


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		:					str          =							[]
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      __lowerCAmelCase    (				snake_case						:     str    ,   snake_case						:     Optional[Any]    ,   snake_case						:     Dict			)							->				Optional[int]:
   __lowerCamelCase:					Tuple	 =						state_dict.pop(snake_case			)
   __lowerCamelCase:					int	 =						val



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

   return new_state_dict



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



def      __lowerCAmelCase    (				)							->				List[str]:
   __lowerCamelCase:					str	 =						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
   __lowerCamelCase:					Optional[int]	 =						Image.open(requests.get(snake_case    ,   stream=snake_case			).raw			)

   return im



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

   # load image processor
   __lowerCamelCase:					str	 =						"""coco_panoptic""" if is_panoptic else """coco_detection"""
   __lowerCamelCase:					int	 =						DetrImageProcessor(format=snake_case			)

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

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

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


if __name__ == "__main__":
   _A		:					List[Any]          =							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		:					Any          =							parser.parse_args()
   convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)






import argparse

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


logging.set_verbosity_info()



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

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

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

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

   # Save tokenizer files
   __lowerCamelCase:					Optional[int]	 =						CanineTokenizer()
   print(f'Save tokenizer files to {pytorch_dump_path}'			)
   tokenizer.save_pretrained(snake_case			)


if __name__ == "__main__":
   _A		:					int          =							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.''',
   )
   _A		:					int          =							parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)


