




'''simple docstring'''


import random
import sys

import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap

_snake_case   :   Optional[int]     							=	'Usage of script: script_name <size_of_canvas:int>'

_snake_case   :   Any     							=	[0] * 100 + [1] * 10
random.shuffle(choice)
def 	snake_case_					(UpperCamelCase :						int   ):





							'''simple docstring'''


							_a			  =       [[False for i in range(UpperCamelCase   )] for j in range(UpperCamelCase   )]
							return canvas
def 	snake_case_					(UpperCamelCase :						list[list[bool]]   ):





							'''simple docstring'''


							for i, row in enumerate(UpperCamelCase   ):
														for j, _ in enumerate(UpperCamelCase   ):
																					_a			  =       bool(random.getrandbits(1   )   )
def 	snake_case_					(UpperCamelCase :						list[list[bool]]   ):





							'''simple docstring'''


							_a			  =       np.array(UpperCamelCase   )
							_a			  =       np.array(create_canvas(current_canvas.shape[0]   )   )
							for r, row in enumerate(UpperCamelCase   ):
														for c, pt in enumerate(UpperCamelCase   ):
																					_a			  =       __judge_point(
																					    UpperCamelCase				,		current_canvas[r - 1 : r + 2, c - 1 : c + 2]   )

							_a			  =       next_gen_canvas
							del next_gen_canvas  # cleaning memory as we move on.
							_a			  =       current_canvas.tolist()
							return return_canvas
def 	snake_case_					(UpperCamelCase :						bool				,		UpperCamelCase :						list[list[bool]]   ):





							'''simple docstring'''


							_a			  =       0
							_a			  =       0
							# finding dead or alive neighbours count.
							for i in neighbours:
														for status in i:
																					if status:
																												alive += 1
																					else:
																												dead += 1

    # handling duplicate entry for focus pt.
							if pt:
														alive -= 1
							else:
														dead -= 1

							# running the rules of game here.
							_a			  =       pt
							if pt:
														if alive < 2:
																					_a			  =       False
														elif alive == 2 or alive == 3:
																					_a			  =       True
														elif alive > 3:
																					_a			  =       False
							else:
														if alive == 3:
																					_a			  =       True

							return state


if __name__ == "__main__":
				if len(sys.argv) != 2:
								raise Exception(usage_doc)

				_snake_case   :   Tuple     							=	int(sys.argv[1])
				# main working structure of this module.
				_snake_case   :   int     							=	create_canvas(canvas_size)
				seed(c)
				_snake_case							,       _snake_case   :   str     							=	plt.subplots()
				fig.show()
				_snake_case   :   List[Any]     							=	ListedColormap(['w', 'k'])
				try:
								while True:
												_snake_case   :   Any     							=	run(c)
												ax.matshow(c, cmap=cmap)
												fig.canvas.draw()
												ax.cla()
				except KeyboardInterrupt:
								# do nothing.
								pass







'''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()
_snake_case   :   Any     							=	logging.get_logger(__name__)
def 	snake_case_					(UpperCamelCase :						List[str]				,		UpperCamelCase :						Any   ):





							'''simple docstring'''


							_a			  =       []
							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 	snake_case_					(UpperCamelCase :						Any				,		UpperCamelCase :						Tuple   ):





							'''simple docstring'''


							for i in range(encoder_config.num_hidden_layers   ):
														# queries, keys and values (only weights, no biases)
														_a			  =       state_dict.pop(f'encoder.deit.blocks.{i}.attn.qkv.weight'   )

														_a			  =       in_proj_weight[
														    : encoder_config.hidden_size, :
														]
														_a			  =       in_proj_weight[
														    encoder_config.hidden_size : encoder_config.hidden_size * 2, :
														]
														_a			  =       in_proj_weight[
														    -encoder_config.hidden_size :, :
														]
def 	snake_case_					(UpperCamelCase :						Optional[int]				,		UpperCamelCase :						Optional[Any]				,		UpperCamelCase :						Optional[int]   ):





							'''simple docstring'''


							_a			  =       dct.pop(UpperCamelCase   )
							_a			  =       val
def 	snake_case_					(UpperCamelCase :						Union[str, Any]   ):





							'''simple docstring'''


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





							'''simple docstring'''


							_a			  =       ViTConfig(image_size=384				,		qkv_bias=UpperCamelCase   )
							_a			  =       TrOCRConfig()

							# size of the architecture
							if "base" in checkpoint_url:
														_a			  =       768
							elif "large" in checkpoint_url:
														# use ViT-large encoder
														_a			  =       1024
														_a			  =       4096
														_a			  =       24
														_a			  =       16
														_a			  =       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:
														_a			  =       False
														_a			  =       '''relu'''
														_a			  =       1024
														_a			  =       True
														_a			  =       False
														_a			  =       False

							# load HuggingFace model
							_a			  =       ViTModel(UpperCamelCase				,		add_pooling_layer=UpperCamelCase   )
							_a			  =       TrOCRForCausalLM(UpperCamelCase   )
							_a			  =       VisionEncoderDecoderModel(encoder=UpperCamelCase				,		decoder=UpperCamelCase   )
							model.eval()

							# load state_dict of original model, rename some keys
							_a			  =       torch.hub.load_state_dict_from_url(UpperCamelCase				,		map_location='''cpu'''				,		check_hash=UpperCamelCase   )['''model''']

							_a			  =       create_rename_keys(UpperCamelCase				,		UpperCamelCase   )
							for src, dest in rename_keys:
														rename_key(UpperCamelCase				,		UpperCamelCase				,		UpperCamelCase   )
							read_in_q_k_v(UpperCamelCase				,		UpperCamelCase   )

							# 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():
														_a			  =       state_dict.pop(UpperCamelCase   )
														if key.startswith('''decoder'''   ) and "output_projection" not in key:
																					_a			  =       val
														else:
																					_a			  =       val

    # load state dict
							model.load_state_dict(UpperCamelCase   )

							# Check outputs on an image
							_a			  =       ViTImageProcessor(size=encoder_config.image_size   )
							_a			  =       RobertaTokenizer.from_pretrained('''roberta-large'''   )
							_a			  =       TrOCRProcessor(UpperCamelCase				,		UpperCamelCase   )

							_a			  =       processor(images=prepare_img(UpperCamelCase   )				,		return_tensors='''pt'''   ).pixel_values

							# verify logits
							_a			  =       torch.tensor([[model.config.decoder.decoder_start_token_id]]   )
							_a			  =       model(pixel_values=UpperCamelCase				,		decoder_input_ids=UpperCamelCase   )
							_a			  =       outputs.logits

							_a			  =       torch.Size([1, 1, 5_0265]   )
							if "trocr-base-handwritten" in checkpoint_url:
														_a			  =       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:
														_a			  =       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:
														_a			  =       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:
														_a			  =       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]				,		UpperCamelCase				,		atol=1e-3   ), "First elements 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 processor to {pytorch_dump_folder_path}'   )
							processor.save_pretrained(UpperCamelCase   )


if __name__ == "__main__":
				_snake_case   :   Tuple     							=	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.'
				)
				_snake_case   :   Dict     							=	parser.parse_args()
				convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)

