
"""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, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import MobileViTImageProcessor
class   _UpperCAmelCase				(		unittest.TestCase				):




      def __init__(				self ,							lowercase_ ,							lowercase_=7 ,							lowercase_=3 ,							lowercase_=1_8 ,							lowercase_=3_0 ,							lowercase_=4_0_0 ,							lowercase_=True ,							lowercase_=None ,							lowercase_=True ,							lowercase_=None ,							lowercase_=True ,							)    ->       str:
          UpperCAmelCase          =       size if size is not None else {'shortest_edge': 2_0}
          UpperCAmelCase          =       crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
          UpperCAmelCase          =       parent
          UpperCAmelCase          =       batch_size
          UpperCAmelCase          =       num_channels
          UpperCAmelCase          =       image_size
          UpperCAmelCase          =       min_resolution
          UpperCAmelCase          =       max_resolution
          UpperCAmelCase          =       do_resize
          UpperCAmelCase          =       size
          UpperCAmelCase          =       do_center_crop
          UpperCAmelCase          =       crop_size
          UpperCAmelCase          =       do_flip_channel_order




      def      a_   (				self				)    ->       Tuple:
          return {
              "do_resize": self.do_resize,
              "size": self.size,
              "do_center_crop": self.do_center_crop,
              "crop_size": self.crop_size,
              "do_flip_channel_order": self.do_flip_channel_order,
          }

@require_torch
@require_vision
class   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_  ,      unittest.TestCase				):
      __SCREAMING_SNAKE_CASE :				Any				   =						MobileViTImageProcessor if is_vision_available() else None




      def      a_   (				self				)    ->       List[str]:
          UpperCAmelCase          =       MobileViTImageProcessingTester(self				)




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




      def      a_   (				self				)    ->       str:
          UpperCAmelCase          =       self.image_processing_class(**self.image_processor_dict				)
          self.assertTrue(hasattr(lowercase_ ,							'do_resize'				)				)
          self.assertTrue(hasattr(lowercase_ ,							'size'				)				)
          self.assertTrue(hasattr(lowercase_ ,							'do_center_crop'				)				)
          self.assertTrue(hasattr(lowercase_ ,							'center_crop'				)				)
          self.assertTrue(hasattr(lowercase_ ,							'do_flip_channel_order'				)				)




      def      a_   (				self				)    ->       List[Any]:
          UpperCAmelCase          =       self.image_processing_class.from_dict(self.image_processor_dict				)
          self.assertEqual(image_processor.size ,							{'shortest_edge': 2_0}				)
          self.assertEqual(image_processor.crop_size ,							{'height': 1_8, 'width': 1_8}				)

          UpperCAmelCase          =       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      a_   (				self				)    ->       Any:
          pass




      def      a_   (				self				)    ->       Optional[Any]:
          # Initialize image_processing
          UpperCAmelCase          =       self.image_processing_class(**self.image_processor_dict				)
          # create random PIL images
          UpperCAmelCase          =       prepare_image_inputs(self.image_processor_tester ,							equal_resolution=lowercase_				)
          for image in image_inputs:
              self.assertIsInstance(lowercase_ ,							Image.Image				)

          # Test not batched input
          UpperCAmelCase          =       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
          UpperCAmelCase          =       image_processing(lowercase_ ,							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      a_   (				self				)    ->       Any:
          # Initialize image_processing
          UpperCAmelCase          =       self.image_processing_class(**self.image_processor_dict				)
          # create random numpy tensors
          UpperCAmelCase          =       prepare_image_inputs(self.image_processor_tester ,							equal_resolution=lowercase_ ,							numpify=lowercase_				)
          for image in image_inputs:
              self.assertIsInstance(lowercase_ ,							np.ndarray				)

          # Test not batched input
          UpperCAmelCase          =       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
          UpperCAmelCase          =       image_processing(lowercase_ ,							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      a_   (				self				)    ->       Optional[Any]:
          # Initialize image_processing
          UpperCAmelCase          =       self.image_processing_class(**self.image_processor_dict				)
          # create random PyTorch tensors
          UpperCAmelCase          =       prepare_image_inputs(self.image_processor_tester ,							equal_resolution=lowercase_ ,							torchify=lowercase_				)
          for image in image_inputs:
              self.assertIsInstance(lowercase_ ,							torch.Tensor				)

          # Test not batched input
          UpperCAmelCase          =       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
          UpperCAmelCase          =       image_processing(lowercase_ ,							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'],
              ) ,							)





"""simple docstring"""

import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union

import torch

from ..utils import BaseOutput


SCREAMING_SNAKE_CASE_            =    '''scheduler_config.json'''
class   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_				):
      __SCREAMING_SNAKE_CASE :				List[Any]				   =						1
      __SCREAMING_SNAKE_CASE :				Dict				   =						2
      __SCREAMING_SNAKE_CASE :				List[Any]				   =						3
      __SCREAMING_SNAKE_CASE :				Any				   =						4
      __SCREAMING_SNAKE_CASE :				Any				   =						5
      __SCREAMING_SNAKE_CASE :				Union[str, Any]				   =						6
      __SCREAMING_SNAKE_CASE :				str				   =						7
      __SCREAMING_SNAKE_CASE :				Any				   =						8
      __SCREAMING_SNAKE_CASE :				Tuple				   =						9
      __SCREAMING_SNAKE_CASE :				int				   =						1_0
      __SCREAMING_SNAKE_CASE :				Union[str, Any]				   =						1_1
      __SCREAMING_SNAKE_CASE :				Union[str, Any]				   =						1_2
      __SCREAMING_SNAKE_CASE :				Dict				   =						1_3
      __SCREAMING_SNAKE_CASE :				Optional[Any]				   =						1_4

@dataclass
class   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_				):
      __SCREAMING_SNAKE_CASE :				torch.FloatTensor

class   _UpperCAmelCase				:
      __SCREAMING_SNAKE_CASE :				str				   =						SCHEDULER_CONFIG_NAME
      __SCREAMING_SNAKE_CASE :				Dict				   =						[]
      __SCREAMING_SNAKE_CASE :				Union[str, Any]				   =						True




      @classmethod
      def      a_   (				cls ,							lowercase_ = None ,							lowercase_ = None ,							lowercase_=False ,							**lowercase_ ,							)    ->       List[Any]:
          UpperCAmelCase      ,				UpperCAmelCase      ,				UpperCAmelCase          =       cls.load_config(
              pretrained_model_name_or_path=lowercase_ ,							subfolder=lowercase_ ,							return_unused_kwargs=lowercase_ ,							return_commit_hash=lowercase_ ,							**lowercase_ ,							)
          return cls.from_config(lowercase_ ,							return_unused_kwargs=lowercase_ ,							**lowercase_				)




      def      a_   (				self ,							lowercase_ ,							lowercase_ = False ,							**lowercase_				)    ->       str:
          self.save_config(save_directory=lowercase_ ,							push_to_hub=lowercase_ ,							**lowercase_				)




      @property
      def      a_   (				self				)    ->       Union[str, Any]:
          return self._get_compatibles()




      @classmethod
      def      a_   (				cls				)    ->       Any:
          UpperCAmelCase          =       list(set([cls.__name__] + cls._compatibles				)				)
          UpperCAmelCase          =       importlib.import_module(__name__.split('.'				)[0]				)
          UpperCAmelCase          =       [
              getattr(lowercase_ ,							lowercase_				) for c in compatible_classes_str if hasattr(lowercase_ ,							lowercase_				)
          ]
          return compatible_classes



