




'''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()
a__         =     logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
a__         =     []
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    snake_case__     (  a       ,      a       ,      a	)			->       int:





			'''simple docstring'''






			snake_case__          =						state_dict.pop(__lowercase	)
			snake_case__          =						val




def    snake_case__     (  a	)			->       Optional[Any]:





			'''simple docstring'''






			snake_case__          =						OrderedDict()
			for key, value in state_dict.items():
						if "backbone.0.body" in key:
									snake_case__          =						key.replace("""backbone.0.body"""       ,      """backbone.conv_encoder.model"""	)
									snake_case__          =						value
						else:
									snake_case__          =						value

			return new_state_dict




def    snake_case__     (  a       ,      a=False	)			->       Any:





			'''simple docstring'''






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




def    snake_case__     (  )			->       Optional[int]:





			'''simple docstring'''






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

			return im




@torch.no_grad()
def    snake_case__     (  a       ,      a	)			->       List[str]:





			'''simple docstring'''






			snake_case__          =						DetrConfig()
			# set backbone and dilation attributes
			if "resnet101" in model_name:
						snake_case__          =						"""resnet101"""
			if "dc5" in model_name:
						snake_case__          =						True
			snake_case__          =						"""panoptic""" in model_name
			if is_panoptic:
						snake_case__          =						250
			else:
						snake_case__          =						91
						snake_case__          =						"""huggingface/label-files"""
						snake_case__          =						"""coco-detection-id2label.json"""
						snake_case__          =						json.load(open(hf_hub_download(__lowercase       ,      __lowercase       ,      repo_type="""dataset"""	)       ,      """r"""	)	)
						snake_case__          =						{int(__lowercase	): v for k, v in idalabel.items()}
						snake_case__          =						idalabel
						snake_case__          =						{v: k for k, v in idalabel.items()}

			# load image processor
			snake_case__          =						"""coco_panoptic""" if is_panoptic else """coco_detection"""
			snake_case__          =						DetrImageProcessor(format=__lowercase	)

			# prepare image
			snake_case__          =						prepare_img()
			snake_case__          =						image_processor(images=__lowercase       ,      return_tensors="""pt"""	)
			snake_case__          =						encoding["""pixel_values"""]

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

			# load original model from torch hub
			snake_case__          =						torch.hub.load("""facebookresearch/detr"""       ,      __lowercase       ,      pretrained=__lowercase	).eval()
			snake_case__          =						detr.state_dict()
			# rename keys
			for src, dest in rename_keys:
						if is_panoptic:
									snake_case__          =						"""detr.""" + src
						rename_key(__lowercase       ,      __lowercase       ,      __lowercase	)
			snake_case__          =						rename_backbone_keys(__lowercase	)
			# query, key and value matrices need special treatment
			read_in_q_k_v(__lowercase       ,      is_panoptic=__lowercase	)
			# 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__          =						"""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__          =						state_dict.pop(__lowercase	)
												snake_case__          =						val
									elif "class_labels_classifier" in key or "bbox_predictor" in key:
												snake_case__          =						state_dict.pop(__lowercase	)
												snake_case__          =						val
									elif key.startswith("""bbox_attention"""	) or key.startswith("""mask_head"""	):
												continue
									else:
												snake_case__          =						state_dict.pop(__lowercase	)
												snake_case__          =						val
						else:
									if not key.startswith("""class_labels_classifier"""	) and not key.startswith("""bbox_predictor"""	):
												snake_case__          =						state_dict.pop(__lowercase	)
												snake_case__          =						val
    # finally, create HuggingFace model and load state dict
			snake_case__          =						DetrForSegmentation(__lowercase	) if is_panoptic else DetrForObjectDetection(__lowercase	)
			model.load_state_dict(__lowercase	)
			model.eval()
			# verify our conversion
			snake_case__          =						detr(__lowercase	)
			snake_case__          =						model(__lowercase	)
			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(__lowercase	).mkdir(exist_ok=__lowercase	)
			model.save_pretrained(__lowercase	)
			image_processor.save_pretrained(__lowercase	)


if __name__ == "__main__":
		a__         =     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.'''
		)
		a__         =     parser.parse_args()
		convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)



"""simple docstring"""


import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from unittest.mock import Mock, patch

import diffusers.utils.hub_utils
class snake_case   (	unittest.TestCase):







		@patch("diffusers.utils.hub_utils.get_full_repo_name"		)
		def 			a_			(       self   :							Any     , a__   :							Mock		)							->    None:





						'''simple docstring'''







						_A       	=      "full_repo_name"
						with TemporaryDirectory() as tmpdir:
										# Dummy args values
										_A       	=      Mock()
										_A       	=      tmpdir
										_A       	=      0
										_A       	=      "hub_token"
										_A       	=      "dataset_name"
										_A       	=      0.0_1
										_A       	=      10_00_00
										_A       	=      1_00_00
										_A       	=      0.0_1
										_A       	=      0.0_2
										_A       	=      0.0_3
										_A       	=      0.0_0_0_5
										_A       	=      0.0_0_0_0_0_1
										_A       	=      1
										_A       	=      10
										_A       	=      0.0_0_1
										_A       	=      0.1
										_A       	=      0.2
										_A       	=      True

										# Model card mush be rendered and saved
										diffusers.utils.hub_utils.create_model_card(a__     , model_name="model_name"		)
										self.assertTrue((Path(a__		) / "README.md").is_file()		)