


import importlib
import inspect
from functools import wraps
from typing import TYPE_CHECKING, Optional, Union

from .download.streaming_download_manager import (
    xbasename,
    xdirname,
    xet_parse,
    xexists,
    xgetsize,
    xglob,
    xgzip_open,
    xisdir,
    xisfile,
    xjoin,
    xlistdir,
    xnumpy_load,
    xopen,
    xpandas_read_csv,
    xpandas_read_excel,
    xPath,
    xrelpath,
    xsio_loadmat,
    xsplit,
    xsplitext,
    xwalk,
    xxml_dom_minidom_parse,
)
from .utils.logging import get_logger
from .utils.patching import patch_submodule
from .utils.py_utils import get_imports


lowerCAmelCase__						=			get_logger(__name__)


if TYPE_CHECKING:
    from .builder import DatasetBuilder
def 							_UpperCAmelCase    (UpperCamelCase__					:  Optional[Any]    ,							UpperCamelCase__					:  Optional[Union[str, bool]] = None						):
       _A				:	List[str]       =			importlib.import_module(UpperCamelCase__						)

       # TODO(QL): always update the module to add subsequent new authentication
       if hasattr(UpperCamelCase__    ,							"_patched_for_streaming"						) and module._patched_for_streaming:
              return

       def wrap_auth(UpperCamelCase__					:  List[Any]						):
              @wraps(UpperCamelCase__						)
              def wrapper(*UpperCamelCase__					:  int    ,							**UpperCamelCase__					:  Tuple						):
                     return function(*UpperCamelCase__    ,							use_auth_token=UpperCamelCase__    ,							**UpperCamelCase__						)

              _A				:	Dict       =			"wrap_auth"
              return wrapper

       # open files in a streaming fashion
       patch_submodule(UpperCamelCase__    ,							"open"    ,							wrap_auth(UpperCamelCase__						)						).start()
       patch_submodule(UpperCamelCase__    ,							"os.listdir"    ,							wrap_auth(UpperCamelCase__						)						).start()
       patch_submodule(UpperCamelCase__    ,							"os.walk"    ,							wrap_auth(UpperCamelCase__						)						).start()
       patch_submodule(UpperCamelCase__    ,							"glob.glob"    ,							wrap_auth(UpperCamelCase__						)						).start()
       # allow to navigate in remote zip files
       patch_submodule(UpperCamelCase__    ,							"os.path.join"    ,							UpperCamelCase__						).start()
       patch_submodule(UpperCamelCase__    ,							"os.path.dirname"    ,							UpperCamelCase__						).start()
       patch_submodule(UpperCamelCase__    ,							"os.path.basename"    ,							UpperCamelCase__						).start()
       patch_submodule(UpperCamelCase__    ,							"os.path.relpath"    ,							UpperCamelCase__						).start()
       patch_submodule(UpperCamelCase__    ,							"os.path.split"    ,							UpperCamelCase__						).start()
       patch_submodule(UpperCamelCase__    ,							"os.path.splitext"    ,							UpperCamelCase__						).start()
       # allow checks on paths
       patch_submodule(UpperCamelCase__    ,							"os.path.exists"    ,							wrap_auth(UpperCamelCase__						)						).start()
       patch_submodule(UpperCamelCase__    ,							"os.path.isdir"    ,							wrap_auth(UpperCamelCase__						)						).start()
       patch_submodule(UpperCamelCase__    ,							"os.path.isfile"    ,							wrap_auth(UpperCamelCase__						)						).start()
       patch_submodule(UpperCamelCase__    ,							"os.path.getsize"    ,							wrap_auth(UpperCamelCase__						)						).start()
       patch_submodule(UpperCamelCase__    ,							"pathlib.Path"    ,							UpperCamelCase__						).start()
       # file readers
       patch_submodule(UpperCamelCase__    ,							"gzip.open"    ,							wrap_auth(UpperCamelCase__						)						).start()
       patch_submodule(UpperCamelCase__    ,							"numpy.load"    ,							wrap_auth(UpperCamelCase__						)						).start()
       patch_submodule(UpperCamelCase__    ,							"pandas.read_csv"    ,							wrap_auth(UpperCamelCase__						)    ,							attrs=["__version__"]						).start()
       patch_submodule(UpperCamelCase__    ,							"pandas.read_excel"    ,							wrap_auth(UpperCamelCase__						)    ,							attrs=["__version__"]						).start()
       patch_submodule(UpperCamelCase__    ,							"scipy.io.loadmat"    ,							wrap_auth(UpperCamelCase__						)    ,							attrs=["__version__"]						).start()
       patch_submodule(UpperCamelCase__    ,							"xml.etree.ElementTree.parse"    ,							wrap_auth(UpperCamelCase__						)						).start()
       patch_submodule(UpperCamelCase__    ,							"xml.dom.minidom.parse"    ,							wrap_auth(UpperCamelCase__						)						).start()
       _A				:	str       =			True






def 							_UpperCAmelCase    (UpperCamelCase__					:  "DatasetBuilder"						):
       extend_module_for_streaming(builder.__module__    ,							use_auth_token=builder.use_auth_token						)
       # if needed, we also have to extend additional internal imports (like wmt14 -> wmt_utils)
       if not builder.__module__.startswith("datasets."						):  # check that it's not a packaged builder like csv
              for imports in get_imports(inspect.getfile(builder.__class__						)						):
                     if imports[0] == "internal":
                            _A				:	Any       =			imports[1]
                            _A				:	Any       =			".".join(builder.__module__.split("."						)[:-1] + [internal_import_name]						)
                            extend_module_for_streaming(UpperCamelCase__    ,							use_auth_token=builder.use_auth_token						)

    # builders can inherit from other builders that might use streaming functionality
    # (for example, ImageFolder and AudioFolder inherit from FolderBuilder which implements examples generation)
    # but these parents builders are not patched automatically as they are not instantiated, so we patch them here
       from .builder import DatasetBuilder

       _A				:	List[str]       =			[
           cls.__module__
           for cls in type(UpperCamelCase__						).__mro__[1:]  # make sure it's not the same module we've already patched
           if issubclass(cls    ,							UpperCamelCase__						) and cls.__module__ != DatasetBuilder.__module__
       ]  # check it's not a standard builder from datasets.builder
       for module in parent_builder_modules:
              extend_module_for_streaming(UpperCamelCase__    ,							use_auth_token=builder.use_auth_token						)




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 					lowerCAmelCase__	(    unittest.TestCase):




     '''simple docstring'''


     def __init__(						self			,						__lowerCamelCase			,						__lowerCamelCase=7			,						__lowerCamelCase=3			,						__lowerCamelCase=1_8			,						__lowerCamelCase=3_0			,						__lowerCamelCase=4_0_0			,						__lowerCamelCase=True			,						__lowerCamelCase=1 / 2_5_5			,						__lowerCamelCase=True			,						__lowerCamelCase=8			,						)      ->    Optional[Any]:
            _A				:	List[str]       =			parent
            _A				:	str       =			batch_size
            _A				:	Any       =			num_channels
            _A				:	Any       =			image_size
            _A				:	Optional[int]       =			min_resolution
            _A				:	Union[str, Any]       =			max_resolution
            _A				:	Optional[Any]       =			do_rescale
            _A				:	str       =			rescale_factor
            _A				:	str       =			do_pad
            _A				:	Dict       =			pad_size


     def 			_lowerCamelCase			(						self)      ->    str:
            return {
                "do_rescale": self.do_rescale,
                "rescale_factor": self.rescale_factor,
                "do_pad": self.do_pad,
                "pad_size": self.pad_size,
            }


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase=False			,						__lowerCamelCase=False			,						__lowerCamelCase=False)      ->    str:
            assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

            if equal_resolution:
                   _A				:	List[str]       =			[]
                   for i in range(self.batch_size):
                          image_inputs.append(
                              np.random.randint(
                                  2_5_5			,						size=(self.num_channels, self.max_resolution, self.max_resolution)			,						dtype=np.uinta))
            else:
                   _A				:	List[str]       =			[]
                   for i in range(self.batch_size):
                          _A   ,      _A				:	int       =			np.random.choice(np.arange(self.min_resolution			,						self.max_resolution)			,						2)
                          image_inputs.append(np.random.randint(2_5_5			,						size=(self.num_channels, width, height)			,						dtype=np.uinta))

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

            if torchify:
                   _A				:	Union[str, Any]       =			[torch.from_numpy(__lowerCamelCase) for x in image_inputs]

            return image_inputs







@require_torch
@require_vision
class 					lowerCAmelCase__	(    a					,   unittest.TestCase):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE        =			SwinaSRImageProcessor if is_vision_available() else None


     def 			_lowerCamelCase			(						self)      ->    Any:
            _A				:	int       =			SwinaSRImageProcessingTester(self)


     @property
     def 			_lowerCamelCase			(						self)      ->    Any:
            return self.image_processor_tester.prepare_image_processor_dict()


     def 			_lowerCamelCase			(						self)      ->    Union[str, Any]:
            _A				:	Tuple       =			self.image_processing_class(**self.image_processor_dict)
            self.assertTrue(hasattr(__lowerCamelCase			,						"do_rescale"))
            self.assertTrue(hasattr(__lowerCamelCase			,						"rescale_factor"))
            self.assertTrue(hasattr(__lowerCamelCase			,						"do_pad"))
            self.assertTrue(hasattr(__lowerCamelCase			,						"pad_size"))


     def 			_lowerCamelCase			(						self)      ->    Dict:
            pass


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase)      ->    Tuple:
            _A   ,      _A				:	Dict       =			get_image_size(__lowerCamelCase)
            _A				:	Tuple       =			self.image_processor_tester.pad_size

            _A				:	Dict       =			(old_height // size + 1) * size - old_height
            _A				:	Any       =			(old_width // size + 1) * size - old_width
            return old_height + pad_height, old_width + pad_width


     def 			_lowerCamelCase			(						self)      ->    Dict:
            # Initialize image_processor
            _A				:	Optional[int]       =			self.image_processing_class(**self.image_processor_dict)
            # create random PIL images
            _A				:	Union[str, Any]       =			self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCamelCase)
            for image in image_inputs:
                   self.assertIsInstance(__lowerCamelCase			,						Image.Image)

            # Test not batched input
            _A				:	str       =			image_processor(image_inputs[0]			,						return_tensors="pt").pixel_values
            _A   ,      _A				:	int       =			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
            _A				:	Any       =			self.image_processing_class(**self.image_processor_dict)
            # create random numpy tensors
            _A				:	int       =			self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCamelCase			,						numpify=__lowerCamelCase)
            for image in image_inputs:
                   self.assertIsInstance(__lowerCamelCase			,						np.ndarray)

            # Test not batched input
            _A				:	Union[str, Any]       =			image_processor(image_inputs[0]			,						return_tensors="pt").pixel_values
            _A   ,      _A				:	Union[str, Any]       =			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)      ->    Optional[Any]:
            # Initialize image_processor
            _A				:	str       =			self.image_processing_class(**self.image_processor_dict)
            # create random PyTorch tensors
            _A				:	Tuple       =			self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCamelCase			,						torchify=__lowerCamelCase)
            for image in image_inputs:
                   self.assertIsInstance(__lowerCamelCase			,						torch.Tensor)

            # Test not batched input
            _A				:	List[str]       =			image_processor(image_inputs[0]			,						return_tensors="pt").pixel_values
            _A   ,      _A				:	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,
                )			,						)
