





'''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 BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCamelCase			       =      logging.get_logger(__name__)




def    __UpperCAmelCase(      lowercase_ ,    lowercase_=False ,    lowercase_=False      ):
       _lowerCamelCase							:     str	=		'''backbone.''' if is_semantic else ''''''

       _lowerCamelCase							:     Optional[Any]	=		[]
       for i in range(config.num_hidden_layers      ):
              # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
              rename_keys.append((F"""{prefix}blocks.{i}.norm1.weight""", F"""beit.encoder.layer.{i}.layernorm_before.weight""")      )
              rename_keys.append((F"""{prefix}blocks.{i}.norm1.bias""", F"""beit.encoder.layer.{i}.layernorm_before.bias""")      )
              rename_keys.append(
                  (F"""{prefix}blocks.{i}.attn.proj.weight""", F"""beit.encoder.layer.{i}.attention.output.dense.weight""")      )
              rename_keys.append(
                  (F"""{prefix}blocks.{i}.attn.proj.bias""", F"""beit.encoder.layer.{i}.attention.output.dense.bias""")      )
              rename_keys.append((F"""{prefix}blocks.{i}.norm2.weight""", F"""beit.encoder.layer.{i}.layernorm_after.weight""")      )
              rename_keys.append((F"""{prefix}blocks.{i}.norm2.bias""", F"""beit.encoder.layer.{i}.layernorm_after.bias""")      )
              rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc1.weight""", F"""beit.encoder.layer.{i}.intermediate.dense.weight""")      )
              rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc1.bias""", F"""beit.encoder.layer.{i}.intermediate.dense.bias""")      )
              rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc2.weight""", F"""beit.encoder.layer.{i}.output.dense.weight""")      )
              rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc2.bias""", F"""beit.encoder.layer.{i}.output.dense.bias""")      )

       # projection layer + position embeddings
       rename_keys.extend(
           [
               (F"""{prefix}cls_token""", '''beit.embeddings.cls_token'''),
               (F"""{prefix}patch_embed.proj.weight""", '''beit.embeddings.patch_embeddings.projection.weight'''),
               (F"""{prefix}patch_embed.proj.bias""", '''beit.embeddings.patch_embeddings.projection.bias'''),
               (F"""{prefix}pos_embed""", '''beit.embeddings.position_embeddings'''),
           ]      )

       if has_lm_head:
              # mask token + layernorm
              rename_keys.extend(
                  [
                      ('''mask_token''', '''beit.embeddings.mask_token'''),
                      ('''norm.weight''', '''layernorm.weight'''),
                      ('''norm.bias''', '''layernorm.bias'''),
                  ]      )
       else:
              # layernorm + classification head
              rename_keys.extend(
                  [
                      ('''fc_norm.weight''', '''beit.pooler.layernorm.weight'''),
                      ('''fc_norm.bias''', '''beit.pooler.layernorm.bias'''),
                      ('''head.weight''', '''classifier.weight'''),
                      ('''head.bias''', '''classifier.bias'''),
                  ]      )

       return rename_keys




def    __UpperCAmelCase(      lowercase_ ,    lowercase_ ,    lowercase_=False ,    lowercase_=False      ):







       for i in range(config.num_hidden_layers      ):
              _lowerCamelCase							:     Optional[Any]	=		'''backbone.''' if is_semantic else ''''''
              # queries, keys and values
              _lowerCamelCase							:     str	=		state_dict.pop(F"""{prefix}blocks.{i}.attn.qkv.weight"""      )
              _lowerCamelCase							:     Dict	=		state_dict.pop(F"""{prefix}blocks.{i}.attn.q_bias"""      )
              _lowerCamelCase							:     Dict	=		state_dict.pop(F"""{prefix}blocks.{i}.attn.v_bias"""      )

              _lowerCamelCase							:     Dict	=		in_proj_weight[
                  : config.hidden_size, :
              ]
              _lowerCamelCase							:     int	=		q_bias
              _lowerCamelCase							:     Optional[int]	=		in_proj_weight[
                  config.hidden_size : config.hidden_size * 2, :
              ]
              _lowerCamelCase							:     Optional[Any]	=		in_proj_weight[
                  -config.hidden_size :, :
              ]
              _lowerCamelCase							:     Tuple	=		v_bias

              # gamma_1 and gamma_2
              # we call them lambda because otherwise they are renamed when using .from_pretrained
              _lowerCamelCase							:     str	=		state_dict.pop(F"""{prefix}blocks.{i}.gamma_1"""      )
              _lowerCamelCase							:     Any	=		state_dict.pop(F"""{prefix}blocks.{i}.gamma_2"""      )

              _lowerCamelCase							:     Dict	=		gamma_a
              _lowerCamelCase							:     str	=		gamma_a




def    __UpperCAmelCase(      lowercase_ ,    lowercase_ ,    lowercase_      ):
       _lowerCamelCase							:     int	=		dct.pop(__UpperCamelCase      )
       _lowerCamelCase							:     Any	=		val




def    __UpperCAmelCase(      ):
       _lowerCamelCase							:     Dict	=		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
       _lowerCamelCase							:     int	=		Image.open(requests.get(__UpperCamelCase ,    stream=__UpperCamelCase      ).raw      )
       return im




@torch.no_grad()
def    __UpperCAmelCase(      lowercase_ ,    lowercase_ ,    lowercase_=False      ):
       _lowerCamelCase							:     List[str]	=		False if '''rvlcdip''' in checkpoint_url else True
       _lowerCamelCase							:     int	=		BeitConfig(use_absolute_position_embeddings=__UpperCamelCase ,    use_mask_token=__UpperCamelCase      )

       # size of the architecture
       if "large" in checkpoint_url or "dit-l" in checkpoint_url:
              _lowerCamelCase							:     Union[str, Any]	=		10_24
              _lowerCamelCase							:     List[Any]	=		40_96
              _lowerCamelCase							:     Any	=		24
              _lowerCamelCase							:     Union[str, Any]	=		16

       # labels
       if "rvlcdip" in checkpoint_url:
              _lowerCamelCase							:     Tuple	=		16
              _lowerCamelCase							:     Union[str, Any]	=		'''huggingface/label-files'''
              _lowerCamelCase							:     int	=		'''rvlcdip-id2label.json'''
              _lowerCamelCase							:     Dict	=		json.load(open(hf_hub_download(__UpperCamelCase ,    __UpperCamelCase ,    repo_type='''dataset'''      ) ,    '''r'''      )      )
              _lowerCamelCase							:     Optional[Any]	=		{int(__UpperCamelCase      ): v for k, v in idalabel.items()}
              _lowerCamelCase							:     int	=		idalabel
              _lowerCamelCase							:     Tuple	=		{v: k for k, v in idalabel.items()}

       # load state_dict of original model, remove and rename some keys
       _lowerCamelCase							:     int	=		torch.hub.load_state_dict_from_url(__UpperCamelCase ,    map_location='''cpu'''      )['''model''']

       _lowerCamelCase							:     List[str]	=		create_rename_keys(__UpperCamelCase ,    has_lm_head=__UpperCamelCase      )
       for src, dest in rename_keys:
              rename_key(__UpperCamelCase ,    __UpperCamelCase ,    __UpperCamelCase      )
       read_in_q_k_v(__UpperCamelCase ,    __UpperCamelCase ,    has_lm_head=__UpperCamelCase      )

       # load HuggingFace model
       _lowerCamelCase							:     Optional[Any]	=		BeitForMaskedImageModeling(__UpperCamelCase      ) if has_lm_head else BeitForImageClassification(__UpperCamelCase      )
       model.eval()
       model.load_state_dict(__UpperCamelCase      )

       # Check outputs on an image
       _lowerCamelCase							:     Optional[int]	=		BeitImageProcessor(
           size=config.image_size ,    resample=PILImageResampling.BILINEAR ,    do_center_crop=__UpperCamelCase      )
       _lowerCamelCase							:     Optional[Any]	=		prepare_img()

       _lowerCamelCase							:     Any	=		image_processor(images=__UpperCamelCase ,    return_tensors='''pt'''      )
       _lowerCamelCase							:     List[str]	=		encoding['''pixel_values''']

       _lowerCamelCase							:     Union[str, Any]	=		model(__UpperCamelCase      )
       _lowerCamelCase							:     Dict	=		outputs.logits

       # verify logits
       _lowerCamelCase							:     str	=		[1, 16] if '''rvlcdip''' in checkpoint_url else [1, 1_96, 81_92]
       assert logits.shape == torch.Size(__UpperCamelCase      ), "Shape of logits not as expected"

       Path(__UpperCamelCase      ).mkdir(exist_ok=__UpperCamelCase      )
       print(F"""Saving model to {pytorch_dump_folder_path}"""      )
       model.save_pretrained(__UpperCamelCase      )
       print(F"""Saving image processor to {pytorch_dump_folder_path}"""      )
       image_processor.save_pretrained(__UpperCamelCase      )

       if push_to_hub:
              if has_lm_head:
                     _lowerCamelCase							:     int	=		'''dit-base''' if '''base''' in checkpoint_url else '''dit-large'''
              else:
                     _lowerCamelCase							:     Dict	=		'''dit-base-finetuned-rvlcdip''' if '''dit-b''' in checkpoint_url else '''dit-large-finetuned-rvlcdip'''
              image_processor.push_to_hub(
                  repo_path_or_name=Path(__UpperCamelCase ,    __UpperCamelCase      ) ,    organization='''nielsr''' ,    commit_message='''Add image processor''' ,    use_temp_dir=__UpperCamelCase ,    )
              model.push_to_hub(
                  repo_path_or_name=Path(__UpperCamelCase ,    __UpperCamelCase      ) ,    organization='''nielsr''' ,    commit_message='''Add model''' ,    use_temp_dir=__UpperCamelCase ,    )


if __name__ == "__main__":
       _lowerCamelCase			       =      argparse.ArgumentParser()

       parser.add_argument(
           '--checkpoint_url',
           default='https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth',
           type=str,
           help='URL to the original PyTorch checkpoint (.pth file).',
       )
       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',
       )
       _lowerCamelCase			       =      parser.parse_args()
       convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)








def    __UpperCAmelCase(      lowercase_      ):
       # if the collection is empty, returns empty
       if collection == []:
              return []

       # get some information about the collection
       _lowerCamelCase							:     Tuple	=		len(lowercase_      )
       _lowerCamelCase							:     List[str]	=		max(lowercase_      )
       _lowerCamelCase							:     Any	=		min(lowercase_      )

       # create the counting array
       _lowerCamelCase							:     str	=		coll_max + 1 - coll_min
       _lowerCamelCase							:     Union[str, Any]	=		[0] * counting_arr_length

       # count how much a number appears in the collection
       for number in collection:
              counting_arr[number - coll_min] += 1

       # sum each position with it's predecessors. now, counting_arr[i] tells
       # us how many elements <= i has in the collection
       for i in range(1 ,    lowercase_      ):
              _lowerCamelCase							:     Union[str, Any]	=		counting_arr[i] + counting_arr[i - 1]

       # create the output collection
       _lowerCamelCase							:     Dict	=		[0] * coll_len

       # place the elements in the output, respecting the original order (stable
       # sort) from end to begin, updating counting_arr
       for i in reversed(range(0 ,    lowercase_      )      ):
              _lowerCamelCase							:     Union[str, Any]	=		collection[i]
              counting_arr[collection[i] - coll_min] -= 1

       return ordered




def    __UpperCAmelCase(      lowercase_      ):
       return "".join([chr(lowercase_      ) for i in counting_sort([ord(lowercase_      ) for c in string]      )]      )


if __name__ == "__main__":
       # Test string sort
       assert counting_sort_string('thisisthestring') == "eghhiiinrsssttt"

       _lowerCamelCase			       =      input('Enter numbers separated by a comma:\n').strip()
       _lowerCamelCase			       =      [int(item) for item in user_input.split(',')]
       print(counting_sort(unsorted))

