
import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()





class 						lowerCamelCase			(					__lowerCamelCase   ,		unittest.TestCase				):
	UpperCamelCase_     :				Optional[int]								=  KandinskyPipeline
	UpperCamelCase_     :				Dict								=  [
	    'prompt',
	    'image_embeds',
	    'negative_image_embeds',
	]
	UpperCamelCase_     :				str								=  ['prompt', 'negative_prompt', 'image_embeds', 'negative_image_embeds']
	UpperCamelCase_     :				Any								=  [
	    'generator',
	    'height',
	    'width',
	    'latents',
	    'guidance_scale',
	    'negative_prompt',
	    'num_inference_steps',
	    'return_dict',
	    'guidance_scale',
	    'num_images_per_prompt',
	    'output_type',
	    'return_dict',
	]
	UpperCamelCase_     :				Any								=  False







	@property
	def    snake_case__    (    self			:Dict )		->			Optional[int]:







				"""simple docstring"""






				return 3_2







	@property
	def    snake_case__    (    self			:Optional[int] )		->			Optional[int]:







				"""simple docstring"""






				return 3_2







	@property
	def    snake_case__    (    self			:List[Any] )		->			List[Any]:







				"""simple docstring"""






				return self.time_input_dim







	@property
	def    snake_case__    (    self			:Dict )		->			Union[str, Any]:







				"""simple docstring"""






				return self.time_input_dim * 4







	@property
	def    snake_case__    (    self			:List[Any] )		->			List[Any]:







				"""simple docstring"""






				return 1_0_0







	@property
	def    snake_case__    (    self			:List[str] )		->			Dict:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE    	=   XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base''' )
				return tokenizer







	@property
	def    snake_case__    (    self			:Optional[int] )		->			Optional[Any]:







				"""simple docstring"""






				torch.manual_seed(0 )
				SCREAMING_SNAKE_CASE    	=   MCLIPConfig(
				    numDims=self.cross_attention_dim      ,    transformerDimensions=self.text_embedder_hidden_size      ,    hidden_size=self.text_embedder_hidden_size      ,    intermediate_size=3_7      ,    num_attention_heads=4      ,    num_hidden_layers=5      ,    vocab_size=1_0_0_5      ,    )

				SCREAMING_SNAKE_CASE    	=   MultilingualCLIP(lowercase )
				SCREAMING_SNAKE_CASE    	=   text_encoder.eval()

				return text_encoder







	@property
	def    snake_case__    (    self			:str )		->			Optional[int]:







				"""simple docstring"""






				torch.manual_seed(0 )

				SCREAMING_SNAKE_CASE    	=   {
				    '''in_channels''': 4,
				    # Out channels is double in channels because predicts mean and variance
				    '''out_channels''': 8,
				    '''addition_embed_type''': '''text_image''',
				    '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
				    '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
				    '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
				    '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
				    '''layers_per_block''': 1,
				    '''encoder_hid_dim''': self.text_embedder_hidden_size,
				    '''encoder_hid_dim_type''': '''text_image_proj''',
				    '''cross_attention_dim''': self.cross_attention_dim,
				    '''attention_head_dim''': 4,
				    '''resnet_time_scale_shift''': '''scale_shift''',
				    '''class_embed_type''': None,
				}

				SCREAMING_SNAKE_CASE    	=   UNetaDConditionModel(**lowercase )
				return model







	@property
	def    snake_case__    (    self			:int )		->			List[str]:







				"""simple docstring"""






				return {
				    "block_out_channels": [3_2, 6_4],
				    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
				    "in_channels": 3,
				    "latent_channels": 4,
				    "layers_per_block": 1,
				    "norm_num_groups": 8,
				    "norm_type": "spatial",
				    "num_vq_embeddings": 1_2,
				    "out_channels": 3,
				    "up_block_types": [
				        "AttnUpDecoderBlock2D",
				        "UpDecoderBlock2D",
				    ],
				    "vq_embed_dim": 4,
				}







	@property
	def    snake_case__    (    self			:List[Any] )		->			List[Any]:







				"""simple docstring"""






				torch.manual_seed(0 )
				SCREAMING_SNAKE_CASE    	=   VQModel(**self.dummy_movq_kwargs )
				return model







	def    snake_case__    (    self			:Dict )		->			Tuple:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE    	=   self.dummy_text_encoder
				SCREAMING_SNAKE_CASE    	=   self.dummy_tokenizer
				SCREAMING_SNAKE_CASE    	=   self.dummy_unet
				SCREAMING_SNAKE_CASE    	=   self.dummy_movq

				SCREAMING_SNAKE_CASE    	=   DDIMScheduler(
				    num_train_timesteps=1_0_0_0      ,    beta_schedule='''linear'''      ,    beta_start=0.0_00_85      ,    beta_end=0.0_12      ,    clip_sample=lowercase      ,    set_alpha_to_one=lowercase      ,    steps_offset=1      ,    prediction_type='''epsilon'''      ,    thresholding=lowercase      ,    )

				SCREAMING_SNAKE_CASE    	=   {
				    '''text_encoder''': text_encoder,
				    '''tokenizer''': tokenizer,
				    '''unet''': unet,
				    '''scheduler''': scheduler,
				    '''movq''': movq,
				}
				return components







	def    snake_case__    (    self			:int      ,    lowercase			:Optional[Any]      ,    lowercase			:Any=0 )		->			Optional[Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE    	=   floats_tensor((1, self.cross_attention_dim)      ,    rng=random.Random(lowercase ) ).to(lowercase )
				SCREAMING_SNAKE_CASE    	=   floats_tensor((1, self.cross_attention_dim)      ,    rng=random.Random(seed + 1 ) ).to(lowercase )
				if str(lowercase ).startswith('''mps''' ):
							SCREAMING_SNAKE_CASE    	=   torch.manual_seed(lowercase )
				else:
							SCREAMING_SNAKE_CASE    	=   torch.Generator(device=lowercase ).manual_seed(lowercase )
				SCREAMING_SNAKE_CASE    	=   {
				    '''prompt''': '''horse''',
				    '''image_embeds''': image_embeds,
				    '''negative_image_embeds''': negative_image_embeds,
				    '''generator''': generator,
				    '''height''': 6_4,
				    '''width''': 6_4,
				    '''guidance_scale''': 4.0,
				    '''num_inference_steps''': 2,
				    '''output_type''': '''np''',
				}
				return inputs







	def    snake_case__    (    self			:Dict )		->			str:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE    	=   '''cpu'''

				SCREAMING_SNAKE_CASE    	=   self.get_dummy_components()

				SCREAMING_SNAKE_CASE    	=   self.pipeline_class(**lowercase )
				SCREAMING_SNAKE_CASE    	=   pipe.to(lowercase )

				pipe.set_progress_bar_config(disable=lowercase )

				SCREAMING_SNAKE_CASE    	=   pipe(**self.get_dummy_inputs(lowercase ) )
				SCREAMING_SNAKE_CASE    	=   output.images

				SCREAMING_SNAKE_CASE    	=   pipe(
				    **self.get_dummy_inputs(lowercase )      ,    return_dict=lowercase      ,    )[0]

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

				assert image.shape == (1, 6_4, 6_4, 3)

				SCREAMING_SNAKE_CASE    	=   np.array(
				    [0.32_86_63, 1.0, 0.23_21_68_73, 1.0, 0.92_71_75_64, 0.4_63_90_46, 0.96_89_47_77, 0.31_71_33_78, 0.6_29_39_53] )

				assert (
				    np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
				), f""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
				assert (
				    np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
				), f""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""





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







	def    snake_case__    (    self			:List[str] )		->			Any:







				"""simple docstring"""






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







	def    snake_case__    (    self			:str )		->			str:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE    	=   load_numpy(
				    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
				    '''/kandinsky/kandinsky_text2img_cat_fp16.npy''' )

				SCREAMING_SNAKE_CASE    	=   KandinskyPriorPipeline.from_pretrained(
				    '''kandinsky-community/kandinsky-2-1-prior'''      ,    torch_dtype=torch.floataa )
				pipe_prior.to(lowercase )

				SCREAMING_SNAKE_CASE    	=   KandinskyPipeline.from_pretrained('''kandinsky-community/kandinsky-2-1'''      ,    torch_dtype=torch.floataa )
				SCREAMING_SNAKE_CASE    	=   pipeline.to(lowercase )
				pipeline.set_progress_bar_config(disable=lowercase )

				SCREAMING_SNAKE_CASE    	=   '''red cat, 4k photo'''

				SCREAMING_SNAKE_CASE    	=   torch.Generator(device='''cuda''' ).manual_seed(0 )
				SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   pipe_prior(
				    lowercase      ,    generator=lowercase      ,    num_inference_steps=5      ,    negative_prompt=''''''      ,    ).to_tuple()

				SCREAMING_SNAKE_CASE    	=   torch.Generator(device='''cuda''' ).manual_seed(0 )
				SCREAMING_SNAKE_CASE    	=   pipeline(
				    lowercase      ,    image_embeds=lowercase      ,    negative_image_embeds=lowercase      ,    generator=lowercase      ,    num_inference_steps=1_0_0      ,    output_type='''np'''      ,    )

				SCREAMING_SNAKE_CASE    	=   output.images[0]

				assert image.shape == (5_1_2, 5_1_2, 3)

				assert_mean_pixel_difference(lowercase      ,    lowercase )

import argparse
import os
from dataclasses import dataclass

import torch
import torchaudio
from tqdm import tqdm

from transformers import AutoConfig, AutoModelForCTC, AutoProcessor





class 						lowerCamelCase			:







	def __init__(    self			:Optional[int]      ,    lowercase			:Optional[Any]      ,    lowercase			:Optional[int]      ,    lowercase			:int )		->			Optional[int]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE    	=   cuda
				SCREAMING_SNAKE_CASE    	=   AutoConfig.from_pretrained(lowercase )
				SCREAMING_SNAKE_CASE    	=   AutoModelForCTC.from_pretrained(lowercase )
				self.model.eval()
				if self.cuda:
							self.model.to(device='''cuda''' )
				SCREAMING_SNAKE_CASE    	=   AutoProcessor.from_pretrained(lowercase )
				SCREAMING_SNAKE_CASE    	=   torchaudio.transforms.Resample(lowercase      ,    1_6_0_0_0 )
				SCREAMING_SNAKE_CASE    	=   0
				SCREAMING_SNAKE_CASE    	=   list(self.processor.tokenizer.get_vocab().keys() )
				for i in range(len(lowercase ) ):
							if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
										SCREAMING_SNAKE_CASE    	=   i
				print('''Blank Token id [PAD]/<pad>'''      ,    lowercase )
				SCREAMING_SNAKE_CASE    	=   blank_id







	def    snake_case__    (    self			:Any      ,    lowercase			:int )		->			Optional[int]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   torchaudio.load(lowercase )
				SCREAMING_SNAKE_CASE    	=   self.resampler(lowercase ).squeeze().numpy()
				return speech







	def    snake_case__    (    self			:Union[str, Any]      ,    lowercase			:Optional[Any] )		->			int:







				"""simple docstring"""






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

				SCREAMING_SNAKE_CASE    	=   self.speech_file_to_array_fn(item['''wav_path'''] )
				SCREAMING_SNAKE_CASE    	=   self.processor(lowercase      ,    sampling_rate=1_6_0_0_0      ,    return_tensors='''pt'''      ,    padding=lowercase )
				if self.cuda:
							SCREAMING_SNAKE_CASE    	=   inputs.to(device='''cuda''' )

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

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

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

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

				def get_trellis(lowercase			:List[Any]      ,    lowercase			:Any      ,    lowercase			:int=0 ):
							SCREAMING_SNAKE_CASE    	=   emission.size(0 )
							SCREAMING_SNAKE_CASE    	=   len(lowercase )

							# 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.
							SCREAMING_SNAKE_CASE    	=   torch.full((num_frames + 1, num_tokens + 1)      ,    -float('''inf''' ) )
							SCREAMING_SNAKE_CASE    	=   0
							for t in range(lowercase ):
										SCREAMING_SNAKE_CASE    	=   torch.maximum(
										    # Score for staying at the same token
										    trellis[t, 1:] + emission[t, blank_id]      ,    trellis[t, :-1] + emission[t, tokens]      ,    )
							return trellis

				SCREAMING_SNAKE_CASE    	=   get_trellis(lowercase      ,    lowercase      ,    lowercase )





				@dataclass
				class 						lowerCamelCase			:
					UpperCamelCase_     :				int
					UpperCamelCase_     :				int
					UpperCamelCase_     :				float

				def backtrack(lowercase			:Optional[int]      ,    lowercase			:List[str]      ,    lowercase			:Union[str, Any]      ,    lowercase			:Optional[Any]=0 ):
							SCREAMING_SNAKE_CASE    	=   trellis.size(1 ) - 1
							SCREAMING_SNAKE_CASE    	=   torch.argmax(trellis[:, j] ).item()

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

										# 2. Store the path with frame-wise probability.
										SCREAMING_SNAKE_CASE    	=   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      ,    lowercase ) )

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

				SCREAMING_SNAKE_CASE    	=   backtrack(lowercase      ,    lowercase      ,    lowercase      ,    lowercase )





				@dataclass
				class 						lowerCamelCase			:
					UpperCamelCase_     :				str
					UpperCamelCase_     :				int
					UpperCamelCase_     :				int
					UpperCamelCase_     :				float







					def __repr__(    self			:Optional[Any] )		->			str:







								"""simple docstring"""






								return f"""{self.label}\t{self.score:4.2f}\t{self.start*2_0:5d}\t{self.end*2_0:5d}"""







					@property
					def    snake_case__    (    self			:List[str] )		->			Any:







								"""simple docstring"""






								return self.end - self.start

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

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







	def    snake_case__    (    self			:List[Any]      ,    lowercase			:Dict      ,    lowercase			:Tuple      ,    lowercase			:str )		->			Dict:







				"""simple docstring"""






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

				# load text file
				SCREAMING_SNAKE_CASE    	=   open(lowercase      ,    encoding='''utf8''' ).readlines()

				SCREAMING_SNAKE_CASE    	=   []
				for line in lines:
							if len(line.strip().split('''\t''' ) ) != 2:
										print('''Script must be in format: 00001  this is my sentence''' )
										exit()

							SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   line.strip().split('''\t''' )
							SCREAMING_SNAKE_CASE    	=   os.path.join(lowercase      ,    wav_name + '''.wav''' )
							SCREAMING_SNAKE_CASE    	=   os.path.join(lowercase      ,    wav_name + '''.txt''' )

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

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



def       a			(   )    ->Optional[int]:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE    	=   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=1_6000		,	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'''  )

			SCREAMING_SNAKE_CASE    	=   parser.parse_args()

			SCREAMING_SNAKE_CASE    	=   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()