


'''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()
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 					lowerCamelCase_ ( __UpperCamelCase					:       str							,    __UpperCamelCase					:       Union[str, Any]							,    __UpperCamelCase					:       Optional[Any]		)					->	Optional[Any]:







     """simple docstring"""
     _A				=							state_dict.pop(__UpperCamelCase		)
     _A				=							val

def 					lowerCamelCase_ ( __UpperCamelCase					:       Optional[int]		)					->	int:







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

     return new_state_dict

def 					lowerCamelCase_ ( __UpperCamelCase					:       str							,    __UpperCamelCase					:       Dict=False		)					->	Optional[Any]:







     """simple docstring"""
     _A				=							''
     if is_panoptic:
          _A				=							'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)
          _A				=							state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'		)
          _A				=							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
          _A				=							in_proj_weight[:2_5_6, :]
          _A				=							in_proj_bias[:2_5_6]
          _A				=							in_proj_weight[2_5_6:5_1_2, :]
          _A				=							in_proj_bias[2_5_6:5_1_2]
          _A				=							in_proj_weight[-2_5_6:, :]
          _A				=							in_proj_bias[-2_5_6:]

def 					lowerCamelCase_ ( )					->	List[Any]:







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

     return im

@torch.no_grad()
def 					lowerCamelCase_ ( __UpperCamelCase					:       Optional[int]							,    __UpperCamelCase					:       Union[str, Any]		)					->	Tuple:







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

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

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

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

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


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)









'''simple docstring'''
import colorsys

from PIL import Image  # type: ignore

def 					lowerCamelCase_ ( __UpperCamelCase					:       float							,    __UpperCamelCase					:       float							,    __UpperCamelCase					:       int		)					->	float:







     """simple docstring"""
     _A				=							x
     _A				=							y
     for step in range(__UpperCamelCase		):  # noqa: B007
          _A				=							a * a - b * b + x
          _A				=							2 * a * b + y
          _A				=							a_new

          # divergence happens for all complex number with an absolute value
          # greater than 4
          if a * a + b * b > 4:
               break
     return step / (max_step - 1)

def 					lowerCamelCase_ ( __UpperCamelCase					:       float		)					->	tuple:







     """simple docstring"""
     if distance == 1:
          return (0, 0, 0)
     else:
          return (2_5_5, 2_5_5, 2_5_5)

def 					lowerCamelCase_ ( __UpperCamelCase					:       float		)					->	tuple:







     """simple docstring"""
     if distance == 1:
          return (0, 0, 0)
     else:
          return tuple(round(i * 2_5_5		) for i in colorsys.hsv_to_rgb(__UpperCamelCase							,    1							,    1		)		)

def 					lowerCamelCase_ ( __UpperCamelCase					:       int = 8_0_0							,    __UpperCamelCase					:       int = 6_0_0							,    __UpperCamelCase					:       float = -0.6							,    __UpperCamelCase					:       float = 0							,    __UpperCamelCase					:       float = 3.2							,    __UpperCamelCase					:       int = 5_0							,    __UpperCamelCase					:       bool = True							,    )					->	Image.Image:







     """simple docstring"""
     _A				=							Image.new('RGB'							,    (image_width, image_height)		)
     _A				=							img.load()

     # loop through the image-coordinates
     for image_x in range(__UpperCamelCase		):
          for image_y in range(__UpperCamelCase		):
               # determine the figure-coordinates based on the image-coordinates
               _A				=							figure_width / image_width * image_height
               _A				=							figure_center_x + (image_x / image_width - 0.5) * figure_width
               _A				=							figure_center_y + (image_y / image_height - 0.5) * figure_height

               _A				=							get_distance(__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase		)

               # color the corresponding pixel based on the selected coloring-function
               if use_distance_color_coding:
                    _A				=							get_color_coded_rgb(__UpperCamelCase		)
               else:
                    _A				=							get_black_and_white_rgb(__UpperCamelCase		)

     return img


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    # colored version, full figure
    lowerCAmelCase						=					get_image()

    # uncomment for colored version, different section, zoomed in
    # img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
    # figure_width = 0.8)

    # uncomment for black and white version, full figure
    # img = get_image(use_distance_color_coding = False)

    # uncomment to save the image
    # img.save("mandelbrot.png")

    img.show()





