


'''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()
UpperCAmelCase_ :     Dict								=      logging.get_logger(__name__)


def 			_lowercase       (				UpperCamelCase__       :					int			):
 # initialize config
 if "resnet-50" in model_name:
  __A  :		Any       =			ResNetConfig.from_pretrained('microsoft/resnet-50'			)
 elif "resnet-101" in model_name:
  __A  :		Tuple       =			ResNetConfig.from_pretrained('microsoft/resnet-101'			)
 else:
  raise ValueError('Model name should include either resnet50 or resnet101'			)

 __A  :		Dict       =			DetrConfig(use_timm_backbone=UpperCamelCase__,				backbone_config=UpperCamelCase__			)

 # set label attributes
 __A  :		int       =			'panoptic' in model_name
 if is_panoptic:
  __A  :		Union[str, Any]       =			250
 else:
  __A  :		int       =			91
  __A  :		str       =			'huggingface/label-files'
  __A  :		Optional[int]       =			'coco-detection-id2label.json'
  __A  :		Any       =			json.load(open(hf_hub_download(UpperCamelCase__,				UpperCamelCase__,				repo_type='dataset'			),				'r'			)			)
  __A  :		int       =			{int(UpperCamelCase__			): v for k, v in idalabel.items()}
  __A  :		int       =			idalabel
  __A  :		List[str]       =			{v: k for k, v in idalabel.items()}

 return config, is_panoptic


def 			_lowercase       (				UpperCamelCase__       :					Dict			):
 # here we list all keys to be renamed (original name on the left, our name on the right)
 __A  :		int       =			[]

 # 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 			_lowercase       (				UpperCamelCase__       :					Union[str, Any],				UpperCamelCase__       :					Dict,				UpperCamelCase__       :					Union[str, Any]			):
 __A  :		int       =			state_dict.pop(UpperCamelCase__			)
 __A  :		int       =			val


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


def 			_lowercase       (				):
 __A  :		int       =			'http://images.cocodataset.org/val2017/000000039769.jpg'
 __A  :		List[Any]       =			Image.open(requests.get(UpperCamelCase__,				stream=UpperCamelCase__			).raw			)

 return im


@torch.no_grad()
def 			_lowercase       (				UpperCamelCase__       :					str,				UpperCamelCase__       :					Optional[int]=None,				UpperCamelCase__       :					str=False			):
 __A     ,__A  :		List[Any]       =			get_detr_config(UpperCamelCase__			)

 # load original model from torch hub
 __A  :		List[Any]       =			{
     'detr-resnet-50': 'detr_resnet50',
     'detr-resnet-101': 'detr_resnet101',
 }
 logger.info(f"""Converting model {model_name}..."""			)
 __A  :		Optional[Any]       =			torch.hub.load('facebookresearch/detr',				model_name_to_original_name[model_name],				pretrained=UpperCamelCase__			).eval()
 __A  :		Optional[int]       =			detr.state_dict()
 # rename keys
 for src, dest in create_rename_keys(UpperCamelCase__			):
  if is_panoptic:
   __A  :		Optional[Any]       =			'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
 __A  :		Tuple       =			'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'			)
   ):
    __A  :		List[str]       =			state_dict.pop(UpperCamelCase__			)
    __A  :		int       =			val
   elif "class_labels_classifier" in key or "bbox_predictor" in key:
    __A  :		Any       =			state_dict.pop(UpperCamelCase__			)
    __A  :		int       =			val
   elif key.startswith('bbox_attention'			) or key.startswith('mask_head'			):
    continue
   else:
    __A  :		Optional[int]       =			state_dict.pop(UpperCamelCase__			)
    __A  :		List[str]       =			val
  else:
   if not key.startswith('class_labels_classifier'			) and not key.startswith('bbox_predictor'			):
    __A  :		Union[str, Any]       =			state_dict.pop(UpperCamelCase__			)
    __A  :		Optional[Any]       =			val

    # finally, create HuggingFace model and load state dict
 __A  :		Tuple       =			DetrForSegmentation(UpperCamelCase__			) if is_panoptic else DetrForObjectDetection(UpperCamelCase__			)
 model.load_state_dict(UpperCamelCase__			)
 model.eval()

 # verify our conversion on an image
 __A  :		int       =			'coco_panoptic' if is_panoptic else 'coco_detection'
 __A  :		int       =			DetrImageProcessor(format=UpperCamelCase__			)

 __A  :		Optional[int]       =			processor(images=prepare_img(),				return_tensors='pt'			)
 __A  :		Optional[Any]       =			encoding['pixel_values']

 __A  :		Any       =			detr(UpperCamelCase__			)
 __A  :		str       =			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__":
 UpperCAmelCase_ :     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.')
 UpperCAmelCase_ :     Dict								=      parser.parse_args()
 convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)










'''simple docstring'''
import unittest

from transformers import DonutProcessor


UpperCAmelCase_ :     str								=      'naver-clova-ix/donut-base'

class 						_lowerCamelCase					(    unittest.TestCase				):




 '''simple docstring'''


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		List[str]       =			DonutProcessor.from_pretrained(__lowercase						)







 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		List[str]       =			{
      'name': 'John Doe',
      'age': '99',
      'city': 'Atlanta',
      'state': 'GA',
      'zip': '30301',
      'phone': '123-4567',
      'nicknames': [{'nickname': 'Johnny'}, {'nickname': 'JD'}],
  }

  __A  :		Optional[Any]       =			(
      '<s_name>John Doe</s_name><s_age>99</s_age><s_city>Atlanta</s_city>'
      '<s_state>GA</s_state><s_zip>30301</s_zip><s_phone>123-4567</s_phone>'
      '<s_nicknames><s_nickname>Johnny</s_nickname>'
      '<sep/><s_nickname>JD</s_nickname></s_nicknames>'
  )
  __A  :		List[Any]       =			self.processor.tokenajson(__lowercase						)

  self.assertDictEqual(__lowercase      ,      __lowercase						)






