





'''simple docstring'''





from dataclasses import dataclass, field
from typing import Optional



@dataclass
class       A__     :
      _UpperCAmelCase			:Optional[str]							=     field(
          default='codeparrot/codeparrot' ,     metadata={'help': 'Model name or path of model to be trained.'}    )
      _UpperCAmelCase			:Optional[str]							=     field(
          default='./' ,     metadata={'help': 'Save dir where model repo is cloned and models updates are saved to.'}    )
      _UpperCAmelCase			:Optional[str]							=     field(
          default='codeparrot/codeparrot-clean-train' ,     metadata={'help': 'Name or path of training dataset.'}    )
      _UpperCAmelCase			:Optional[str]							=     field(
          default='codeparrot/codeparrot-clean-valid' ,     metadata={'help': 'Name or path of validation dataset.'}    )
      _UpperCAmelCase			:Optional[int]							=     field(default=2 ,     metadata={'help': 'Batch size for training.'}    )
      _UpperCAmelCase			:Optional[int]							=     field(default=2 ,     metadata={'help': 'Batch size for evaluation.'}    )
      _UpperCAmelCase			:Optional[float]							=     field(default=0.1 ,     metadata={'help': 'Value of weight decay.'}    )
      _UpperCAmelCase			:Optional[int]							=     field(
          default=1_0_0_0_0 ,     metadata={'help': 'Size of buffer used to shuffle streaming dataset.'}    )
      _UpperCAmelCase			:Optional[float]							=     field(default=2e-4 ,     metadata={'help': 'Learning rate fo training.'}    )
      _UpperCAmelCase			:Optional[str]							=     field(default='cosine' ,     metadata={'help': 'Learning rate.'}    )
      _UpperCAmelCase			:Optional[int]							=     field(
          default=7_5_0 ,     metadata={'help': 'Number of warmup steps in the learning rate schedule.'}    )
      _UpperCAmelCase			:Optional[int]							=     field(
          default=1_6 ,     metadata={'help': 'Number of gradient accumulation steps.'}    )
      _UpperCAmelCase			:Optional[bool]							=     field(
          default=__snake_case ,     metadata={'help': 'Use gradient checkpointing to reduce memory footprint.'}    )
      _UpperCAmelCase			:Optional[int]							=     field(default=5_0_0_0_0 ,     metadata={'help': 'Maximum number of training steps.'}    )
      _UpperCAmelCase			:Optional[int]							=     field(
          default=-1 ,     metadata={'help': 'Maximum number of evaluation steps. If -1 the full dataset is evaluated.'}    )
      _UpperCAmelCase			:Optional[int]							=     field(default=1_0_2_4 ,     metadata={'help': 'Sequence lengths used for training.'}    )
      _UpperCAmelCase			:Optional[int]							=     field(default=1 ,     metadata={'help': 'Training seed.'}    )
      _UpperCAmelCase			:Optional[int]							=     field(
          default=1_0_2_4 ,     metadata={'help': 'Interval to save checkpoints. Measured as number of forward passes not training steps.'} ,     )
      _UpperCAmelCase			:Optional[str]							=     field(
          default=__snake_case ,     metadata={'help': 'States path if the training should continue from a checkpoint folder.'}    )
      _UpperCAmelCase			:Optional[bool]							=     field(default=__snake_case ,     metadata={'help': 'If True the data is pretokenized.'}    )



@dataclass
class       A__     :
      _UpperCAmelCase			:Optional[str]							=     field(
          default='codeparrot/codeparrot' ,     metadata={'help': 'Model name or path of model to be evaluated.'}    )
      _UpperCAmelCase			:Optional[str]							=     field(
          default='codeparrot/codeparrot-clean-valid' ,     metadata={'help': 'Name or path of validation dataset.'}    )
      _UpperCAmelCase			:Optional[int]							=     field(default=2 ,     metadata={'help': 'Batch size used for evaluation.'}    )
      _UpperCAmelCase			:Optional[int]							=     field(
          default=-1 ,     metadata={'help': 'Maximum number of evaluation steps. If -1 the full dataset is evaluated.'}    )
      _UpperCAmelCase			:Optional[int]							=     field(default=1_0_2_4 ,     metadata={'help': 'Length of sequences to be evaluated.'}    )
      _UpperCAmelCase			:Optional[int]							=     field(default=1 ,     metadata={'help': 'Random seed used for evaluation.'}    )



@dataclass
class       A__     :
      _UpperCAmelCase			:Optional[str]							=     field(
          default='codeparrot/codeparrot' ,     metadata={'help': 'Model name or path of model to be evaluated.'}    )
      _UpperCAmelCase			:Optional[int]							=     field(default=__snake_case ,     metadata={'help': 'Number of workers used for code evaluation.'}    )
      _UpperCAmelCase			:Optional[int]							=     field(
          default=__snake_case ,     metadata={'help': 'The number of human-eval tasks to run. If not included all tasks are evaluated.'} ,     )
      _UpperCAmelCase			:Optional[bool]							=     field(
          default=__snake_case ,     metadata={'help': 'Sample from the language model\'s output distribution.'}    )
      _UpperCAmelCase			:Optional[float]							=     field(default=0.2 ,     metadata={'help': 'Sampling temperature used for generation.'}    )
      _UpperCAmelCase			:Optional[int]							=     field(default=2_5_6 ,     metadata={'help': 'Maximum number of newly generated tokens.'}    )
      _UpperCAmelCase			:Optional[int]							=     field(default=0 ,     metadata={'help': 'Top-k parameter used for generation.'}    )
      _UpperCAmelCase			:Optional[float]							=     field(default=0.95 ,     metadata={'help': 'Top-p parameter used for nucleus sampling.'}    )
      _UpperCAmelCase			:Optional[int]							=     field(default=1_0 ,     metadata={'help': 'Number of generations to run in parallel.'}    )
      _UpperCAmelCase			:Optional[int]							=     field(
          default=2_0_0 ,     metadata={'help': 'Number of completions to generate for each sample.'}    )
      _UpperCAmelCase			:Optional[int]							=     field(default=1 ,     metadata={'help': 'Random seed used for evaluation.'}    )
      _UpperCAmelCase			:Optional[str]							=     field(
          default='eval_results.json' ,     metadata={'help': 'Random seed used for evaluation.'}    )
      _UpperCAmelCase			:Optional[str]							=     field(
          default='0' ,     metadata={'help': 'Allow `code_eval` to execute Python code on machine'}    )
      _UpperCAmelCase			:Optional[int]							=     field(
          default=-1 ,     metadata={
              'help': (
                  'Determine which device to run the `text-generation` Pipeline on. -1 is CPU and any zero or positive'
                  ' number corresponds to which GPU device id to run on.'
              )
          } ,     )



@dataclass
class       A__     :
      _UpperCAmelCase			:Optional[int]							=     field(
          default=__snake_case ,     metadata={
              'help': 'The number of CPU cores to use for parallel preprocessing. Default uses the maximum available.'
          } ,     )
      _UpperCAmelCase			:Optional[str]							=     field(
          default='transformersbook/codeparrot' ,     metadata={'help': 'Folder or name of dataset to process.'}    )
      _UpperCAmelCase			:Optional[str]							=     field(
          default='codeparrot-clean' ,     metadata={'help': 'Folder to save processed processed dataset.'}    )
      _UpperCAmelCase			:Optional[int]							=     field(
          default=1_0_0_0_0_0 ,     metadata={'help': 'Number of files to save per JSON output file.'}    )
      _UpperCAmelCase			:Optional[str]							=     field(default='content' ,     metadata={'help': 'Column containing text data to process.'}    )
      _UpperCAmelCase			:Optional[float]							=     field(
          default=1_0_0_0 ,     metadata={'help': 'Maximum line length in file, otherwise file is filtered.'}    )
      _UpperCAmelCase			:Optional[float]							=     field(
          default=1_0_0 ,     metadata={'help': 'Maximum mean line length in file, otherwise file is filtered.'}    )
      _UpperCAmelCase			:Optional[float]							=     field(
          default=0.25 ,     metadata={'help': 'Maximum fraction of non-alphanumeric characters, otherwise file is filtered.'}    )
      _UpperCAmelCase			:Optional[float]							=     field(
          default=1.5 ,     metadata={'help': 'Minimum character token ratio for the file, otherwise file is filtered.'}    )
      _UpperCAmelCase			:Optional[float]							=     field(
          default=0.7 ,     metadata={'help': 'Probability for filtering config, test and uncommon files.'}    )
      _UpperCAmelCase			:Optional[str]							=     field(
          default='codeparrot/codeparrot' ,     metadata={'help': 'Name or path to the tokenizer.'} ,     )
      _UpperCAmelCase			:Optional[bool]							=     field(
          default=__snake_case ,     metadata={'help': 'If True, near-duplicate samples are removed.'}    )
      _UpperCAmelCase			:Optional[float]							=     field(
          default=0.85 ,     metadata={'help': 'Jaccard threshold for near-duplicate samples.'}    )



@dataclass
class       A__     :
      _UpperCAmelCase			:Optional[str]							=     field(
          default='gpt2' ,     metadata={'help': 'Base tokenizer to build new tokenizer from.'}    )
      _UpperCAmelCase			:Optional[str]							=     field(
          default='transformersbook/codeparrot-train' ,     metadata={'help': 'Dataset to train tokenizer on.'}    )
      _UpperCAmelCase			:Optional[str]							=     field(default='content' ,     metadata={'help': 'Column containing text data to process.'}    )
      _UpperCAmelCase			:Optional[int]							=     field(default=2_0_0_0_0_0 ,     metadata={'help': 'Number of examples to train tokenizer on.'}    )
      _UpperCAmelCase			:Optional[int]							=     field(
          default=3_2_7_6_8 ,     metadata={'help': 'Number of examples to train the tokenizer on.'}    )
      _UpperCAmelCase			:Optional[str]							=     field(default='codeparrot' ,     metadata={'help': 'Name of new tokenizer.'}    )
      _UpperCAmelCase			:Optional[bool]							=     field(default=__snake_case ,     metadata={'help': 'Push saved tokenizer to the hub.'}    )



@dataclass
class       A__     :
      _UpperCAmelCase			:Optional[str]							=     field(
          default='codeparrot/codeparrot' ,     metadata={'help': 'Name or path to the tokenizer.'}    )
      _UpperCAmelCase			:Optional[str]							=     field(
          default='codeparrot/codeparrot-clean-train' ,     metadata={'help': 'Name or path to the dataset to pretokenize.'}    )
      _UpperCAmelCase			:Optional[str]							=     field(
          default='tokenized-codeparrot-train' ,     metadata={'help': 'Repo name of the pretokenized data.'}    )
      _UpperCAmelCase			:Optional[int]							=     field(default=__snake_case ,     metadata={'help': 'Number of workers used for code evaluation.'}    )



@dataclass
class       A__     :
      _UpperCAmelCase			:Optional[str]							=     field(
          default='gpt2-large' ,     metadata={'help': 'Configuration to use for model initialization.'}    )
      _UpperCAmelCase			:Optional[str]							=     field(
          default='codeparrot/codeparrot' ,     metadata={'help': 'Tokenizer attached to model.'}    )
      _UpperCAmelCase			:Optional[str]							=     field(default='codeparrot' ,     metadata={'help': 'Name of the created model.'}    )
      _UpperCAmelCase			:Optional[bool]							=     field(default=__snake_case ,     metadata={'help': 'Push saved tokenizer to the hub.'}    )







from math import ceil
from typing import List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging


__lowerCamelCase :  Dict										=  logging.get_logger(__name__)



class       A__     ( __snake_case    ):
      _UpperCAmelCase			:Tuple							=     ['audio_values', 'audio_mask']





      def __init__(				self ,							A_=2048 ,							A_=1 ,							A_=[16, 16] ,							A_=128 ,							A_=4_4100 ,							A_=86 ,							A_=2048 ,							A_=0.0 ,							**A_ ,							):
       '''simple docstring'''




       super().__init__(
           feature_size=A_ ,							sampling_rate=A_ ,							padding_value=A_ ,							**A_ ,							)

       UpperCamelCase			:      Optional[int] 			=     spectrogram_length
       UpperCamelCase			:      Dict 			=     num_channels
       UpperCamelCase			:      Optional[Any] 			=     patch_size
       UpperCamelCase			:      str 			=     feature_size // self.patch_size[1]
       UpperCamelCase			:      List[str] 			=     n_fft
       UpperCamelCase			:      int 			=     sampling_rate // hop_length_to_sampling_rate
       UpperCamelCase			:      Optional[int] 			=     sampling_rate
       UpperCamelCase			:      int 			=     padding_value
       UpperCamelCase			:      str 			=     mel_filter_bank(
           num_frequency_bins=1 + n_fft // 2 ,							num_mel_filters=A_ ,							min_frequency=0.0 ,							max_frequency=2_20_50.0 ,							sampling_rate=A_ ,							norm="slaney" ,							mel_scale="slaney" ,							).T





      def  __UpperCamelCase(				self ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      Union[str, Any] 			=     spectrogram(
           A_ ,							window_function(self.n_fft ,							"hann"     ) ,							frame_length=self.n_fft ,							hop_length=self.hop_length ,							power=2.0 ,							mel_filters=self.mel_filters.T ,							log_mel="dB" ,							db_range=80.0 ,							)
       UpperCamelCase			:      List[Any] 			=     log_spec[:, :-1]
       UpperCamelCase			:      Optional[int] 			=     log_spec - 20.0
       UpperCamelCase			:      str 			=     np.clip(log_spec / 40.0 ,							-2.0 ,							0.0     ) + 1.0
       return log_spec





      def __call__(				self ,							A_ ,							A_ = None ,							A_ = True ,							A_ = None ,							A_ = False ,							A_ = False ,							**A_ ,							):
       '''simple docstring'''




       if sampling_rate is not None:
        if sampling_rate != self.sampling_rate:
         raise ValueError(
             "This feature extractor is set to support sampling rate"
             F""" of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled"""
             F""" with {self.sampling_rate} and not {sampling_rate}."""     )
       else:
        logger.warning(
            "It is strongly recommended to pass the `sampling_rate` argument to this function. "
            "Failing to do so can result in silent errors that might be hard to debug."     )

       UpperCamelCase			:      Optional[int] 			=     isinstance(A_ ,							np.ndarray     ) and len(raw_speech.shape     ) > 1
       if is_batched_numpy and len(raw_speech.shape     ) > 2:
        raise ValueError(F"""Only mono-channel audio is supported for input to {self}"""     )
       UpperCamelCase			:      Union[str, Any] 			=     is_batched_numpy or (
           isinstance(A_ ,							(list, tuple)     ) and (isinstance(raw_speech[0] ,							(np.ndarray, tuple, list)     ))
       )
       if is_batched:
        UpperCamelCase			:      int 			=     [np.asarray([speech] ,							dtype=np.floataa     ).T for speech in raw_speech]
       elif not is_batched and not isinstance(A_ ,							np.ndarray     ):
        UpperCamelCase			:      str 			=     np.asarray(A_ ,							dtype=np.floataa     )
       elif isinstance(A_ ,							np.ndarray     ) and raw_speech.dtype is np.dtype(np.floataa     ):
        UpperCamelCase			:      List[Any] 			=     raw_speech.astype(np.floataa     )
       # always return batch
       if not is_batched:
        UpperCamelCase			:      Tuple 			=     [np.asarray([raw_speech]     ).T]

       # Convert audio signals to log mel spectrograms, truncate by time axis
       UpperCamelCase			:      str 			=     [
           self._np_extract_fbank_features(waveform.squeeze()     ).T[: self.spectrogram_length] for waveform in raw_speech
       ]
       if isinstance(audio_features[0] ,							A_     ):
        UpperCamelCase			:      int 			=     [np.asarray(A_ ,							dtype=np.floataa     ) for feature in audio_features]

       # Create audio attention mask
       UpperCamelCase			:      List[str] 			=     max(
           [ceil(feature.shape[0] / self.patch_size[0]     ) * self.freq_len for feature in audio_features]     )  # The maximum number of audio patches in a batch
       if return_attention_mask:
        UpperCamelCase			:      str 			=     [
            (ceil(feature.shape[0] / self.patch_size[0]     ) * self.freq_len) * [1]
            + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]     ) * self.freq_len) * [0]
            for feature in audio_features
        ]
        UpperCamelCase			:      Tuple 			=     np.array(A_     ).astype(np.floataa     )

       # convert into correct format for padding
       UpperCamelCase			:      Union[str, Any] 			=     max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
       UpperCamelCase			:      Any 			=     np.ones([len(A_     ), 1, max_time_len, self.feature_size]     ).astype(np.floataa     )
       UpperCamelCase			:      List[str] 			=     padded_audio_features * self.padding_value
       for i in range(len(A_     )     ):
        UpperCamelCase			:      Union[str, Any] 			=     audio_features[i]
        UpperCamelCase			:      Optional[int] 			=     feature

       # return as BatchFeature
       if return_attention_mask:
        UpperCamelCase			:      Optional[Any] 			=     {"audio_values": padded_audio_features, "audio_mask": audio_mask}
       else:
        UpperCamelCase			:      int 			=     {"audio_values": padded_audio_features}

       UpperCamelCase			:      Any 			=     BatchFeature(data=A_ ,							tensor_type=A_     )
       return encoded_inputs
