




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			:							str    ,					A			:							List[Any]    ,					A			:							Dict    ,					A			:							int     )      ->      Dict:






         """simple docstring"""
         a__  			=							cuda
         a__  			=							AutoConfig.from_pretrained(A     )
         a__  			=							AutoModelForCTC.from_pretrained(A     )
         self.model.eval()
         if self.cuda:
             self.model.to(device="""cuda"""     )
         a__  			=							AutoProcessor.from_pretrained(A     )
         a__  			=							torchaudio.transforms.Resample(A    ,					1_60_00     )
         a__  			=							0
         a__  			=							list(self.processor.tokenizer.get_vocab().keys()     )
         for i in range(len(A     )     ):
             if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
                 a__  			=							i
         print("""Blank Token id [PAD]/<pad>"""    ,					A     )
         a__  			=							blank_id

     def 					__SCREAMING_SNAKE_CASE		(   self			:							int    ,					A			:							List[str]     )      ->      Dict:






         """simple docstring"""
         a__			,		a__  			=							torchaudio.load(A     )
         a__  			=							self.resampler(A     ).squeeze().numpy()
         return speech

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[Any]    ,					A			:							Optional[int]     )      ->      int:






         """simple docstring"""
         a__  			=							self.blank_id
         a__  			=							"""|""".join(item["""sent"""].split(""" """     )     )
         if not os.path.isfile(item["""wav_path"""]     ):
             print(item["""wav_path"""]    ,					"""not found in wavs directory"""     )

         a__  			=							self.speech_file_to_array_fn(item["""wav_path"""]     )
         a__  			=							self.processor(A    ,					sampling_rate=1_60_00    ,					return_tensors="""pt"""    ,					padding=A     )
         if self.cuda:
             a__  			=							inputs.to(device="""cuda"""     )

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

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

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

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

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

         a__  			=							get_trellis(A    ,					A    ,					A     )







         @dataclass
         class 		__UpperCAmelCase:





              """simple docstring"""



              __UpperCAmelCase						:			int
              __UpperCAmelCase						:			int
              __UpperCAmelCase						:			float

         def backtrack(A			:							str    ,					A			:							Dict    ,					A			:							int    ,					A			:							Optional[int]=0     ):
             a__  			=							trellis.size(1     ) - 1
             a__  			=							torch.argmax(trellis[:, j]     ).item()

             a__  			=							[]
             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.
                 a__  			=							trellis[t - 1, j] + emission[t - 1, blank_id]
                 # Score for token changing from C-1 at T-1 to J at T.
                 a__  			=							trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

                 # 2. Store the path with frame-wise probability.
                 a__  			=							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]

         a__  			=							backtrack(A    ,					A    ,					A    ,					A     )







         @dataclass
         class 		__UpperCAmelCase:





              """simple docstring"""



              __UpperCAmelCase						:			str
              __UpperCAmelCase						:			int
              __UpperCAmelCase						:			int
              __UpperCAmelCase						:			float

              def __repr__(   self			:							List[Any]     )      ->      Union[str, Any]:






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






              @property
              def 					__SCREAMING_SNAKE_CASE		(   self			:							Dict     )      ->      int:






                  """simple docstring"""
                  return self.end - self.start

         def merge_repeats(A			:							Tuple     ):
             a__			,		a__  			=							0, 0
             a__  			=							[]
             while ia < len(A     ):
                 while ia < len(A     ) and path[ia].token_index == path[ia].token_index:
                     ia += 1
                 a__  			=							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    ,					)     )
                 a__  			=							ia
             return segments

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






     def 					__SCREAMING_SNAKE_CASE		(   self			:							List[Any]    ,					A			:							Optional[int]    ,					A			:							Union[str, Any]    ,					A			:							Any     )      ->      int:






         """simple docstring"""
         if not os.path.exists(A     ):
             os.makedirs(A     )

         # load text file
         a__  			=							open(A    ,					encoding="""utf8"""     ).readlines()

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

             a__			,		a__  			=							line.strip().split("""\t"""     )
             a__  			=							os.path.join(A    ,					wav_name + """.wav"""     )
             a__  			=							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			():
    a__  			=							argparse.ArgumentParser()

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

    a__  			=							parser.parse_args()

    a__  			=							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 List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


SCREAMING_SNAKE_CASE_    :       int       =							logging.get_logger(__name__)  # pylint: disable=invalid-name

SCREAMING_SNAKE_CASE_    :       int       =							'''
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)["depth"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline("depth-estimation")

        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to("cuda")

        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to("cuda")

        >>> img = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/cat.png"
        ... ).resize((768, 768))


        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")

        >>> prompt = "A robot, 4k photo"
        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"

        >>> generator = torch.Generator(device="cuda").manual_seed(43)

        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)

        >>> images = pipe(
        ...     image=img,
        ...     strength=0.5,
        ...     image_embeds=img_emb.image_embeds,
        ...     negative_image_embeds=negative_emb.image_embeds,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save("robot_cat.png")
        ```
'''
def 							__lowerCAmelCase			(UpperCAmelCase_ ,     UpperCAmelCase_ ,     UpperCAmelCase_=8		):
    a__  			=							height // scale_factor**2
    if height % scale_factor**2 != 0:
        new_height += 1
    a__  			=							width // scale_factor**2
    if width % scale_factor**2 != 0:
        new_width += 1
    return new_height * scale_factor, new_width * scale_factor
def 							__lowerCAmelCase			(UpperCAmelCase_ ,     UpperCAmelCase_=512 ,     UpperCAmelCase_=512		):
    a__  			=							pil_image.resize((w, h) ,     resample=Image.BICUBIC ,     reducing_gap=1		)
    a__  			=							np.array(pil_image.convert("""RGB"""		)		)
    a__  			=							arr.astype(np.floataa		) / 1_2_7.5 - 1
    a__  			=							np.transpose(UpperCAmelCase_ ,     [2, 0, 1]		)
    a__  			=							torch.from_numpy(UpperCAmelCase_		).unsqueeze(0		)
    return image







class 		__UpperCAmelCase(		lowercase__			):





     """simple docstring"""



     def __init__(   self			:							Optional[Any]    ,					A			:							UNetaDConditionModel    ,					A			:							DDPMScheduler    ,					A			:							VQModel    ,					)      ->      Any:






         """simple docstring"""
         super().__init__()

         self.register_modules(
             unet=A    ,					scheduler=A    ,					movq=A    ,					)
         a__  			=							2 ** (len(self.movq.config.block_out_channels     ) - 1)

     def 					__SCREAMING_SNAKE_CASE		(   self			:							int    ,					A			:							Optional[int]    ,					A			:							int    ,					A			:							Any     )      ->      Dict:






         """simple docstring"""
         # get the original timestep using init_timestep
         a__  			=							min(int(num_inference_steps * strength     )    ,					A     )

         a__  			=							max(num_inference_steps - init_timestep    ,					0     )
         a__  			=							self.scheduler.timesteps[t_start:]

         return timesteps, num_inference_steps - t_start

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Dict    ,					A			:							List[Any]    ,					A			:							Dict    ,					A			:							str    ,					A			:							Union[str, Any]    ,					A			:							Union[str, Any]    ,					A			:							str    ,					A			:							str=None     )      ->      List[Any]:






         """simple docstring"""
         if not isinstance(A    ,					(torch.Tensor, PIL.Image.Image, list)     ):
             raise ValueError(
                 f"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(A     )}"""     )

         a__  			=							image.to(device=A    ,					dtype=A     )

         a__  			=							batch_size * num_images_per_prompt

         if image.shape[1] == 4:
             a__  			=							image

         else:
             if isinstance(A    ,					A     ) and len(A     ) != batch_size:
                 raise ValueError(
                     f"""You have passed a list of generators of length {len(A     )}, but requested an effective batch"""
                     f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""     )

             elif isinstance(A    ,					A     ):
                 a__  			=							[
                     self.movq.encode(image[i : i + 1]     ).latent_dist.sample(generator[i]     ) for i in range(A     )
                 ]
                 a__  			=							torch.cat(A    ,					dim=0     )
             else:
                 a__  			=							self.movq.encode(A     ).latent_dist.sample(A     )

             a__  			=							self.movq.config.scaling_factor * init_latents

         a__  			=							torch.cat([init_latents]    ,					dim=0     )

         a__  			=							init_latents.shape
         a__  			=							randn_tensor(A    ,					generator=A    ,					device=A    ,					dtype=A     )

         # get latents
         a__  			=							self.scheduler.add_noise(A    ,					A    ,					A     )

         a__  			=							init_latents

         return latents

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Any    ,					A			:							Union[str, Any]=0     )      ->      Tuple:






         """simple docstring"""
         if is_accelerate_available():
             from accelerate import cpu_offload
         else:
             raise ImportError("""Please install accelerate via `pip install accelerate`"""     )

         a__  			=							torch.device(f"""cuda:{gpu_id}"""     )

         a__  			=							[
             self.unet,
             self.movq,
         ]
         for cpu_offloaded_model in models:
             if cpu_offloaded_model is not None:
                 cpu_offload(A    ,					A     )

     def 					__SCREAMING_SNAKE_CASE		(   self			:							int    ,					A			:							Optional[Any]=0     )      ->      Dict:






         """simple docstring"""
         if is_accelerate_available() and is_accelerate_version(""">="""    ,					"""0.17.0.dev0"""     ):
             from accelerate import cpu_offload_with_hook
         else:
             raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""     )

         a__  			=							torch.device(f"""cuda:{gpu_id}"""     )

         if self.device.type != "cpu":
             self.to("""cpu"""    ,					silence_dtype_warnings=A     )
             torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

         a__  			=							None
         for cpu_offloaded_model in [self.unet, self.movq]:
             a__			,		a__  			=							cpu_offload_with_hook(A    ,					A    ,					prev_module_hook=A     )

         # We'll offload the last model manually.
         a__  			=							hook

     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def 					__SCREAMING_SNAKE_CASE		(   self			:							Union[str, Any]     )      ->      Optional[int]:






         """simple docstring"""
         if not hasattr(self.unet    ,					"""_hf_hook"""     ):
             return self.device
         for module in self.unet.modules():
             if (
                 hasattr(A    ,					"""_hf_hook"""     )
                 and hasattr(module._hf_hook    ,					"""execution_device"""     )
                 and module._hf_hook.execution_device is not None
             ):
                 return torch.device(module._hf_hook.execution_device     )
         return self.device






     @torch.no_grad()
     @replace_example_docstring(A     )
     def __call__(   self			:							int    ,					A			:							Union[torch.FloatTensor, List[torch.FloatTensor]]    ,					A			:							Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]    ,					A			:							Union[torch.FloatTensor, List[torch.FloatTensor]]    ,					A			:							torch.FloatTensor    ,					A			:							int = 5_12    ,					A			:							int = 5_12    ,					A			:							int = 1_00    ,					A			:							float = 4.0    ,					A			:							float = 0.3    ,					A			:							int = 1    ,					A			:							Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,					A			:							Optional[str] = "pil"    ,					A			:							bool = True    ,					)      ->      List[Any]:






         """simple docstring"""
         a__  			=							self._execution_device

         a__  			=							guidance_scale > 1.0

         if isinstance(A    ,					A     ):
             a__  			=							torch.cat(A    ,					dim=0     )
         if isinstance(A    ,					A     ):
             a__  			=							torch.cat(A    ,					dim=0     )
         if isinstance(A    ,					A     ):
             a__  			=							torch.cat(A    ,					dim=0     )

         a__  			=							image_embeds.shape[0]

         if do_classifier_free_guidance:
             a__  			=							image_embeds.repeat_interleave(A    ,					dim=0     )
             a__  			=							negative_image_embeds.repeat_interleave(A    ,					dim=0     )
             a__  			=							hint.repeat_interleave(A    ,					dim=0     )

         a__  			=							torch.cat([negative_image_embeds, image_embeds]    ,					dim=0     ).to(dtype=self.unet.dtype    ,					device=A     )
         a__  			=							torch.cat([hint, hint]    ,					dim=0     ).to(dtype=self.unet.dtype    ,					device=A     )

         if not isinstance(A    ,					A     ):
             a__  			=							[image]
         if not all(isinstance(A    ,					(PIL.Image.Image, torch.Tensor)     ) for i in image     ):
             raise ValueError(
                 f"""Input is in incorrect format: {[type(A     ) for i in image]}. Currently, we only support  PIL image and pytorch tensor"""     )

         a__  			=							torch.cat([prepare_image(A    ,					A    ,					A     ) for i in image]    ,					dim=0     )
         a__  			=							image.to(dtype=image_embeds.dtype    ,					device=A     )

         a__  			=							self.movq.encode(A     )["""latents"""]
         a__  			=							latents.repeat_interleave(A    ,					dim=0     )
         self.scheduler.set_timesteps(A    ,					device=A     )
         a__			,		a__  			=							self.get_timesteps(A    ,					A    ,					A     )
         a__  			=							timesteps[:1].repeat(batch_size * num_images_per_prompt     )
         a__			,		a__  			=							downscale_height_and_width(A    ,					A    ,					self.movq_scale_factor     )
         a__  			=							self.prepare_latents(
             A    ,					A    ,					A    ,					A    ,					image_embeds.dtype    ,					A    ,					A     )
         for i, t in enumerate(self.progress_bar(A     )     ):
             # expand the latents if we are doing classifier free guidance
             a__  			=							torch.cat([latents] * 2     ) if do_classifier_free_guidance else latents

             a__  			=							{"""image_embeds""": image_embeds, """hint""": hint}
             a__  			=							self.unet(
                 sample=A    ,					timestep=A    ,					encoder_hidden_states=A    ,					added_cond_kwargs=A    ,					return_dict=A    ,					)[0]

             if do_classifier_free_guidance:
                 a__			,		a__  			=							noise_pred.split(latents.shape[1]    ,					dim=1     )
                 a__			,		a__  			=							noise_pred.chunk(2     )
                 a__			,		a__  			=							variance_pred.chunk(2     )
                 a__  			=							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                 a__  			=							torch.cat([noise_pred, variance_pred_text]    ,					dim=1     )

             if not (
                 hasattr(self.scheduler.config    ,					"""variance_type"""     )
                 and self.scheduler.config.variance_type in ["learned", "learned_range"]
             ):
                 a__			,		a__  			=							noise_pred.split(latents.shape[1]    ,					dim=1     )

             # compute the previous noisy sample x_t -> x_t-1

             a__  			=							self.scheduler.step(
                 A    ,					A    ,					A    ,					generator=A    ,					)[0]

         # post-processing
         a__  			=							self.movq.decode(A    ,					force_not_quantize=A     )["""sample"""]

         if output_type not in ["pt", "np", "pil"]:
             raise ValueError(f"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"""     )

         if output_type in ["np", "pil"]:
             a__  			=							image * 0.5 + 0.5
             a__  			=							image.clamp(0    ,					1     )
             a__  			=							image.cpu().permute(0    ,					2    ,					3    ,					1     ).float().numpy()

         if output_type == "pil":
             a__  			=							self.numpy_to_pil(A     )

         if not return_dict:
             return (image,)

         return ImagePipelineOutput(images=A     )






