


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							:	int					      =				logging.get_logger(__name__)






def 							__lowercase       (			__lowerCAmelCase							:    Optional[Any] ,   __lowerCAmelCase							:    str       ):
							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 							__lowercase       (			__lowerCAmelCase							:    Dict ,   __lowerCAmelCase							:    Dict       ):
							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 							__lowercase       (			__lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    Optional[Any]       ):
							a__					=					dct.pop(__lowerCAmelCase       )
							a__					=					val






def 							__lowercase       (			__lowerCAmelCase							:    Optional[Any]       ):
							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(__lowerCAmelCase ,   stream=__lowerCAmelCase       ).raw       ).convert('RGB'       )
							return im






@torch.no_grad()
def 							__lowercase       (			__lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    int       ):
							a__					=					ViTConfig(image_size=3_8_4 ,   qkv_bias=__lowerCAmelCase       )
							a__					=					TrOCRConfig()

							# size of the architecture
							if "base" in checkpoint_url:
														a__					=					7_6_8
							elif "large" in checkpoint_url:
														# use ViT-large encoder
														a__					=					1_0_2_4
														a__					=					4_0_9_6
														a__					=					2_4
														a__					=					1_6
														a__					=					1_0_2_4
							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__					=					1_0_2_4
														a__					=					True
														a__					=					False
														a__					=					False

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

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

							a__					=					create_rename_keys(__lowerCAmelCase ,   __lowerCAmelCase       )
							for src, dest in rename_keys:
														rename_key(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase       )
							read_in_q_k_v(__lowerCAmelCase ,   __lowerCAmelCase       )

							# 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(__lowerCAmelCase       )
														if key.startswith('decoder'       ) and "output_projection" not in key:
																					a__					=					val
														else:
																					a__					=					val

    # load state dict
							model.load_state_dict(__lowerCAmelCase       )

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

							a__					=					processor(images=prepare_img(__lowerCAmelCase       ) ,   return_tensors='pt'       ).pixel_values

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

							a__					=					torch.Size([1, 1, 5_0_2_6_5]       )
							if "trocr-base-handwritten" in checkpoint_url:
														a__					=					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:
														a__					=					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:
														a__					=					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:
														a__					=					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, :1_0] ,   __lowerCAmelCase ,   atol=1E-3       ), "First elements of logits not as expected"

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


if __name__ == "__main__":
						snake_case							:	Optional[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.'''
						)
						snake_case							:	int					      =				parser.parse_args()
						convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)








def 							__lowercase       (			__lowerCAmelCase							:    int       ):

							if num <= 0:
														raise ValueError('Input must be a positive integer'       )

							a__					=					[True] * (num + 1)

							a__					=					2
							while p * p <= num:
														if primes[p]:
																					for i in range(p * p ,   num + 1 ,   __lowerCAmelCase       ):
																												a__					=					False
														p += 1

							return [prime for prime in range(2 ,   num + 1       ) if primes[prime]]


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						snake_case							:	Optional[Any]					      =				int(input('''Enter a positive integer: ''').strip())
						print(prime_sieve_eratosthenes(user_num))




