
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		:   str         =    logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
A		:   Optional[int]         =    []
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_  :  Optional[int]		,      SCREAMING_SNAKE_CASE_  :  List[str]		,      SCREAMING_SNAKE_CASE_  :  Any					)     ->    Optional[Any]:
						_lowercase									=				state_dict.pop(SCREAMING_SNAKE_CASE_					)
						_lowercase									=				val




def   UpperCamelCase__     (    SCREAMING_SNAKE_CASE_  :  Any					)     ->    Any:
						_lowercase									=				OrderedDict()
						for key, value in state_dict.items():
												if "backbone.0.body" in key:
																		_lowercase									=				key.replace("""backbone.0.body"""		,      """backbone.conv_encoder.model"""					)
																		_lowercase									=				value
												else:
																		_lowercase									=				value

						return new_state_dict




def   UpperCamelCase__     (    SCREAMING_SNAKE_CASE_  :  Optional[int]		,      SCREAMING_SNAKE_CASE_  :  Optional[int]=False					)     ->    Dict:
						_lowercase									=				""""""
						if is_panoptic:
												_lowercase									=				"""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)
												_lowercase									=				state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""					)
												_lowercase									=				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
												_lowercase									=				in_proj_weight[:2_56, :]
												_lowercase									=				in_proj_bias[:2_56]
												_lowercase									=				in_proj_weight[2_56:5_12, :]
												_lowercase									=				in_proj_bias[2_56:5_12]
												_lowercase									=				in_proj_weight[-2_56:, :]
												_lowercase									=				in_proj_bias[-2_56:]
						# 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
												_lowercase									=				state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""					)
												_lowercase									=				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
												_lowercase									=				in_proj_weight[:2_56, :]
												_lowercase									=				in_proj_bias[:2_56]
												_lowercase									=				in_proj_weight[2_56:5_12, :]
												_lowercase									=				in_proj_bias[2_56:5_12]
												_lowercase									=				in_proj_weight[-2_56:, :]
												_lowercase									=				in_proj_bias[-2_56:]
												# read in weights + bias of input projection layer of cross-attention
												_lowercase									=				state_dict.pop(
												    f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""					)
												_lowercase									=				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
												_lowercase									=				in_proj_weight_cross_attn[:2_56, :]
												_lowercase									=				in_proj_bias_cross_attn[:2_56]
												_lowercase									=				in_proj_weight_cross_attn[2_56:5_12, :]
												_lowercase									=				in_proj_bias_cross_attn[2_56:5_12]
												_lowercase									=				in_proj_weight_cross_attn[-2_56:, :]
												_lowercase									=				in_proj_bias_cross_attn[-2_56:]




def   UpperCamelCase__     (    )     ->    int:
						_lowercase									=				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
						_lowercase									=				Image.open(requests.get(SCREAMING_SNAKE_CASE_		,      stream=SCREAMING_SNAKE_CASE_					).raw					)

						return im




@torch.no_grad()
def   UpperCamelCase__     (    SCREAMING_SNAKE_CASE_  :  Optional[Any]		,      SCREAMING_SNAKE_CASE_  :  str					)     ->    int:
						_lowercase									=				DetrConfig()
						# set backbone and dilation attributes
						if "resnet101" in model_name:
												_lowercase									=				"""resnet101"""
						if "dc5" in model_name:
												_lowercase									=				True
						_lowercase									=				"""panoptic""" in model_name
						if is_panoptic:
												_lowercase									=				2_50
						else:
												_lowercase									=				91
												_lowercase									=				"""huggingface/label-files"""
												_lowercase									=				"""coco-detection-id2label.json"""
												_lowercase									=				json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		,      repo_type="""dataset"""					)		,      """r"""					)					)
												_lowercase									=				{int(SCREAMING_SNAKE_CASE_					): v for k, v in idalabel.items()}
												_lowercase									=				idalabel
												_lowercase									=				{v: k for k, v in idalabel.items()}

						# load image processor
						_lowercase									=				"""coco_panoptic""" if is_panoptic else """coco_detection"""
						_lowercase									=				DetrImageProcessor(format=SCREAMING_SNAKE_CASE_					)

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

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

						# load original model from torch hub
						_lowercase									=				torch.hub.load("""facebookresearch/detr"""		,      SCREAMING_SNAKE_CASE_		,      pretrained=SCREAMING_SNAKE_CASE_					).eval()
						_lowercase									=				detr.state_dict()
						# rename keys
						for src, dest in rename_keys:
												if is_panoptic:
																		_lowercase									=				"""detr.""" + src
												rename_key(SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_					)
						_lowercase									=				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
						_lowercase									=				"""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"""					)
																		):
																								_lowercase									=				state_dict.pop(SCREAMING_SNAKE_CASE_					)
																								_lowercase									=				val
																		elif "class_labels_classifier" in key or "bbox_predictor" in key:
																								_lowercase									=				state_dict.pop(SCREAMING_SNAKE_CASE_					)
																								_lowercase									=				val
																		elif key.startswith("""bbox_attention"""					) or key.startswith("""mask_head"""					):
																								continue
																		else:
																								_lowercase									=				state_dict.pop(SCREAMING_SNAKE_CASE_					)
																								_lowercase									=				val
												else:
																		if not key.startswith("""class_labels_classifier"""					) and not key.startswith("""bbox_predictor"""					):
																								_lowercase									=				state_dict.pop(SCREAMING_SNAKE_CASE_					)
																								_lowercase									=				val
    # finally, create HuggingFace model and load state dict
						_lowercase									=				DetrForSegmentation(SCREAMING_SNAKE_CASE_					) if is_panoptic else DetrForObjectDetection(SCREAMING_SNAKE_CASE_					)
						model.load_state_dict(SCREAMING_SNAKE_CASE_					)
						model.eval()
						# verify our conversion
						_lowercase									=				detr(SCREAMING_SNAKE_CASE_					)
						_lowercase									=				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__":
		A		:   Dict         =    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		:   Tuple         =    parser.parse_args()
		convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A		:   Any         =    {'''configuration_bit''': ['''BIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BitConfig''', '''BitOnnxConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A		:   Any         =    [
		    '''BIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''BitForImageClassification''',
		    '''BitModel''',
		    '''BitPreTrainedModel''',
		    '''BitBackbone''',
		]


try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A		:   Tuple         =    ['''BitImageProcessor''']


if TYPE_CHECKING:
		from .configuration_bit import BIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BitConfig, BitOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_bit import (
				    BIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BitBackbone,
				    BitForImageClassification,
				    BitModel,
				    BitPreTrainedModel,
				)

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .image_processing_bit import BitImageProcessor

else:
		import sys

		A		:   int         =    _LazyModule(__name__, globals()['''__file__'''], _import_structure)