






'''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      UpperCamelCase (				snake_case__ ):







		"""simple docstring"""



		snake_case    =   "naver-clova-ix/donut-base-finetuned-docvqa"
		snake_case    =   (
		    "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."
		)
		snake_case    =   "document_qa"
		snake_case    =   AutoProcessor
		snake_case    =   VisionEncoderDecoderModel

		snake_case    =   ["image", "text"]
		snake_case    =   ["text"]
		def __init__(  self		:       Tuple						,*_SCREAMING_SNAKE_CASE		:       Tuple						,**_SCREAMING_SNAKE_CASE		:       Any		)    ->   int:


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

						super().__init__(*_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       List[str]						,_SCREAMING_SNAKE_CASE		:       "Image"						,_SCREAMING_SNAKE_CASE		:       str		)    ->   Optional[Any]:


						'''simple docstring'''
						A	 =      '<s_docvqa><s_question>{user_input}</s_question><s_answer>'
						A	 =      task_prompt.replace('{user_input}'						,_SCREAMING_SNAKE_CASE		)
						A	 =      self.pre_processor.tokenizer(
						    _SCREAMING_SNAKE_CASE						,add_special_tokens=_SCREAMING_SNAKE_CASE						,return_tensors='pt'		).input_ids
						A	 =      self.pre_processor(_SCREAMING_SNAKE_CASE						,return_tensors='pt'		).pixel_values

						return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}
		def       A(  self		:       int						,_SCREAMING_SNAKE_CASE		:       List[Any]		)    ->   Dict:


						'''simple docstring'''
						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=_SCREAMING_SNAKE_CASE						,pad_token_id=self.pre_processor.tokenizer.pad_token_id						,eos_token_id=self.pre_processor.tokenizer.eos_token_id						,use_cache=_SCREAMING_SNAKE_CASE						,num_beams=1						,bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]]						,return_dict_in_generate=_SCREAMING_SNAKE_CASE						,).sequences







		def       A(  self		:       Optional[Any]						,_SCREAMING_SNAKE_CASE		:       Tuple		)    ->   Tuple:


						'''simple docstring'''
						A	 =      self.pre_processor.batch_decode(_SCREAMING_SNAKE_CASE		)[0]
						A	 =      sequence.replace(self.pre_processor.tokenizer.eos_token						,''		)
						A	 =      sequence.replace(self.pre_processor.tokenizer.pad_token						,''		)
						A	 =      re.sub(R'<.*?>'						,''						,_SCREAMING_SNAKE_CASE						,count=1		).strip()  # remove first task start token
						A	 =      self.pre_processor.tokenajson(_SCREAMING_SNAKE_CASE		)

						return sequence["answer"]













import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM







def      snake_case		(							UpperCAmelCase	:       List[Any]   ):
				if "model" in orig_key:
								A	 =      orig_key.replace('model.',						''   )
				if "norm1" in orig_key:
								A	 =      orig_key.replace('norm1',						'attention.output.LayerNorm'   )
				if "norm2" in orig_key:
								A	 =      orig_key.replace('norm2',						'output.LayerNorm'   )
				if "norm" in orig_key:
								A	 =      orig_key.replace('norm',						'LayerNorm'   )
				if "transformer" in orig_key:
								A	 =      orig_key.split('.'   )[0].split('_'   )[-1]
								A	 =      orig_key.replace(f'transformer_{layer_num}',						f'encoder.layer.{layer_num}'   )
				if "mha.attn" in orig_key:
								A	 =      orig_key.replace('mha.attn',						'attention.self'   )
				if "mha" in orig_key:
								A	 =      orig_key.replace('mha',						'attention'   )
				if "W_q" in orig_key:
								A	 =      orig_key.replace('W_q',						'self.query'   )
				if "W_k" in orig_key:
								A	 =      orig_key.replace('W_k',						'self.key'   )
				if "W_v" in orig_key:
								A	 =      orig_key.replace('W_v',						'self.value'   )
				if "ff1" in orig_key:
								A	 =      orig_key.replace('ff1',						'intermediate.dense'   )
				if "ff2" in orig_key:
								A	 =      orig_key.replace('ff2',						'output.dense'   )
				if "ff" in orig_key:
								A	 =      orig_key.replace('ff',						'output.dense'   )
				if "mlm_class" in orig_key:
								A	 =      orig_key.replace('mlm.mlm_class',						'cls.predictions.decoder'   )
				if "mlm" in orig_key:
								A	 =      orig_key.replace('mlm',						'cls.predictions.transform'   )
				if "cls" not in orig_key:
								A	 =      'yoso.' + orig_key

				return orig_key







def      snake_case		(							UpperCAmelCase	:       Tuple,						UpperCAmelCase	:       str   ):
				for key in orig_state_dict.copy().keys():
								A	 =      orig_state_dict.pop(UpperCAmelCase   )

								if ("pooler" in key) or ("sen_class" in key):
												continue
								else:
												A	 =      val

				A	 =      orig_state_dict['cls.predictions.decoder.bias']
				A	 =      torch.arange(UpperCAmelCase   ).expand((1, -1)   ) + 2

				return orig_state_dict







def      snake_case		(							UpperCAmelCase	:       int,						UpperCAmelCase	:       Union[str, Any],						UpperCAmelCase	:       str   ):
				A	 =      torch.load(UpperCAmelCase,						map_location='cpu'   )['model_state_dict']
				A	 =      YosoConfig.from_json_file(UpperCAmelCase   )
				A	 =      YosoForMaskedLM(UpperCAmelCase   )

				A	 =      convert_checkpoint_helper(config.max_position_embeddings,						UpperCAmelCase   )

				print(model.load_state_dict(UpperCAmelCase   )   )
				model.eval()
				model.save_pretrained(UpperCAmelCase   )

				print(f'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'   )


if __name__ == "__main__":
					lowerCAmelCase_           = argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
					)
					parser.add_argument(
					    '--config_file',
					    default=None,
					    type=str,
					    required=True,
					    help='The json file for YOSO model config.',
					)
					parser.add_argument(
					    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
					)
					lowerCAmelCase_           = parser.parse_args()
					convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





