





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
				import PIL


UpperCamelCase__   : List[str]					  = logging.get_logger(__name__)




def    A_(			A				,      A   ):
	UpperCAmelCase_								=     b.T
	UpperCAmelCase_								=     np.sum(np.square(A   )				,      axis=1   )
	UpperCAmelCase_								=     np.sum(np.square(A   )				,      axis=0   )
	UpperCAmelCase_								=     np.matmul(A				,      A   )
	UpperCAmelCase_								=     aa[:, None] - 2 * ab + ba[None, :]
	return d




def    A_(			A				,      A   ):
	UpperCAmelCase_								=     x.reshape(-1				,      3   )
	UpperCAmelCase_								=     squared_euclidean_distance(A				,      A   )
	return np.argmin(A				,      axis=1   )







class   _UpperCamelCase  (       A_  ):







					'''simple docstring'''

					lowerCamelCase     :     int			    =       ['pixel_values']





					def __init__(						self    :  Union[str, Any] ,					__lowercase    :  Optional[Union[List[List[int]], np.ndarray]] = None ,					__lowercase    :  bool = True ,					__lowercase    :  Dict[str, int] = None ,					__lowercase    :  PILImageResampling = PILImageResampling.BILINEAR ,					__lowercase    :  bool = True ,					__lowercase    :  bool = True ,					**__lowercase    :  Optional[Any] ,					):






						'''simple docstring'''







						super().__init__(**__lowercase		)
						UpperCAmelCase_								=     size if size is not None else {"""height""": 2_56, """width""": 2_56}
						UpperCAmelCase_								=     get_size_dict(__lowercase		)
						UpperCAmelCase_								=     np.array(__lowercase		) if clusters is not None else None
						UpperCAmelCase_								=     do_resize
						UpperCAmelCase_								=     size
						UpperCAmelCase_								=     resample
						UpperCAmelCase_								=     do_normalize
						UpperCAmelCase_								=     do_color_quantize





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[Any] ,					__lowercase    :  np.ndarray ,					__lowercase    :  Dict[str, int] ,					__lowercase    :  PILImageResampling = PILImageResampling.BILINEAR ,					__lowercase    :  Optional[Union[str, ChannelDimension]] = None ,					**__lowercase    :  Optional[Any] ,					):






						'''simple docstring'''







						UpperCAmelCase_								=     get_size_dict(__lowercase		)
						if "height" not in size or "width" not in size:
							raise ValueError(F"""Size dictionary must contain both height and width keys. Got {size.keys()}"""		)
						return resize(
						    __lowercase ,					size=(size["""height"""], size["""width"""]) ,					resample=__lowercase ,					data_format=__lowercase ,					**__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Union[str, Any] ,					__lowercase    :  np.ndarray ,					__lowercase    :  Optional[Union[str, ChannelDimension]] = None ,					):






						'''simple docstring'''







						UpperCAmelCase_								=     rescale(image=__lowercase ,					scale=1 / 127.5 ,					data_format=__lowercase		)
						UpperCAmelCase_								=     image - 1
						return image





					def        SCREAMING_SNAKE_CASE							(						self    :  List[str] ,					__lowercase    :  ImageInput ,					__lowercase    :  bool = None ,					__lowercase    :  Dict[str, int] = None ,					__lowercase    :  PILImageResampling = None ,					__lowercase    :  bool = None ,					__lowercase    :  Optional[bool] = None ,					__lowercase    :  Optional[Union[List[List[int]], np.ndarray]] = None ,					__lowercase    :  Optional[Union[str, TensorType]] = None ,					__lowercase    :  Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST ,					**__lowercase    :  Optional[Any] ,					):






						'''simple docstring'''







						UpperCAmelCase_								=     do_resize if do_resize is not None else self.do_resize
						UpperCAmelCase_								=     size if size is not None else self.size
						UpperCAmelCase_								=     get_size_dict(__lowercase		)
						UpperCAmelCase_								=     resample if resample is not None else self.resample
						UpperCAmelCase_								=     do_normalize if do_normalize is not None else self.do_normalize
						UpperCAmelCase_								=     do_color_quantize if do_color_quantize is not None else self.do_color_quantize
						UpperCAmelCase_								=     clusters if clusters is not None else self.clusters
						UpperCAmelCase_								=     np.array(__lowercase		)

						UpperCAmelCase_								=     make_list_of_images(__lowercase		)

						if not valid_images(__lowercase		):
							raise ValueError(
							    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
							    """torch.Tensor, tf.Tensor or jax.ndarray."""		)

						if do_resize and size is None or resample is None:
							raise ValueError("""Size and resample must be specified if do_resize is True."""		)

						if do_color_quantize and clusters is None:
							raise ValueError("""Clusters must be specified if do_color_quantize is True."""		)

						# All transformations expect numpy arrays.
						UpperCAmelCase_								=     [to_numpy_array(__lowercase		) for image in images]

						if do_resize:
							UpperCAmelCase_								=     [self.resize(image=__lowercase ,					size=__lowercase ,					resample=__lowercase		) for image in images]

						if do_normalize:
							UpperCAmelCase_								=     [self.normalize(image=__lowercase		) for image in images]

						if do_color_quantize:
							UpperCAmelCase_								=     [to_channel_dimension_format(__lowercase ,					ChannelDimension.LAST		) for image in images]
							# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
							UpperCAmelCase_								=     np.array(__lowercase		)
							UpperCAmelCase_								=     color_quantize(__lowercase ,					__lowercase		).reshape(images.shape[:-1]		)

							# flatten to (batch_size, height*width)
							UpperCAmelCase_								=     images.shape[0]
							UpperCAmelCase_								=     images.reshape(__lowercase ,					-1		)

							# We need to convert back to a list of images to keep consistent behaviour across processors.
							UpperCAmelCase_								=     list(__lowercase		)
						else:
							UpperCAmelCase_								=     [to_channel_dimension_format(__lowercase ,					__lowercase		) for image in images]

						UpperCAmelCase_								=     {"""input_ids""": images}
						return BatchFeature(data=__lowercase ,					tensor_type=__lowercase		)









import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases




def    A_(			A				,      A   ):
	assert isinstance(A				,      A   )
	assert dataset.num_rows == 4
	assert dataset.num_columns == 1
	assert dataset.column_names == ["text"]
	for feature, expected_dtype in expected_features.items():
		assert dataset.features[feature].dtype == expected_dtype




@pytest.mark.parametrize("""keep_in_memory"""				,      [False, True]   )
def    A_(			A				,      A				,      A   ):
	UpperCAmelCase_								=     tmp_path / """cache"""
	UpperCAmelCase_								=     {"""text""": """string"""}
	with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
		UpperCAmelCase_								=     TextDatasetReader(A				,      cache_dir=A				,      keep_in_memory=A   ).read()
	_check_text_dataset(A				,      A   )




@pytest.mark.parametrize(
    """features"""				,      [
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ]				,      )
def    A_(			A				,      A				,      A   ):
	UpperCAmelCase_								=     tmp_path / """cache"""
	UpperCAmelCase_								=     {"""text""": """string"""}
	UpperCAmelCase_								=     features.copy() if features else default_expected_features
	UpperCAmelCase_								=     (
	    Features({feature: Value(A   ) for feature, dtype in features.items()}   ) if features is not None else None
	)
	UpperCAmelCase_								=     TextDatasetReader(A				,      features=A				,      cache_dir=A   ).read()
	_check_text_dataset(A				,      A   )




@pytest.mark.parametrize("""split"""				,      [None, NamedSplit("""train"""   ), """train""", """test"""]   )
def    A_(			A				,      A				,      A   ):
	UpperCAmelCase_								=     tmp_path / """cache"""
	UpperCAmelCase_								=     {"""text""": """string"""}
	UpperCAmelCase_								=     TextDatasetReader(A				,      cache_dir=A				,      split=A   ).read()
	_check_text_dataset(A				,      A   )
	assert dataset.split == split if split else "train"




@pytest.mark.parametrize("""path_type"""				,      [str, list]   )
def    A_(			A				,      A				,      A   ):
	if issubclass(A				,      A   ):
		UpperCAmelCase_								=     text_path
	elif issubclass(A				,      A   ):
		UpperCAmelCase_								=     [text_path]
	UpperCAmelCase_								=     tmp_path / """cache"""
	UpperCAmelCase_								=     {"""text""": """string"""}
	UpperCAmelCase_								=     TextDatasetReader(A				,      cache_dir=A   ).read()
	_check_text_dataset(A				,      A   )




def    A_(			A				,      A				,      A=("train",)   ):
	assert isinstance(A				,      A   )
	for split in splits:
		UpperCAmelCase_								=     dataset_dict[split]
		assert dataset.num_rows == 4
		assert dataset.num_columns == 1
		assert dataset.column_names == ["text"]
		for feature, expected_dtype in expected_features.items():
			assert dataset.features[feature].dtype == expected_dtype




@pytest.mark.parametrize("""keep_in_memory"""				,      [False, True]   )
def    A_(			A				,      A				,      A   ):
	UpperCAmelCase_								=     tmp_path / """cache"""
	UpperCAmelCase_								=     {"""text""": """string"""}
	with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
		UpperCAmelCase_								=     TextDatasetReader({"""train""": text_path}				,      cache_dir=A				,      keep_in_memory=A   ).read()
	_check_text_datasetdict(A				,      A   )




@pytest.mark.parametrize(
    """features"""				,      [
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ]				,      )
def    A_(			A				,      A				,      A   ):
	UpperCAmelCase_								=     tmp_path / """cache"""
	# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
	UpperCAmelCase_								=     {"""text""": """string"""}
	UpperCAmelCase_								=     features.copy() if features else default_expected_features
	UpperCAmelCase_								=     (
	    Features({feature: Value(A   ) for feature, dtype in features.items()}   ) if features is not None else None
	)
	UpperCAmelCase_								=     TextDatasetReader({"""train""": text_path}				,      features=A				,      cache_dir=A   ).read()
	_check_text_datasetdict(A				,      A   )




@pytest.mark.parametrize("""split"""				,      [None, NamedSplit("""train"""   ), """train""", """test"""]   )
def    A_(			A				,      A				,      A   ):
	if split:
		UpperCAmelCase_								=     {split: text_path}
	else:
		UpperCAmelCase_								=     """train"""
		UpperCAmelCase_								=     {"""train""": text_path, """test""": text_path}
	UpperCAmelCase_								=     tmp_path / """cache"""
	UpperCAmelCase_								=     {"""text""": """string"""}
	UpperCAmelCase_								=     TextDatasetReader(A				,      cache_dir=A   ).read()
	_check_text_datasetdict(A				,      A				,      splits=list(path.keys()   )   )
	assert all(dataset[split].split == split for split in path.keys()   )


