
'''simple docstring'''





import argparse
import os
from dataclasses import dataclass

import torch
import torchaudio
from tqdm import tqdm

from transformers import AutoConfig, AutoModelForCTC, AutoProcessor



class 			lowerCAmelCase__ :



  '''simple docstring'''






  def __init__(self	:			int						,			lowerCAmelCase_	:			Any						,			lowerCAmelCase_	:			List[str]						,			lowerCAmelCase_	:			str     ) ->							List[Any]:
        snake_case_    							=				cuda
        snake_case_    							=				AutoConfig.from_pretrained(lowerCAmelCase_     )
        snake_case_    							=				AutoModelForCTC.from_pretrained(lowerCAmelCase_     )
        self.model.eval()
        if self.cuda:
              self.model.to(device="cuda"     )
        snake_case_    							=				AutoProcessor.from_pretrained(lowerCAmelCase_     )
        snake_case_    							=				torchaudio.transforms.Resample(lowerCAmelCase_						,			1_60_00     )
        snake_case_    							=				0
        snake_case_    							=				list(self.processor.tokenizer.get_vocab().keys()     )
        for i in range(len(lowerCAmelCase_     )     ):
              if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
                    snake_case_    							=				i
        print("Blank Token id [PAD]/<pad>"						,			lowerCAmelCase_     )
        snake_case_    							=				blank_id

  def 		_lowerCamelCase						(self	:			Optional[int]						,			lowerCAmelCase_	:			Optional[int]     ) ->							Tuple:
        snake_case_							,						snake_case_    							=				torchaudio.load(lowerCAmelCase_     )
        snake_case_    							=				self.resampler(lowerCAmelCase_     ).squeeze().numpy()
        return speech

  def 		_lowerCamelCase						(self	:			Optional[int]						,			lowerCAmelCase_	:			Tuple     ) ->							Optional[Any]:
        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(lowerCAmelCase_						,			sampling_rate=1_60_00						,			return_tensors="pt"						,			padding=lowerCAmelCase_     )
        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(lowerCAmelCase_						,			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(lowerCAmelCase_     )}
        snake_case_    							=				[]
        for c in transcript:
              if c in dictionary:
                    tokens.append(dictionary[c]     )

        def get_trellis(lowerCAmelCase_	:			List[Any]						,			lowerCAmelCase_	:			Union[str, Any]						,			lowerCAmelCase_	:			Tuple=0     ):
              snake_case_    							=				emission.size(0     )
              snake_case_    							=				len(lowerCAmelCase_     )

              # 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(lowerCAmelCase_     ):
                    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(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_     )



        @dataclass
        class 			lowerCAmelCase__ :



          '''simple docstring'''






          _UpperCamelCase							=			42
          _UpperCamelCase							=			42
          _UpperCamelCase							=			42

        def backtrack(lowerCAmelCase_	:			Dict						,			lowerCAmelCase_	:			Dict						,			lowerCAmelCase_	:			Union[str, Any]						,			lowerCAmelCase_	:			Any=0     ):
              snake_case_    							=				trellis.size(1     ) - 1
              snake_case_    							=				torch.argmax(trellis[:, j]     ).item()

              snake_case_    							=				[]
              for t in range(lowerCAmelCase_						,			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						,			lowerCAmelCase_     )     )

                    # 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(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_     )



        @dataclass
        class 			lowerCAmelCase__ :



          '''simple docstring'''






          _UpperCamelCase							=			42
          _UpperCamelCase							=			42
          _UpperCamelCase							=			42
          _UpperCamelCase							=			42

          def __repr__(self	:			Optional[Any]     ) ->							Union[str, Any]:
                return f'''{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}'''


          @property
          def 		_lowerCamelCase						(self	:			Optional[int]     ) ->							Optional[Any]:
                return self.end - self.start

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

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


  def 		_lowerCamelCase						(self	:			Tuple						,			lowerCAmelCase_	:			Optional[int]						,			lowerCAmelCase_	:			List[str]						,			lowerCAmelCase_	:			Dict     ) ->							Union[str, Any]:
        if not os.path.exists(lowerCAmelCase_     ):
              os.makedirs(lowerCAmelCase_     )

        # load text file
        snake_case_    							=				open(lowerCAmelCase_						,			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(lowerCAmelCase_						,			wav_name + ".wav"     )
              snake_case_    							=				os.path.join(lowerCAmelCase_						,			wav_name + ".txt"     )

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

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

def 	snake_case_							(				):
      snake_case_    							=				argparse.ArgumentParser()

      parser.add_argument(
          "--model_name"			,       type=_a			,       default="arijitx/wav2vec2-xls-r-300m-bengali"			,       help="wav2vec model name"      )
      parser.add_argument("--wav_dir"			,       type=_a			,       default="./wavs"			,       help="directory containing wavs"      )
      parser.add_argument("--text_file"			,       type=_a			,       default="script.txt"			,       help="file containing text"      )
      parser.add_argument("--input_wavs_sr"			,       type=_a			,       default=16_000			,       help="sampling rate of input audios"      )
      parser.add_argument(
          "--output_dir"			,       type=_a			,       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()







'''simple docstring'''




import jax
import jax.numpy as jnp
from bigbird_flax import FlaxBigBirdForNaturalQuestions
from datasets import load_from_disk

from transformers import BigBirdTokenizerFast


snake_case__ 							=    {0: """null""", 1: """short""", 2: """long""", 3: """yes""", 4: """no"""}
snake_case__ 							=    set("""""".join(["""‘""", """’""", """´""", """`""", """.""", """,""", """-""", """\""""]))




def     snake_case__     (			lowerCamelCase__					: List[str]      ,		lowerCamelCase__					: Tuple=0      ,		lowerCamelCase__					: Tuple=None      ) -> Optional[Any]:
					return [" ".join(x.split(''' '''      )[begin:end]      ) for x in answers if len(x.split(''' '''      )      ) > 1]




def     snake_case__     (			lowerCamelCase__					: Any      ,		lowerCamelCase__					: Any=False      ) -> List[Any]:
					if make_sub_answers:
										# if answers are longer than one word, make sure a predictions is correct if it coresponds to the complete 1: or :-1 sub word
										# *e.g.* if the correct answer contains a prefix such as "the", or "a"
										A_			:  Union[str, Any]			=				(
										    given_answers + get_sub_answers(lowerCamelCase__      ,		begin=1      ) + get_sub_answers(lowerCamelCase__      ,		end=-1      )
										)
					A_			:  List[Any]			=				[]
					for answer in given_answers:
										A_			:  Dict			=				answer.replace('''_'''      ,		''' '''      ).lower()
										A_			:  str			=				''''''.join(c if c not in PUNCTUATION_SET_TO_EXCLUDE else ''' ''' for c in alias      )
										answers.append(''' '''.join(alias.split()      ).strip()      )
					return set(lowerCamelCase__      )




def     snake_case__     (			lowerCamelCase__					: Optional[Any]      ,		lowerCamelCase__					: Dict      ,		lowerCamelCase__					: Union[str, Any]=1      ,		lowerCamelCase__					: Any=1_0_0      ) -> Any:
					A_			,A_			:  str			=				jax.lax.top_k(lowerCamelCase__      ,		lowerCamelCase__      )
					A_			,A_			:  str			=				jax.lax.top_k(lowerCamelCase__      ,		lowerCamelCase__      )

					A_			:  List[Any]			=				best_end_idx[:, None] - best_start_idx[None, :]
					A_			:  Optional[Any]			=				jnp.logical_or(widths < 0      ,		widths > max_size      )
					A_			:  Optional[int]			=				(best_end_scores[:, None] + best_start_scores[None, :]) - (1e8 * mask)
					A_			:  str			=				jnp.argmax(lowerCamelCase__      ).item()

					return best_start_idx[best_score % top_k], best_end_idx[best_score // top_k]




def     snake_case__     (			lowerCamelCase__					: List[str]      ) -> Union[str, Any]:
					A_			:  str			=				sample['''question''']['''text''']
					A_			:  Tuple			=				sample['''document''']['''tokens''']['''token''']
					A_			:  List[Any]			=				sample['''document''']['''tokens''']['''is_html''']
					A_			:  Tuple			=				sample['''annotations''']['''long_answer''']
					A_			:  List[Any]			=				sample['''annotations''']['''short_answers''']

					A_			:  Optional[Any]			=				''' '''.join([context[i] for i in range(len(lowerCamelCase__      )      ) if not is_html[i]]      )

					# 0 - No ; 1 - Yes
					for answer in sample["annotations"]["yes_no_answer"]:
										if answer == 0 or answer == 1:
															return {
															    "question": question,
															    "context": context_string,
															    "short": [],
															    "long": [],
															    "category": "no" if answer == 0 else "yes",
															}

					A_			:  List[Any]			=				[]
					for s in short_answers:
										short_targets.extend(s['''text''']      )
					A_			:  Tuple			=				list(set(lowerCamelCase__      )      )

					A_			:  Tuple			=				[]
					for s in long_answers:
										if s["start_token"] == -1:
															continue
										A_			:  Dict			=				context[s['''start_token'''] : s['''end_token''']]
										A_			:  Tuple			=				is_html[s['''start_token'''] : s['''end_token''']]
										A_			:  Optional[Any]			=				''' '''.join([answer[i] for i in range(len(lowerCamelCase__      )      ) if not html[i]]      )
										if new_answer not in long_targets:
															long_targets.append(lowerCamelCase__      )

					A_			:  str			=				'''long_short''' if len(short_targets + long_targets      ) > 0 else '''null'''

					return {
					    "question": question,
					    "context": context_string,
					    "short": short_targets,
					    "long": long_targets,
					    "category": category,
					}




def     snake_case__     (			) -> List[Any]:
					A_			:  int			=				load_from_disk('''natural-questions-validation'''      )
					A_			:  Optional[int]			=				dataset.map(lowerCamelCase__      ).remove_columns(['''annotations''', '''document''', '''id''']      )
					print(lowerCamelCase__      )

					A_			:  Tuple			=				dataset.filter(lambda lowerCamelCase__      : (len(x['''question''']      ) + len(x['''context''']      )) < 4 * 4_0_9_6      )
					A_			:  Optional[Any]			=				short_validation_dataset.filter(lambda lowerCamelCase__      : x["category"] != "null"      )
					short_validation_dataset

					A_			:  Union[str, Any]			=				'''vasudevgupta/flax-bigbird-natural-questions'''
					A_			:  List[str]			=				FlaxBigBirdForNaturalQuestions.from_pretrained(lowerCamelCase__      )
					A_			:  int			=				BigBirdTokenizerFast.from_pretrained(lowerCamelCase__      )

					@jax.jit
					def forward(*lowerCamelCase__					: Any      ,		**lowerCamelCase__					: Optional[Any]      ):
										A_			,A_			,A_			:  Tuple			=				model(*lowerCamelCase__      ,		**lowerCamelCase__      )
										return start_logits, end_logits, jnp.argmax(lowerCamelCase__      ,		axis=-1      )

					def evaluate(lowerCamelCase__					: List[str]      ):
										# encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length
										A_			:  List[Any]			=				tokenizer(
										    example['''question''']      ,		example['''context''']      ,		return_tensors='''np'''      ,		max_length=4_0_9_6      ,		padding='''max_length'''      ,		truncation=lowerCamelCase__      ,		)

										A_			,A_			,A_			:  Optional[Any]			=				forward(**lowerCamelCase__      )

										A_			:  Optional[Any]			=				CATEGORY_MAPPING[category.item()]

										A_			:  str			=				example['''long'''] + example['''short''']
										if example["category"] in ["yes", "no", "null"]:
															A_			:  str			=				[example['''category''']]
										A_			:  Optional[Any]			=				example['''category'''] != '''null'''
										# Now target can be: "yes", "no", "null", "list of long & short answers"

										if predicted_category in ["yes", "no", "null"]:
															A_			:  int			=				[predicted_category]
															A_			:  Optional[int]			=				example['''output'''] == example['''targets''']
															A_			:  Dict			=				predicted_category != '''null'''
															return example

										A_			:  List[Any]			=				3_8 if predicted_category == '''short''' else 1_0_2_4
										A_			,A_			:  str			=				get_best_valid_start_end_idx(
										    start_scores[0]      ,		end_scores[0]      ,		top_k=8      ,		max_size=lowerCamelCase__      )

										A_			:  List[Any]			=				inputs['''input_ids'''][0].tolist()
										A_			:  Dict			=				[tokenizer.decode(input_ids[start_score : end_score + 1]      )]

										A_			:  Optional[int]			=				expand_to_aliases(example['''targets''']      ,		make_sub_answers=lowerCamelCase__      )
										A_			:  str			=				expand_to_aliases(example['''output''']      )

										# some preprocessing to both prediction and answer
										A_			:  Tuple			=				{''''''.join(a.split()      ) for a in answers}
										A_			:  Optional[int]			=				{''''''.join(p.split()      ) for p in predictions}
										A_			:  List[str]			=				{s for s in predictions if s not in ['''``''', '''\'\'''', '''`''', '''\'''']}

										# if there is a common element, it's a exact match
										A_			:  List[Any]			=				len(list(answers & predictions      )      ) > 0
										A_			:  Dict			=				predicted_category != '''null''' and len(lowerCamelCase__      ) > 0

										return example

					A_			:  Dict			=				short_validation_dataset.map(lowerCamelCase__      )

					A_			:  List[str]			=				len(lowerCamelCase__      )
					A_			:  Any			=				len(short_validation_dataset.filter(lambda lowerCamelCase__      : x["match"] == 1      )      )
					print('''EM score:'''      ,		(matched / total) * 1_0_0      ,		'''%'''      )


if __name__ == "__main__":
							main()



