


import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
   import torch_xla.core.xla_model as xm
   import torch_xla.debug.metrics as met


class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  def __init__(  self							:							Union[str, Any]     ,					*_lowerCAmelCase							:							Dict     ,					_lowerCAmelCase							:							List[Any]=None     ,					_lowerCAmelCase							:							str=None     ,					**_lowerCAmelCase							:							str	) ->			Dict:




     """simple docstring"""
     super().__init__(*_lowerCAmelCase     ,					**_lowerCAmelCase	)
     snake_case_            =       eval_examples
     snake_case_            =       post_process_function



  def        lowerCAmelCase__	(  self							:							Union[str, Any]     ,					_lowerCAmelCase							:							Optional[Dataset] = None     ,					_lowerCAmelCase							:							Any=None     ,					_lowerCAmelCase							:							Optional[List[str]] = None     ,					_lowerCAmelCase							:							str = "eval"     ,					**_lowerCAmelCase							:							Tuple     ,					) ->			Dict[str, float]:




     """simple docstring"""
     snake_case_            =       gen_kwargs.copy()
     snake_case_            =       (
         gen_kwargs["max_length"] if gen_kwargs.get("max_length"	) is not None else self.args.generation_max_length
     )
     snake_case_            =       (
         gen_kwargs["num_beams"] if gen_kwargs.get("num_beams"	) is not None else self.args.generation_num_beams
     )
     snake_case_            =       gen_kwargs

     snake_case_            =       self.eval_dataset if eval_dataset is None else eval_dataset
     snake_case_            =       self.get_eval_dataloader(_lowerCAmelCase	)
     snake_case_            =       self.eval_examples if eval_examples is None else eval_examples

     # Temporarily disable metric computation, we will do it in the loop here.
     snake_case_            =       self.compute_metrics
     snake_case_            =       None
     snake_case_            =       time.time()
     snake_case_            =       self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
     try:
        snake_case_            =       eval_loop(
            _lowerCAmelCase     ,					description="Evaluation"     ,					prediction_loss_only=True if compute_metrics is None else None     ,					ignore_keys=_lowerCAmelCase     ,					metric_key_prefix=_lowerCAmelCase     ,					)
     finally:
        snake_case_            =       compute_metrics
     snake_case_            =       self.args.eval_batch_size * self.args.world_size
     if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
        start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
     output.metrics.update(
         speed_metrics(
             _lowerCAmelCase     ,					_lowerCAmelCase     ,					num_samples=output.num_samples     ,					num_steps=math.ceil(output.num_samples / total_batch_size	)     ,					)	)

     if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
        # Only the main node write the results by default
        snake_case_            =       self.post_process_function(_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase	)
        snake_case_            =       self.compute_metrics(_lowerCAmelCase	)

        # Prefix all keys with metric_key_prefix + '_'
        for key in list(metrics.keys()	):
           if not key.startswith(F'''{metric_key_prefix}_'''	):
              snake_case_            =       metrics.pop(_lowerCAmelCase	)

        metrics.update(output.metrics	)
     else:
        snake_case_            =       output.metrics

     if self.args.should_log:
        # Only the main node log the results by default
        self.log(_lowerCAmelCase	)

     if self.args.tpu_metrics_debug or self.args.debug:
        # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
        xm.master_print(met.metrics_report()	)

     snake_case_            =       self.callback_handler.on_evaluate(self.args     ,					self.state     ,					self.control     ,					_lowerCAmelCase	)
     return metrics



  def        lowerCAmelCase__	(  self							:							Optional[Any]     ,					_lowerCAmelCase							:							Optional[Any]     ,					_lowerCAmelCase							:							Union[str, Any]     ,					_lowerCAmelCase							:							List[str]=None     ,					_lowerCAmelCase							:							str = "test"     ,					**_lowerCAmelCase							:							Optional[Any]	) ->			Union[str, Any]:




     """simple docstring"""
     snake_case_            =       gen_kwargs.copy()

     snake_case_            =       self.get_test_dataloader(_lowerCAmelCase	)

     # Temporarily disable metric computation, we will do it in the loop here.
     snake_case_            =       self.compute_metrics
     snake_case_            =       None
     snake_case_            =       time.time()
     snake_case_            =       self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
     try:
        snake_case_            =       eval_loop(
            _lowerCAmelCase     ,					description="Prediction"     ,					prediction_loss_only=True if compute_metrics is None else None     ,					ignore_keys=_lowerCAmelCase     ,					metric_key_prefix=_lowerCAmelCase     ,					)
     finally:
        snake_case_            =       compute_metrics

     snake_case_            =       self.args.eval_batch_size * self.args.world_size
     if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
        start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
     output.metrics.update(
         speed_metrics(
             _lowerCAmelCase     ,					_lowerCAmelCase     ,					num_samples=output.num_samples     ,					num_steps=math.ceil(output.num_samples / total_batch_size	)     ,					)	)
     if self.post_process_function is None or self.compute_metrics is None:
        return output

     snake_case_            =       self.post_process_function(_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase     ,					"predict"	)
     snake_case_            =       self.compute_metrics(_lowerCAmelCase	)

     # Prefix all keys with metric_key_prefix + '_'
     for key in list(metrics.keys()	):
        if not key.startswith(F'''{metric_key_prefix}_'''	):
           snake_case_            =       metrics.pop(_lowerCAmelCase	)
     metrics.update(output.metrics	)
     return PredictionOutput(predictions=predictions.predictions     ,					label_ids=predictions.label_ids     ,					metrics=_lowerCAmelCase	)








import functools
import operator

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


SCREAMING_SNAKE_CASE						:List[str]			=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE						:Dict			=  {
    '''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      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							'wav2vec2'



  def __init__(  self							:							Any     ,					_lowerCAmelCase							:							Optional[Any]=3_2     ,					_lowerCAmelCase							:							List[Any]=7_6_8     ,					_lowerCAmelCase							:							Dict=1_2     ,					_lowerCAmelCase							:							Tuple=1_2     ,					_lowerCAmelCase							:							Optional[int]=3_0_7_2     ,					_lowerCAmelCase							:							Any="gelu"     ,					_lowerCAmelCase							:							List[str]=0.1     ,					_lowerCAmelCase							:							Dict=0.1     ,					_lowerCAmelCase							:							Union[str, Any]=0.1     ,					_lowerCAmelCase							:							Dict=0.0     ,					_lowerCAmelCase							:							Optional[int]=0.0     ,					_lowerCAmelCase							:							List[Any]=0.1     ,					_lowerCAmelCase							:							List[Any]=0.1     ,					_lowerCAmelCase							:							Dict=0.02     ,					_lowerCAmelCase							:							Any=1e-5     ,					_lowerCAmelCase							:							Any="group"     ,					_lowerCAmelCase							:							Union[str, Any]="gelu"     ,					_lowerCAmelCase							:							str=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)     ,					_lowerCAmelCase							:							Optional[Any]=(5, 2, 2, 2, 2, 2, 2)     ,					_lowerCAmelCase							:							Optional[Any]=(1_0, 3, 3, 3, 3, 2, 2)     ,					_lowerCAmelCase							:							int=False     ,					_lowerCAmelCase							:							int=1_2_8     ,					_lowerCAmelCase							:							Union[str, Any]=1_6     ,					_lowerCAmelCase							:							Union[str, Any]=False     ,					_lowerCAmelCase							:							List[Any]=True     ,					_lowerCAmelCase							:							Dict=0.05     ,					_lowerCAmelCase							:							List[str]=1_0     ,					_lowerCAmelCase							:							Tuple=2     ,					_lowerCAmelCase							:							str=0.0     ,					_lowerCAmelCase							:							Union[str, Any]=1_0     ,					_lowerCAmelCase							:							int=0     ,					_lowerCAmelCase							:							Optional[int]=3_2_0     ,					_lowerCAmelCase							:							Optional[Any]=2     ,					_lowerCAmelCase							:							str=0.1     ,					_lowerCAmelCase							:							Optional[Any]=1_0_0     ,					_lowerCAmelCase							:							Tuple=2_5_6     ,					_lowerCAmelCase							:							Union[str, Any]=2_5_6     ,					_lowerCAmelCase							:							Union[str, Any]=0.1     ,					_lowerCAmelCase							:							Optional[Any]="sum"     ,					_lowerCAmelCase							:							List[str]=False     ,					_lowerCAmelCase							:							List[Any]=False     ,					_lowerCAmelCase							:							List[str]=2_5_6     ,					_lowerCAmelCase							:							List[Any]=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0)     ,					_lowerCAmelCase							:							List[str]=(5, 3, 3, 1, 1)     ,					_lowerCAmelCase							:							Union[str, Any]=(1, 2, 3, 1, 1)     ,					_lowerCAmelCase							:							Optional[Any]=5_1_2     ,					_lowerCAmelCase							:							Any=0     ,					_lowerCAmelCase							:							Optional[int]=1     ,					_lowerCAmelCase							:							List[str]=2     ,					_lowerCAmelCase							:							Tuple=False     ,					_lowerCAmelCase							:							Any=3     ,					_lowerCAmelCase							:							List[Any]=2     ,					_lowerCAmelCase							:							Optional[Any]=3     ,					_lowerCAmelCase							:							List[str]=None     ,					_lowerCAmelCase							:							str=None     ,					**_lowerCAmelCase							:							Tuple     ,					) ->			Optional[int]:




     """simple docstring"""
     super().__init__(**_lowerCAmelCase     ,					pad_token_id=_lowerCAmelCase     ,					bos_token_id=_lowerCAmelCase     ,					eos_token_id=_lowerCAmelCase	)
     snake_case_            =       hidden_size
     snake_case_            =       feat_extract_norm
     snake_case_            =       feat_extract_activation
     snake_case_            =       list(_lowerCAmelCase	)
     snake_case_            =       list(_lowerCAmelCase	)
     snake_case_            =       list(_lowerCAmelCase	)
     snake_case_            =       conv_bias
     snake_case_            =       num_conv_pos_embeddings
     snake_case_            =       num_conv_pos_embedding_groups
     snake_case_            =       len(self.conv_dim	)
     snake_case_            =       num_hidden_layers
     snake_case_            =       intermediate_size
     snake_case_            =       hidden_act
     snake_case_            =       num_attention_heads
     snake_case_            =       hidden_dropout
     snake_case_            =       attention_dropout
     snake_case_            =       activation_dropout
     snake_case_            =       feat_proj_dropout
     snake_case_            =       final_dropout
     snake_case_            =       layerdrop
     snake_case_            =       layer_norm_eps
     snake_case_            =       initializer_range
     snake_case_            =       vocab_size
     snake_case_            =       do_stable_layer_norm
     snake_case_            =       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
     snake_case_            =       apply_spec_augment
     snake_case_            =       mask_time_prob
     snake_case_            =       mask_time_length
     snake_case_            =       mask_time_min_masks
     snake_case_            =       mask_feature_prob
     snake_case_            =       mask_feature_length
     snake_case_            =       mask_feature_min_masks

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

     # ctc loss
     snake_case_            =       ctc_loss_reduction
     snake_case_            =       ctc_zero_infinity

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

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

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



  @property
  def        lowerCAmelCase__	(  self							:							str	) ->			int:




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




