

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

# here we list all keys to be renamed (original name on the left, our name on the right)
lowerCAmelCase_     =      []
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      __magic_name__							(			A       , A       , A				)      ->		str:
	snake_case							=					state_dict.pop(A				)
	snake_case							=					val

def      __magic_name__							(			A				)      ->		str:
	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      __magic_name__							(			A       , A=False				)      ->		Dict:
	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[:2_5_6, :]
		snake_case							=					in_proj_bias[:2_5_6]
		snake_case							=					in_proj_weight[2_5_6:5_1_2, :]
		snake_case							=					in_proj_bias[2_5_6:5_1_2]
		snake_case							=					in_proj_weight[-2_5_6:, :]
		snake_case							=					in_proj_bias[-2_5_6:]
	# 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[:2_5_6, :]
		snake_case							=					in_proj_bias[:2_5_6]
		snake_case							=					in_proj_weight[2_5_6:5_1_2, :]
		snake_case							=					in_proj_bias[2_5_6:5_1_2]
		snake_case							=					in_proj_weight[-2_5_6:, :]
		snake_case							=					in_proj_bias[-2_5_6:]
		# 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[:2_5_6, :]
		snake_case							=					in_proj_bias_cross_attn[:2_5_6]
		snake_case							=					in_proj_weight_cross_attn[2_5_6:5_1_2, :]
		snake_case							=					in_proj_bias_cross_attn[2_5_6:5_1_2]
		snake_case							=					in_proj_weight_cross_attn[-2_5_6:, :]
		snake_case							=					in_proj_bias_cross_attn[-2_5_6:]

def      __magic_name__							(			)      ->		int:
	snake_case							=					'http://images.cocodataset.org/val2017/000000039769.jpg'
	snake_case							=					Image.open(requests.get(A       , stream=A				).raw				)

	return im

@torch.no_grad()
def      __magic_name__							(			A       , A				)      ->		int:
	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							=					2_5_0
	else:
		snake_case							=					9_1
		snake_case							=					'huggingface/label-files'
		snake_case							=					'coco-detection-id2label.json'
		snake_case							=					json.load(open(hf_hub_download(A       , A       , repo_type='dataset'				)       , 'r'				)				)
		snake_case							=					{int(A				): 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=A				)

	# prepare image
	snake_case							=					prepare_img()
	snake_case							=					image_processor(images=A       , 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'       , A       , pretrained=A				).eval()
	snake_case							=					detr.state_dict()
	# rename keys
	for src, dest in rename_keys:
		if is_panoptic:
			snake_case							=					'detr.' + src
		rename_key(A       , A       , A				)
	snake_case							=					rename_backbone_keys(A				)
	# query, key and value matrices need special treatment
	read_in_q_k_v(A       , is_panoptic=A				)
	# 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(A				)
				snake_case							=					val
			elif "class_labels_classifier" in key or "bbox_predictor" in key:
				snake_case							=					state_dict.pop(A				)
				snake_case							=					val
			elif key.startswith('bbox_attention'				) or key.startswith('mask_head'				):
				continue
			else:
				snake_case							=					state_dict.pop(A				)
				snake_case							=					val
		else:
			if not key.startswith('class_labels_classifier'				) and not key.startswith('bbox_predictor'				):
				snake_case							=					state_dict.pop(A				)
				snake_case							=					val
    # finally, create HuggingFace model and load state dict
	snake_case							=					DetrForSegmentation(A				) if is_panoptic else DetrForObjectDetection(A				)
	model.load_state_dict(A				)
	model.eval()
	# verify our conversion
	snake_case							=					detr(A				)
	snake_case							=					model(A				)
	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(A				).mkdir(exist_ok=A				)
	model.save_pretrained(A				)
	image_processor.save_pretrained(A				)


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






# This test is meant to be run in on an instance with TPUs like this:
#
#   python examples/pytorch/xla_spawn.py --num_cores=8 tests/test_trainer_tpu.py
#
# Replace 8 with the number of TPU cores you have.
#

import sys
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.utils import logging


__lowerCamelCase     :					List[str]				  =						logging.get_logger(__name__)


if is_torch_available():
   import torch
   from torch import nn
   from torch.utils.data import Dataset

   from transformers import Trainer







   class __snake_case							(		lowerCamelCase_						):




      def __init__(       self     :							Tuple			,      _lowercase     :							int = 1_01 ):



        """simple docstring"""






        SCREAMING_SNAKE_CASE__     				= length




      def __len__(       self     :							str ):



        """simple docstring"""






        return self.length




      def __getitem__(       self     :							Optional[Any]			,      _lowercase     :							Union[str, Any] ):



        """simple docstring"""






        return i







   class __snake_case							:




      def __call__(       self     :							Dict			,      _lowercase     :							Dict ):



        """simple docstring"""






        return {"input_ids": torch.tensor(_lowercase ), "labels": torch.tensor(_lowercase )}







   class __snake_case							(		nn.Module						):




      def __init__(       self     :							Tuple ):



        """simple docstring"""






        super().__init__()
        # Add some (unused) params otherwise DDP will complain.
        SCREAMING_SNAKE_CASE__     				= nn.Linear(1_20			,      80 )




      def       __a		(       self     :							Any			,      _lowercase     :							Union[str, Any]			,      _lowercase     :							Dict=None ):



        """simple docstring"""






        if labels is not None:
          return torch.tensor(0.0			,      device=input_ids.device ), input_ids
        else:
          return input_ids



def       __SCREAMING_SNAKE_CASE    (   )				->   Optional[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= HfArgumentParser((TrainingArguments,)      )
  sys.argv += ["--output_dir", "./examples"]
  SCREAMING_SNAKE_CASE__     				= parser.parse_args_into_dataclasses()[0]

  logger.warning(
      f"""Process rank: {training_args.local_rank}, device: {training_args.device}, """
      f"""tpu_num_cores: {training_args.tpu_num_cores}"""					,	)

  # Essentially, what we want to verify in the distributed case is
  # that we get all samples back, in the right order.
  # (this is crucial for prediction for instance)
  for dataset_length in [10_01, 2_56, 15]:
    SCREAMING_SNAKE_CASE__     				= DummyDataset(__UpperCamelCase      )

    def compute_metrics(__UpperCamelCase       :			EvalPrediction      ) -> Dict:
      SCREAMING_SNAKE_CASE__     				= list(range(len(__UpperCamelCase      )      )      )
      SCREAMING_SNAKE_CASE__     				= p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
      return {"success": success}

    SCREAMING_SNAKE_CASE__     				= Trainer(
        model=DummyModel()					,	args=__UpperCamelCase					,	data_collator=DummyDataCollator()					,	eval_dataset=__UpperCamelCase					,	compute_metrics=__UpperCamelCase					,	)
    SCREAMING_SNAKE_CASE__     				= trainer.evaluate()
    logger.info(__UpperCamelCase      )
    if metrics["eval_success"] is not True:
      logger.error(__UpperCamelCase      )
      exit(1      )

    SCREAMING_SNAKE_CASE__     				= trainer.predict(__UpperCamelCase      )
    logger.info(p.metrics      )
    if p.metrics["test_success"] is not True:
      logger.error(p.metrics      )
      exit(1      )

    SCREAMING_SNAKE_CASE__     				= 2

    SCREAMING_SNAKE_CASE__     				= trainer.evaluate()
    logger.info(__UpperCamelCase      )
    if metrics["eval_success"] is not True:
      logger.error(__UpperCamelCase      )
      exit(1      )

    SCREAMING_SNAKE_CASE__     				= trainer.predict(__UpperCamelCase      )
    logger.info(p.metrics      )
    if p.metrics["test_success"] is not True:
      logger.error(p.metrics      )
      exit(1      )

    SCREAMING_SNAKE_CASE__     				= None

  logger.info("""🔥 All distributed tests successful"""      )



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			Union[str, Any]      )				->   Dict:







  """simple docstring"""





  main()


if __name__ == "__main__":
   main()
