


'''simple docstring'''



# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile

from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version


A__						:   Union[str, Any]				=    get_logger(__name__)





class   UpperCAmelCase_    :





    """simple docstring"""






    lowerCamelCase    :   int					=     'dummy_data'
    lowerCamelCase    :   Tuple					=     'datasets'
    lowerCamelCase    :   Union[str, Any]					=     False







    def __init__(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = False     ,	SCREAMING_SNAKE_CASE_ = True     ,	SCREAMING_SNAKE_CASE_ = None     ,	)							->	str:
         __lowerCamelCase  :      Optional[Any]														=						0
         __lowerCamelCase  :      List[str]														=						dataset_name
         __lowerCamelCase  :      Any														=						cache_dir
         __lowerCamelCase  :      List[str]														=						use_local_dummy_data
         __lowerCamelCase  :      Any														=						config
         # download_callbacks take a single url as input
         __lowerCamelCase  :      List[Callable]														=						download_callbacks or []
         # if False, it doesn't load existing files and it returns the paths of the dummy files relative
         # to the dummy_data zip file root
         __lowerCamelCase  :      Any														=						load_existing_dummy_data

         # TODO(PVP, QL) might need to make this more general
         __lowerCamelCase  :      List[Any]														=						str(SCREAMING_SNAKE_CASE_      )
         # to be downloaded
         __lowerCamelCase  :      int														=						None
         __lowerCamelCase  :      List[Any]														=						None







    @property
    def  lowercase_							(				self      )							->	Union[str, Any]:
         if self._dummy_file is None:
              __lowerCamelCase  :      Dict														=						self.download_dummy_data()
         return self._dummy_file







    @property
    def  lowercase_							(				self      )							->	int:
         if self.config is not None:
              # structure is dummy / config_name / version_name
              return os.path.join('dummy'     ,	self.config.name     ,	self.version_name      )
         # structure is dummy / version_name
         return os.path.join('dummy'     ,	self.version_name      )







    @property
    def  lowercase_							(				self      )							->	Any:
         return os.path.join(self.dummy_data_folder     ,	'dummy_data.zip'      )







    def  lowercase_							(				self      )							->	Any:
         __lowerCamelCase  :      int														=						(
             self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
         )

         __lowerCamelCase  :      Union[str, Any]														=						cached_path(
             SCREAMING_SNAKE_CASE_     ,	cache_dir=self.cache_dir     ,	extract_compressed_file=SCREAMING_SNAKE_CASE_     ,	force_extract=SCREAMING_SNAKE_CASE_      )

         return os.path.join(SCREAMING_SNAKE_CASE_     ,	self.dummy_file_name      )







    @property
    def  lowercase_							(				self      )							->	List[Any]:
         return os.path.join(self.datasets_scripts_dir     ,	self.dataset_name     ,	self.dummy_zip_file      )







    @property
    def  lowercase_							(				self      )							->	Optional[Any]:
         if self._bucket_url is None:
              __lowerCamelCase  :      Dict														=						hf_github_url(self.dataset_name     ,	self.dummy_zip_file.replace(os.sep     ,	'/'      )      )
         return self._bucket_url







    @property
    def  lowercase_							(				self      )							->	str:
         # return full path if its a dir
         if os.path.isdir(self.dummy_file      ):
              return self.dummy_file
         # else cut off path to file -> example `xsum`.
         return "/".join(self.dummy_file.replace(os.sep     ,	'/'      ).split('/'      )[:-1]      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	*SCREAMING_SNAKE_CASE_      )							->	List[str]:
         if self.load_existing_dummy_data:
              # dummy data is downloaded and tested
              __lowerCamelCase  :      Optional[Any]														=						self.dummy_file
         else:
              # dummy data cannot be downloaded and only the path to dummy file is returned
              __lowerCamelCase  :      Any														=						self.dummy_file_name

         # special case when data_url is a dict
         if isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      ):
              return self.create_dummy_data_dict(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )
         elif isinstance(SCREAMING_SNAKE_CASE_     ,	(list, tuple)      ):
              return self.create_dummy_data_list(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )
         else:
              return self.create_dummy_data_single(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	*SCREAMING_SNAKE_CASE_      )							->	Any:
         return self.download_and_extract(SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	List[str]:
         return self.download_and_extract(SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	*SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )							->	List[Any]:
         return path







    def  lowercase_							(				self      )							->	List[str]:
         return {}







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	Union[str, Any]:
         __lowerCamelCase  :      Optional[Any]														=						{}
         for key, single_urls in data_url.items():
              for download_callback in self.download_callbacks:
                   if isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      ):
                        for single_url in single_urls:
                             download_callback(SCREAMING_SNAKE_CASE_      )
                   else:
                        __lowerCamelCase  :      Union[str, Any]														=						single_urls
                        download_callback(SCREAMING_SNAKE_CASE_      )
            # we force the name of each key to be the last file / folder name of the url path
            # if the url has arguments, we need to encode them with urllib.parse.quote_plus
              if isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      ):
                   __lowerCamelCase  :      int														=						[os.path.join(SCREAMING_SNAKE_CASE_     ,	urllib.parse.quote_plus(Path(SCREAMING_SNAKE_CASE_      ).name      )      ) for x in single_urls]
              else:
                   __lowerCamelCase  :      str														=						single_urls
                   __lowerCamelCase  :      Union[str, Any]														=						os.path.join(SCREAMING_SNAKE_CASE_     ,	urllib.parse.quote_plus(Path(SCREAMING_SNAKE_CASE_      ).name      )      )
              __lowerCamelCase  :      List[Any]														=						value

         # make sure that values are unique
         if all(isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      ) for i in dummy_data_dict.values()      ) and len(set(dummy_data_dict.values()      )      ) < len(
             dummy_data_dict.values()      ):
              # append key to value to make its name unique
              __lowerCamelCase  :      int														=						{key: value + key for key, value in dummy_data_dict.items()}

         return dummy_data_dict







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	Any:
         __lowerCamelCase  :      Tuple														=						[]
         # trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
         __lowerCamelCase  :      Optional[Any]														=						all(bool(re.findall('[0-9]{3,}-of-[0-9]{3,}'     ,	SCREAMING_SNAKE_CASE_      )      ) for url in data_url      )
         __lowerCamelCase  :      Dict														=						all(
             url.startswith('https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed'      ) for url in data_url      )
         if data_url and (is_tf_records or is_pubmed_records):
              __lowerCamelCase  :      Dict														=						[data_url[0]] * len(SCREAMING_SNAKE_CASE_      )
         for single_url in data_url:
              for download_callback in self.download_callbacks:
                   download_callback(SCREAMING_SNAKE_CASE_      )
              # we force the name of each key to be the last file / folder name of the url path
              # if the url has arguments, we need to encode them with urllib.parse.quote_plus
              __lowerCamelCase  :      str														=						os.path.join(SCREAMING_SNAKE_CASE_     ,	urllib.parse.quote_plus(single_url.split('/'      )[-1]      )      )
              dummy_data_list.append(SCREAMING_SNAKE_CASE_      )
         return dummy_data_list







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	Optional[int]:
         for download_callback in self.download_callbacks:
              download_callback(SCREAMING_SNAKE_CASE_      )
         # we force the name of each key to be the last file / folder name of the url path
         # if the url has arguments, we need to encode them with urllib.parse.quote_plus
         __lowerCamelCase  :      Tuple														=						os.path.join(SCREAMING_SNAKE_CASE_     ,	urllib.parse.quote_plus(data_url.split('/'      )[-1]      )      )
         if os.path.exists(SCREAMING_SNAKE_CASE_      ) or not self.load_existing_dummy_data:
              return value
         else:
              # Backward compatibility, maybe deprecate at one point.
              # For many datasets with single url calls to dl_manager.download_and_extract,
              # the dummy_data.zip file is actually the zipped downloaded file
              # while now we expected the dummy_data.zip file to be a directory containing
              # the downloaded file.
              return path_to_dummy_data







    def  lowercase_							(				self      )							->	Union[str, Any]:
         pass







    def  lowercase_							(				self      )							->	Tuple:
         pass







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_      )							->	Tuple:
         def _iter_archive_members(SCREAMING_SNAKE_CASE_      ):
              # this preserves the order of the members inside the ZIP archive
              __lowerCamelCase  :      List[Any]														=						Path(self.dummy_file      ).parent
              __lowerCamelCase  :      str														=						path.relative_to(SCREAMING_SNAKE_CASE_      )
              with ZipFile(self.local_path_to_dummy_data      ) as zip_file:
                   __lowerCamelCase  :      Tuple														=						zip_file.namelist()
              for member in members:
                   if member.startswith(relative_path.as_posix()      ):
                        yield dummy_parent_path.joinpath(SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      str														=						Path(SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Tuple														=						_iter_archive_members(SCREAMING_SNAKE_CASE_      ) if self.use_local_dummy_data else path.rglob('*'      )
         for file_path in file_paths:
              if file_path.is_file() and not file_path.name.startswith(('.', '__')      ):
                   yield file_path.relative_to(SCREAMING_SNAKE_CASE_      ).as_posix(), file_path.open('rb'      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_      )							->	List[Any]:
         if not isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      ):
              __lowerCamelCase  :      List[Any]														=						[paths]
         for path in paths:
              if os.path.isfile(SCREAMING_SNAKE_CASE_      ):
                   if os.path.basename(SCREAMING_SNAKE_CASE_      ).startswith(('.', '__')      ):
                        return
                   yield path
              else:
                   for dirpath, dirnames, filenames in os.walk(SCREAMING_SNAKE_CASE_      ):
                        if os.path.basename(SCREAMING_SNAKE_CASE_      ).startswith(('.', '__')      ):
                             continue
                        dirnames.sort()
                        for filename in sorted(SCREAMING_SNAKE_CASE_      ):
                             if filename.startswith(('.', '__')      ):
                                  continue
                             yield os.path.join(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )










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 PoolFormerImageProcessor






class      UpperCamelCase(	unittest.TestCase ):


					def __init__(    self						:		Tuple  ,     SCREAMING_SNAKE_CASE						:		List[Any]  ,     SCREAMING_SNAKE_CASE						:		Optional[Any]=7  ,     SCREAMING_SNAKE_CASE						:		Dict=3  ,     SCREAMING_SNAKE_CASE						:		Any=3_0  ,     SCREAMING_SNAKE_CASE						:		List[str]=4_0_0  ,     SCREAMING_SNAKE_CASE						:		Any=True  ,     SCREAMING_SNAKE_CASE						:		List[Any]=None  ,     SCREAMING_SNAKE_CASE						:		int=0.9  ,     SCREAMING_SNAKE_CASE						:		Tuple=None  ,     SCREAMING_SNAKE_CASE						:		int=True  ,     SCREAMING_SNAKE_CASE						:		Union[str, Any]=[0.5, 0.5, 0.5]  ,     SCREAMING_SNAKE_CASE						:		Optional[Any]=[0.5, 0.5, 0.5]  ,     )      ->						Union[str, Any]:




											'''simple docstring'''

											__snake_case				=		size if size is not None else {"shortest_edge": 3_0}
											__snake_case				=		crop_size if crop_size is not None else {"height": 3_0, "width": 3_0}
											__snake_case				=		parent
											__snake_case				=		batch_size
											__snake_case				=		num_channels
											__snake_case				=		min_resolution
											__snake_case				=		max_resolution
											__snake_case				=		do_resize_and_center_crop
											__snake_case				=		size
											__snake_case				=		crop_pct
											__snake_case				=		crop_size
											__snake_case				=		do_normalize
											__snake_case				=		image_mean
											__snake_case				=		image_std






					def 				SCREAMING_SNAKE_CASE_	(    self						:		str    )      ->						Any:




											'''simple docstring'''

											return {
											    "size": self.size,
											    "do_resize_and_center_crop": self.do_resize_and_center_crop,
											    "crop_pct": self.crop_pct,
											    "crop_size": self.crop_size,
											    "do_normalize": self.do_normalize,
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											}






@require_torch
@require_vision
class      UpperCamelCase(	_a  ,				unittest.TestCase ):
					snake_case_		:					List[str]    					=						PoolFormerImageProcessor if is_vision_available() else None


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Union[str, Any]    )      ->						str:




											'''simple docstring'''

											__snake_case				=		PoolFormerImageProcessingTester(self    )


					@property
					def 				SCREAMING_SNAKE_CASE_	(    self						:		int    )      ->						Dict:




											'''simple docstring'''

											return self.image_processor_tester.prepare_image_processor_dict()


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Dict    )      ->						List[str]:




											'''simple docstring'''

											__snake_case				=		self.image_processing_class(**self.image_processor_dict    )
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE  ,     "do_resize_and_center_crop"    )    )
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE  ,     "size"    )    )
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE  ,     "crop_pct"    )    )
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE  ,     "do_normalize"    )    )
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE  ,     "image_mean"    )    )
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE  ,     "image_std"    )    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Union[str, Any]    )      ->						Any:




											'''simple docstring'''

											__snake_case				=		self.image_processing_class.from_dict(self.image_processor_dict    )
											self.assertEqual(image_processor.size  ,     {"shortest_edge": 3_0}    )
											self.assertEqual(image_processor.crop_size  ,     {"height": 3_0, "width": 3_0}    )

											__snake_case				=		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 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[int]    )      ->						List[Any]:




											'''simple docstring'''

											pass


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[Any]    )      ->						Dict:




											'''simple docstring'''

											__snake_case				=		self.image_processing_class(**self.image_processor_dict    )
											# create random PIL images
											__snake_case				=		prepare_image_inputs(self.image_processor_tester  ,     equal_resolution=SCREAMING_SNAKE_CASE    )
											for image in image_inputs:
																	self.assertIsInstance(SCREAMING_SNAKE_CASE  ,     Image.Image    )

											# Test not batched input
											__snake_case				=		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
											__snake_case				=		image_processing(SCREAMING_SNAKE_CASE  ,     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 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[Any]    )      ->						Optional[int]:




											'''simple docstring'''

											__snake_case				=		self.image_processing_class(**self.image_processor_dict    )
											# create random numpy tensors
											__snake_case				=		prepare_image_inputs(self.image_processor_tester  ,     equal_resolution=SCREAMING_SNAKE_CASE  ,     numpify=SCREAMING_SNAKE_CASE    )
											for image in image_inputs:
																	self.assertIsInstance(SCREAMING_SNAKE_CASE  ,     np.ndarray    )

											# Test not batched input
											__snake_case				=		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
											__snake_case				=		image_processing(SCREAMING_SNAKE_CASE  ,     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 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[int]    )      ->						Union[str, Any]:




											'''simple docstring'''

											__snake_case				=		self.image_processing_class(**self.image_processor_dict    )
											# create random PyTorch tensors
											__snake_case				=		prepare_image_inputs(self.image_processor_tester  ,     equal_resolution=SCREAMING_SNAKE_CASE  ,     torchify=SCREAMING_SNAKE_CASE    )
											for image in image_inputs:
																	self.assertIsInstance(SCREAMING_SNAKE_CASE  ,     torch.Tensor    )

											# Test not batched input
											__snake_case				=		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
											__snake_case				=		image_processing(SCREAMING_SNAKE_CASE  ,     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"],
											    )  ,     )





