"""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()
__lowerCamelCase							 =     logging.get_logger(__name__)






def      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





							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=UpperCamelCase__				,			backbone_config=UpperCamelCase__ )

							# 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(UpperCamelCase__				,			UpperCamelCase__				,			repo_type='dataset' )				,			'r' ) )
														A__		   =							{int(UpperCamelCase__ ): v for k, v in idalabel.items()}
														A__		   =							idalabel
														A__		   =							{v: k for k, v in idalabel.items()}

							return config, is_panoptic






def      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





							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      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





							A__		   =							state_dict.pop(UpperCamelCase__ )
							A__		   =							val






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__=False ):






							"""simple docstring"""





							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      UpperCAmelCase    (		):






							"""simple docstring"""





							A__		   =							'http://images.cocodataset.org/val2017/000000039769.jpg'
							A__		   =							Image.open(requests.get(UpperCamelCase__				,			stream=UpperCamelCase__ ).raw )

							return im






@torch.no_grad()
def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__=None				,			UpperCamelCase__=False ):






							"""simple docstring"""





							A__   ,     A__		   =							get_detr_config(UpperCamelCase__ )

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

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

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

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

							A__		   =							detr(UpperCamelCase__ )
							A__		   =							model(UpperCamelCase__ )

							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(UpperCamelCase__ ).mkdir(exist_ok=UpperCamelCase__ )
														model.save_pretrained(UpperCamelCase__ )
														processor.save_pretrained(UpperCamelCase__ )

							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__":
				__lowerCamelCase							 =     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.")
				__lowerCamelCase							 =     parser.parse_args()
				convert_detr_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_torch_available, is_vision_available


__lowerCamelCase							 =     {
    "configuration_chinese_clip": [
        "CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ChineseCLIPConfig",
        "ChineseCLIPOnnxConfig",
        "ChineseCLIPTextConfig",
        "ChineseCLIPVisionConfig",
    ],
    "processing_chinese_clip": ["ChineseCLIPProcessor"],
}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase							 =     ["ChineseCLIPFeatureExtractor"]
				__lowerCamelCase							 =     ["ChineseCLIPImageProcessor"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase							 =     [
				    "CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "ChineseCLIPModel",
				    "ChineseCLIPPreTrainedModel",
				    "ChineseCLIPTextModel",
				    "ChineseCLIPVisionModel",
				]

if TYPE_CHECKING:
				from .configuration_chinese_clip import (
				    CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    ChineseCLIPConfig,
				    ChineseCLIPOnnxConfig,
				    ChineseCLIPTextConfig,
				    ChineseCLIPVisionConfig,
				)
				from .processing_chinese_clip import ChineseCLIPProcessor

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_chinese_clip import ChineseCLIPFeatureExtractor, ChineseCLIPImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_chinese_clip import (
								    CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
								    ChineseCLIPModel,
								    ChineseCLIPPreTrainedModel,
								    ChineseCLIPTextModel,
								    ChineseCLIPVisionModel,
								)

else:
				import sys

				__lowerCamelCase							 =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





