
"""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      _SCREAMING_SNAKE_CASE   :






			def __init__(   self			,  __A			,  __A			,  __A			)     ->	Optional[int]:
						lowerCAmelCase_       :str     	=    cuda
						lowerCAmelCase_       :Tuple     	=    AutoConfig.from_pretrained(__A			)
						lowerCAmelCase_       :Tuple     	=    AutoModelForCTC.from_pretrained(__A			)
						self.model.eval()
						if self.cuda:
									self.model.to(device="""cuda"""			)
						lowerCAmelCase_       :int     	=    AutoProcessor.from_pretrained(__A			)
						lowerCAmelCase_       :List[str]     	=    torchaudio.transforms.Resample(__A			,  1_6000			)
						lowerCAmelCase_       :Tuple     	=    0
						lowerCAmelCase_       :Optional[Any]     	=    list(self.processor.tokenizer.get_vocab().keys()			)
						for i in range(len(__A			)			):
									if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
												lowerCAmelCase_       :List[Any]     	=    i
						print("""Blank Token id [PAD]/<pad>"""			,  __A			)
						lowerCAmelCase_       :Any     	=    blank_id






			def 					__lowerCAmelCase					(   self			,  __A			)     ->	int:
						lowerCAmelCase_	,			lowerCAmelCase_       :Optional[Any]     	=    torchaudio.load(__A			)
						lowerCAmelCase_       :int     	=    self.resampler(__A			).squeeze().numpy()
						return speech






			def 					__lowerCAmelCase					(   self			,  __A			)     ->	List[Any]:
						lowerCAmelCase_       :Optional[Any]     	=    self.blank_id
						lowerCAmelCase_       :Any     	=    """|""".join(item["""sent"""].split(""" """			)			)
						if not os.path.isfile(item["""wav_path"""]			):
									print(item["""wav_path"""]			,  """not found in wavs directory"""			)

						lowerCAmelCase_       :int     	=    self.speech_file_to_array_fn(item["""wav_path"""]			)
						lowerCAmelCase_       :int     	=    self.processor(__A			,  sampling_rate=1_6000			,  return_tensors="""pt"""			,  padding=__A			)
						if self.cuda:
									lowerCAmelCase_       :Optional[Any]     	=    inputs.to(device="""cuda"""			)

						with torch.no_grad():
									lowerCAmelCase_       :List[Any]     	=    self.model(inputs.input_values			).logits

						# get the emission probability at frame level
						lowerCAmelCase_       :List[str]     	=    torch.log_softmax(__A			,  dim=-1			)
						lowerCAmelCase_       :Any     	=    emissions[0].cpu().detach()

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

						lowerCAmelCase_       :Optional[int]     	=    {c: i for i, c in enumerate(__A			)}
						lowerCAmelCase_       :Dict     	=    []
						for c in transcript:
									if c in dictionary:
												tokens.append(dictionary[c]			)

						def get_trellis(__A			,  __A			,  __A=0			):
									lowerCAmelCase_       :Any     	=    emission.size(0			)
									lowerCAmelCase_       :Optional[int]     	=    len(__A			)

									# 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.
									lowerCAmelCase_       :Union[str, Any]     	=    torch.full((num_frames + 1, num_tokens + 1)			,  -float("""inf"""			)			)
									lowerCAmelCase_       :List[str]     	=    0
									for t in range(__A			):
												lowerCAmelCase_       :Dict     	=    torch.maximum(
												    # Score for staying at the same token
												    trellis[t, 1:] + emission[t, blank_id]			,  trellis[t, :-1] + emission[t, tokens]			,  )
									return trellis

						lowerCAmelCase_       :Any     	=    get_trellis(__A			,  __A			,  __A			)




						@dataclass
						class      _SCREAMING_SNAKE_CASE   :
									UpperCAmelCase_    :int
									UpperCAmelCase_    :int
									UpperCAmelCase_    :float

						def backtrack(__A			,  __A			,  __A			,  __A=0			):
									lowerCAmelCase_       :Tuple     	=    trellis.size(1			) - 1
									lowerCAmelCase_       :Any     	=    torch.argmax(trellis[:, j]			).item()

									lowerCAmelCase_       :Dict     	=    []
									for t in range(__A			,  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.
												lowerCAmelCase_       :Any     	=    trellis[t - 1, j] + emission[t - 1, blank_id]
												# Score for token changing from C-1 at T-1 to J at T.
												lowerCAmelCase_       :Optional[int]     	=    trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

												# 2. Store the path with frame-wise probability.
												lowerCAmelCase_       :Optional[Any]     	=    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			,  __A			)			)

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

						lowerCAmelCase_       :List[str]     	=    backtrack(__A			,  __A			,  __A			,  __A			)




						@dataclass
						class      _SCREAMING_SNAKE_CASE   :
									UpperCAmelCase_    :str
									UpperCAmelCase_    :int
									UpperCAmelCase_    :int
									UpperCAmelCase_    :float






									def __repr__(   self			)     ->	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[Any]:
												return self.end - self.start

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

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






			def 					__lowerCAmelCase					(   self			,  __A			,  __A			,  __A			)     ->	str:
						if not os.path.exists(__A			):
									os.makedirs(__A			)

						# load text file
						lowerCAmelCase_       :Tuple     	=    open(__A			,  encoding="""utf8"""			).readlines()

						lowerCAmelCase_       :List[str]     	=    []
						for line in lines:
									if len(line.strip().split("""\t"""			)			) != 2:
												print("""Script must be in format: 00001  this is my sentence"""			)
												exit()

									lowerCAmelCase_	,			lowerCAmelCase_       :List[Any]     	=    line.strip().split("""\t"""			)
									lowerCAmelCase_       :Dict     	=    os.path.join(__A			,  wav_name + """.wav"""			)
									lowerCAmelCase_       :Optional[Any]     	=    os.path.join(__A			,  wav_name + """.txt"""			)

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

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






def 		_snake_case			(      )    ->							Any:


			'''simple docstring'''





			lowerCAmelCase_       :str     	=    argparse.ArgumentParser()

			parser.add_argument(
			    """--model_name""" ,	type=lowercase__ ,	default="""arijitx/wav2vec2-xls-r-300m-bengali""" ,	help="""wav2vec model name"""						)
			parser.add_argument("""--wav_dir""" ,	type=lowercase__ ,	default="""./wavs""" ,	help="""directory containing wavs"""						)
			parser.add_argument("""--text_file""" ,	type=lowercase__ ,	default="""script.txt""" ,	help="""file containing text"""						)
			parser.add_argument("""--input_wavs_sr""" ,	type=lowercase__ ,	default=1_6_0_0_0 ,	help="""sampling rate of input audios"""						)
			parser.add_argument(
			    """--output_dir""" ,	type=lowercase__ ,	default="""./out_alignment""" ,	help="""output directory containing the alignment files"""						)
			parser.add_argument("""--cuda""" ,	action="""store_true"""						)

			lowerCAmelCase_       :List[Any]     	=    parser.parse_args()

			lowerCAmelCase_       :int     	=    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 json
import os
import unittest

from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import (
    VOCAB_FILES_NAMES,
    GPTNeoXJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class      _SCREAMING_SNAKE_CASE   (     A__      ,					unittest.TestCase				):
			UpperCAmelCase_    :List[str]												=		GPTNeoXJapaneseTokenizer
			UpperCAmelCase_    :List[str]												=		False
			UpperCAmelCase_    :int												=		{"do_clean_text": False, "add_prefix_space": False}






			def 					__lowerCAmelCase					(   self			)     ->	Optional[Any]:
						super().setUp()

						lowerCAmelCase_       :int     	=    [
						    """こん""",
						    """こんに""",
						    """にちは""",
						    """ばんは""",
						    """世界,㔺界""",
						    """、""",
						    """。""",
						    """<BR>""",
						    """<SP>""",
						    """<TAB>""",
						    """<URL>""",
						    """<EMAIL>""",
						    """<TEL>""",
						    """<DATE>""",
						    """<PRICE>""",
						    """<BLOCK>""",
						    """<KIGOU>""",
						    """<U2000U2BFF>""",
						    """<|emoji1|>""",
						    """<unk>""",
						    """<|startoftext|>""",
						    """<|endoftext|>""",
						]
						lowerCAmelCase_       :str     	=    {"""emoji""": {"""\ud83d\ude00""": """<|emoji1|>"""}, """emoji_inv""": {"""<|emoji1|>""": """\ud83d\ude00"""}}  # 😀
						lowerCAmelCase_       :Dict     	=    {"""unk_token""": """<unk>"""}

						lowerCAmelCase_       :Union[str, Any]     	=    os.path.join(self.tmpdirname			,  VOCAB_FILES_NAMES["""vocab_file"""]			)
						lowerCAmelCase_       :Union[str, Any]     	=    os.path.join(self.tmpdirname			,  VOCAB_FILES_NAMES["""emoji_file"""]			)
						with open(self.vocab_file			,  """w"""			,  encoding="""utf-8"""			) as vocab_writer:
									vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]			)			)
						with open(self.emoji_file			,  """w"""			) as emoji_writer:
									emoji_writer.write(json.dumps(__A			)			)






			def 					__lowerCAmelCase					(   self			,  **__A			)     ->	str:
						kwargs.update(self.special_tokens_map			)
						return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname			,  **__A			)






			def 					__lowerCAmelCase					(   self			,  __A			)     ->	List[Any]:
						lowerCAmelCase_       :Optional[Any]     	=    """こんにちは、世界。 \nこんばんは、㔺界。😀"""
						lowerCAmelCase_       :List[Any]     	=    """こんにちは、世界。 \nこんばんは、世界。😀"""
						return input_text, output_text






			def 					__lowerCAmelCase					(   self			,  __A			)     ->	Optional[Any]:
						lowerCAmelCase_	,			lowerCAmelCase_       :Optional[int]     	=    self.get_input_output_texts(__A			)
						lowerCAmelCase_       :List[Any]     	=    tokenizer.encode(__A			,  add_special_tokens=__A			)
						lowerCAmelCase_       :Optional[Any]     	=    tokenizer.decode(__A			,  clean_up_tokenization_spaces=__A			)
						return text, ids






			def 					__lowerCAmelCase					(   self			)     ->	str:
						pass  # TODO add if relevant






			def 					__lowerCAmelCase					(   self			)     ->	Any:
						pass  # TODO add if relevant






			def 					__lowerCAmelCase					(   self			)     ->	Optional[int]:
						pass  # TODO add if relevant






			def 					__lowerCAmelCase					(   self			)     ->	Optional[Any]:
						lowerCAmelCase_       :int     	=    self.get_tokenizer()

						# Testing tokenization
						lowerCAmelCase_       :List[str]     	=    """こんにちは、世界。　こんばんは、㔺界。"""
						lowerCAmelCase_       :int     	=    ["""こん""", """にちは""", """、""", """世界""", """。""", """<SP>""", """こん""", """ばんは""", """、""", """㔺界""", """。"""]
						lowerCAmelCase_       :List[Any]     	=    tokenizer.tokenize(__A			)
						self.assertListEqual(__A			,  __A			)

						# Testing conversion to ids without special tokens
						lowerCAmelCase_       :List[str]     	=    [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
						lowerCAmelCase_       :Dict     	=    tokenizer.convert_tokens_to_ids(__A			)
						self.assertListEqual(__A			,  __A			)

						# Testing conversion to ids with special tokens
						lowerCAmelCase_       :Tuple     	=    tokens + [tokenizer.unk_token]
						lowerCAmelCase_       :str     	=    [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
						lowerCAmelCase_       :Any     	=    tokenizer.convert_tokens_to_ids(__A			)
						self.assertListEqual(__A			,  __A			)






			@slow
			def 					__lowerCAmelCase					(   self			)     ->	Any:
						lowerCAmelCase_       :str     	=    self.tokenizer_class.from_pretrained("""abeja/gpt-neox-japanese-2.7b"""			)

						lowerCAmelCase_       :Union[str, Any]     	=    tokenizer.encode("""ありがとう。"""			,  add_special_tokens=__A			)
						lowerCAmelCase_       :Dict     	=    tokenizer.encode("""どういたしまして。"""			,  add_special_tokens=__A			)

						lowerCAmelCase_       :Union[str, Any]     	=    tokenizer.build_inputs_with_special_tokens(__A			)
						lowerCAmelCase_       :Optional[int]     	=    tokenizer.build_inputs_with_special_tokens(__A			,  __A			)

						assert encoded_sentence == ids_a
						assert encoded_pair == ids_a + ids_a






			def 					__lowerCAmelCase					(   self			)     ->	Dict:
						# Intentionally convert some words to accommodate character fluctuations unique to Japanese
						pass






			def 					__lowerCAmelCase					(   self			)     ->	List[str]:
						# tokenizer has no padding token
						pass






