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


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

# here we list all keys to be renamed (original name on the left, our name on the right)
lowercase__		    =	[]
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}.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"),
    ]
)




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__       )   ->				Optional[Any]:


		'''simple docstring'''


		snake_case   :    Optional[int]			       =		state_dict.pop(SCREAMING_SNAKE_CASE__       )
		snake_case   :    str			       =		val




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__       )   ->				List[Any]:


		'''simple docstring'''


		snake_case   :    Tuple			       =		OrderedDict()
		for key, value in state_dict.items():
				if "backbone.0.body" in key:
						snake_case   :    int			       =		key.replace('''backbone.0.body'''      ,    '''backbone.conv_encoder.model'''       )
						snake_case   :    Union[str, Any]			       =		value
				else:
						snake_case   :    Tuple			       =		value

		return new_state_dict




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__=False       )   ->				int:


		'''simple docstring'''


		snake_case   :    int			       =		''''''
		if is_panoptic:
				snake_case   :    List[str]			       =		'''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)
				snake_case   :    List[Any]			       =		state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'       )
				snake_case   :    Union[str, Any]			       =		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
				snake_case   :    Dict			       =		in_proj_weight[:256, :]
				snake_case   :    Tuple			       =		in_proj_bias[:256]
				snake_case   :    Tuple			       =		in_proj_weight[256:512, :]
				snake_case   :    str			       =		in_proj_bias[256:512]
				snake_case   :    Optional[Any]			       =		in_proj_weight[-256:, :]
				snake_case   :    Tuple			       =		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
				snake_case   :    Union[str, Any]			       =		state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'       )
				snake_case   :    int			       =		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
				snake_case   :    int			       =		in_proj_weight[:256, :]
				snake_case   :    Dict			       =		in_proj_bias[:256]
				snake_case   :    List[str]			       =		in_proj_weight[256:512, :]
				snake_case   :    Optional[Any]			       =		in_proj_bias[256:512]
				snake_case   :    Dict			       =		in_proj_weight[-256:, :]
				snake_case   :    Union[str, Any]			       =		in_proj_bias[-256:]
				# read in weights + bias of input projection layer of cross-attention
				snake_case   :    Any			       =		state_dict.pop(
				    F'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'       )
				snake_case   :    List[str]			       =		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
				snake_case   :    List[str]			       =		in_proj_weight_cross_attn[:256, :]
				snake_case   :    Tuple			       =		in_proj_bias_cross_attn[:256]
				snake_case   :    int			       =		in_proj_weight_cross_attn[256:512, :]
				snake_case   :    Any			       =		in_proj_bias_cross_attn[256:512]
				snake_case   :    Optional[int]			       =		in_proj_weight_cross_attn[-256:, :]
				snake_case   :    Optional[int]			       =		in_proj_bias_cross_attn[-256:]




def 					_UpperCamelCase		(    )   ->				Union[str, Any]:


		'''simple docstring'''


		snake_case   :    int			       =		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
		snake_case   :    Dict			       =		Image.open(requests.get(SCREAMING_SNAKE_CASE__      ,    stream=SCREAMING_SNAKE_CASE__       ).raw       )

		return im




@torch.no_grad()
def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__       )   ->				Dict:


		'''simple docstring'''


		snake_case   :    Optional[Any]			       =		DetrConfig()
		# set backbone and dilation attributes
		if "resnet101" in model_name:
				snake_case   :    str			       =		'''resnet101'''
		if "dc5" in model_name:
				snake_case   :    List[Any]			       =		True
		snake_case   :    int			       =		'''panoptic''' in model_name
		if is_panoptic:
				snake_case   :    Any			       =		250
		else:
				snake_case   :    str			       =		91
				snake_case   :    Optional[int]			       =		'''huggingface/label-files'''
				snake_case   :    Optional[int]			       =		'''coco-detection-id2label.json'''
				snake_case   :    Tuple			       =		json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__      ,    repo_type='''dataset'''       )      ,    '''r'''       )       )
				snake_case   :    Any			       =		{int(SCREAMING_SNAKE_CASE__       ): v for k, v in idalabel.items()}
				snake_case   :    Any			       =		idalabel
				snake_case   :    Union[str, Any]			       =		{v: k for k, v in idalabel.items()}

		# load image processor
		snake_case   :    Tuple			       =		'''coco_panoptic''' if is_panoptic else '''coco_detection'''
		snake_case   :    Optional[int]			       =		DetrImageProcessor(format=SCREAMING_SNAKE_CASE__       )

		# prepare image
		snake_case   :    Dict			       =		prepare_img()
		snake_case   :    Optional[int]			       =		image_processor(images=SCREAMING_SNAKE_CASE__      ,    return_tensors='''pt'''       )
		snake_case   :    Optional[int]			       =		encoding['''pixel_values''']

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

		# load original model from torch hub
		snake_case   :    List[str]			       =		torch.hub.load('''facebookresearch/detr'''      ,    SCREAMING_SNAKE_CASE__      ,    pretrained=SCREAMING_SNAKE_CASE__       ).eval()
		snake_case   :    Any			       =		detr.state_dict()
		# rename keys
		for src, dest in rename_keys:
				if is_panoptic:
						snake_case   :    str			       =		'''detr.''' + src
				rename_key(SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__       )
		snake_case   :    Tuple			       =		rename_backbone_keys(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
		snake_case   :    List[str]			       =		'''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'''       )
						):
								snake_case   :    Tuple			       =		state_dict.pop(SCREAMING_SNAKE_CASE__       )
								snake_case   :    str			       =		val
						elif "class_labels_classifier" in key or "bbox_predictor" in key:
								snake_case   :    Union[str, Any]			       =		state_dict.pop(SCREAMING_SNAKE_CASE__       )
								snake_case   :    Any			       =		val
						elif key.startswith('''bbox_attention'''       ) or key.startswith('''mask_head'''       ):
								continue
						else:
								snake_case   :    List[Any]			       =		state_dict.pop(SCREAMING_SNAKE_CASE__       )
								snake_case   :    int			       =		val
				else:
						if not key.startswith('''class_labels_classifier'''       ) and not key.startswith('''bbox_predictor'''       ):
								snake_case   :    str			       =		state_dict.pop(SCREAMING_SNAKE_CASE__       )
								snake_case   :    Any			       =		val
    # finally, create HuggingFace model and load state dict
		snake_case   :    Dict			       =		DetrForSegmentation(SCREAMING_SNAKE_CASE__       ) if is_panoptic else DetrForObjectDetection(SCREAMING_SNAKE_CASE__       )
		model.load_state_dict(SCREAMING_SNAKE_CASE__       )
		model.eval()
		# verify our conversion
		snake_case   :    Optional[Any]			       =		detr(SCREAMING_SNAKE_CASE__       )
		snake_case   :    Any			       =		model(SCREAMING_SNAKE_CASE__       )
		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(SCREAMING_SNAKE_CASE__       ).mkdir(exist_ok=SCREAMING_SNAKE_CASE__       )
		model.save_pretrained(SCREAMING_SNAKE_CASE__       )
		image_processor.save_pretrained(SCREAMING_SNAKE_CASE__       )


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

						parser.add_argument(
						    "--model_name", default="detr_resnet50", type=str, 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."
						)
						lowercase__		    =	parser.parse_args()
						convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)




'''simple docstring'''
from __future__ import annotations

from collections.abc import Callable




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__ = 100      ,    )   ->				float:


		'''simple docstring'''


		snake_case   :    int			       =		x_start
		snake_case   :    Tuple			       =		fnc(SCREAMING_SNAKE_CASE__       )
		snake_case   :    Dict			       =		0.0

		for _ in range(SCREAMING_SNAKE_CASE__       ):
				# Approximates small segments of curve as linear and solve
				# for trapezoidal area
				snake_case   :    str			       =		(x_end - x_start) / steps + xa
				snake_case   :    str			       =		fnc(SCREAMING_SNAKE_CASE__       )
				area += abs(fxa + fxa       ) * (xa - xa) / 2

				# Increment step
				snake_case   :    Any			       =		xa
				snake_case   :    List[str]			       =		fxa
		return area


if __name__ == "__main__":




						def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__       )   ->				str:


								'''simple docstring'''


								return x**3

						print("f(x) = x^3")
						print("The area between the curve, x = -10, x = 10 and the x axis is:")
						lowercase__		    =	1_0
						while i <= 1_0_0_0_0_0:
												lowercase__		    =	trapezoidal_area(f, -5, 5, i)
												print(f"with {i} steps: {area}")
												i *= 1_0


