



# 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      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					A__				:		List[Any]			       =    "naver-clova-ix/donut-base-finetuned-docvqa"
					A__				:		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."
					)
					A__				:		Tuple			       =    "document_qa"
					A__				:		str			       =    AutoProcessor
					A__				:		Union[str, Any]			       =    VisionEncoderDecoderModel

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



					def __init__(     self					:       Optional[int]				,					*_snake_case					:       Tuple				,					**_snake_case					:       Tuple					):






								"""simple docstring"""


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

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



					def     _a							(     self					:       Union[str, Any]				,					_snake_case					:       "Image"				,					_snake_case					:       str					):






								"""simple docstring"""


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

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



					def     _a							(     self					:       int				,					_snake_case					:       Optional[Any]					):






								"""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=_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     _a							(     self					:       List[Any]				,					_snake_case					:       List[str]					):






								"""simple docstring"""


								A__       =    self.pre_processor.batch_decode(_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'<.*?>'				,					''				,					_snake_case				,					count=1					).strip()  # remove first task start token
								A__       =    self.pre_processor.tokenajson(_snake_case					)

								return sequence["answer"]




import argparse
import re
from typing import Dict

import torch
from datasets import Audio, Dataset, load_dataset, load_metric

from transformers import AutoFeatureExtractor, pipeline


def A						(       __UpperCamelCase ,	__UpperCamelCase					)						->      Tuple:
			A__       =    args.log_outputs
			A__       =    '_'.join(args.dataset.split('/'					) + [args.config, args.split]					)

			# load metric
			A__       =    load_metric('wer'					)
			A__       =    load_metric('cer'					)

			# compute metrics
			A__       =    wer.compute(references=result['target'] ,	predictions=result['prediction']					)
			A__       =    cer.compute(references=result['target'] ,	predictions=result['prediction']					)

			# print & log results
			A__       =    f'''WER: {wer_result}\nCER: {cer_result}'''
			print(__UpperCamelCase					)

			with open(f'''{dataset_id}_eval_results.txt''' ,	'w'					) as f:
						f.write(__UpperCamelCase					)

			# log all results in text file. Possibly interesting for analysis
			if log_outputs is not None:
						A__       =    f'''log_{dataset_id}_predictions.txt'''
						A__       =    f'''log_{dataset_id}_targets.txt'''

						with open(__UpperCamelCase ,	'w'					) as p, open(__UpperCamelCase ,	'w'					) as t:
									# mapping function to write output
									def write_to_file(__UpperCamelCase ,	__UpperCamelCase					):
												p.write(f'''{i}''' + '\n'					)
												p.write(batch['prediction'] + '\n'					)
												t.write(f'''{i}''' + '\n'					)
												t.write(batch['target'] + '\n'					)

									result.map(__UpperCamelCase ,	with_indices=__UpperCamelCase					)


def A						(       __UpperCamelCase					)						->      str:
			A__       =    '[,?.!\-\;\:"“%‘”�—’…–]'  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

			A__       =    re.sub(__UpperCamelCase ,	'' ,	text.lower()					)

			# In addition, we can normalize the target text, e.g. removing new lines characters etc...
			# note that order is important here!
			A__       =    ['\n\n', '\n', '   ', '  ']

			for t in token_sequences_to_ignore:
						A__       =    ' '.join(text.split(__UpperCamelCase					)					)

			return text


def A						(       __UpperCamelCase					)						->      Union[str, Any]:
			# load dataset
			A__       =    load_dataset(args.dataset ,	args.config ,	split=args.split ,	use_auth_token=__UpperCamelCase					)

			# for testing: only process the first two examples as a test
			# dataset = dataset.select(range(10))

			# load processor
			A__       =    AutoFeatureExtractor.from_pretrained(args.model_id					)
			A__       =    feature_extractor.sampling_rate

			# resample audio
			A__       =    dataset.cast_column('audio' ,	Audio(sampling_rate=__UpperCamelCase					)					)

			# load eval pipeline
			if args.device is None:
						A__       =    0 if torch.cuda.is_available() else -1
			A__       =    pipeline('automatic-speech-recognition' ,	model=args.model_id ,	device=args.device					)

			# map function to decode audio
			def map_to_pred(__UpperCamelCase					):
						A__       =    asr(
						    batch['audio']['array'] ,	chunk_length_s=args.chunk_length_s ,	stride_length_s=args.stride_length_s					)

						A__       =    prediction['text']
						A__       =    normalize_text(batch['sentence']					)
						return batch

			# run inference on all examples
			A__       =    dataset.map(__UpperCamelCase ,	remove_columns=dataset.column_names					)

			# compute and log_results
			# do not change function below
			log_results(__UpperCamelCase ,	__UpperCamelCase					)


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__       =      argparse.ArgumentParser()

							parser.add_argument(
							    '''--model_id''', type=str, required=True, help='''Model identifier. Should be loadable with 🤗 Transformers'''
							)
							parser.add_argument(
							    '''--dataset''',
							    type=str,
							    required=True,
							    help='''Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets''',
							)
							parser.add_argument(
							    '''--config''', type=str, required=True, help='''Config of the dataset. *E.g.* `\'en\'`  for Common Voice'''
							)
							parser.add_argument('''--split''', type=str, required=True, help='''Split of the dataset. *E.g.* `\'test\'`''')
							parser.add_argument(
							    '''--chunk_length_s''', type=float, default=None, help='''Chunk length in seconds. Defaults to 5 seconds.'''
							)
							parser.add_argument(
							    '''--stride_length_s''', type=float, default=None, help='''Stride of the audio chunks. Defaults to 1 second.'''
							)
							parser.add_argument(
							    '''--log_outputs''', action='''store_true''', help='''If defined, write outputs to log file for analysis.'''
							)
							parser.add_argument(
							    '''--device''',
							    type=int,
							    default=None,
							    help='''The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.''',
							)
							SCREAMING_SNAKE_CASE__       =      parser.parse_args()

							main(args)




