






"""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.
from ..models.whisper import WhisperForConditionalGeneration, WhisperProcessor
from .base import PipelineTool





class       snake_case_       (    _lowerCamelCase       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							Union[str, Any]   			=					"""openai/whisper-base"""
	SCREAMING_SNAKE_CASE_:							Any   			=					(
	    """This is a tool that transcribes an audio into text. It takes an input named `audio` and returns the """
	    """transcribed text."""
	)
	SCREAMING_SNAKE_CASE_:							Any   			=					"""transcriber"""
	SCREAMING_SNAKE_CASE_:							Optional[int]   			=					WhisperProcessor
	SCREAMING_SNAKE_CASE_:							Dict   			=					WhisperForConditionalGeneration

	SCREAMING_SNAKE_CASE_:							str   			=					["""audio"""]
	SCREAMING_SNAKE_CASE_:							Union[str, Any]   			=					["""text"""]


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					return self.pre_processor(__a  ,					return_tensors='pt'						).input_features


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					return self.model.generate(inputs=__a						)






	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					return self.pre_processor.batch_decode(__a  ,					skip_special_tokens=__a						)[0]










"""simple docstring"""






import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
SCREAMING_SNAKE_CASE						:						Optional[int]					   =       logging.getLogger(__name__)



def  __lowerCamelCase					(					):
				A__							 =       argparse.ArgumentParser(
				    description='Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).'				)
				parser.add_argument('--file_path'       ,type=lowerCAmelCase__       ,default='data/dump.txt'       ,help='The path to the data.'				)
				parser.add_argument('--tokenizer_type'       ,type=lowerCAmelCase__       ,default='bert'       ,choices=['bert', 'roberta', 'gpt2']				)
				parser.add_argument('--tokenizer_name'       ,type=lowerCAmelCase__       ,default='bert-base-uncased'       ,help='The tokenizer to use.'				)
				parser.add_argument('--dump_file'       ,type=lowerCAmelCase__       ,default='data/dump'       ,help='The dump file prefix.'				)
				A__							 =       parser.parse_args()

				logger.info(f'''Loading Tokenizer ({args.tokenizer_name})'''				)
				if args.tokenizer_type == "bert":
								A__							 =       BertTokenizer.from_pretrained(args.tokenizer_name				)
								A__							 =       tokenizer.special_tokens_map['cls_token']  # `[CLS]`
								A__							 =       tokenizer.special_tokens_map['sep_token']  # `[SEP]`
				elif args.tokenizer_type == "roberta":
								A__							 =       RobertaTokenizer.from_pretrained(args.tokenizer_name				)
								A__							 =       tokenizer.special_tokens_map['cls_token']  # `<s>`
								A__							 =       tokenizer.special_tokens_map['sep_token']  # `</s>`
				elif args.tokenizer_type == "gpt2":
								A__							 =       GPTaTokenizer.from_pretrained(args.tokenizer_name				)
								A__							 =       tokenizer.special_tokens_map['bos_token']  # `<|endoftext|>`
								A__							 =       tokenizer.special_tokens_map['eos_token']  # `<|endoftext|>`

				logger.info(f'''Loading text from {args.file_path}'''				)
				with open(args.file_path       ,'r'       ,encoding='utf8'				) as fp:
								A__							 =       fp.readlines()

				logger.info('Start encoding'				)
				logger.info(f'''{len(lowerCAmelCase__				)} examples to process.'''				)

				A__							 =       []
				A__							 =       0
				A__							 =       1_0000
				A__							 =       time.time()
				for text in data:
								A__							 =       f'''{bos} {text.strip()} {sep}'''
								A__							 =       tokenizer.encode(lowerCAmelCase__       ,add_special_tokens=lowerCAmelCase__				)
								rslt.append(lowerCAmelCase__				)

								iter += 1
								if iter % interval == 0:
												A__							 =       time.time()
												logger.info(f'''{iter} examples processed. - {(end-start):.2f}s/{interval}expl'''				)
												A__							 =       time.time()
				logger.info('Finished binarization'				)
				logger.info(f'''{len(lowerCAmelCase__				)} examples processed.'''				)

				A__							 =       f'''{args.dump_file}.{args.tokenizer_name}.pickle'''
				A__							 =       tokenizer.vocab_size
				if vocab_size < (1 << 16):
								A__							 =       [np.uintaa(lowerCAmelCase__				) for d in rslt]
				else:
								A__							 =       [np.intaa(lowerCAmelCase__				) for d in rslt]
				random.shuffle(rslt_				)
				logger.info(f'''Dump to {dp_file}'''				)
				with open(lowerCAmelCase__       ,'wb'				) as handle:
								pickle.dump(rslt_       ,lowerCAmelCase__       ,protocol=pickle.HIGHEST_PROTOCOL				)


if __name__ == "__main__":
					main()


