




'''simple docstring'''


import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case				:				Tuple       							=		logging.get_logger(__name__)

_snake_case				:				List[str]       							=		{
    'facebook/wav2vec2-base-960h': 'https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json',
    # See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}

class       A   (		_a   ):
							lowercase_          =    'wav2vec2'







							def __init__(  self	:  str							,      lowerCAmelCase_	:  List[str]=32							,      lowerCAmelCase_	:  str=7_68							,      lowerCAmelCase_	:  Tuple=12							,      lowerCAmelCase_	:  Any=12							,      lowerCAmelCase_	:  Union[str, Any]=30_72							,      lowerCAmelCase_	:  Optional[int]="gelu"							,      lowerCAmelCase_	:  List[str]=0.1							,      lowerCAmelCase_	:  Dict=0.1							,      lowerCAmelCase_	:  int=0.1							,      lowerCAmelCase_	:  List[str]=0.0							,      lowerCAmelCase_	:  List[str]=0.0							,      lowerCAmelCase_	:  Optional[int]=0.1							,      lowerCAmelCase_	:  List[str]=0.1							,      lowerCAmelCase_	:  Optional[int]=0.0_2							,      lowerCAmelCase_	:  Optional[Any]=1e-5							,      lowerCAmelCase_	:  Dict="group"							,      lowerCAmelCase_	:  Optional[int]="gelu"							,      lowerCAmelCase_	:  int=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)							,      lowerCAmelCase_	:  Optional[int]=(5, 2, 2, 2, 2, 2, 2)							,      lowerCAmelCase_	:  Optional[Any]=(10, 3, 3, 3, 3, 2, 2)							,      lowerCAmelCase_	:  List[Any]=False							,      lowerCAmelCase_	:  List[Any]=1_28							,      lowerCAmelCase_	:  Any=16							,      lowerCAmelCase_	:  Any=False							,      lowerCAmelCase_	:  Union[str, Any]=True							,      lowerCAmelCase_	:  Optional[Any]=0.0_5							,      lowerCAmelCase_	:  Tuple=10							,      lowerCAmelCase_	:  int=2							,      lowerCAmelCase_	:  Optional[int]=0.0							,      lowerCAmelCase_	:  Union[str, Any]=10							,      lowerCAmelCase_	:  Union[str, Any]=0							,      lowerCAmelCase_	:  int=3_20							,      lowerCAmelCase_	:  List[str]=2							,      lowerCAmelCase_	:  Union[str, Any]=0.1							,      lowerCAmelCase_	:  List[str]=1_00							,      lowerCAmelCase_	:  Tuple=2_56							,      lowerCAmelCase_	:  Dict=2_56							,      lowerCAmelCase_	:  Dict=0.1							,      lowerCAmelCase_	:  Any="sum"							,      lowerCAmelCase_	:  Union[str, Any]=False							,      lowerCAmelCase_	:  int=False							,      lowerCAmelCase_	:  List[Any]=2_56							,      lowerCAmelCase_	:  List[str]=(5_12, 5_12, 5_12, 5_12, 15_00)							,      lowerCAmelCase_	:  Optional[Any]=(5, 3, 3, 1, 1)							,      lowerCAmelCase_	:  Union[str, Any]=(1, 2, 3, 1, 1)							,      lowerCAmelCase_	:  Dict=5_12							,      lowerCAmelCase_	:  Dict=0							,      lowerCAmelCase_	:  Union[str, Any]=1							,      lowerCAmelCase_	:  Optional[int]=2							,      lowerCAmelCase_	:  Optional[Any]=False							,      lowerCAmelCase_	:  Dict=3							,      lowerCAmelCase_	:  List[str]=2							,      lowerCAmelCase_	:  int=3							,      lowerCAmelCase_	:  Optional[Any]=None							,      lowerCAmelCase_	:  List[str]=None							,      **lowerCAmelCase_	:  str							,      )      ->							int:


														"""simple docstring"""


														super().__init__(**lowerCAmelCase_							,      pad_token_id=lowerCAmelCase_							,      bos_token_id=lowerCAmelCase_							,      eos_token_id=lowerCAmelCase_ )
														_a     					=    hidden_size
														_a     					=    feat_extract_norm
														_a     					=    feat_extract_activation
														_a     					=    list(lowerCAmelCase_ )
														_a     					=    list(lowerCAmelCase_ )
														_a     					=    list(lowerCAmelCase_ )
														_a     					=    conv_bias
														_a     					=    num_conv_pos_embeddings
														_a     					=    num_conv_pos_embedding_groups
														_a     					=    len(self.conv_dim )
														_a     					=    num_hidden_layers
														_a     					=    intermediate_size
														_a     					=    hidden_act
														_a     					=    num_attention_heads
														_a     					=    hidden_dropout
														_a     					=    attention_dropout
														_a     					=    activation_dropout
														_a     					=    feat_proj_dropout
														_a     					=    final_dropout
														_a     					=    layerdrop
														_a     					=    layer_norm_eps
														_a     					=    initializer_range
														_a     					=    vocab_size
														_a     					=    do_stable_layer_norm
														_a     					=    use_weighted_layer_sum

														if (
														    (len(self.conv_stride ) != self.num_feat_extract_layers)
														    or (len(self.conv_kernel ) != self.num_feat_extract_layers)
														    or (len(self.conv_dim ) != self.num_feat_extract_layers)
														):
																					raise ValueError(
																					    '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
																					    ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
																					    F' {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,'
																					    F' `len(config.conv_kernel) = {len(self.conv_kernel )}`.' )

														# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
														_a     					=    apply_spec_augment
														_a     					=    mask_time_prob
														_a     					=    mask_time_length
														_a     					=    mask_time_min_masks
														_a     					=    mask_feature_prob
														_a     					=    mask_feature_length
														_a     					=    mask_feature_min_masks

														# parameters for pretraining with codevector quantized representations
														_a     					=    num_codevectors_per_group
														_a     					=    num_codevector_groups
														_a     					=    contrastive_logits_temperature
														_a     					=    feat_quantizer_dropout
														_a     					=    num_negatives
														_a     					=    codevector_dim
														_a     					=    proj_codevector_dim
														_a     					=    diversity_loss_weight

														# ctc loss
														_a     					=    ctc_loss_reduction
														_a     					=    ctc_zero_infinity

														# adapter
														_a     					=    add_adapter
														_a     					=    adapter_kernel_size
														_a     					=    adapter_stride
														_a     					=    num_adapter_layers
														_a     					=    output_hidden_size or hidden_size
														_a     					=    adapter_attn_dim

														# SequenceClassification-specific parameter. Feel free to ignore for other classes.
														_a     					=    classifier_proj_size

														# XVector-specific parameters. Feel free to ignore for other classes.
														_a     					=    list(lowerCAmelCase_ )
														_a     					=    list(lowerCAmelCase_ )
														_a     					=    list(lowerCAmelCase_ )
														_a     					=    xvector_output_dim







							@property
							def        __lowerCAmelCase      (  self	:  List[Any] )      ->							int:


														"""simple docstring"""


														return functools.reduce(operator.mul							,      self.conv_stride							,      1 )







'''simple docstring'''


import logging

import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader

from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed

class       A   (		_a   ):







							def __init__(  self	:  Optional[int]							,      lowerCAmelCase_	:  Optional[Any]							,      lowerCAmelCase_	:  Union[str, Any]							,      lowerCAmelCase_	:  List[str]=10_24							,      lowerCAmelCase_	:  Optional[Any]=10_24							,      lowerCAmelCase_	:  Tuple=3.6 )      ->							List[Any]:


														"""simple docstring"""


														_a     					=    tokenizer
														_a     					=    tokenizer.bos_token_id
														_a     					=    dataset
														_a     					=    seq_length
														_a     					=    seq_length * chars_per_token * num_of_sequences







							def __iter__(  self	:  Any )      ->							int:


														"""simple docstring"""


														_a     					=    iter(self.dataset )
														_a     					=    True
														while more_examples:
																					_a					,							_a     					=    [], 0
																					while True:
																												if buffer_len >= self.input_characters:
																																			break
																												try:
																																			buffer.append(next(lowerCAmelCase_ )['''content'''] )
																																			buffer_len += len(buffer[-1] )
																												except StopIteration:
																																			_a     					=    False
																																			break
																					_a     					=    tokenizer(lowerCAmelCase_							,      truncation=lowerCAmelCase_ )['''input_ids''']
																					_a     					=    []
																					for tokenized_input in tokenized_inputs:
																												all_token_ids.extend(tokenized_input + [self.concat_token_id] )
																					for i in range(0							,      len(lowerCAmelCase_ )							,      self.seq_length ):
																												_a     					=    all_token_ids[i : i + self.seq_length]
																												if len(lowerCAmelCase_ ) == self.seq_length:
																																			yield torch.tensor(lowerCAmelCase_ )



def 		snake_case_   (UpperCamelCase	:  int				):





							'''simple docstring'''


							_a     					=    {'''streaming''': True}
							_a     					=    load_dataset(args.dataset_name			,						split='''train'''			,						**UpperCamelCase				)
							_a     					=    ConstantLengthDataset(UpperCamelCase			,						UpperCamelCase			,						seq_length=args.seq_length				)
							_a     					=    DataLoader(UpperCamelCase			,						batch_size=args.batch_size				)
							return eval_dataloader
def 		snake_case_   (UpperCamelCase	:  int				):





							'''simple docstring'''


							model.eval()
							_a     					=    []
							for step, batch in enumerate(UpperCamelCase				):
														with torch.no_grad():
																					_a     					=    model(UpperCamelCase			,						labels=UpperCamelCase				)
														_a     					=    outputs.loss.repeat(args.batch_size				)
														losses.append(accelerator.gather(UpperCamelCase				)				)

														if args.max_eval_steps > 0 and step >= args.max_eval_steps:
																					break
							_a     					=    torch.mean(torch.cat(UpperCamelCase				)				)
							try:
														_a     					=    torch.exp(UpperCamelCase				)
							except OverflowError:
														_a     					=    float('''inf'''				)
							return loss.item(), perplexity.item()


# Setup Accelerator
_snake_case				:				List[str]       							=		Accelerator()

# Parse configuration
_snake_case				:				List[str]       							=		HfArgumentParser(EvaluationArguments)
_snake_case				:				Optional[int]       							=		parser.parse_args()
set_seed(args.seed)

# Logging
_snake_case				:				Any       							=		logging.getLogger(__name__)
logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)

# Load model and tokenizer
_snake_case				:				Dict       							=		AutoModelForCausalLM.from_pretrained(args.model_ckpt)
_snake_case				:				Optional[Any]       							=		AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
_snake_case				:				List[str]       							=		create_dataloader(args)

# Prepare everything with our `accelerator`.
_snake_case							,    _snake_case				:				Optional[int]       							=		accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info('Evaluating and saving model after training')
_snake_case							,    _snake_case				:				int       							=		evaluate(args)

logger.info(F'''loss/eval: {eval_loss}, perplexity: {perplexity}''')

