



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()
_UpperCamelCase							    =						logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_UpperCamelCase							    =						[]
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 	a_					(							_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	)				->				Dict:
  __lowerCamelCase	:	List[str]   	=			state_dict.pop(_lowerCAmelCase	)
  __lowerCamelCase	:	Dict   	=			val

def 	a_					(							_lowerCAmelCase	)				->				int:
  __lowerCamelCase	:	Any   	=			OrderedDict()
  for key, value in state_dict.items():
    if "backbone.0.body" in key:
      __lowerCamelCase	:	int   	=			key.replace('backbone.0.body'	,'backbone.conv_encoder.model'	)
      __lowerCamelCase	:	List[str]   	=			value
    else:
      __lowerCamelCase	:	Dict   	=			value

  return new_state_dict

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

def 	a_					(							)				->				Dict:
  __lowerCamelCase	:	str   	=			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
  __lowerCamelCase	:	Any   	=			Image.open(requests.get(_lowerCAmelCase	,stream=_lowerCAmelCase	).raw	)

  return im

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

  # load image processor
  __lowerCamelCase	:	Optional[int]   	=			"""coco_panoptic""" if is_panoptic else """coco_detection"""
  __lowerCamelCase	:	Dict   	=			DetrImageProcessor(format=_lowerCAmelCase	)

  # prepare image
  __lowerCamelCase	:	Any   	=			prepare_img()
  __lowerCamelCase	:	int   	=			image_processor(images=_lowerCAmelCase	,return_tensors='pt'	)
  __lowerCamelCase	:	List[Any]   	=			encoding["""pixel_values"""]

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

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


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











'''simple docstring'''





import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()

def 	a_					(							_lowerCAmelCase	,_lowerCAmelCase	)				->				List[Any]:
  # Initialize PyTorch model
  __lowerCamelCase	:	Union[str, Any]   	=			CanineConfig()
  __lowerCamelCase	:	Optional[Any]   	=			CanineModel(_lowerCAmelCase	)
  model.eval()

  print(F'Building PyTorch model from configuration: {config}'	)

  # Load weights from tf checkpoint
  load_tf_weights_in_canine(_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	)

  # Save pytorch-model (weights and configuration)
  print(F'Save PyTorch model to {pytorch_dump_path}'	)
  model.save_pretrained(_lowerCAmelCase	)

  # Save tokenizer files
  __lowerCamelCase	:	str   	=			CanineTokenizer()
  print(F'Save tokenizer files to {pytorch_dump_path}'	)
  tokenizer.save_pretrained(_lowerCAmelCase	)


if __name__ == "__main__":
   _UpperCamelCase							    =						argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--tf_checkpoint_path',
       default=None,
       type=str,
       required=True,
       help='Path to the TensorFlow checkpoint. Should end with model.ckpt',
   )
   parser.add_argument(
       '--pytorch_dump_path',
       default=None,
       type=str,
       required=True,
       help='Path to a folder where the PyTorch model will be placed.',
   )
   _UpperCamelCase							    =						parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)






