




'''simple docstring'''






from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)



class    A						(					SCREAMING_SNAKE_CASE__						):




				'''simple docstring'''





				def __init__(self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase = 0.93					,    _UpperCAmelCase = True					,    )      ->			Any:
							__UpperCamelCase :     Tuple										=  mixing_coeff
							__UpperCamelCase :     Tuple										=  leapfrog_steps

							super().__init__()
							self.register_modules(
							    vae=_UpperCAmelCase					,    text_encoder=_UpperCAmelCase					,    tokenizer=_UpperCAmelCase					,    unet=_UpperCAmelCase					,    scheduler=_UpperCAmelCase					,    )

							__UpperCamelCase :     Tuple										=  2 ** (len(self.vae.config.block_out_channels     ) - 1)
							__UpperCamelCase :     Dict										=  VaeImageProcessor(vae_scale_factor=self.vae_scale_factor     )
				def 			a_			(self					,    _UpperCAmelCase					,    _UpperCAmelCase = None					,    _UpperCAmelCase = False     )      ->			Optional[Any]:
							__UpperCamelCase :     Dict										=  self.tokenizer(
							    _UpperCAmelCase					,    padding="max_length"					,    max_length=self.tokenizer.model_max_length					,    truncation=_UpperCAmelCase					,    return_tensors="pt"					,    )

							__UpperCamelCase :     Any										=  self.text_encoder(text_inputs.input_ids.to(self.device     )     ).last_hidden_state

							__UpperCamelCase :     Tuple										=  prompt_embeds.to(dtype=self.text_encoder.dtype					,    device=self.device     )

							if do_classifier_free_guidance:
										__UpperCamelCase :     int										=  "" if negative_prompt is None else negative_prompt

										__UpperCamelCase :     Tuple										=  self.tokenizer(
										    _UpperCAmelCase					,    padding="max_length"					,    max_length=self.tokenizer.model_max_length					,    truncation=_UpperCAmelCase					,    return_tensors="pt"					,    )

										__UpperCamelCase :     Dict										=  self.text_encoder(uncond_input.input_ids.to(self.device     )     ).last_hidden_state

										__UpperCamelCase :     Optional[int]										=  torch.cat([negative_prompt_embeds, prompt_embeds]     )

							return prompt_embeds
				def 			a_			(self					,    _UpperCAmelCase					,    _UpperCAmelCase     )      ->			int:
							__UpperCamelCase :     Optional[Any]										=  self.mixing_coeff * x + (1 - self.mixing_coeff) * y
							__UpperCamelCase :     List[str]										=  self.mixing_coeff * y + (1 - self.mixing_coeff) * x

							return [x, y]
				def 			a_			(self					,    _UpperCAmelCase					,    _UpperCAmelCase     )      ->			Union[str, Any]:
							__UpperCamelCase :     Optional[Any]										=  (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
							__UpperCamelCase :     str										=  (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

							return [x, y]
				def 			a_			(self					,    _UpperCAmelCase     )      ->			List[Any]:
							# as self.alphas_cumprod is always in cpu
							__UpperCamelCase :     Union[str, Any]										=  int(_UpperCAmelCase     )

							__UpperCamelCase :     Optional[int]										=  self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

							return alpha_prod, 1 - alpha_prod
				def 			a_			(self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    )      ->			Optional[int]:
							__UpperCamelCase :     int										=  timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

							__UpperCamelCase						,   __UpperCamelCase :     str										=  self._get_alpha_and_beta(_UpperCAmelCase     )
							__UpperCamelCase						,   __UpperCamelCase :     List[Any]										=  self._get_alpha_and_beta(_UpperCAmelCase     )

							__UpperCamelCase :     Tuple										=  (alpha_prod_t_prev / alpha_prod_t) ** 0.5
							__UpperCamelCase :     Dict										=  -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

							__UpperCamelCase :     Any										=  (base - b_t * model_output) / a_t

							return model_input, next_model_input.to(base.dtype     )
				def 			a_			(self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    )      ->			int:
							__UpperCamelCase :     Tuple										=  timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

							__UpperCamelCase						,   __UpperCamelCase :     Union[str, Any]										=  self._get_alpha_and_beta(_UpperCAmelCase     )
							__UpperCamelCase						,   __UpperCamelCase :     Union[str, Any]										=  self._get_alpha_and_beta(_UpperCAmelCase     )

							__UpperCamelCase :     Any										=  (alpha_prod_t_prev / alpha_prod_t) ** 0.5
							__UpperCamelCase :     int										=  -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
							__UpperCamelCase :     Tuple										=  a_t * base + b_t * model_output

							return model_input, next_model_input.to(base.dtype     )
				@torch.no_grad()
				def 			a_			(self					,    _UpperCAmelCase     )      ->			Union[str, Any]:
							__UpperCamelCase :     str										=  1 / self.vae.config.scaling_factor * latents
							__UpperCamelCase :     Tuple										=  self.vae.decode(_UpperCAmelCase     ).sample
							__UpperCamelCase :     Optional[Any]										=  (image / 2 + 0.5).clamp(0					,    1     )
							return image
				@torch.no_grad()
				def 			a_			(self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase = None					,    )      ->			int:
							__UpperCamelCase :     Tuple										=  guidance_scale > 1.0

							__UpperCamelCase :     List[Any]										=  image.to(device=self.device					,    dtype=text_embeds.dtype     )
							__UpperCamelCase :     List[str]										=  self.vae.encode(_UpperCAmelCase     ).latent_dist.sample(_UpperCAmelCase     )

							__UpperCamelCase :     Tuple										=  self.vae.config.scaling_factor * latent

							__UpperCamelCase :     Tuple										=  [latent.clone(), latent.clone()]

							for i, t in tqdm(enumerate(_UpperCAmelCase     )					,    total=len(_UpperCAmelCase     )     ):
										__UpperCamelCase :     str										=  self.noise_mixing_layer(x=coupled_latents[0]					,    y=coupled_latents[1]     )

										# j - model_input index, k - base index
										for j in range(2     ):
													__UpperCamelCase :     Optional[Any]										=  j ^ 1

													if self.leapfrog_steps:
																if i % 2 == 0:
																			__UpperCamelCase						,   __UpperCamelCase :     int										=  j, k

													__UpperCamelCase :     Dict										=  coupled_latents[j]
													__UpperCamelCase :     Dict										=  coupled_latents[k]

													__UpperCamelCase :     Optional[Any]										=  torch.cat([model_input] * 2     ) if do_classifier_free_guidance else model_input

													__UpperCamelCase :     int										=  self.unet(_UpperCAmelCase					,    _UpperCAmelCase					,    encoder_hidden_states=_UpperCAmelCase     ).sample

													if do_classifier_free_guidance:
																__UpperCamelCase						,   __UpperCamelCase :     Optional[Any]										=  noise_pred.chunk(2     )
																__UpperCamelCase :     Tuple										=  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

													__UpperCamelCase						,   __UpperCamelCase :     Tuple										=  self.noise_step(
													    base=_UpperCAmelCase					,    model_input=_UpperCAmelCase					,    model_output=_UpperCAmelCase					,    timestep=_UpperCAmelCase					,    )

													__UpperCamelCase :     List[Any]										=  model_input

							return coupled_latents





				@torch.no_grad()
				def __call__(self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase = 3.0					,    _UpperCAmelCase = 5_0					,    _UpperCAmelCase = 0.8					,    _UpperCAmelCase = None					,    _UpperCAmelCase = None					,    _UpperCAmelCase = "pil"					,    )      ->			int:
							__UpperCamelCase :     List[str]										=  guidance_scale > 1.0

							__UpperCamelCase :     Optional[Any]										=  self.image_processor.preprocess(_UpperCAmelCase     )

							__UpperCamelCase :     Tuple										=  self._encode_prompt(_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase     )
							__UpperCamelCase :     List[Any]										=  self._encode_prompt(_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase     )

							self.scheduler.set_timesteps(_UpperCAmelCase					,    self.device     )

							__UpperCamelCase :     List[Any]										=  num_inference_steps - int(num_inference_steps * strength     )
							__UpperCamelCase :     Tuple										=  self.scheduler.timesteps[t_limit:]
							__UpperCamelCase :     Union[str, Any]										=  fwd_timesteps.flip(0     )

							__UpperCamelCase :     List[str]										=  self.prepare_latents(_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase     )

							for i, t in tqdm(enumerate(_UpperCAmelCase     )					,    total=len(_UpperCAmelCase     )     ):
										# j - model_input index, k - base index
										for k in range(2     ):
													__UpperCamelCase :     List[Any]										=  k ^ 1

													if self.leapfrog_steps:
																if i % 2 == 1:
																			__UpperCamelCase						,   __UpperCamelCase :     Dict										=  j, k

													__UpperCamelCase :     List[Any]										=  coupled_latents[j]
													__UpperCamelCase :     Optional[int]										=  coupled_latents[k]

													__UpperCamelCase :     List[str]										=  torch.cat([model_input] * 2     ) if do_classifier_free_guidance else model_input

													__UpperCamelCase :     str										=  self.unet(_UpperCAmelCase					,    _UpperCAmelCase					,    encoder_hidden_states=_UpperCAmelCase     ).sample

													if do_classifier_free_guidance:
																__UpperCamelCase						,   __UpperCamelCase :     List[Any]										=  noise_pred.chunk(2     )
																__UpperCamelCase :     Union[str, Any]										=  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

													__UpperCamelCase						,   __UpperCamelCase :     Union[str, Any]										=  self.denoise_step(
													    base=_UpperCAmelCase					,    model_input=_UpperCAmelCase					,    model_output=_UpperCAmelCase					,    timestep=_UpperCAmelCase					,    )

													__UpperCamelCase :     List[Any]										=  model_input

										__UpperCamelCase :     Dict										=  self.denoise_mixing_layer(x=coupled_latents[0]					,    y=coupled_latents[1]     )

							# either one is fine
							__UpperCamelCase :     List[Any]										=  coupled_latents[0]

							if output_type not in ["latent", "pt", "np", "pil"]:
										__UpperCamelCase :     List[str]										=  (
										    f"the output_type {output_type} is outdated. Please make sure to set it to one of these instead: "
										    "`pil`, `np`, `pt`, `latent`"
										)
										deprecate("Unsupported output_type"					,    "1.0.0"					,    _UpperCAmelCase					,    standard_warn=_UpperCAmelCase     )
										__UpperCamelCase :     int										=  "np"

							if output_type == "latent":
										__UpperCamelCase :     str										=  final_latent
							else:
										__UpperCamelCase :     Optional[Any]										=  self.decode_latents(_UpperCAmelCase     )
										__UpperCamelCase :     Optional[int]										=  self.image_processor.postprocess(_UpperCAmelCase					,    output_type=_UpperCAmelCase     )

							return image









'''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    A						:




				'''simple docstring'''





				def __init__(self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase     )      ->			str:
							__UpperCamelCase :     str										=  cuda
							__UpperCamelCase :     Union[str, Any]										=  AutoConfig.from_pretrained(_UpperCAmelCase     )
							__UpperCamelCase :     List[str]										=  AutoModelForCTC.from_pretrained(_UpperCAmelCase     )
							self.model.eval()
							if self.cuda:
										self.model.to(device="cuda"     )
							__UpperCamelCase :     Any										=  AutoProcessor.from_pretrained(_UpperCAmelCase     )
							__UpperCamelCase :     Any										=  torchaudio.transforms.Resample(_UpperCAmelCase					,    1_6_0_0_0     )
							__UpperCamelCase :     List[Any]										=  0
							__UpperCamelCase :     Dict										=  list(self.processor.tokenizer.get_vocab().keys()     )
							for i in range(len(_UpperCAmelCase     )     ):
										if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
													__UpperCamelCase :     Union[str, Any]										=  i
							print("Blank Token id [PAD]/<pad>"					,    _UpperCAmelCase     )
							__UpperCamelCase :     Tuple										=  blank_id
				def 			a_			(self					,    _UpperCAmelCase     )      ->			Union[str, Any]:
							__UpperCamelCase						,   __UpperCamelCase :     str										=  torchaudio.load(_UpperCAmelCase     )
							__UpperCamelCase :     Dict										=  self.resampler(_UpperCAmelCase     ).squeeze().numpy()
							return speech
				def 			a_			(self					,    _UpperCAmelCase     )      ->			int:
							__UpperCamelCase :     Union[str, Any]										=  self.blank_id
							__UpperCamelCase :     Any										=  "|".join(item["sent"].split(" "     )     )
							if not os.path.isfile(item["wav_path"]     ):
										print(item["wav_path"]					,    "not found in wavs directory"     )

							__UpperCamelCase :     Optional[Any]										=  self.speech_file_to_array_fn(item["wav_path"]     )
							__UpperCamelCase :     Tuple										=  self.processor(_UpperCAmelCase					,    sampling_rate=1_6_0_0_0					,    return_tensors="pt"					,    padding=_UpperCAmelCase     )
							if self.cuda:
										__UpperCamelCase :     str										=  inputs.to(device="cuda"     )

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

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

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

							__UpperCamelCase :     Tuple										=  {c: i for i, c in enumerate(_UpperCAmelCase     )}
							__UpperCamelCase :     List[str]										=  []
							for c in transcript:
										if c in dictionary:
													tokens.append(dictionary[c]     )

							def get_trellis(_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase=0     ):
										__UpperCamelCase :     List[Any]										=  emission.size(0     )
										__UpperCamelCase :     Optional[Any]										=  len(_UpperCAmelCase     )

										# 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.
										__UpperCamelCase :     Optional[int]										=  torch.full((num_frames + 1, num_tokens + 1)					,    -float("inf"     )     )
										__UpperCamelCase :     Union[str, Any]										=  0
										for t in range(_UpperCAmelCase     ):
													__UpperCamelCase :     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

							__UpperCamelCase :     Optional[Any]										=  get_trellis(_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase     )



							@dataclass
							class    A						:




											'''simple docstring'''





											A       =							42
											A       =							42
											A       =							42

							def backtrack(_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase=0     ):
										__UpperCamelCase :     Dict										=  trellis.size(1     ) - 1
										__UpperCamelCase :     List[str]										=  torch.argmax(trellis[:, j]     ).item()

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

													# 2. Store the path with frame-wise probability.
													__UpperCamelCase :     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					,    _UpperCAmelCase     )     )

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

							__UpperCamelCase :     Optional[Any]										=  backtrack(_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase     )



							@dataclass
							class    A						:




											'''simple docstring'''





											A       =							42
											A       =							42
											A       =							42
											A       =							42
											def __repr__(self     )      ->			Optional[int]:
														return f"{self.label}\t{self.score:4.2f}\t{self.start*2_0:5d}\t{self.end*2_0:5d}"





											@property
											def 			a_			(self     )      ->			Tuple:
														return self.end - self.start

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

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





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

							# load text file
							__UpperCamelCase :     Optional[int]										=  open(_UpperCAmelCase					,    encoding="utf8"     ).readlines()

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

										__UpperCamelCase						,   __UpperCamelCase :     List[Any]										=  line.strip().split("\t"     )
										__UpperCamelCase :     List[Any]										=  os.path.join(_UpperCAmelCase					,    wav_name + ".wav"     )
										__UpperCamelCase :     Tuple										=  os.path.join(_UpperCAmelCase					,    wav_name + ".txt"     )

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

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







def      __lowerCAmelCase   (       ):
			__UpperCamelCase :     Union[str, Any]										=  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=16_000   , 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"		)

			__UpperCamelCase :     Any										=  parser.parse_args()

			__UpperCamelCase :     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()



