


'''simple docstring'''

import gc
import random
import tempfile
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.semantic_stable_diffusion import SemanticStableDiffusionPipeline as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()







class 			UpperCAmelCase			(							unittest.TestCase):

 def 		lowercase_    (			self			:						Tuple       ):





    """simple docstring"""



    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()

 @property
 def 		lowercase_    (			self			:						List[str]       ):





    """simple docstring"""



    UpperCamelCase__			    =    1
    UpperCamelCase__			    =    3
    UpperCamelCase__			    =    (32, 32)

    UpperCamelCase__			    =    floats_tensor((batch_size, num_channels) + sizes,       rng=random.Random(0       )       ).to(a_       )
    return image

 @property
 def 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



    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"),       cross_attention_dim=32,       )
    return model

 @property
 def 		lowercase_    (			self			:						Dict       ):





    """simple docstring"""



    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,       )
    return model

 @property
 def 		lowercase_    (			self			:						List[Any]       ):





    """simple docstring"""



    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=1000,       )
    return CLIPTextModel(a_       )

 @property
 def 		lowercase_    (			self			:						Dict       ):





    """simple docstring"""



    def extract(*a_			:						List[Any],       **a_			:						str       ):







       class 			UpperCAmelCase			:

        def __init__(			self			:						Dict       ):





           """simple docstring"""



           UpperCamelCase__			    =    torch.ones([0]       )

        def 		lowercase_    (			self			:						Optional[int],       a_			:						str       ):





           """simple docstring"""



           self.pixel_values.to(a_       )
           return self







       return Out()

    return extract

 def 		lowercase_    (			self			:						int       ):





    """simple docstring"""



    UpperCamelCase__			    =    "cpu"  # ensure determinism for the device-dependent torch.Generator
    UpperCamelCase__			    =    self.dummy_cond_unet
    UpperCamelCase__			    =    DDIMScheduler(
        beta_start=0.00_085,       beta_end=0.012,       beta_schedule="scaled_linear",       clip_sample=a_,       set_alpha_to_one=a_,       )

    UpperCamelCase__			    =    self.dummy_vae
    UpperCamelCase__			    =    self.dummy_text_encoder
    UpperCamelCase__			    =    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )

    # make sure here that pndm scheduler skips prk
    UpperCamelCase__			    =    StableDiffusionPipeline(
        unet=a_,       scheduler=a_,       vae=a_,       text_encoder=a_,       tokenizer=a_,       safety_checker=a_,       feature_extractor=self.dummy_extractor,       )
    UpperCamelCase__			    =    sd_pipe.to(a_       )
    sd_pipe.set_progress_bar_config(disable=a_       )

    UpperCamelCase__			    =    "A painting of a squirrel eating a burger"

    UpperCamelCase__			    =    torch.Generator(device=a_       ).manual_seed(0       )
    UpperCamelCase__			    =    sd_pipe([prompt],       generator=a_,       guidance_scale=6.0,       num_inference_steps=2,       output_type="np"       )
    UpperCamelCase__			    =    output.images

    UpperCamelCase__			    =    torch.Generator(device=a_       ).manual_seed(0       )
    UpperCamelCase__			    =    sd_pipe(
        [prompt],       generator=a_,       guidance_scale=6.0,       num_inference_steps=2,       output_type="np",       return_dict=a_,       )[0]

    UpperCamelCase__			    =    image[0, -3:, -3:, -1]
    UpperCamelCase__			    =    image_from_tuple[0, -3:, -3:, -1]

    assert image.shape == (1, 64, 64, 3)
    UpperCamelCase__			    =    np.array([0.5_753, 0.6_114, 0.5_001, 0.5_034, 0.5_470, 0.4_729, 0.4_971, 0.4_867, 0.4_867]       )

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

 def 		lowercase_    (			self			:						Dict       ):





    """simple docstring"""



    UpperCamelCase__			    =    "cpu"  # ensure determinism for the device-dependent torch.Generator
    UpperCamelCase__			    =    self.dummy_cond_unet
    UpperCamelCase__			    =    PNDMScheduler(skip_prk_steps=a_       )
    UpperCamelCase__			    =    self.dummy_vae
    UpperCamelCase__			    =    self.dummy_text_encoder
    UpperCamelCase__			    =    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )

    # make sure here that pndm scheduler skips prk
    UpperCamelCase__			    =    StableDiffusionPipeline(
        unet=a_,       scheduler=a_,       vae=a_,       text_encoder=a_,       tokenizer=a_,       safety_checker=a_,       feature_extractor=self.dummy_extractor,       )
    UpperCamelCase__			    =    sd_pipe.to(a_       )
    sd_pipe.set_progress_bar_config(disable=a_       )

    UpperCamelCase__			    =    "A painting of a squirrel eating a burger"
    UpperCamelCase__			    =    torch.Generator(device=a_       ).manual_seed(0       )
    UpperCamelCase__			    =    sd_pipe([prompt],       generator=a_,       guidance_scale=6.0,       num_inference_steps=2,       output_type="np"       )

    UpperCamelCase__			    =    output.images

    UpperCamelCase__			    =    torch.Generator(device=a_       ).manual_seed(0       )
    UpperCamelCase__			    =    sd_pipe(
        [prompt],       generator=a_,       guidance_scale=6.0,       num_inference_steps=2,       output_type="np",       return_dict=a_,       )[0]

    UpperCamelCase__			    =    image[0, -3:, -3:, -1]
    UpperCamelCase__			    =    image_from_tuple[0, -3:, -3:, -1]

    assert image.shape == (1, 64, 64, 3)
    UpperCamelCase__			    =    np.array([0.5_122, 0.5_712, 0.4_825, 0.5_053, 0.5_646, 0.4_769, 0.5_179, 0.4_894, 0.4_994]       )

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

 def 		lowercase_    (			self			:						Tuple       ):





    """simple docstring"""



    UpperCamelCase__			    =    StableDiffusionPipeline.from_pretrained(
        "hf-internal-testing/tiny-stable-diffusion-lms-pipe",       safety_checker=a_       )
    assert isinstance(a_,       a_       )
    assert isinstance(pipe.scheduler,       a_       )
    assert pipe.safety_checker is None

    UpperCamelCase__			    =    pipe("example prompt",       num_inference_steps=2       ).images[0]
    assert image is not None

    # check that there's no error when saving a pipeline with one of the models being None
    with tempfile.TemporaryDirectory() as tmpdirname:
       pipe.save_pretrained(a_       )
       UpperCamelCase__			    =    StableDiffusionPipeline.from_pretrained(a_       )

    # sanity check that the pipeline still works
    assert pipe.safety_checker is None
    UpperCamelCase__			    =    pipe("example prompt",       num_inference_steps=2       ).images[0]
    assert image is not None

 @unittest.skipIf(torch_device != "cuda",       "This test requires a GPU"       )
 def 		lowercase_    (			self			:						int       ):





    """simple docstring"""



    UpperCamelCase__			    =    self.dummy_cond_unet
    UpperCamelCase__			    =    PNDMScheduler(skip_prk_steps=a_       )
    UpperCamelCase__			    =    self.dummy_vae
    UpperCamelCase__			    =    self.dummy_text_encoder
    UpperCamelCase__			    =    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )

    # put models in fp16
    UpperCamelCase__			    =    unet.half()
    UpperCamelCase__			    =    vae.half()
    UpperCamelCase__			    =    bert.half()

    # make sure here that pndm scheduler skips prk
    UpperCamelCase__			    =    StableDiffusionPipeline(
        unet=a_,       scheduler=a_,       vae=a_,       text_encoder=a_,       tokenizer=a_,       safety_checker=a_,       feature_extractor=self.dummy_extractor,       )
    UpperCamelCase__			    =    sd_pipe.to(a_       )
    sd_pipe.set_progress_bar_config(disable=a_       )

    UpperCamelCase__			    =    "A painting of a squirrel eating a burger"
    UpperCamelCase__			    =    sd_pipe([prompt],       num_inference_steps=2,       output_type="np"       ).images

    assert image.shape == (1, 64, 64, 3)







@nightly
@require_torch_gpu
class 			UpperCAmelCase			(							unittest.TestCase):

 def 		lowercase_    (			self			:						List[Any]       ):





    """simple docstring"""



    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()

 def 		lowercase_    (			self			:						Dict       ):





    """simple docstring"""



    UpperCamelCase__			    =    "cuda"
    UpperCamelCase__			    =    StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"       )
    UpperCamelCase__			    =    pipe.to(a_       )
    pipe.set_progress_bar_config(disable=a_       )

    UpperCamelCase__			    =    "a photo of a cat"
    UpperCamelCase__			    =    {
        "editing_prompt": ["sunglasses"],
        "reverse_editing_direction": [False],
        "edit_warmup_steps": 10,
        "edit_guidance_scale": 6,
        "edit_threshold": 0.95,
        "edit_momentum_scale": 0.5,
        "edit_mom_beta": 0.6,
    }

    UpperCamelCase__			    =    3
    UpperCamelCase__			    =    7

    # no sega enabled
    UpperCamelCase__			    =    torch.Generator(a_       )
    generator.manual_seed(a_       )
    UpperCamelCase__			    =    pipe(
        [prompt],       generator=a_,       guidance_scale=a_,       num_inference_steps=50,       output_type="np",       width=512,       height=512,       )

    UpperCamelCase__			    =    output.images
    UpperCamelCase__			    =    image[0, -3:, -3:, -1]
    UpperCamelCase__			    =    [
        0.34_673_113,
        0.38_492_733,
        0.37_597_352,
        0.34_086_335,
        0.35_650_748,
        0.35_579_205,
        0.3_384_763,
        0.34_340_236,
        0.3_573_271,
    ]

    assert image.shape == (1, 512, 512, 3)

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

    # with sega enabled
    # generator = torch.manual_seed(seed)
    generator.manual_seed(a_       )
    UpperCamelCase__			    =    pipe(
        [prompt],       generator=a_,       guidance_scale=a_,       num_inference_steps=50,       output_type="np",       width=512,       height=512,       **a_,       )

    UpperCamelCase__			    =    output.images
    UpperCamelCase__			    =    image[0, -3:, -3:, -1]
    UpperCamelCase__			    =    [
        0.41_887_826,
        0.37_728_766,
        0.30_138_272,
        0.41_416_335,
        0.41_664_985,
        0.36_283_392,
        0.36_191_246,
        0.43_364_465,
        0.43_001_732,
    ]

    assert image.shape == (1, 512, 512, 3)

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

 def 		lowercase_    (			self			:						int       ):





    """simple docstring"""



    UpperCamelCase__			    =    "cuda"
    UpperCamelCase__			    =    StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"       )
    UpperCamelCase__			    =    pipe.to(a_       )
    pipe.set_progress_bar_config(disable=a_       )

    UpperCamelCase__			    =    "an image of a crowded boulevard, realistic, 4k"
    UpperCamelCase__			    =    {
        "editing_prompt": "crowd, crowded, people",
        "reverse_editing_direction": True,
        "edit_warmup_steps": 10,
        "edit_guidance_scale": 8.3,
        "edit_threshold": 0.9,
        "edit_momentum_scale": 0.5,
        "edit_mom_beta": 0.6,
    }

    UpperCamelCase__			    =    9
    UpperCamelCase__			    =    7

    # no sega enabled
    UpperCamelCase__			    =    torch.Generator(a_       )
    generator.manual_seed(a_       )
    UpperCamelCase__			    =    pipe(
        [prompt],       generator=a_,       guidance_scale=a_,       num_inference_steps=50,       output_type="np",       width=512,       height=512,       )

    UpperCamelCase__			    =    output.images
    UpperCamelCase__			    =    image[0, -3:, -3:, -1]
    UpperCamelCase__			    =    [
        0.43_497_998,
        0.91_814_065,
        0.7_540_739,
        0.55_580_205,
        0.8_467_265,
        0.5_389_691,
        0.62_574_506,
        0.58_897_763,
        0.50_926_757,
    ]

    assert image.shape == (1, 512, 512, 3)

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

    # with sega enabled
    # generator = torch.manual_seed(seed)
    generator.manual_seed(a_       )
    UpperCamelCase__			    =    pipe(
        [prompt],       generator=a_,       guidance_scale=a_,       num_inference_steps=50,       output_type="np",       width=512,       height=512,       **a_,       )

    UpperCamelCase__			    =    output.images
    UpperCamelCase__			    =    image[0, -3:, -3:, -1]
    UpperCamelCase__			    =    [
        0.3_089_719,
        0.30_500_144,
        0.29_016_042,
        0.30_630_964,
        0.325_687,
        0.29_419_225,
        0.2_908_091,
        0.28_723_598,
        0.27_696_294,
    ]

    assert image.shape == (1, 512, 512, 3)

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

 def 		lowercase_    (			self			:						Dict       ):





    """simple docstring"""



    UpperCamelCase__			    =    "cuda"
    UpperCamelCase__			    =    StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"       )
    UpperCamelCase__			    =    pipe.to(a_       )
    pipe.set_progress_bar_config(disable=a_       )

    UpperCamelCase__			    =    "a castle next to a river"
    UpperCamelCase__			    =    {
        "editing_prompt": ["boat on a river, boat", "monet, impression, sunrise"],
        "reverse_editing_direction": False,
        "edit_warmup_steps": [15, 18],
        "edit_guidance_scale": 6,
        "edit_threshold": [0.9, 0.8],
        "edit_momentum_scale": 0.5,
        "edit_mom_beta": 0.6,
    }

    UpperCamelCase__			    =    48
    UpperCamelCase__			    =    7

    # no sega enabled
    UpperCamelCase__			    =    torch.Generator(a_       )
    generator.manual_seed(a_       )
    UpperCamelCase__			    =    pipe(
        [prompt],       generator=a_,       guidance_scale=a_,       num_inference_steps=50,       output_type="np",       width=512,       height=512,       )

    UpperCamelCase__			    =    output.images
    UpperCamelCase__			    =    image[0, -3:, -3:, -1]
    UpperCamelCase__			    =    [
        0.75_163_555,
        0.76_037_145,
        0.61_785,
        0.9_189_673,
        0.8_627_701,
        0.85_189_694,
        0.8_512_813,
        0.87_012_076,
        0.8_312_857,
    ]

    assert image.shape == (1, 512, 512, 3)

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

    # with sega enabled
    # generator = torch.manual_seed(seed)
    generator.manual_seed(a_       )
    UpperCamelCase__			    =    pipe(
        [prompt],       generator=a_,       guidance_scale=a_,       num_inference_steps=50,       output_type="np",       width=512,       height=512,       **a_,       )

    UpperCamelCase__			    =    output.images
    UpperCamelCase__			    =    image[0, -3:, -3:, -1]
    UpperCamelCase__			    =    [
        0.73_553_365,
        0.7_537_271,
        0.74_341_905,
        0.66_480_356,
        0.6_472_925,
        0.63_039_416,
        0.64_812_905,
        0.6_749_717,
        0.6_517_102,
    ]

    assert image.shape == (1, 512, 512, 3)

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

 def 		lowercase_    (			self			:						int       ):





    """simple docstring"""



    UpperCamelCase__			    =    "cuda"
    UpperCamelCase__			    =    StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5",       torch_dtype=torch.floataa       )
    UpperCamelCase__			    =    pipe.to(a_       )
    pipe.set_progress_bar_config(disable=a_       )

    UpperCamelCase__			    =    "a photo of a cat"
    UpperCamelCase__			    =    {
        "editing_prompt": ["sunglasses"],
        "reverse_editing_direction": [False],
        "edit_warmup_steps": 10,
        "edit_guidance_scale": 6,
        "edit_threshold": 0.95,
        "edit_momentum_scale": 0.5,
        "edit_mom_beta": 0.6,
    }

    UpperCamelCase__			    =    3
    UpperCamelCase__			    =    7

    # no sega enabled
    UpperCamelCase__			    =    torch.Generator(a_       )
    generator.manual_seed(a_       )
    UpperCamelCase__			    =    pipe(
        [prompt],       generator=a_,       guidance_scale=a_,       num_inference_steps=50,       output_type="np",       width=512,       height=512,       )

    UpperCamelCase__			    =    output.images
    UpperCamelCase__			    =    image[0, -3:, -3:, -1]
    UpperCamelCase__			    =    [
        0.34_887_695,
        0.3_876_953,
        0.375,
        0.34_423_828,
        0.3_581_543,
        0.35_717_773,
        0.3_383_789,
        0.34_570_312,
        0.359_375,
    ]

    assert image.shape == (1, 512, 512, 3)

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

    # with sega enabled
    # generator = torch.manual_seed(seed)
    generator.manual_seed(a_       )
    UpperCamelCase__			    =    pipe(
        [prompt],       generator=a_,       guidance_scale=a_,       num_inference_steps=50,       output_type="np",       width=512,       height=512,       **a_,       )

    UpperCamelCase__			    =    output.images
    UpperCamelCase__			    =    image[0, -3:, -3:, -1]
    UpperCamelCase__			    =    [
        0.42_285_156,
        0.36_914_062,
        0.29_077_148,
        0.42_041_016,
        0.41_918_945,
        0.35_498_047,
        0.3_618_164,
        0.4_423_828,
        0.43_115_234,
    ]

    assert image.shape == (1, 512, 512, 3)

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





'''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()



