

'''simple docstring'''


# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re

from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool


if is_vision_available():
						from PIL import Image



class      lowercase						(  UpperCAmelCase_ ):
							_lowerCamelCase		:      List[Any]=   "naver-clova-ix/donut-base-finetuned-docvqa"
							_lowerCamelCase		:      Optional[Any]=   (
							    "This is a tool that answers a question about an document (pdf). It takes an input named `document` which "
							    "should be the document containing the information, as well as a `question` that is the question about the "
							    "document. It returns a text that contains the answer to the question."
							)
							_lowerCamelCase		:      Tuple=   "document_qa"
							_lowerCamelCase		:      str=   AutoProcessor
							_lowerCamelCase		:      Union[str, Any]=   VisionEncoderDecoderModel

							_lowerCamelCase		:      int=   ["image", "text"]
							_lowerCamelCase		:      Any=   ["text"]


							def __init__(			self    ,			*_snake_case    ,			**_snake_case)							->				List[Any]:


								if not is_vision_available():
									raise ValueError('Pillow must be installed to use the DocumentQuestionAnsweringTool.')

								super().__init__(*_snake_case    ,			**_snake_case)


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case)							->				Optional[int]:
								UpperCAmelCase_		:      Optional[int]       	=					'<s_docvqa><s_question>{user_input}</s_question><s_answer>'
								UpperCAmelCase_		:      int       	=					task_prompt.replace('{user_input}'    ,			_snake_case)
								UpperCAmelCase_		:      Dict       	=					self.pre_processor.tokenizer(
								    _snake_case    ,			add_special_tokens=_snake_case    ,			return_tensors='pt').input_ids
								UpperCAmelCase_		:      List[str]       	=					self.pre_processor(_snake_case    ,			return_tensors='pt').pixel_values

								return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}


							def 						_snake_case							(			self    ,			_snake_case)							->				Tuple:
								return self.model.generate(
								    inputs['pixel_values'].to(self.device)    ,			decoder_input_ids=inputs['decoder_input_ids'].to(self.device)    ,			max_length=self.model.decoder.config.max_position_embeddings    ,			early_stopping=_snake_case    ,			pad_token_id=self.pre_processor.tokenizer.pad_token_id    ,			eos_token_id=self.pre_processor.tokenizer.eos_token_id    ,			use_cache=_snake_case    ,			num_beams=1    ,			bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]]    ,			return_dict_in_generate=_snake_case    ,			).sequences





							def 						_snake_case							(			self    ,			_snake_case)							->				Union[str, Any]:
								UpperCAmelCase_		:      List[Any]       	=					self.pre_processor.batch_decode(_snake_case)[0]
								UpperCAmelCase_		:      Optional[Any]       	=					sequence.replace(self.pre_processor.tokenizer.eos_token    ,			'')
								UpperCAmelCase_		:      Optional[Any]       	=					sequence.replace(self.pre_processor.tokenizer.pad_token    ,			'')
								UpperCAmelCase_		:      Any       	=					re.sub(r'<.*?>'    ,			''    ,			_snake_case    ,			count=1).strip()  # remove first task start token
								UpperCAmelCase_		:      Dict       	=					self.pre_processor.tokenajson(_snake_case)

								return sequence["answer"]









'''simple docstring'''


import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )    ->    Tuple:
	# load base model
	UpperCAmelCase_		:      List[str]       	=					StableDiffusionPipeline.from_pretrained(UpperCamelCase   ,torch_dtype=torch.floataa  )

	# load LoRA weight from .safetensors
	UpperCAmelCase_		:      Union[str, Any]       	=					load_file(UpperCamelCase  )

	UpperCAmelCase_		:      Any       	=					[]

	# 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:
			UpperCAmelCase_		:      Optional[Any]       	=					key.split('.'  )[0].split(LORA_PREFIX_TEXT_ENCODER + '_'  )[-1].split('_'  )
			UpperCAmelCase_		:      str       	=					pipeline.text_encoder
		else:
			UpperCAmelCase_		:      List[str]       	=					key.split('.'  )[0].split(LORA_PREFIX_UNET + '_'  )[-1].split('_'  )
			UpperCAmelCase_		:      List[str]       	=					pipeline.unet

		# find the target layer
		UpperCAmelCase_		:      Dict       	=					layer_infos.pop(0  )
		while len(UpperCamelCase  ) > -1:
			try:
				UpperCAmelCase_		:      List[str]       	=					curr_layer.__getattr__(UpperCamelCase  )
				if len(UpperCamelCase  ) > 0:
					UpperCAmelCase_		:      Tuple       	=					layer_infos.pop(0  )
				elif len(UpperCamelCase  ) == 0:
					break
			except Exception:
				if len(UpperCamelCase  ) > 0:
					temp_name += "_" + layer_infos.pop(0  )
				else:
					UpperCAmelCase_		:      List[Any]       	=					layer_infos.pop(0  )

		UpperCAmelCase_		:      str       	=					[]
		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:
			UpperCAmelCase_		:      Dict       	=					state_dict[pair_keys[0]].squeeze(3  ).squeeze(2  ).to(torch.floataa  )
			UpperCAmelCase_		:      List[str]       	=					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:
			UpperCAmelCase_		:      Optional[int]       	=					state_dict[pair_keys[0]].to(torch.floataa  )
			UpperCAmelCase_		:      Tuple       	=					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__":
						lowerCAmelCase__										= 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.)")

						lowerCAmelCase__										= parser.parse_args()

						lowerCAmelCase__										= args.base_model_path
						lowerCAmelCase__										= args.checkpoint_path
						lowerCAmelCase__										= args.dump_path
						lowerCAmelCase__										= args.lora_prefix_unet
						lowerCAmelCase__										= args.lora_prefix_text_encoder
						lowerCAmelCase__										= args.alpha

						lowerCAmelCase__										= convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

						lowerCAmelCase__										= pipe.to(args.device)
						pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)






