import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser

def        SCREAMING_SNAKE_CASE_					(						snake_case__=None       ,  snake_case__=None							)					->     Optional[Any]:






							"""simple docstring"""




							return field(default_factory=lambda: default       ,  metadata=snake_case__							)




@dataclass
class        lowercase_  :



	"""simple docstring"""





	UpperCAmelCase_  : str       =    field(
	    metadata={"""help""": """The csv file to plot."""}				,					)
	UpperCAmelCase_  : bool       =    field(
	    default=UpperCamelCase_				,					metadata={"""help""": """Whether to plot along batch size or sequence length. Defaults to sequence length."""}				,					)
	UpperCAmelCase_  : bool       =    field(
	    default=UpperCamelCase_				,					metadata={"""help""": """Whether the csv file has time results or memory results. Defaults to memory results."""}				,					)
	UpperCAmelCase_  : bool       =    field(
	    default=UpperCamelCase_				,					metadata={"""help""": """Disable logarithmic scale when plotting"""}				,					)
	UpperCAmelCase_  : bool       =    field(
	    default=UpperCamelCase_				,					metadata={
	        """help""": """Whether the csv file has training results or inference results. Defaults to inference results."""
	    }				,					)
	UpperCAmelCase_  : Optional[str]       =    field(
	    default=UpperCamelCase_				,					metadata={"""help""": """Filename under which the plot will be saved. If unused no plot is saved."""}				,					)
	UpperCAmelCase_  : Optional[List[str]]       =    list_field(
	    default=UpperCamelCase_				,					metadata={"""help""": """List of model names that are used instead of the ones in the csv file."""}						)

def        SCREAMING_SNAKE_CASE_					(						snake_case__							)					->     Dict:






							"""simple docstring"""




							try:
														int(snake_case__							)
														return True
							except ValueError:
														return False

def        SCREAMING_SNAKE_CASE_					(						snake_case__							)					->     Union[str, Any]:






							"""simple docstring"""




							try:
														float(snake_case__							)
														return True
							except ValueError:
														return False




class        lowercase_  :



	"""simple docstring"""





	def __init__(							self		,      __SCREAMING_SNAKE_CASE    )						->Optional[int]:
								lowerCAmelCase     		=		args
								lowerCAmelCase     		=		defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}    )

								with open(self.args.csv_file		,      newline=''''''    ) as csv_file:
															lowerCAmelCase     		=		csv.DictReader(__SCREAMING_SNAKE_CASE    )
															for row in reader:
																						lowerCAmelCase     		=		row['''model''']
																						self.result_dict[model_name]["bsz"].append(int(row['''batch_size''']    )    )
																						self.result_dict[model_name]["seq_len"].append(int(row['''sequence_length''']    )    )
																						if can_convert_to_int(row['''result''']    ):
																													# value is not None
																													lowerCAmelCase     		=		int(row['''result''']    )
																						elif can_convert_to_float(row['''result''']    ):
																													# value is not None
																													lowerCAmelCase     		=		float(row['''result''']    )




	def       SCREAMING_SNAKE_CASE_    (							self    )						->Tuple:
								lowerCAmelCase		,				lowerCAmelCase     		=		plt.subplots()
								lowerCAmelCase     		=		'''Time usage''' if self.args.is_time else '''Memory usage'''
								lowerCAmelCase     		=		title_str + ''' for training''' if self.args.is_train else title_str + ''' for inference'''

								if not self.args.no_log_scale:
															# set logarithm scales
															ax.set_xscale('''log'''    )
															ax.set_yscale('''log'''    )

								for axis in [ax.xaxis, ax.yaxis]:
															axis.set_major_formatter(ScalarFormatter()    )

								for model_name_idx, model_name in enumerate(self.result_dict.keys()    ):
															lowerCAmelCase     		=		sorted(set(self.result_dict[model_name]['''bsz''']    )    )
															lowerCAmelCase     		=		sorted(set(self.result_dict[model_name]['''seq_len''']    )    )
															lowerCAmelCase     		=		self.result_dict[model_name]['''result''']

															((lowerCAmelCase)		,				(lowerCAmelCase))     		=		(
															    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
															)

															lowerCAmelCase     		=		(
															    model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
															)

															for inner_loop_value in inner_loop_array:
																						if self.args.plot_along_batch:
																													lowerCAmelCase     		=		np.asarray(
																													    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]		,      dtype=__SCREAMING_SNAKE_CASE		,      )
																						else:
																													lowerCAmelCase     		=		np.asarray(
																													    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]		,      dtype=np.floataa		,      )

																						((lowerCAmelCase)		,				(lowerCAmelCase))     		=		(
																						    ('''batch_size''', '''len''') if self.args.plot_along_batch else ('''in #tokens''', '''bsz''')
																						)

																						lowerCAmelCase     		=		np.asarray(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )[: len(__SCREAMING_SNAKE_CASE    )]
																						plt.scatter(
																						    __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      label=F"{label_model_name} - {inner_loop_label}: {inner_loop_value}"    )
																						plt.plot(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      '''--'''    )

															title_str += F" {label_model_name} vs."

								lowerCAmelCase     		=		title_str[:-4]
								lowerCAmelCase     		=		'''Time in s''' if self.args.is_time else '''Memory in MB'''

								# plot
								plt.title(__SCREAMING_SNAKE_CASE    )
								plt.xlabel(__SCREAMING_SNAKE_CASE    )
								plt.ylabel(__SCREAMING_SNAKE_CASE    )
								plt.legend()

								if self.args.figure_png_file is not None:
															plt.savefig(self.args.figure_png_file    )
								else:
															plt.show()

def        SCREAMING_SNAKE_CASE_					(						)					->     List[str]:






							"""simple docstring"""




							lowerCAmelCase     		=		HfArgumentParser(snake_case__							)
							lowerCAmelCase     		=		parser.parse_args_into_dataclasses()[0]
							lowerCAmelCase     		=		Plot(args=snake_case__							)
							plot.plot()


if __name__ == "__main__":
	main()




import argparse
import os
from dataclasses import dataclass

import torch
import torchaudio
from tqdm import tqdm

from transformers import AutoConfig, AutoModelForCTC, AutoProcessor




class        lowercase_  :



	"""simple docstring"""





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


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE    )						->Dict:
								lowerCAmelCase		,				lowerCAmelCase     		=		torchaudio.load(__SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		self.resampler(__SCREAMING_SNAKE_CASE    ).squeeze().numpy()
								return speech


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE    )						->str:
								lowerCAmelCase     		=		self.blank_id
								lowerCAmelCase     		=		'''|'''.join(item['''sent'''].split(''' '''    )    )
								if not os.path.isfile(item['''wav_path''']    ):
															print(item['''wav_path''']		,      '''not found in wavs directory'''    )

								lowerCAmelCase     		=		self.speech_file_to_array_fn(item['''wav_path''']    )
								lowerCAmelCase     		=		self.processor(__SCREAMING_SNAKE_CASE		,      sampling_rate=16000		,      return_tensors='''pt'''		,      padding=__SCREAMING_SNAKE_CASE    )
								if self.cuda:
															lowerCAmelCase     		=		inputs.to(device='''cuda'''    )

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

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

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

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

								def get_trellis(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE=0    ):
															lowerCAmelCase     		=		emission.size(0    )
															lowerCAmelCase     		=		len(__SCREAMING_SNAKE_CASE    )

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

								lowerCAmelCase     		=		get_trellis(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )




								@dataclass
								class        lowercase_  :



									"""simple docstring"""





									UpperCAmelCase_  : int
									UpperCAmelCase_  : int
									UpperCAmelCase_  : float

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

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

																						# 2. Store the path with frame-wise probability.
																						lowerCAmelCase     		=		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		,      __SCREAMING_SNAKE_CASE    )    )

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

								lowerCAmelCase     		=		backtrack(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )




								@dataclass
								class        lowercase_  :



									"""simple docstring"""





									UpperCAmelCase_  : str
									UpperCAmelCase_  : int
									UpperCAmelCase_  : int
									UpperCAmelCase_  : float


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




									@property
									def       SCREAMING_SNAKE_CASE_    (							self    )						->str:
																return self.end - self.start

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

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




	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )						->Dict:
								if not os.path.exists(__SCREAMING_SNAKE_CASE    ):
															os.makedirs(__SCREAMING_SNAKE_CASE    )

								# load text file
								lowerCAmelCase     		=		open(__SCREAMING_SNAKE_CASE		,      encoding='''utf8'''    ).readlines()

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

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

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

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

def        SCREAMING_SNAKE_CASE_					(						)					->     Optional[Any]:
							lowerCAmelCase     		=		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=1_6_0_0_0       ,  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'''							)

							lowerCAmelCase     		=		parser.parse_args()

							lowerCAmelCase     		=		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()



