




"""simple docstring"""


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_						=				logging.get_logger(__name__)


def 					UpperCAmelCase__       (snake_case__							:					str		,   snake_case__							:					str    ):
  """simple docstring"""

  _snake_case							:		int								=  []
  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 					UpperCAmelCase__       (snake_case__							:					List[Any]		,   snake_case__							:					int    ):
  """simple docstring"""

  for i in range(encoder_config.num_hidden_layers    ):
    # queries, keys and values (only weights, no biases)
    _snake_case							:		Union[str, Any]								=  state_dict.pop(F"encoder.deit.blocks.{i}.attn.qkv.weight"    )

    _snake_case							:		Union[str, Any]								=  in_proj_weight[
        : encoder_config.hidden_size, :
    ]
    _snake_case							:		List[Any]								=  in_proj_weight[
        encoder_config.hidden_size : encoder_config.hidden_size * 2, :
    ]
    _snake_case							:		str								=  in_proj_weight[
        -encoder_config.hidden_size :, :
    ]


def 					UpperCAmelCase__       (snake_case__							:					List[str]		,   snake_case__							:					List[str]		,   snake_case__							:					Any    ):
  """simple docstring"""

  _snake_case							:		Tuple								=  dct.pop(__snake_case    )
  _snake_case							:		Union[str, Any]								=  val


def 					UpperCAmelCase__       (snake_case__							:					Dict    ):
  """simple docstring"""

  if "handwritten" in checkpoint_url:
    _snake_case							:		Any								=  """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:
    _snake_case							:		Dict								=  """https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg"""
  _snake_case							:		Any								=  Image.open(requests.get(__snake_case		,   stream=__snake_case    ).raw    ).convert("""RGB"""    )
  return im


@torch.no_grad()
def 					UpperCAmelCase__       (snake_case__							:					Optional[Any]		,   snake_case__							:					Dict    ):
  """simple docstring"""

  _snake_case							:		Optional[Any]								=  ViTConfig(image_size=3_84		,   qkv_bias=__snake_case    )
  _snake_case							:		Tuple								=  TrOCRConfig()

  # size of the architecture
  if "base" in checkpoint_url:
    _snake_case							:		Union[str, Any]								=  7_68
  elif "large" in checkpoint_url:
    # use ViT-large encoder
    _snake_case							:		Tuple								=  10_24
    _snake_case							:		int								=  40_96
    _snake_case							:		List[str]								=  24
    _snake_case							:		Union[str, Any]								=  16
    _snake_case							:		Any								=  10_24
  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:
    _snake_case							:		Optional[int]								=  False
    _snake_case							:		Optional[Any]								=  """relu"""
    _snake_case							:		str								=  10_24
    _snake_case							:		Any								=  True
    _snake_case							:		Tuple								=  False
    _snake_case							:		Optional[int]								=  False

  # load HuggingFace model
  _snake_case							:		Tuple								=  ViTModel(__snake_case		,   add_pooling_layer=__snake_case    )
  _snake_case							:		int								=  TrOCRForCausalLM(__snake_case    )
  _snake_case							:		Union[str, Any]								=  VisionEncoderDecoderModel(encoder=__snake_case		,   decoder=__snake_case    )
  model.eval()

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

  _snake_case							:		str								=  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():
    _snake_case							:		Union[str, Any]								=  state_dict.pop(__snake_case    )
    if key.startswith("""decoder"""    ) and "output_projection" not in key:
      _snake_case							:		str								=  val
    else:
      _snake_case							:		str								=  val

    # load state dict
  model.load_state_dict(__snake_case    )

  # Check outputs on an image
  _snake_case							:		List[str]								=  ViTImageProcessor(size=encoder_config.image_size    )
  _snake_case							:		Optional[Any]								=  RobertaTokenizer.from_pretrained("""roberta-large"""    )
  _snake_case							:		List[Any]								=  TrOCRProcessor(__snake_case		,   __snake_case    )

  _snake_case							:		List[str]								=  processor(images=prepare_img(__snake_case    )		,   return_tensors="""pt"""    ).pixel_values

  # verify logits
  _snake_case							:		List[str]								=  torch.tensor([[model.config.decoder.decoder_start_token_id]]    )
  _snake_case							:		Any								=  model(pixel_values=__snake_case		,   decoder_input_ids=__snake_case    )
  _snake_case							:		Dict								=  outputs.logits

  _snake_case							:		str								=  torch.Size([1, 1, 5_02_65]    )
  if "trocr-base-handwritten" in checkpoint_url:
    _snake_case							:		Optional[Any]								=  torch.tensor(
        [-1.45_02, -4.66_83, -0.53_47, -2.92_91, 9.14_35, -3.05_71, 8.97_64, 1.75_60, 8.73_58, -1.53_11]    )
  elif "trocr-large-handwritten" in checkpoint_url:
    _snake_case							:		str								=  torch.tensor(
        [-2.64_37, -1.31_29, -2.25_96, -5.34_55, 6.35_39, 1.76_04, 5.49_91, 1.47_02, 5.61_13, 2.01_70]    )
  elif "trocr-base-printed" in checkpoint_url:
    _snake_case							:		str								=  torch.tensor(
        [-5.68_16, -5.83_88, 1.13_98, -6.90_34, 6.85_05, -2.43_93, 1.22_84, -1.02_32, -1.96_61, -3.92_10]    )
  elif "trocr-large-printed" in checkpoint_url:
    _snake_case							:		Tuple								=  torch.tensor(
        [-6.01_62, -7.09_59, 4.41_55, -5.10_63, 7.04_68, -3.16_31, 2.64_66, -0.30_81, -0.81_06, -1.75_35]    )

  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_						=				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_						=				parser.parse_args()
     convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




"""simple docstring"""





class     _UpperCAmelCase:



		def __init__(					self)       ->       Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase							 =  {}



		def 						UpperCAmelCase							(					self)       ->       None:






							'''simple docstring'''
							print(self.vertex)
							for i in self.vertex:
												print(__a				,		''' -> '''				,		''' -> '''.join([str(__a) for j in self.vertex[i]]))



		def 						UpperCAmelCase							(					self				,		__a				,		__a)       ->       None:






							'''simple docstring'''
							# check if vertex is already present,
							if from_vertex in self.vertex:
												self.vertex[from_vertex].append(__a)
							else:
												# else make a new vertex
												_UpperCamelCase							 =  [to_vertex]



		def 						UpperCAmelCase							(					self)       ->       None:






							'''simple docstring'''
							# visited array for storing already visited nodes
							_UpperCamelCase							 =  [False] * len(self.vertex)

							# call the recursive helper function
							for i in range(len(self.vertex)):
												if not visited[i]:
																	self.dfs_recursive(__a				,		__a)




		def 						UpperCAmelCase							(					self				,		__a				,		__a)       ->       None:






							'''simple docstring'''
							# mark start vertex as visited
							_UpperCamelCase							 =  True

							print(__a				,		end=''' ''')

							# Recur for all the vertices that are adjacent to this node
							for i in self.vertex:
												if not visited[i]:
																	self.dfs_recursive(__a				,		__a)


if __name__ == "__main__":
				_a														=		Graph()
				g.add_edge(0, 1)
				g.add_edge(0, 2)
				g.add_edge(1, 2)
				g.add_edge(2, 0)
				g.add_edge(2, 3)
				g.add_edge(3, 3)

				g.print_graph()
				print("""DFS:""")
				g.dfs()

				# OUTPUT:
				# 0  ->  1 -> 2
				# 1  ->  2
				# 2  ->  0 -> 3
				# 3  ->  3
				# DFS:
				#  0 1 2 3






