


"""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()
A  :     Optional[Any]    					=    logging.get_logger(__name__)


def _lowerCamelCase			(	_UpperCamelCase     :   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			(	_UpperCamelCase     :   List[str]   ):

			'''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			(	_UpperCamelCase     :   Any					,				_UpperCamelCase     :   Tuple					,				_UpperCamelCase     :   int   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    state_dict.pop(_lowerCAmelCase   )
			__lowerCAmelCase       					=    val


def _lowerCamelCase			(	_UpperCamelCase     :   Optional[int]					,				_UpperCamelCase     :   Optional[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			(	_UpperCamelCase     :   List[Any]					,				_UpperCamelCase     :   Tuple=None					,				_UpperCamelCase     :   Union[str, Any]=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  :     Union[str, Any]    					=    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  :     Dict    					=    parser.parse_args()
	convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








"""simple docstring"""


from itertools import product


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    sides_number
			__lowerCAmelCase       					=    max_face_number * dice_number
			__lowerCAmelCase       					=    [0] * (max_total + 1)

			__lowerCAmelCase       					=    1
			__lowerCAmelCase       					=    range(_UpperCamelCase					,				max_face_number + 1   )
			for dice_numbers in product(_UpperCamelCase					,				repeat=_UpperCamelCase   ):
						__lowerCAmelCase       					=    sum(_UpperCamelCase   )
						totals_frequencies[total] += 1

			return totals_frequencies


def _lowerCamelCase			(	):

			'''simple docstring'''




			__lowerCAmelCase       					=    total_frequency_distribution(
			    sides_number=4					,				dice_number=9   )
			__lowerCAmelCase       					=    total_frequency_distribution(
			    sides_number=6					,				dice_number=6   )

			__lowerCAmelCase       					=    0
			__lowerCAmelCase       					=    9
			__lowerCAmelCase       					=    4 * 9
			__lowerCAmelCase       					=    6
			for peter_total in range(_UpperCamelCase					,				max_peter_total + 1   ):
						peter_wins_count += peter_totals_frequencies[peter_total] * sum(
						    colin_totals_frequencies[min_colin_total:peter_total]   )

			__lowerCAmelCase       					=    (4**9) * (6**6)
			__lowerCAmelCase       					=    peter_wins_count / total_games_number

			__lowerCAmelCase       					=    round(_UpperCamelCase					,				ndigits=7   )

			return rounded_peter_win_probability


if __name__ == "__main__":
	print(f'''{solution() = }''')




