





'''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 			_lowercase						(       __a					):







     def __init__( self    ,   A__    ,   A__    ,   A__=10_24    ,   A__=10_24    ,   A__=3.6    ) ->	Dict:
      snake_case				=			tokenizer
      snake_case				=			tokenizer.bos_token_id
      snake_case				=			dataset
      snake_case				=			seq_length
      snake_case				=			seq_length * chars_per_token * num_of_sequences







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




def    __UpperCamelCase	(	a    : Optional[int]     ) ->str:
 snake_case				=			{'''streaming''': True}
 snake_case				=			load_dataset(args.dataset_name					,   split='''train'''					,   **a     )
 snake_case				=			ConstantLengthDataset(a					,   a					,   seq_length=args.seq_length     )
 snake_case				=			DataLoader(a					,   batch_size=args.batch_size     )
 return eval_dataloader

def    __UpperCamelCase	(	a    : List[Any]     ) ->Dict:
 model.eval()
 snake_case				=			[]
 for step, batch in enumerate(a     ):
  with torch.no_grad():
   snake_case				=			model(a					,   labels=a     )
  snake_case				=			outputs.loss.repeat(args.batch_size     )
  losses.append(accelerator.gather(a     )     )

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


# Setup Accelerator
_lowercase       						=			Accelerator()

# Parse configuration
_lowercase       						=			HfArgumentParser(EvaluationArguments)
_lowercase       						=			parser.parse_args()
set_seed(args.seed)

# Logging
_lowercase       						=			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
_lowercase       						=			AutoModelForCausalLM.from_pretrained(args.model_ckpt)
_lowercase       						=			AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
_lowercase       						=			create_dataloader(args)

# Prepare everything with our `accelerator`.
_lowercase						,				_lowercase       						=			accelerator.prepare(model, eval_dataloader)

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






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








'''simple docstring'''







import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
   from .features import FeatureType


_lowercase						,				_lowercase						,				_lowercase       						=			False, False, False
@dataclass
class 			_lowercase						:
     _UpperCAmelCase										=					None
     _UpperCAmelCase										=					True
     _UpperCAmelCase										=					True
     _UpperCAmelCase										=					None
     # Automatically constructed
     _UpperCAmelCase										=					"dict"
     _UpperCAmelCase										=					pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()}					)
     _UpperCAmelCase										=					field(default='''Audio'''				,		init=__a				,		repr=__a					)







     def __call__( self    ) ->	Optional[Any]:
      return self.pa_type







     def     UpperCamelCase			( self    ,   A__    ) ->	dict:
      try:
       import soundfile as sf  # soundfile is a dependency of librosa, needed to decode audio files.
      except ImportError as err:
       raise ImportError('''To support encoding audio data, please install \'soundfile\'.'''    ) from err
      if isinstance(A__    ,   A__    ):
       return {"bytes": None, "path": value}
      elif isinstance(A__    ,   A__    ):
       return {"bytes": value, "path": None}
      elif "array" in value:
       # convert the audio array to wav bytes
       snake_case				=			BytesIO()
       sf.write(A__    ,   value['''array''']    ,   value['''sampling_rate''']    ,   format='''wav'''    )
       return {"bytes": buffer.getvalue(), "path": None}
      elif value.get('''path'''    ) is not None and os.path.isfile(value['''path''']    ):
       # we set "bytes": None to not duplicate the data if they're already available locally
       if value["path"].endswith('''pcm'''    ):
        # "PCM" only has raw audio bytes
        if value.get('''sampling_rate'''    ) is None:
         # At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
         raise KeyError('''To use PCM files, please specify a \'sampling_rate\' in Audio object'''    )
        if value.get('''bytes'''    ):
         # If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
         snake_case				=			np.frombuffer(value['''bytes''']    ,   dtype=np.intaa    ).astype(np.floataa    ) / 3_27_67
        else:
         snake_case				=			np.memmap(value['''path''']    ,   dtype='''h'''    ,   mode='''r'''    ).astype(np.floataa    ) / 3_27_67

        snake_case				=			BytesIO(bytes()    )
        sf.write(A__    ,   A__    ,   value['''sampling_rate''']    ,   format='''wav'''    )
        return {"bytes": buffer.getvalue(), "path": None}
       else:
        return {"bytes": None, "path": value.get('''path'''    )}
      elif value.get('''bytes'''    ) is not None or value.get('''path'''    ) is not None:
       # store the audio bytes, and path is used to infer the audio format using the file extension
       return {"bytes": value.get('''bytes'''    ), "path": value.get('''path'''    )}
      else:
       raise ValueError(
           F"""An audio sample should have one of 'path' or 'bytes' but they are missing or None in {value}."""    )







     def     UpperCamelCase			( self    ,   A__    ,   A__ = None    ) ->	dict:
      if not self.decode:
       raise RuntimeError('''Decoding is disabled for this feature. Please use Audio(decode=True) instead.'''    )

      snake_case				,		snake_case				=			(value['''path'''], BytesIO(value['''bytes''']    )) if value['''bytes'''] is not None else (value['''path'''], None)
      if path is None and file is None:
       raise ValueError(F"""An audio sample should have one of 'path' or 'bytes' but both are None in {value}."""    )

      try:
       import librosa
       import soundfile as sf
      except ImportError as err:
       raise ImportError('''To support decoding audio files, please install \'librosa\' and \'soundfile\'.'''    ) from err

      snake_case				=			xsplitext(A__    )[1][1:].lower() if path is not None else None
      if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
       raise RuntimeError(
           '''Decoding \'opus\' files requires system library \'libsndfile\'>=1.0.31, '''
           '''You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. '''    )
      elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
       raise RuntimeError(
           '''Decoding \'mp3\' files requires system library \'libsndfile\'>=1.1.0, '''
           '''You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. '''    )

      if file is None:
       snake_case				=			token_per_repo_id or {}
       snake_case				=			path.split('''::'''    )[-1]
       try:
        snake_case				=			string_to_dict(A__    ,   config.HUB_DATASETS_URL    )['''repo_id''']
        snake_case				=			token_per_repo_id[repo_id]
       except (ValueError, KeyError):
        snake_case				=			None

       with xopen(A__    ,   '''rb'''    ,   use_auth_token=A__    ) as f:
        snake_case				,		snake_case				=			sf.read(A__    )

      else:
       snake_case				,		snake_case				=			sf.read(A__    )

      snake_case				=			array.T
      if self.mono:
       snake_case				=			librosa.to_mono(A__    )
      if self.sampling_rate and self.sampling_rate != sampling_rate:
       snake_case				=			librosa.resample(A__    ,   orig_sr=A__    ,   target_sr=self.sampling_rate    )
       snake_case				=			self.sampling_rate

      return {"path": path, "array": array, "sampling_rate": sampling_rate}







     def     UpperCamelCase			( self    ) ->	Union["FeatureType", Dict[str, "FeatureType"]]:
      from .features import Value

      if self.decode:
       raise ValueError('''Cannot flatten a decoded Audio feature.'''    )
      return {
          "bytes": Value('''binary'''    ),
          "path": Value('''string'''    ),
      }







     def     UpperCamelCase			( self    ,   A__    ) ->	pa.StructArray:
      if pa.types.is_string(storage.type    ):
       snake_case				=			pa.array([None] * len(A__    )    ,   type=pa.binary()    )
       snake_case				=			pa.StructArray.from_arrays([bytes_array, storage]    ,   ['''bytes''', '''path''']    ,   mask=storage.is_null()    )
      elif pa.types.is_binary(storage.type    ):
       snake_case				=			pa.array([None] * len(A__    )    ,   type=pa.string()    )
       snake_case				=			pa.StructArray.from_arrays([storage, path_array]    ,   ['''bytes''', '''path''']    ,   mask=storage.is_null()    )
      elif pa.types.is_struct(storage.type    ) and storage.type.get_all_field_indices('''array'''    ):
       snake_case				=			pa.array([Audio().encode_example(A__    ) if x is not None else None for x in storage.to_pylist()]    )
      elif pa.types.is_struct(storage.type    ):
       if storage.type.get_field_index('''bytes'''    ) >= 0:
        snake_case				=			storage.field('''bytes'''    )
       else:
        snake_case				=			pa.array([None] * len(A__    )    ,   type=pa.binary()    )
       if storage.type.get_field_index('''path'''    ) >= 0:
        snake_case				=			storage.field('''path'''    )
       else:
        snake_case				=			pa.array([None] * len(A__    )    ,   type=pa.string()    )
       snake_case				=			pa.StructArray.from_arrays([bytes_array, path_array]    ,   ['''bytes''', '''path''']    ,   mask=storage.is_null()    )
      return array_cast(A__    ,   self.pa_type    )







     def     UpperCamelCase			( self    ,   A__    ) ->	pa.StructArray:

      @no_op_if_value_is_null
      def path_to_bytes(A__    ):
       with xopen(A__    ,   '''rb'''    ) as f:
        snake_case				=			f.read()
       return bytes_

      snake_case				=			pa.array(
          [
              (path_to_bytes(x['''path''']    ) if x['''bytes'''] is None else x['''bytes''']) if x is not None else None
              for x in storage.to_pylist()
          ]    ,   type=pa.binary()    ,   )
      snake_case				=			pa.array(
          [os.path.basename(A__    ) if path is not None else None for path in storage.field('''path'''    ).to_pylist()]    ,   type=pa.string()    ,   )
      snake_case				=			pa.StructArray.from_arrays([bytes_array, path_array]    ,   ['''bytes''', '''path''']    ,   mask=bytes_array.is_null()    )
      return array_cast(A__    ,   self.pa_type    )

