




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       UpperCAmelCase_							(   __UpperCAmelCase		:			List[Any]    ,    __UpperCAmelCase		:			Dict							)      ->				List[Any]:
      assert isinstance(__UpperCAmelCase    ,    __UpperCAmelCase							)
      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       UpperCAmelCase_							(   __UpperCAmelCase		:			List[Any]    ,    __UpperCAmelCase		:			Dict    ,    __UpperCAmelCase		:			str							)      ->				Union[str, Any]:
      SCREAMING_SNAKE_CASE_					=    tmp_path / 'cache'
      SCREAMING_SNAKE_CASE_					=    {'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_					=    CsvDatasetReader(__UpperCAmelCase    ,    cache_dir=__UpperCAmelCase    ,    keep_in_memory=__UpperCAmelCase							).read()
      _check_csv_dataset(__UpperCAmelCase    ,    __UpperCAmelCase							)

@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       UpperCAmelCase_							(   __UpperCAmelCase		:			Dict    ,    __UpperCAmelCase		:			Optional[int]    ,    __UpperCAmelCase		:			Union[str, Any]							)      ->				int:
      SCREAMING_SNAKE_CASE_					=    tmp_path / 'cache'
      # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
      SCREAMING_SNAKE_CASE_					=    {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
      SCREAMING_SNAKE_CASE_					=    features.copy() if features else default_expected_features
      SCREAMING_SNAKE_CASE_					=    (
          Features({feature: Value(__UpperCAmelCase							) for feature, dtype in features.items()}							) if features is not None else None
      )
      SCREAMING_SNAKE_CASE_					=    CsvDatasetReader(__UpperCAmelCase    ,    features=__UpperCAmelCase    ,    cache_dir=__UpperCAmelCase							).read()
      _check_csv_dataset(__UpperCAmelCase    ,    __UpperCAmelCase							)

@pytest.mark.parametrize('split'    ,    [None, NamedSplit('train'							), 'train', 'test']							)
def       UpperCAmelCase_							(   __UpperCAmelCase		:			Tuple    ,    __UpperCAmelCase		:			Optional[int]    ,    __UpperCAmelCase		:			List[str]							)      ->				Dict:
      SCREAMING_SNAKE_CASE_					=    tmp_path / 'cache'
      SCREAMING_SNAKE_CASE_					=    {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
      SCREAMING_SNAKE_CASE_					=    CsvDatasetReader(__UpperCAmelCase    ,    cache_dir=__UpperCAmelCase    ,    split=__UpperCAmelCase							).read()
      _check_csv_dataset(__UpperCAmelCase    ,    __UpperCAmelCase							)
      assert dataset.split == split if split else "train"

@pytest.mark.parametrize('path_type'    ,    [str, list]							)
def       UpperCAmelCase_							(   __UpperCAmelCase		:			List[str]    ,    __UpperCAmelCase		:			str    ,    __UpperCAmelCase		:			str							)      ->				int:
      if issubclass(__UpperCAmelCase    ,    __UpperCAmelCase							):
            SCREAMING_SNAKE_CASE_					=    csv_path
      elif issubclass(__UpperCAmelCase    ,    __UpperCAmelCase							):
            SCREAMING_SNAKE_CASE_					=    [csv_path]
      SCREAMING_SNAKE_CASE_					=    tmp_path / 'cache'
      SCREAMING_SNAKE_CASE_					=    {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
      SCREAMING_SNAKE_CASE_					=    CsvDatasetReader(__UpperCAmelCase    ,    cache_dir=__UpperCAmelCase							).read()
      _check_csv_dataset(__UpperCAmelCase    ,    __UpperCAmelCase							)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			Optional[Any]    ,    __UpperCAmelCase		:			Optional[Any]    ,    __UpperCAmelCase		:			List[str]=("train",)							)      ->				str:
      assert isinstance(__UpperCAmelCase    ,    __UpperCAmelCase							)
      for split in splits:
            SCREAMING_SNAKE_CASE_					=    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       UpperCAmelCase_							(   __UpperCAmelCase		:			int    ,    __UpperCAmelCase		:			int    ,    __UpperCAmelCase		:			List[Any]							)      ->				List[str]:
      SCREAMING_SNAKE_CASE_					=    tmp_path / 'cache'
      SCREAMING_SNAKE_CASE_					=    {'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_					=    CsvDatasetReader({'train': csv_path}    ,    cache_dir=__UpperCAmelCase    ,    keep_in_memory=__UpperCAmelCase							).read()
      _check_csv_datasetdict(__UpperCAmelCase    ,    __UpperCAmelCase							)

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

@pytest.mark.parametrize('split'    ,    [None, NamedSplit('train'							), 'train', 'test']							)
def       UpperCAmelCase_							(   __UpperCAmelCase		:			Tuple    ,    __UpperCAmelCase		:			Optional[Any]    ,    __UpperCAmelCase		:			Dict							)      ->				str:
      if split:
            SCREAMING_SNAKE_CASE_					=    {split: csv_path}
      else:
            SCREAMING_SNAKE_CASE_					=    {'train': csv_path, 'test': csv_path}
      SCREAMING_SNAKE_CASE_					=    tmp_path / 'cache'
      SCREAMING_SNAKE_CASE_					=    {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
      SCREAMING_SNAKE_CASE_					=    CsvDatasetReader(__UpperCAmelCase    ,    cache_dir=__UpperCAmelCase							).read()
      _check_csv_datasetdict(__UpperCAmelCase    ,    __UpperCAmelCase    ,    splits=list(path.keys()							)							)
      assert all(dataset[split].split == split for split in path.keys()							)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			Optional[int]							)      ->				Optional[Any]:
      with open(__UpperCAmelCase    ,    encoding='utf-8'							) as csvfile:
            yield from csv.reader(__UpperCAmelCase							)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			Dict    ,    __UpperCAmelCase		:			List[Any]							)      ->				List[Any]:
      SCREAMING_SNAKE_CASE_					=    tmp_path / 'cache'
      SCREAMING_SNAKE_CASE_					=    os.path.join(__UpperCAmelCase    ,    'tmp.csv'							)
      SCREAMING_SNAKE_CASE_					=    CsvDatasetReader({'train': csv_path}    ,    cache_dir=__UpperCAmelCase							).read()
      CsvDatasetWriter(dataset['train']    ,    __UpperCAmelCase    ,    num_proc=1							).write()

      SCREAMING_SNAKE_CASE_					=    iter_csv_file(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_					=    iter_csv_file(__UpperCAmelCase							)

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

def       UpperCAmelCase_							(   __UpperCAmelCase		:			List[str]    ,    __UpperCAmelCase		:			List[str]							)      ->				Any:
      SCREAMING_SNAKE_CASE_					=    tmp_path / 'cache'
      SCREAMING_SNAKE_CASE_					=    os.path.join(__UpperCAmelCase    ,    'tmp.csv'							)
      SCREAMING_SNAKE_CASE_					=    CsvDatasetReader({'train': csv_path}    ,    cache_dir=__UpperCAmelCase							).read()
      CsvDatasetWriter(dataset['train']    ,    __UpperCAmelCase    ,    num_proc=2							).write()

      SCREAMING_SNAKE_CASE_					=    iter_csv_file(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_					=    iter_csv_file(__UpperCAmelCase							)

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





def       UpperCAmelCase_							(   __UpperCAmelCase		:			Union[str, Any]    ,    __UpperCAmelCase		:			Any							)      ->				List[str]:
      SCREAMING_SNAKE_CASE_					=    tmp_path / 'cache'
      SCREAMING_SNAKE_CASE_					=    os.path.join(__UpperCAmelCase    ,    'tmp.csv'							)
      SCREAMING_SNAKE_CASE_					=    CsvDatasetReader({'train': csv_path}    ,    cache_dir=__UpperCAmelCase							).read()
      with pytest.raises(__UpperCAmelCase							):
            CsvDatasetWriter(dataset['train']    ,    __UpperCAmelCase    ,    num_proc=0							)





import os
from pathlib import Path
from typing import Dict, List

import fire
import torch

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.utils.logging import get_logger


lowerCamelCase__				:     Optional[Any]			       =							get_logger(__name__)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			str    ,    __UpperCAmelCase		:			str							)      ->				List[str]:
      if text.startswith(__UpperCAmelCase							):
            return text[len(__UpperCAmelCase							) :]
      return text  # or whatever

def       UpperCAmelCase_							(   __UpperCAmelCase		:			Union[str, Any]							)      ->				List[Any]:
      return {remove_prefix(__UpperCAmelCase    ,    'model.'							): v for k, v in sd.items()}

def       UpperCAmelCase_							(   __UpperCAmelCase		:			List[Dict[str, torch.Tensor]]							)      ->				Dict:
      SCREAMING_SNAKE_CASE_					=    {}
      for k in state_dicts[0].keys():
            SCREAMING_SNAKE_CASE_					=    [sd[k] for sd in state_dicts]
            SCREAMING_SNAKE_CASE_					=    sum(__UpperCAmelCase							) / len(__UpperCAmelCase							)
            assert isinstance(__UpperCAmelCase    ,    torch.Tensor							)
            SCREAMING_SNAKE_CASE_					=    new_t
      return new_sd

def       UpperCAmelCase_							(   __UpperCAmelCase		:			str    ,    __UpperCAmelCase		:			str    ,    __UpperCAmelCase		:			str							)      ->				None:
      SCREAMING_SNAKE_CASE_					=    AutoModelForSeqaSeqLM.from_pretrained(__UpperCAmelCase							)
      if os.path.isfile(__UpperCAmelCase							):
            SCREAMING_SNAKE_CASE_					=    [pl_ckpt_path]
      else:
            assert os.path.isdir(__UpperCAmelCase							)
            SCREAMING_SNAKE_CASE_					=    list(Path(__UpperCAmelCase							).glob('*.ckpt'							)							)
            assert ckpt_files, f"could not find any ckpt files inside the {pl_ckpt_path} directory"

      if len(__UpperCAmelCase							) > 1:
            logger.info(f"averaging the weights of {ckpt_files}"							)

      SCREAMING_SNAKE_CASE_					=    [sanitize(torch.load(__UpperCAmelCase    ,    map_location='cpu'							)['state_dict']							) for x in ckpt_files]
      SCREAMING_SNAKE_CASE_					=    average_state_dicts(__UpperCAmelCase							)

      SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_					=    hf_model.load_state_dict(__UpperCAmelCase    ,    strict=__UpperCAmelCase							)
      assert not missing, f"missing keys: {missing}"
      hf_model.save_pretrained(__UpperCAmelCase							)
      try:
            SCREAMING_SNAKE_CASE_					=    AutoTokenizer.from_pretrained(__UpperCAmelCase							)
            tok.save_pretrained(__UpperCAmelCase							)
      except Exception:
            pass
            # dont copy tokenizer if cant


if __name__ == "__main__":
   fire.Fire(convert_pl_to_hf)