




"""simple docstring"""






import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


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

# here we list all keys to be renamed (original name on the left, our name on the right)
lowercase__  		=		[]
for i in range(6):
			# 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}.cross_attn.out_proj.weight",
			        F"decoder.layers.{i}.encoder_attn.out_proj.weight",
			    )
			)
			rename_keys.append(
			    (
			        F"transformer.decoder.layers.{i}.cross_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"))

			# q, k, v projections in self/cross-attention in decoder for conditional DETR
			rename_keys.append(
			    (F"transformer.decoder.layers.{i}.sa_qcontent_proj.weight", F"decoder.layers.{i}.sa_qcontent_proj.weight")
			)
			rename_keys.append(
			    (F"transformer.decoder.layers.{i}.sa_kcontent_proj.weight", F"decoder.layers.{i}.sa_kcontent_proj.weight")
			)
			rename_keys.append(
			    (F"transformer.decoder.layers.{i}.sa_qpos_proj.weight", F"decoder.layers.{i}.sa_qpos_proj.weight")
			)
			rename_keys.append(
			    (F"transformer.decoder.layers.{i}.sa_kpos_proj.weight", F"decoder.layers.{i}.sa_kpos_proj.weight")
			)
			rename_keys.append((F"transformer.decoder.layers.{i}.sa_v_proj.weight", F"decoder.layers.{i}.sa_v_proj.weight"))
			rename_keys.append(
			    (F"transformer.decoder.layers.{i}.ca_qcontent_proj.weight", F"decoder.layers.{i}.ca_qcontent_proj.weight")
			)
			# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
			rename_keys.append(
			    (F"transformer.decoder.layers.{i}.ca_kcontent_proj.weight", F"decoder.layers.{i}.ca_kcontent_proj.weight")
			)
			rename_keys.append(
			    (F"transformer.decoder.layers.{i}.ca_kpos_proj.weight", F"decoder.layers.{i}.ca_kpos_proj.weight")
			)
			rename_keys.append((F"transformer.decoder.layers.{i}.ca_v_proj.weight", F"decoder.layers.{i}.ca_v_proj.weight"))
			rename_keys.append(
			    (F"transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight", F"decoder.layers.{i}.ca_qpos_sine_proj.weight")
			)

			rename_keys.append(
			    (F"transformer.decoder.layers.{i}.sa_qcontent_proj.bias", F"decoder.layers.{i}.sa_qcontent_proj.bias")
			)
			rename_keys.append(
			    (F"transformer.decoder.layers.{i}.sa_kcontent_proj.bias", F"decoder.layers.{i}.sa_kcontent_proj.bias")
			)
			rename_keys.append((F"transformer.decoder.layers.{i}.sa_qpos_proj.bias", F"decoder.layers.{i}.sa_qpos_proj.bias"))
			rename_keys.append((F"transformer.decoder.layers.{i}.sa_kpos_proj.bias", F"decoder.layers.{i}.sa_kpos_proj.bias"))
			rename_keys.append((F"transformer.decoder.layers.{i}.sa_v_proj.bias", F"decoder.layers.{i}.sa_v_proj.bias"))
			rename_keys.append(
			    (F"transformer.decoder.layers.{i}.ca_qcontent_proj.bias", F"decoder.layers.{i}.ca_qcontent_proj.bias")
			)
			# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
			rename_keys.append(
			    (F"transformer.decoder.layers.{i}.ca_kcontent_proj.bias", F"decoder.layers.{i}.ca_kcontent_proj.bias")
			)
			rename_keys.append((F"transformer.decoder.layers.{i}.ca_kpos_proj.bias", F"decoder.layers.{i}.ca_kpos_proj.bias"))
			rename_keys.append((F"transformer.decoder.layers.{i}.ca_v_proj.bias", F"decoder.layers.{i}.ca_v_proj.bias"))
			rename_keys.append(
			    (F"transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias", F"decoder.layers.{i}.ca_qpos_sine_proj.bias")
			)

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
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"""),
        ("""transformer.decoder.ref_point_head.layers.0.weight""", """decoder.ref_point_head.layers.0.weight"""),
        ("""transformer.decoder.ref_point_head.layers.0.bias""", """decoder.ref_point_head.layers.0.bias"""),
        ("""transformer.decoder.ref_point_head.layers.1.weight""", """decoder.ref_point_head.layers.1.weight"""),
        ("""transformer.decoder.ref_point_head.layers.1.bias""", """decoder.ref_point_head.layers.1.bias"""),
        ("""transformer.decoder.query_scale.layers.0.weight""", """decoder.query_scale.layers.0.weight"""),
        ("""transformer.decoder.query_scale.layers.0.bias""", """decoder.query_scale.layers.0.bias"""),
        ("""transformer.decoder.query_scale.layers.1.weight""", """decoder.query_scale.layers.1.weight"""),
        ("""transformer.decoder.query_scale.layers.1.bias""", """decoder.query_scale.layers.1.bias"""),
        ("""transformer.decoder.layers.0.ca_qpos_proj.weight""", """decoder.layers.0.ca_qpos_proj.weight"""),
        ("""transformer.decoder.layers.0.ca_qpos_proj.bias""", """decoder.layers.0.ca_qpos_proj.bias"""),
    ]
)







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__	):
				_lowerCamelCase  :    Optional[int]					=					state_dict.pop(lowercase__	)
				_lowerCamelCase  :    Union[str, Any]					=					val







def   _snake_case    (       lowercase__	):
				_lowerCamelCase  :    Dict					=					OrderedDict()
				for key, value in state_dict.items():
								if "backbone.0.body" in key:
												_lowerCamelCase  :    Union[str, Any]					=					key.replace('backbone.0.body'							,					'backbone.conv_encoder.model'	)
												_lowerCamelCase  :    List[str]					=					value
								else:
												_lowerCamelCase  :    Any					=					value

				return new_state_dict







def   _snake_case    (       lowercase__							,					lowercase__=False	):
				_lowerCamelCase  :    Optional[int]					=					''
				if is_panoptic:
								_lowerCamelCase  :    List[str]					=					'conditional_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)
								_lowerCamelCase  :    int					=					state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''	)
								_lowerCamelCase  :    Any					=					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
								_lowerCamelCase  :    Dict					=					in_proj_weight[:256, :]
								_lowerCamelCase  :    Optional[Any]					=					in_proj_bias[:256]
								_lowerCamelCase  :    Any					=					in_proj_weight[256:512, :]
								_lowerCamelCase  :    List[Any]					=					in_proj_bias[256:512]
								_lowerCamelCase  :    Optional[Any]					=					in_proj_weight[-256:, :]
								_lowerCamelCase  :    List[Any]					=					in_proj_bias[-256:]







def   _snake_case    (       ):
				_lowerCamelCase  :    Any					=					'http://images.cocodataset.org/val2017/000000039769.jpg'
				_lowerCamelCase  :    Union[str, Any]					=					Image.open(requests.get(lowercase__							,					stream=lowercase__	).raw	)

				return im







@torch.no_grad()
def   _snake_case    (       lowercase__							,					lowercase__	):
				_lowerCamelCase  :    Any					=					ConditionalDetrConfig()
				# set backbone and dilation attributes
				if "resnet101" in model_name:
								_lowerCamelCase  :    Tuple					=					'resnet101'
				if "dc5" in model_name:
								_lowerCamelCase  :    List[str]					=					True
				_lowerCamelCase  :    int					=					'panoptic' in model_name
				if is_panoptic:
								_lowerCamelCase  :    List[str]					=					250
				else:
								_lowerCamelCase  :    Tuple					=					91
								_lowerCamelCase  :    int					=					'huggingface/label-files'
								_lowerCamelCase  :    List[str]					=					'coco-detection-id2label.json'
								_lowerCamelCase  :    Dict					=					json.load(open(hf_hub_download(lowercase__							,					lowercase__							,					repo_type='dataset'	)							,					'r'	)	)
								_lowerCamelCase  :    Any					=					{int(lowercase__	): v for k, v in idalabel.items()}
								_lowerCamelCase  :    List[Any]					=					idalabel
								_lowerCamelCase  :    List[str]					=					{v: k for k, v in idalabel.items()}

				# load image processor
				_lowerCamelCase  :    Dict					=					'coco_panoptic' if is_panoptic else 'coco_detection'
				_lowerCamelCase  :    int					=					ConditionalDetrImageProcessor(format=lowercase__	)

				# prepare image
				_lowerCamelCase  :    Optional[Any]					=					prepare_img()
				_lowerCamelCase  :    Any					=					image_processor(images=lowercase__							,					return_tensors='pt'	)
				_lowerCamelCase  :    Dict					=					encoding['pixel_values']

				logger.info(f'''Converting model {model_name}...'''	)

				# load original model from torch hub
				_lowerCamelCase  :    Optional[int]					=					torch.hub.load('DeppMeng/ConditionalDETR'							,					lowercase__							,					pretrained=lowercase__	).eval()
				_lowerCamelCase  :    Dict					=					conditional_detr.state_dict()
				# rename keys
				for src, dest in rename_keys:
								if is_panoptic:
												_lowerCamelCase  :    int					=					'conditional_detr.' + src
								rename_key(lowercase__							,					lowercase__							,					lowercase__	)
				_lowerCamelCase  :    Union[str, Any]					=					rename_backbone_keys(lowercase__	)
				# query, key and value matrices need special treatment
				read_in_q_k_v(lowercase__							,					is_panoptic=lowercase__	)
				# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
				_lowerCamelCase  :    Optional[Any]					=					'conditional_detr.model.' if is_panoptic else 'model.'
				for key in state_dict.copy().keys():
								if is_panoptic:
												if (
												    key.startswith('conditional_detr'	)
												    and not key.startswith('class_labels_classifier'	)
												    and not key.startswith('bbox_predictor'	)
												):
																_lowerCamelCase  :    Optional[int]					=					state_dict.pop(lowercase__	)
																_lowerCamelCase  :    int					=					val
												elif "class_labels_classifier" in key or "bbox_predictor" in key:
																_lowerCamelCase  :    Optional[int]					=					state_dict.pop(lowercase__	)
																_lowerCamelCase  :    int					=					val
												elif key.startswith('bbox_attention'	) or key.startswith('mask_head'	):
																continue
												else:
																_lowerCamelCase  :    List[Any]					=					state_dict.pop(lowercase__	)
																_lowerCamelCase  :    Tuple					=					val
								else:
												if not key.startswith('class_labels_classifier'	) and not key.startswith('bbox_predictor'	):
																_lowerCamelCase  :    Dict					=					state_dict.pop(lowercase__	)
																_lowerCamelCase  :    List[Any]					=					val
    # finally, create HuggingFace model and load state dict
				_lowerCamelCase  :    Tuple					=					ConditionalDetrForSegmentation(lowercase__	) if is_panoptic else ConditionalDetrForObjectDetection(lowercase__	)
				model.load_state_dict(lowercase__	)
				model.eval()
				model.push_to_hub(repo_id=lowercase__							,					organization='DepuMeng'							,					commit_message='Add model'	)
				# verify our conversion
				_lowerCamelCase  :    Union[str, Any]					=					conditional_detr(lowercase__	)
				_lowerCamelCase  :    int					=					model(lowercase__	)
				assert torch.allclose(outputs.logits							,					original_outputs['pred_logits']							,					atol=1E-4	)
				assert torch.allclose(outputs.pred_boxes							,					original_outputs['pred_boxes']							,					atol=1E-4	)
				if is_panoptic:
								assert torch.allclose(outputs.pred_masks							,					original_outputs['pred_masks']							,					atol=1E-4	)

				# Save model and image processor
				logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''	)
				Path(lowercase__	).mkdir(exist_ok=lowercase__	)
				model.save_pretrained(lowercase__	)
				image_processor.save_pretrained(lowercase__	)


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

			parser.add_argument(
			    """--model_name""",
			    default="""conditional_detr_resnet50""",
			    type=str,
			    help="""Name of the CONDITIONAL_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."""
			)
			lowercase__  		=		parser.parse_args()
			convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)





"""simple docstring"""






import copy
import os

import cva
import numpy as np
from matplotlib import pyplot as plt






class lowerCAmelCase__			:






			'''simple docstring'''



			def __init__(						self						):
							_lowerCamelCase  :    Tuple					=					''
							_lowerCamelCase  :    Any					=					''
							_lowerCamelCase  :    List[Any]					=					[]
							_lowerCamelCase  :    Tuple					=					0
							_lowerCamelCase  :    Dict					=					256
							_lowerCamelCase  :    Optional[Any]					=					0
							_lowerCamelCase  :    List[Any]					=					0
							_lowerCamelCase  :    int					=					0
							_lowerCamelCase  :    Dict					=					0
			def       A_					(						self						,   lowercase						):
							_lowerCamelCase  :    Any					=					cva.imread(lowercase						,   0						)
							_lowerCamelCase  :    Optional[int]					=					copy.deepcopy(self.img						)
							_lowerCamelCase,				_lowerCamelCase,				_lowerCamelCase  :    List[str]					=					plt.hist(self.img.ravel()						,   256						,   [0, 256]						,   label='x'						)
							_lowerCamelCase  :    Optional[int]					=					np.sum(lowercase						)
							for i in range(len(lowercase						)						):
											_lowerCamelCase  :    List[Any]					=					x[i] / self.k
											self.sk += prk
											_lowerCamelCase  :    int					=					(self.L - 1) * self.sk
											if self.rem != 0:
															_lowerCamelCase  :    Union[str, Any]					=					int(last % last						)
											_lowerCamelCase  :    int					=					int(last + 1 if self.rem >= 0.5 else last						)
											self.last_list.append(lowercase						)
											_lowerCamelCase  :    List[str]					=					int(np.ma.count(self.img						) / self.img[1].size						)
											_lowerCamelCase  :    int					=					self.img[1].size
							for i in range(self.number_of_cols						):
											for j in range(self.number_of_rows						):
															_lowerCamelCase  :    Optional[Any]					=					self.img[j][i]
															if num != self.last_list[num]:
																			_lowerCamelCase  :    Any					=					self.last_list[num]
							cva.imwrite('output_data/output.jpg'						,   self.img						)
			def       A_					(						self						):
							plt.hist(self.img.ravel()						,   256						,   [0, 256]						)






			def       A_					(						self						):
							cva.imshow('Output-Image'						,   self.img						)
							cva.imshow('Input-Image'						,   self.original_image						)
							cva.waitKey(5000						)
							cva.destroyAllWindows()


if __name__ == "__main__":
			lowercase__  		=		os.path.join(os.path.basename(__file__), """image_data/input.jpg""")
			lowercase__  		=		ConstantStretch()
			stretcher.stretch(file_path)
			stretcher.plot_histogram()
			stretcher.show_image()