
'''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()
_lowercase			:  List[Any]   							=	logging.get_logger(__name__)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Tuple				,      __SCREAMING_SNAKE_CASE :			Union[str, Any]							):



						"""simple docstring"""





						lowercase_     :					Optional[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  snake_case_		(    __SCREAMING_SNAKE_CASE :			str				,      __SCREAMING_SNAKE_CASE :			Any							):



						"""simple docstring"""





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

												lowercase_     :					Optional[Any]													=	in_proj_weight[
												    : encoder_config.hidden_size, :
												]
												lowercase_     :					Union[str, Any]													=	in_proj_weight[
												    encoder_config.hidden_size : encoder_config.hidden_size * 2, :
												]
												lowercase_     :					str													=	in_proj_weight[
												    -encoder_config.hidden_size :, :
												]
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Union[str, Any]				,      __SCREAMING_SNAKE_CASE :			Dict				,      __SCREAMING_SNAKE_CASE :			int							):



						"""simple docstring"""





						lowercase_     :					Any													=	dct.pop(lowerCAmelCase__							)
						lowercase_     :					List[Any]													=	val
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			List[Any]							):



						"""simple docstring"""





						if "handwritten" in checkpoint_url:
												lowercase_     :					Dict													=	'''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:
												lowercase_     :					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'''
						lowercase_     :					int													=	Image.open(requests.get(lowerCAmelCase__				,      stream=lowerCAmelCase__							).raw							).convert('''RGB'''							)
						return im
@torch.no_grad()
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			int				,      __SCREAMING_SNAKE_CASE :			Union[str, Any]							):



						"""simple docstring"""





						lowercase_     :					str													=	ViTConfig(image_size=384				,      qkv_bias=lowerCAmelCase__							)
						lowercase_     :					Optional[int]													=	TrOCRConfig()

						# size of the architecture
						if "base" in checkpoint_url:
												lowercase_     :					int													=	768
						elif "large" in checkpoint_url:
												# use ViT-large encoder
												lowercase_     :					List[Any]													=	1024
												lowercase_     :					Tuple													=	4096
												lowercase_     :					List[str]													=	24
												lowercase_     :					str													=	16
												lowercase_     :					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:
												lowercase_     :					List[str]													=	False
												lowercase_     :					str													=	'''relu'''
												lowercase_     :					List[Any]													=	1024
												lowercase_     :					int													=	True
												lowercase_     :					Optional[int]													=	False
												lowercase_     :					Union[str, Any]													=	False

						# load HuggingFace model
						lowercase_     :					Optional[int]													=	ViTModel(lowerCAmelCase__				,      add_pooling_layer=lowerCAmelCase__							)
						lowercase_     :					Tuple													=	TrOCRForCausalLM(lowerCAmelCase__							)
						lowercase_     :					Tuple													=	VisionEncoderDecoderModel(encoder=lowerCAmelCase__				,      decoder=lowerCAmelCase__							)
						model.eval()

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

						lowercase_     :					Tuple													=	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():
												lowercase_     :					int													=	state_dict.pop(lowerCAmelCase__							)
												if key.startswith('''decoder'''							) and "output_projection" not in key:
																		lowercase_     :					Any													=	val
												else:
																		lowercase_     :					List[Any]													=	val

    # load state dict
						model.load_state_dict(lowerCAmelCase__							)

						# Check outputs on an image
						lowercase_     :					List[Any]													=	ViTImageProcessor(size=encoder_config.image_size							)
						lowercase_     :					Optional[int]													=	RobertaTokenizer.from_pretrained('''roberta-large'''							)
						lowercase_     :					Tuple													=	TrOCRProcessor(lowerCAmelCase__				,      lowerCAmelCase__							)

						lowercase_     :					Union[str, Any]													=	processor(images=prepare_img(lowerCAmelCase__							)				,      return_tensors='''pt'''							).pixel_values

						# verify logits
						lowercase_     :					str													=	torch.tensor([[model.config.decoder.decoder_start_token_id]]							)
						lowercase_     :					Optional[Any]													=	model(pixel_values=lowerCAmelCase__				,      decoder_input_ids=lowerCAmelCase__							)
						lowercase_     :					Optional[int]													=	outputs.logits

						lowercase_     :					List[Any]													=	torch.Size([1, 1, 50265]							)
						if "trocr-base-handwritten" in checkpoint_url:
												lowercase_     :					Optional[int]													=	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:
												lowercase_     :					Any													=	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:
												lowercase_     :					str													=	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:
												lowercase_     :					str													=	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]				,      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__":
							_lowercase			:  Union[str, 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."
							)
							_lowercase			:  Dict   							=	parser.parse_args()
							convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)





'''simple docstring'''







from typing import Optional
from urllib.parse import quote

import huggingface_hub as hfh
from packaging import version
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			str				,				__SCREAMING_SNAKE_CASE :			str				,				__SCREAMING_SNAKE_CASE :			Optional[str] = None							):



						"""simple docstring"""





						if version.parse(hfh.__version__							).release < version.parse('''0.11.0'''							).release:
												# old versions of hfh don't url-encode the file path
												lowercase_     :					int													=	quote(__SCREAMING_SNAKE_CASE							)
						return hfh.hf_hub_url(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE				,				repo_type='''dataset'''				,				revision=__SCREAMING_SNAKE_CASE							)



