


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   							=						logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__snake_case   							=						[]
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 		_A		(		_lowercase					,  _lowercase					,  _lowercase )					-> Union[str, Any]:


     """simple docstring"""


     __UpperCamelCase =			state_dict.pop(_lowercase )
     __UpperCamelCase =			val






def 		_A		(		_lowercase )					-> int:


     """simple docstring"""


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

     return new_state_dict






def 		_A		(		_lowercase					,  _lowercase=False )					-> Union[str, Any]:


     """simple docstring"""


     __UpperCamelCase =			''
     if is_panoptic:
          __UpperCamelCase =			'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 =			state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''' )
          __UpperCamelCase =			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 =			in_proj_weight[:2_56, :]
          __UpperCamelCase =			in_proj_bias[:2_56]
          __UpperCamelCase =			in_proj_weight[2_56:5_12, :]
          __UpperCamelCase =			in_proj_bias[2_56:5_12]
          __UpperCamelCase =			in_proj_weight[-2_56:, :]
          __UpperCamelCase =			in_proj_bias[-2_56:]






def 		_A		(		)					-> List[Any]:


     """simple docstring"""


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

     return im






@torch.no_grad()
def 		_A		(		_lowercase					,  _lowercase )					-> int:


     """simple docstring"""


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

     # load image processor
     __UpperCamelCase =			'coco_panoptic' if is_panoptic else 'coco_detection'
     __UpperCamelCase =			ConditionalDetrImageProcessor(format=_lowercase )

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

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

     # load original model from torch hub
     __UpperCamelCase =			torch.hub.load('DeppMeng/ConditionalDETR'					,  _lowercase					,  pretrained=_lowercase ).eval()
     __UpperCamelCase =			conditional_detr.state_dict()
     # rename keys
     for src, dest in rename_keys:
          if is_panoptic:
               __UpperCamelCase =			'conditional_detr.' + src
          rename_key(_lowercase					,  _lowercase					,  _lowercase )
     __UpperCamelCase =			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 =			'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 =			state_dict.pop(_lowercase )
                    __UpperCamelCase =			val
               elif "class_labels_classifier" in key or "bbox_predictor" in key:
                    __UpperCamelCase =			state_dict.pop(_lowercase )
                    __UpperCamelCase =			val
               elif key.startswith('bbox_attention' ) or key.startswith('mask_head' ):
                    continue
               else:
                    __UpperCamelCase =			state_dict.pop(_lowercase )
                    __UpperCamelCase =			val
          else:
               if not key.startswith('class_labels_classifier' ) and not key.startswith('bbox_predictor' ):
                    __UpperCamelCase =			state_dict.pop(_lowercase )
                    __UpperCamelCase =			val
    # finally, create HuggingFace model and load state dict
     __UpperCamelCase =			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 =			conditional_detr(_lowercase )
     __UpperCamelCase =			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   							=						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   							=						parser.parse_args()
    convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)






'''simple docstring'''

from __future__ import annotations

lowerCamelCase				:  List[str]    		=      []

def _SCREAMING_SNAKE_CASE (A    ,					A    ,					A			)			->			bool:






       """simple docstring"""







       for i in range(len(A			)			):
              if board[row][i] == 1:
                     return False
       for i in range(len(A			)			):
              if board[i][column] == 1:
                     return False
       for i, j in zip(range(A    ,					-1    ,					-1			)    ,					range(A    ,					-1    ,					-1			)			):
              if board[i][j] == 1:
                     return False
       for i, j in zip(range(A    ,					-1    ,					-1			)    ,					range(A    ,					len(A			)			)			):
              if board[i][j] == 1:
                     return False
       return True

def _SCREAMING_SNAKE_CASE (A    ,					A			)			->			bool:






       """simple docstring"""







       if row >= len(A			):
              solution.append(A			)
              printboard(A			)
              print()
              return True
       for i in range(len(A			)			):
              if is_safe(A    ,					A    ,					A			):
                     lowercase__      =					1
                     solve(A    ,					row + 1			)
                     lowercase__      =					0
       return False

def _SCREAMING_SNAKE_CASE (A			)			->			None:






       """simple docstring"""







       for i in range(len(A			)			):
              for j in range(len(A			)			):
                     if board[i][j] == 1:
                            print('''Q'''    ,					end=''' '''			)
                     else:
                            print('''.'''    ,					end=''' '''			)
              print()


# n=int(input("The no. of queens"))
lowerCamelCase				:  Optional[Any]    		=      8
lowerCamelCase				:  Optional[int]    		=      [[0 for i in range(n)] for j in range(n)]
solve(board, 0)

print('The total no. of solutions are :', len(solution))




