
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()
_A  =	logging.get_logger(__name__)






def   lowerCamelCase__							(							__lowerCAmelCase    :    Union[str, Any]							):




			"""simple docstring"""


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

			lowerCAmelCase_         =      DetrConfig(use_timm_backbone=__lowerCAmelCase       ,				backbone_config=__lowerCAmelCase							)

			# set label attributes
			lowerCAmelCase_         =      "panoptic" in model_name
			if is_panoptic:
						lowerCAmelCase_         =      250
			else:
						lowerCAmelCase_         =      91
						lowerCAmelCase_         =      "huggingface/label-files"
						lowerCAmelCase_         =      "coco-detection-id2label.json"
						lowerCAmelCase_         =      json.load(open(hf_hub_download(__lowerCAmelCase       ,				__lowerCAmelCase       ,				repo_type="dataset"							)       ,				"r"							)							)
						lowerCAmelCase_         =      {int(__lowerCAmelCase							): v for k, v in idalabel.items()}
						lowerCAmelCase_         =      idalabel
						lowerCAmelCase_         =      {v: k for k, v in idalabel.items()}

			return config, is_panoptic






def   lowerCamelCase__							(							__lowerCAmelCase    :    Dict							):




			"""simple docstring"""


			lowerCAmelCase_         =      []

			# 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   lowerCamelCase__							(							__lowerCAmelCase    :    List[str]       ,				__lowerCAmelCase    :    Tuple       ,				__lowerCAmelCase    :    List[str]							):




			"""simple docstring"""


			lowerCAmelCase_         =      state_dict.pop(__lowerCAmelCase							)
			lowerCAmelCase_         =      val






def   lowerCamelCase__							(							__lowerCAmelCase    :    int       ,				__lowerCAmelCase    :    Any=False							):




			"""simple docstring"""


			lowerCAmelCase_         =      ""
			if is_panoptic:
						lowerCAmelCase_         =      "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)
						lowerCAmelCase_         =      state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""							)
						lowerCAmelCase_         =      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
						lowerCAmelCase_         =      in_proj_weight[:256, :]
						lowerCAmelCase_         =      in_proj_bias[:256]
						lowerCAmelCase_         =      in_proj_weight[256:512, :]
						lowerCAmelCase_         =      in_proj_bias[256:512]
						lowerCAmelCase_         =      in_proj_weight[-256:, :]
						lowerCAmelCase_         =      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
						lowerCAmelCase_         =      state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""							)
						lowerCAmelCase_         =      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
						lowerCAmelCase_         =      in_proj_weight[:256, :]
						lowerCAmelCase_         =      in_proj_bias[:256]
						lowerCAmelCase_         =      in_proj_weight[256:512, :]
						lowerCAmelCase_         =      in_proj_bias[256:512]
						lowerCAmelCase_         =      in_proj_weight[-256:, :]
						lowerCAmelCase_         =      in_proj_bias[-256:]
						# read in weights + bias of input projection layer of cross-attention
						lowerCAmelCase_         =      state_dict.pop(
						    F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""							)
						lowerCAmelCase_         =      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
						lowerCAmelCase_         =      in_proj_weight_cross_attn[:256, :]
						lowerCAmelCase_         =      in_proj_bias_cross_attn[:256]
						lowerCAmelCase_         =      in_proj_weight_cross_attn[256:512, :]
						lowerCAmelCase_         =      in_proj_bias_cross_attn[256:512]
						lowerCAmelCase_         =      in_proj_weight_cross_attn[-256:, :]
						lowerCAmelCase_         =      in_proj_bias_cross_attn[-256:]






def   lowerCamelCase__							(							):




			"""simple docstring"""


			lowerCAmelCase_         =      "http://images.cocodataset.org/val2017/000000039769.jpg"
			lowerCAmelCase_         =      Image.open(requests.get(__lowerCAmelCase       ,				stream=__lowerCAmelCase							).raw							)

			return im






@torch.no_grad()
def   lowerCamelCase__							(							__lowerCAmelCase    :    int       ,				__lowerCAmelCase    :    Union[str, Any]=None       ,				__lowerCAmelCase    :    int=False							):




			"""simple docstring"""


			lowerCAmelCase_      ,    lowerCAmelCase_         =      get_detr_config(__lowerCAmelCase							)

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

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

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

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

			lowerCAmelCase_         =      detr(__lowerCAmelCase							)
			lowerCAmelCase_         =      model(__lowerCAmelCase							)

			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(__lowerCAmelCase							).mkdir(exist_ok=__lowerCAmelCase							)
						model.save_pretrained(__lowerCAmelCase							)
						processor.save_pretrained(__lowerCAmelCase							)

			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__":
			_A  =	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.")
			_A  =	parser.parse_args()
			convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





def   lowerCamelCase__							(							__lowerCAmelCase    :    list							):




			"""simple docstring"""


			for i in range(len(__lowerCAmelCase							) - 1       ,				0       ,				-1							):
						lowerCAmelCase_         =      False

						for j in range(__lowerCAmelCase       ,				0       ,				-1							):
									if unsorted[j] < unsorted[j - 1]:
												lowerCAmelCase_      ,    lowerCAmelCase_         =      unsorted[j - 1], unsorted[j]
												lowerCAmelCase_         =      True

						for j in range(__lowerCAmelCase							):
									if unsorted[j] > unsorted[j + 1]:
												lowerCAmelCase_      ,    lowerCAmelCase_         =      unsorted[j + 1], unsorted[j]
												lowerCAmelCase_         =      True

						if not swapped:
									break
			return unsorted


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			_A  =	input("Enter numbers separated by a comma:\n").strip()
			_A  =	[int(item) for item in user_input.split(",")]
			print(f"""{cocktail_shaker_sort(unsorted) = }""")



