





'''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 							__UpperCAmelCase  :







		'''simple docstring'''







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







		def        A     (self						:      Any      ,      _lowerCAmelCase						:      int      ):
					A  ,    A							  =      torchaudio.load(_lowerCAmelCase      )
					A							  =      self.resampler(_lowerCAmelCase      ).squeeze().numpy()
					return speech







		def        A     (self						:      Union[str, Any]      ,      _lowerCAmelCase						:      Optional[Any]      ):
					A							  =      self.blank_id
					A							  =      """|""".join(item["""sent"""].split(""" """      )      )
					if not os.path.isfile(item["""wav_path"""]      ):
								print(item["""wav_path"""]      ,      """not found in wavs directory"""      )

					A							  =      self.speech_file_to_array_fn(item["""wav_path"""]      )
					A							  =      self.processor(_lowerCAmelCase      ,      sampling_rate=1_6000      ,      return_tensors="""pt"""      ,      padding=_lowerCAmelCase      )
					if self.cuda:
								A							  =      inputs.to(device="""cuda"""      )

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

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

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

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

					def get_trellis(_lowerCAmelCase						:      List[Any]      ,      _lowerCAmelCase						:      Any      ,      _lowerCAmelCase						:      int=0      ):
								A							  =      emission.size(0      )
								A							  =      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.
								A							  =      torch.full((num_frames + 1, num_tokens + 1)      ,      -float("""inf"""      )      )
								A							  =      0
								for t in range(_lowerCAmelCase      ):
											A							  =      torch.maximum(
											    # Score for staying at the same token
											    trellis[t, 1:] + emission[t, blank_id]      ,      trellis[t, :-1] + emission[t, tokens]      ,      )
								return trellis

					A							  =      get_trellis(_lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      )



					@dataclass
					class 							__UpperCAmelCase  :







							'''simple docstring'''







							__lowerCAmelCase 				=    42
							__lowerCAmelCase 				=    42
							__lowerCAmelCase 				=    42

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

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

											# 2. Store the path with frame-wise probability.
											A							  =      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]

					A							  =      backtrack(_lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      )



					@dataclass
					class 							__UpperCAmelCase  :







							'''simple docstring'''







							__lowerCAmelCase 				=    42
							__lowerCAmelCase 				=    42
							__lowerCAmelCase 				=    42
							__lowerCAmelCase 				=    42







							def __repr__(self						:      Optional[Any]      ):
										return F"""{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}"""







							@property
							def        A     (self						:      List[str]      ):
										return self.end - self.start

					def merge_repeats(_lowerCAmelCase						:      List[str]      ):
								A  ,    A							  =      0, 0
								A							  =      []
								while ia < len(_lowerCAmelCase      ):
											while ia < len(_lowerCAmelCase      ) and path[ia].token_index == path[ia].token_index:
														ia += 1
											A							  =      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      ,      )      )
											A							  =      ia
								return segments

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







		def        A     (self						:      List[Any]      ,      _lowerCAmelCase						:      Dict      ,      _lowerCAmelCase						:      Tuple      ,      _lowerCAmelCase						:      str      ):






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

					# load text file
					A							  =      open(_lowerCAmelCase      ,      encoding="""utf8"""      ).readlines()

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

								A  ,    A							  =      line.strip().split("""\t"""      )
								A							  =      os.path.join(_lowerCAmelCase      ,      wav_name + """.wav"""      )
								A							  =      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    __a (     )      ->Optional[int]:



			"""simple docstring"""







			A							  =      argparse.ArgumentParser()

			parser.add_argument(
			    """--model_name"""       ,			type=UpperCAmelCase       ,			default="""arijitx/wav2vec2-xls-r-300m-bengali"""       ,			help="""wav2vec model name"""    )
			parser.add_argument("""--wav_dir"""       ,			type=UpperCAmelCase       ,			default="""./wavs"""       ,			help="""directory containing wavs"""    )
			parser.add_argument("""--text_file"""       ,			type=UpperCAmelCase       ,			default="""script.txt"""       ,			help="""file containing text"""    )
			parser.add_argument("""--input_wavs_sr"""       ,			type=UpperCAmelCase       ,			default=16000       ,			help="""sampling rate of input audios"""    )
			parser.add_argument(
			    """--output_dir"""       ,			type=UpperCAmelCase       ,			default="""./out_alignment"""       ,			help="""output directory containing the alignment files"""    )
			parser.add_argument("""--cuda"""       ,			action="""store_true"""    )

			A							  =      parser.parse_args()

			A							  =      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()








'''simple docstring'''


import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig




def    __a (     UpperCAmelCase       ,			UpperCAmelCase       ,			UpperCAmelCase    )      ->List[Any]:



	"""simple docstring"""







	A							  =      TaConfig.from_pretrained(UpperCAmelCase    )
	A							  =      FlaxTaForConditionalGeneration(config=UpperCAmelCase    )
	A							  =      checkpoints.load_tax_checkpoint(UpperCAmelCase    )

	A							  =      """wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]

	# Encoder
	for layer_index in range(config.num_layers    ):
		A							  =      f"""layers_{str(UpperCAmelCase    )}"""

		# Self-Attention
		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""key"""]["""kernel"""]
		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""out"""]["""kernel"""]
		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""query"""]["""kernel"""]
		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""value"""]["""kernel"""]

		# Layer Normalization
		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""pre_attention_layer_norm"""]["""scale"""]

		if split_mlp_wi:
			A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
			A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
		else:
			A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

		# Layer Normalization
		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

		# Assigning
		A							  =      tax_attention_key
		A							  =      tax_attention_out
		A							  =      tax_attention_query
		A							  =      tax_attention_value

		A							  =      tax_attention_layer_norm

		if split_mlp_wi:
			A							  =      tax_mlp_wi_a
			A							  =      tax_mlp_wi_a
		else:
			A							  =      tax_mlp_wi

		A							  =      tax_mlp_wo
		A							  =      tax_mlp_layer_norm

	# Only for layer 0:
	A							  =      tax_model["""target"""]["""encoder"""]["""relpos_bias"""]["""rel_embedding"""].T
	A							  =      tax_encoder_rel_embedding

	# Assigning
	A							  =      tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
	A							  =      tax_encoder_norm

	# Decoder
	for layer_index in range(config.num_decoder_layers    ):
		A							  =      f"""layers_{str(UpperCAmelCase    )}"""

		# Self-Attention
		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""key"""]["""kernel"""]
		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""out"""]["""kernel"""]
		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""query"""]["""kernel"""]
		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""value"""]["""kernel"""]

		# Layer Normalization
		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""pre_self_attention_layer_norm"""][
		    """scale"""
		]

		# Encoder-Decoder-Attention
		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""key"""][
		    """kernel"""
		]
		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""out"""][
		    """kernel"""
		]
		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""query"""][
		    """kernel"""
		]
		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""value"""][
		    """kernel"""
		]

		# Layer Normalization
		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]

		# MLP
		if split_mlp_wi:
			A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
			A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
		else:
			A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

		# Layer Normalization
		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

		# Assigning
		A							  =      tax_attention_key
		A							  =      tax_attention_out
		A							  =      tax_attention_query
		A							  =      tax_attention_value

		A							  =      tax_pre_attention_layer_norm

		A							  =      tax_enc_dec_attention_key
		A							  =      tax_enc_dec_attention_out
		A							  =      tax_enc_dec_attention_query
		A							  =      tax_enc_dec_attention_value

		A							  =      tax_cross_layer_norm

		if split_mlp_wi:
			A							  =      tax_mlp_wi_a
			A							  =      tax_mlp_wi_a
		else:
			A							  =      tax_mlp_wi

		A							  =      tax_mlp_wo

		A							  =      txa_mlp_layer_norm

	# Decoder Normalization
	A							  =      tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
	A							  =      txa_decoder_norm

	# Only for layer 0:
	A							  =      tax_model["""target"""]["""decoder"""]["""relpos_bias"""]["""rel_embedding"""].T
	A							  =      tax_decoder_rel_embedding

	# Token Embeddings
	A							  =      tax_model["""target"""]["""token_embedder"""]["""embedding"""]
	A							  =      txa_token_embeddings

	# LM Head (only in v1.1 checkpoints)
	if "logits_dense" in tax_model["target"]["decoder"]:
		A							  =      tax_model["""target"""]["""decoder"""]["""logits_dense"""]["""kernel"""]

	flax_model.save_pretrained(UpperCAmelCase    )
	print("""T5X Model was sucessfully converted!"""    )


if __name__ == "__main__":
				_lowerCamelCase				:	Tuple				    =       argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path the TX5 checkpoint.'
				)
				parser.add_argument('--config_name', default=None, type=str, required=True, help='Config name of T5 model.')
				parser.add_argument(
				    '--flax_dump_folder_path', default=None, type=str, required=True, help='Path to the output FLAX model.'
				)
				_lowerCamelCase				:	Union[str, Any]				    =       parser.parse_args()
				convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)

