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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor
from transformers.utils import logging


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

# here we list all keys to be renamed (original name on the left, our name on the right)
lowerCAmelCase__							= []
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}.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'),
    ]
)






def       __lowerCamelCase		(						lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	):
       lowerCAmelCase__     	=					state_dict.pop(lowerCAmelCase__	)
       lowerCAmelCase__     	=					val






def       __lowerCamelCase		(						lowerCAmelCase__	):
       lowerCAmelCase__     	=					OrderedDict()
       for key, value in state_dict.items():
              if "backbone.0.body" in key:
                     lowerCAmelCase__     	=					key.replace('backbone.0.body'	,  'backbone.conv_encoder.model'	)
                     lowerCAmelCase__     	=					value
              else:
                     lowerCAmelCase__     	=					value

       return new_state_dict






def       __lowerCamelCase		(						lowerCAmelCase__	,  lowerCAmelCase__=False	):
       lowerCAmelCase__     	=					''
       if is_panoptic:
              lowerCAmelCase__     	=					'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__     	=					state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""	)
              lowerCAmelCase__     	=					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__     	=					in_proj_weight[:2_5_6, :]
              lowerCAmelCase__     	=					in_proj_bias[:2_5_6]
              lowerCAmelCase__     	=					in_proj_weight[2_5_6:5_1_2, :]
              lowerCAmelCase__     	=					in_proj_bias[2_5_6:5_1_2]
              lowerCAmelCase__     	=					in_proj_weight[-2_5_6:, :]
              lowerCAmelCase__     	=					in_proj_bias[-2_5_6:]
       # 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
              lowerCAmelCase__     	=					state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""	)
              lowerCAmelCase__     	=					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
              lowerCAmelCase__     	=					in_proj_weight[:2_5_6, :]
              lowerCAmelCase__     	=					in_proj_bias[:2_5_6]
              lowerCAmelCase__     	=					in_proj_weight[2_5_6:5_1_2, :]
              lowerCAmelCase__     	=					in_proj_bias[2_5_6:5_1_2]
              lowerCAmelCase__     	=					in_proj_weight[-2_5_6:, :]
              lowerCAmelCase__     	=					in_proj_bias[-2_5_6:]
              # read in weights + bias of input projection layer of cross-attention
              lowerCAmelCase__     	=					state_dict.pop(
                  F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""	)
              lowerCAmelCase__     	=					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
              lowerCAmelCase__     	=					in_proj_weight_cross_attn[:2_5_6, :]
              lowerCAmelCase__     	=					in_proj_bias_cross_attn[:2_5_6]
              lowerCAmelCase__     	=					in_proj_weight_cross_attn[2_5_6:5_1_2, :]
              lowerCAmelCase__     	=					in_proj_bias_cross_attn[2_5_6:5_1_2]
              lowerCAmelCase__     	=					in_proj_weight_cross_attn[-2_5_6:, :]
              lowerCAmelCase__     	=					in_proj_bias_cross_attn[-2_5_6:]






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

       return im






@torch.no_grad()
def       __lowerCamelCase		(						lowerCAmelCase__	,  lowerCAmelCase__	):
       lowerCAmelCase__     	=					DetrConfig()
       # set backbone and dilation attributes
       if "resnet101" in model_name:
              lowerCAmelCase__     	=					'resnet101'
       if "dc5" in model_name:
              lowerCAmelCase__     	=					True
       lowerCAmelCase__     	=					'panoptic' in model_name
       if is_panoptic:
              lowerCAmelCase__     	=					2_5_0
       else:
              lowerCAmelCase__     	=					9_1
              lowerCAmelCase__     	=					'huggingface/label-files'
              lowerCAmelCase__     	=					'coco-detection-id2label.json'
              lowerCAmelCase__     	=					json.load(open(hf_hub_download(lowerCAmelCase__	,  lowerCAmelCase__	,  repo_type='dataset'	)	,  'r'	)	)
              lowerCAmelCase__     	=					{int(lowerCAmelCase__	): v for k, v in idalabel.items()}
              lowerCAmelCase__     	=					idalabel
              lowerCAmelCase__     	=					{v: k for k, v in idalabel.items()}

       # load image processor
       lowerCAmelCase__     	=					'coco_panoptic' if is_panoptic else 'coco_detection'
       lowerCAmelCase__     	=					DetrImageProcessor(format=lowerCAmelCase__	)

       # prepare image
       lowerCAmelCase__     	=					prepare_img()
       lowerCAmelCase__     	=					image_processor(images=lowerCAmelCase__	,  return_tensors='pt'	)
       lowerCAmelCase__     	=					encoding['pixel_values']

       logger.info(F"""Converting model {model_name}..."""	)

       # load original model from torch hub
       lowerCAmelCase__     	=					torch.hub.load('facebookresearch/detr'	,  lowerCAmelCase__	,  pretrained=lowerCAmelCase__	).eval()
       lowerCAmelCase__     	=					detr.state_dict()
       # rename keys
       for src, dest in rename_keys:
              if is_panoptic:
                     lowerCAmelCase__     	=					'detr.' + src
              rename_key(lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	)
       lowerCAmelCase__     	=					rename_backbone_keys(lowerCAmelCase__	)
       # query, key and value matrices need special treatment
       read_in_q_k_v(lowerCAmelCase__	,  is_panoptic=lowerCAmelCase__	)
       # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
       lowerCAmelCase__     	=					'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'	)
                     ):
                            lowerCAmelCase__     	=					state_dict.pop(lowerCAmelCase__	)
                            lowerCAmelCase__     	=					val
                     elif "class_labels_classifier" in key or "bbox_predictor" in key:
                            lowerCAmelCase__     	=					state_dict.pop(lowerCAmelCase__	)
                            lowerCAmelCase__     	=					val
                     elif key.startswith('bbox_attention'	) or key.startswith('mask_head'	):
                            continue
                     else:
                            lowerCAmelCase__     	=					state_dict.pop(lowerCAmelCase__	)
                            lowerCAmelCase__     	=					val
              else:
                     if not key.startswith('class_labels_classifier'	) and not key.startswith('bbox_predictor'	):
                            lowerCAmelCase__     	=					state_dict.pop(lowerCAmelCase__	)
                            lowerCAmelCase__     	=					val
    # finally, create HuggingFace model and load state dict
       lowerCAmelCase__     	=					DetrForSegmentation(lowerCAmelCase__	) if is_panoptic else DetrForObjectDetection(lowerCAmelCase__	)
       model.load_state_dict(lowerCAmelCase__	)
       model.eval()
       # verify our conversion
       lowerCAmelCase__     	=					detr(lowerCAmelCase__	)
       lowerCAmelCase__     	=					model(lowerCAmelCase__	)
       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(lowerCAmelCase__	).mkdir(exist_ok=lowerCAmelCase__	)
       model.save_pretrained(lowerCAmelCase__	)
       image_processor.save_pretrained(lowerCAmelCase__	)


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

    parser.add_argument(
        '--model_name', default='detr_resnet50', type=str, 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.'
    )
    lowerCAmelCase__							= parser.parse_args()
    convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)


'''simple docstring'''
def 						A_(			A		:						list[list[int]]):
							if len(A) == 0 or not all(len(A) == 2 for pair in coordinates):
														raise ValueError('The program cannot work out a fitting polynomial.')

							if len({tuple(A) for pair in coordinates}) != len(A):
														raise ValueError('The program cannot work out a fitting polynomial.')

							UpperCamelCase					       =			{x for x, _ in coordinates}
							if len(A) == 1:
														return f'''x={coordinates[0][0]}'''

							if len(A) != len(A):
														raise ValueError('The program cannot work out a fitting polynomial.')

							UpperCamelCase					       =			len(A)

							UpperCamelCase					       =			0
							UpperCamelCase					       =			[]
							# put the x and x to the power values in a matrix
							while count_of_line < x:
														UpperCamelCase					       =			0
														UpperCamelCase					       =			coordinates[count_of_line][0]
														UpperCamelCase					       =			[]
														while count_in_line < x:
																					count_line.append(a ** (x - (count_in_line + 1)))
																					count_in_line += 1
														matrix.append(A)
														count_of_line += 1

							UpperCamelCase					       =			0
							# put the y values into a vector
							UpperCamelCase					       =			[]
							while count_of_line < x:
														vector.append(coordinates[count_of_line][1])
														count_of_line += 1

							UpperCamelCase					       =			0

							while count < x:
														UpperCamelCase					       =			0
														while zahlen < x:
																					if count == zahlen:
																												zahlen += 1
																					if zahlen == x:
																												break
																					UpperCamelCase					       =			matrix[zahlen][count] / matrix[count][count]
																					for counting_columns, item in enumerate(matrix[count]):
																												# manipulating all the values in the matrix
																												matrix[zahlen][counting_columns] -= item * bruch
																					# manipulating the values in the vector
																					vector[zahlen] -= vector[count] * bruch
																					zahlen += 1
														count += 1

							UpperCamelCase					       =			0
							# make solutions
							UpperCamelCase					       =			[]
							while count < x:
														solution.append(str(vector[count] / matrix[count][count]))
														count += 1

							UpperCamelCase					       =			0
							UpperCamelCase					       =			'f(x)='

							while count < x:
														UpperCamelCase					       =			solution[count].split('E')
														if len(A) > 1:
																					UpperCamelCase					       =			f'''{remove_e[0]}*10^{remove_e[1]}'''
														solved += f'''x^{x - (count + 1)}*{solution[count]}'''
														if count + 1 != x:
																					solved += "+"
														count += 1

							return solved


if __name__ == "__main__":
						print(points_to_polynomial([]))
						print(points_to_polynomial([[]]))
						print(points_to_polynomial([[1, 0], [2, 0], [3, 0]]))
						print(points_to_polynomial([[1, 1], [2, 1], [3, 1]]))
						print(points_to_polynomial([[1, 3], [2, 3], [3, 3]]))
						print(points_to_polynomial([[1, 1], [2, 2], [3, 3]]))
						print(points_to_polynomial([[1, 1], [2, 4], [3, 9]]))
						print(points_to_polynomial([[1, 3], [2, 6], [3, 11]]))
						print(points_to_polynomial([[1, -3], [2, -6], [3, -11]]))
						print(points_to_polynomial([[1, 5], [2, 2], [3, 9]]))




