
"""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
from transformers.utils import logging


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



def 					_lowerCamelCase()       ->				Any:
	_lowerCAmelCase			=DetaConfig(
	    num_queries=900					,				encoder_ffn_dim=2048					,				decoder_ffn_dim=2048					,				num_feature_levels=5					,				assign_first_stage=__a					,				with_box_refine=__a					,				two_stage=__a					,				)

	# set labels
	_lowerCAmelCase			=91
	_lowerCAmelCase			='''huggingface/label-files'''
	_lowerCAmelCase			='''coco-detection-id2label.json'''
	_lowerCAmelCase			=json.load(open(cached_download(hf_hub_url(__a					,				__a					,				repo_type="""dataset""" ) )					,				"""r""" ) )
	_lowerCAmelCase			={int(__a ): v for k, v in idalabel.items()}
	_lowerCAmelCase			=idalabel
	_lowerCAmelCase			={v: k for k, v in idalabel.items()}

	return config



def 					_lowerCamelCase(__UpperCamelCase )       ->				Union[str, Any]:
	_lowerCAmelCase			=[]

	# stem
	# fmt: off
	rename_keys.append(("""backbone.0.body.conv1.weight""", """model.backbone.model.embedder.embedder.convolution.weight""") )
	rename_keys.append(("""backbone.0.body.bn1.weight""", """model.backbone.model.embedder.embedder.normalization.weight""") )
	rename_keys.append(("""backbone.0.body.bn1.bias""", """model.backbone.model.embedder.embedder.normalization.bias""") )
	rename_keys.append(("""backbone.0.body.bn1.running_mean""", """model.backbone.model.embedder.embedder.normalization.running_mean""") )
	rename_keys.append(("""backbone.0.body.bn1.running_var""", """model.backbone.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'''model.backbone.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'''model.backbone.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'''model.backbone.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'''model.backbone.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'''model.backbone.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'''model.backbone.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'''model.backbone.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'''model.backbone.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'''model.backbone.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'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var''',
				    ) )
    # 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 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase )       ->				Union[str, Any]:
	_lowerCAmelCase			=dct.pop(__a )
	_lowerCAmelCase			=val



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase )       ->				Dict:
	_lowerCAmelCase			=config.d_model
	for i in range(config.decoder_layers ):
		# read in weights + bias of input projection layer of self-attention
		_lowerCAmelCase			=state_dict.pop(F'''transformer.decoder.layers.{i}.self_attn.in_proj_weight''' )
		_lowerCAmelCase			=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
		_lowerCAmelCase			=in_proj_weight[:hidden_size, :]
		_lowerCAmelCase			=in_proj_bias[:hidden_size]
		_lowerCAmelCase			=in_proj_weight[
		    hidden_size : hidden_size * 2, :
		]
		_lowerCAmelCase			=in_proj_bias[hidden_size : hidden_size * 2]
		_lowerCAmelCase			=in_proj_weight[-hidden_size:, :]
		_lowerCAmelCase			=in_proj_bias[-hidden_size:]



def 					_lowerCamelCase()       ->				int:
	_lowerCAmelCase			='''http://images.cocodataset.org/val2017/000000039769.jpg'''
	_lowerCAmelCase			=Image.open(requests.get(__a					,				stream=__a ).raw )

	return im



@torch.no_grad()
def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase )       ->				Optional[Any]:
	_lowerCAmelCase			=get_deta_config()

	# load original state dict
	if model_name == "deta-resnet-50":
		_lowerCAmelCase			='''adet_checkpoint0011.pth'''
	elif model_name == "deta-resnet-50-24-epochs":
		_lowerCAmelCase			='''adet_2x_checkpoint0023.pth'''
	else:
		raise ValueError(F'''Model name {model_name} not supported''' )
	_lowerCAmelCase			=hf_hub_download(repo_id="""nielsr/deta-checkpoints"""					,				filename=__a )
	_lowerCAmelCase			=torch.load(__a					,				map_location="""cpu""" )['''model''']

	# rename keys
	_lowerCAmelCase			=create_rename_keys(__a )
	for src, dest in rename_keys:
		rename_key(__a					,				__a					,				__a )
	read_in_decoder_q_k_v(__a					,				__a )

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

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

	_lowerCAmelCase			='''cuda''' if torch.cuda.is_available() else '''cpu'''
	model.to(__a )

	# load image processor
	_lowerCAmelCase			=DetaImageProcessor(format="""coco_detection""" )

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

	# verify logits
	if model_name == "deta-resnet-50":
		_lowerCAmelCase			=torch.tensor(
		    [[-7.39_78, -2.54_06, -4.16_68], [-8.26_84, -3.99_33, -3.80_96], [-7.05_15, -3.79_73, -5.85_16]] )
		_lowerCAmelCase			=torch.tensor([[0.50_43, 0.49_73, 0.99_98], [0.25_42, 0.54_89, 0.47_48], [0.54_90, 0.27_65, 0.05_70]] )
	elif model_name == "deta-resnet-50-24-epochs":
		_lowerCAmelCase			=torch.tensor(
		    [[-7.16_88, -2.48_57, -4.86_69], [-7.86_30, -3.81_54, -4.26_74], [-7.27_30, -4.18_65, -5.53_23]] )
		_lowerCAmelCase			=torch.tensor([[0.50_21, 0.49_71, 0.99_94], [0.25_46, 0.54_86, 0.47_31], [0.16_86, 0.19_86, 0.21_42]] )

	assert torch.allclose(outputs.logits[0, :3, :3]					,				expected_logits.to(__a )					,				atol=1E-4 )
	assert torch.allclose(outputs.pred_boxes[0, :3, :3]					,				expected_boxes.to(__a )					,				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(__a ).mkdir(exist_ok=__a )
		model.save_pretrained(__a )
		processor.save_pretrained(__a )

	# 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__":
			__A 		= argparse.ArgumentParser()

			parser.add_argument(
			    '--model_name',
			    type=str,
			    default='deta-resnet-50',
			    choices=['deta-resnet-50', 'deta-resnet-50-24-epochs'],
			    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.'
			)
			__A 		= parser.parse_args()
			convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A 		= {
    'configuration_led': ['LED_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LEDConfig'],
    'tokenization_led': ['LEDTokenizer'],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A 		= ['LEDTokenizerFast']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A 		= [
			    'LED_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'LEDForConditionalGeneration',
			    'LEDForQuestionAnswering',
			    'LEDForSequenceClassification',
			    'LEDModel',
			    'LEDPreTrainedModel',
			]


try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A 		= ['TFLEDForConditionalGeneration', 'TFLEDModel', 'TFLEDPreTrainedModel']


if TYPE_CHECKING:
			from .configuration_led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig
			from .tokenization_led import LEDTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_led_fast import LEDTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_led import (
						    LED_PRETRAINED_MODEL_ARCHIVE_LIST,
						    LEDForConditionalGeneration,
						    LEDForQuestionAnswering,
						    LEDForSequenceClassification,
						    LEDModel,
						    LEDPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel

else:
			import sys

			__A 		= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



