

import argparse
import os
from dataclasses import dataclass

import torch
import torchaudio
from tqdm import tqdm

from transformers import AutoConfig, AutoModelForCTC, AutoProcessor






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(     self							:	int , a							:	Any , a							:	Optional[int] , a							:	List[Any]		)							->    Optional[Any]:

				"""simple docstring"""

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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE				: Tuple			       =    torchaudio.load(a		)
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    self.resampler(a		).squeeze().numpy()
				return speech
			def 							__UpperCamelCase	(     self							:	Tuple , a							:	Optional[int]		)							->    Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Tuple			       =    self.blank_id
				SCREAMING_SNAKE_CASE				: Optional[int]			       =    "|".join(item["sent"].split(" "		)		)
				if not os.path.isfile(item["wav_path"]		):
					print(item["wav_path"] , "not found in wavs directory"		)

				SCREAMING_SNAKE_CASE				: Dict			       =    self.speech_file_to_array_fn(item["wav_path"]		)
				SCREAMING_SNAKE_CASE				: Optional[int]			       =    self.processor(a , sampling_rate=1_6000 , return_tensors="pt" , padding=a		)
				if self.cuda:
					SCREAMING_SNAKE_CASE				: Tuple			       =    inputs.to(device="cuda"		)

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

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

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

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

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

				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    get_trellis(a , a , a		)






				@dataclass
				class _UpperCamelCase							:
							'''simple docstring'''







							lowerCamelCase__          =42
							lowerCamelCase__          =42
							lowerCamelCase__          =42

				def backtrack(a							:	Tuple , a							:	Any , a							:	Union[str, Any] , a							:	Dict=0		):
					SCREAMING_SNAKE_CASE				: Optional[int]			       =    trellis.size(1		) - 1
					SCREAMING_SNAKE_CASE				: Any			       =    torch.argmax(trellis[:, j]		).item()

					SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    []
					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.
						SCREAMING_SNAKE_CASE				: Dict			       =    trellis[t - 1, j] + emission[t - 1, blank_id]
						# Score for token changing from C-1 at T-1 to J at T.
						SCREAMING_SNAKE_CASE				: Dict			       =    trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

						# 2. Store the path with frame-wise probability.
						SCREAMING_SNAKE_CASE				: List[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]

				SCREAMING_SNAKE_CASE				: str			       =    backtrack(a , a , a , a		)






				@dataclass
				class _UpperCamelCase							:
							'''simple docstring'''







							lowerCamelCase__          =42
							lowerCamelCase__          =42
							lowerCamelCase__          =42
							lowerCamelCase__          =42
							def __repr__(     self							:	List[str]		)							->    int:

								"""simple docstring"""

								return F"{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}"






							@property
							def 							__UpperCamelCase	(     self							:	str		)							->    List[Any]:

								"""simple docstring"""

								return self.end - self.start

				def merge_repeats(a							:	Optional[Any]		):
					SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE				: int			       =    0, 0
					SCREAMING_SNAKE_CASE				: List[str]			       =    []
					while ia < len(a		):
						while ia < len(a		) and path[ia].token_index == path[ia].token_index:
							ia += 1
						SCREAMING_SNAKE_CASE				: 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 , )		)
						SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    ia
					return segments

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






			def 							__UpperCamelCase	(     self							:	Tuple , a							:	List[str] , a							:	int , a							:	List[str]		)							->    Optional[Any]:

				"""simple docstring"""

				if not os.path.exists(a		):
					os.makedirs(a		)

				# load text file
				SCREAMING_SNAKE_CASE				: Dict			       =    open(a , encoding="utf8"		).readlines()

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

					SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE				: Tuple			       =    line.strip().split("\t"		)
					SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    os.path.join(a , wav_name + ".wav"		)
					SCREAMING_SNAKE_CASE				: int			       =    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 	lowerCamelCase__	(   ):
	SCREAMING_SNAKE_CASE				: Tuple			       =    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=16000							,     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")

	SCREAMING_SNAKE_CASE				: Tuple			       =    parser.parse_args()

	SCREAMING_SNAKE_CASE				: List[Any]			       =    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()


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a_         =      {
    'configuration_codegen': ['CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CodeGenConfig', 'CodeGenOnnxConfig'],
    'tokenization_codegen': ['CodeGenTokenizer'],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_         =      ['CodeGenTokenizerFast']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_         =      [
			    'CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'CodeGenForCausalLM',
			    'CodeGenModel',
			    'CodeGenPreTrainedModel',
			]

if TYPE_CHECKING:
			from .configuration_codegen import CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP, CodeGenConfig, CodeGenOnnxConfig
			from .tokenization_codegen import CodeGenTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_codegen_fast import CodeGenTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_codegen import (
						    CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST,
						    CodeGenForCausalLM,
						    CodeGenModel,
						    CodeGenPreTrainedModel,
						)

else:
			import sys

			a_         =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)