"""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


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

UpperCAmelCase       =  ["names", "prefix"]
UpperCAmelCase       =  ["warn_bad_lines", "error_bad_lines", "mangle_dupe_cols"]
UpperCAmelCase       =  ["encoding_errors", "on_bad_lines"]
UpperCAmelCase       =  ["date_format"]







@dataclass
class      UpperCAmelCase_       (						datasets.BuilderConfig):
							snake_case__        =      ''','''
							snake_case__        =      None
							snake_case__        =      '''infer'''
							snake_case__        =      None
							snake_case__        =      None
							snake_case__        =      None
							snake_case__        =      None
							snake_case__        =      None
							snake_case__        =      True
							snake_case__        =      None
							snake_case__        =      None
							snake_case__        =      None
							snake_case__        =      None
							snake_case__        =      False
							snake_case__        =      None
							snake_case__        =      None
							snake_case__        =      None
							snake_case__        =      True
							snake_case__        =      True
							snake_case__        =      False
							snake_case__        =      True
							snake_case__        =      None
							snake_case__        =      '''.'''
							snake_case__        =      None
							snake_case__        =      '''"'''
							snake_case__        =      0
							snake_case__        =      None
							snake_case__        =      None
							snake_case__        =      None
							snake_case__        =      None
							snake_case__        =      True
							snake_case__        =      True
							snake_case__        =      0
							snake_case__        =      True
							snake_case__        =      False
							snake_case__        =      None
							snake_case__        =      1_00_00
							snake_case__        =      None
							snake_case__        =      '''strict'''
							snake_case__        =      '''error'''
							snake_case__        =      None






							def       _UpperCamelCase			(		self :      Dict  )       ->       str:
												if self.delimiter is not None:
																	_UpperCamelCase      						=	self.delimiter
												if self.column_names is not None:
																	_UpperCamelCase      						=	self.column_names






							@property
							def       _UpperCamelCase			(		self :      Optional[Any]  )       ->       List[Any]:
												_UpperCamelCase      						=	{
												    '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      UpperCAmelCase_       (						datasets.ArrowBasedBuilder):
							snake_case__        =      CsvConfig






							def       _UpperCamelCase			(		self :      Union[str, Any]  )       ->       Any:
												return datasets.DatasetInfo(features=self.config.features  )






							def       _UpperCamelCase			(		self :      int     ,						__UpperCamelCase :      Optional[Any]  )       ->       Any:
												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      						=	dl_manager.download_and_extract(self.config.data_files  )
												if isinstance(_snake_case     ,						(str, list, tuple)  ):
																	_UpperCamelCase      						=	data_files
																	if isinstance(_snake_case     ,						_snake_case  ):
																						_UpperCamelCase      						=	[files]
																	_UpperCamelCase      						=	[dl_manager.iter_files(_snake_case  ) for file in files]
																	return [datasets.SplitGenerator(name=datasets.Split.TRAIN     ,						gen_kwargs={'''files''': files}  )]
												_UpperCamelCase      						=	[]
												for split_name, files in data_files.items():
																	if isinstance(_snake_case     ,						_snake_case  ):
																						_UpperCamelCase      						=	[files]
																	_UpperCamelCase      						=	[dl_manager.iter_files(_snake_case  ) for file in files]
																	splits.append(datasets.SplitGenerator(name=_snake_case     ,						gen_kwargs={'''files''': files}  )  )
												return splits






							def       _UpperCamelCase			(		self :      List[str]     ,						__UpperCamelCase :      pa.Table  )       ->       pa.Table:
												if self.config.features is not None:
																	_UpperCamelCase      						=	self.config.features.arrow_schema
																	if all(not require_storage_cast(_snake_case  ) for feature in self.config.features.values()  ):
																						# cheaper cast
																						_UpperCamelCase      						=	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      						=	table_cast(_snake_case     ,						_snake_case  )
												return pa_table






							def       _UpperCamelCase			(		self :      str     ,						__UpperCamelCase :      List[str]  )       ->       List[Any]:
												_UpperCamelCase      						=	self.config.features.arrow_schema if self.config.features else None
												# dtype allows reading an int column as str
												_UpperCamelCase      						=	(
												    {
												        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      						=	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      						=	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





import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import PoolFormerImageProcessor




class 					snake_case__						(       unittest.TestCase     ):






     """simple docstring"""


     def __init__(       self  :  Optional[int], _snake_case  :  List[Any], _snake_case  :  str=7, _snake_case  :  Tuple=3, _snake_case  :  List[str]=3_0, _snake_case  :  Tuple=4_0_0, _snake_case  :  Any=True, _snake_case  :  List[Any]=None, _snake_case  :  int=0.9, _snake_case  :  Optional[Any]=None, _snake_case  :  str=True, _snake_case  :  Union[str, Any]=[0.5, 0.5, 0.5], _snake_case  :  Union[str, Any]=[0.5, 0.5, 0.5], )   ->List[Any]:
            snake_case__							:	int   	=	size if size is not None else {'shortest_edge': 3_0}
            snake_case__							:	Tuple   	=	crop_size if crop_size is not None else {'height': 3_0, 'width': 3_0}
            snake_case__							:	Union[str, Any]   	=	parent
            snake_case__							:	Dict   	=	batch_size
            snake_case__							:	int   	=	num_channels
            snake_case__							:	Tuple   	=	min_resolution
            snake_case__							:	Any   	=	max_resolution
            snake_case__							:	List[Any]   	=	do_resize_and_center_crop
            snake_case__							:	str   	=	size
            snake_case__							:	str   	=	crop_pct
            snake_case__							:	List[str]   	=	crop_size
            snake_case__							:	Optional[int]   	=	do_normalize
            snake_case__							:	Tuple   	=	image_mean
            snake_case__							:	Tuple   	=	image_std






     def    lowercase_   (       self  :  Optional[int]					)   ->int:
            return {
                "size": self.size,
                "do_resize_and_center_crop": self.do_resize_and_center_crop,
                "crop_pct": self.crop_pct,
                "crop_size": self.crop_size,
                "do_normalize": self.do_normalize,
                "image_mean": self.image_mean,
                "image_std": self.image_std,
            }




@require_torch
@require_vision
class 					snake_case__						(       lowerCAmelCase_				,     unittest.TestCase     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	PoolFormerImageProcessor if is_vision_available() else None






     def    lowercase_   (       self  :  Union[str, Any]					)   ->Dict:
            snake_case__							:	Union[str, Any]   	=	PoolFormerImageProcessingTester(self					)






     @property
     def    lowercase_   (       self  :  int					)   ->Dict:
            return self.image_processor_tester.prepare_image_processor_dict()






     def    lowercase_   (       self  :  Union[str, Any]					)   ->Optional[int]:
            snake_case__							:	List[str]   	=	self.image_processing_class(**self.image_processor_dict					)
            self.assertTrue(hasattr(_snake_case, 'do_resize_and_center_crop'					)					)
            self.assertTrue(hasattr(_snake_case, 'size'					)					)
            self.assertTrue(hasattr(_snake_case, 'crop_pct'					)					)
            self.assertTrue(hasattr(_snake_case, 'do_normalize'					)					)
            self.assertTrue(hasattr(_snake_case, 'image_mean'					)					)
            self.assertTrue(hasattr(_snake_case, 'image_std'					)					)






     def    lowercase_   (       self  :  List[str]					)   ->List[str]:
            snake_case__							:	Union[str, Any]   	=	self.image_processing_class.from_dict(self.image_processor_dict					)
            self.assertEqual(image_processor.size, {'shortest_edge': 3_0}					)
            self.assertEqual(image_processor.crop_size, {'height': 3_0, 'width': 3_0}					)

            snake_case__							:	int   	=	self.image_processing_class.from_dict(self.image_processor_dict, size=4_2, crop_size=8_4					)
            self.assertEqual(image_processor.size, {'shortest_edge': 4_2}					)
            self.assertEqual(image_processor.crop_size, {'height': 8_4, 'width': 8_4}					)






     def    lowercase_   (       self  :  List[Any]					)   ->List[Any]:
            pass






     def    lowercase_   (       self  :  List[str]					)   ->str:
            # Initialize image_processing
            snake_case__							:	Union[str, Any]   	=	self.image_processing_class(**self.image_processor_dict					)
            # create random PIL images
            snake_case__							:	List[str]   	=	prepare_image_inputs(self.image_processor_tester, equal_resolution=_snake_case					)
            for image in image_inputs:
                   self.assertIsInstance(_snake_case, Image.Image					)

            # Test not batched input
            snake_case__							:	Optional[int]   	=	image_processing(image_inputs[0], return_tensors='pt'					).pixel_values

            self.assertEqual(
                encoded_images.shape, (
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ), )

            # Test batched
            snake_case__							:	str   	=	image_processing(_snake_case, return_tensors='pt'					).pixel_values
            self.assertEqual(
                encoded_images.shape, (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ), )






     def    lowercase_   (       self  :  int					)   ->List[Any]:
            # Initialize image_processing
            snake_case__							:	Optional[int]   	=	self.image_processing_class(**self.image_processor_dict					)
            # create random numpy tensors
            snake_case__							:	Dict   	=	prepare_image_inputs(self.image_processor_tester, equal_resolution=_snake_case, numpify=_snake_case					)
            for image in image_inputs:
                   self.assertIsInstance(_snake_case, np.ndarray					)

            # Test not batched input
            snake_case__							:	Dict   	=	image_processing(image_inputs[0], return_tensors='pt'					).pixel_values
            self.assertEqual(
                encoded_images.shape, (
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ), )

            # Test batched
            snake_case__							:	List[Any]   	=	image_processing(_snake_case, return_tensors='pt'					).pixel_values
            self.assertEqual(
                encoded_images.shape, (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ), )






     def    lowercase_   (       self  :  List[str]					)   ->List[str]:
            # Initialize image_processing
            snake_case__							:	Tuple   	=	self.image_processing_class(**self.image_processor_dict					)
            # create random PyTorch tensors
            snake_case__							:	List[str]   	=	prepare_image_inputs(self.image_processor_tester, equal_resolution=_snake_case, torchify=_snake_case					)
            for image in image_inputs:
                   self.assertIsInstance(_snake_case, torch.Tensor					)

            # Test not batched input
            snake_case__							:	Tuple   	=	image_processing(image_inputs[0], return_tensors='pt'					).pixel_values
            self.assertEqual(
                encoded_images.shape, (
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ), )

            # Test batched
            snake_case__							:	Optional[Any]   	=	image_processing(_snake_case, return_tensors='pt'					).pixel_values
            self.assertEqual(
                encoded_images.shape, (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ), )






