


"""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


lowerCAmelCase__      				=   get_logger(__name__)


class       SCREAMING_SNAKE_CASE__				:
       """simple docstring"""


       a  :				Optional[Any]   ="dummy_data"
       a  :				int   ="datasets"
       a  :				Tuple   =False

       def __init__(						self		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__ = None		,     snake_case__ = False		,     snake_case__ = True		,     snake_case__ = None		,     ):






         """simple docstring"""





         lowerCAmelCase     :     Tuple								=							0
         lowerCAmelCase     :     int								=							dataset_name
         lowerCAmelCase     :     List[Any]								=							cache_dir
         lowerCAmelCase     :     List[str]								=							use_local_dummy_data
         lowerCAmelCase     :     List[str]								=							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     :     Tuple								=							load_existing_dummy_data

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

       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         if self._dummy_file is None:
           lowerCAmelCase     :     Any								=							self.download_dummy_data()
         return self._dummy_file

       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         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						):






         """simple docstring"""





         return os.path.join(self.dummy_data_folder		,     "dummy_data.zip"						)

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Optional[Any]								=							(
             self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
         )

         lowerCAmelCase     :     str								=							cached_path(
             snake_case__		,     cache_dir=self.cache_dir		,     extract_compressed_file=snake_case__		,     force_extract=snake_case__						)

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

       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         return os.path.join(self.datasets_scripts_dir		,     self.dataset_name		,     self.dummy_zip_file						)

       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         if self._bucket_url is None:
           lowerCAmelCase     :     Union[str, Any]								=							hf_github_url(self.dataset_name		,     self.dummy_zip_file.replace(os.sep		,     "/"						)						)
         return self._bucket_url

       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         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		,     snake_case__		,     *snake_case__						):






         """simple docstring"""





         if self.load_existing_dummy_data:
           # dummy data is downloaded and tested
           lowerCAmelCase     :     int								=							self.dummy_file
         else:
           # dummy data cannot be downloaded and only the path to dummy file is returned
           lowerCAmelCase     :     List[Any]								=							self.dummy_file_name

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

       def        lowercase__							(						self		,     snake_case__		,     *snake_case__						):






         """simple docstring"""





         return self.download_and_extract(snake_case__						)

       def        lowercase__							(						self		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         return self.download_and_extract(snake_case__						)

       def        lowercase__							(						self		,     snake_case__		,     *snake_case__		,     **snake_case__						):






         """simple docstring"""





         return path

       def        lowercase__							(						self						):






         """simple docstring"""





         return {}

       def        lowercase__							(						self		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     List[Any]								=							{}
         for key, single_urls in data_url.items():
           for download_callback in self.download_callbacks:
             if isinstance(snake_case__		,     snake_case__						):
               for single_url in single_urls:
                 download_callback(snake_case__						)
             else:
               lowerCAmelCase     :     List[str]								=							single_urls
               download_callback(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(snake_case__		,     snake_case__						):
             lowerCAmelCase     :     Tuple								=							[os.path.join(snake_case__		,     urllib.parse.quote_plus(Path(snake_case__						).name						)						) for x in single_urls]
           else:
             lowerCAmelCase     :     int								=							single_urls
             lowerCAmelCase     :     Any								=							os.path.join(snake_case__		,     urllib.parse.quote_plus(Path(snake_case__						).name						)						)
           lowerCAmelCase     :     Union[str, Any]								=							value

         # make sure that values are unique
         if all(isinstance(snake_case__		,     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     :     Union[str, Any]								=							{key: value + key for key, value in dummy_data_dict.items()}

         return dummy_data_dict

       def        lowercase__							(						self		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     Dict								=							[]
         # 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,}"		,     snake_case__						)						) for url in data_url						)
         lowerCAmelCase     :     Any								=							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     :     int								=							[data_url[0]] * len(snake_case__						)
         for single_url in data_url:
           for download_callback in self.download_callbacks:
             download_callback(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     :     Dict								=							os.path.join(snake_case__		,     urllib.parse.quote_plus(single_url.split("/"						)[-1]						)						)
           dummy_data_list.append(snake_case__						)
         return dummy_data_list

       def        lowercase__							(						self		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         for download_callback in self.download_callbacks:
           download_callback(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(snake_case__		,     urllib.parse.quote_plus(data_url.split("/"						)[-1]						)						)
         if os.path.exists(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						):






         """simple docstring"""





         pass

       def        lowercase__							(						self						):






         """simple docstring"""





         pass

       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





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

         lowerCAmelCase     :     List[Any]								=							Path(snake_case__						)
         lowerCAmelCase     :     str								=							_iter_archive_members(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(snake_case__						).as_posix(), file_path.open("rb"						)







       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         if not isinstance(snake_case__		,     snake_case__						):
           lowerCAmelCase     :     List[Any]								=							[paths]
         for path in paths:
           if os.path.isfile(snake_case__						):
             if os.path.basename(snake_case__						).startswith((".", "__")						):
               return
             yield path
           else:
             for dirpath, dirnames, filenames in os.walk(snake_case__						):
               if os.path.basename(snake_case__						).startswith((".", "__")						):
                 continue
               dirnames.sort()
               for filename in sorted(snake_case__						):
                 if filename.startswith((".", "__")						):
                   continue
                 yield os.path.join(snake_case__		,     snake_case__						)







import unittest

import numpy as np
import requests

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

					from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
					SCREAMING_SNAKE_CASE__   :			str												=    False

if is_vision_available():
					from PIL import Image

					from transformers import PixaStructImageProcessor

class    lowerCAmelCase__     (	unittest.TestCase			):






		def __init__(		self		:							Tuple							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]							,			SCREAMING_SNAKE_CASE__		:							List[Any]=7							,			SCREAMING_SNAKE_CASE__		:							List[str]=3							,			SCREAMING_SNAKE_CASE__		:							Tuple=18							,			SCREAMING_SNAKE_CASE__		:							int=30							,			SCREAMING_SNAKE_CASE__		:							Any=4_00							,			SCREAMING_SNAKE_CASE__		:							Any=None							,			SCREAMING_SNAKE_CASE__		:							str=True							,			SCREAMING_SNAKE_CASE__		:							str=True							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]=None							,			)     ->							Union[str, Any]:
						__lowerCamelCase    					=	size if size is not None else {'''height''': 20, '''width''': 20}
						__lowerCamelCase    					=	parent
						__lowerCamelCase    					=	batch_size
						__lowerCamelCase    					=	num_channels
						__lowerCamelCase    					=	image_size
						__lowerCamelCase    					=	min_resolution
						__lowerCamelCase    					=	max_resolution
						__lowerCamelCase    					=	size
						__lowerCamelCase    					=	do_normalize
						__lowerCamelCase    					=	do_convert_rgb
						__lowerCamelCase    					=	[5_12, 10_24, 20_48, 40_96]
						__lowerCamelCase    					=	patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}






		def  __A       (		self		:							Union[str, Any]  )     ->							Union[str, Any]:
						return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}







		def  __A       (		self		:							int  )     ->							Any:
						__lowerCamelCase    					=	'''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'''
						__lowerCamelCase    					=	Image.open(requests.get(SCREAMING_SNAKE_CASE__							,			stream=SCREAMING_SNAKE_CASE__  ).raw  ).convert('''RGB'''  )
						return raw_image





@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11  ,	reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""  ,	)
@require_torch
@require_vision
class    lowerCAmelCase__     (	__lowercase  ,	unittest.TestCase			):
		a__       :						Dict        =		PixaStructImageProcessor if is_vision_available() else None






		def  __A       (		self		:							Optional[Any]  )     ->							List[Any]:
						__lowerCamelCase    					=	PixaStructImageProcessingTester(self  )






		@property
		def  __A       (		self		:							Optional[int]  )     ->							Optional[int]:
						return self.image_processor_tester.prepare_image_processor_dict()






		def  __A       (		self		:							Tuple  )     ->							Dict:
						__lowerCamelCase    					=	self.image_processing_class(**self.image_processor_dict  )
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__							,			'''do_normalize'''  )  )
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__							,			'''do_convert_rgb'''  )  )






		def  __A       (		self		:							int  )     ->							str:
						__lowerCamelCase    					=	self.image_processor_tester.prepare_dummy_image()

						__lowerCamelCase    					=	self.image_processing_class(**self.image_processor_dict  )
						__lowerCamelCase    					=	20_48

						__lowerCamelCase    					=	image_processor(SCREAMING_SNAKE_CASE__							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__  )
						self.assertTrue(torch.allclose(inputs.flattened_patches.mean()							,			torch.tensor(0.0606  )							,			atol=1e-3							,			rtol=1e-3  )  )






		def  __A       (		self		:							Union[str, Any]  )     ->							Dict:
						# Initialize image_processor
						__lowerCamelCase    					=	self.image_processing_class(**self.image_processor_dict  )
						# create random PIL images
						__lowerCamelCase    					=	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
						__lowerCamelCase    					=	(
						    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
						    * self.image_processor_tester.num_channels
						) + 2

						for max_patch in self.image_processor_tester.max_patches:
										# Test not batched input
										__lowerCamelCase    					=	image_processor(
										    image_inputs[0]							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__  ).flattened_patches
										self.assertEqual(
										    encoded_images.shape							,			(1, max_patch, expected_hidden_dim)							,			)

										# Test batched
										__lowerCamelCase    					=	image_processor(
										    SCREAMING_SNAKE_CASE__							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__  ).flattened_patches
										self.assertEqual(
										    encoded_images.shape							,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,			)






		def  __A       (		self		:							Dict  )     ->							str:
						# Initialize image_processor
						__lowerCamelCase    					=	self.image_processing_class(**self.image_processor_dict  )
						# create random PIL images
						__lowerCamelCase    					=	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
						__lowerCamelCase    					=	(
						    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
						    * self.image_processor_tester.num_channels
						) + 2

						__lowerCamelCase    					=	True

						for max_patch in self.image_processor_tester.max_patches:
										# Test not batched input
										with self.assertRaises(SCREAMING_SNAKE_CASE__  ):
														__lowerCamelCase    					=	image_processor(
														    image_inputs[0]							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__  ).flattened_patches

										__lowerCamelCase    					=	'''Hello'''

										__lowerCamelCase    					=	image_processor(
										    image_inputs[0]							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__							,			header_text=SCREAMING_SNAKE_CASE__  ).flattened_patches
										self.assertEqual(
										    encoded_images.shape							,			(1, max_patch, expected_hidden_dim)							,			)

										# Test batched
										__lowerCamelCase    					=	image_processor(
										    SCREAMING_SNAKE_CASE__							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__							,			header_text=SCREAMING_SNAKE_CASE__  ).flattened_patches
										self.assertEqual(
										    encoded_images.shape							,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,			)






		def  __A       (		self		:							List[str]  )     ->							Any:
						# Initialize image_processor
						__lowerCamelCase    					=	self.image_processing_class(**self.image_processor_dict  )
						# create random numpy tensors
						__lowerCamelCase    					=	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  )

						__lowerCamelCase    					=	(
						    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
						    * self.image_processor_tester.num_channels
						) + 2

						for max_patch in self.image_processor_tester.max_patches:
										# Test not batched input
										__lowerCamelCase    					=	image_processor(
										    image_inputs[0]							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__  ).flattened_patches
										self.assertEqual(
										    encoded_images.shape							,			(1, max_patch, expected_hidden_dim)							,			)

										# Test batched
										__lowerCamelCase    					=	image_processor(
										    SCREAMING_SNAKE_CASE__							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__  ).flattened_patches
										self.assertEqual(
										    encoded_images.shape							,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,			)







		def  __A       (		self		:							Tuple  )     ->							List[str]:
						# Initialize image_processor
						__lowerCamelCase    					=	self.image_processing_class(**self.image_processor_dict  )
						# create random PyTorch tensors
						__lowerCamelCase    					=	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
						__lowerCamelCase    					=	(
						    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
						    * self.image_processor_tester.num_channels
						) + 2

						for max_patch in self.image_processor_tester.max_patches:
										# Test not batched input
										__lowerCamelCase    					=	image_processor(
										    image_inputs[0]							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__  ).flattened_patches
										self.assertEqual(
										    encoded_images.shape							,			(1, max_patch, expected_hidden_dim)							,			)

										# Test batched
										__lowerCamelCase    					=	image_processor(
										    SCREAMING_SNAKE_CASE__							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__  ).flattened_patches
										self.assertEqual(
										    encoded_images.shape							,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,			)





@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11  ,	reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""  ,	)
@require_torch
@require_vision
class    lowerCAmelCase__     (	__lowercase  ,	unittest.TestCase			):
		a__       :						Any        =		PixaStructImageProcessor if is_vision_available() else None






		def  __A       (		self		:							Union[str, Any]  )     ->							Optional[Any]:
						__lowerCamelCase    					=	PixaStructImageProcessingTester(self							,			num_channels=4  )
						__lowerCamelCase    					=	3






		@property
		def  __A       (		self		:							List[Any]  )     ->							Optional[Any]:
						return self.image_processor_tester.prepare_image_processor_dict()






		def  __A       (		self		:							Union[str, Any]  )     ->							List[str]:
						__lowerCamelCase    					=	self.image_processing_class(**self.image_processor_dict  )
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__							,			'''do_normalize'''  )  )
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__							,			'''do_convert_rgb'''  )  )







		def  __A       (		self		:							Optional[int]  )     ->							str:
						# Initialize image_processor
						__lowerCamelCase    					=	self.image_processing_class(**self.image_processor_dict  )
						# create random PIL images
						__lowerCamelCase    					=	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
						__lowerCamelCase    					=	(
						    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
						    * (self.image_processor_tester.num_channels - 1)
						) + 2

						for max_patch in self.image_processor_tester.max_patches:
										# Test not batched input
										__lowerCamelCase    					=	image_processor(
										    image_inputs[0]							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__  ).flattened_patches
										self.assertEqual(
										    encoded_images.shape							,			(1, max_patch, expected_hidden_dim)							,			)

										# Test batched
										__lowerCamelCase    					=	image_processor(
										    SCREAMING_SNAKE_CASE__							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__  ).flattened_patches
										self.assertEqual(
										    encoded_images.shape							,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,			)



