





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




   def       a	(					self			,     A_   ):
        _UpperCamelCase				,	_UpperCamelCase											=   torchaudio.load(A_   )
        _UpperCamelCase											=   self.resampler(A_   ).squeeze().numpy()
        return speech




   def       a	(					self			,     A_   ):
        _UpperCamelCase											=   self.blank_id
        _UpperCamelCase											=   "|".join(item["sent"].split(" "   )   )
        if not os.path.isfile(item["wav_path"]   ):
             print(item["wav_path"]			,     "not found in wavs directory"   )

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

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

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

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

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

        def get_trellis(A_			,     A_			,     A_=0   ):
             _UpperCamelCase											=   emission.size(0   )
             _UpperCamelCase											=   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.
             _UpperCamelCase											=   torch.full((num_frames + 1, num_tokens + 1)			,     -float("inf"   )   )
             _UpperCamelCase											=   0
             for t in range(A_   ):
                  _UpperCamelCase											=   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											=   get_trellis(A_			,     A_			,     A_   )

        @dataclass
        class     A_ :

           '''simple docstring'''





           _lowerCAmelCase						=    42
           _lowerCAmelCase						=    42
           _lowerCAmelCase						=    42

        def backtrack(A_			,     A_			,     A_			,     A_=0   ):
             _UpperCamelCase											=   trellis.size(1   ) - 1
             _UpperCamelCase											=   torch.argmax(trellis[:, j]   ).item()

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

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

        _UpperCamelCase											=   backtrack(A_			,     A_			,     A_			,     A_   )

        @dataclass
        class     A_ :

           '''simple docstring'''





           _lowerCAmelCase						=    42
           _lowerCAmelCase						=    42
           _lowerCAmelCase						=    42
           _lowerCAmelCase						=    42




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




           @property
           def       a	(					self   ):
                return self.end - self.start

        def merge_repeats(A_   ):
             _UpperCamelCase				,	_UpperCamelCase											=   0, 0
             _UpperCamelCase											=   []
             while ia < len(A_   ):
                  while ia < len(A_   ) and path[ia].token_index == path[ia].token_index:
                       ia += 1
                  _UpperCamelCase											=   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_			,     )   )
                  _UpperCamelCase											=   ia
             return segments

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




   def       a	(					self			,     A_			,     A_			,     A_   ):






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

        # load text file
        _UpperCamelCase											=   open(A_			,     encoding="utf8"   ).readlines()

        _UpperCamelCase											=   []
        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											=   line.strip().split("\t"   )
             _UpperCamelCase											=   os.path.join(A_			,     wav_name + ".wav"   )
             _UpperCamelCase											=   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  lowercase__(		)->   Optional[int]:






     """simple docstring"""

     _UpperCamelCase											=   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=16000 ,   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"				)

     _UpperCamelCase											=   parser.parse_args()

     _UpperCamelCase											=   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 gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class     A_ (  lowerCAmelCase_    ,			lowerCAmelCase_    ,			unittest.TestCase  ):

			'''simple docstring'''





			_lowerCAmelCase						=    StableDiffusionXLPipeline
			_lowerCAmelCase						=    TEXT_TO_IMAGE_PARAMS
			_lowerCAmelCase						=    TEXT_TO_IMAGE_BATCH_PARAMS
			_lowerCAmelCase						=    TEXT_TO_IMAGE_IMAGE_PARAMS
			_lowerCAmelCase						=    TEXT_TO_IMAGE_IMAGE_PARAMS




			def       a	(					self   ):
								torch.manual_seed(0   )
								_UpperCamelCase											=   UNetaDConditionModel(
								    block_out_channels=(32, 64)			,     layers_per_block=2			,     sample_size=32			,     in_channels=4			,     out_channels=4			,     down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")			,     up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")			,     attention_head_dim=(2, 4)			,     use_linear_projection=A_			,     addition_embed_type="text_time"			,     addition_time_embed_dim=8			,     transformer_layers_per_block=(1, 2)			,     projection_class_embeddings_input_dim=80			,     cross_attention_dim=64			,     )
								_UpperCamelCase											=   EulerDiscreteScheduler(
								    beta_start=0.0_0085			,     beta_end=0.012			,     steps_offset=1			,     beta_schedule="scaled_linear"			,     timestep_spacing="leading"			,     )
								torch.manual_seed(0   )
								_UpperCamelCase											=   AutoencoderKL(
								    block_out_channels=[32, 64]			,     in_channels=3			,     out_channels=3			,     down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]			,     up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]			,     latent_channels=4			,     sample_size=1_28			,     )
								torch.manual_seed(0   )
								_UpperCamelCase											=   CLIPTextConfig(
								    bos_token_id=0			,     eos_token_id=2			,     hidden_size=32			,     intermediate_size=37			,     layer_norm_eps=1e-0_5			,     num_attention_heads=4			,     num_hidden_layers=5			,     pad_token_id=1			,     vocab_size=10_00			,     hidden_act="gelu"			,     projection_dim=32			,     )
								_UpperCamelCase											=   CLIPTextModel(A_   )
								_UpperCamelCase											=   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"			,     local_files_only=A_   )

								_UpperCamelCase											=   CLIPTextModelWithProjection(A_   )
								_UpperCamelCase											=   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"			,     local_files_only=A_   )

								_UpperCamelCase											=   {
								    "unet": unet,
								    "scheduler": scheduler,
								    "vae": vae,
								    "text_encoder": text_encoder,
								    "tokenizer": tokenizer,
								    "text_encoder_2": text_encoder_a,
								    "tokenizer_2": tokenizer_a,
								    # "safety_checker": None,
								    # "feature_extractor": None,
								}
								return components




			def       a	(					self			,     A_			,     A_=0   ):
								if str(A_   ).startswith("mps"   ):
													_UpperCamelCase											=   torch.manual_seed(A_   )
								else:
													_UpperCamelCase											=   torch.Generator(device=A_   ).manual_seed(A_   )
								_UpperCamelCase											=   {
								    "prompt": "A painting of a squirrel eating a burger",
								    "generator": generator,
								    "num_inference_steps": 2,
								    "guidance_scale": 5.0,
								    "output_type": "numpy",
								}
								return inputs




			def       a	(					self   ):
								_UpperCamelCase											=   "cpu"  # ensure determinism for the device-dependent torch.Generator
								_UpperCamelCase											=   self.get_dummy_components()
								_UpperCamelCase											=   StableDiffusionXLPipeline(**A_   )
								_UpperCamelCase											=   sd_pipe.to(A_   )
								sd_pipe.set_progress_bar_config(disable=A_   )

								_UpperCamelCase											=   self.get_dummy_inputs(A_   )
								_UpperCamelCase											=   sd_pipe(**A_   ).images
								_UpperCamelCase											=   image[0, -3:, -3:, -1]

								assert image.shape == (1, 64, 64, 3)
								_UpperCamelCase											=   np.array([0.5873, 0.6128, 0.4797, 0.5122, 0.5674, 0.4639, 0.5227, 0.5149, 0.4747]   )

								assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2




			def       a	(					self   ):
								_UpperCamelCase											=   self.get_dummy_components()
								_UpperCamelCase											=   StableDiffusionXLPipeline(**A_   )
								_UpperCamelCase											=   sd_pipe.to(A_   )
								_UpperCamelCase											=   sd_pipe.to(A_   )
								sd_pipe.set_progress_bar_config(disable=A_   )

								# forward without prompt embeds
								_UpperCamelCase											=   self.get_dummy_inputs(A_   )
								_UpperCamelCase											=   3 * ["this is a negative prompt"]
								_UpperCamelCase											=   negative_prompt
								_UpperCamelCase											=   3 * [inputs["prompt"]]

								_UpperCamelCase											=   sd_pipe(**A_   )
								_UpperCamelCase											=   output.images[0, -3:, -3:, -1]

								# forward with prompt embeds
								_UpperCamelCase											=   self.get_dummy_inputs(A_   )
								_UpperCamelCase											=   3 * ["this is a negative prompt"]
								_UpperCamelCase											=   3 * [inputs.pop("prompt"   )]

								(
								    (
								    _UpperCamelCase
								)				,	(
								    _UpperCamelCase
								)				,	(
								    _UpperCamelCase
								)				,	(
								    _UpperCamelCase
								)				,	
								)											=   sd_pipe.encode_prompt(A_			,     negative_prompt=A_   )

								_UpperCamelCase											=   sd_pipe(
								    **A_			,     prompt_embeds=A_			,     negative_prompt_embeds=A_			,     pooled_prompt_embeds=A_			,     negative_pooled_prompt_embeds=A_			,     )
								_UpperCamelCase											=   output.images[0, -3:, -3:, -1]

								# make sure that it's equal
								assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()   ).max() < 1e-4




			def       a	(					self   ):
								super().test_attention_slicing_forward_pass(expected_max_diff=3e-3   )




			def       a	(					self   ):
								super().test_inference_batch_single_identical(expected_max_diff=3e-3   )




@slow
@require_torch_gpu
class     A_ (  unittest.TestCase  ):

			'''simple docstring'''





			def       a	(					self   ):
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()




			def       a	(					self			,     A_			,     A_="cpu"			,     A_=torch.floataa			,     A_=0   ):
								_UpperCamelCase											=   torch.Generator(device=A_   ).manual_seed(A_   )
								_UpperCamelCase											=   np.random.RandomState(A_   ).standard_normal((1, 4, 64, 64)   )
								_UpperCamelCase											=   torch.from_numpy(A_   ).to(device=A_			,     dtype=A_   )
								_UpperCamelCase											=   {
								    "prompt": "a photograph of an astronaut riding a horse",
								    "latents": latents,
								    "generator": generator,
								    "num_inference_steps": 3,
								    "guidance_scale": 7.5,
								    "output_type": "numpy",
								}
								return inputs




			def       a	(					self   ):
								_UpperCamelCase											=   DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-base"   )
								pipe.to(A_   )
								pipe.set_progress_bar_config(disable=A_   )

								_UpperCamelCase											=   self.get_inputs(A_   )
								_UpperCamelCase											=   pipe(**A_   ).images
								_UpperCamelCase											=   image[0, -3:, -3:, -1].flatten()

								assert image.shape == (1, 5_12, 5_12, 3)
								_UpperCamelCase											=   np.array([0.4_9493, 0.4_7896, 0.4_0798, 0.5_4214, 0.5_3212, 0.4_8202, 0.4_7656, 0.4_6329, 0.4_8506]   )
								assert np.abs(image_slice - expected_slice   ).max() < 7e-3



