





'''simple docstring'''


import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline
def 	snake_case_					(UpperCamelCase :						Tuple				,		UpperCamelCase :						List[str]				,		UpperCamelCase :						Dict				,		UpperCamelCase :						Union[str, Any]				,		UpperCamelCase :						Dict   ):





							'''simple docstring'''


							_a			  =       StableDiffusionPipeline.from_pretrained(UpperCamelCase				,		torch_dtype=torch.floataa   )

							# load LoRA weight from .safetensors
							_a			  =       load_file(UpperCamelCase   )

							_a			  =       []

							# directly update weight in diffusers model
							for key in state_dict:
														# it is suggested to print out the key, it usually will be something like below
														# "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"

														# as we have set the alpha beforehand, so just skip
														if ".alpha" in key or key in visited:
																					continue

														if "text" in key:
																					_a			  =       key.split('''.'''   )[0].split(LORA_PREFIX_TEXT_ENCODER + '''_'''   )[-1].split('''_'''   )
																					_a			  =       pipeline.text_encoder
														else:
																					_a			  =       key.split('''.'''   )[0].split(LORA_PREFIX_UNET + '''_'''   )[-1].split('''_'''   )
																					_a			  =       pipeline.unet

														# find the target layer
														_a			  =       layer_infos.pop(0   )
														while len(UpperCamelCase   ) > -1:
																					try:
																												_a			  =       curr_layer.__getattr__(UpperCamelCase   )
																												if len(UpperCamelCase   ) > 0:
																																			_a			  =       layer_infos.pop(0   )
																												elif len(UpperCamelCase   ) == 0:
																																			break
																					except Exception:
																												if len(UpperCamelCase   ) > 0:
																																			temp_name += "_" + layer_infos.pop(0   )
																												else:
																																			_a			  =       layer_infos.pop(0   )

														_a			  =       []
														if "lora_down" in key:
																					pair_keys.append(key.replace('''lora_down'''				,		'''lora_up'''   )   )
																					pair_keys.append(UpperCamelCase   )
														else:
																					pair_keys.append(UpperCamelCase   )
																					pair_keys.append(key.replace('''lora_up'''				,		'''lora_down'''   )   )

														# update weight
														if len(state_dict[pair_keys[0]].shape   ) == 4:
																					_a			  =       state_dict[pair_keys[0]].squeeze(3   ).squeeze(2   ).to(torch.floataa   )
																					_a			  =       state_dict[pair_keys[1]].squeeze(3   ).squeeze(2   ).to(torch.floataa   )
																					curr_layer.weight.data += alpha * torch.mm(UpperCamelCase				,		UpperCamelCase   ).unsqueeze(2   ).unsqueeze(3   )
														else:
																					_a			  =       state_dict[pair_keys[0]].to(torch.floataa   )
																					_a			  =       state_dict[pair_keys[1]].to(torch.floataa   )
																					curr_layer.weight.data += alpha * torch.mm(UpperCamelCase				,		UpperCamelCase   )

														# update visited list
														for item in pair_keys:
																					visited.append(UpperCamelCase   )

							return pipeline


if __name__ == "__main__":
				_snake_case   :   int     							=	argparse.ArgumentParser()

				parser.add_argument(
				    '--base_model_path', default=None, type=str, required=True, help='Path to the base model in diffusers format.'
				)
				parser.add_argument(
				    '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
				)
				parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
				parser.add_argument(
				    '--lora_prefix_unet', default='lora_unet', type=str, help='The prefix of UNet weight in safetensors'
				)
				parser.add_argument(
				    '--lora_prefix_text_encoder',
				    default='lora_te',
				    type=str,
				    help='The prefix of text encoder weight in safetensors',
				)
				parser.add_argument('--alpha', default=0.75, type=float, help='The merging ratio in W = W0 + alpha * deltaW')
				parser.add_argument(
				    '--to_safetensors', action='store_true', help='Whether to store pipeline in safetensors format or not.'
				)
				parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')

				_snake_case   :   List[str]     							=	parser.parse_args()

				_snake_case   :   str     							=	args.base_model_path
				_snake_case   :   int     							=	args.checkpoint_path
				_snake_case   :   List[str]     							=	args.dump_path
				_snake_case   :   Tuple     							=	args.lora_prefix_unet
				_snake_case   :   Union[str, Any]     							=	args.lora_prefix_text_encoder
				_snake_case   :   List[str]     							=	args.alpha

				_snake_case   :   Union[str, Any]     							=	convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

				_snake_case   :   Optional[Any]     							=	pipe.to(args.device)
				pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)







'''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)

