
"""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__   :     Union[str, Any]     ):
	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__   :     Optional[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__   :     Dict	,      snake_case__   :     str	,      snake_case__   :     str     ):
	A						   =   dct.pop(snake_case__     )
	A						   =   val






def       _snake_case				(		snake_case__   :     List[str]	,      snake_case__   :     str     ):
	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__   :     List[Any]	,      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__   :     List[str]	,      snake_case__   :     Union[str, Any]	,      snake_case__   :     List[Any]     ):
	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 math import pi, sqrt






def       _snake_case				(		snake_case__   :     float     ):
	if num <= 0:
		raise ValueError('math domain error'     )
	if num > 171.5:
		raise OverflowError('math range error'     )
	elif num - int(snake_case__     ) not in (0, 0.5):
		raise NotImplementedError('num must be an integer or a half-integer'     )
	elif num == 0.5:
		return sqrt(snake_case__     )
	else:
		return 1.0 if num == 1 else (num - 1) * gamma(num - 1     )






def       _snake_case				(		):
	assert gamma(0.5     ) == sqrt(snake_case__     )
	assert gamma(1     ) == 1.0
	assert gamma(2     ) == 1.0


if __name__ == "__main__":
		from doctest import testmod

		testmod()
		_lowercase							   =				1.0
		while num:
				_lowercase							   =				float(input('''Gamma of: '''))
				print(F"""gamma({num}) = {gamma(num)}""")
				print('''\nEnter 0 to exit...''')