
'''simple docstring'''
import functools
import math

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







def 							A_  (    __SCREAMING_SNAKE_CASE    :	Optional[int]      ,  __SCREAMING_SNAKE_CASE    :	Optional[int]      ,  __SCREAMING_SNAKE_CASE    :	int      ,  __SCREAMING_SNAKE_CASE    :	Union[str, Any]      ,  __SCREAMING_SNAKE_CASE    :	List[Any] = 4096			)					->							List[str]:





  """simple docstring"""

  __A				,       __A				,       __A		:  Optional[int]       				=		key.shape[-3:]
  __A		:  Optional[Any]       				=		value.shape[-1]
  __A		:  Any       				=		min(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE			)
  __A		:  Optional[int]       				=		query / jnp.sqrt(__SCREAMING_SNAKE_CASE			)

  @functools.partial(jax.checkpoint      ,  prevent_cse=__SCREAMING_SNAKE_CASE			)
  def summarize_chunk(__SCREAMING_SNAKE_CASE    :	Optional[Any]      ,  __SCREAMING_SNAKE_CASE    :	List[str]      ,  __SCREAMING_SNAKE_CASE    :	Tuple			):
    __A		:  Dict       				=		jnp.einsum("""...qhd,...khd->...qhk"""      ,  __SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE      ,  precision=__SCREAMING_SNAKE_CASE			)

    __A		:  Optional[int]       				=		jnp.max(__SCREAMING_SNAKE_CASE      ,  axis=-1      ,  keepdims=__SCREAMING_SNAKE_CASE			)
    __A		:  Tuple       				=		jax.lax.stop_gradient(__SCREAMING_SNAKE_CASE			)
    __A		:  int       				=		jnp.exp(attn_weights - max_score			)

    __A		:  Any       				=		jnp.einsum("""...vhf,...qhv->...qhf"""      ,  __SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE      ,  precision=__SCREAMING_SNAKE_CASE			)
    __A		:  Tuple       				=		jnp.einsum("""...qhk->...qh"""      ,  __SCREAMING_SNAKE_CASE			)

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

  def chunk_scanner(__SCREAMING_SNAKE_CASE    :	List[Any]			):
    # julienne key array
    __A		:  List[str]       				=		jax.lax.dynamic_slice(
        operand=__SCREAMING_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
    __A		:  Optional[int]       				=		jax.lax.dynamic_slice(
        operand=__SCREAMING_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(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE			)

  __A				,       __A				,       __A		:  int       				=		jax.lax.map(f=__SCREAMING_SNAKE_CASE      ,  xs=jnp.arange(0      ,  __SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE			)			)

  __A		:  Union[str, Any]       				=		jnp.max(__SCREAMING_SNAKE_CASE      ,  axis=0      ,  keepdims=__SCREAMING_SNAKE_CASE			)
  __A		:  List[str]       				=		jnp.exp(chunk_max - global_max			)

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

  __A		:  str       				=		chunk_values.sum(axis=0			)
  __A		:  Optional[int]       				=		jnp.expand_dims(__SCREAMING_SNAKE_CASE      ,  -1			).sum(axis=0			)

  return all_values / all_weights







def 							A_  (    __SCREAMING_SNAKE_CASE    :	Tuple      ,  __SCREAMING_SNAKE_CASE    :	List[str]      ,  __SCREAMING_SNAKE_CASE    :	Tuple      ,  __SCREAMING_SNAKE_CASE    :	Optional[Any]=jax.lax.Precision.HIGHEST      ,  __SCREAMING_SNAKE_CASE    :	Union[str, Any] = 1024      ,  __SCREAMING_SNAKE_CASE    :	Union[str, Any] = 4096			)					->							Dict:





  """simple docstring"""

  __A				,       __A				,       __A		:  List[str]       				=		query.shape[-3:]

  def chunk_scanner(__SCREAMING_SNAKE_CASE    :	Optional[int]      ,  __SCREAMING_SNAKE_CASE    :	Union[str, Any]			):
    # julienne query array
    __A		:  str       				=		jax.lax.dynamic_slice(
        operand=__SCREAMING_SNAKE_CASE      ,  start_indices=([0] * (query.ndim - 3)) + [chunk_idx, 0, 0]      ,  slice_sizes=list(query.shape[:-3]			) + [min(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE			), num_heads, q_features]      ,  )

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

  __A				,       __A		:  List[str]       				=		jax.lax.scan(
      f=__SCREAMING_SNAKE_CASE      ,  init=0      ,  xs=__SCREAMING_SNAKE_CASE      ,  length=math.ceil(num_q / query_chunk_size			)			)

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







class 			__A					(			nn.Module      ):
 lowerCamelCase												=42
 lowerCamelCase												=8
 lowerCamelCase												=64
 lowerCamelCase												=0.0
 lowerCamelCase												=False
 lowerCamelCase												=jnp.floataa
 def 		lowercase_( self      :     Any							):



   """simple docstring"""





   __A		:  Any       				=		self.dim_head * self.heads
   __A		:  Dict       				=		self.dim_head**-0.5

   # Weights were exported with old names {to_q, to_k, to_v, to_out}
   __A		:  str       				=		nn.Dense(_lowerCAmelCase					, use_bias=_lowerCAmelCase					, dtype=self.dtype					, name="""to_q"""							)
   __A		:  str       				=		nn.Dense(_lowerCAmelCase					, use_bias=_lowerCAmelCase					, dtype=self.dtype					, name="""to_k"""							)
   __A		:  Tuple       				=		nn.Dense(_lowerCAmelCase					, use_bias=_lowerCAmelCase					, dtype=self.dtype					, name="""to_v"""							)

   __A		:  str       				=		nn.Dense(self.query_dim					, dtype=self.dtype					, name="""to_out_0"""							)
   __A		:  Any       				=		nn.Dropout(rate=self.dropout							)
 def 		lowercase_( self      :     Any					, lowerCamelCase      :     Tuple							):



   """simple docstring"""





   __A				,       __A				,       __A		:  Dict       				=		tensor.shape
   __A		:  Tuple       				=		self.heads
   __A		:  Optional[Any]       				=		tensor.reshape(_lowerCAmelCase					, _lowerCAmelCase					, _lowerCAmelCase					, dim // head_size							)
   __A		:  Union[str, Any]       				=		jnp.transpose(_lowerCAmelCase					, (0, 2, 1, 3)							)
   __A		:  int       				=		tensor.reshape(batch_size * head_size					, _lowerCAmelCase					, dim // head_size							)
   return tensor
 def 		lowercase_( self      :     Any					, lowerCamelCase      :     Union[str, Any]							):



   """simple docstring"""





   __A				,       __A				,       __A		:  Optional[Any]       				=		tensor.shape
   __A		:  Optional[int]       				=		self.heads
   __A		:  Optional[Any]       				=		tensor.reshape(batch_size // head_size					, _lowerCAmelCase					, _lowerCAmelCase					, _lowerCAmelCase							)
   __A		:  Optional[Any]       				=		jnp.transpose(_lowerCAmelCase					, (0, 2, 1, 3)							)
   __A		:  Optional[Any]       				=		tensor.reshape(batch_size // head_size					, _lowerCAmelCase					, dim * head_size							)
   return tensor






 def __call__( self      :     Tuple					, lowerCamelCase      :     Tuple					, lowerCamelCase      :     List[str]=None					, lowerCamelCase      :     Tuple=True							):



   """simple docstring"""





   __A		:  List[Any]       				=		hidden_states if context is None else context

   __A		:  Tuple       				=		self.query(_lowerCAmelCase							)
   __A		:  List[str]       				=		self.key(_lowerCAmelCase							)
   __A		:  int       				=		self.value(_lowerCAmelCase							)

   __A		:  Optional[Any]       				=		self.reshape_heads_to_batch_dim(_lowerCAmelCase							)
   __A		:  Dict       				=		self.reshape_heads_to_batch_dim(_lowerCAmelCase							)
   __A		:  Tuple       				=		self.reshape_heads_to_batch_dim(_lowerCAmelCase							)

   if self.use_memory_efficient_attention:
     __A		:  List[str]       				=		query_states.transpose(1					, 0					, 2							)
     __A		:  Optional[int]       				=		key_states.transpose(1					, 0					, 2							)
     __A		:  Tuple       				=		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

     __A		:  Any       				=		query_states.shape[-3]
     if flatten_latent_dim % 64 == 0:
       __A		:  List[str]       				=		int(flatten_latent_dim / 64							)
     elif flatten_latent_dim % 16 == 0:
       __A		:  Optional[Any]       				=		int(flatten_latent_dim / 16							)
     elif flatten_latent_dim % 4 == 0:
       __A		:  str       				=		int(flatten_latent_dim / 4							)
     else:
       __A		:  Optional[int]       				=		int(_lowerCAmelCase							)

     __A		:  Tuple       				=		jax_memory_efficient_attention(
         _lowerCAmelCase					, _lowerCAmelCase					, _lowerCAmelCase					, query_chunk_size=_lowerCAmelCase					, key_chunk_size=40_96 * 4							)

     __A		:  Dict       				=		hidden_states.transpose(1					, 0					, 2							)
   else:
     # compute attentions
     __A		:  Any       				=		jnp.einsum("""b i d, b j d->b i j"""					, _lowerCAmelCase					, _lowerCAmelCase							)
     __A		:  int       				=		attention_scores * self.scale
     __A		:  int       				=		nn.softmax(_lowerCAmelCase					, axis=2							)

     # attend to values
     __A		:  Any       				=		jnp.einsum("""b i j, b j d -> b i d"""					, _lowerCAmelCase					, _lowerCAmelCase							)

   __A		:  Optional[int]       				=		self.reshape_batch_dim_to_heads(_lowerCAmelCase							)
   __A		:  List[Any]       				=		self.proj_attn(_lowerCAmelCase							)
   return self.dropout_layer(_lowerCAmelCase					, deterministic=_lowerCAmelCase							)







class 			__A					(			nn.Module      ):
 lowerCamelCase												=42
 lowerCamelCase												=42
 lowerCamelCase												=42
 lowerCamelCase												=0.0
 lowerCamelCase												=False
 lowerCamelCase												=jnp.floataa
 lowerCamelCase												=False
 def 		lowercase_( self      :     Tuple							):



   """simple docstring"""





   __A		:  str       				=		FlaxAttention(
       self.dim					, self.n_heads					, self.d_head					, self.dropout					, self.use_memory_efficient_attention					, dtype=self.dtype							)
   # cross attention
   __A		:  Optional[int]       				=		FlaxAttention(
       self.dim					, self.n_heads					, self.d_head					, self.dropout					, self.use_memory_efficient_attention					, dtype=self.dtype							)
   __A		:  Tuple       				=		FlaxFeedForward(dim=self.dim					, dropout=self.dropout					, dtype=self.dtype							)
   __A		:  int       				=		nn.LayerNorm(epsilon=1e-5					, dtype=self.dtype							)
   __A		:  Any       				=		nn.LayerNorm(epsilon=1e-5					, dtype=self.dtype							)
   __A		:  Optional[Any]       				=		nn.LayerNorm(epsilon=1e-5					, dtype=self.dtype							)
   __A		:  List[str]       				=		nn.Dropout(rate=self.dropout							)






 def __call__( self      :     Dict					, lowerCamelCase      :     Optional[int]					, lowerCamelCase      :     int					, lowerCamelCase      :     Union[str, Any]=True							):



   """simple docstring"""





   __A		:  List[str]       				=		hidden_states
   if self.only_cross_attention:
     __A		:  Optional[int]       				=		self.attna(self.norma(_lowerCAmelCase							)					, _lowerCAmelCase					, deterministic=_lowerCAmelCase							)
   else:
     __A		:  Tuple       				=		self.attna(self.norma(_lowerCAmelCase							)					, deterministic=_lowerCAmelCase							)
   __A		:  Dict       				=		hidden_states + residual

   # cross attention
   __A		:  Optional[Any]       				=		hidden_states
   __A		:  Optional[int]       				=		self.attna(self.norma(_lowerCAmelCase							)					, _lowerCAmelCase					, deterministic=_lowerCAmelCase							)
   __A		:  Any       				=		hidden_states + residual

   # feed forward
   __A		:  Tuple       				=		hidden_states
   __A		:  Tuple       				=		self.ff(self.norma(_lowerCAmelCase							)					, deterministic=_lowerCAmelCase							)
   __A		:  Dict       				=		hidden_states + residual

   return self.dropout_layer(_lowerCAmelCase					, deterministic=_lowerCAmelCase							)







class 			__A					(			nn.Module      ):
 lowerCamelCase												=42
 lowerCamelCase												=42
 lowerCamelCase												=42
 lowerCamelCase												=1
 lowerCamelCase												=0.0
 lowerCamelCase												=False
 lowerCamelCase												=False
 lowerCamelCase												=jnp.floataa
 lowerCamelCase												=False
 def 		lowercase_( self      :     Optional[int]							):



   """simple docstring"""





   __A		:  Union[str, Any]       				=		nn.GroupNorm(num_groups=32					, epsilon=1e-5							)

   __A		:  Optional[Any]       				=		self.n_heads * self.d_head
   if self.use_linear_projection:
     __A		:  Optional[Any]       				=		nn.Dense(_lowerCAmelCase					, dtype=self.dtype							)
   else:
     __A		:  Optional[int]       				=		nn.Conv(
         _lowerCAmelCase					, kernel_size=(1, 1)					, strides=(1, 1)					, padding="""VALID"""					, dtype=self.dtype					, )

   __A		:  Dict       				=		[
       FlaxBasicTransformerBlock(
           _lowerCAmelCase					, 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:
     __A		:  Any       				=		nn.Dense(_lowerCAmelCase					, dtype=self.dtype							)
   else:
     __A		:  Any       				=		nn.Conv(
         _lowerCAmelCase					, kernel_size=(1, 1)					, strides=(1, 1)					, padding="""VALID"""					, dtype=self.dtype					, )

   __A		:  List[Any]       				=		nn.Dropout(rate=self.dropout							)






 def __call__( self      :     Any					, lowerCamelCase      :     Tuple					, lowerCamelCase      :     int					, lowerCamelCase      :     List[Any]=True							):



   """simple docstring"""





   __A				,       __A				,       __A				,       __A		:  Optional[int]       				=		hidden_states.shape
   __A		:  int       				=		hidden_states
   __A		:  int       				=		self.norm(_lowerCAmelCase							)
   if self.use_linear_projection:
     __A		:  Dict       				=		hidden_states.reshape(_lowerCAmelCase					, height * width					, _lowerCAmelCase							)
     __A		:  Optional[int]       				=		self.proj_in(_lowerCAmelCase							)
   else:
     __A		:  Optional[int]       				=		self.proj_in(_lowerCAmelCase							)
     __A		:  Union[str, Any]       				=		hidden_states.reshape(_lowerCAmelCase					, height * width					, _lowerCAmelCase							)

   for transformer_block in self.transformer_blocks:
     __A		:  Tuple       				=		transformer_block(_lowerCAmelCase					, _lowerCAmelCase					, deterministic=_lowerCAmelCase							)

   if self.use_linear_projection:
     __A		:  Optional[int]       				=		self.proj_out(_lowerCAmelCase							)
     __A		:  int       				=		hidden_states.reshape(_lowerCAmelCase					, _lowerCAmelCase					, _lowerCAmelCase					, _lowerCAmelCase							)
   else:
     __A		:  List[str]       				=		hidden_states.reshape(_lowerCAmelCase					, _lowerCAmelCase					, _lowerCAmelCase					, _lowerCAmelCase							)
     __A		:  Optional[Any]       				=		self.proj_out(_lowerCAmelCase							)

   __A		:  int       				=		hidden_states + residual
   return self.dropout_layer(_lowerCAmelCase					, deterministic=_lowerCAmelCase							)







class 			__A					(			nn.Module      ):
 lowerCamelCase												=42
 lowerCamelCase												=0.0
 lowerCamelCase												=jnp.floataa
 def 		lowercase_( self      :     str							):



   """simple docstring"""





   __A		:  Tuple       				=		FlaxGEGLU(self.dim					, self.dropout					, self.dtype							)
   __A		:  List[Any]       				=		nn.Dense(self.dim					, dtype=self.dtype							)






 def __call__( self      :     List[Any]					, lowerCamelCase      :     str					, lowerCamelCase      :     List[Any]=True							):



   """simple docstring"""





   __A		:  Optional[int]       				=		self.net_a(_lowerCAmelCase					, deterministic=_lowerCAmelCase							)
   __A		:  List[str]       				=		self.net_a(_lowerCAmelCase							)
   return hidden_states







class 			__A					(			nn.Module      ):
 lowerCamelCase												=42
 lowerCamelCase												=0.0
 lowerCamelCase												=jnp.floataa
 def 		lowercase_( self      :     List[Any]							):



   """simple docstring"""





   __A		:  List[Any]       				=		self.dim * 4
   __A		:  Union[str, Any]       				=		nn.Dense(inner_dim * 2					, dtype=self.dtype							)
   __A		:  str       				=		nn.Dropout(rate=self.dropout							)






 def __call__( self      :     Optional[Any]					, lowerCamelCase      :     Dict					, lowerCamelCase      :     List[Any]=True							):



   """simple docstring"""





   __A		:  Dict       				=		self.proj(_lowerCAmelCase							)
   __A				,       __A		:  Tuple       				=		jnp.split(_lowerCAmelCase					, 2					, axis=2							)
   return self.dropout_layer(hidden_linear * nn.gelu(_lowerCAmelCase							)					, deterministic=_lowerCAmelCase							)










'''simple docstring'''


import logging
import pathlib
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Callable, Dict, List, Optional, Set, Union

import datasets
import librosa
import numpy as np
import torch
from lang_trans import arabic
from packaging import version
from torch import nn

from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaProcessor,
    is_apex_available,
    trainer_utils,
)


if is_apex_available():
				from apex import amp

if version.parse(version.parse(torch.__version__).base_version) >= version.parse('1.6'):
				_lowerCamelCase					:						Optional[Any]					     =							True
				from torch.cuda.amp import autocast


_lowerCamelCase					:						List[Any]					     =							logging.getLogger(__name__)



@dataclass
class 				__UpperCAmelCase	:







		'''simple docstring'''







		__lowerCAmelCase								=     field(
		    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}    )
		__lowerCAmelCase								=     field(
		    default=A__		,       metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}		,       )
		__lowerCAmelCase								=     field(
		    default=A__		,       metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''}    )
		__lowerCAmelCase								=     field(
		    default=A__		,       metadata={'''help''': '''Whether to log verbose messages or not.'''}		,       )






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



	"""simple docstring"""







	logging.basicConfig(
	    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""							,      datefmt="""%m/%d/%Y %H:%M:%S"""							,      handlers=[logging.StreamHandler(sys.stdout     )]							,      )
	A					=   logging.WARNING
	if model_args.verbose_logging:
		A					=   logging.DEBUG
	elif trainer_utils.is_main_process(training_args.local_rank     ):
		A					=   logging.INFO
	logger.setLevel(UpperCAmelCase     )



@dataclass
class 				__UpperCAmelCase	:







		'''simple docstring'''







		__lowerCAmelCase								=     field(
		    default=A__		,       metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}    )
		__lowerCAmelCase								=     field(
		    default=A__		,       metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}    )
		__lowerCAmelCase								=     field(
		    default='''train'''		,       metadata={
		        '''help''': '''The name of the training data set split to use (via the datasets library). Defaults to \'train\''''
		    }		,       )
		__lowerCAmelCase								=     field(
		    default='''validation'''		,       metadata={
		        '''help''': (
		            '''The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''''
		        )
		    }		,       )
		__lowerCAmelCase								=     field(
		    default='''text'''		,       metadata={'''help''': '''Column in the dataset that contains label (target text). Defaults to \'text\''''}		,       )
		__lowerCAmelCase								=     field(
		    default='''file'''		,       metadata={'''help''': '''Column in the dataset that contains speech file path. Defaults to \'file\''''}		,       )
		__lowerCAmelCase								=     field(
		    default=A__		,       metadata={'''help''': '''Resample loaded audio to target feature extractor\'s sampling rate or not.'''}		,       )
		__lowerCAmelCase								=     field(
		    default=A__		,       metadata={'''help''': '''Filters out examples longer than specified. Defaults to no filtering.'''}		,       )
		__lowerCAmelCase								=     field(
		    default='''librispeech'''		,       metadata={
		        '''help''': (
		            '''Orthography used for normalization and tokenization: \'librispeech\' (default), \'timit\', or'''
		            ''' \'buckwalter\'.'''
		        )
		    }		,       )
		__lowerCAmelCase								=     field(
		    default=A__		,       metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}    )
		__lowerCAmelCase								=     field(
		    default=A__		,       metadata={'''help''': '''The number of processes to use for the preprocessing.'''}		,       )






@dataclass
class 				__UpperCAmelCase	:







		'''simple docstring'''







		__lowerCAmelCase								=     False
		__lowerCAmelCase								=     None
		__lowerCAmelCase								=     "|"
		__lowerCAmelCase								=     field(default_factory=A__    )
		__lowerCAmelCase								=     field(default_factory=A__    )
		__lowerCAmelCase								=     None







		@classmethod
		def 							A  (cls    :   List[str]   ,   _lowerCAmelCase    :   str   ):
			if name == "librispeech":
				return cls()
			if name == "timit":
				return cls(
				    do_lower_case=_lowerCAmelCase   ,   translation_table=str.maketrans({"""-""": """ """}   )   ,   )
			if name == "buckwalter":
				A					=   {
				    """-""": """ """,  # sometimes used to represent pauses
				    """^""": """v""",  # fixing "tha" in arabic_speech_corpus dataset
				}
				return cls(
				    vocab_file=pathlib.Path(_lowerCAmelCase   ).parent.joinpath("""vocab/buckwalter.json"""   )   ,   word_delimiter_token="""/"""   ,   translation_table=str.maketrans(_lowerCAmelCase   )   ,   words_to_remove={"""sil"""}   ,   untransliterator=arabic.buckwalter.untransliterate   ,   )
			raise ValueError(F"""Unsupported orthography: '{name}'."""   )







		def 							A  (self    :   Union[str, Any]   ,   _lowerCAmelCase    :   str   ):
			# TODO(elgeish) return a pipeline (e.g., from jiwer) instead? Or rely on branch predictor as is
			if len(self.translation_table   ) > 0:
				A					=   text.translate(self.translation_table   )
			if len(self.words_to_remove   ) == 0:
				A					=   """ """.join(text.split()   )  # clean up whitespaces
			else:
				A					=   """ """.join(w for w in text.split() if w not in self.words_to_remove   )  # and clean up whilespaces
			return text







		def 							A  (self    :   str   ,   _lowerCAmelCase    :   ModelArguments   ):
			A					=   WavaVecaFeatureExtractor.from_pretrained(
			    model_args.model_name_or_path   ,   cache_dir=model_args.cache_dir   )
			if self.vocab_file:
				A					=   WavaVecaCTCTokenizer(
				    self.vocab_file   ,   cache_dir=model_args.cache_dir   ,   do_lower_case=self.do_lower_case   ,   word_delimiter_token=self.word_delimiter_token   ,   )
			else:
				A					=   WavaVecaCTCTokenizer.from_pretrained(
				    model_args.model_name_or_path   ,   cache_dir=model_args.cache_dir   ,   do_lower_case=self.do_lower_case   ,   word_delimiter_token=self.word_delimiter_token   ,   )
			return WavaVecaProcessor(_lowerCAmelCase   ,   _lowerCAmelCase   )






@dataclass
class 				__UpperCAmelCase	:







		'''simple docstring'''







		__lowerCAmelCase								=     42
		__lowerCAmelCase								=     True
		__lowerCAmelCase								=     None
		__lowerCAmelCase								=     None
		__lowerCAmelCase								=     None
		__lowerCAmelCase								=     None







		def __call__(self    :   List[str]   ,   _lowerCAmelCase    :   List[Dict[str, Union[List[int], torch.Tensor]]]   ):
			# split inputs and labels since they have to be of different lenghts and need
			# different padding methods
			A					=   [{"""input_values""": feature["""input_values"""]} for feature in features]
			A					=   [{"""input_ids""": feature["""labels"""]} for feature in features]

			A					=   self.processor.pad(
			    _lowerCAmelCase   ,   padding=self.padding   ,   max_length=self.max_length   ,   pad_to_multiple_of=self.pad_to_multiple_of   ,   return_tensors="""pt"""   ,   )
			A					=   self.processor.pad(
			    labels=_lowerCAmelCase   ,   padding=self.padding   ,   max_length=self.max_length_labels   ,   pad_to_multiple_of=self.pad_to_multiple_of_labels   ,   return_tensors="""pt"""   ,   )

			# replace padding with -100 to ignore loss correctly
			A					=   labels_batch["""input_ids"""].masked_fill(labels_batch.attention_mask.ne(1   )   ,   -100   )

			A					=   labels

			return batch






class 				__UpperCAmelCase	(							A__    ):







		'''simple docstring'''







		def 							A  (self    :   str   ,   _lowerCAmelCase    :   nn.Module   ,   _lowerCAmelCase    :   Dict[str, Union[torch.Tensor, Any]]   ):
			model.train()
			A					=   self._prepare_inputs(_lowerCAmelCase   )

			if self.use_amp:
				with autocast():
					A					=   self.compute_loss(_lowerCAmelCase   ,   _lowerCAmelCase   )
			else:
				A					=   self.compute_loss(_lowerCAmelCase   ,   _lowerCAmelCase   )

			if self.args.n_gpu > 1:
				if model.module.config.ctc_loss_reduction == "mean":
					A					=   loss.mean()
				elif model.module.config.ctc_loss_reduction == "sum":
					A					=   loss.sum() / (inputs["""labels"""] >= 0).sum()
				else:
					raise ValueError(F"""{model.config.ctc_loss_reduction} is not valid. Choose one of ['mean', 'sum']"""   )

			if self.args.gradient_accumulation_steps > 1:
				A					=   loss / self.args.gradient_accumulation_steps

			if self.use_amp:
				self.scaler.scale(_lowerCAmelCase   ).backward()
			elif self.use_apex:
				with amp.scale_loss(_lowerCAmelCase   ,   self.optimizer   ) as scaled_loss:
					scaled_loss.backward()
			elif self.deepspeed:
				self.deepspeed.backward(_lowerCAmelCase   )
			else:
				loss.backward()

			return loss.detach()






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



	"""simple docstring"""







	A					=   HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)     )

	A	,     A	,     A					=   parser.parse_args_into_dataclasses()
	configure_logger(UpperCAmelCase							,      UpperCAmelCase     )

	A					=   Orthography.from_name(data_args.orthography.lower()     )
	A					=   orthography.create_processor(UpperCAmelCase     )
	A					=   WavaVecaForCTC.from_pretrained(
	    model_args.model_name_or_path							,      cache_dir=model_args.cache_dir							,      gradient_checkpointing=training_args.gradient_checkpointing							,      vocab_size=len(processor.tokenizer     )							,      )

	A					=   datasets.load_dataset(
	    data_args.dataset_name							,      data_args.dataset_config_name							,      split=data_args.train_split_name     )
	A					=   datasets.load_dataset(
	    data_args.dataset_name							,      data_args.dataset_config_name							,      split=data_args.validation_split_name     )

	A					=   datasets.load_metric("""wer"""     )
	A					=   processor.feature_extractor.sampling_rate if data_args.target_feature_extractor_sampling_rate else None
	A					=   """""".join(t for t in processor.tokenizer.get_vocab().keys() if len(UpperCAmelCase     ) == 1     )
	A					=   re.compile(  # remove characters not in vocabulary
	    Rf"""[^\s{re.escape(UpperCAmelCase     )}]"""							,      flags=re.IGNORECASE if processor.tokenizer.do_lower_case else 0							,      )
	A					=   []

	def prepare_example(UpperCAmelCase     ):  # TODO(elgeish) make use of multiprocessing?
		A	,     A					=   librosa.load(example[data_args.speech_file_column]							,      sr=UpperCAmelCase     )
		if data_args.max_duration_in_seconds is not None:
			A					=   len(example["""speech"""]     ) / example["""sampling_rate"""]
		# Normalize and clean up text; order matters!
		A					=   orthography.preprocess_for_training(example[data_args.target_text_column]     )
		A					=   vocabulary_text_cleaner.sub(""""""							,      UpperCAmelCase     )
		if updated_text != example[data_args.target_text_column]:
			text_updates.append((example[data_args.target_text_column], updated_text)     )
			A					=   updated_text
		return example

	A					=   train_dataset.map(UpperCAmelCase							,      remove_columns=[data_args.speech_file_column]     )
	A					=   val_dataset.map(UpperCAmelCase							,      remove_columns=[data_args.speech_file_column]     )

	if data_args.max_duration_in_seconds is not None:

		def filter_by_max_duration(UpperCAmelCase     ):
			return example["duration_in_seconds"] <= data_args.max_duration_in_seconds

		A					=   len(UpperCAmelCase     )
		A					=   len(UpperCAmelCase     )
		A					=   train_dataset.filter(UpperCAmelCase							,      remove_columns=["""duration_in_seconds"""]     )
		A					=   val_dataset.filter(UpperCAmelCase							,      remove_columns=["""duration_in_seconds"""]     )
		if len(UpperCAmelCase     ) > old_train_size:
			logger.warning(
			    f"""Filtered out {len(UpperCAmelCase     ) - old_train_size} train example(s) longer than"""
			    f""" {data_args.max_duration_in_seconds} second(s)."""     )
		if len(UpperCAmelCase     ) > old_val_size:
			logger.warning(
			    f"""Filtered out {len(UpperCAmelCase     ) - old_val_size} validation example(s) longer than"""
			    f""" {data_args.max_duration_in_seconds} second(s)."""     )
	logger.info(f"""Split sizes: {len(UpperCAmelCase     )} train and {len(UpperCAmelCase     )} validation."""     )

	logger.warning(f"""Updated {len(UpperCAmelCase     )} transcript(s) using '{data_args.orthography}' orthography rules."""     )
	if logger.isEnabledFor(logging.DEBUG     ):
		for original_text, updated_text in text_updates:
			logger.debug(f"""Updated text: \"{original_text}\" -> \"{updated_text}\""""     )
	A					=   None

	def prepare_dataset(UpperCAmelCase     ):
		# check that all files have the correct sampling rate
		assert (
		    len(set(batch["""sampling_rate"""]     )     ) == 1
		), f"""Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}."""

		A					=   processor(
		    audio=batch["""speech"""]							,      text=batch[data_args.target_text_column]							,      sampling_rate=batch["""sampling_rate"""][0]     )
		batch.update(UpperCAmelCase     )
		return batch

	A					=   train_dataset.map(
	    UpperCAmelCase							,      batch_size=training_args.per_device_train_batch_size							,      batched=UpperCAmelCase							,      num_proc=data_args.preprocessing_num_workers							,      )
	A					=   val_dataset.map(
	    UpperCAmelCase							,      batch_size=training_args.per_device_train_batch_size							,      batched=UpperCAmelCase							,      num_proc=data_args.preprocessing_num_workers							,      )

	A					=   DataCollatorCTCWithPadding(processor=UpperCAmelCase							,      padding=UpperCAmelCase     )

	def compute_metrics(UpperCAmelCase     ):
		A					=   pred.predictions
		A					=   np.argmax(UpperCAmelCase							,      axis=-1     )

		A					=   processor.tokenizer.pad_token_id

		A					=   processor.batch_decode(UpperCAmelCase     )
		# we do not want to group tokens when computing the metrics
		A					=   processor.batch_decode(pred.label_ids							,      group_tokens=UpperCAmelCase     )
		if logger.isEnabledFor(logging.DEBUG     ):
			for reference, predicted in zip(UpperCAmelCase							,      UpperCAmelCase     ):
				logger.debug(f"""reference: \"{reference}\""""     )
				logger.debug(f"""predicted: \"{predicted}\""""     )
				if orthography.untransliterator is not None:
					logger.debug(f"""reference (untransliterated): \"{orthography.untransliterator(UpperCAmelCase     )}\""""     )
					logger.debug(f"""predicted (untransliterated): \"{orthography.untransliterator(UpperCAmelCase     )}\""""     )

		A					=   wer_metric.compute(predictions=UpperCAmelCase							,      references=UpperCAmelCase     )

		return {"wer": wer}

	if model_args.freeze_feature_extractor:
		model.freeze_feature_extractor()

	A					=   CTCTrainer(
	    model=UpperCAmelCase							,      data_collator=UpperCAmelCase							,      args=UpperCAmelCase							,      compute_metrics=UpperCAmelCase							,      train_dataset=UpperCAmelCase							,      eval_dataset=UpperCAmelCase							,      tokenizer=processor.feature_extractor							,      )

	trainer.train()


if __name__ == "__main__":
				main()

