


from collections.abc import Sequence
from queue import Queue



class        a					:





     def __init__(    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]=None    ,	SCREAMING_SNAKE_CASE_					:				List[Any]=None							):
        __lowerCamelCase:				List[str]     =	start
        __lowerCamelCase:				Optional[int]     =	end
        __lowerCamelCase:				Optional[Any]     =	val
        __lowerCamelCase:				Tuple     =	(start + end) // 2
        __lowerCamelCase:				int     =	left
        __lowerCamelCase:				Dict     =	right







     def __repr__(    self					:				Optional[Any]							):
        return F'SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})'



class        a					:





     def __init__(    self					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				Sequence    ,	SCREAMING_SNAKE_CASE_					:				Dict							):
        __lowerCamelCase:				Dict     =	collection
        __lowerCamelCase:				Union[str, Any]     =	function
        if self.collection:
           __lowerCamelCase:				Union[str, Any]     =	self._build_tree(0    ,	len(SCREAMING_SNAKE_CASE_							) - 1							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Any							):
        self._update_tree(self.root    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]							):
        return self._query_range(self.root    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				Dict							):
        if start == end:
           return SegmentTreeNode(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	self.collection[start]							)
        __lowerCamelCase:				Any     =	(start + end) // 2
        __lowerCamelCase:				List[Any]     =	self._build_tree(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				List[str]     =	self._build_tree(mid + 1    ,	SCREAMING_SNAKE_CASE_							)
        return SegmentTreeNode(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	self.fn(left.val    ,	right.val							)    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				str    ,	SCREAMING_SNAKE_CASE_					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]							):
        if node.start == i and node.end == i:
           __lowerCamelCase:				List[str]     =	val
           return
        if i <= node.mid:
           self._update_tree(node.left    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)
        else:
           self._update_tree(node.right    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Optional[int]     =	self.fn(node.left.val    ,	node.right.val							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]							):
        if node.start == i and node.end == j:
           return node.val

        if i <= node.mid:
           if j <= node.mid:
              # range in left child tree
              return self._query_range(node.left    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)
           else:
              # range in left child tree and right child tree
              return self.fn(
                  self._query_range(node.left    ,	SCREAMING_SNAKE_CASE_    ,	node.mid							)    ,	self._query_range(node.right    ,	node.mid + 1    ,	SCREAMING_SNAKE_CASE_							)    ,	)
        else:
           # range in right child tree
           return self._query_range(node.right    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)







     def  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        if self.root is not None:
           __lowerCamelCase:				Dict     =	Queue()
           queue.put(self.root							)
           while not queue.empty():
              __lowerCamelCase:				Optional[Any]     =	queue.get()
              yield node

              if node.left is not None:
                 queue.put(node.left							)

              if node.right is not None:
                 queue.put(node.right							)


if __name__ == "__main__":
   import operator

   for fn in [operator.add, max, min]:
      print('''*''' * 50)
      _A		:				int									=       SegmentTree([2, 1, 5, 3, 4], fn)
      for node in arr.traverse():
         print(node)
      print()

      arr.update(1, 5)
      for node in arr.traverse():
         print(node)
      print()

      print(arr.query_range(3, 4))  # 7
      print(arr.query_range(2, 2))  # 5
      print(arr.query_range(1, 3))  # 13
      print()






import argparse
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import (
    RobertaTokenizer,
    TrOCRConfig,
    TrOCRForCausalLM,
    TrOCRProcessor,
    VisionEncoderDecoderModel,
    ViTConfig,
    ViTImageProcessor,
    ViTModel,
)
from transformers.utils import logging


logging.set_verbosity_info()
_A		:				List[Any]									=       logging.get_logger(__name__)



def     __lowerCAmelCase						(							snake_case	:    Any						,      snake_case	:    int )       ->							Dict:
   __lowerCamelCase:				Tuple     =	[]
   for i in range(encoder_config.num_hidden_layers ):
      # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
      rename_keys.append(
          (f'encoder.deit.blocks.{i}.norm1.weight', f'encoder.encoder.layer.{i}.layernorm_before.weight') )
      rename_keys.append((f'encoder.deit.blocks.{i}.norm1.bias', f'encoder.encoder.layer.{i}.layernorm_before.bias') )
      rename_keys.append(
          (f'encoder.deit.blocks.{i}.attn.proj.weight', f'encoder.encoder.layer.{i}.attention.output.dense.weight') )
      rename_keys.append(
          (f'encoder.deit.blocks.{i}.attn.proj.bias', f'encoder.encoder.layer.{i}.attention.output.dense.bias') )
      rename_keys.append(
          (f'encoder.deit.blocks.{i}.norm2.weight', f'encoder.encoder.layer.{i}.layernorm_after.weight') )
      rename_keys.append((f'encoder.deit.blocks.{i}.norm2.bias', f'encoder.encoder.layer.{i}.layernorm_after.bias') )
      rename_keys.append(
          (f'encoder.deit.blocks.{i}.mlp.fc1.weight', f'encoder.encoder.layer.{i}.intermediate.dense.weight') )
      rename_keys.append(
          (f'encoder.deit.blocks.{i}.mlp.fc1.bias', f'encoder.encoder.layer.{i}.intermediate.dense.bias') )
      rename_keys.append(
          (f'encoder.deit.blocks.{i}.mlp.fc2.weight', f'encoder.encoder.layer.{i}.output.dense.weight') )
      rename_keys.append((f'encoder.deit.blocks.{i}.mlp.fc2.bias', f'encoder.encoder.layer.{i}.output.dense.bias') )

   # cls token, position embeddings and patch embeddings of encoder
   rename_keys.extend(
       [
           ("""encoder.deit.cls_token""", """encoder.embeddings.cls_token"""),
           ("""encoder.deit.pos_embed""", """encoder.embeddings.position_embeddings"""),
           ("""encoder.deit.patch_embed.proj.weight""", """encoder.embeddings.patch_embeddings.projection.weight"""),
           ("""encoder.deit.patch_embed.proj.bias""", """encoder.embeddings.patch_embeddings.projection.bias"""),
           ("""encoder.deit.norm.weight""", """encoder.layernorm.weight"""),
           ("""encoder.deit.norm.bias""", """encoder.layernorm.bias"""),
       ] )

   return rename_keys



def     __lowerCAmelCase						(							snake_case	:    str						,      snake_case	:    Union[str, Any] )       ->							Tuple:
   for i in range(encoder_config.num_hidden_layers ):
      # queries, keys and values (only weights, no biases)
      __lowerCamelCase:				Union[str, Any]     =	state_dict.pop(f'encoder.deit.blocks.{i}.attn.qkv.weight' )

      __lowerCamelCase:				Any     =	in_proj_weight[
          : encoder_config.hidden_size, :
      ]
      __lowerCamelCase:				Dict     =	in_proj_weight[
          encoder_config.hidden_size : encoder_config.hidden_size * 2, :
      ]
      __lowerCamelCase:				Any     =	in_proj_weight[
          -encoder_config.hidden_size :, :
      ]



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



def     __lowerCAmelCase						(							snake_case	:    Dict )       ->							Any:
   if "handwritten" in checkpoint_url:
      __lowerCamelCase:				Tuple     =	"""https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg"""  # industry
      # url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-12.jpg" # have
      # url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-10.jpg" # let
      # url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg"  #
      # url = "https://fki.tic.heia-fr.ch/static/img/a01-122.jpg"
   elif "printed" in checkpoint_url or "stage1" in checkpoint_url:
      __lowerCamelCase:				Union[str, Any]     =	"""https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg"""
   __lowerCamelCase:				Union[str, Any]     =	Image.open(requests.get(snake_case						,      stream=snake_case ).raw ).convert("""RGB""" )
   return im



@torch.no_grad()
def     __lowerCAmelCase						(							snake_case	:    List[Any]						,      snake_case	:    Any )       ->							List[str]:
   __lowerCamelCase:				Union[str, Any]     =	ViTConfig(image_size=384						,      qkv_bias=snake_case )
   __lowerCamelCase:				Optional[int]     =	TrOCRConfig()

   # size of the architecture
   if "base" in checkpoint_url:
      __lowerCamelCase:				Optional[int]     =	768
   elif "large" in checkpoint_url:
      # use ViT-large encoder
      __lowerCamelCase:				Optional[int]     =	1024
      __lowerCamelCase:				int     =	4096
      __lowerCamelCase:				Tuple     =	24
      __lowerCamelCase:				Optional[Any]     =	16
      __lowerCamelCase:				str     =	1024
   else:
      raise ValueError("""Should either find 'base' or 'large' in checkpoint URL""" )

   # the large-printed + stage1 checkpoints uses sinusoidal position embeddings, no layernorm afterwards
   if "large-printed" in checkpoint_url or "stage1" in checkpoint_url:
      __lowerCamelCase:				Any     =	False
      __lowerCamelCase:				Optional[Any]     =	"""relu"""
      __lowerCamelCase:				List[Any]     =	1024
      __lowerCamelCase:				List[str]     =	True
      __lowerCamelCase:				int     =	False
      __lowerCamelCase:				int     =	False

   # load HuggingFace model
   __lowerCamelCase:				Any     =	ViTModel(snake_case						,      add_pooling_layer=snake_case )
   __lowerCamelCase:				Dict     =	TrOCRForCausalLM(snake_case )
   __lowerCamelCase:				Any     =	VisionEncoderDecoderModel(encoder=snake_case						,      decoder=snake_case )
   model.eval()

   # load state_dict of original model, rename some keys
   __lowerCamelCase:				Any     =	torch.hub.load_state_dict_from_url(snake_case						,      map_location="""cpu"""						,      check_hash=snake_case )["""model"""]

   __lowerCamelCase:				Optional[Any]     =	create_rename_keys(snake_case						,      snake_case )
   for src, dest in rename_keys:
      rename_key(snake_case						,      snake_case						,      snake_case )
   read_in_q_k_v(snake_case						,      snake_case )

   # remove parameters we don't need
   del state_dict["encoder.deit.head.weight"]
   del state_dict["encoder.deit.head.bias"]
   del state_dict["decoder.version"]

   # add prefix to decoder keys
   for key, val in state_dict.copy().items():
      __lowerCamelCase:				int     =	state_dict.pop(snake_case )
      if key.startswith("""decoder""" ) and "output_projection" not in key:
         __lowerCamelCase:				Any     =	val
      else:
         __lowerCamelCase:				List[str]     =	val

    # load state dict
   model.load_state_dict(snake_case )

   # Check outputs on an image
   __lowerCamelCase:				Any     =	ViTImageProcessor(size=encoder_config.image_size )
   __lowerCamelCase:				Tuple     =	RobertaTokenizer.from_pretrained("""roberta-large""" )
   __lowerCamelCase:				List[Any]     =	TrOCRProcessor(snake_case						,      snake_case )

   __lowerCamelCase:				Optional[int]     =	processor(images=prepare_img(snake_case )						,      return_tensors="""pt""" ).pixel_values

   # verify logits
   __lowerCamelCase:				Tuple     =	torch.tensor([[model.config.decoder.decoder_start_token_id]] )
   __lowerCamelCase:				Dict     =	model(pixel_values=snake_case						,      decoder_input_ids=snake_case )
   __lowerCamelCase:				Any     =	outputs.logits

   __lowerCamelCase:				Dict     =	torch.Size([1, 1, 50265] )
   if "trocr-base-handwritten" in checkpoint_url:
      __lowerCamelCase:				Tuple     =	torch.tensor(
          [-1.4_502, -4.6_683, -0.5_347, -2.9_291, 9.1_435, -3.0_571, 8.9_764, 1.7_560, 8.7_358, -1.5_311] )
   elif "trocr-large-handwritten" in checkpoint_url:
      __lowerCamelCase:				Optional[int]     =	torch.tensor(
          [-2.6_437, -1.3_129, -2.2_596, -5.3_455, 6.3_539, 1.7_604, 5.4_991, 1.4_702, 5.6_113, 2.0_170] )
   elif "trocr-base-printed" in checkpoint_url:
      __lowerCamelCase:				Union[str, Any]     =	torch.tensor(
          [-5.6_816, -5.8_388, 1.1_398, -6.9_034, 6.8_505, -2.4_393, 1.2_284, -1.0_232, -1.9_661, -3.9_210] )
   elif "trocr-large-printed" in checkpoint_url:
      __lowerCamelCase:				Union[str, Any]     =	torch.tensor(
          [-6.0_162, -7.0_959, 4.4_155, -5.1_063, 7.0_468, -3.1_631, 2.6_466, -0.3_081, -0.8_106, -1.7_535] )

   if "stage1" not in checkpoint_url:
      assert logits.shape == expected_shape, "Shape of logits not as expected"
      assert torch.allclose(logits[0, 0, :10]						,      snake_case						,      atol=1e-3 ), "First elements of logits not as expected"

   Path(snake_case ).mkdir(exist_ok=snake_case )
   print(f'Saving model to {pytorch_dump_folder_path}' )
   model.save_pretrained(snake_case )
   print(f'Saving processor to {pytorch_dump_folder_path}' )
   processor.save_pretrained(snake_case )


if __name__ == "__main__":
   _A		:				int									=       argparse.ArgumentParser()

   parser.add_argument(
       '''--checkpoint_url''',
       default='''https://layoutlm.blob.core.windows.net/trocr/model_zoo/fairseq/trocr-base-handwritten.pt''',
       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.'''
   )
   _A		:				Optional[int]									=       parser.parse_args()
   convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)


