

import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(	self							:       Optional[Any]					,					a							:       str					,					a							:       Any=sys.maxsize		)							->				str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					str    =				"bilinear"
				SCREAMING_SNAKE_CASE      :					List[str]    =				max_size
				SCREAMING_SNAKE_CASE      :					int    =				short_edge_length






			def __call__(	self							:       List[Any]					,					a							:       str		)							->				int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					int    =				[]
				for img in imgs:
					SCREAMING_SNAKE_CASE   ,SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				img.shape[:2]
					# later: provide list and randomly choose index for resize
					SCREAMING_SNAKE_CASE      :					str    =				np.random.randint(self.short_edge_length[0]					,					self.short_edge_length[1] + 1		)
					if size == 0:
						return img
					SCREAMING_SNAKE_CASE      :					str    =				size * 1.0 / min(a					,					a		)
					if h < w:
						SCREAMING_SNAKE_CASE   ,SCREAMING_SNAKE_CASE      :					str    =				size, scale * w
					else:
						SCREAMING_SNAKE_CASE   ,SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				scale * h, size
					if max(a					,					a		) > self.max_size:
						SCREAMING_SNAKE_CASE      :					Any    =				self.max_size * 1.0 / max(a					,					a		)
						SCREAMING_SNAKE_CASE      :					Optional[Any]    =				newh * scale
						SCREAMING_SNAKE_CASE      :					Any    =				neww * scale
					SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				int(neww + 0.5		)
					SCREAMING_SNAKE_CASE      :					Dict    =				int(newh + 0.5		)

					if img.dtype == np.uinta:
						SCREAMING_SNAKE_CASE      :					Dict    =				Image.fromarray(a		)
						SCREAMING_SNAKE_CASE      :					Dict    =				pil_image.resize((neww, newh)					,					PILImageResampling.BILINEAR		)
						SCREAMING_SNAKE_CASE      :					Tuple    =				np.asarray(a		)
					else:
						SCREAMING_SNAKE_CASE      :					List[str]    =				img.permute(2					,					0					,					1		).unsqueeze(0		)  # 3, 0, 1)  # hw(c) -> nchw
						SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				nn.functional.interpolate(
						    a					,					(newh, neww)					,					mode=self.interp_method					,					align_corners=a		).squeeze(0		)
					img_augs.append(a		)

				return img_augs






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(	self							:       List[str]					,					a							:       int		)							->				Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					int    =				ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]					,					cfg.INPUT.MAX_SIZE_TEST		)
				SCREAMING_SNAKE_CASE      :					int    =				cfg.INPUT.FORMAT
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				cfg.SIZE_DIVISIBILITY
				SCREAMING_SNAKE_CASE      :					str    =				cfg.PAD_VALUE
				SCREAMING_SNAKE_CASE      :					int    =				cfg.INPUT.MAX_SIZE_TEST
				SCREAMING_SNAKE_CASE      :					Any    =				cfg.MODEL.DEVICE
				SCREAMING_SNAKE_CASE      :					int    =				torch.tensor(cfg.MODEL.PIXEL_STD		).to(self.device		).view(len(cfg.MODEL.PIXEL_STD		)					,					1					,					1		)
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				torch.tensor(cfg.MODEL.PIXEL_MEAN		).to(self.device		).view(len(cfg.MODEL.PIXEL_STD		)					,					1					,					1		)
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				lambda a		: (x - self.pixel_mean) / self.pixel_std
			def 							__UpperCamelCase       (	self							:       Tuple					,					a							:       Tuple		)							->				str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					List[str]    =				tuple(max(a		) for s in zip(*[img.shape for img in images]		)		)
				SCREAMING_SNAKE_CASE      :					str    =				[im.shape[-2:] for im in images]
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				[
				    nn.functional.pad(
				        a					,					[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]					,					value=self.pad_value					,					)
				    for size, im in zip(a					,					a		)
				]

				return torch.stack(a		), torch.tensor(a		)






			def __call__(	self							:       Optional[Any]					,					a							:       List[Any]					,					a							:       Dict=False		)							->				Optional[Any]:

				"""simple docstring"""

				with torch.no_grad():
					if not isinstance(a					,					a		):
						SCREAMING_SNAKE_CASE      :					int    =				[images]
					if single_image:
						assert len(a		) == 1
					for i in range(len(a		)		):
						if isinstance(images[i]					,					torch.Tensor		):
							images.insert(a					,					images.pop(a		).to(self.device		).float()		)
						elif not isinstance(images[i]					,					torch.Tensor		):
							images.insert(
							    a					,					torch.as_tensor(img_tensorize(images.pop(a		)					,					input_format=self.input_format		)		)
							    .to(self.device		)
							    .float()					,					)
            # resize smallest edge
					SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				torch.tensor([im.shape[:2] for im in images]		)
					SCREAMING_SNAKE_CASE      :					Optional[int]    =				self.aug(a		)
					# transpose images and convert to torch tensors
					# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
					# now normalize before pad to avoid useless arithmetic
					SCREAMING_SNAKE_CASE      :					Optional[Any]    =				[self.normalizer(a		) for x in images]
					# now pad them to do the following operations
					SCREAMING_SNAKE_CASE   ,SCREAMING_SNAKE_CASE      :					Any    =				self.pad(a		)
					# Normalize

					if self.size_divisibility > 0:
						raise NotImplementedError()
					# pad
					SCREAMING_SNAKE_CASE      :					Tuple    =				torch.true_divide(a					,					a		)
					if single_image:
						return images[0], sizes[0], scales_yx[0]
					else:
						return images, sizes, scales_yx

def        lowerCamelCase__       (    _a							,	_a):
	boxes[:, 0::2] *= scale_yx[:, 1]
	boxes[:, 1::2] *= scale_yx[:, 0]
	return boxes


def        lowerCamelCase__       (    _a							,	_a):
	assert torch.isfinite(_a).all(), "Box tensor contains infinite or NaN!"
	SCREAMING_SNAKE_CASE   ,SCREAMING_SNAKE_CASE      :					Tuple    =				box_size
	tensor[:, 0].clamp_(min=0							,	max=_a)
	tensor[:, 1].clamp_(min=0							,	max=_a)
	tensor[:, 2].clamp_(min=0							,	max=_a)
	tensor[:, 3].clamp_(min=0							,	max=_a)

import os
from tempfile import TemporaryDirectory
from unittest import TestCase

import pytest
from absl.testing import parameterized

from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path


__lowerCAmelCase			:     Any           =   [
    {'dataset': 'wikipedia', 'config_name': '20220301.de'},
    {'dataset': 'wikipedia', 'config_name': '20220301.en'},
    {'dataset': 'wikipedia', 'config_name': '20220301.fr'},
    {'dataset': 'wikipedia', 'config_name': '20220301.frr'},
    {'dataset': 'wikipedia', 'config_name': '20220301.it'},
    {'dataset': 'wikipedia', 'config_name': '20220301.simple'},
    {'dataset': 'snli', 'config_name': 'plain_text'},
    {'dataset': 'eli5', 'config_name': 'LFQA_reddit'},
    {'dataset': 'wiki40b', 'config_name': 'en'},
    {'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.nq.compressed'},
    {'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.nq.no_index'},
    {'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.multiset.no_index'},
    {'dataset': 'natural_questions', 'config_name': 'default'},
]

def a__    (       A_=True					):






  '''simple docstring'''





  if with_config:
    return [
        {
            "testcase_name": d["dataset"] + "/" + d["config_name"],
            "dataset": d["dataset"],
            "config_name": d["config_name"],
        }
        for d in DATASETS_ON_HF_GCP
    ]
  else:
    return [
        {"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
    ]




@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=_A     )     )
class        UpperCAmelCase_			( _A     ):


  '''simple docstring'''







  a__           =		None
  a__           =		None
  def 							_lowercase							( self			: List[Any]			,							UpperCamelCase__			: int			,							UpperCamelCase__			: Union[str, Any]    )			->	Tuple:







    """simple docstring"""






    with TemporaryDirectory() as tmp_dir:
      __magic_name__				     =       dataset_module_factory(UpperCamelCase__			,							cache_dir=UpperCamelCase__    )

      __magic_name__				     =       import_main_class(dataset_module.module_path			,							dataset=UpperCamelCase__    )

      __magic_name__				     =       builder_cls(
          cache_dir=UpperCamelCase__			,							config_name=UpperCamelCase__			,							hash=dataset_module.hash			,							)

      __magic_name__				     =       """/""".join(
          [
              HF_GCP_BASE_URL,
              builder_instance._relative_data_dir(with_hash=UpperCamelCase__    ).replace(os.sep			,							"""/"""    ),
              config.DATASET_INFO_FILENAME,
          ]    )
      __magic_name__				     =       cached_path(UpperCamelCase__			,							cache_dir=UpperCamelCase__    )
      self.assertTrue(os.path.exists(UpperCamelCase__    )    )







@pytest.mark.integration
def a__    (       A_					):






  '''simple docstring'''





  __magic_name__				     =       tmp_path_factory.mktemp("""test_hf_gcp"""					) / """test_wikipedia_simple"""
  __magic_name__				     =       dataset_module_factory("""wikipedia""",     cache_dir=A_					)
  __magic_name__				     =       import_main_class(dataset_module.module_path					)
  __magic_name__				     =       builder_cls(
      cache_dir=A_,     config_name="""20220301.frr""",     hash=dataset_module.hash,     )
  # use the HF cloud storage, not the original download_and_prepare that uses apache-beam
  __magic_name__				     =       None
  builder_instance.download_and_prepare()
  __magic_name__				     =       builder_instance.as_dataset()
  assert ds





@pytest.mark.integration
def a__    (       A_					):






  '''simple docstring'''





  __magic_name__				     =       dataset_module_factory("""wikipedia""",     cache_dir=A_					)
  __magic_name__				     =       import_main_class(dataset_module.module_path,     dataset=A_					)
  __magic_name__				     =       builder_cls(
      cache_dir=A_,     config_name="""20220301.frr""",     hash=dataset_module.hash,     )
  __magic_name__				     =       builder_instance.as_streaming_dataset()
  assert ds
  assert isinstance(A_,     A_					)
  assert "train" in ds
  assert isinstance(ds["""train"""],     A_					)
  assert next(iter(ds["""train"""]					)					)

