






'''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


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import SwinaSRImageProcessor
						from transformers.image_transforms import get_image_size






class 				__magic_name__  (						unittest.TestCase					):







	"""simple docstring"""



	def __init__(				self	,   _lowercase	,   _lowercase=7	,   _lowercase=3	,   _lowercase=18	,   _lowercase=30	,   _lowercase=400	,   _lowercase=True	,   _lowercase=1 / 255	,   _lowercase=True	,   _lowercase=8	,   )    ->	Dict:
							lowercase_   : Optional[Any]   	=      parent
							lowercase_   : List[str]   	=      batch_size
							lowercase_   : Optional[int]   	=      num_channels
							lowercase_   : Union[str, Any]   	=      image_size
							lowercase_   : Any   	=      min_resolution
							lowercase_   : Optional[Any]   	=      max_resolution
							lowercase_   : Dict   	=      do_rescale
							lowercase_   : str   	=      rescale_factor
							lowercase_   : Any   	=      do_pad
							lowercase_   : Dict   	=      pad_size
	def 							lowerCamelCase__		(				self							)    ->	Optional[int]:
							return {
							    "do_rescale": self.do_rescale,
							    "rescale_factor": self.rescale_factor,
							    "do_pad": self.do_pad,
							    "pad_size": self.pad_size,
							}







	def 							lowerCamelCase__		(				self	,   _lowercase=False	,   _lowercase=False	,   _lowercase=False							)    ->	Union[str, Any]:
							assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

							if equal_resolution:
													lowercase_   : Tuple   	=      []
													for i in range(self.batch_size							):
																			image_inputs.append(
																			    np.random.randint(
																			        255	,   size=(self.num_channels, self.max_resolution, self.max_resolution)	,   dtype=np.uinta							)							)
							else:
													lowercase_   : Optional[int]   	=      []
													for i in range(self.batch_size							):
																			lowercase_	,   lowercase_   : Optional[int]   	=      np.random.choice(np.arange(self.min_resolution	,   self.max_resolution							)	,   2							)
																			image_inputs.append(np.random.randint(255	,   size=(self.num_channels, width, height)	,   dtype=np.uinta							)							)

							if not numpify and not torchify:
													# PIL expects the channel dimension as last dimension
													lowercase_   : Optional[int]   	=      [Image.fromarray(np.moveaxis(_lowercase	,   0	,   -1							)							) for x in image_inputs]

							if torchify:
													lowercase_   : int   	=      [torch.from_numpy(_lowercase							) for x in image_inputs]

							return image_inputs






@require_torch
@require_vision
class 				__magic_name__  (						UpperCAmelCase_,    unittest.TestCase					):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_       :   Union[str, Any]   =	SwinaSRImageProcessor if is_vision_available() else None
	def 							lowerCamelCase__		(				self							)    ->	List[Any]:
							lowercase_   : Tuple   	=      SwinaSRImageProcessingTester(self							)
	@property
	def 							lowerCamelCase__		(				self							)    ->	int:
							return self.image_processor_tester.prepare_image_processor_dict()
	def 							lowerCamelCase__		(				self							)    ->	Any:
							lowercase_   : Dict   	=      self.image_processing_class(**self.image_processor_dict							)
							self.assertTrue(hasattr(_lowercase	,   'do_rescale'							)							)
							self.assertTrue(hasattr(_lowercase	,   'rescale_factor'							)							)
							self.assertTrue(hasattr(_lowercase	,   'do_pad'							)							)
							self.assertTrue(hasattr(_lowercase	,   'pad_size'							)							)
	def 							lowerCamelCase__		(				self							)    ->	int:
							pass
	def 							lowerCamelCase__		(				self	,   _lowercase							)    ->	List[Any]:
							lowercase_	,   lowercase_   : Tuple   	=      get_image_size(_lowercase							)
							lowercase_   : List[str]   	=      self.image_processor_tester.pad_size

							lowercase_   : int   	=      (old_height // size + 1) * size - old_height
							lowercase_   : Union[str, Any]   	=      (old_width // size + 1) * size - old_width
							return old_height + pad_height, old_width + pad_width
	def 							lowerCamelCase__		(				self							)    ->	Optional[Any]:
							# Initialize image_processor
							lowercase_   : Dict   	=      self.image_processing_class(**self.image_processor_dict							)
							# create random PIL images
							lowercase_   : Union[str, Any]   	=      self.image_processor_tester.prepare_inputs(equal_resolution=_lowercase							)
							for image in image_inputs:
													self.assertIsInstance(_lowercase	,   Image.Image							)

							# Test not batched input
							lowercase_   : Tuple   	=      image_processor(image_inputs[0]	,   return_tensors='pt'							).pixel_values
							lowercase_	,   lowercase_   : List[Any]   	=      self.calculate_expected_size(np.array(image_inputs[0]							)							)
							self.assertEqual(
							    encoded_images.shape	,   (
							        1,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )	,   )
	def 							lowerCamelCase__		(				self							)    ->	int:
							# Initialize image_processor
							lowercase_   : Any   	=      self.image_processing_class(**self.image_processor_dict							)
							# create random numpy tensors
							lowercase_   : Tuple   	=      self.image_processor_tester.prepare_inputs(equal_resolution=_lowercase	,   numpify=_lowercase							)
							for image in image_inputs:
													self.assertIsInstance(_lowercase	,   np.ndarray							)

							# Test not batched input
							lowercase_   : Optional[Any]   	=      image_processor(image_inputs[0]	,   return_tensors='pt'							).pixel_values
							lowercase_	,   lowercase_   : Optional[int]   	=      self.calculate_expected_size(image_inputs[0]							)
							self.assertEqual(
							    encoded_images.shape	,   (
							        1,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )	,   )







	def 							lowerCamelCase__		(				self							)    ->	Union[str, Any]:
							# Initialize image_processor
							lowercase_   : Optional[Any]   	=      self.image_processing_class(**self.image_processor_dict							)
							# create random PyTorch tensors
							lowercase_   : int   	=      self.image_processor_tester.prepare_inputs(equal_resolution=_lowercase	,   torchify=_lowercase							)
							for image in image_inputs:
													self.assertIsInstance(_lowercase	,   torch.Tensor							)

							# Test not batched input
							lowercase_   : Dict   	=      image_processor(image_inputs[0]	,   return_tensors='pt'							).pixel_values
							lowercase_	,   lowercase_   : List[str]   	=      self.calculate_expected_size(image_inputs[0]							)
							self.assertEqual(
							    encoded_images.shape	,   (
							        1,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )	,   )








'''simple docstring'''

import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


A:   str						=					get_logger(__name__)

# Datasets
A:   Optional[Any]						=					"https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets"
A:   Any						=					"https://cdn-datasets.huggingface.co/datasets/datasets"
A:   Dict						=					"https://raw.githubusercontent.com/huggingface/datasets/{revision}/datasets/{path}/{name}"

# Metrics
A:   List[str]						=					"https://s3.amazonaws.com/datasets.huggingface.co/datasets/metrics"
A:   Any						=					"https://cdn-datasets.huggingface.co/datasets/metric"
A:   List[str]						=					"https://raw.githubusercontent.com/huggingface/datasets/{revision}/metrics/{path}/{name}"

# Hub
A:   str						=					os.environ.get("HF_ENDPOINT", "https://huggingface.co")
A:   List[Any]						=					HF_ENDPOINT + "/datasets/{repo_id}/resolve/{revision}/{path}"
A:   List[Any]						=					"main"

A:   Any						=					version.parse(platform.python_version())

if PY_VERSION < version.parse("3.8"):
						import importlib_metadata
else:
						import importlib.metadata as importlib_metadata

# General environment variables accepted values for booleans
A:   Optional[Any]						=					{"1", "ON", "YES", "TRUE"}
A:   Optional[int]						=					ENV_VARS_TRUE_VALUES.union({"AUTO"})


# Imports
A:   Any						=					version.parse(importlib_metadata.version("dill"))
A:   str						=					version.parse(importlib_metadata.version("pandas"))
A:   Tuple						=					version.parse(importlib_metadata.version("pyarrow"))

A:   str						=					os.environ.get("USE_TF", "AUTO").upper()
A:   List[Any]						=					os.environ.get("USE_TORCH", "AUTO").upper()
A:   Union[str, Any]						=					os.environ.get("USE_JAX", "AUTO").upper()

A:   Dict						=					"N/A"
A:   Optional[int]						=					False

if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
						A:   List[str]						=					importlib.util.find_spec("torch") is not None
						if TORCH_AVAILABLE:
												try:
																		A:   List[str]						=					version.parse(importlib_metadata.version("torch"))
																		logger.info(f"""PyTorch version {TORCH_VERSION} available.""")
												except importlib_metadata.PackageNotFoundError:
																		pass
else:
						logger.info("Disabling PyTorch because USE_TF is set")

A:   Union[str, Any]						=					"N/A"
A:   Dict						=					False

if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
						A:   Optional[Any]						=					importlib.util.find_spec("tensorflow") is not None
						if TF_AVAILABLE:
												# For the metadata, we have to look for both tensorflow and tensorflow-cpu
												for package in [
												    "tensorflow",
												    "tensorflow-cpu",
												    "tensorflow-gpu",
												    "tf-nightly",
												    "tf-nightly-cpu",
												    "tf-nightly-gpu",
												    "intel-tensorflow",
												    "tensorflow-rocm",
												    "tensorflow-macos",
												]:
																		try:
																								A:   Any						=					version.parse(importlib_metadata.version(package))
																		except importlib_metadata.PackageNotFoundError:
																								continue
																		else:
																								break
												else:
																		A:   Any						=					False
						if TF_AVAILABLE:
												if TF_VERSION.major < 2:
																		logger.info(f"""TensorFlow found but with version {TF_VERSION}. `datasets` requires version 2 minimum.""")
																		A:   Tuple						=					False
												else:
																		logger.info(f"""TensorFlow version {TF_VERSION} available.""")
else:
						logger.info("Disabling Tensorflow because USE_TORCH is set")


A:   Any						=					"N/A"
A:   int						=					False

if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
						A:   Dict						=					importlib.util.find_spec("jax") is not None and importlib.util.find_spec("jaxlib") is not None
						if JAX_AVAILABLE:
												try:
																		A:   List[str]						=					version.parse(importlib_metadata.version("jax"))
																		logger.info(f"""JAX version {JAX_VERSION} available.""")
												except importlib_metadata.PackageNotFoundError:
																		pass
else:
						logger.info("Disabling JAX because USE_JAX is set to False")


A:   Optional[Any]						=					os.environ.get("USE_BEAM", "AUTO").upper()
A:   Any						=					"N/A"
A:   List[Any]						=					False
if USE_BEAM in ENV_VARS_TRUE_AND_AUTO_VALUES:
						try:
												A:   Tuple						=					version.parse(importlib_metadata.version("apache_beam"))
												A:   Dict						=					True
												logger.info(f"""Apache Beam version {BEAM_VERSION} available.""")
						except importlib_metadata.PackageNotFoundError:
												pass
else:
						logger.info("Disabling Apache Beam because USE_BEAM is set to False")


# Optional tools for data loading
A:   Union[str, Any]						=					importlib.util.find_spec("sqlalchemy") is not None

# Optional tools for feature decoding
A:   Optional[int]						=					importlib.util.find_spec("PIL") is not None
A:   Dict						=					importlib.util.find_spec("soundfile") is not None and version.parse(
    importlib.import_module("soundfile").__libsndfile_version__
) >= version.parse("1.0.31")
A:   Any						=					importlib.util.find_spec("soundfile") is not None and version.parse(
    importlib.import_module("soundfile").__libsndfile_version__
) >= version.parse("1.1.0")

# Optional compression tools
A:   Optional[Any]						=					importlib.util.find_spec("rarfile") is not None
A:   Any						=					importlib.util.find_spec("zstandard") is not None
A:   Union[str, Any]						=					importlib.util.find_spec("lz4") is not None
A:   str						=					importlib.util.find_spec("py7zr") is not None


# Cache location
A:   Any						=					"~/.cache"
A:   Tuple						=					os.getenv("XDG_CACHE_HOME", DEFAULT_XDG_CACHE_HOME)
A:   Dict						=					os.path.join(XDG_CACHE_HOME, "huggingface")
A:   List[str]						=					os.path.expanduser(os.getenv("HF_HOME", DEFAULT_HF_CACHE_HOME))

A:   int						=					os.path.join(HF_CACHE_HOME, "datasets")
A:   int						=					Path(os.getenv("HF_DATASETS_CACHE", DEFAULT_HF_DATASETS_CACHE))

A:   int						=					os.path.join(HF_CACHE_HOME, "metrics")
A:   int						=					Path(os.getenv("HF_METRICS_CACHE", DEFAULT_HF_METRICS_CACHE))

A:   Any						=					os.path.join(HF_CACHE_HOME, "modules")
A:   Tuple						=					Path(os.getenv("HF_MODULES_CACHE", DEFAULT_HF_MODULES_CACHE))

A:   List[str]						=					"downloads"
A:   Tuple						=					os.path.join(HF_DATASETS_CACHE, DOWNLOADED_DATASETS_DIR)
A:   Union[str, Any]						=					Path(os.getenv("HF_DATASETS_DOWNLOADED_DATASETS_PATH", DEFAULT_DOWNLOADED_DATASETS_PATH))

A:   Tuple						=					"extracted"
A:   Optional[int]						=					os.path.join(DEFAULT_DOWNLOADED_DATASETS_PATH, EXTRACTED_DATASETS_DIR)
A:   Optional[int]						=					Path(os.getenv("HF_DATASETS_EXTRACTED_DATASETS_PATH", DEFAULT_EXTRACTED_DATASETS_PATH))

# Download count for the website
A:   Union[str, Any]						=					(
    os.environ.get("HF_UPDATE_DOWNLOAD_COUNTS", "AUTO").upper() in ENV_VARS_TRUE_AND_AUTO_VALUES
)

# Batch size constants. For more info, see:
# https://github.com/apache/arrow/blob/master/docs/source/cpp/arrays.rst#size-limitations-and-recommendations)
A:   List[str]						=					1_0_0_0

# Size of the preloaded record batch in `Dataset.__iter__`
A:   Tuple						=					1_0

# Pickling tables works only for small tables (<4GiB)
# For big tables, we write them on disk instead
A:   List[Any]						=					4 << 3_0

# Max shard size in bytes (e.g. to shard parquet datasets in push_to_hub or download_and_prepare)
A:   List[str]						=					"500MB"

# Parquet configuration
A:   Union[str, Any]						=					1_0_0
A:   Optional[int]						=					1_0_0
A:   Any						=					1_0_0

# Offline mode
A:   List[str]						=					os.environ.get("HF_DATASETS_OFFLINE", "AUTO").upper() in ENV_VARS_TRUE_VALUES

# In-memory
A:   Any						=					0  # Disabled
A:   List[str]						=					float(os.environ.get("HF_DATASETS_IN_MEMORY_MAX_SIZE", DEFAULT_IN_MEMORY_MAX_SIZE))

# File names
A:   List[str]						=					"dataset.arrow"
A:   Optional[int]						=					"indices.arrow"
A:   List[str]						=					"state.json"
A:   Tuple						=					"dataset_info.json"
A:   Tuple						=					"dataset_infos.json"
A:   Dict						=					"LICENSE"
A:   Optional[Any]						=					"metric_info.json"
A:   Any						=					"dataset_dict.json"

A:   Dict						=					"datasets_modules"

A:   List[str]						=					2_5_5

# Streaming
A:   Any						=					2_0
A:   Dict						=					5

# Datasets without script
A:   Union[str, Any]						=					2_0_0
A:   Union[str, Any]						=					1_0
A:   List[Any]						=					2_0_0

# Progress bars
A:   Tuple						=					0.05  # 20 progress updates per sec
