


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()
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}.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 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			,					lowercase_			)		->    int:




						'''simple docstring'''







						__UpperCAmelCase :       Tuple  						=  state_dict.pop(lowercase_			)
						__UpperCAmelCase :       Tuple  						=  val




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			)		->    int:




						'''simple docstring'''







						__UpperCAmelCase :       Optional[Any]  						=  OrderedDict()
						for key, value in state_dict.items():
												if "backbone.0.body" in key:
																		__UpperCAmelCase :       Dict  						=  key.replace('''backbone.0.body'''			,					'''backbone.conv_encoder.model'''			)
																		__UpperCAmelCase :       int  						=  value
												else:
																		__UpperCAmelCase :       List[str]  						=  value

						return new_state_dict




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_=False			)		->    Union[str, Any]:




						'''simple docstring'''







						__UpperCAmelCase :       int  						=  ''''''
						if is_panoptic:
												__UpperCAmelCase :       List[Any]  						=  '''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)
												__UpperCAmelCase :       Dict  						=  state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"			)
												__UpperCAmelCase :       Dict  						=  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
												__UpperCAmelCase :       Any  						=  in_proj_weight[:256, :]
												__UpperCAmelCase :       Dict  						=  in_proj_bias[:256]
												__UpperCAmelCase :       str  						=  in_proj_weight[256:512, :]
												__UpperCAmelCase :       Any  						=  in_proj_bias[256:512]
												__UpperCAmelCase :       List[str]  						=  in_proj_weight[-256:, :]
												__UpperCAmelCase :       Tuple  						=  in_proj_bias[-256:]




def 						__SCREAMING_SNAKE_CASE       (					)		->    int:




						'''simple docstring'''







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

						return im




@torch.no_grad()
def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			)		->    Union[str, Any]:




						'''simple docstring'''







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

						# load image processor
						__UpperCAmelCase :       Any  						=  '''coco_panoptic''' if is_panoptic else '''coco_detection'''
						__UpperCAmelCase :       str  						=  ConditionalDetrImageProcessor(format=lowercase_			)

						# prepare image
						__UpperCAmelCase :       Optional[int]  						=  prepare_img()
						__UpperCAmelCase :       Optional[int]  						=  image_processor(images=lowercase_			,					return_tensors='''pt'''			)
						__UpperCAmelCase :       List[Any]  						=  encoding['''pixel_values''']

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

						# load original model from torch hub
						__UpperCAmelCase :       Optional[Any]  						=  torch.hub.load('''DeppMeng/ConditionalDETR'''			,					lowercase_			,					pretrained=lowercase_			).eval()
						__UpperCAmelCase :       Any  						=  conditional_detr.state_dict()
						# rename keys
						for src, dest in rename_keys:
												if is_panoptic:
																		__UpperCAmelCase :       str  						=  '''conditional_detr.''' + src
												rename_key(lowercase_			,					lowercase_			,					lowercase_			)
						__UpperCAmelCase :       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
						__UpperCAmelCase :       Optional[int]  						=  '''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'''			)
																		):
																								__UpperCAmelCase :       Union[str, Any]  						=  state_dict.pop(lowercase_			)
																								__UpperCAmelCase :       str  						=  val
																		elif "class_labels_classifier" in key or "bbox_predictor" in key:
																								__UpperCAmelCase :       Tuple  						=  state_dict.pop(lowercase_			)
																								__UpperCAmelCase :       int  						=  val
																		elif key.startswith('''bbox_attention'''			) or key.startswith('''mask_head'''			):
																								continue
																		else:
																								__UpperCAmelCase :       Dict  						=  state_dict.pop(lowercase_			)
																								__UpperCAmelCase :       str  						=  val
												else:
																		if not key.startswith('''class_labels_classifier'''			) and not key.startswith('''bbox_predictor'''			):
																								__UpperCAmelCase :       List[Any]  						=  state_dict.pop(lowercase_			)
																								__UpperCAmelCase :       int  						=  val
    # finally, create HuggingFace model and load state dict
						__UpperCAmelCase :       str  						=  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
						__UpperCAmelCase :       List[Any]  						=  conditional_detr(lowercase_			)
						__UpperCAmelCase :       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__":
				lowerCAmelCase							=						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."""
				)
				lowerCAmelCase							=						parser.parse_args()
				convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)





def 						__SCREAMING_SNAKE_CASE       (					)		->    list[list[int]]:




						'''simple docstring'''







						return [list(range(1000 - i			,					-1000 - i			,					-1			)			) for i in range(1000			)]


lowerCAmelCase							=						generate_large_matrix()
lowerCAmelCase							=						(
    [[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
    [[3, 2], [1, 0]],
    [[7, 7, 6]],
    [[7, 7, 6], [-1, -2, -3]],
    grid,
)




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			)		->    None:




						'''simple docstring'''







						assert all(row == sorted(lowercase_			,					reverse=lowercase_			) for row in grid			)
						assert all(list(lowercase_			) == sorted(lowercase_			,					reverse=lowercase_			) for col in zip(*lowercase_			)			)




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			)		->    int:




						'''simple docstring'''







						__UpperCAmelCase :       Dict  						=  0
						__UpperCAmelCase :       List[Any]  						=  len(lowercase_			) - 1

						# Edge cases such as no values or all numbers are negative.
						if not array or array[0] < 0:
												return 0

						while right + 1 > left:
												__UpperCAmelCase :       List[Any]  						=  (left + right) // 2
												__UpperCAmelCase :       Dict  						=  array[mid]

												# Num must be negative and the index must be greater than or equal to 0.
												if num < 0 and array[mid - 1] >= 0:
																		return mid

												if num >= 0:
																		__UpperCAmelCase :       Dict  						=  mid + 1
												else:
																		__UpperCAmelCase :       Optional[Any]  						=  mid - 1
    # No negative numbers so return the last index of the array + 1 which is the length.
						return len(lowercase_			)




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			)		->    int:




						'''simple docstring'''







						__UpperCAmelCase :       int  						=  0
						__UpperCAmelCase :       Dict  						=  len(grid[0]			)

						for i in range(len(lowercase_			)			):
												__UpperCAmelCase :       Any  						=  find_negative_index(grid[i][:bound]			)
												total += bound
						return (len(lowercase_			) * len(grid[0]			)) - total




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			)		->    int:




						'''simple docstring'''







						return len([number for row in grid for number in row if number < 0]			)




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			)		->    int:




						'''simple docstring'''







						__UpperCAmelCase :       List[Any]  						=  0
						for row in grid:
												for i, number in enumerate(lowercase_			):
																		if number < 0:
																								total += len(lowercase_			) - i
																								break
						return total




def 						__SCREAMING_SNAKE_CASE       (					)		->    None:




						'''simple docstring'''







						from timeit import timeit

						print('''Running benchmarks'''			)
						__UpperCAmelCase :       Tuple  						=  (
						    '''from __main__ import count_negatives_binary_search, '''
						    '''count_negatives_brute_force, count_negatives_brute_force_with_break, grid'''
						)
						for func in (
						    "count_negatives_binary_search",  # took 0.7727 seconds
						    "count_negatives_brute_force_with_break",  # took 4.6505 seconds
						    "count_negatives_brute_force",  # took 12.8160 seconds
						):
												__UpperCAmelCase :       Union[str, Any]  						=  timeit(f"{func}(grid=grid)"			,					setup=lowercase_			,					number=500			)
												print(f"{func}() took {time:0.4f} seconds"			)


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				benchmark()

