

"""simple docstring"""





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 MobileViTImageProcessor




class 					_lowerCAmelCase  (	unittest.TestCase   ):







 """simple docstring"""







 def __init__(			self							:			Tuple,					UpperCAmelCase__							:			List[Any],					UpperCAmelCase__							:			int=7,					UpperCAmelCase__							:			Optional[int]=3,					UpperCAmelCase__							:			Dict=1_8,					UpperCAmelCase__							:			Any=3_0,					UpperCAmelCase__							:			Any=4_0_0,					UpperCAmelCase__							:			Dict=True,					UpperCAmelCase__							:			Optional[int]=None,					UpperCAmelCase__							:			List[Any]=True,					UpperCAmelCase__							:			Union[str, Any]=None,					UpperCAmelCase__							:			int=True,					):
    __lowercase								=						size if size is not None else {"shortest_edge": 2_0}
    __lowercase								=						crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
    __lowercase								=						parent
    __lowercase								=						batch_size
    __lowercase								=						num_channels
    __lowercase								=						image_size
    __lowercase								=						min_resolution
    __lowercase								=						max_resolution
    __lowercase								=						do_resize
    __lowercase								=						size
    __lowercase								=						do_center_crop
    __lowercase								=						crop_size
    __lowercase								=						do_flip_channel_order



 def 					_lowercase (			self							:			Optional[int]	):
    return {
        "do_resize": self.do_resize,
        "size": self.size,
        "do_center_crop": self.do_center_crop,
        "crop_size": self.crop_size,
        "do_flip_channel_order": self.do_flip_channel_order,
    }







@require_torch
@require_vision
class 					_lowerCAmelCase  (	lowercase     ,unittest.TestCase   ):







 """simple docstring"""







 __UpperCAmelCase	:					Tuple   		=	MobileViTImageProcessor if is_vision_available() else None


 def 					_lowercase (			self							:			Any	):
    __lowercase								=						MobileViTImageProcessingTester(self	)


 @property
 def 					_lowercase (			self							:			Any	):
    return self.image_processor_tester.prepare_image_processor_dict()


 def 					_lowercase (			self							:			List[str]	):
    __lowercase								=						self.image_processing_class(**self.image_processor_dict	)
    self.assertTrue(hasattr(UpperCAmelCase__,					"do_resize"	)	)
    self.assertTrue(hasattr(UpperCAmelCase__,					"size"	)	)
    self.assertTrue(hasattr(UpperCAmelCase__,					"do_center_crop"	)	)
    self.assertTrue(hasattr(UpperCAmelCase__,					"center_crop"	)	)
    self.assertTrue(hasattr(UpperCAmelCase__,					"do_flip_channel_order"	)	)


 def 					_lowercase (			self							:			List[str]	):
    __lowercase								=						self.image_processing_class.from_dict(self.image_processor_dict	)
    self.assertEqual(image_processor.size,					{"shortest_edge": 2_0}	)
    self.assertEqual(image_processor.crop_size,					{"height": 1_8, "width": 1_8}	)

    __lowercase								=						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							:			str	):
    pass


 def 					_lowercase (			self							:			Any	):
    # Initialize image_processing
    __lowercase								=						self.image_processing_class(**self.image_processor_dict	)
    # create random PIL images
    __lowercase								=						prepare_image_inputs(self.image_processor_tester,					equal_resolution=UpperCAmelCase__	)
    for image in image_inputs:
       self.assertIsInstance(UpperCAmelCase__,					Image.Image	)

    # Test not batched input
    __lowercase								=						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
    __lowercase								=						image_processing(UpperCAmelCase__,					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[Any]	):
    # Initialize image_processing
    __lowercase								=						self.image_processing_class(**self.image_processor_dict	)
    # create random numpy tensors
    __lowercase								=						prepare_image_inputs(self.image_processor_tester,					equal_resolution=UpperCAmelCase__,					numpify=UpperCAmelCase__	)
    for image in image_inputs:
       self.assertIsInstance(UpperCAmelCase__,					np.ndarray	)

    # Test not batched input
    __lowercase								=						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
    __lowercase								=						image_processing(UpperCAmelCase__,					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							:			Dict	):
    # Initialize image_processing
    __lowercase								=						self.image_processing_class(**self.image_processor_dict	)
    # create random PyTorch tensors
    __lowercase								=						prepare_image_inputs(self.image_processor_tester,					equal_resolution=UpperCAmelCase__,					torchify=UpperCAmelCase__	)
    for image in image_inputs:
       self.assertIsInstance(UpperCAmelCase__,					torch.Tensor	)

    # Test not batched input
    __lowercase								=						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
    __lowercase								=						image_processing(UpperCAmelCase__,					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"],
        ),					)







"""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


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

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




@dataclass
class 					_lowerCAmelCase  (	datasets.BuilderConfig   ):







 """simple docstring"""







 __UpperCAmelCase	:					str   		=	","
 __UpperCAmelCase	:					Optional[str]   		=	None
 __UpperCAmelCase	:					Optional[Union[int, List[int], str]]   		=	"infer"
 __UpperCAmelCase	:					Optional[List[str]]   		=	None
 __UpperCAmelCase	:					Optional[List[str]]   		=	None
 __UpperCAmelCase	:					Optional[Union[int, str, List[int], List[str]]]   		=	None
 __UpperCAmelCase	:					Optional[Union[List[int], List[str]]]   		=	None
 __UpperCAmelCase	:					Optional[str]   		=	None
 __UpperCAmelCase	:					bool   		=	True
 __UpperCAmelCase	:					Optional[Literal["c", "python", "pyarrow"]]   		=	None
 __UpperCAmelCase	:					Dict[Union[int, str], Callable[[Any], Any]]   		=	None
 __UpperCAmelCase	:					Optional[list]   		=	None
 __UpperCAmelCase	:					Optional[list]   		=	None
 __UpperCAmelCase	:					bool   		=	False
 __UpperCAmelCase	:					Optional[Union[int, List[int]]]   		=	None
 __UpperCAmelCase	:					Optional[int]   		=	None
 __UpperCAmelCase	:					Optional[Union[str, List[str]]]   		=	None
 __UpperCAmelCase	:					bool   		=	True
 __UpperCAmelCase	:					bool   		=	True
 __UpperCAmelCase	:					bool   		=	False
 __UpperCAmelCase	:					bool   		=	True
 __UpperCAmelCase	:					Optional[str]   		=	None
 __UpperCAmelCase	:					str   		=	"."
 __UpperCAmelCase	:					Optional[str]   		=	None
 __UpperCAmelCase	:					str   		=	'"'
 __UpperCAmelCase	:					int   		=	0
 __UpperCAmelCase	:					Optional[str]   		=	None
 __UpperCAmelCase	:					Optional[str]   		=	None
 __UpperCAmelCase	:					Optional[str]   		=	None
 __UpperCAmelCase	:					Optional[str]   		=	None
 __UpperCAmelCase	:					bool   		=	True
 __UpperCAmelCase	:					bool   		=	True
 __UpperCAmelCase	:					int   		=	0
 __UpperCAmelCase	:					bool   		=	True
 __UpperCAmelCase	:					bool   		=	False
 __UpperCAmelCase	:					Optional[str]   		=	None
 __UpperCAmelCase	:					int   		=	1_0_0_0_0
 __UpperCAmelCase	:					Optional[datasets.Features]   		=	None
 __UpperCAmelCase	:					Optional[str]   		=	"strict"
 __UpperCAmelCase	:					Literal["error", "warn", "skip"]   		=	"error"
 __UpperCAmelCase	:					Optional[str]   		=	None


 def 					_lowercase (			self							:			Tuple	):
    if self.delimiter is not None:
       __lowercase								=						self.delimiter
    if self.column_names is not None:
       __lowercase								=						self.column_names



 @property
 def 					_lowercase (			self							:			Union[str, Any]	):
    __lowercase								=						{
        "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(),					UpperCAmelCase__	):
          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 					_lowerCAmelCase  (	datasets.ArrowBasedBuilder   ):







 """simple docstring"""







 __UpperCAmelCase	:					Tuple   		=	CsvConfig


 def 					_lowercase (			self							:			List[str]	):
    return datasets.DatasetInfo(features=self.config.features	)


 def 					_lowercase (			self							:			List[Any],					UpperCAmelCase__							:			Dict	):
    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}"""	)
    __lowercase								=						dl_manager.download_and_extract(self.config.data_files	)
    if isinstance(UpperCAmelCase__,					(str, list, tuple)	):
       __lowercase								=						data_files
       if isinstance(UpperCAmelCase__,					UpperCAmelCase__	):
          __lowercase								=						[files]
       __lowercase								=						[dl_manager.iter_files(UpperCAmelCase__	) for file in files]
       return [datasets.SplitGenerator(name=datasets.Split.TRAIN,					gen_kwargs={"files": files}	)]
    __lowercase								=						[]
    for split_name, files in data_files.items():
       if isinstance(UpperCAmelCase__,					UpperCAmelCase__	):
          __lowercase								=						[files]
       __lowercase								=						[dl_manager.iter_files(UpperCAmelCase__	) for file in files]
       splits.append(datasets.SplitGenerator(name=UpperCAmelCase__,					gen_kwargs={"files": files}	)	)
    return splits


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



 def 					_lowercase (			self							:			Optional[Any],					UpperCAmelCase__							:			List[str]	):
    __lowercase								=						self.config.features.arrow_schema if self.config.features else None
    # dtype allows reading an int column as str
    __lowercase								=						(
        {
            name: dtype.to_pandas_dtype() if not require_storage_cast(UpperCAmelCase__	) 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(UpperCAmelCase__	)	):
       __lowercase								=						pd.read_csv(UpperCAmelCase__,					iterator=UpperCAmelCase__,					dtype=UpperCAmelCase__,					**self.config.pd_read_csv_kwargs	)
       try:
          for batch_idx, df in enumerate(UpperCAmelCase__	):
             __lowercase								=						pa.Table.from_pandas(UpperCAmelCase__	)
             # 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(UpperCAmelCase__	)
       except ValueError as e:
          logger.error(F"""Failed to read file '{file}' with error {type(UpperCAmelCase__	)}: {e}"""	)
          raise




