import argparse

# utilities to import the model weights and config file
import os
import pickle as pkl

# PyTorch + new model classes
import torch
from torch import nn

from transformers import AutoTokenizer, MegaConfig, MegaForMaskedLM


# import the EncoderLayer class used to pretrain
# !! NOTE !! this requires the version of fairseq that is built when you install the Mega source
try:
	from fairseq.modules.mega_layer import MegaEncoderLayer
except ImportError:
	raise ImportError('''You need to install the version of fairseq from the Mega repo!''')




class        lowercase_  (  nn.Module						):



	"""simple docstring"""





	def __init__(							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )						->List[Any]:
								super().__init__()
								lowerCAmelCase     		=		mega_args
								lowerCAmelCase     		=		nn.Embedding(__SCREAMING_SNAKE_CASE		,      self.mega_args.encoder_embed_dim    )
								lowerCAmelCase     		=		nn.ModuleList([MegaEncoderLayer(self.mega_args    ) for _ in range(__SCREAMING_SNAKE_CASE    )]    )
								lowerCAmelCase     		=		depth




	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE=True		,      __SCREAMING_SNAKE_CASE=0    )						->Any:

								# Mega expects embeddings to be (time, batch, embedding size), but
								# Hugging Face returns tokens as (batch, time)
								if batch_first:
															lowerCAmelCase     		=		input_ids.T

								# to make things more confusing, Mega expects the attention mask to
								# be (batch, time), but with values of 0 (normal token) and 1 (ignore token)
								# which is the opposite of what HF returns
								if ignore_mask_value == 0:
															lowerCAmelCase     		=		1 - attention_mask

								# get token embeddings from IDs
								lowerCAmelCase     		=		self.embedding_layer(__SCREAMING_SNAKE_CASE    )

								# pass through the Mega layers
								# input is (time, batch, encoder dim) and output is the same
								for encoder in self.encoders:
															lowerCAmelCase     		=		encoder(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )

								# return according to the shape specified
								if batch_first:
															# (T, B, H) --> (B, T, H)
															return torch.transpose(__SCREAMING_SNAKE_CASE		,      0		,      1    )
								else:
															return embeds




class        lowercase_  (  nn.Module						):



	"""simple docstring"""





	def __init__(							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )						->str:
								super().__init__()
								lowerCAmelCase     		=		MegaLM(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		nn.Linear(mega_args.encoder_embed_dim		,      __SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		nn.Dropout(p=0.1    )




	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE=True		,      __SCREAMING_SNAKE_CASE=0    )						->str:
								lowerCAmelCase     		=		self.mega(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )
								return self.mlm_head(self.dropout(__SCREAMING_SNAKE_CASE    )    )

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__       ,  snake_case__							)					->     List[str]:
							with open(os.path.join(snake_case__       ,  '''model_args.pkl'''							)       ,  '''rb'''							) as f:
														lowerCAmelCase     		=		pkl.load(snake_case__							)

							# load the original encoder
							lowerCAmelCase     		=		OriginalMegaForMaskedLM(**snake_case__							).eval()

							# load its weights
							print(
							    '''Original Mega encoder:'''       ,  original_mlm.mega.load_state_dict(
							        torch.load(os.path.join(snake_case__       ,  '''encoder_weights.pt'''							)       ,  map_location='''cpu'''							)							)       ,  )
							print(
							    '''Original Mega MLM layer:'''       ,  original_mlm.mlm_head.load_state_dict(
							        torch.load(os.path.join(snake_case__       ,  '''mlm_head_weights.pt'''							)       ,  map_location='''cpu'''							)							)       ,  )

							# create a new config from the old one
							lowerCAmelCase     		=		MegaConfig(
							    num_hidden_layers=mega_original_args['''depth''']       ,  vocab_size=mega_original_args['''vocab_size''']       ,  hidden_size=mega_original_args['''mega_args'''].encoder_embed_dim       ,  shared_representation_size=mega_original_args['''mega_args'''].encoder_z_dim       ,  intermediate_size=mega_original_args['''mega_args'''].encoder_hidden_dim       ,  ema_projection_size=mega_original_args['''mega_args'''].encoder_n_dim       ,  dropout_prob=mega_original_args['''mega_args'''].dropout       ,  attention_probs_dropout_prob=mega_original_args['''mega_args'''].attention_dropout       ,  hidden_dropout_prob=mega_original_args['''mega_args'''].hidden_dropout       ,  activation=mega_original_args['''mega_args'''].activation_fn       ,  attention_activation=mega_original_args['''mega_args'''].attention_activation_fn       ,  bidirectional=mega_original_args['''mega_args'''].bidirectional       ,  use_chunking=mega_original_args['''mega_args'''].encoder_chunk_size > 0       ,  chunk_size=mega_original_args['''mega_args'''].encoder_chunk_size       ,  truncation=mega_original_args['''mega_args'''].truncation_length       ,  normalization_type=mega_original_args['''mega_args'''].normalization_type       ,  normalize_before_mega=snake_case__       ,  norm_affine=snake_case__       ,  use_feature_dropout=mega_original_args['''mega_args'''].feature_dropout       ,  relative_positional_bias=mega_original_args['''mega_args'''].rel_pos_bias       ,  max_positions=mega_original_args['''mega_args'''].max_source_positions       ,  nffn_hidden_size=mega_original_args['''mega_args'''].encoder_ffn_embed_dim       ,  normalize_before_ffn=mega_original_args['''mega_args'''].normalize_before       ,  nffn_activation_dropout_prob=0.0       ,  add_token_type_embeddings=snake_case__       ,  add_lm_hidden_dense_layer=snake_case__       ,  )

							lowerCAmelCase     		=		MegaForMaskedLM(snake_case__							).eval()

							# the originl checkpoint just uses nn.Embedding for the word embeddings
							# we use a wrapper module for embeddings to add support for positional embeddings
							lowerCAmelCase     		=		original_mlm.mega.embedding_layer.weight

							# modify the state dictionary of the original checkpoint to account for naming issues in the Hugging Face
							# ecosystem -- any names containing "beta" or "gamma" aren't safe to use and are renamed upon _load_pretrained,
							# also renaming previously confusing parameter names
							lowerCAmelCase     		=		original_mlm.mega.encoders.state_dict()
							lowerCAmelCase     		=		{}
							for module_name in original_state_dict.keys():
														lowerCAmelCase     		=		None
														# have to handle gamma, beta, and alpha differently due to their use
														# in multiple modules within the original repository;
														# beta is used in EMA, MovingAverageGatedAttention, and RotaryRelativePositionalBias, and must be renamed due to flax/tf weights
														# the EMA sublayer was renamed from "move" to "ema_gate" for readability, so that is also done here
														if "beta" in module_name:
																					# EMA sub-layers were always called "move" in the original repo
																					if "move.beta" in module_name:
																												lowerCAmelCase     		=		module_name.replace('''move.beta'''       ,  '''ema_gate.ema_expansion_matrix'''							)
																					elif "mega_layer.beta" in module_name:
																												lowerCAmelCase     		=		module_name.replace('''beta'''       ,  '''qk_bias'''							)
																					else:
																												lowerCAmelCase     		=		module_name.replace('''beta'''       ,  '''b_param'''							)
        # beta is used in EMA and MovingAverageGatedAttention, and must be renamed due to flax/tf weights
														elif "gamma" in module_name:
																					if "move.gamma" in module_name:
																												lowerCAmelCase     		=		module_name.replace('''move.gamma'''       ,  '''ema_gate.kernel_projection_matrix'''							)
																					elif "mega_layer.gamma" in module_name:
																												lowerCAmelCase     		=		module_name.replace('''gamma'''       ,  '''qk_weight'''							)
																					else:
																												lowerCAmelCase     		=		module_name.replace('''gamma'''       ,  '''g_param'''							)
        # alpha is used in EMA and positional bias; renaming to improve readability
														elif "move.alpha" in module_name:
																					lowerCAmelCase     		=		module_name.replace('''move.alpha'''       ,  '''ema_gate.decay_factor'''							)
														# delta is only used in EMA; renaming to improve readability
														elif "move.delta" in module_name:
																					lowerCAmelCase     		=		module_name.replace('''move.delta'''       ,  '''ema_gate.damping_factor'''							)
														# omega is only used in EMA; renaming to improve readability
														elif "omega" in module_name:
																					lowerCAmelCase     		=		module_name.replace('''move.omega'''       ,  '''ema_gate.residual_weight'''							)

														if new_module_name:
																					lowerCAmelCase     		=		new_module_name

							if len(snake_case__							) != 0:
														print(f"Renaming these keys: {updated_keys.keys()}"							)
							else:
														print('''No need to rename state dict entries'''							)
							for old, new in updated_keys.items():
														lowerCAmelCase     		=		original_state_dict.pop(snake_case__							)

							# now attempt to load the state dictionary with updated names
							# note that we now call it `mega.layers` instead of `mega.encoders` due to hugging face style
							print('''HF Mega encoder:'''       ,  hf_mlm.mega.layers.load_state_dict(snake_case__							)							)

							# load the MLM head weights directly
							print(
							    '''HF Mega MLM layer:'''       ,  hf_mlm.mlm_head.load_state_dict(
							        torch.load(os.path.join(snake_case__       ,  '''mlm_head_weights.pt'''							)       ,  map_location='''cpu'''							)							)       ,  )

							# test on a randomly generated input sequence
							lowerCAmelCase     		=		torch.randint(0       ,  hf_config.vocab_size       ,  size=(4, 2_5_6)							)
							lowerCAmelCase     		=		torch.ones_like(snake_case__							)
							# mask a few tokens to make sure masking is applied appropriately :)
							lowerCAmelCase     		=		0

							# run forward passes
							lowerCAmelCase     		=		original_mlm(snake_case__       ,  snake_case__       ,  batch_first=snake_case__       ,  ignore_mask_value=0							)
							lowerCAmelCase     		=		hf_mlm(snake_case__       ,  snake_case__							)[0]

							# print shapes and diff
							print(f"original output {original_output.shape}"							)
							print(f"hf output {hf_output.shape}"							)
							print(f"max diff: {(original_output - hf_output).max()}"							)  # 0.0
							lowerCAmelCase     		=		torch.allclose(snake_case__       ,  snake_case__       ,  atol=1E-3							)

							if success:
														print('''Yay!'''							)
														hf_mlm.save_pretrained(snake_case__							)
							else:
														raise RuntimeError(f"Something's broken :(\nOriginal:\n{original_output}\n\nHF\n{hf_output}\n{hf_mlm}"							)

							if includes_tokenizer:
														print('''Transferring tokenizer'''							)
														lowerCAmelCase     		=		AutoTokenizer.from_pretrained(snake_case__							)
														tokenizer.save_pretrained(snake_case__							)


if __name__ == "__main__":
	lowercase__      :    Dict          =     argparse.ArgumentParser()

	parser.add_argument(
	    '''--pretrained_checkpoint_path''',
	    default=None,
	    type=str,
	    required=True,
	    help='''Point to the directory containing your model weights using the official Mega repo''',
	)

	parser.add_argument(
	    '''--output_path''', default=None, type=str, required=True, help='''Location to save the Hugging Face version'''
	)

	parser.add_argument(
	    '''--includes_tokenizer''',
	    action='''store_true''',
	    help='''Use this flag if there is a Hugging Face tokenizer in the original checkpoint repo''',
	)

	lowercase__      :    Optional[int]          =     parser.parse_args()

	convert_checkpoint_to_huggingface(args.pretrained_checkpoint_path, args.output_path, args.includes_tokenizer)




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()



