

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

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







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

   return new_state_dict







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







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

   return im







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

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

   # prepare image
   _lowercase    :   Tuple      =     prepare_img()
   _lowercase    :   List[str]      =     image_processor(images=snake_case	,							return_tensors="pt"  )
   _lowercase    :   List[str]      =     encoding["pixel_values"]

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

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









'''simple docstring'''
import warnings
from dataclasses import dataclass, field
from typing import Optional, Tuple

from .training_args import TrainingArguments
from .utils import cached_property, is_tf_available, logging, requires_backends


_snake_case     						=						logging.get_logger(__name__)

if is_tf_available():
    import tensorflow as tf


@dataclass
class      a__						(							lowerCamelCase_						):
   _SCREAMING_SNAKE_CASE					:  int			 =							'tf'
   _SCREAMING_SNAKE_CASE					:  Optional[str]			 =							field(
       default=lowerCamelCase_						,   metadata={'help': 'Name of TPU'}						,   )

   _SCREAMING_SNAKE_CASE					:  Optional[str]			 =							field(
       default=lowerCamelCase_						,   metadata={'help': 'Zone of TPU'}						,   )

   _SCREAMING_SNAKE_CASE					:  Optional[str]			 =							field(
       default=lowerCamelCase_						,   metadata={'help': 'Name of Cloud TPU-enabled project'}						,   )

   _SCREAMING_SNAKE_CASE					:  float			 =							field(
       default=1.0						,   metadata={'help': 'Power for the Polynomial decay LR scheduler.'}						,   )

   _SCREAMING_SNAKE_CASE					:  bool			 =							field(default=lowerCamelCase_						,   metadata={'help': 'Whether to activate the XLA compilation or not'}						)


   @cached_property
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      requires_backends(self    ,       ["tf"]						)
      logger.info("Tensorflow: setting up strategy"						)

      _lowercase    :   Optional[int]      =     tf.config.list_physical_devices("GPU"						)

      # Set to float16 at first
      if self.fpaa:
         tf.keras.mixed_precision.set_global_policy("mixed_float16"						)

      if self.no_cuda:
         _lowercase    :   Optional[int]      =     tf.distribute.OneDeviceStrategy(device="/cpu:0"						)
      else:
         try:
            if self.tpu_name:
               _lowercase    :   int      =     tf.distribute.cluster_resolver.TPUClusterResolver(
                   self.tpu_name    ,       zone=self.tpu_zone    ,       project=self.gcp_project						)
            else:
               _lowercase    :   Any      =     tf.distribute.cluster_resolver.TPUClusterResolver()
         except ValueError:
            if self.tpu_name:
               raise RuntimeError(f'''Couldn\'t connect to TPU {self.tpu_name}!'''						)
            else:
               _lowercase    :   Tuple      =     None

         if tpu:
            # Set to bfloat16 in case of TPU
            if self.fpaa:
               tf.keras.mixed_precision.set_global_policy("mixed_bfloat16"						)

            tf.config.experimental_connect_to_cluster(_UpperCamelCase						)
            tf.tpu.experimental.initialize_tpu_system(_UpperCamelCase						)

            _lowercase    :   List[str]      =     tf.distribute.TPUStrategy(_UpperCamelCase						)

         elif len(_UpperCamelCase						) == 0:
            _lowercase    :   Optional[Any]      =     tf.distribute.OneDeviceStrategy(device="/cpu:0"						)
         elif len(_UpperCamelCase						) == 1:
            _lowercase    :   Optional[int]      =     tf.distribute.OneDeviceStrategy(device="/gpu:0"						)
         elif len(_UpperCamelCase						) > 1:
            # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
            _lowercase    :   Any      =     tf.distribute.MirroredStrategy()
         else:
            raise ValueError("Cannot find the proper strategy, please check your environment properties."						)

      return strategy


   @property
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      requires_backends(self    ,       ["tf"]						)
      return self._setup_strategy


   @property
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      requires_backends(self    ,       ["tf"]						)
      return self._setup_strategy.num_replicas_in_sync


   @property
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      return False  # TF Logging is handled by Keras not the Trainer


   @property
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      if self.per_gpu_train_batch_size:
         logger.warning(
             "Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future "
             "version. Using `--per_device_train_batch_size` is preferred."						)
      _lowercase    :   str      =     self.per_gpu_train_batch_size or self.per_device_train_batch_size
      return per_device_batch_size * self.n_replicas


   @property
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      if self.per_gpu_eval_batch_size:
         logger.warning(
             "Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future "
             "version. Using `--per_device_eval_batch_size` is preferred."						)
      _lowercase    :   List[Any]      =     self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
      return per_device_batch_size * self.n_replicas






   @property
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      requires_backends(self    ,       ["tf"]						)
      warnings.warn(
          "The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead."    ,       _UpperCamelCase    ,       )
      return self._setup_strategy.num_replicas_in_sync






