






"""simple docstring"""
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


_lowerCAmelCase         =							logging.get_logger(__name__)






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	A_		:						str	    =   b.T
	A_		:						Dict	    =   np.sum(np.square(snake_case__					)   ,   axis=1					)
	A_		:						Optional[int]	    =   np.sum(np.square(snake_case__					)   ,   axis=0					)
	A_		:						Optional[Any]	    =   np.matmul(snake_case__   ,   snake_case__					)
	A_		:						List[str]	    =   aa[:, None] - 2 * ab + ba[None, :]
	return d






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	A_		:						Union[str, Any]	    =   x.reshape(-1   ,   3					)
	A_		:						Dict	    =   squared_euclidean_distance(snake_case__   ,   snake_case__					)
	return np.argmin(snake_case__   ,   axis=1					)




class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				Optional[Any]    =				["""pixel_values"""]





	def __init__(self     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = True     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = PILImageResampling.BILINEAR     ,     lowerCAmelCase_ = True     ,     lowerCAmelCase_ = True     ,     **lowerCAmelCase_     ,     ):
		super().__init__(**lowerCAmelCase_							)
		A_		:						List[Any]	    =   size if size is not None else {"""height""": 256, """width""": 256}
		A_		:						Dict	    =   get_size_dict(lowerCAmelCase_							)
		A_		:						Any	    =   np.array(lowerCAmelCase_							) if clusters is not None else None
		A_		:						int	    =   do_resize
		A_		:						Optional[int]	    =   size
		A_		:						int	    =   resample
		A_		:						Dict	    =   do_normalize
		A_		:						List[Any]	    =   do_color_quantize





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = PILImageResampling.BILINEAR     ,     lowerCAmelCase_ = None     ,     **lowerCAmelCase_     ,     ):
		A_		:						List[Any]	    =   get_size_dict(lowerCAmelCase_							)
		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(
		    lowerCAmelCase_     ,     size=(size["""height"""], size["""width"""])     ,     resample=lowerCAmelCase_     ,     data_format=lowerCAmelCase_     ,     **lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = None     ,     ):
		A_		:						Union[str, Any]	    =   rescale(image=lowerCAmelCase_     ,     scale=1 / 127.5     ,     data_format=lowerCAmelCase_							)
		A_		:						List[Any]	    =   image - 1
		return image





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = ChannelDimension.FIRST     ,     **lowerCAmelCase_     ,     ):
		A_		:						Any	    =   do_resize if do_resize is not None else self.do_resize
		A_		:						Any	    =   size if size is not None else self.size
		A_		:						Union[str, Any]	    =   get_size_dict(lowerCAmelCase_							)
		A_		:						Optional[Any]	    =   resample if resample is not None else self.resample
		A_		:						Any	    =   do_normalize if do_normalize is not None else self.do_normalize
		A_		:						List[str]	    =   do_color_quantize if do_color_quantize is not None else self.do_color_quantize
		A_		:						Optional[Any]	    =   clusters if clusters is not None else self.clusters
		A_		:						int	    =   np.array(lowerCAmelCase_							)

		A_		:						Any	    =   make_list_of_images(lowerCAmelCase_							)

		if not valid_images(lowerCAmelCase_							):
			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.
		A_		:						str	    =   [to_numpy_array(lowerCAmelCase_							) for image in images]

		if do_resize:
			A_		:						Dict	    =   [self.resize(image=lowerCAmelCase_     ,     size=lowerCAmelCase_     ,     resample=lowerCAmelCase_							) for image in images]

		if do_normalize:
			A_		:						int	    =   [self.normalize(image=lowerCAmelCase_							) for image in images]

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

			# flatten to (batch_size, height*width)
			A_		:						int	    =   images.shape[0]
			A_		:						Union[str, Any]	    =   images.reshape(lowerCAmelCase_     ,     -1							)

			# We need to convert back to a list of images to keep consistent behaviour across processors.
			A_		:						List[str]	    =   list(lowerCAmelCase_							)
		else:
			A_		:						Union[str, Any]	    =   [to_channel_dimension_format(lowerCAmelCase_     ,     lowerCAmelCase_							) for image in images]

		A_		:						Tuple	    =   {"""input_ids""": images}
		return BatchFeature(data=lowerCAmelCase_     ,     tensor_type=lowerCAmelCase_							)









"""simple docstring"""
import json
import os
from pathlib import Path

import pytest

from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename


_lowerCAmelCase         =							"http://www.mocksite.com/file1.txt"
_lowerCAmelCase         =							"\"text\": [\"foo\", \"foo\"]"
_lowerCAmelCase         =							"6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8"



class      SCREAMING_SNAKE_CASE  :





	"""simple docstring"""




	_A       :				Optional[int]    =				200
	_A       :				Dict    =				{"""Content-Length""": """100"""}
	_A       :				List[Any]    =				{}





	def    lowerCamelCase(self     ,     **lowerCAmelCase_							):
		return [bytes(lowerCAmelCase_     ,     """utf-8"""							)]






def 			__UpperCamelCase	(		*snake_case__   ,   **snake_case__					):
	return MockResponse()






@pytest.mark.parametrize("""urls_type"""   ,   [str, list, dict]					)
def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	import requests

	monkeypatch.setattr(snake_case__   ,   """request"""   ,   snake_case__					)

	A_		:						Optional[int]	    =   URL
	if issubclass(snake_case__   ,   snake_case__					):
		A_		:						str	    =   url
	elif issubclass(snake_case__   ,   snake_case__					):
		A_		:						Union[str, Any]	    =   [url]
	elif issubclass(snake_case__   ,   snake_case__					):
		A_		:						List[Any]	    =   {"""train""": url}
	A_		:						Tuple	    =   """dummy"""
	A_		:						List[str]	    =   """downloads"""
	A_		:						List[str]	    =   tmp_path
	A_		:						str	    =   DownloadConfig(
	    cache_dir=os.path.join(snake_case__   ,   snake_case__					)   ,   use_etag=snake_case__   ,   )
	A_		:						Any	    =   DownloadManager(dataset_name=snake_case__   ,   download_config=snake_case__					)
	A_		:						Dict	    =   dl_manager.download(snake_case__					)
	A_		:						List[str]	    =   urls
	for downloaded_paths in [downloaded_paths]:
		if isinstance(snake_case__   ,   snake_case__					):
			A_		:						Optional[Any]	    =   [downloaded_paths]
			A_		:						Tuple	    =   [urls]
		elif isinstance(snake_case__   ,   snake_case__					):
			assert "train" in downloaded_paths.keys()
			A_		:						Union[str, Any]	    =   downloaded_paths.values()
			A_		:						Tuple	    =   urls.values()
		assert downloaded_paths
		for downloaded_path, input_url in zip(snake_case__   ,   snake_case__					):
			assert downloaded_path == dl_manager.downloaded_paths[input_url]
			A_		:						Union[str, Any]	    =   Path(snake_case__					)
			A_		:						str	    =   downloaded_path.parts
			assert parts[-1] == HASH
			assert parts[-2] == cache_subdir
			assert downloaded_path.exists()
			A_		:						List[str]	    =   downloaded_path.read_text()
			assert content == CONTENT
			A_		:						Union[str, Any]	    =   downloaded_path.with_suffix(""".json"""					)
			assert metadata_downloaded_path.exists()
			A_		:						List[str]	    =   json.loads(metadata_downloaded_path.read_text()					)
			assert metadata_content == {"url": URL, "etag": None}






@pytest.mark.parametrize("""paths_type"""   ,   [str, list, dict]					)
def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	A_		:						str	    =   str(snake_case__					)
	if issubclass(snake_case__   ,   snake_case__					):
		A_		:						List[str]	    =   filename
	elif issubclass(snake_case__   ,   snake_case__					):
		A_		:						Tuple	    =   [filename]
	elif issubclass(snake_case__   ,   snake_case__					):
		A_		:						Dict	    =   {"""train""": filename}
	A_		:						Union[str, Any]	    =   """dummy"""
	A_		:						Dict	    =   xz_file.parent
	A_		:						List[str]	    =   """extracted"""
	A_		:						int	    =   DownloadConfig(
	    cache_dir=snake_case__   ,   use_etag=snake_case__   ,   )
	A_		:						str	    =   DownloadManager(dataset_name=snake_case__   ,   download_config=snake_case__					)
	A_		:						int	    =   dl_manager.extract(snake_case__					)
	A_		:						int	    =   paths
	for extracted_paths in [extracted_paths]:
		if isinstance(snake_case__   ,   snake_case__					):
			A_		:						Dict	    =   [extracted_paths]
			A_		:						str	    =   [paths]
		elif isinstance(snake_case__   ,   snake_case__					):
			assert "train" in extracted_paths.keys()
			A_		:						Any	    =   extracted_paths.values()
			A_		:						str	    =   paths.values()
		assert extracted_paths
		for extracted_path, input_path in zip(snake_case__   ,   snake_case__					):
			assert extracted_path == dl_manager.extracted_paths[input_path]
			A_		:						str	    =   Path(snake_case__					)
			A_		:						List[str]	    =   extracted_path.parts
			assert parts[-1] == hash_url_to_filename(snake_case__   ,   etag=snake_case__					)
			assert parts[-2] == extracted_subdir
			assert extracted_path.exists()
			A_		:						Optional[Any]	    =   extracted_path.read_text()
			A_		:						Optional[int]	    =   text_file.read_text()
			assert extracted_file_content == expected_file_content






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	assert path.endswith(""".jsonl"""					)
	for num_items, line in enumerate(snake_case__   ,   start=1					):
		A_		:						List[str]	    =   json.loads(line.decode("""utf-8"""					)					)
		assert item.keys() == {"col_1", "col_2", "col_3"}
	assert num_items == 4






@pytest.mark.parametrize("""archive_jsonl"""   ,   ["""tar_jsonl_path""", """zip_jsonl_path"""]					)
def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	A_		:						Optional[int]	    =   request.getfixturevalue(snake_case__					)
	A_		:						List[str]	    =   DownloadManager()
	for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(snake_case__					)   ,   start=1					):
		_test_jsonl(snake_case__   ,   snake_case__					)
	assert num_jsonl == 2






@pytest.mark.parametrize("""archive_nested_jsonl"""   ,   ["""tar_nested_jsonl_path""", """zip_nested_jsonl_path"""]					)
def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	A_		:						int	    =   request.getfixturevalue(snake_case__					)
	A_		:						Union[str, Any]	    =   DownloadManager()
	for num_tar, (path, file) in enumerate(dl_manager.iter_archive(snake_case__					)   ,   start=1					):
		for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(snake_case__					)   ,   start=1					):
			_test_jsonl(snake_case__   ,   snake_case__					)
	assert num_tar == 1
	assert num_jsonl == 2






def 			__UpperCamelCase	(		snake_case__					):
	A_		:						int	    =   DownloadManager()
	for num_file, file in enumerate(dl_manager.iter_files(snake_case__					)   ,   start=1					):
		assert os.path.basename(snake_case__					) == ("test.txt" if num_file == 1 else "train.txt")
	assert num_file == 2

