


"""simple docstring"""






import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor


class 			__lowerCamelCase     (    unittest.TestCase ):

     '''simple docstring'''


     def __init__(					self			, __UpperCAmelCase			, __UpperCAmelCase = True			, __UpperCAmelCase = None			, __UpperCAmelCase = 32			, __UpperCAmelCase = True			, __UpperCAmelCase = 1 / 255			, __UpperCAmelCase = True			, __UpperCAmelCase = True			, __UpperCAmelCase = [0.48145466, 0.4578275, 0.40821073]			, __UpperCAmelCase = [0.26862954, 0.26130258, 0.27577711]			, __UpperCAmelCase = True			, __UpperCAmelCase=7			, __UpperCAmelCase=30			, __UpperCAmelCase=400			, __UpperCAmelCase=3			, )				->							List[str]:
          _a					= parent
          _a					= do_resize
          _a					= size if size is not None else {'''shortest_edge''': 288}
          _a					= size_divisor
          _a					= do_rescale
          _a					= rescale_factor
          _a					= do_normalize
          _a					= do_center_crop
          _a					= image_mean
          _a					= image_std
          _a					= do_pad
          _a					= batch_size
          _a					= num_channels
          _a					= min_resolution
          _a					= max_resolution


     def 							_UpperCAmelCase					(					self							)				->							List[Any]:
          return {
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_normalize": self.do_normalize,
              "do_resize": self.do_resize,
              "size": self.size,
              "size_divisor": self.size_divisor,
          }





     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase			, __UpperCAmelCase=False							)				->							int:
          if not batched:
               _a					= self.size['''shortest_edge''']
               _a					= image_inputs[0]
               if isinstance(SCREAMING_SNAKE_CASE__			, Image.Image							):
                    _a		,					_a					= image.size
               else:
                    _a		,					_a					= image.shape[1], image.shape[2]
               _a					= size / min(SCREAMING_SNAKE_CASE__			, SCREAMING_SNAKE_CASE__							)
               if h < w:
                    _a		,					_a					= size, scale * w
               else:
                    _a		,					_a					= scale * h, size

               _a					= int((1333 / 800) * size							)
               if max(SCREAMING_SNAKE_CASE__			, SCREAMING_SNAKE_CASE__							) > max_size:
                    _a					= max_size / max(SCREAMING_SNAKE_CASE__			, SCREAMING_SNAKE_CASE__							)
                    _a					= newh * scale
                    _a					= neww * scale

               _a		,					_a					= int(newh + 0.5							), int(neww + 0.5							)
               _a		,					_a					= (
                   newh // self.size_divisor * self.size_divisor,
                   neww // self.size_divisor * self.size_divisor,
               )

          else:
               _a					= []
               for image in image_inputs:
                    _a		,					_a					= self.get_expected_values([image]							)
                    expected_values.append((expected_height, expected_width)							)
               _a					= max(SCREAMING_SNAKE_CASE__			, key=lambda __UpperCAmelCase							: item[0]							)[0]
               _a					= max(SCREAMING_SNAKE_CASE__			, key=lambda __UpperCAmelCase							: item[1]							)[1]

          return expected_height, expected_width







@require_torch
@require_vision
class 			__lowerCamelCase     (    __lowercase     ,    unittest.TestCase ):

     '''simple docstring'''
     A_				:		Union[str, Any]			=						BridgeTowerImageProcessor if is_vision_available() else None


     def 							_UpperCAmelCase					(					self							)				->							Union[str, Any]:
          _a					= BridgeTowerImageProcessingTester(self							)


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


     def 							_UpperCAmelCase					(					self							)				->							int:
          _a					= self.image_processing_class(**self.image_processor_dict							)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__			, '''image_mean'''							)							)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__			, '''image_std'''							)							)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__			, '''do_normalize'''							)							)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__			, '''do_resize'''							)							)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__			, '''size'''							)							)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__			, '''size_divisor'''							)							)


     def 							_UpperCAmelCase					(					self							)				->							Optional[int]:
          pass


     def 							_UpperCAmelCase					(					self							)				->							str:
          # Initialize image processor
          _a					= self.image_processing_class(**self.image_processor_dict							)
          # create random PIL images
          _a					= 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
          _a					= image_processing(image_inputs[0]			, return_tensors='''pt'''							).pixel_values

          _a		,					_a					= self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__							)
          self.assertEqual(
              encoded_images.shape			, (1, self.image_processor_tester.num_channels, expected_height, expected_width)			, )

          # Test batched
          _a					= image_processing(SCREAMING_SNAKE_CASE__			, return_tensors='''pt'''							).pixel_values

          _a		,					_a					= self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__			, batched=SCREAMING_SNAKE_CASE__							)
          self.assertEqual(
              encoded_images.shape			, (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )			, )


     def 							_UpperCAmelCase					(					self							)				->							List[str]:
          # Initialize image processor
          _a					= self.image_processing_class(**self.image_processor_dict							)
          # create random numpy tensors
          _a					= 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
          _a					= image_processing(image_inputs[0]			, return_tensors='''pt'''							).pixel_values

          _a		,					_a					= self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__							)
          self.assertEqual(
              encoded_images.shape			, (1, self.image_processor_tester.num_channels, expected_height, expected_width)			, )

          # Test batched
          _a					= image_processing(SCREAMING_SNAKE_CASE__			, return_tensors='''pt'''							).pixel_values

          _a		,					_a					= self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__			, batched=SCREAMING_SNAKE_CASE__							)
          self.assertEqual(
              encoded_images.shape			, (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )			, )





     def 							_UpperCAmelCase					(					self							)				->							str:
          # Initialize image processor
          _a					= self.image_processing_class(**self.image_processor_dict							)
          # create random PyTorch tensors
          _a					= 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
          _a					= image_processing(image_inputs[0]			, return_tensors='''pt'''							).pixel_values

          _a		,					_a					= self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__							)
          self.assertEqual(
              encoded_images.shape			, (1, self.image_processor_tester.num_channels, expected_height, expected_width)			, )

          # Test batched
          _a					= image_processing(SCREAMING_SNAKE_CASE__			, return_tensors='''pt'''							).pixel_values

          _a		,					_a					= self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__			, batched=SCREAMING_SNAKE_CASE__							)
          self.assertEqual(
              encoded_images.shape			, (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )			, )

from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader

class    lowerCAmelCase__     (	__lowercase			):






		def __init__(		self		:							Tuple							,			SCREAMING_SNAKE_CASE__		:							NestedDataStructureLike[PathLike]							,			SCREAMING_SNAKE_CASE__		:							Optional[NamedSplit] = None							,			SCREAMING_SNAKE_CASE__		:							Optional[Features] = None							,			SCREAMING_SNAKE_CASE__		:							str = None							,			SCREAMING_SNAKE_CASE__		:							bool = False							,			SCREAMING_SNAKE_CASE__		:							bool = False							,			SCREAMING_SNAKE_CASE__		:							Optional[int] = None							,			**SCREAMING_SNAKE_CASE__		:							str							,			)     ->							Union[str, Any]:
						super().__init__(
						    SCREAMING_SNAKE_CASE__							,			split=SCREAMING_SNAKE_CASE__							,			features=SCREAMING_SNAKE_CASE__							,			cache_dir=SCREAMING_SNAKE_CASE__							,			keep_in_memory=SCREAMING_SNAKE_CASE__							,			streaming=SCREAMING_SNAKE_CASE__							,			num_proc=SCREAMING_SNAKE_CASE__							,			**SCREAMING_SNAKE_CASE__							,			)
						__lowerCamelCase    					=	path_or_paths if isinstance(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  ) else {self.split: path_or_paths}
						__lowerCamelCase    					=	Text(
						    cache_dir=SCREAMING_SNAKE_CASE__							,			data_files=SCREAMING_SNAKE_CASE__							,			features=SCREAMING_SNAKE_CASE__							,			**SCREAMING_SNAKE_CASE__							,			)







		def  __A       (		self		:							int  )     ->							Dict:
						# Build iterable dataset
						if self.streaming:
										__lowerCamelCase    					=	self.builder.as_streaming_dataset(split=self.split  )
						# Build regular (map-style) dataset
						else:
										__lowerCamelCase    					=	None
										__lowerCamelCase    					=	None
										__lowerCamelCase    					=	None
										__lowerCamelCase    					=	None

										self.builder.download_and_prepare(
										    download_config=SCREAMING_SNAKE_CASE__							,			download_mode=SCREAMING_SNAKE_CASE__							,			verification_mode=SCREAMING_SNAKE_CASE__							,			base_path=SCREAMING_SNAKE_CASE__							,			num_proc=self.num_proc							,			)
										__lowerCamelCase    					=	self.builder.as_dataset(
										    split=self.split							,			verification_mode=SCREAMING_SNAKE_CASE__							,			in_memory=self.keep_in_memory  )
						return dataset



