
'''simple docstring'''
import glob
import os
from typing import Dict, List, Union

import torch

from diffusers.utils import is_safetensors_available


if is_safetensors_available():
						import safetensors.torch

from huggingface_hub import snapshot_download

from diffusers import DiffusionPipeline, __version__
from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
from diffusers.utils import CONFIG_NAME, DIFFUSERS_CACHE, ONNX_WEIGHTS_NAME, WEIGHTS_NAME






class      snake_case__    (	__SCREAMING_SNAKE_CASE ):







		"""simple docstring"""

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






				"""simple docstring"""







				self.register_to_config()
				super().__init__()



		def 	lowerCAmelCase							(	self							:       Dict   ,     UpperCamelCase__							:       Dict   ,     UpperCamelCase__							:       int	)		->       Any:






				"""simple docstring"""







				if dicta == dicta:
						return True
				else:
						snake_case       ,snake_case   :    List[Any]			       =		self._remove_meta_keys(UpperCamelCase__	)
						snake_case       ,snake_case   :    List[Any]			       =		self._remove_meta_keys(UpperCamelCase__	)
						if configa == configa:
								print(f'Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}.'	)
								return True
				return False



		def 	lowerCAmelCase							(	self							:       Union[str, Any]   ,     UpperCamelCase__							:       Dict	)		->       Union[str, Any]:






				"""simple docstring"""







				snake_case   :    int			       =		[]
				snake_case   :    List[str]			       =		config_dict.copy()
				for key in config_dict.keys():
						if key.startswith('''_'''	):
								temp_dict.pop(UpperCamelCase__	)
								meta_keys.append(UpperCamelCase__	)
				return (temp_dict, meta_keys)



		@torch.no_grad()
		def 	lowerCAmelCase							(	self							:       Optional[int]   ,     UpperCamelCase__							:       List[Union[str, os.PathLike]]   ,     **UpperCamelCase__							:       Tuple	)		->       str:






				"""simple docstring"""







				snake_case   :    Tuple			       =		kwargs.pop('''cache_dir'''   ,     UpperCamelCase__	)
				snake_case   :    Optional[Any]			       =		kwargs.pop('''resume_download'''   ,     UpperCamelCase__	)
				snake_case   :    List[str]			       =		kwargs.pop('''force_download'''   ,     UpperCamelCase__	)
				snake_case   :    str			       =		kwargs.pop('''proxies'''   ,     UpperCamelCase__	)
				snake_case   :    List[str]			       =		kwargs.pop('''local_files_only'''   ,     UpperCamelCase__	)
				snake_case   :    List[Any]			       =		kwargs.pop('''use_auth_token'''   ,     UpperCamelCase__	)
				snake_case   :    Tuple			       =		kwargs.pop('''revision'''   ,     UpperCamelCase__	)
				snake_case   :    Dict			       =		kwargs.pop('''torch_dtype'''   ,     UpperCamelCase__	)
				snake_case   :    str			       =		kwargs.pop('''device_map'''   ,     UpperCamelCase__	)

				snake_case   :    Tuple			       =		kwargs.pop('''alpha'''   ,     0.5	)
				snake_case   :    Any			       =		kwargs.pop('''interp'''   ,     UpperCamelCase__	)

				print('''Received list'''   ,     UpperCamelCase__	)
				print(f'Combining with alpha={alpha}, interpolation mode={interp}'	)

				snake_case   :    List[Any]			       =		len(UpperCamelCase__	)
				# Ignore result from model_index_json comparision of the two checkpoints
				snake_case   :    Optional[int]			       =		kwargs.pop('''force'''   ,     UpperCamelCase__	)

				# If less than 2 checkpoints, nothing to merge. If more than 3, not supported for now.
				if checkpoint_count > 3 or checkpoint_count < 2:
						raise ValueError(
						    '''Received incorrect number of checkpoints to merge. Ensure that either 2 or 3 checkpoints are being'''
						    ''' passed.'''	)

				print('''Received the right number of checkpoints'''	)
				# chkpt0, chkpt1 = pretrained_model_name_or_path_list[0:2]
				# chkpt2 = pretrained_model_name_or_path_list[2] if checkpoint_count == 3 else None

				# Validate that the checkpoints can be merged
				# Step 1: Load the model config and compare the checkpoints. We'll compare the model_index.json first while ignoring the keys starting with '_'
				snake_case   :    Any			       =		[]
				for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
						snake_case   :    Tuple			       =		DiffusionPipeline.load_config(
						    UpperCamelCase__   ,     cache_dir=UpperCamelCase__   ,     resume_download=UpperCamelCase__   ,     force_download=UpperCamelCase__   ,     proxies=UpperCamelCase__   ,     local_files_only=UpperCamelCase__   ,     use_auth_token=UpperCamelCase__   ,     revision=UpperCamelCase__   ,     )
						config_dicts.append(UpperCamelCase__	)

				snake_case   :    Tuple			       =		True
				for idx in range(1   ,     len(UpperCamelCase__	)	):
						comparison_result &= self._compare_model_configs(config_dicts[idx - 1]   ,     config_dicts[idx]	)
						if not force and comparison_result is False:
								raise ValueError('''Incompatible checkpoints. Please check model_index.json for the models.'''	)
								print(config_dicts[0]   ,     config_dicts[1]	)
				print('''Compatible model_index.json files found'''	)
				# Step 2: Basic Validation has succeeded. Let's download the models and save them into our local files.
				snake_case   :    Tuple			       =		[]
				for pretrained_model_name_or_path, config_dict in zip(UpperCamelCase__   ,     UpperCamelCase__	):
						snake_case   :    int			       =		[k for k in config_dict.keys() if not k.startswith('''_'''	)]
						snake_case   :    str			       =		[os.path.join(UpperCamelCase__   ,     '''*'''	) for k in folder_names]
						allow_patterns += [
						    WEIGHTS_NAME,
						    SCHEDULER_CONFIG_NAME,
						    CONFIG_NAME,
						    ONNX_WEIGHTS_NAME,
						    DiffusionPipeline.config_name,
						]
						snake_case   :    Any			       =		config_dict.get('''_class_name'''	)
						snake_case   :    str			       =		{'''diffusers''': __version__, '''pipeline_class''': requested_pipeline_class}

						snake_case   :    List[Any]			       =		(
						    pretrained_model_name_or_path
						    if os.path.isdir(UpperCamelCase__	)
						    else snapshot_download(
						        UpperCamelCase__   ,     cache_dir=UpperCamelCase__   ,     resume_download=UpperCamelCase__   ,     proxies=UpperCamelCase__   ,     local_files_only=UpperCamelCase__   ,     use_auth_token=UpperCamelCase__   ,     revision=UpperCamelCase__   ,     allow_patterns=UpperCamelCase__   ,     user_agent=UpperCamelCase__   ,     )
						)
						print('''Cached Folder'''   ,     UpperCamelCase__	)
						cached_folders.append(UpperCamelCase__	)

				# Step 3:-
				# Load the first checkpoint as a diffusion pipeline and modify its module state_dict in place
				snake_case   :    Optional[Any]			       =		DiffusionPipeline.from_pretrained(
				    cached_folders[0]   ,     torch_dtype=UpperCamelCase__   ,     device_map=UpperCamelCase__	)
				final_pipe.to(self.device	)

				snake_case   :    Dict			       =		None
				if len(UpperCamelCase__	) > 2:
						snake_case   :    Tuple			       =		os.path.join(cached_folders[2]	)

				if interp == "sigmoid":
						snake_case   :    Tuple			       =		CheckpointMergerPipeline.sigmoid
				elif interp == "inv_sigmoid":
						snake_case   :    Tuple			       =		CheckpointMergerPipeline.inv_sigmoid
				elif interp == "add_diff":
						snake_case   :    Optional[int]			       =		CheckpointMergerPipeline.add_difference
				else:
						snake_case   :    Tuple			       =		CheckpointMergerPipeline.weighted_sum

				# Find each module's state dict.
				for attr in final_pipe.config.keys():
						if not attr.startswith('''_'''	):
								snake_case   :    str			       =		os.path.join(cached_folders[1]   ,     UpperCamelCase__	)
								if os.path.exists(UpperCamelCase__	):
										snake_case   :    Tuple			       =		[
										    *glob.glob(os.path.join(UpperCamelCase__   ,     '''*.safetensors'''	)	),
										    *glob.glob(os.path.join(UpperCamelCase__   ,     '''*.bin'''	)	),
										]
										snake_case   :    int			       =		files[0] if len(UpperCamelCase__	) > 0 else None
								if len(UpperCamelCase__	) < 3:
										snake_case   :    int			       =		None
								else:
										snake_case   :    Optional[int]			       =		os.path.join(cached_folders[2]   ,     UpperCamelCase__	)
										if os.path.exists(UpperCamelCase__	):
												snake_case   :    Optional[Any]			       =		[
												    *glob.glob(os.path.join(UpperCamelCase__   ,     '''*.safetensors'''	)	),
												    *glob.glob(os.path.join(UpperCamelCase__   ,     '''*.bin'''	)	),
												]
												snake_case   :    Tuple			       =		files[0] if len(UpperCamelCase__	) > 0 else None
                # For an attr if both checkpoint_path_1 and 2 are None, ignore.
                # If atleast one is present, deal with it according to interp method, of course only if the state_dict keys match.
								if checkpoint_path_a is None and checkpoint_path_a is None:
										print(f'Skipping {attr}: not present in 2nd or 3d model'	)
										continue
								try:
										snake_case   :    Tuple			       =		getattr(UpperCamelCase__   ,     UpperCamelCase__	)
										if isinstance(UpperCamelCase__   ,     UpperCamelCase__	):  # ignore requires_safety_checker boolean
												continue
										snake_case   :    Dict			       =		getattr(UpperCamelCase__   ,     '''state_dict'''	)
										snake_case   :    Dict			       =		theta_a()

										snake_case   :    Dict			       =		getattr(UpperCamelCase__   ,     '''load_state_dict'''	)
										snake_case   :    Any			       =		(
										    safetensors.torch.load_file(UpperCamelCase__	)
										    if (is_safetensors_available() and checkpoint_path_a.endswith('''.safetensors'''	))
										    else torch.load(UpperCamelCase__   ,     map_location='''cpu'''	)
										)
										snake_case   :    Tuple			       =		None
										if checkpoint_path_a:
												snake_case   :    Any			       =		(
												    safetensors.torch.load_file(UpperCamelCase__	)
												    if (is_safetensors_available() and checkpoint_path_a.endswith('''.safetensors'''	))
												    else torch.load(UpperCamelCase__   ,     map_location='''cpu'''	)
												)

										if not theta_a.keys() == theta_a.keys():
												print(f'Skipping {attr}: key mismatch'	)
												continue
										if theta_a and not theta_a.keys() == theta_a.keys():
												print(f'Skipping {attr}:y mismatch'	)
								except Exception as e:
										print(f'Skipping {attr} do to an unexpected error: {str(UpperCamelCase__	)}'	)
										continue
								print(f'MERGING {attr}'	)

								for key in theta_a.keys():
										if theta_a:
												snake_case   :    Union[str, Any]			       =		theta_func(theta_a[key]   ,     theta_a[key]   ,     theta_a[key]   ,     UpperCamelCase__	)
										else:
												snake_case   :    Tuple			       =		theta_func(theta_a[key]   ,     theta_a[key]   ,     UpperCamelCase__   ,     UpperCamelCase__	)

								del theta_a
								del theta_a
								update_theta_a(UpperCamelCase__	)

								del theta_a
				return final_pipe



		@staticmethod
		def 	lowerCAmelCase							(	UpperCamelCase__							:       Optional[int]   ,     UpperCamelCase__							:       int   ,     UpperCamelCase__							:       Optional[Any]   ,     UpperCamelCase__							:       Any	)		->       Union[str, Any]:






				"""simple docstring"""







				return ((1 - alpha) * thetaa) + (alpha * thetaa)



		@staticmethod
		def 	lowerCAmelCase							(	UpperCamelCase__							:       Optional[Any]   ,     UpperCamelCase__							:       Optional[int]   ,     UpperCamelCase__							:       Union[str, Any]   ,     UpperCamelCase__							:       List[str]	)		->       int:






				"""simple docstring"""







				snake_case   :    Optional[int]			       =		alpha * alpha * (3 - (2 * alpha))
				return thetaa + ((thetaa - thetaa) * alpha)



		@staticmethod
		def 	lowerCAmelCase							(	UpperCamelCase__							:       Optional[int]   ,     UpperCamelCase__							:       Tuple   ,     UpperCamelCase__							:       Any   ,     UpperCamelCase__							:       List[str]	)		->       Optional[Any]:






				"""simple docstring"""







				import math

				snake_case   :    int			       =		0.5 - math.sin(math.asin(1.0 - 2.0 * alpha	) / 3.0	)
				return thetaa + ((thetaa - thetaa) * alpha)



		@staticmethod
		def 	lowerCAmelCase							(	UpperCamelCase__							:       List[str]   ,     UpperCamelCase__							:       Any   ,     UpperCamelCase__							:       Optional[int]   ,     UpperCamelCase__							:       Tuple	)		->       Optional[Any]:






				"""simple docstring"""







				return thetaa + (thetaa - thetaa) * (1.0 - alpha)




'''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      snake_case__    :







		"""simple docstring"""

		def __init__(	self							:       str   ,     UpperCamelCase__							:       List[str]   ,     UpperCamelCase__							:       Any   ,     UpperCamelCase__							:       Dict	)		->       List[Any]:






				"""simple docstring"""







				snake_case   :    Union[str, Any]			       =		cuda
				snake_case   :    int			       =		AutoConfig.from_pretrained(UpperCamelCase__	)
				snake_case   :    int			       =		AutoModelForCTC.from_pretrained(UpperCamelCase__	)
				self.model.eval()
				if self.cuda:
						self.model.to(device='''cuda'''	)
				snake_case   :    Tuple			       =		AutoProcessor.from_pretrained(UpperCamelCase__	)
				snake_case   :    str			       =		torchaudio.transforms.Resample(UpperCamelCase__   ,     1_6000	)
				snake_case   :    Any			       =		0
				snake_case   :    int			       =		list(self.processor.tokenizer.get_vocab().keys()	)
				for i in range(len(UpperCamelCase__	)	):
						if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
								snake_case   :    Union[str, Any]			       =		i
				print('''Blank Token id [PAD]/<pad>'''   ,     UpperCamelCase__	)
				snake_case   :    List[Any]			       =		blank_id



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






				"""simple docstring"""







				snake_case       ,snake_case   :    Tuple			       =		torchaudio.load(UpperCamelCase__	)
				snake_case   :    Optional[Any]			       =		self.resampler(UpperCamelCase__	).squeeze().numpy()
				return speech



		def 	lowerCAmelCase							(	self							:       Optional[Any]   ,     UpperCamelCase__							:       int	)		->       Dict:






				"""simple docstring"""







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

				snake_case   :    List[Any]			       =		self.speech_file_to_array_fn(item['''wav_path''']	)
				snake_case   :    List[str]			       =		self.processor(UpperCamelCase__   ,     sampling_rate=1_6000   ,     return_tensors='''pt'''   ,     padding=UpperCamelCase__	)
				if self.cuda:
						snake_case   :    str			       =		inputs.to(device='''cuda'''	)

				with torch.no_grad():
						snake_case   :    List[Any]			       =		self.model(inputs.input_values	).logits

				# get the emission probability at frame level
				snake_case   :    str			       =		torch.log_softmax(UpperCamelCase__   ,     dim=-1	)
				snake_case   :    Optional[Any]			       =		emissions[0].cpu().detach()

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

				snake_case   :    List[str]			       =		{c: i for i, c in enumerate(UpperCamelCase__	)}
				snake_case   :    Any			       =		[]
				for c in transcript:
						if c in dictionary:
								tokens.append(dictionary[c]	)

				def get_trellis(UpperCamelCase__							:       str   ,     UpperCamelCase__							:       Dict   ,     UpperCamelCase__							:       str=0	):
						snake_case   :    List[str]			       =		emission.size(0	)
						snake_case   :    List[str]			       =		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.
						snake_case   :    List[Any]			       =		torch.full((num_frames + 1, num_tokens + 1)   ,     -float('''inf'''	)	)
						snake_case   :    Optional[int]			       =		0
						for t in range(UpperCamelCase__	):
								snake_case   :    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

				snake_case   :    Optional[Any]			       =		get_trellis(UpperCamelCase__   ,     UpperCamelCase__   ,     UpperCamelCase__	)






				@dataclass
				class      snake_case__    :







						"""simple docstring"""

						lowerCamelCase							=		42
						lowerCamelCase							=		42
						lowerCamelCase							=		42

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

						snake_case   :    Dict			       =		[]
						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.
								snake_case   :    Union[str, Any]			       =		trellis[t - 1, j] + emission[t - 1, blank_id]
								# Score for token changing from C-1 at T-1 to J at T.
								snake_case   :    Optional[int]			       =		trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

								# 2. Store the path with frame-wise probability.
								snake_case   :    Optional[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]

				snake_case   :    Any			       =		backtrack(UpperCamelCase__   ,     UpperCamelCase__   ,     UpperCamelCase__   ,     UpperCamelCase__	)






				@dataclass
				class      snake_case__    :







						"""simple docstring"""

						lowerCamelCase							=		42
						lowerCamelCase							=		42
						lowerCamelCase							=		42
						lowerCamelCase							=		42



						def __repr__(	self							:       Dict	)		->       int:






								"""simple docstring"""







								return f'{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}'



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






								"""simple docstring"""







								return self.end - self.start

				def merge_repeats(UpperCamelCase__							:       str	):
						snake_case       ,snake_case   :    Union[str, Any]			       =		0, 0
						snake_case   :    List[Any]			       =		[]
						while ia < len(UpperCamelCase__	):
								while ia < len(UpperCamelCase__	) and path[ia].token_index == path[ia].token_index:
										ia += 1
								snake_case   :    Dict			       =		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__   ,     )	)
								snake_case   :    int			       =		ia
						return segments

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



		def 	lowerCAmelCase							(	self							:       Dict   ,     UpperCamelCase__							:       Dict   ,     UpperCamelCase__							:       Union[str, Any]   ,     UpperCamelCase__							:       List[str]	)		->       List[str]:






				"""simple docstring"""







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

				# load text file
				snake_case   :    List[Any]			       =		open(UpperCamelCase__   ,     encoding='''utf8'''	).readlines()

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

						snake_case       ,snake_case   :    Dict			       =		line.strip().split('''\t'''	)
						snake_case   :    Tuple			       =		os.path.join(UpperCamelCase__   ,     wav_name + '''.wav'''	)
						snake_case   :    str			       =		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 					_UpperCamelCase		(    )   ->				List[Any]:


		'''simple docstring'''


		snake_case   :    Optional[int]			       =		argparse.ArgumentParser()

		parser.add_argument(
		    '''--model_name'''      ,    type=SCREAMING_SNAKE_CASE__      ,    default='''arijitx/wav2vec2-xls-r-300m-bengali'''      ,    help='''wav2vec model name'''       )
		parser.add_argument('''--wav_dir'''      ,    type=SCREAMING_SNAKE_CASE__      ,    default='''./wavs'''      ,    help='''directory containing wavs'''       )
		parser.add_argument('''--text_file'''      ,    type=SCREAMING_SNAKE_CASE__      ,    default='''script.txt'''      ,    help='''file containing text'''       )
		parser.add_argument('''--input_wavs_sr'''      ,    type=SCREAMING_SNAKE_CASE__      ,    default=1_6000      ,    help='''sampling rate of input audios'''       )
		parser.add_argument(
		    '''--output_dir'''      ,    type=SCREAMING_SNAKE_CASE__      ,    default='''./out_alignment'''      ,    help='''output directory containing the alignment files'''       )
		parser.add_argument('''--cuda'''      ,    action='''store_true'''       )

		snake_case   :    Tuple			       =		parser.parse_args()

		snake_case   :    Tuple			       =		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()


