

"""simple docstring"""





import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile
from typing import List

from datasets import config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.info_utils import VerificationMode

def 						__SCREAMING_SNAKE_CASE						(   A_		,  **A_				):
	return RunBeamCommand(
	    args.dataset		,  args.name		,  args.cache_dir		,  args.beam_pipeline_options		,  args.data_dir		,  args.all_configs		,  args.save_info or args.save_infos		,  args.ignore_verifications		,  args.force_redownload		,  **A_		,  )







class        SCREAMING_SNAKE_CASE      (	a_ ):






					"""simple docstring"""



					@staticmethod
					def  __lowerCAmelCase				( lowercase_    :       ArgumentParser	):
						lowerCAmelCase__			:		Optional[Any]						       =			parser.add_parser('''run_beam'''     ,help='''Run a Beam dataset processing pipeline'''	)
						run_beam_parser.add_argument('''dataset'''     ,type=lowercase_     ,help='''Name of the dataset to download'''	)
						run_beam_parser.add_argument('''--name'''     ,type=lowercase_     ,default=lowercase_     ,help='''Dataset config name'''	)
						run_beam_parser.add_argument(
						    '''--cache_dir'''     ,type=lowercase_     ,default=lowercase_     ,help='''Cache directory where the datasets are stored'''     ,)
						run_beam_parser.add_argument(
						    '''--beam_pipeline_options'''     ,type=lowercase_     ,default=''''''     ,help='''Beam pipeline options, separated by commas. Example:: `--beam_pipeline_options=job_name=my-job,project=my-project`'''     ,)
						run_beam_parser.add_argument(
						    '''--data_dir'''     ,type=lowercase_     ,default=lowercase_     ,help='''Can be used to specify a manual directory to get the files from'''     ,)
						run_beam_parser.add_argument('''--all_configs'''     ,action='''store_true'''     ,help='''Test all dataset configurations'''	)
						run_beam_parser.add_argument('''--save_info'''     ,action='''store_true'''     ,help='''Save the dataset infos file'''	)
						run_beam_parser.add_argument(
						    '''--ignore_verifications'''     ,action='''store_true'''     ,help='''Run the test without checksums and splits checks'''	)
						run_beam_parser.add_argument('''--force_redownload'''     ,action='''store_true'''     ,help='''Force dataset redownload'''	)
						# aliases
						run_beam_parser.add_argument('''--save_infos'''     ,action='''store_true'''     ,help='''alias for save_info'''	)
						run_beam_parser.set_defaults(func=lowercase_	)







					def __init__( self    :       Optional[Any]     ,lowercase_    :       str     ,lowercase_    :       str     ,lowercase_    :       str     ,lowercase_    :       str     ,lowercase_    :       str     ,lowercase_    :       bool     ,lowercase_    :       bool     ,lowercase_    :       bool     ,lowercase_    :       bool     ,**lowercase_    :       List[str]     ,):
						lowerCAmelCase__			:		List[str]						       =			dataset
						lowerCAmelCase__			:		Optional[Any]						       =			name
						lowerCAmelCase__			:		Union[str, Any]						       =			cache_dir
						lowerCAmelCase__			:		Tuple						       =			beam_pipeline_options
						lowerCAmelCase__			:		str						       =			data_dir
						lowerCAmelCase__			:		Any						       =			all_configs
						lowerCAmelCase__			:		Optional[int]						       =			save_infos
						lowerCAmelCase__			:		str						       =			ignore_verifications
						lowerCAmelCase__			:		int						       =			force_redownload
						lowerCAmelCase__			:		Union[str, Any]						       =			config_kwargs







					def  __lowerCAmelCase				( self    :       int	):
						import apache_beam as beam

						if self._name is not None and self._all_configs:
							print('''Both parameters `name` and `all_configs` can\'t be used at once.'''	)
							exit(1	)
						lowerCAmelCase__       ,lowerCAmelCase__			:		Union[str, Any]						       =			self._dataset, self._name
						lowerCAmelCase__			:		Union[str, Any]						       =			dataset_module_factory(lowercase_	)
						lowerCAmelCase__			:		List[str]						       =			import_main_class(dataset_module.module_path	)
						lowerCAmelCase__			:		List[DatasetBuilder]						       =			[]
						if self._beam_pipeline_options:
							lowerCAmelCase__			:		Union[str, Any]						       =			beam.options.pipeline_options.PipelineOptions(
							    flags=[F'--{opt.strip()}' for opt in self._beam_pipeline_options.split(''','''	) if opt]	)
						else:
							lowerCAmelCase__			:		Dict						       =			None
						if self._all_configs and len(builder_cls.BUILDER_CONFIGS	) > 0:
							for builder_config in builder_cls.BUILDER_CONFIGS:
								builders.append(
								    builder_cls(
								        config_name=builder_config.name     ,data_dir=self._data_dir     ,hash=dataset_module.hash     ,beam_options=lowercase_     ,cache_dir=self._cache_dir     ,base_path=dataset_module.builder_kwargs.get('''base_path'''	)     ,)	)
						else:
							builders.append(
							    builder_cls(
							        config_name=lowercase_     ,data_dir=self._data_dir     ,beam_options=lowercase_     ,cache_dir=self._cache_dir     ,base_path=dataset_module.builder_kwargs.get('''base_path'''	)     ,**self._config_kwargs     ,)	)

						for builder in builders:
							builder.download_and_prepare(
							    download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
							    if not self._force_redownload
							    else DownloadMode.FORCE_REDOWNLOAD     ,download_config=DownloadConfig(cache_dir=config.DOWNLOADED_DATASETS_PATH	)     ,verification_mode=VerificationMode.NO_CHECKS
							    if self._ignore_verifications
							    else VerificationMode.ALL_CHECKS     ,try_from_hf_gcs=lowercase_     ,)
							if self._save_infos:
								builder._save_infos()

						print('''Apache beam run successful.'''	)

						# If save_infos=True, the dataset infos file is created next to the loaded module file.
						# Let's move it to the original directory of the dataset script, to allow the user to
						# upload them on S3 at the same time afterwards.
						if self._save_infos:
							lowerCAmelCase__			:		int						       =			os.path.join(builder_cls.get_imported_module_dir()     ,config.DATASETDICT_INFOS_FILENAME	)

							lowerCAmelCase__			:		List[str]						       =			Path(lowercase_	).name + '''.py'''

							lowerCAmelCase__			:		int						       =			os.path.join(lowercase_     ,lowercase_	)
							if os.path.isfile(lowercase_	):
								lowerCAmelCase__			:		List[Any]						       =			os.path.dirname(lowercase_	)
							elif os.path.isfile(lowercase_	):
								lowerCAmelCase__			:		Union[str, Any]						       =			path
							else:  # in case of a remote dataset
								print(F'Dataset Infos file saved at {dataset_infos_path}'	)
								exit(1	)

							# Move datasetinfo back to the user
							lowerCAmelCase__			:		Optional[int]						       =			os.path.join(lowercase_     ,config.DATASETDICT_INFOS_FILENAME	)
							copyfile(lowercase_     ,lowercase_	)
							print(F'Dataset Infos file saved at {user_dataset_infos_path}'	)







"""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        SCREAMING_SNAKE_CASE      (	unittest.TestCase ):






					"""simple docstring"""



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







					def  __lowerCAmelCase				( self    :       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    :       Union[str, Any]     ,lowercase_    :       List[str]=False     ,lowercase_    :       Any=False     ,lowercase_    :       Dict=False	):
						assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

						if equal_resolution:
							lowerCAmelCase__			:		int						       =			[]
							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:
							lowerCAmelCase__			:		str						       =			[]
							for i in range(self.batch_size	):
								lowerCAmelCase__       ,lowerCAmelCase__			:		Any						       =			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
							lowerCAmelCase__			:		Optional[int]						       =			[Image.fromarray(np.moveaxis(lowercase_     ,0     ,-1	)	) for x in image_inputs]

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

						return image_inputs







@require_torch
@require_vision
class        SCREAMING_SNAKE_CASE      (	a_					,							unittest.TestCase ):






					"""simple docstring"""



					lowercase__       				= SwinaSRImageProcessor if is_vision_available() else None







					def  __lowerCAmelCase				( self    :       Dict	):
						lowerCAmelCase__			:		Dict						       =			SwinaSRImageProcessingTester(self	)







					@property
					def  __lowerCAmelCase				( self    :       str	):
						return self.image_processor_tester.prepare_image_processor_dict()







					def  __lowerCAmelCase				( self    :       List[str]	):
						lowerCAmelCase__			:		Any						       =			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    :       Dict	):
						pass







					def  __lowerCAmelCase				( self    :       List[str]     ,lowercase_    :       Optional[Any]	):
						lowerCAmelCase__       ,lowerCAmelCase__			:		Tuple						       =			get_image_size(lowercase_	)
						lowerCAmelCase__			:		Dict						       =			self.image_processor_tester.pad_size

						lowerCAmelCase__			:		List[Any]						       =			(old_height // size + 1) * size - old_height
						lowerCAmelCase__			:		Union[str, Any]						       =			(old_width // size + 1) * size - old_width
						return old_height + pad_height, old_width + pad_width







					def  __lowerCAmelCase				( self    :       List[Any]	):
						# Initialize image_processor
						lowerCAmelCase__			:		str						       =			self.image_processing_class(**self.image_processor_dict	)
						# create random PIL images
						lowerCAmelCase__			:		int						       =			self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_	)
						for image in image_inputs:
							self.assertIsInstance(lowercase_     ,Image.Image	)

						# Test not batched input
						lowerCAmelCase__			:		List[str]						       =			image_processor(image_inputs[0]     ,return_tensors='''pt'''	).pixel_values
						lowerCAmelCase__       ,lowerCAmelCase__			:		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
						lowerCAmelCase__			:		str						       =			self.image_processing_class(**self.image_processor_dict	)
						# create random numpy tensors
						lowerCAmelCase__			:		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
						lowerCAmelCase__			:		Optional[Any]						       =			image_processor(image_inputs[0]     ,return_tensors='''pt'''	).pixel_values
						lowerCAmelCase__       ,lowerCAmelCase__			:		List[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    :       Dict	):
						# Initialize image_processor
						lowerCAmelCase__			:		Tuple						       =			self.image_processing_class(**self.image_processor_dict	)
						# create random PyTorch tensors
						lowerCAmelCase__			:		Union[str, Any]						       =			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
						lowerCAmelCase__			:		Tuple						       =			image_processor(image_inputs[0]     ,return_tensors='''pt'''	).pixel_values
						lowerCAmelCase__       ,lowerCAmelCase__			:		int						       =			self.calculate_expected_size(image_inputs[0]	)
						self.assertEqual(
						    encoded_images.shape     ,(
						        1,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )     ,)




