
import argparse
import os
from dataclasses import dataclass

import torch
import torchaudio
from tqdm import tqdm

from transformers import AutoConfig, AutoModelForCTC, AutoProcessor







class  _lowerCamelCase			:

     def __init__(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ):
            __snake_case						       =   cuda
            __snake_case						       =   AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   AutoModelForCTC.from_pretrained(SCREAMING_SNAKE_CASE_    )
            self.model.eval()
            if self.cuda:
                   self.model.to(device='cuda'    )
            __snake_case						       =   AutoProcessor.from_pretrained(SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   torchaudio.transforms.Resample(SCREAMING_SNAKE_CASE_							,       16_000    )
            __snake_case						       =   0
            __snake_case						       =   list(self.processor.tokenizer.get_vocab().keys()    )
            for i in range(len(SCREAMING_SNAKE_CASE_    )    ):
                   if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
                          __snake_case						       =   i
            print('Blank Token id [PAD]/<pad>'							,       SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   blank_id

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_    ):
            __snake_case       ,       __snake_case						       =   torchaudio.load(SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   self.resampler(SCREAMING_SNAKE_CASE_    ).squeeze().numpy()
            return speech

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_    ):
            __snake_case						       =   self.blank_id
            __snake_case						       =   '|'.join(item['sent'].split(' '    )    )
            if not os.path.isfile(item['wav_path']    ):
                   print(item['wav_path']							,       'not found in wavs directory'    )

            __snake_case						       =   self.speech_file_to_array_fn(item['wav_path']    )
            __snake_case						       =   self.processor(SCREAMING_SNAKE_CASE_							,       sampling_rate=16_000							,       return_tensors='pt'							,       padding=SCREAMING_SNAKE_CASE_    )
            if self.cuda:
                   __snake_case						       =   inputs.to(device='cuda'    )

            with torch.no_grad():
                   __snake_case						       =   self.model(inputs.input_values    ).logits

            # get the emission probability at frame level
            __snake_case						       =   torch.log_softmax(SCREAMING_SNAKE_CASE_							,       dim=-1    )
            __snake_case						       =   emissions[0].cpu().detach()

            # get labels from vocab
            __snake_case						       =   ([''] + list(self.processor.tokenizer.get_vocab().keys()    ))[
                :-1
            ]  # logits don't align with the tokenizer's vocab

            __snake_case						       =   {c: i for i, c in enumerate(SCREAMING_SNAKE_CASE_    )}
            __snake_case						       =   []
            for c in transcript:
                   if c in dictionary:
                          tokens.append(dictionary[c]    )

            def get_trellis(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_=0    ):
                   __snake_case						       =   emission.size(0    )
                   __snake_case						       =   len(SCREAMING_SNAKE_CASE_    )

                   # Trellis has extra diemsions for both time axis and tokens.
                   # The extra dim for tokens represents <SoS> (start-of-sentence)
                   # The extra dim for time axis is for simplification of the code.
                   __snake_case						       =   torch.full((num_frames + 1, num_tokens + 1)							,       -float('inf'    )    )
                   __snake_case						       =   0
                   for t in range(SCREAMING_SNAKE_CASE_    ):
                          __snake_case						       =   torch.maximum(
                              # Score for staying at the same token
                              trellis[t, 1:] + emission[t, blank_id]							,       trellis[t, :-1] + emission[t, tokens]							,       )
                   return trellis

            __snake_case						       =   get_trellis(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )







            @dataclass
            class  _lowerCamelCase			:
                 lowercase__				  =			42
                 lowercase__				  =			42
                 lowercase__				  =			42

            def backtrack(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_=0    ):
                   __snake_case						       =   trellis.size(1    ) - 1
                   __snake_case						       =   torch.argmax(trellis[:, j]    ).item()

                   __snake_case						       =   []
                   for t in range(SCREAMING_SNAKE_CASE_							,       0							,       -1    ):
                          # 1. Figure out if the current position was stay or change
                          # Note (again):
                          # `emission[J-1]` is the emission at time frame `J` of trellis dimension.
                          # Score for token staying the same from time frame J-1 to T.
                          __snake_case						       =   trellis[t - 1, j] + emission[t - 1, blank_id]
                          # Score for token changing from C-1 at T-1 to J at T.
                          __snake_case						       =   trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

                          # 2. Store the path with frame-wise probability.
                          __snake_case						       =   emission[t - 1, tokens[j - 1] if changed > stayed else 0].exp().item()
                          # Return token index and time index in non-trellis coordinate.
                          path.append(Point(j - 1							,       t - 1							,       SCREAMING_SNAKE_CASE_    )    )

                          # 3. Update the token
                          if changed > stayed:
                                 j -= 1
                                 if j == 0:
                                        break
                   else:
                          raise ValueError('Failed to align'    )
                   return path[::-1]

            __snake_case						       =   backtrack(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )







            @dataclass
            class  _lowerCamelCase			:
                 lowercase__				  =			42
                 lowercase__				  =			42
                 lowercase__				  =			42
                 lowercase__				  =			42

                 def __repr__(  self    ):
                        return f'''{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}'''

                 @property
                 def 						__lowerCamelCase				(  self    ):
                        return self.end - self.start

            def merge_repeats(SCREAMING_SNAKE_CASE_    ):
                   __snake_case       ,       __snake_case						       =   0, 0
                   __snake_case						       =   []
                   while ia < len(SCREAMING_SNAKE_CASE_    ):
                          while ia < len(SCREAMING_SNAKE_CASE_    ) and path[ia].token_index == path[ia].token_index:
                                 ia += 1
                          __snake_case						       =   sum(path[k].score for k in range(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )    ) / (ia - ia)
                          segments.append(
                              Segment(
                                  transcript[path[ia].token_index]							,       path[ia].time_index							,       path[ia - 1].time_index + 1							,       SCREAMING_SNAKE_CASE_							,       )    )
                          __snake_case						       =   ia
                   return segments

            __snake_case						       =   merge_repeats(SCREAMING_SNAKE_CASE_    )
            with open(item['out_path']							,       'w'    ) as out_align:
                   for seg in segments:
                          out_align.write(str(SCREAMING_SNAKE_CASE_    ) + '\n'    )

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ):
            if not os.path.exists(SCREAMING_SNAKE_CASE_    ):
                   os.makedirs(SCREAMING_SNAKE_CASE_    )

            # load text file
            __snake_case						       =   open(SCREAMING_SNAKE_CASE_							,       encoding='utf8'    ).readlines()

            __snake_case						       =   []
            for line in lines:
                   if len(line.strip().split('\t'    )    ) != 2:
                          print('Script must be in format: 00001  this is my sentence'    )
                          exit()

                   __snake_case       ,       __snake_case						       =   line.strip().split('\t'    )
                   __snake_case						       =   os.path.join(SCREAMING_SNAKE_CASE_							,       wav_name + '.wav'    )
                   __snake_case						       =   os.path.join(SCREAMING_SNAKE_CASE_							,       wav_name + '.txt'    )

                   items.append({'sent': sentence, 'wav_path': wav_path, 'out_path': out_path}    )
            print('Number of samples found in script file'							,       len(SCREAMING_SNAKE_CASE_    )    )

            for item in tqdm(SCREAMING_SNAKE_CASE_    ):
                   self.align_single_sample(SCREAMING_SNAKE_CASE_    )





def        __lowercase(							)       ->   List[Any]:
       __snake_case						       =   argparse.ArgumentParser()

       parser.add_argument(
           '--model_name'    ,type=__snake_case    ,default='arijitx/wav2vec2-xls-r-300m-bengali'    ,help='wav2vec model name'					)
       parser.add_argument('--wav_dir'    ,type=__snake_case    ,default='./wavs'    ,help='directory containing wavs'					)
       parser.add_argument('--text_file'    ,type=__snake_case    ,default='script.txt'    ,help='file containing text'					)
       parser.add_argument('--input_wavs_sr'    ,type=__snake_case    ,default=1_60_00    ,help='sampling rate of input audios'					)
       parser.add_argument(
           '--output_dir'    ,type=__snake_case    ,default='./out_alignment'    ,help='output directory containing the alignment files'					)
       parser.add_argument('--cuda'    ,action='store_true'					)

       __snake_case						       =   parser.parse_args()

       __snake_case						       =   WavaVecaAligner(args.model_name    ,args.input_wavs_sr    ,args.cuda					)
       aligner.align_data(args.wav_dir    ,args.text_file    ,args.output_dir					)


if __name__ == "__main__":
 main()


import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


lowerCamelCase_				:							List[str]       =    getLogger(__name__)


lowerCamelCase_				:							Dict       =    "cuda" if torch.cuda.is_available() else "cpu"
def        __lowercase(							__snake_case				:		List[str]    ,__snake_case				:		str    ,__snake_case				:		str    ,__snake_case				:		int = 8    ,__snake_case				:		str = DEFAULT_DEVICE    ,__snake_case				:		str=False    ,__snake_case				:		Tuple="summarization"    ,__snake_case				:		List[str]=None    ,**__snake_case				:		Dict    ,)       ->   Dict:
       __snake_case						       =   Path(__snake_case					).open('w'    ,encoding='utf-8'					)
       __snake_case						       =   str(__snake_case					)
       __snake_case						       =   AutoModelForSeqaSeqLM.from_pretrained(__snake_case					).to(__snake_case					)
       if fpaa:
              __snake_case						       =   model.half()

       __snake_case						       =   AutoTokenizer.from_pretrained(__snake_case					)
       logger.info(f'''Inferred tokenizer type: {tokenizer.__class__}'''					)  # if this is wrong, check config.model_type.

       __snake_case						       =   time.time()
       # update config with task specific params
       use_task_specific_params(__snake_case    ,__snake_case					)
       if prefix is None:
              __snake_case						       =   prefix or getattr(model.config    ,'prefix'    ,''					) or ''
       for examples_chunk in tqdm(list(chunks(__snake_case    ,__snake_case					)					)					):
              __snake_case						       =   [prefix + text for text in examples_chunk]
              __snake_case						       =   tokenizer(__snake_case    ,return_tensors='pt'    ,truncation=__snake_case    ,padding='longest'					).to(__snake_case					)
              __snake_case						       =   model.generate(
                  input_ids=batch.input_ids    ,attention_mask=batch.attention_mask    ,**__snake_case    ,)
              __snake_case						       =   tokenizer.batch_decode(__snake_case    ,skip_special_tokens=__snake_case    ,clean_up_tokenization_spaces=__snake_case					)
              for hypothesis in dec:
                     fout.write(hypothesis + '\n'					)
                     fout.flush()
       fout.close()
       __snake_case						       =   int(time.time() - start_time					)  # seconds
       __snake_case						       =   len(__snake_case					)
       return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs    ,4					)}
def        __lowercase(							)       ->   Optional[int]:
       return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'					)
def        __lowercase(							__snake_case				:		Optional[int]=True					)       ->   Dict:
       __snake_case						       =   argparse.ArgumentParser()
       parser.add_argument('model_name'    ,type=__snake_case    ,help='like facebook/bart-large-cnn,t5-base, etc.'					)
       parser.add_argument('input_path'    ,type=__snake_case    ,help='like cnn_dm/test.source'					)
       parser.add_argument('save_path'    ,type=__snake_case    ,help='where to save summaries'					)
       parser.add_argument('--reference_path'    ,type=__snake_case    ,required=__snake_case    ,help='like cnn_dm/test.target'					)
       parser.add_argument('--score_path'    ,type=__snake_case    ,required=__snake_case    ,default='metrics.json'    ,help='where to save metrics'					)
       parser.add_argument('--device'    ,type=__snake_case    ,required=__snake_case    ,default=__snake_case    ,help='cuda, cuda:1, cpu etc.'					)
       parser.add_argument(
           '--prefix'    ,type=__snake_case    ,required=__snake_case    ,default=__snake_case    ,help='will be added to the begininng of src examples'					)
       parser.add_argument('--task'    ,type=__snake_case    ,default='summarization'    ,help='used for task_specific_params + metrics'					)
       parser.add_argument('--bs'    ,type=__snake_case    ,default=8    ,required=__snake_case    ,help='batch size'					)
       parser.add_argument(
           '--n_obs'    ,type=__snake_case    ,default=-1    ,required=__snake_case    ,help='How many observations. Defaults to all.'					)
       parser.add_argument('--fp16'    ,action='store_true'					)
       parser.add_argument('--dump-args'    ,action='store_true'    ,help='print the custom hparams with the results'					)
       parser.add_argument(
           '--info'    ,nargs='?'    ,type=__snake_case    ,const=datetime_now()    ,help=(
               'use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'
               ' lang=en-ru. If no value is passed, the current datetime string will be used.'
           )    ,)
       # Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
       __snake_case       ,       __snake_case						       =   parser.parse_known_args()
       __snake_case						       =   parse_numeric_n_bool_cl_kwargs(__snake_case					)
       if parsed_args and verbose:
              print(f'''parsed the following generate kwargs: {parsed_args}'''					)
       with open(args.input_path					) as f:
              __snake_case						       =   [' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in f.readlines()]
       if args.n_obs > 0:
              __snake_case						       =   examples[: args.n_obs]
       Path(args.save_path					).parent.mkdir(exist_ok=__snake_case					)
       if args.reference_path is None and Path(args.score_path					).exists():
              warnings.warn(f'''score_path {args.score_path} will be overwritten unless you type ctrl-c.'''					)
       __snake_case						       =   generate_summaries_or_translations(
           __snake_case    ,args.save_path    ,args.model_name    ,batch_size=args.bs    ,device=args.device    ,fpaa=args.fpaa    ,task=args.task    ,prefix=args.prefix    ,**__snake_case    ,)

       if args.reference_path is None:
              return {}

       # Compute scores
       __snake_case						       =   calculate_bleu if 'translation' in args.task else calculate_rouge
       __snake_case						       =   [x.rstrip() for x in open(args.save_path					).readlines()]
       __snake_case						       =   [x.rstrip() for x in open(args.reference_path					).readlines()][: len(__snake_case					)]
       __snake_case						       =   score_fn(__snake_case    ,__snake_case					)
       scores.update(__snake_case					)

       if args.dump_args:
              scores.update(__snake_case					)
       if args.info:
              __snake_case						       =   args.info

       if verbose:
              print(__snake_case					)

       if args.score_path is not None:
              json.dump(__snake_case    ,open(args.score_path    ,'w'					)					)

       return scores


if __name__ == "__main__":
 # Usage for MT:
 # python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
 run_generate(verbose=True)
