'''simple docstring'''






import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal


SCREAMING_SNAKE_CASE_	   =			datasets.utils.logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_	   =			['names', 'prefix']
SCREAMING_SNAKE_CASE_	   =			['warn_bad_lines', 'error_bad_lines', 'mangle_dupe_cols']
SCREAMING_SNAKE_CASE_	   =			['encoding_errors', 'on_bad_lines']
SCREAMING_SNAKE_CASE_	   =			['date_format']




@dataclass
class    a (       datasets.BuilderConfig				):



  """simple docstring"""
  __lowerCAmelCase				      =					","
  __lowerCAmelCase				      =					None
  __lowerCAmelCase				      =					"infer"
  __lowerCAmelCase				      =					None
  __lowerCAmelCase				      =					None
  __lowerCAmelCase				      =					None
  __lowerCAmelCase				      =					None
  __lowerCAmelCase				      =					None
  __lowerCAmelCase				      =					True
  __lowerCAmelCase				      =					None
  __lowerCAmelCase				      =					None
  __lowerCAmelCase				      =					None
  __lowerCAmelCase				      =					None
  __lowerCAmelCase				      =					False
  __lowerCAmelCase				      =					None
  __lowerCAmelCase				      =					None
  __lowerCAmelCase				      =					None
  __lowerCAmelCase				      =					True
  __lowerCAmelCase				      =					True
  __lowerCAmelCase				      =					False
  __lowerCAmelCase				      =					True
  __lowerCAmelCase				      =					None
  __lowerCAmelCase				      =					"."
  __lowerCAmelCase				      =					None
  __lowerCAmelCase				      =					'"'
  __lowerCAmelCase				      =					0
  __lowerCAmelCase				      =					None
  __lowerCAmelCase				      =					None
  __lowerCAmelCase				      =					None
  __lowerCAmelCase				      =					None
  __lowerCAmelCase				      =					True
  __lowerCAmelCase				      =					True
  __lowerCAmelCase				      =					0
  __lowerCAmelCase				      =					True
  __lowerCAmelCase				      =					False
  __lowerCAmelCase				      =					None
  __lowerCAmelCase				      =					1_0_0_0_0
  __lowerCAmelCase				      =					None
  __lowerCAmelCase				      =					"strict"
  __lowerCAmelCase				      =					"error"
  __lowerCAmelCase				      =					None







  def    lowercase_  (							self					):

    '''simple docstring'''







    if self.delimiter is not None:
      __UpperCAmelCase:					Optional[Any]		= self.delimiter
    if self.column_names is not None:
      __UpperCAmelCase:					Tuple		= self.column_names







  @property
  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Dict		= {
        """sep""": self.sep,
        """header""": self.header,
        """names""": self.names,
        """index_col""": self.index_col,
        """usecols""": self.usecols,
        """prefix""": self.prefix,
        """mangle_dupe_cols""": self.mangle_dupe_cols,
        """engine""": self.engine,
        """converters""": self.converters,
        """true_values""": self.true_values,
        """false_values""": self.false_values,
        """skipinitialspace""": self.skipinitialspace,
        """skiprows""": self.skiprows,
        """nrows""": self.nrows,
        """na_values""": self.na_values,
        """keep_default_na""": self.keep_default_na,
        """na_filter""": self.na_filter,
        """verbose""": self.verbose,
        """skip_blank_lines""": self.skip_blank_lines,
        """thousands""": self.thousands,
        """decimal""": self.decimal,
        """lineterminator""": self.lineterminator,
        """quotechar""": self.quotechar,
        """quoting""": self.quoting,
        """escapechar""": self.escapechar,
        """comment""": self.comment,
        """encoding""": self.encoding,
        """dialect""": self.dialect,
        """error_bad_lines""": self.error_bad_lines,
        """warn_bad_lines""": self.warn_bad_lines,
        """skipfooter""": self.skipfooter,
        """doublequote""": self.doublequote,
        """memory_map""": self.memory_map,
        """float_precision""": self.float_precision,
        """chunksize""": self.chunksize,
        """encoding_errors""": self.encoding_errors,
        """on_bad_lines""": self.on_bad_lines,
        """date_format""": self.date_format,
    }

    # some kwargs must not be passed if they don't have a default value
    # some others are deprecated and we can also not pass them if they are the default value
    for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
      if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig()   ,    snake_case_					):
        del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 2.0 new arguments
    if not (datasets.config.PANDAS_VERSION.major >= 2):
      for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
        del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 1.3 new arguments
    if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
      for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
        del pd_read_csv_kwargs[pd_read_csv_parameter]

    return pd_read_csv_kwargs




class    a (       datasets.ArrowBasedBuilder				):



  """simple docstring"""
  __lowerCAmelCase				      =					CsvConfig







  def    lowercase_  (							self					):

    '''simple docstring'''







    return datasets.DatasetInfo(features=self.config.features					)







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    if not self.config.data_files:
      raise ValueError(F'''At least one data file must be specified, but got data_files={self.config.data_files}'''					)
    __UpperCAmelCase:					str		= dl_manager.download_and_extract(self.config.data_files					)
    if isinstance(snake_case_   ,    (str, list, tuple)					):
      __UpperCAmelCase:					List[Any]		= data_files
      if isinstance(snake_case_   ,    snake_case_					):
        __UpperCAmelCase:					List[Any]		= [files]
      __UpperCAmelCase:					Union[str, Any]		= [dl_manager.iter_files(snake_case_					) for file in files]
      return [datasets.SplitGenerator(name=datasets.Split.TRAIN   ,    gen_kwargs={"""files""": files}					)]
    __UpperCAmelCase:					str		= []
    for split_name, files in data_files.items():
      if isinstance(snake_case_   ,    snake_case_					):
        __UpperCAmelCase:					int		= [files]
      __UpperCAmelCase:					Tuple		= [dl_manager.iter_files(snake_case_					) for file in files]
      splits.append(datasets.SplitGenerator(name=snake_case_   ,    gen_kwargs={"""files""": files}					)					)
    return splits







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    if self.config.features is not None:
      __UpperCAmelCase:					Union[str, Any]		= self.config.features.arrow_schema
      if all(not require_storage_cast(snake_case_					) for feature in self.config.features.values()					):
        # cheaper cast
        __UpperCAmelCase:					Optional[int]		= pa.Table.from_arrays([pa_table[field.name] for field in schema]   ,    schema=snake_case_					)
      else:
        # more expensive cast; allows str <-> int/float or str to Audio for example
        __UpperCAmelCase:					Optional[Any]		= table_cast(snake_case_   ,    snake_case_					)
    return pa_table







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					str		= self.config.features.arrow_schema if self.config.features else None
    # dtype allows reading an int column as str
    __UpperCAmelCase:					List[str]		= (
        {
            name: dtype.to_pandas_dtype() if not require_storage_cast(snake_case_					) else object
            for name, dtype, feature in zip(schema.names   ,    schema.types   ,    self.config.features.values()					)
        }
        if schema is not None
        else None
    )
    for file_idx, file in enumerate(itertools.chain.from_iterable(snake_case_					)					):
      __UpperCAmelCase:					Optional[int]		= pd.read_csv(snake_case_   ,    iterator=snake_case_   ,    dtype=snake_case_   ,    **self.config.pd_read_csv_kwargs					)
      try:
        for batch_idx, df in enumerate(snake_case_					):
          __UpperCAmelCase:					Any		= pa.Table.from_pandas(snake_case_					)
          # Uncomment for debugging (will print the Arrow table size and elements)
          # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
          # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
          yield (file_idx, batch_idx), self._cast_table(snake_case_					)
      except ValueError as e:
        logger.error(F'''Failed to read file \'{file}\' with error {type(snake_case_					)}: {e}'''					)
        raise
'''simple docstring'''






import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import ImageGPTImageProcessor




class    a (       unittest.TestCase				):



  """simple docstring"""







  def __init__(							self   ,    snake_case_   ,    snake_case_=7   ,    snake_case_=3   ,    snake_case_=18   ,    snake_case_=30   ,    snake_case_=400   ,    snake_case_=True   ,    snake_case_=None   ,    snake_case_=True   ,    ):

    '''simple docstring'''







    __UpperCAmelCase:					Optional[int]		= size if size is not None else {"""height""": 18, """width""": 18}
    __UpperCAmelCase:					Tuple		= parent
    __UpperCAmelCase:					Any		= batch_size
    __UpperCAmelCase:					str		= num_channels
    __UpperCAmelCase:					Any		= image_size
    __UpperCAmelCase:					Optional[int]		= min_resolution
    __UpperCAmelCase:					Tuple		= max_resolution
    __UpperCAmelCase:					Any		= do_resize
    __UpperCAmelCase:					Any		= size
    __UpperCAmelCase:					Optional[Any]		= do_normalize







  def    lowercase_  (							self					):

    '''simple docstring'''







    return {
        # here we create 2 clusters for the sake of simplicity
        "clusters": np.asarray(
            [
                [0.8_8_6_6_4_4_3_6_3_4_0_3_3_2_0_3, 0.6_6_1_8_8_2_9_3_6_9_5_4_4_9_8_3, 0.3_8_9_1_7_4_6_4_0_1_7_8_6_8_0_4],
                [-0.6_0_4_2_5_5_9_1_4_6_8_8_1_1_0_4, -0.0_2_2_9_5_0_0_8_8_6_0_5_2_8_4_6_9, 0.5_4_2_3_7_9_7_3_6_9_0_0_3_2_9_6],
            ]					),
        "do_resize": self.do_resize,
        "size": self.size,
        "do_normalize": self.do_normalize,
    }




@require_torch
@require_vision
class    a (       __lowerCAmelCase						,  unittest.TestCase				):



  """simple docstring"""
  __lowerCAmelCase				      =					ImageGPTImageProcessor if is_vision_available() else None







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					str		= ImageGPTImageProcessingTester(self					)







  @property
  def    lowercase_  (							self					):

    '''simple docstring'''







    return self.image_processor_tester.prepare_image_processor_dict()







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Tuple		= self.image_processing_class(**self.image_processor_dict					)
    self.assertTrue(hasattr(snake_case_   ,    """clusters"""					)					)
    self.assertTrue(hasattr(snake_case_   ,    """do_resize"""					)					)
    self.assertTrue(hasattr(snake_case_   ,    """size"""					)					)
    self.assertTrue(hasattr(snake_case_   ,    """do_normalize"""					)					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					List[Any]		= self.image_processing_class.from_dict(self.image_processor_dict					)
    self.assertEqual(image_processor.size   ,    {"""height""": 18, """width""": 18}					)

    __UpperCAmelCase:					int		= self.image_processing_class.from_dict(self.image_processor_dict   ,    size=42					)
    self.assertEqual(image_processor.size   ,    {"""height""": 42, """width""": 42}					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					int		= self.image_processing_class(**self.image_processor_dict					)
    __UpperCAmelCase:					str		= json.loads(image_processor.to_json_string()					)
    for key, value in self.image_processor_dict.items():
      if key == "clusters":
        self.assertTrue(np.array_equal(snake_case_   ,    obj[key]					)					)
      else:
        self.assertEqual(obj[key]   ,    snake_case_					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Any		= self.image_processing_class(**self.image_processor_dict					)

    with tempfile.TemporaryDirectory() as tmpdirname:
      __UpperCAmelCase:					List[Any]		= os.path.join(snake_case_   ,    """image_processor.json"""					)
      image_processor_first.to_json_file(snake_case_					)
      __UpperCAmelCase:					List[str]		= self.image_processing_class.from_json_file(snake_case_					).to_dict()

    __UpperCAmelCase:					str		= image_processor_first.to_dict()
    for key, value in image_processor_first.items():
      if key == "clusters":
        self.assertTrue(np.array_equal(snake_case_   ,    image_processor_second[key]					)					)
      else:
        self.assertEqual(image_processor_first[key]   ,    snake_case_					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Any		= self.image_processing_class(**self.image_processor_dict					)

    with tempfile.TemporaryDirectory() as tmpdirname:
      image_processor_first.save_pretrained(snake_case_					)
      __UpperCAmelCase:					Any		= self.image_processing_class.from_pretrained(snake_case_					).to_dict()

    __UpperCAmelCase:					str		= image_processor_first.to_dict()
    for key, value in image_processor_first.items():
      if key == "clusters":
        self.assertTrue(np.array_equal(snake_case_   ,    image_processor_second[key]					)					)
      else:
        self.assertEqual(image_processor_first[key]   ,    snake_case_					)







  @unittest.skip("""ImageGPT requires clusters at initialization"""					)
  def    lowercase_  (							self					):

    '''simple docstring'''







    pass






def  UpperCamelCase__					(					)							->	List[str]:
  __UpperCAmelCase:					Any		= load_dataset("""hf-internal-testing/fixtures_image_utils"""   ,    split="""test"""     )

  __UpperCAmelCase:					List[Any]		= Image.open(dataset[4]["""file"""]     )
  __UpperCAmelCase:					Dict		= Image.open(dataset[5]["""file"""]     )

  __UpperCAmelCase:					Dict		= [imagea, imagea]

  return images







@require_vision
@require_torch
class    a (       unittest.TestCase				):



  """simple docstring"""







  @slow
  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Union[str, Any]		= ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small"""					)

    __UpperCAmelCase:					Dict		= prepare_images()

    # test non-batched
    __UpperCAmelCase:					Tuple		= image_processing(images[0]   ,    return_tensors="""pt"""					)

    self.assertIsInstance(encoding.input_ids   ,    torch.LongTensor					)
    self.assertEqual(encoding.input_ids.shape   ,    (1, 1024)					)

    __UpperCAmelCase:					int		= [306, 191, 191]
    self.assertEqual(encoding.input_ids[0, :3].tolist()   ,    snake_case_					)

    # test batched
    __UpperCAmelCase:					Union[str, Any]		= image_processing(snake_case_   ,    return_tensors="""pt"""					)

    self.assertIsInstance(encoding.input_ids   ,    torch.LongTensor					)
    self.assertEqual(encoding.input_ids.shape   ,    (2, 1024)					)

    __UpperCAmelCase:					Tuple		= [303, 13, 13]
    self.assertEqual(encoding.input_ids[1, -3:].tolist()   ,    snake_case_					)