

"""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()
snake_case_   :   List[str]        =       logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
snake_case_   :   Union[str, Any]        =       []
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   lowercase_	(    _lowercase       :		Tuple							,      _lowercase       :		Optional[Any]							,      _lowercase       :		Dict	):




					'''simple docstring'''






					UpperCAmelCase			:   int      =				state_dict.pop(_lowercase	)
					UpperCAmelCase			:   List[str]      =				val



def   lowercase_	(    _lowercase       :		str	):




					'''simple docstring'''






					UpperCAmelCase			:   Optional[Any]      =				OrderedDict()
					for key, value in state_dict.items():
										if "backbone.0.body" in key:
															UpperCAmelCase			:   Union[str, Any]      =				key.replace("backbone.0.body"							,      "backbone.conv_encoder.model"	)
															UpperCAmelCase			:   str      =				value
										else:
															UpperCAmelCase			:   Union[str, Any]      =				value

					return new_state_dict



def   lowercase_	(    _lowercase       :		List[Any]							,      _lowercase       :		List[str]=False	):




					'''simple docstring'''






					UpperCAmelCase			:   List[Any]      =				""
					if is_panoptic:
										UpperCAmelCase			:   Union[str, 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			:   int      =				state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""	)
										UpperCAmelCase			:   Optional[int]      =				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			:   Optional[int]      =				in_proj_weight[:2_56, :]
										UpperCAmelCase			:   int      =				in_proj_bias[:2_56]
										UpperCAmelCase			:   Optional[int]      =				in_proj_weight[2_56:5_12, :]
										UpperCAmelCase			:   List[Any]      =				in_proj_bias[2_56:5_12]
										UpperCAmelCase			:   List[Any]      =				in_proj_weight[-2_56:, :]
										UpperCAmelCase			:   List[Any]      =				in_proj_bias[-2_56:]



def   lowercase_	(    ):




					'''simple docstring'''






					UpperCAmelCase			:   Dict      =				"http://images.cocodataset.org/val2017/000000039769.jpg"
					UpperCAmelCase			:   Any      =				Image.open(requests.get(_lowercase							,      stream=_lowercase	).raw	)

					return im



@torch.no_grad()
def   lowercase_	(    _lowercase       :		Union[str, Any]							,      _lowercase       :		List[Any]	):




					'''simple docstring'''






					UpperCAmelCase			:   List[Any]      =				ConditionalDetrConfig()
					# set backbone and dilation attributes
					if "resnet101" in model_name:
										UpperCAmelCase			:   Optional[int]      =				"resnet101"
					if "dc5" in model_name:
										UpperCAmelCase			:   int      =				True
					UpperCAmelCase			:   str      =				"panoptic" in model_name
					if is_panoptic:
										UpperCAmelCase			:   str      =				2_50
					else:
										UpperCAmelCase			:   List[Any]      =				91
										UpperCAmelCase			:   Dict      =				"huggingface/label-files"
										UpperCAmelCase			:   List[str]      =				"coco-detection-id2label.json"
										UpperCAmelCase			:   Optional[Any]      =				json.load(open(hf_hub_download(_lowercase							,      _lowercase							,      repo_type="dataset"	)							,      "r"	)	)
										UpperCAmelCase			:   List[str]      =				{int(_lowercase	): v for k, v in idalabel.items()}
										UpperCAmelCase			:   int      =				idalabel
										UpperCAmelCase			:   int      =				{v: k for k, v in idalabel.items()}

					# load image processor
					UpperCAmelCase			:   List[str]      =				"coco_panoptic" if is_panoptic else "coco_detection"
					UpperCAmelCase			:   Union[str, Any]      =				ConditionalDetrImageProcessor(format=_lowercase	)

					# prepare image
					UpperCAmelCase			:   Dict      =				prepare_img()
					UpperCAmelCase			:   Optional[Any]      =				image_processor(images=_lowercase							,      return_tensors="pt"	)
					UpperCAmelCase			:   Optional[Any]      =				encoding["pixel_values"]

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

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





"""simple docstring"""






from __future__ import annotations

snake_case_   :   str        =       list[list[int]]

# assigning initial values to the grid
snake_case_   :   Matrix        =       [
    [3, 0, 6, 5, 0, 8, 4, 0, 0],
    [5, 2, 0, 0, 0, 0, 0, 0, 0],
    [0, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]

# a grid with no solution
snake_case_   :   Matrix        =       [
    [5, 0, 6, 5, 0, 8, 4, 0, 3],
    [5, 2, 0, 0, 0, 0, 0, 0, 2],
    [1, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]



def   lowercase_	(    _lowercase       :		Matrix							,      _lowercase       :		int							,      _lowercase       :		int							,      _lowercase       :		int	):




					'''simple docstring'''






					for i in range(9	):
										if grid[row][i] == n or grid[i][column] == n:
															return False

					for i in range(3	):
										for j in range(3	):
															if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
																				return False

					return True



def   lowercase_	(    _lowercase       :		Matrix	):




					'''simple docstring'''






					for i in range(9	):
										for j in range(9	):
															if grid[i][j] == 0:
																				return i, j
					return None



def   lowercase_	(    _lowercase       :		Matrix	):




					'''simple docstring'''






					if location := find_empty_location(_lowercase	):
										UpperCAmelCase      ,							UpperCAmelCase			:   List[str]      =				location
					else:
										# If the location is ``None``, then the grid is solved.
										return grid

					for digit in range(1							,      10	):
										if is_safe(_lowercase							,      _lowercase							,      _lowercase							,      _lowercase	):
															UpperCAmelCase			:   int      =				digit

															if sudoku(_lowercase	) is not None:
																				return grid

															UpperCAmelCase			:   Optional[Any]      =				0

					return None



def   lowercase_	(    _lowercase       :		Matrix	):




					'''simple docstring'''






					for row in grid:
										for cell in row:
															print(_lowercase							,      end=" "	)
										print()


if __name__ == "__main__":
	# make a copy of grid so that you can compare with the unmodified grid
	for example_grid in (initial_grid, no_solution):
		print("""\nExample grid:\n""" + """=""" * 2_0)
		print_solution(example_grid)
		print("""\nExample grid solution:""")
		snake_case_   :   Dict        =       sudoku(example_grid)
		if solution is not None:
			print_solution(solution)
		else:
			print("""Cannot find a solution.""")


