_offsets_mapping=A_	,return_token_type_ids=A_	,return_length=A_	,verbose=A_	,return_tensors=A_	,**A_	,)
								return text_encoding

							if not self.image_processor.is_vqa:
								# add pixel_values
								A     = self.image_processor(
								    A_	,return_tensors=A_	,max_patches=A_	,**A_   )
							else:
								# add pixel_values and bbox
								A     = self.image_processor(
								    A_	,return_tensors=A_	,max_patches=A_	,header_text=A_	,**A_   )

							if text is not None and not self.image_processor.is_vqa:
								A     = self.tokenizer(
								    text=A_	,add_special_tokens=A_	,padding=A_	,truncation=A_	,max_length=A_	,stride=A_	,pad_to_multiple_of=A_	,return_attention_mask=A_	,return_overflowing_tokens=A_	,return_special_tokens_mask=A_	,return_offsets_mapping=A_	,return_token_type_ids=A_	,return_length=A_	,verbose=A_	,return_tensors=A_	,**A_	,)

								if "attention_mask" in text_encoding:
									A     = text_encoding.pop('attention_mask'   )
								if "input_ids" in text_encoding:
									A     = text_encoding.pop('input_ids'   )
							else:
								A     = None

							if text_encoding is not None:
								encoding_image_processor.update(A_   )

							return encoding_image_processor


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]	,*A_  :   Optional[Any]	,**A_  :   Dict   )  -> Union[str, Any]:
							return self.tokenizer.batch_decode(*A_	,**A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]	,*A_  :   Tuple	,**A_  :   List[str]   )  -> Any:
							return self.tokenizer.decode(*A_	,**A_   )




						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]   )  -> Any:
							A     = self.tokenizer.model_input_names
							A     = self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )