



"""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_    					=	logging.get_logger(__name__)

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

												UpperCAmelCase					=			in_proj_weight[
												    : encoder_config.hidden_size, :
												]
												UpperCAmelCase					=			in_proj_weight[
												    encoder_config.hidden_size : encoder_config.hidden_size * 2, :
												]
												UpperCAmelCase					=			in_proj_weight[
												    -encoder_config.hidden_size :, :
												]

def 			_lowerCAmelCase				(       lowercase_		,     lowercase_		,     lowercase_						):
						UpperCAmelCase					=			dct.pop(lowercase_						)
						UpperCAmelCase					=			val

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

@torch.no_grad()
def 			_lowerCAmelCase				(       lowercase_		,     lowercase_						):
						UpperCAmelCase					=			ViTConfig(image_size=384		,     qkv_bias=lowercase_						)
						UpperCAmelCase					=			TrOCRConfig()

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

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

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

						UpperCAmelCase					=			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():
												UpperCAmelCase					=			state_dict.pop(lowercase_						)
												if key.startswith('decoder'						) and "output_projection" not in key:
																		UpperCAmelCase					=			val
												else:
																		UpperCAmelCase					=			val

    # load state dict
						model.load_state_dict(lowercase_						)

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

						UpperCAmelCase					=			processor(images=prepare_img(lowercase_						)		,     return_tensors='pt'						).pixel_values

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

						UpperCAmelCase					=			torch.Size([1, 1, 50265]						)
						if "trocr-base-handwritten" in checkpoint_url:
												UpperCAmelCase					=			torch.tensor(
												    [-1.4_5_0_2, -4.6_6_8_3, -0.5_3_4_7, -2.9_2_9_1, 9.1_4_3_5, -3.0_5_7_1, 8.9_7_6_4, 1.7_5_6_0, 8.7_3_5_8, -1.5_3_1_1]						)
						elif "trocr-large-handwritten" in checkpoint_url:
												UpperCAmelCase					=			torch.tensor(
												    [-2.6_4_3_7, -1.3_1_2_9, -2.2_5_9_6, -5.3_4_5_5, 6.3_5_3_9, 1.7_6_0_4, 5.4_9_9_1, 1.4_7_0_2, 5.6_1_1_3, 2.0_1_7_0]						)
						elif "trocr-base-printed" in checkpoint_url:
												UpperCAmelCase					=			torch.tensor(
												    [-5.6_8_1_6, -5.8_3_8_8, 1.1_3_9_8, -6.9_0_3_4, 6.8_5_0_5, -2.4_3_9_3, 1.2_2_8_4, -1.0_2_3_2, -1.9_6_6_1, -3.9_2_1_0]						)
						elif "trocr-large-printed" in checkpoint_url:
												UpperCAmelCase					=			torch.tensor(
												    [-6.0_1_6_2, -7.0_9_5_9, 4.4_1_5_5, -5.1_0_6_3, 7.0_4_6_8, -3.1_6_3_1, 2.6_4_6_6, -0.3_0_8_1, -0.8_1_0_6, -1.7_5_3_5]						)

						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__":
				snake_case_    					=	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_    					=	parser.parse_args()
				convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)










"""simple docstring"""


def 			_lowerCAmelCase				(       lowercase_						):

						if not isinstance(lowercase_		,     lowercase_						):
												UpperCAmelCase					=			F"""Input value of [number={number}] must be an integer"""
												raise TypeError(lowercase_						)

						if number < 1:
												UpperCAmelCase					=			F"""Input value of [number={number}] must be > 0"""
												raise ValueError(lowercase_						)

						UpperCAmelCase					=			1

						for i in range(1		,     lowercase_						):
												current_number *= 4 * i - 2
												current_number //= i + 1

						return current_number


if __name__ == "__main__":
				import doctest

				doctest.testmod()





