"""simple docstring"""



import argparse
import json
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, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
__lowercase		:		Any								=				logging.get_logger(__name__)





def  SCREAMING_SNAKE_CASE							(  snake_case):




     if "resnet-50" in model_name:
          __snake_case				=				ResNetConfig.from_pretrained('''microsoft/resnet-50''')
     elif "resnet-101" in model_name:
          __snake_case				=				ResNetConfig.from_pretrained('''microsoft/resnet-101''')
     else:
          raise ValueError('''Model name should include either resnet50 or resnet101''')

     __snake_case				=				DetrConfig(use_timm_backbone=_UpperCamelCase,   backbone_config=_UpperCamelCase)

     # set label attributes
     __snake_case				=				"panoptic" in model_name
     if is_panoptic:
          __snake_case				=				2_50
     else:
          __snake_case				=				91
          __snake_case				=				"huggingface/label-files"
          __snake_case				=				"coco-detection-id2label.json"
          __snake_case				=				json.load(open(hf_hub_download(_UpperCamelCase,   _UpperCamelCase,   repo_type='''dataset'''),   '''r'''))
          __snake_case				=				{int(_UpperCamelCase): v for k, v in idalabel.items()}
          __snake_case				=				idalabel
          __snake_case				=				{v: k for k, v in idalabel.items()}

     return config, is_panoptic





def  SCREAMING_SNAKE_CASE							(  snake_case):
     __snake_case				=				[]

     # stem
     # fmt: off
     rename_keys.append(('''backbone.0.body.conv1.weight''', '''backbone.conv_encoder.model.embedder.embedder.convolution.weight'''))
     rename_keys.append(('''backbone.0.body.bn1.weight''', '''backbone.conv_encoder.model.embedder.embedder.normalization.weight'''))
     rename_keys.append(('''backbone.0.body.bn1.bias''', '''backbone.conv_encoder.model.embedder.embedder.normalization.bias'''))
     rename_keys.append(('''backbone.0.body.bn1.running_mean''', '''backbone.conv_encoder.model.embedder.embedder.normalization.running_mean'''))
     rename_keys.append(('''backbone.0.body.bn1.running_var''', '''backbone.conv_encoder.model.embedder.embedder.normalization.running_var'''))
     # stages
     for stage_idx in range(len(config.backbone_config.depths)):
          for layer_idx in range(config.backbone_config.depths[stage_idx]):
               # shortcut
               if layer_idx == 0:
                    rename_keys.append(
                        (
                            f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight",
                            f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight",
                        ))
                    rename_keys.append(
                        (
                            f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight",
                            f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight",
                        ))
                    rename_keys.append(
                        (
                            f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias",
                            f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias",
                        ))
                    rename_keys.append(
                        (
                            f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean",
                            f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean",
                        ))
                    rename_keys.append(
                        (
                            f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var",
                            f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var",
                        ))
               # 3 convs
               for i in range(3):
                    rename_keys.append(
                        (
                            f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight",
                            f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight",
                        ))
                    rename_keys.append(
                        (
                            f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight",
                            f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight",
                        ))
                    rename_keys.append(
                        (
                            f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias",
                            f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias",
                        ))
                    rename_keys.append(
                        (
                            f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean",
                            f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean",
                        ))
                    rename_keys.append(
                        (
                            f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var",
                            f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var",
                        ))
    # fmt: on

     for i in range(config.encoder_layers):
          # 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'''),
         ])

     return rename_keys





def  SCREAMING_SNAKE_CASE							(  snake_case,   snake_case,   snake_case):
     __snake_case				=				state_dict.pop(_UpperCamelCase)
     __snake_case				=				val





def  SCREAMING_SNAKE_CASE							(  snake_case,   snake_case=False):
     __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_56, :]
          __snake_case				=				in_proj_bias[:2_56]
          __snake_case				=				in_proj_weight[2_56:5_12, :]
          __snake_case				=				in_proj_bias[2_56:5_12]
          __snake_case				=				in_proj_weight[-2_56:, :]
          __snake_case				=				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
          __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_56, :]
          __snake_case				=				in_proj_bias[:2_56]
          __snake_case				=				in_proj_weight[2_56:5_12, :]
          __snake_case				=				in_proj_bias[2_56:5_12]
          __snake_case				=				in_proj_weight[-2_56:, :]
          __snake_case				=				in_proj_bias[-2_56:]
          # 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_56, :]
          __snake_case				=				in_proj_bias_cross_attn[:2_56]
          __snake_case				=				in_proj_weight_cross_attn[2_56:5_12, :]
          __snake_case				=				in_proj_bias_cross_attn[2_56:5_12]
          __snake_case				=				in_proj_weight_cross_attn[-2_56:, :]
          __snake_case				=				in_proj_bias_cross_attn[-2_56:]





def  SCREAMING_SNAKE_CASE							(  ):
     __snake_case				=				"http://images.cocodataset.org/val2017/000000039769.jpg"
     __snake_case				=				Image.open(requests.get(_UpperCamelCase,   stream=_UpperCamelCase).raw)

     return im





@torch.no_grad()
def  SCREAMING_SNAKE_CASE							(  snake_case,   snake_case=None,   snake_case=False):
     __snake_case				=				get_detr_config(_UpperCamelCase)

     # load original model from torch hub
     __snake_case				=				{
         "detr-resnet-50": "detr_resnet50",
         "detr-resnet-101": "detr_resnet101",
     }
     logger.info(f"Converting model {model_name}...")
     __snake_case				=				torch.hub.load('''facebookresearch/detr''',   model_name_to_original_name[model_name],   pretrained=_UpperCamelCase).eval()
     __snake_case				=				detr.state_dict()
     # rename keys
     for src, dest in create_rename_keys(_UpperCamelCase):
          if is_panoptic:
               __snake_case				=				"detr." + src
          rename_key(_UpperCamelCase,   _UpperCamelCase,   _UpperCamelCase)
     # query, key and value matrices need special treatment
     read_in_q_k_v(_UpperCamelCase,   is_panoptic=_UpperCamelCase)
     # 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(_UpperCamelCase)
                    __snake_case				=				val
               elif "class_labels_classifier" in key or "bbox_predictor" in key:
                    __snake_case				=				state_dict.pop(_UpperCamelCase)
                    __snake_case				=				val
               elif key.startswith('''bbox_attention''') or key.startswith('''mask_head'''):
                    continue
               else:
                    __snake_case				=				state_dict.pop(_UpperCamelCase)
                    __snake_case				=				val
          else:
               if not key.startswith('''class_labels_classifier''') and not key.startswith('''bbox_predictor'''):
                    __snake_case				=				state_dict.pop(_UpperCamelCase)
                    __snake_case				=				val

    # finally, create HuggingFace model and load state dict
     __snake_case				=				DetrForSegmentation(_UpperCamelCase) if is_panoptic else DetrForObjectDetection(_UpperCamelCase)
     model.load_state_dict(_UpperCamelCase)
     model.eval()

     # verify our conversion on an image
     __snake_case				=				"coco_panoptic" if is_panoptic else "coco_detection"
     __snake_case				=				DetrImageProcessor(format=_UpperCamelCase)

     __snake_case				=				processor(images=prepare_img(),   return_tensors='''pt''')
     __snake_case				=				encoding["pixel_values"]

     __snake_case				=				detr(_UpperCamelCase)
     __snake_case				=				model(_UpperCamelCase)

     assert torch.allclose(outputs.logits,   original_outputs['''pred_logits'''],   atol=1E-3)
     assert torch.allclose(outputs.pred_boxes,   original_outputs['''pred_boxes'''],   atol=1E-3)
     if is_panoptic:
          assert torch.allclose(outputs.pred_masks,   original_outputs['''pred_masks'''],   atol=1E-4)
     print('''Looks ok!''')

     if pytorch_dump_folder_path is not None:
          # Save model and image processor
          logger.info(f"Saving PyTorch model and image processor to {pytorch_dump_folder_path}...")
          Path(_UpperCamelCase).mkdir(exist_ok=_UpperCamelCase)
          model.save_pretrained(_UpperCamelCase)
          processor.save_pretrained(_UpperCamelCase)

     if push_to_hub:
          # Upload model and image processor to the hub
          logger.info('''Uploading PyTorch model and image processor to the hub...''')
          model.push_to_hub(f"nielsr/{model_name}")
          processor.push_to_hub(f"nielsr/{model_name}")


if __name__ == "__main__":
       __lowercase		:		Optional[int]								=				argparse.ArgumentParser()

       parser.add_argument(
           "--model_name",
           default="detr-resnet-50",
           type=str,
           choices=["detr-resnet-50", "detr-resnet-101"],
           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."
       )
       parser.add_argument("--push_to_hub", action="store_true", help="Whether to push the model to the hub or not.")
       __lowercase		:		Optional[Any]								=				parser.parse_args()
       convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


UpperCamelCase__			=      {
    'configuration_layoutlmv3': [
        'LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'LayoutLMv3Config',
        'LayoutLMv3OnnxConfig',
    ],
    'processing_layoutlmv3': ['LayoutLMv3Processor'],
    'tokenization_layoutlmv3': ['LayoutLMv3Tokenizer'],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCamelCase__			=      ['LayoutLMv3TokenizerFast']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCamelCase__			=      [
     'LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
     'LayoutLMv3ForQuestionAnswering',
     'LayoutLMv3ForSequenceClassification',
     'LayoutLMv3ForTokenClassification',
     'LayoutLMv3Model',
     'LayoutLMv3PreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCamelCase__			=      [
     'TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFLayoutLMv3ForQuestionAnswering',
     'TFLayoutLMv3ForSequenceClassification',
     'TFLayoutLMv3ForTokenClassification',
     'TFLayoutLMv3Model',
     'TFLayoutLMv3PreTrainedModel',
 ]

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCamelCase__			=      ['LayoutLMv3FeatureExtractor']
 UpperCamelCase__			=      ['LayoutLMv3ImageProcessor']


if TYPE_CHECKING:
 from .configuration_layoutlmva import (
     LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
     LayoutLMvaConfig,
     LayoutLMvaOnnxConfig,
 )
 from .processing_layoutlmva import LayoutLMvaProcessor
 from .tokenization_layoutlmva import LayoutLMvaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_layoutlmva import (
      LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
      LayoutLMvaForQuestionAnswering,
      LayoutLMvaForSequenceClassification,
      LayoutLMvaForTokenClassification,
      LayoutLMvaModel,
      LayoutLMvaPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_layoutlmva import (
      TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFLayoutLMvaForQuestionAnswering,
      TFLayoutLMvaForSequenceClassification,
      TFLayoutLMvaForTokenClassification,
      TFLayoutLMvaModel,
      TFLayoutLMvaPreTrainedModel,
  )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
  from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
 import sys

 UpperCamelCase__			=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




