


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 				_lowercase     (		SCREAMING_SNAKE_CASE_ :		Optional[int]							,	SCREAMING_SNAKE_CASE_ :		Any							,	SCREAMING_SNAKE_CASE_ :		Optional[Any]						):





			"""simple docstring"""




			UpperCamelCase								=       state_dict.pop(UpperCamelCase__						)
			UpperCamelCase								=       val






def 				_lowercase     (		SCREAMING_SNAKE_CASE_ :		str						):





			"""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 				_lowercase     (		SCREAMING_SNAKE_CASE_ :		str							,	SCREAMING_SNAKE_CASE_ :		List[Any]=False						):





			"""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[:256, :]
						UpperCamelCase								=       in_proj_bias[:256]
						UpperCamelCase								=       in_proj_weight[256:512, :]
						UpperCamelCase								=       in_proj_bias[256:512]
						UpperCamelCase								=       in_proj_weight[-256:, :]
						UpperCamelCase								=       in_proj_bias[-256:]






def 				_lowercase     (		):





			"""simple docstring"""




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

			return im






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





			"""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								=       250
			else:
						UpperCamelCase								=       91
						UpperCamelCase								=       'huggingface/label-files'
						UpperCamelCase								=       'coco-detection-id2label.json'
						UpperCamelCase								=       json.load(open(hf_hub_download(UpperCamelCase__							,	UpperCamelCase__							,	repo_type="""dataset"""						)							,	"""r"""						)						)
						UpperCamelCase								=       {int(UpperCamelCase__						): 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=UpperCamelCase__						)

			# prepare image
			UpperCamelCase								=       prepare_img()
			UpperCamelCase								=       image_processor(images=UpperCamelCase__							,	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"""							,	UpperCamelCase__							,	pretrained=UpperCamelCase__						).eval()
			UpperCamelCase								=       conditional_detr.state_dict()
			# rename keys
			for src, dest in rename_keys:
						if is_panoptic:
									UpperCamelCase								=       'conditional_detr.' + src
						rename_key(UpperCamelCase__							,	UpperCamelCase__							,	UpperCamelCase__						)
			UpperCamelCase								=       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
			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(UpperCamelCase__						)
												UpperCamelCase								=       val
									elif "class_labels_classifier" in key or "bbox_predictor" in key:
												UpperCamelCase								=       state_dict.pop(UpperCamelCase__						)
												UpperCamelCase								=       val
									elif key.startswith("""bbox_attention"""						) or key.startswith("""mask_head"""						):
												continue
									else:
												UpperCamelCase								=       state_dict.pop(UpperCamelCase__						)
												UpperCamelCase								=       val
						else:
									if not key.startswith("""class_labels_classifier"""						) and not key.startswith("""bbox_predictor"""						):
												UpperCamelCase								=       state_dict.pop(UpperCamelCase__						)
												UpperCamelCase								=       val
												# finally, create HuggingFace model and load state dict
			UpperCamelCase								=       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
			UpperCamelCase								=       conditional_detr(UpperCamelCase__						)
			UpperCamelCase								=       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__":
			__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'''
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings('ignore', category=UserWarning, module='torch.optim.lr_scheduler')

class     _lowerCamelCase       :




 '''simple docstring'''


 def __init__(   self   ,   __lowercase   ,   __lowercase   ,   __lowercase = True   ,   __lowercase = False    ):

  """simple docstring"""







  __A	:		List[str]	  =      scheduler
  __A	:		Dict	  =      optimizers if isinstance(__lowercase   ,   (list, tuple)    ) else [optimizers]
  __A	:		List[Any]	  =      split_batches
  __A	:		Any	  =      step_with_optimizer
  __A	:		List[Any]	  =      GradientState()


 def 			snake_case__ (   self   ,   *__lowercase   ,   **__lowercase    ):

  """simple docstring"""







  if not self.step_with_optimizer:
   # No link between scheduler and optimizer -> just step
   self.scheduler.step(*__lowercase   ,   **__lowercase    )
   return

  # Otherwise, first make sure the optimizer was stepped.
  if not self.gradient_state.sync_gradients:
   if self.gradient_state.adjust_scheduler:
    self.scheduler._step_count += 1
   return

  for opt in self.optimizers:
   if opt.step_was_skipped:
    return
  if self.split_batches:
   # Split batches -> the training dataloader batch size is not changed so one step per training step
   self.scheduler.step(*__lowercase   ,   **__lowercase    )
  else:
   # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
   # num_processes steps per training step
   __A	:		Tuple	  =      AcceleratorState().num_processes
   for _ in range(__lowercase    ):
    # Special case when using OneCycle and `drop_last` was not used
    if hasattr(self.scheduler   ,   'total_steps'    ):
     if self.scheduler._step_count <= self.scheduler.total_steps:
      self.scheduler.step(*__lowercase   ,   **__lowercase    )
    else:
     self.scheduler.step(*__lowercase   ,   **__lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.scheduler.get_last_lr()


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.scheduler.state_dict()


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  self.scheduler.load_state_dict(__lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.scheduler.get_lr()







 def 			snake_case__ (   self   ,   *__lowercase   ,   **__lowercase    ):

  """simple docstring"""







  return self.scheduler.print_lr(*__lowercase   ,   **__lowercase    )






