import functools
import math

import flax.linen as nn
import jax
import jax.numpy as jnp

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__ = 4_0_9_6							)					->     List[str]:
							lowerCAmelCase		,				lowerCAmelCase		,				lowerCAmelCase     		=		key.shape[-3:]
							lowerCAmelCase     		=		value.shape[-1]
							lowerCAmelCase     		=		min(snake_case__       ,  snake_case__							)
							lowerCAmelCase     		=		query / jnp.sqrt(snake_case__							)

							@functools.partial(jax.checkpoint       ,  prevent_cse=snake_case__							)
							def summarize_chunk(snake_case__       ,  snake_case__       ,  snake_case__							):
														lowerCAmelCase     		=		jnp.einsum('''...qhd,...khd->...qhk'''       ,  snake_case__       ,  snake_case__       ,  precision=snake_case__							)

														lowerCAmelCase     		=		jnp.max(snake_case__       ,  axis=-1       ,  keepdims=snake_case__							)
														lowerCAmelCase     		=		jax.lax.stop_gradient(snake_case__							)
														lowerCAmelCase     		=		jnp.exp(attn_weights - max_score							)

														lowerCAmelCase     		=		jnp.einsum('''...vhf,...qhv->...qhf'''       ,  snake_case__       ,  snake_case__       ,  precision=snake_case__							)
														lowerCAmelCase     		=		jnp.einsum('''...qhk->...qh'''       ,  snake_case__							)

														return (exp_values, exp_weights.sum(axis=-1							), max_score)

							def chunk_scanner(snake_case__							):
														# julienne key array
														lowerCAmelCase     		=		jax.lax.dynamic_slice(
														    operand=snake_case__       ,  start_indices=[0] * (key.ndim - 3) + [chunk_idx, 0, 0]       ,  slice_sizes=list(key.shape[:-3]							) + [key_chunk_size, num_heads, k_features]       ,  )

														# julienne value array
														lowerCAmelCase     		=		jax.lax.dynamic_slice(
														    operand=snake_case__       ,  start_indices=[0] * (value.ndim - 3) + [chunk_idx, 0, 0]       ,  slice_sizes=list(value.shape[:-3]							) + [key_chunk_size, num_heads, v_features]       ,  )

														return summarize_chunk(snake_case__       ,  snake_case__       ,  snake_case__							)

							lowerCAmelCase		,				lowerCAmelCase		,				lowerCAmelCase     		=		jax.lax.map(f=snake_case__       ,  xs=jnp.arange(0       ,  snake_case__       ,  snake_case__							)							)

							lowerCAmelCase     		=		jnp.max(snake_case__       ,  axis=0       ,  keepdims=snake_case__							)
							lowerCAmelCase     		=		jnp.exp(chunk_max - global_max							)

							chunk_values *= jnp.expand_dims(snake_case__       ,  axis=-1							)
							chunk_weights *= max_diffs

							lowerCAmelCase     		=		chunk_values.sum(axis=0							)
							lowerCAmelCase     		=		jnp.expand_dims(snake_case__       ,  -1							).sum(axis=0							)

							return all_values / all_weights

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__=jax.lax.Precision.HIGHEST       ,  snake_case__ = 1_0_2_4       ,  snake_case__ = 4_0_9_6							)					->     Dict:
							lowerCAmelCase		,				lowerCAmelCase		,				lowerCAmelCase     		=		query.shape[-3:]

							def chunk_scanner(snake_case__       ,  snake_case__							):
														# julienne query array
														lowerCAmelCase     		=		jax.lax.dynamic_slice(
														    operand=snake_case__       ,  start_indices=([0] * (query.ndim - 3)) + [chunk_idx, 0, 0]       ,  slice_sizes=list(query.shape[:-3]							) + [min(snake_case__       ,  snake_case__							), num_heads, q_features]       ,  )

														return (
														    chunk_idx + query_chunk_size,  # unused ignore it
														    _query_chunk_attention(
														        query=snake_case__       ,  key=snake_case__       ,  value=snake_case__       ,  precision=snake_case__       ,  key_chunk_size=snake_case__							),
														)

							lowerCAmelCase		,				lowerCAmelCase     		=		jax.lax.scan(
							    f=snake_case__       ,  init=0       ,  xs=snake_case__       ,  length=math.ceil(num_q / query_chunk_size							)							)

							return jnp.concatenate(snake_case__       ,  axis=-3							)  # fuse the chunked result back




class        lowercase_  (  nn.Module						):



	"""simple docstring"""





	UpperCAmelCase_  : int
	UpperCAmelCase_  : int       =    8
	UpperCAmelCase_  : int       =    64
	UpperCAmelCase_  : float       =    0.0
	UpperCAmelCase_  : bool       =    False
	UpperCAmelCase_  : jnp.dtype       =    jnp.floataa


	def       SCREAMING_SNAKE_CASE_    (							self    )						->Union[str, Any]:
								lowerCAmelCase     		=		self.dim_head * self.heads
								lowerCAmelCase     		=		self.dim_head**-0.5

								# Weights were exported with old names {to_q, to_k, to_v, to_out}
								lowerCAmelCase     		=		nn.Dense(__SCREAMING_SNAKE_CASE		,      use_bias=__SCREAMING_SNAKE_CASE		,      dtype=self.dtype		,      name='''to_q'''    )
								lowerCAmelCase     		=		nn.Dense(__SCREAMING_SNAKE_CASE		,      use_bias=__SCREAMING_SNAKE_CASE		,      dtype=self.dtype		,      name='''to_k'''    )
								lowerCAmelCase     		=		nn.Dense(__SCREAMING_SNAKE_CASE		,      use_bias=__SCREAMING_SNAKE_CASE		,      dtype=self.dtype		,      name='''to_v'''    )

								lowerCAmelCase     		=		nn.Dense(self.query_dim		,      dtype=self.dtype		,      name='''to_out_0'''    )
								lowerCAmelCase     		=		nn.Dropout(rate=self.dropout    )


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE    )						->List[str]:
								lowerCAmelCase		,				lowerCAmelCase		,				lowerCAmelCase     		=		tensor.shape
								lowerCAmelCase     		=		self.heads
								lowerCAmelCase     		=		tensor.reshape(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      dim // head_size    )
								lowerCAmelCase     		=		jnp.transpose(__SCREAMING_SNAKE_CASE		,      (0, 2, 1, 3)    )
								lowerCAmelCase     		=		tensor.reshape(batch_size * head_size		,      __SCREAMING_SNAKE_CASE		,      dim // head_size    )
								return tensor


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE    )						->Tuple:
								lowerCAmelCase		,				lowerCAmelCase		,				lowerCAmelCase     		=		tensor.shape
								lowerCAmelCase     		=		self.heads
								lowerCAmelCase     		=		tensor.reshape(batch_size // head_size		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		jnp.transpose(__SCREAMING_SNAKE_CASE		,      (0, 2, 1, 3)    )
								lowerCAmelCase     		=		tensor.reshape(batch_size // head_size		,      __SCREAMING_SNAKE_CASE		,      dim * head_size    )
								return tensor




	def __call__(							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE=None		,      __SCREAMING_SNAKE_CASE=True    )						->Tuple:
								lowerCAmelCase     		=		hidden_states if context is None else context

								lowerCAmelCase     		=		self.query(__SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		self.key(__SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		self.value(__SCREAMING_SNAKE_CASE    )

								lowerCAmelCase     		=		self.reshape_heads_to_batch_dim(__SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		self.reshape_heads_to_batch_dim(__SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		self.reshape_heads_to_batch_dim(__SCREAMING_SNAKE_CASE    )

								if self.use_memory_efficient_attention:
															lowerCAmelCase     		=		query_states.transpose(1		,      0		,      2    )
															lowerCAmelCase     		=		key_states.transpose(1		,      0		,      2    )
															lowerCAmelCase     		=		value_states.transpose(1		,      0		,      2    )

															# this if statement create a chunk size for each layer of the unet
															# the chunk size is equal to the query_length dimension of the deepest layer of the unet

															lowerCAmelCase     		=		query_states.shape[-3]
															if flatten_latent_dim % 64 == 0:
																						lowerCAmelCase     		=		int(flatten_latent_dim / 64    )
															elif flatten_latent_dim % 16 == 0:
																						lowerCAmelCase     		=		int(flatten_latent_dim / 16    )
															elif flatten_latent_dim % 4 == 0:
																						lowerCAmelCase     		=		int(flatten_latent_dim / 4    )
															else:
																						lowerCAmelCase     		=		int(__SCREAMING_SNAKE_CASE    )

															lowerCAmelCase     		=		jax_memory_efficient_attention(
															    __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      query_chunk_size=__SCREAMING_SNAKE_CASE		,      key_chunk_size=4096 * 4    )

															lowerCAmelCase     		=		hidden_states.transpose(1		,      0		,      2    )
								else:
															# compute attentions
															lowerCAmelCase     		=		jnp.einsum('''b i d, b j d->b i j'''		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )
															lowerCAmelCase     		=		attention_scores * self.scale
															lowerCAmelCase     		=		nn.softmax(__SCREAMING_SNAKE_CASE		,      axis=2    )

															# attend to values
															lowerCAmelCase     		=		jnp.einsum('''b i j, b j d -> b i d'''		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )

								lowerCAmelCase     		=		self.reshape_batch_dim_to_heads(__SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		self.proj_attn(__SCREAMING_SNAKE_CASE    )
								return self.dropout_layer(__SCREAMING_SNAKE_CASE		,      deterministic=__SCREAMING_SNAKE_CASE    )




class        lowercase_  (  nn.Module						):



	"""simple docstring"""





	UpperCAmelCase_  : int
	UpperCAmelCase_  : int
	UpperCAmelCase_  : int
	UpperCAmelCase_  : float       =    0.0
	UpperCAmelCase_  : bool       =    False
	UpperCAmelCase_  : jnp.dtype       =    jnp.floataa
	UpperCAmelCase_  : bool       =    False


	def       SCREAMING_SNAKE_CASE_    (							self    )						->int:
								# self attention (or cross_attention if only_cross_attention is True)
								lowerCAmelCase     		=		FlaxAttention(
								    self.dim		,      self.n_heads		,      self.d_head		,      self.dropout		,      self.use_memory_efficient_attention		,      dtype=self.dtype    )
								# cross attention
								lowerCAmelCase     		=		FlaxAttention(
								    self.dim		,      self.n_heads		,      self.d_head		,      self.dropout		,      self.use_memory_efficient_attention		,      dtype=self.dtype    )
								lowerCAmelCase     		=		FlaxFeedForward(dim=self.dim		,      dropout=self.dropout		,      dtype=self.dtype    )
								lowerCAmelCase     		=		nn.LayerNorm(epsilon=1e-5		,      dtype=self.dtype    )
								lowerCAmelCase     		=		nn.LayerNorm(epsilon=1e-5		,      dtype=self.dtype    )
								lowerCAmelCase     		=		nn.LayerNorm(epsilon=1e-5		,      dtype=self.dtype    )
								lowerCAmelCase     		=		nn.Dropout(rate=self.dropout    )




	def __call__(							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE=True    )						->Optional[int]:
								# self attention
								lowerCAmelCase     		=		hidden_states
								if self.only_cross_attention:
															lowerCAmelCase     		=		self.attna(self.norma(__SCREAMING_SNAKE_CASE    )		,      __SCREAMING_SNAKE_CASE		,      deterministic=__SCREAMING_SNAKE_CASE    )
								else:
															lowerCAmelCase     		=		self.attna(self.norma(__SCREAMING_SNAKE_CASE    )		,      deterministic=__SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		hidden_states + residual

								# cross attention
								lowerCAmelCase     		=		hidden_states
								lowerCAmelCase     		=		self.attna(self.norma(__SCREAMING_SNAKE_CASE    )		,      __SCREAMING_SNAKE_CASE		,      deterministic=__SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		hidden_states + residual

								# feed forward
								lowerCAmelCase     		=		hidden_states
								lowerCAmelCase     		=		self.ff(self.norma(__SCREAMING_SNAKE_CASE    )		,      deterministic=__SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		hidden_states + residual

								return self.dropout_layer(__SCREAMING_SNAKE_CASE		,      deterministic=__SCREAMING_SNAKE_CASE    )




class        lowercase_  (  nn.Module						):



	"""simple docstring"""





	UpperCAmelCase_  : int
	UpperCAmelCase_  : int
	UpperCAmelCase_  : int
	UpperCAmelCase_  : int       =    1
	UpperCAmelCase_  : float       =    0.0
	UpperCAmelCase_  : bool       =    False
	UpperCAmelCase_  : bool       =    False
	UpperCAmelCase_  : jnp.dtype       =    jnp.floataa
	UpperCAmelCase_  : bool       =    False


	def       SCREAMING_SNAKE_CASE_    (							self    )						->Dict:
								lowerCAmelCase     		=		nn.GroupNorm(num_groups=32		,      epsilon=1e-5    )

								lowerCAmelCase     		=		self.n_heads * self.d_head
								if self.use_linear_projection:
															lowerCAmelCase     		=		nn.Dense(__SCREAMING_SNAKE_CASE		,      dtype=self.dtype    )
								else:
															lowerCAmelCase     		=		nn.Conv(
															    __SCREAMING_SNAKE_CASE		,      kernel_size=(1, 1)		,      strides=(1, 1)		,      padding='''VALID'''		,      dtype=self.dtype		,      )

								lowerCAmelCase     		=		[
								    FlaxBasicTransformerBlock(
								        __SCREAMING_SNAKE_CASE		,      self.n_heads		,      self.d_head		,      dropout=self.dropout		,      only_cross_attention=self.only_cross_attention		,      dtype=self.dtype		,      use_memory_efficient_attention=self.use_memory_efficient_attention		,      )
								    for _ in range(self.depth    )
								]

								if self.use_linear_projection:
															lowerCAmelCase     		=		nn.Dense(__SCREAMING_SNAKE_CASE		,      dtype=self.dtype    )
								else:
															lowerCAmelCase     		=		nn.Conv(
															    __SCREAMING_SNAKE_CASE		,      kernel_size=(1, 1)		,      strides=(1, 1)		,      padding='''VALID'''		,      dtype=self.dtype		,      )

								lowerCAmelCase     		=		nn.Dropout(rate=self.dropout    )




	def __call__(							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE=True    )						->List[Any]:
								lowerCAmelCase		,				lowerCAmelCase		,				lowerCAmelCase		,				lowerCAmelCase     		=		hidden_states.shape
								lowerCAmelCase     		=		hidden_states
								lowerCAmelCase     		=		self.norm(__SCREAMING_SNAKE_CASE    )
								if self.use_linear_projection:
															lowerCAmelCase     		=		hidden_states.reshape(__SCREAMING_SNAKE_CASE		,      height * width		,      __SCREAMING_SNAKE_CASE    )
															lowerCAmelCase     		=		self.proj_in(__SCREAMING_SNAKE_CASE    )
								else:
															lowerCAmelCase     		=		self.proj_in(__SCREAMING_SNAKE_CASE    )
															lowerCAmelCase     		=		hidden_states.reshape(__SCREAMING_SNAKE_CASE		,      height * width		,      __SCREAMING_SNAKE_CASE    )

								for transformer_block in self.transformer_blocks:
															lowerCAmelCase     		=		transformer_block(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      deterministic=__SCREAMING_SNAKE_CASE    )

								if self.use_linear_projection:
															lowerCAmelCase     		=		self.proj_out(__SCREAMING_SNAKE_CASE    )
															lowerCAmelCase     		=		hidden_states.reshape(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )
								else:
															lowerCAmelCase     		=		hidden_states.reshape(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )
															lowerCAmelCase     		=		self.proj_out(__SCREAMING_SNAKE_CASE    )

								lowerCAmelCase     		=		hidden_states + residual
								return self.dropout_layer(__SCREAMING_SNAKE_CASE		,      deterministic=__SCREAMING_SNAKE_CASE    )




class        lowercase_  (  nn.Module						):



	"""simple docstring"""





	UpperCAmelCase_  : int
	UpperCAmelCase_  : float       =    0.0
	UpperCAmelCase_  : jnp.dtype       =    jnp.floataa


	def       SCREAMING_SNAKE_CASE_    (							self    )						->Optional[int]:
								# The second linear layer needs to be called
								# net_2 for now to match the index of the Sequential layer
								lowerCAmelCase     		=		FlaxGEGLU(self.dim		,      self.dropout		,      self.dtype    )
								lowerCAmelCase     		=		nn.Dense(self.dim		,      dtype=self.dtype    )




	def __call__(							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE=True    )						->List[Any]:
								lowerCAmelCase     		=		self.net_a(__SCREAMING_SNAKE_CASE		,      deterministic=__SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		self.net_a(__SCREAMING_SNAKE_CASE    )
								return hidden_states




class        lowercase_  (  nn.Module						):



	"""simple docstring"""





	UpperCAmelCase_  : int
	UpperCAmelCase_  : float       =    0.0
	UpperCAmelCase_  : jnp.dtype       =    jnp.floataa


	def       SCREAMING_SNAKE_CASE_    (							self    )						->Optional[int]:
								lowerCAmelCase     		=		self.dim * 4
								lowerCAmelCase     		=		nn.Dense(inner_dim * 2		,      dtype=self.dtype    )
								lowerCAmelCase     		=		nn.Dropout(rate=self.dropout    )




	def __call__(							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE=True    )						->str:
								lowerCAmelCase     		=		self.proj(__SCREAMING_SNAKE_CASE    )
								lowerCAmelCase		,				lowerCAmelCase     		=		jnp.split(__SCREAMING_SNAKE_CASE		,      2		,      axis=2    )
								return self.dropout_layer(hidden_linear * nn.gelu(__SCREAMING_SNAKE_CASE    )		,      deterministic=__SCREAMING_SNAKE_CASE    )




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()



