
'''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()
__A								=			logging.get_logger(__name__)





def        _A						(							lowercase__						,						lowercase__		):
						lowercase__							       =				[]
						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)
												lowercase__							       =				state_dict.pop(f'''encoder.deit.blocks.{i}.attn.qkv.weight'''		)

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





def        _A						(							lowercase__						,						lowercase__						,						lowercase__		):
						lowercase__							       =				dct.pop(lowercase__		)
						lowercase__							       =				val





def        _A						(							lowercase__		):
						if "handwritten" in checkpoint_url:
												lowercase__							       =				"""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__							       =				"""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__							       =				Image.open(requests.get(lowercase__						,						stream=lowercase__		).raw		).convert("""RGB"""		)
						return im





@torch.no_grad()
def        _A						(							lowercase__						,						lowercase__		):
						lowercase__							       =				ViTConfig(image_size=384						,						qkv_bias=lowercase__		)
						lowercase__							       =				TrOCRConfig()

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

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

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

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

																		 # load state dict
						model.load_state_dict(lowercase__		)

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

						lowercase__							       =				processor(images=prepare_img(lowercase__		)						,						return_tensors="""pt"""		).pixel_values

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

						lowercase__							       =				torch.Size([1, 1, 50265]		)
						if "trocr-base-handwritten" in checkpoint_url:
												lowercase__							       =				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:
												lowercase__							       =				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:
												lowercase__							       =				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:
												lowercase__							       =				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__":
				__A								=			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."
				)
				__A								=			parser.parse_args()
				convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)











"""simple docstring"""
from collections.abc import Callable

import numpy as np

def       _snake_case						(						UpperCamelCase						:		Callable			,		UpperCamelCase						:		float			,		UpperCamelCase						:		float			,		UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 UpperCAmelCase	:					Any	=	int(np.ceil((x_end - xa) / step_size						)						)
 UpperCAmelCase	:					Optional[Any]	=	np.zeros((n + 1,)						)
 UpperCAmelCase	:					Optional[int]	=	ya
 UpperCAmelCase	:					int	=	xa

 for k in range(UpperCamelCase						):
  UpperCAmelCase	:					Optional[int]	=	y[k] + step_size * ode_func(UpperCamelCase			,		y[k]						)
  UpperCAmelCase	:					Optional[int]	=	y[k] + (
      (step_size / 2) * (ode_func(UpperCamelCase			,		y[k]						) + ode_func(x + step_size			,		UpperCamelCase						))
  )
  x += step_size

 return y


if __name__ == "__main__":
      import doctest

      doctest.testmod()
