






import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases


def      lowerCamelCase__	(   lowercase  ,   lowercase ):

  """simple docstring"""






  assert isinstance(lowercase  ,   lowercase )
  assert dataset.num_rows == 4
  assert dataset.num_columns == 3
  assert dataset.column_names == ["col_1", "col_2", "col_3"]
  for feature, expected_dtype in expected_features.items():
    assert dataset.features[feature].dtype == expected_dtype


@pytest.mark.parametrize("keep_in_memory"  ,   [False, True] )
def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			tmp_path / "cache"
  SCREAMING_SNAKE_CASE				:					Dict		  =			{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			CsvDatasetReader(lowercase  ,   cache_dir=lowercase  ,   keep_in_memory=lowercase ).read()
  _check_csv_dataset(lowercase  ,   lowercase )


@pytest.mark.parametrize(
    "features"  ,   [
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]  ,   )
def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Any		  =			tmp_path / "cache"
  # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  SCREAMING_SNAKE_CASE				:					Any		  =			features.copy() if features else default_expected_features
  SCREAMING_SNAKE_CASE				:					List[Any]		  =			(
      Features({feature: Value(lowercase ) for feature, dtype in features.items()} ) if features is not None else None
  )
  SCREAMING_SNAKE_CASE				:					Tuple		  =			CsvDatasetReader(lowercase  ,   features=lowercase  ,   cache_dir=lowercase ).read()
  _check_csv_dataset(lowercase  ,   lowercase )


@pytest.mark.parametrize("split"  ,   [None, NamedSplit("train" ), "train", "test"] )
def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Dict		  =			tmp_path / "cache"
  SCREAMING_SNAKE_CASE				:					Dict		  =			{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  SCREAMING_SNAKE_CASE				:					List[Any]		  =			CsvDatasetReader(lowercase  ,   cache_dir=lowercase  ,   split=lowercase ).read()
  _check_csv_dataset(lowercase  ,   lowercase )
  assert dataset.split == split if split else "train"


@pytest.mark.parametrize("path_type"  ,   [str, list] )
def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  if issubclass(lowercase  ,   lowercase ):
    SCREAMING_SNAKE_CASE				:					Tuple		  =			csv_path
  elif issubclass(lowercase  ,   lowercase ):
    SCREAMING_SNAKE_CASE				:					int		  =			[csv_path]
  SCREAMING_SNAKE_CASE				:					Any		  =			tmp_path / "cache"
  SCREAMING_SNAKE_CASE				:					Optional[int]		  =			{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  SCREAMING_SNAKE_CASE				:					Tuple		  =			CsvDatasetReader(lowercase  ,   cache_dir=lowercase ).read()
  _check_csv_dataset(lowercase  ,   lowercase )


def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase=("train",) ):

  """simple docstring"""






  assert isinstance(lowercase  ,   lowercase )
  for split in splits:
    SCREAMING_SNAKE_CASE				:					str		  =			dataset_dict[split]
    assert dataset.num_rows == 4
    assert dataset.num_columns == 3
    assert dataset.column_names == ["col_1", "col_2", "col_3"]
    for feature, expected_dtype in expected_features.items():
      assert dataset.features[feature].dtype == expected_dtype


@pytest.mark.parametrize("keep_in_memory"  ,   [False, True] )
def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			tmp_path / "cache"
  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			CsvDatasetReader({"train": csv_path}  ,   cache_dir=lowercase  ,   keep_in_memory=lowercase ).read()
  _check_csv_datasetdict(lowercase  ,   lowercase )


@pytest.mark.parametrize(
    "features"  ,   [
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]  ,   )
def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					int		  =			tmp_path / "cache"
  # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
  SCREAMING_SNAKE_CASE				:					Any		  =			{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			features.copy() if features else default_expected_features
  SCREAMING_SNAKE_CASE				:					List[Any]		  =			(
      Features({feature: Value(lowercase ) for feature, dtype in features.items()} ) if features is not None else None
  )
  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			CsvDatasetReader({"train": csv_path}  ,   features=lowercase  ,   cache_dir=lowercase ).read()
  _check_csv_datasetdict(lowercase  ,   lowercase )


@pytest.mark.parametrize("split"  ,   [None, NamedSplit("train" ), "train", "test"] )
def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  if split:
    SCREAMING_SNAKE_CASE				:					Dict		  =			{split: csv_path}
  else:
    SCREAMING_SNAKE_CASE				:					str		  =			{"train": csv_path, "test": csv_path}
  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			tmp_path / "cache"
  SCREAMING_SNAKE_CASE				:					Dict		  =			{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  SCREAMING_SNAKE_CASE				:					Tuple		  =			CsvDatasetReader(lowercase  ,   cache_dir=lowercase ).read()
  _check_csv_datasetdict(lowercase  ,   lowercase  ,   splits=list(path.keys() ) )
  assert all(dataset[split].split == split for split in path.keys() )


def      lowerCamelCase__	(   lowercase ):

  """simple docstring"""






  with open(lowercase  ,   encoding="utf-8" ) as csvfile:
    yield from csv.reader(lowercase )


def      lowerCamelCase__	(   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					List[Any]		  =			tmp_path / "cache"
  SCREAMING_SNAKE_CASE				:					Optional[int]		  =			os.path.join(lowercase  ,   "tmp.csv" )
  SCREAMING_SNAKE_CASE				:					Optional[int]		  =			CsvDatasetReader({"train": csv_path}  ,   cache_dir=lowercase ).read()
  CsvDatasetWriter(dataset["train"]  ,   lowercase  ,   num_proc=1 ).write()

  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			iter_csv_file(lowercase )
  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			iter_csv_file(lowercase )

  for rowa, rowa in zip(lowercase  ,   lowercase ):
    assert rowa == rowa


def      lowerCamelCase__	(   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			tmp_path / "cache"
  SCREAMING_SNAKE_CASE				:					List[Any]		  =			os.path.join(lowercase  ,   "tmp.csv" )
  SCREAMING_SNAKE_CASE				:					List[str]		  =			CsvDatasetReader({"train": csv_path}  ,   cache_dir=lowercase ).read()
  CsvDatasetWriter(dataset["train"]  ,   lowercase  ,   num_proc=2 ).write()

  SCREAMING_SNAKE_CASE				:					Dict		  =			iter_csv_file(lowercase )
  SCREAMING_SNAKE_CASE				:					Tuple		  =			iter_csv_file(lowercase )

  for rowa, rowa in zip(lowercase  ,   lowercase ):
    assert rowa == rowa


def      lowerCamelCase__	(   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			tmp_path / "cache"
  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			os.path.join(lowercase  ,   "tmp.csv" )
  SCREAMING_SNAKE_CASE				:					List[str]		  =			CsvDatasetReader({"train": csv_path}  ,   cache_dir=lowercase ).read()
  with pytest.raises(lowercase ):
    CsvDatasetWriter(dataset["train"]  ,   lowercase  ,   num_proc=0 )














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,
)
snake_case			 =						logging.getLogger(__name__)


def      lowerCamelCase__	(   lowercase ):

  """simple docstring"""






  np.random.seed(args.seed )
  torch.manual_seed(args.seed )
  if args.n_gpu > 0:
    torch.cuda.manual_seed_all(args.seed )


def      lowerCamelCase__	(   ):

  """simple docstring"""






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

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

  parser.add_argument("--seed"  ,   type=lowercase  ,   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"  ,   )
  SCREAMING_SNAKE_CASE				:					str		  =			parser.parse_args()

  SCREAMING_SNAKE_CASE				:					Dict		  =			torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu" )
  SCREAMING_SNAKE_CASE				:					List[str]		  =			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(lowercase )

  # Initialize the model and tokenizer
  SCREAMING_SNAKE_CASE				:					Tuple		  =			AutoTokenizer.from_pretrained(args.model_name_or_path )
  SCREAMING_SNAKE_CASE				:					int		  =			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(lowercase )
  SCREAMING_SNAKE_CASE				:					Any		  =			args.prompt if args.prompt else input("Model prompt >>> " )

  SCREAMING_SNAKE_CASE				:					str		  =			tokenizer(lowercase  ,   return_tensors="pt"  ,   add_special_tokens=lowercase )
  SCREAMING_SNAKE_CASE				:					Dict		  =			{key: value.to(args.device ) for key, value in inputs.items()}

  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			model.generate(
      **lowercase  ,   max_length=args.length + len(inputs["input_ids"][0] )  ,   penalty_alpha=args.penalty_alpha  ,   top_k=args.k  ,   )

  SCREAMING_SNAKE_CASE				:					str		  =			[]
  for generated_sequence_idx, generated_sequence in enumerate(lowercase ):
    print(F'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ===''' )
    SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			generated_sequence.tolist()

    # Decode text
    SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			tokenizer.decode(lowercase  ,   clean_up_tokenization_spaces=lowercase  ,   add_special_tokens=lowercase )

    # Remove all text after the stop token
    SCREAMING_SNAKE_CASE				:					List[str]		  =			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
    SCREAMING_SNAKE_CASE				:					str		  =			(
        prompt_text + text[len(tokenizer.decode(inputs["input_ids"][0]  ,   clean_up_tokenization_spaces=lowercase ) ) :]
    )

    generated_sequences.append(lowercase )
    print(lowercase )

  return generated_sequences


if __name__ == "__main__":
   main()






