
import multiprocessing
import os
from typing import BinaryIO, Optional, Union

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.csv.csv import Csv
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader


class 							lowerCamelCase       (		__snake_case					):



  """simple docstring"""





  def __init__(					self						,    __UpperCamelCase						,    __UpperCamelCase = None						,    __UpperCamelCase = None						,    __UpperCamelCase = None						,    __UpperCamelCase = False						,    __UpperCamelCase = False						,    __UpperCamelCase = None						,    **__UpperCamelCase						,    ):
      super().__init__(
          __UpperCamelCase						,    split=__UpperCamelCase						,    features=__UpperCamelCase						,    cache_dir=__UpperCamelCase						,    keep_in_memory=__UpperCamelCase						,    streaming=__UpperCamelCase						,    num_proc=__UpperCamelCase						,    **__UpperCamelCase						,    )
      A_								=					path_or_paths if isinstance(__UpperCamelCase						,    __UpperCamelCase					) else {self.split: path_or_paths}
      A_								=					Csv(
          cache_dir=__UpperCamelCase						,    data_files=__UpperCamelCase						,    features=__UpperCamelCase						,    **__UpperCamelCase						,    )

  def 					lowercase_						(					self					):
      # Build iterable dataset
      if self.streaming:
          A_								=					self.builder.as_streaming_dataset(split=self.split					)
      # Build regular (map-style) dataset
      else:
          A_								=					None
          A_								=					None
          A_								=					None
          A_								=					None

          self.builder.download_and_prepare(
              download_config=__UpperCamelCase						,    download_mode=__UpperCamelCase						,    verification_mode=__UpperCamelCase						,    base_path=__UpperCamelCase						,    num_proc=self.num_proc						,    )
          A_								=					self.builder.as_dataset(
              split=self.split						,    verification_mode=__UpperCamelCase						,    in_memory=self.keep_in_memory					)
      return dataset



class 							lowerCamelCase       :



  """simple docstring"""





  def __init__(					self						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase = None						,    __UpperCamelCase = None						,    **__UpperCamelCase						,    ):
      if num_proc is not None and num_proc <= 0:
          raise ValueError(f'num_proc {num_proc} must be an integer > 0.'					)

      A_								=					dataset
      A_								=					path_or_buf
      A_								=					batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
      A_								=					num_proc
      A_								=					"utf-8"
      A_								=					to_csv_kwargs

  def 					lowercase_						(					self					):
      A_								=					self.to_csv_kwargs.pop("path_or_buf"						,    __UpperCamelCase					)
      A_								=					self.to_csv_kwargs.pop("header"						,    __UpperCamelCase					)
      A_								=					self.to_csv_kwargs.pop("index"						,    __UpperCamelCase					)

      if isinstance(self.path_or_buf						,    (str, bytes, os.PathLike)					):
          with open(self.path_or_buf						,    "wb+"					) as buffer:
              A_								=					self._write(file_obj=__UpperCamelCase						,    header=__UpperCamelCase						,    index=__UpperCamelCase						,    **self.to_csv_kwargs					)
      else:
          A_								=					self._write(file_obj=self.path_or_buf						,    header=__UpperCamelCase						,    index=__UpperCamelCase						,    **self.to_csv_kwargs					)
      return written

  def 					lowercase_						(					self						,    __UpperCamelCase					):
      A_	,	A_	,	A_	,	A_								=					args

      A_								=					query_table(
          table=self.dataset.data						,    key=slice(__UpperCamelCase						,    offset + self.batch_size					)						,    indices=self.dataset._indices						,    )
      A_								=					batch.to_pandas().to_csv(
          path_or_buf=__UpperCamelCase						,    header=header if (offset == 0) else False						,    index=__UpperCamelCase						,    **__UpperCamelCase					)
      return csv_str.encode(self.encoding					)

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    **__UpperCamelCase					):
      A_								=					0

      if self.num_proc is None or self.num_proc == 1:
          for offset in logging.tqdm(
              range(0						,    len(self.dataset					)						,    self.batch_size					)						,    unit="ba"						,    disable=not logging.is_progress_bar_enabled()						,    desc="Creating CSV from Arrow format"						,    ):
              A_								=					self._batch_csv((offset, header, index, to_csv_kwargs)					)
              written += file_obj.write(__UpperCamelCase					)

      else:
          A_	,	A_								=					len(self.dataset					), self.batch_size
          with multiprocessing.Pool(self.num_proc					) as pool:
              for csv_str in logging.tqdm(
                  pool.imap(
                      self._batch_csv						,    [(offset, header, index, to_csv_kwargs) for offset in range(0						,    __UpperCamelCase						,    __UpperCamelCase					)]						,    )						,    total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size						,    unit="ba"						,    disable=not logging.is_progress_bar_enabled()						,    desc="Creating CSV from Arrow format"						,    ):
                  written += file_obj.write(__UpperCamelCase					)

      return written








import argparse
import logging

import numpy as np
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
    datefmt='%m/%d/%Y %H:%M:%S',
    level=logging.INFO,
)
__magic_name__  :  List[str]     			=   logging.getLogger(__name__)





def        lowerCAmelCase       (				snake_case__   :      Union[str, Any] )->			Optional[Any]:
    np.random.seed(args.seed )
    torch.manual_seed(args.seed )
    if args.n_gpu > 0:
        torch.cuda.manual_seed_all(args.seed )





def        lowerCAmelCase       (				)->			int:
    A_								=					argparse.ArgumentParser()
    parser.add_argument(
        "--model_name_or_path"				,					default=snake_case__				,					type=snake_case__				,					required=snake_case__				,					)
    parser.add_argument("--prompt"				,					type=snake_case__				,					default="" )
    parser.add_argument("--length"				,					type=snake_case__				,					default=20 )
    parser.add_argument("--stop_token"				,					type=snake_case__				,					default=snake_case__				,					help="Token at which text generation is stopped" )
    parser.add_argument(
        "--temperature"				,					type=snake_case__				,					default=1.0				,					help="temperature of 1.0 has no effect, lower tend toward greedy sampling"				,					)
    parser.add_argument(
        "--repetition_penalty"				,					type=snake_case__				,					default=1.0				,					help="primarily useful for CTRL model; in that case, use 1.2" )
    parser.add_argument("--k"				,					type=snake_case__				,					default=0 )
    parser.add_argument("--penalty_alpha"				,					type=snake_case__				,					default=0.0 )
    parser.add_argument("--p"				,					type=snake_case__				,					default=0.9 )

    parser.add_argument("--prefix"				,					type=snake_case__				,					default=""				,					help="Text added prior to input." )
    parser.add_argument("--padding_text"				,					type=snake_case__				,					default=""				,					help="Deprecated, the use of `--prefix` is preferred." )
    parser.add_argument("--xlm_language"				,					type=snake_case__				,					default=""				,					help="Optional language when used with the XLM model." )

    parser.add_argument("--seed"				,					type=snake_case__				,					default=42				,					help="random seed for initialization" )
    parser.add_argument("--no_cuda"				,					action="store_true"				,					help="Avoid using CUDA when available" )
    parser.add_argument(
        "--fp16"				,					action="store_true"				,					help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"				,					)
    A_								=					parser.parse_args()

    A_								=					torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu" )
    A_								=					0 if args.no_cuda else torch.cuda.device_count()

    logger.warning(f'device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}' )

    set_seed(snake_case__ )

    # Initialize the model and tokenizer
    A_								=					AutoTokenizer.from_pretrained(args.model_name_or_path )
    A_								=					AutoModelForCausalLM.from_pretrained(args.model_name_or_path )

    # tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
    # model = OPTForCausalLM.from_pretrained(args.model_name_or_path)
    model.to(args.device )

    if args.fpaa:
        model.half()

    logger.info(snake_case__ )
    A_								=					args.prompt if args.prompt else input("Model prompt >>> " )

    A_								=					tokenizer(snake_case__				,					return_tensors="pt"				,					add_special_tokens=snake_case__ )
    A_								=					{key: value.to(args.device ) for key, value in inputs.items()}

    A_								=					model.generate(
        **snake_case__				,					max_length=args.length + len(inputs["input_ids"][0] )				,					penalty_alpha=args.penalty_alpha				,					top_k=args.k				,					)

    A_								=					[]
    for generated_sequence_idx, generated_sequence in enumerate(snake_case__ ):
        print(f'=== GENERATED SEQUENCE {generated_sequence_idx + 1} ===' )
        A_								=					generated_sequence.tolist()

        # Decode text
        A_								=					tokenizer.decode(snake_case__				,					clean_up_tokenization_spaces=snake_case__				,					add_special_tokens=snake_case__ )

        # Remove all text after the stop token
        A_								=					text[: text.find(args.stop_token ) if args.stop_token else None]

        # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
        A_								=					(
            prompt_text + text[len(tokenizer.decode(inputs["input_ids"][0]				,					clean_up_tokenization_spaces=snake_case__ ) ) :]
        )

        generated_sequences.append(snake_case__ )
        print(snake_case__ )

    return generated_sequences


if __name__ == "__main__":
     main()






