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()
__UpperCamelCase		:       int									=     logging.get_logger(__name__)
def       A	(    _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    List[str]							=							[]
						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       A	(    _lowercase		,  _lowercase						):






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

												SCREAMING_SNAKE_CASE							:    Tuple							=							in_proj_weight[
												    : encoder_config.hidden_size, :
												]
												SCREAMING_SNAKE_CASE							:    int							=							in_proj_weight[
												    encoder_config.hidden_size : encoder_config.hidden_size * 2, :
												]
												SCREAMING_SNAKE_CASE							:    Dict							=							in_proj_weight[
												    -encoder_config.hidden_size :, :
												]
def       A	(    _lowercase		,  _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							dct.pop(__lowercase						)
						SCREAMING_SNAKE_CASE							:    List[str]							=							val
def       A	(    _lowercase						):






						if "handwritten" in checkpoint_url:
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							'''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:
												SCREAMING_SNAKE_CASE							:    Tuple							=							'''https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg'''
						SCREAMING_SNAKE_CASE							:    Tuple							=							Image.open(requests.get(__lowercase		,  stream=__lowercase						).raw						).convert('''RGB'''						)
						return im
@torch.no_grad()
def       A	(    _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    int							=							ViTConfig(image_size=384		,  qkv_bias=__lowercase						)
						SCREAMING_SNAKE_CASE							:    Dict							=							TrOCRConfig()

						# size of the architecture
						if "base" in checkpoint_url:
												SCREAMING_SNAKE_CASE							:    Tuple							=							768
						elif "large" in checkpoint_url:
												# use ViT-large encoder
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							1_024
												SCREAMING_SNAKE_CASE							:    int							=							4_096
												SCREAMING_SNAKE_CASE							:    Any							=							24
												SCREAMING_SNAKE_CASE							:    str							=							16
												SCREAMING_SNAKE_CASE							:    str							=							1_024
						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:
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							False
												SCREAMING_SNAKE_CASE							:    str							=							'''relu'''
												SCREAMING_SNAKE_CASE							:    List[str]							=							1_024
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							True
												SCREAMING_SNAKE_CASE							:    Tuple							=							False
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							False

						# load HuggingFace model
						SCREAMING_SNAKE_CASE							:    Any							=							ViTModel(__lowercase		,  add_pooling_layer=__lowercase						)
						SCREAMING_SNAKE_CASE							:    str							=							TrOCRForCausalLM(__lowercase						)
						SCREAMING_SNAKE_CASE							:    Any							=							VisionEncoderDecoderModel(encoder=__lowercase		,  decoder=__lowercase						)
						model.eval()

						# load state_dict of original model, rename some keys
						SCREAMING_SNAKE_CASE							:    Optional[Any]							=							torch.hub.load_state_dict_from_url(__lowercase		,  map_location='''cpu'''		,  check_hash=__lowercase						)['''model''']

						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							create_rename_keys(__lowercase		,  __lowercase						)
						for src, dest in rename_keys:
												rename_key(__lowercase		,  __lowercase		,  __lowercase						)
						read_in_q_k_v(__lowercase		,  __lowercase						)

						# 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():
												SCREAMING_SNAKE_CASE							:    int							=							state_dict.pop(__lowercase						)
												if key.startswith('''decoder'''						) and "output_projection" not in key:
																		SCREAMING_SNAKE_CASE							:    str							=							val
												else:
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							val

    # load state dict
						model.load_state_dict(__lowercase						)

						# Check outputs on an image
						SCREAMING_SNAKE_CASE							:    Dict							=							ViTImageProcessor(size=encoder_config.image_size						)
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							RobertaTokenizer.from_pretrained('''roberta-large'''						)
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							TrOCRProcessor(__lowercase		,  __lowercase						)

						SCREAMING_SNAKE_CASE							:    int							=							processor(images=prepare_img(__lowercase						)		,  return_tensors='''pt'''						).pixel_values

						# verify logits
						SCREAMING_SNAKE_CASE							:    List[str]							=							torch.tensor([[model.config.decoder.decoder_start_token_id]]						)
						SCREAMING_SNAKE_CASE							:    str							=							model(pixel_values=__lowercase		,  decoder_input_ids=__lowercase						)
						SCREAMING_SNAKE_CASE							:    List[str]							=							outputs.logits

						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							torch.Size([1, 1, 50_265]						)
						if "trocr-base-handwritten" in checkpoint_url:
												SCREAMING_SNAKE_CASE							:    List[Any]							=							torch.tensor(
												    [-1.4502, -4.6683, -0.5347, -2.9291, 9.1435, -3.0571, 8.9764, 1.7560, 8.7358, -1.5311]						)
						elif "trocr-large-handwritten" in checkpoint_url:
												SCREAMING_SNAKE_CASE							:    List[str]							=							torch.tensor(
												    [-2.6437, -1.3129, -2.2596, -5.3455, 6.3539, 1.7604, 5.4991, 1.4702, 5.6113, 2.0170]						)
						elif "trocr-base-printed" in checkpoint_url:
												SCREAMING_SNAKE_CASE							:    Any							=							torch.tensor(
												    [-5.6816, -5.8388, 1.1398, -6.9034, 6.8505, -2.4393, 1.2284, -1.0232, -1.9661, -3.9210]						)
						elif "trocr-large-printed" in checkpoint_url:
												SCREAMING_SNAKE_CASE							:    int							=							torch.tensor(
												    [-6.0162, -7.0959, 4.4155, -5.1063, 7.0468, -3.1631, 2.6466, -0.3081, -0.8106, -1.7535]						)

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

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


if __name__ == "__main__":
					__UpperCamelCase		:       Any									=     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.'
					)
					__UpperCamelCase		:       str									=     parser.parse_args()
					convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)












'''simple docstring'''
a__							:							Optional[Any] =256
# Modulus to hash a string
a__							:							Dict =1_000_003
def 				lowercase__   (   __lowercase						:  str				,  __lowercase						:  str     ) ->      bool:



       """simple docstring"""






       __UpperCamelCase       =  len(__lowercase     )
       __UpperCamelCase       =  len(__lowercase     )
       if p_len > t_len:
              return False

       __UpperCamelCase       =  0
       __UpperCamelCase       =  0
       __UpperCamelCase       =  1

       # Calculating the hash of pattern and substring of text
       for i in range(__lowercase     ):
              __UpperCamelCase       =  (ord(pattern[i]     ) + p_hash * alphabet_size) % modulus
              __UpperCamelCase       =  (ord(text[i]     ) + text_hash * alphabet_size) % modulus
              if i == p_len - 1:
                     continue
              __UpperCamelCase       =  (modulus_power * alphabet_size) % modulus

       for i in range(0				,  t_len - p_len + 1     ):
              if text_hash == p_hash and text[i : i + p_len] == pattern:
                     return True
              if i == t_len - p_len:
                     continue
              # Calculate the https://en.wikipedia.org/wiki/Rolling_hash
              __UpperCamelCase       =  (
                  (text_hash - ord(text[i]     ) * modulus_power) * alphabet_size
                  + ord(text[i + p_len]     )
              ) % modulus
       return False
def 				lowercase__   (   ) ->      None:



       """simple docstring"""






       __UpperCamelCase       =  'abc1abc12'
       __UpperCamelCase       =  'alskfjaldsabc1abc1abc12k23adsfabcabc'
       __UpperCamelCase       =  'alskfjaldsk23adsfabcabc'
       assert rabin_karp(__lowercase				,  __lowercase     ) and not rabin_karp(__lowercase				,  __lowercase     )

       # Test 2)
       __UpperCamelCase       =  'ABABX'
       __UpperCamelCase       =  'ABABZABABYABABX'
       assert rabin_karp(__lowercase				,  __lowercase     )

       # Test 3)
       __UpperCamelCase       =  'AAAB'
       __UpperCamelCase       =  'ABAAAAAB'
       assert rabin_karp(__lowercase				,  __lowercase     )

       # Test 4)
       __UpperCamelCase       =  'abcdabcy'
       __UpperCamelCase       =  'abcxabcdabxabcdabcdabcy'
       assert rabin_karp(__lowercase				,  __lowercase     )

       # Test 5)
       __UpperCamelCase       =  'Lü'
       __UpperCamelCase       =  'Lüsai'
       assert rabin_karp(__lowercase				,  __lowercase     )
       __UpperCamelCase       =  'Lue'
       assert not rabin_karp(__lowercase				,  __lowercase     )
       print('Success.'     )


if __name__ == "__main__":
    test_rabin_karp()
