






'''simple docstring'''

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__											=     logging.get_logger(__name__)







def 					lowercase__				(   __UpperCamelCase					)->				List[Any]:

						if "resnet-50" in model_name:
												UpperCamelCase       						=      ResNetConfig.from_pretrained("""microsoft/resnet-50"""					)
						elif "resnet-101" in model_name:
												UpperCamelCase       						=      ResNetConfig.from_pretrained("""microsoft/resnet-101"""					)
						else:
												raise ValueError("""Model name should include either resnet50 or resnet101"""					)

						UpperCamelCase       						=      DetrConfig(use_timm_backbone=SCREAMING_SNAKE_CASE_	,		backbone_config=SCREAMING_SNAKE_CASE_					)

						# set label attributes
						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(SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		repo_type="""dataset"""					)	,		"""r"""					)					)
												UpperCamelCase       						=      {int(SCREAMING_SNAKE_CASE_					): v for k, v in idalabel.items()}
												UpperCamelCase       						=      idalabel
												UpperCamelCase       						=      {v: k for k, v in idalabel.items()}

						return config, is_panoptic







def 					lowercase__				(   __UpperCamelCase					)->				int:
						UpperCamelCase       						=      []

						# stem
						# fmt: off
						rename_keys.append(("""backbone.0.body.conv1.weight""", """backbone.conv_encoder.model.embedder.embedder.convolution.weight""")					)
						rename_keys.append(("""backbone.0.body.bn1.weight""", """backbone.conv_encoder.model.embedder.embedder.normalization.weight""")					)
						rename_keys.append(("""backbone.0.body.bn1.bias""", """backbone.conv_encoder.model.embedder.embedder.normalization.bias""")					)
						rename_keys.append(("""backbone.0.body.bn1.running_mean""", """backbone.conv_encoder.model.embedder.embedder.normalization.running_mean""")					)
						rename_keys.append(("""backbone.0.body.bn1.running_var""", """backbone.conv_encoder.model.embedder.embedder.normalization.running_var""")					)
						# stages
						for stage_idx in range(len(config.backbone_config.depths					)					):
												for layer_idx in range(config.backbone_config.depths[stage_idx]					):
																		# shortcut
																		if layer_idx == 0:
																								rename_keys.append(
																								    (
																								        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight",
																								        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight",
																								    )					)
																								rename_keys.append(
																								    (
																								        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight",
																								        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight",
																								    )					)
																								rename_keys.append(
																								    (
																								        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias",
																								        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias",
																								    )					)
																								rename_keys.append(
																								    (
																								        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean",
																								        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean",
																								    )					)
																								rename_keys.append(
																								    (
																								        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var",
																								        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var",
																								    )					)
																		# 3 convs
																		for i in range(3					):
																								rename_keys.append(
																								    (
																								        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight",
																								        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight",
																								    )					)
																								rename_keys.append(
																								    (
																								        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight",
																								        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight",
																								    )					)
																								rename_keys.append(
																								    (
																								        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias",
																								        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias",
																								    )					)
																								rename_keys.append(
																								    (
																								        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean",
																								        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean",
																								    )					)
																								rename_keys.append(
																								    (
																								        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var",
																								        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var",
																								    )					)
    # fmt: on

						for i in range(config.encoder_layers					):
												# 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}.multihead_attn.out_proj.weight",
												        F"decoder.layers.{i}.encoder_attn.out_proj.weight",
												    )					)
												rename_keys.append(
												    (
												        F"transformer.decoder.layers.{i}.multihead_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")					)

						# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
						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"""),
						    ]					)

						return rename_keys







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase					)->				Tuple:
						UpperCamelCase       						=      state_dict.pop(SCREAMING_SNAKE_CASE_					)
						UpperCamelCase       						=      val







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase=False					)->				Dict:
						UpperCamelCase       						=      ""
						if is_panoptic:
												UpperCamelCase       						=      "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:]
						# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
						for i in range(6					):
												# read in weights + bias of input projection layer of self-attention
												UpperCamelCase       						=      state_dict.pop(F"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"					)
												UpperCamelCase       						=      state_dict.pop(F"{prefix}transformer.decoder.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:]
												# read in weights + bias of input projection layer of cross-attention
												UpperCamelCase       						=      state_dict.pop(
												    F"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"					)
												UpperCamelCase       						=      state_dict.pop(F"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"					)
												# next, add query, keys and values (in that order) of cross-attention to the state dict
												UpperCamelCase       						=      in_proj_weight_cross_attn[:256, :]
												UpperCamelCase       						=      in_proj_bias_cross_attn[:256]
												UpperCamelCase       						=      in_proj_weight_cross_attn[256:512, :]
												UpperCamelCase       						=      in_proj_bias_cross_attn[256:512]
												UpperCamelCase       						=      in_proj_weight_cross_attn[-256:, :]
												UpperCamelCase       						=      in_proj_bias_cross_attn[-256:]







def 					lowercase__				(   )->				Union[str, Any]:
						UpperCamelCase       						=      "http://images.cocodataset.org/val2017/000000039769.jpg"
						UpperCamelCase       						=      Image.open(requests.get(SCREAMING_SNAKE_CASE_	,		stream=SCREAMING_SNAKE_CASE_					).raw					)

						return im







@torch.no_grad()
def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase=None	,		__UpperCamelCase=False					)->				Union[str, Any]:
						UpperCamelCase       						=      get_detr_config(SCREAMING_SNAKE_CASE_					)

						# load original model from torch hub
						UpperCamelCase       						=      {
						    "detr-resnet-50": "detr_resnet50",
						    "detr-resnet-101": "detr_resnet101",
						}
						logger.info(F"Converting model {model_name}..."					)
						UpperCamelCase       						=      torch.hub.load("""facebookresearch/detr"""	,		model_name_to_original_name[model_name]	,		pretrained=SCREAMING_SNAKE_CASE_					).eval()
						UpperCamelCase       						=      detr.state_dict()
						# rename keys
						for src, dest in create_rename_keys(SCREAMING_SNAKE_CASE_					):
												if is_panoptic:
																		UpperCamelCase       						=      "detr." + src
												rename_key(SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_					)
						# query, key and value matrices need special treatment
						read_in_q_k_v(SCREAMING_SNAKE_CASE_	,		is_panoptic=SCREAMING_SNAKE_CASE_					)
						# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
						UpperCamelCase       						=      "detr.model." if is_panoptic else "model."
						for key in state_dict.copy().keys():
												if is_panoptic:
																		if (
																		    key.startswith("""detr"""					)
																		    and not key.startswith("""class_labels_classifier"""					)
																		    and not key.startswith("""bbox_predictor"""					)
																		):
																								UpperCamelCase       						=      state_dict.pop(SCREAMING_SNAKE_CASE_					)
																								UpperCamelCase       						=      val
																		elif "class_labels_classifier" in key or "bbox_predictor" in key:
																								UpperCamelCase       						=      state_dict.pop(SCREAMING_SNAKE_CASE_					)
																								UpperCamelCase       						=      val
																		elif key.startswith("""bbox_attention"""					) or key.startswith("""mask_head"""					):
																								continue
																		else:
																								UpperCamelCase       						=      state_dict.pop(SCREAMING_SNAKE_CASE_					)
																								UpperCamelCase       						=      val
												else:
																		if not key.startswith("""class_labels_classifier"""					) and not key.startswith("""bbox_predictor"""					):
																								UpperCamelCase       						=      state_dict.pop(SCREAMING_SNAKE_CASE_					)
																								UpperCamelCase       						=      val

    # finally, create HuggingFace model and load state dict
						UpperCamelCase       						=      DetrForSegmentation(SCREAMING_SNAKE_CASE_					) if is_panoptic else DetrForObjectDetection(SCREAMING_SNAKE_CASE_					)
						model.load_state_dict(SCREAMING_SNAKE_CASE_					)
						model.eval()

						# verify our conversion on an image
						UpperCamelCase       						=      "coco_panoptic" if is_panoptic else "coco_detection"
						UpperCamelCase       						=      DetrImageProcessor(format=SCREAMING_SNAKE_CASE_					)

						UpperCamelCase       						=      processor(images=prepare_img()	,		return_tensors="""pt"""					)
						UpperCamelCase       						=      encoding["pixel_values"]

						UpperCamelCase       						=      detr(SCREAMING_SNAKE_CASE_					)
						UpperCamelCase       						=      model(SCREAMING_SNAKE_CASE_					)

						assert torch.allclose(outputs.logits	,		original_outputs["""pred_logits"""]	,		atol=1E-3					)
						assert torch.allclose(outputs.pred_boxes	,		original_outputs["""pred_boxes"""]	,		atol=1E-3					)
						if is_panoptic:
												assert torch.allclose(outputs.pred_masks	,		original_outputs["""pred_masks"""]	,		atol=1E-4					)
						print("""Looks ok!"""					)

						if pytorch_dump_folder_path is not None:
												# Save model and image processor
												logger.info(F"Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."					)
												Path(SCREAMING_SNAKE_CASE_					).mkdir(exist_ok=SCREAMING_SNAKE_CASE_					)
												model.save_pretrained(SCREAMING_SNAKE_CASE_					)
												processor.save_pretrained(SCREAMING_SNAKE_CASE_					)

						if push_to_hub:
												# Upload model and image processor to the hub
												logger.info("""Uploading PyTorch model and image processor to the hub..."""					)
												model.push_to_hub(F"nielsr/{model_name}"					)
												processor.push_to_hub(F"nielsr/{model_name}"					)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__											=     argparse.ArgumentParser()

					parser.add_argument(
					    '--model_name',
					    default='detr-resnet-50',
					    type=str,
					    choices=['detr-resnet-50', 'detr-resnet-101'],
					    help='Name of the 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.'
					)
					parser.add_argument('--push_to_hub', action='store_true', help='Whether to push the model to the hub or not.')
					SCREAMING_SNAKE_CASE__											=     parser.parse_args()
					convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








'''simple docstring'''

from __future__ import annotations

from typing import Any






class        a_		:

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE = 6   )						->   None:



        """simple docstring"""






        UpperCamelCase       						=      None
        UpperCamelCase       						=      None
        self.create_linked_list(_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE   )						->   None:



        """simple docstring"""






        UpperCamelCase       						=      Node()
        UpperCamelCase       						=      current_node
        UpperCamelCase       						=      current_node
        UpperCamelCase       						=      current_node
        for _ in range(1    ,	_SCREAMING_SNAKE_CASE   ):
              UpperCamelCase       						=      Node()
              UpperCamelCase       						=      current_node
              UpperCamelCase       						=      previous_node
              UpperCamelCase       						=      current_node
        UpperCamelCase       						=      self.front
        UpperCamelCase       						=      previous_node

  def 						A__		(   self   )						->   bool:



        """simple docstring"""






        return (
            self.front == self.rear
            and self.front is not None
            and self.front.data is None
        )

  def 						A__		(   self   )						->   Any | None:



        """simple docstring"""






        self.check_can_perform_operation()
        return self.front.data if self.front else None

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE   )						->   None:



        """simple docstring"""






        if self.rear is None:
              return

        self.check_is_full()
        if not self.is_empty():
              UpperCamelCase       						=      self.rear.next
        if self.rear:
              UpperCamelCase       						=      data

  def 						A__		(   self   )						->   Any:



        """simple docstring"""






        self.check_can_perform_operation()
        if self.rear is None or self.front is None:
              return None
        if self.front == self.rear:
              UpperCamelCase       						=      self.front.data
              UpperCamelCase       						=      None
              return data

        UpperCamelCase       						=      self.front
        UpperCamelCase       						=      old_front.next
        UpperCamelCase       						=      old_front.data
        UpperCamelCase       						=      None
        return data

  def 						A__		(   self   )						->   None:



        """simple docstring"""






        if self.is_empty():
              raise Exception("""Empty Queue"""   )


  def 						A__		(   self   )						->   None:



        """simple docstring"""






        if self.rear and self.rear.next == self.front:
              raise Exception("""Full Queue"""   )






class        a_		:

  def __init__(   self   )						->   None:



        """simple docstring"""






        UpperCamelCase       						=      None
        UpperCamelCase       						=      None
        UpperCamelCase       						=      None


if __name__ == "__main__":
     import doctest

     doctest.testmod()
