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       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		_lowercase   =cuda
		_lowercase   =AutoConfig.from_pretrained(lowerCAmelCase_       )
		_lowercase   =AutoModelForCTC.from_pretrained(lowerCAmelCase_       )
		self.model.eval()
		if self.cuda:
			self.model.to(device="cuda"       )
		_lowercase   =AutoProcessor.from_pretrained(lowerCAmelCase_       )
		_lowercase   =torchaudio.transforms.Resample(lowerCAmelCase_       ,  16000       )
		_lowercase   =0
		_lowercase   =list(self.processor.tokenizer.get_vocab().keys()       )
		for i in range(len(lowerCAmelCase_       )       ):
			if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
				_lowercase   =i
		print("Blank Token id [PAD]/<pad>"       ,  lowerCAmelCase_       )
		_lowercase   =blank_id


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		_lowercase   ,  _lowercase   =torchaudio.load(lowerCAmelCase_       )
		_lowercase   =self.resampler(lowerCAmelCase_       ).squeeze().numpy()
		return speech


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

		_lowercase   =self.speech_file_to_array_fn(item["wav_path"]       )
		_lowercase   =self.processor(lowerCAmelCase_       ,  sampling_rate=16000       ,  return_tensors="pt"       ,  padding=lowerCAmelCase_       )
		if self.cuda:
			_lowercase   =inputs.to(device="cuda"       )

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

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

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

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

		def get_trellis(lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_=0       ):
			_lowercase   =emission.size(0       )
			_lowercase   =len(lowerCAmelCase_       )

			# 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.
			_lowercase   =torch.full((num_frames + 1, num_tokens + 1)       ,  -float("inf"       )       )
			_lowercase   =0
			for t in range(lowerCAmelCase_       ):
				_lowercase   =torch.maximum(
				    # Score for staying at the same token
				    trellis[t, 1:] + emission[t, blank_id]       ,  trellis[t, :-1] + emission[t, tokens]       ,  )
			return trellis

		_lowercase   =get_trellis(lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       )
		@dataclass
		class 	_a  :



			"""simple docstring"""



			__SCREAMING_SNAKE_CASE              =					42
			__SCREAMING_SNAKE_CASE              =					42
			__SCREAMING_SNAKE_CASE              =					42

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

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

				# 2. Store the path with frame-wise probability.
				_lowercase   =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       ,  lowerCAmelCase_       )       )

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

		_lowercase   =backtrack(lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       )
		@dataclass
		class 	_a  :



			"""simple docstring"""



			__SCREAMING_SNAKE_CASE              =					42
			__SCREAMING_SNAKE_CASE              =					42
			__SCREAMING_SNAKE_CASE              =					42
			__SCREAMING_SNAKE_CASE              =					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 __lowerCAmelCase      (   self       ):
				return self.end - self.start

		def merge_repeats(lowerCAmelCase_       ):
			_lowercase   ,  _lowercase   =0, 0
			_lowercase   =[]
			while ia < len(lowerCAmelCase_       ):
				while ia < len(lowerCAmelCase_       ) and path[ia].token_index == path[ia].token_index:
					ia += 1
				_lowercase   =sum(path[k].score for k in range(lowerCAmelCase_       ,  lowerCAmelCase_       )       ) / (ia - ia)
				segments.append(
				    Segment(
				        transcript[path[ia].token_index]       ,  path[ia].time_index       ,  path[ia - 1].time_index + 1       ,  lowerCAmelCase_       ,  )       )
				_lowercase   =ia
			return segments

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


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		if not os.path.exists(lowerCAmelCase_       ):
			os.makedirs(lowerCAmelCase_       )

		# load text file
		_lowercase   =open(lowerCAmelCase_       ,  encoding="utf8"       ).readlines()

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

			_lowercase   ,  _lowercase   =line.strip().split("\t"       )
			_lowercase   =os.path.join(lowerCAmelCase_       ,  wav_name + ".wav"       )
			_lowercase   =os.path.join(lowerCAmelCase_       ,  wav_name + ".txt"       )

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

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

def 	__lowerCamelCase							(				)      ->       Union[str, Any]:
	_lowercase   =argparse.ArgumentParser()

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

	_lowercase   =parser.parse_args()

	_lowercase   =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()





import gc
import unittest

import numpy as np
import torch

from diffusers import DDPMScheduler, MidiProcessor, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder
from diffusers.utils import require_torch_gpu, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_note_seq, require_onnxruntime

from ..pipeline_params import TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS, TOKENS_TO_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


lowerCAmelCase__      		=					'./tests/fixtures/elise_format0.mid'
@unittest.skip('The note-seq package currently throws an error on import'		)
class 							a_					(		SCREAMING_SNAKE_CASE			,	unittest.TestCase		):






 '''simple docstring'''






 UpperCAmelCase_      					=   SpectrogramDiffusionPipeline
 UpperCAmelCase_      					=   PipelineTesterMixin.required_optional_params - {
     'callback',
     'latents',
     'callback_steps',
     'output_type',
     'num_images_per_prompt',
 }
 UpperCAmelCase_      					=   False
 UpperCAmelCase_      					=   False
 UpperCAmelCase_      					=   TOKENS_TO_AUDIO_GENERATION_PARAMS
 UpperCAmelCase_      					=   TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS




 def 						__snake_case		(     self   :    List[Any]):







        '''simple docstring'''



        torch.manual_seed(0)
        lowerCAmelCase__	      =   SpectrogramNotesEncoder(
            max_length=2_048     ,       vocab_size=1_536     ,       d_model=768     ,       dropout_rate=0.1     ,       num_layers=1     ,       num_heads=1     ,       d_kv=4     ,       d_ff=2_048     ,       feed_forward_proj='gated-gelu'     ,       )

        lowerCAmelCase__	      =   SpectrogramContEncoder(
            input_dims=128     ,       targets_context_length=256     ,       d_model=768     ,       dropout_rate=0.1     ,       num_layers=1     ,       num_heads=1     ,       d_kv=4     ,       d_ff=2_048     ,       feed_forward_proj='gated-gelu'     ,       )

        lowerCAmelCase__	      =   TaFilmDecoder(
            input_dims=128     ,       targets_length=256     ,       max_decoder_noise_time=20_000.0     ,       d_model=768     ,       num_layers=1     ,       num_heads=1     ,       d_kv=4     ,       d_ff=2_048     ,       dropout_rate=0.1     ,       )

        lowerCAmelCase__	      =   DDPMScheduler()

        lowerCAmelCase__	      =   {
            'notes_encoder': notes_encoder.eval(),
            'continuous_encoder': continuous_encoder.eval(),
            'decoder': decoder.eval(),
            'scheduler': scheduler,
            'melgan': None,
        }
        return components




 def 						__snake_case		(     self   :    Optional[int]     ,       lowercase__   :    int     ,       lowercase__   :    List[str]=0):







        '''simple docstring'''



        if str(lowercase__).startswith('mps'):
               lowerCAmelCase__	      =   torch.manual_seed(lowercase__)
        else:
               lowerCAmelCase__	      =   torch.Generator(device=lowercase__).manual_seed(lowercase__)
        lowerCAmelCase__	      =   {
            'input_tokens': [
                [1_134, 90, 1_135, 1_133, 1_080, 112, 1_132, 1_080, 1_133, 1_079, 133, 1_132, 1_079, 1_133, 1] + [0] * 2_033
            ],
            'generator': generator,
            'num_inference_steps': 4,
            'output_type': 'mel',
        }
        return inputs




 def 						__snake_case		(     self   :    List[str]):







        '''simple docstring'''



        lowerCAmelCase__	      =   'cpu'  # ensure determinism for the device-dependent torch.Generator
        lowerCAmelCase__	      =   self.get_dummy_components()
        lowerCAmelCase__	      =   SpectrogramDiffusionPipeline(**lowercase__)
        lowerCAmelCase__	      =   pipe.to(lowercase__)
        pipe.set_progress_bar_config(disable=lowercase__)

        lowerCAmelCase__	      =   self.get_dummy_inputs(lowercase__)
        lowerCAmelCase__	      =   pipe(**lowercase__)
        lowerCAmelCase__	      =   output.audios

        lowerCAmelCase__	      =   mel[0, -3:, -3:]

        assert mel_slice.shape == (3, 3)
        lowerCAmelCase__	      =   np.array(
            [-11.512_925, -4.788_215, -0.46_172_905, -2.051_715, -10.539_147, -10.970_963, -9.091_634, 4.0, 4.0])
        assert np.abs(mel_slice.flatten() - expected_slice).max() < 1e-2




 @skip_mps
 def 						__snake_case		(     self   :    List[Any]):







        '''simple docstring'''



        return super().test_save_load_local()




 @skip_mps
 def 						__snake_case		(     self   :    Optional[Any]):







        '''simple docstring'''



        return super().test_dict_tuple_outputs_equivalent()




 @skip_mps
 def 						__snake_case		(     self   :    Dict):







        '''simple docstring'''



        return super().test_save_load_optional_components()




 @skip_mps
 def 						__snake_case		(     self   :    str):







        '''simple docstring'''



        return super().test_attention_slicing_forward_pass()




 def 						__snake_case		(     self   :    Optional[Any]):







        '''simple docstring'''



        pass




 def 						__snake_case		(     self   :    List[Any]):







        '''simple docstring'''



        pass




 @skip_mps
 def 						__snake_case		(     self   :    Optional[Any]):







        '''simple docstring'''



        return super().test_progress_bar()






@slow
@require_torch_gpu
@require_onnxruntime
@require_note_seq
class 							a_					(		unittest.TestCase		):






 '''simple docstring'''






 def 						__snake_case		(     self   :    Optional[int]):







        '''simple docstring'''



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




 def 						__snake_case		(     self   :    Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__	      =   torch_device

        lowerCAmelCase__	      =   SpectrogramDiffusionPipeline.from_pretrained('google/music-spectrogram-diffusion')
        lowerCAmelCase__	      =   pipe.melgan
        lowerCAmelCase__	      =   None

        lowerCAmelCase__	      =   pipe.to(lowercase__)
        pipe.set_progress_bar_config(disable=lowercase__)

        def callback(lowercase__   :    int     ,       lowercase__   :    List[Any]):
               # decode mel to audio
               lowerCAmelCase__	      =   melgan(input_features=mel_output.astype(np.floataa))[0]
               assert len(audio[0]) == 81_920 * (step + 1)
               # simulate that audio is played
               return audio

        lowerCAmelCase__	      =   MidiProcessor()
        lowerCAmelCase__	      =   processor(lowercase__)

        lowerCAmelCase__	      =   input_tokens[:3]
        lowerCAmelCase__	      =   torch.manual_seed(0)
        pipe(lowercase__     ,       num_inference_steps=5     ,       generator=lowercase__     ,       callback=lowercase__     ,       output_type='mel')




 def 						__snake_case		(     self   :    str):







        '''simple docstring'''



        lowerCAmelCase__	      =   torch_device

        lowerCAmelCase__	      =   SpectrogramDiffusionPipeline.from_pretrained('google/music-spectrogram-diffusion')
        lowerCAmelCase__	      =   pipe.to(lowercase__)
        pipe.set_progress_bar_config(disable=lowercase__)
        lowerCAmelCase__	      =   MidiProcessor()

        lowerCAmelCase__	      =   processor(lowercase__)
        # just run two denoising loops
        lowerCAmelCase__	      =   input_tokens[:2]

        lowerCAmelCase__	      =   torch.manual_seed(0)
        lowerCAmelCase__	      =   pipe(lowercase__     ,       num_inference_steps=2     ,       generator=lowercase__)

        lowerCAmelCase__	      =   output.audios[0]

        assert abs(np.abs(lowercase__).sum() - 3_612.841) < 1e-1




 def 						__snake_case		(     self   :    Dict):







        '''simple docstring'''



        lowerCAmelCase__	      =   torch_device

        lowerCAmelCase__	      =   SpectrogramDiffusionPipeline.from_pretrained('google/music-spectrogram-diffusion')
        lowerCAmelCase__	      =   pipe.to(lowercase__)
        pipe.set_progress_bar_config(disable=lowercase__)

        lowerCAmelCase__	      =   MidiProcessor()

        lowerCAmelCase__	      =   processor(lowercase__)

        # just run 4 denoising loops
        lowerCAmelCase__	      =   input_tokens[:4]

        lowerCAmelCase__	      =   torch.manual_seed(0)
        lowerCAmelCase__	      =   pipe(lowercase__     ,       num_inference_steps=100     ,       generator=lowercase__)

        lowerCAmelCase__	      =   output.audios[0]
        assert abs(np.abs(lowercase__).sum() - 9_389.1_111) < 5e-2
