


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 MobileNetVaImageProcessor

class      lowerCamelCase	(  unittest.TestCase     ):

       def __init__(			self	,						a__	,						a__=7	,						a__=3	,						a__=18	,						a__=30	,						a__=4_00	,						a__=True	,						a__=None	,						a__=True	,						a__=None	,						):






        '''simple docstring'''






        snake_case   : str	  =      size if size is not None else {'''shortest_edge''': 20}
        snake_case   : List[str]	  =      crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
        snake_case   : Optional[Any]	  =      parent
        snake_case   : Dict	  =      batch_size
        snake_case   : List[str]	  =      num_channels
        snake_case   : Union[str, Any]	  =      image_size
        snake_case   : Optional[Any]	  =      min_resolution
        snake_case   : Union[str, Any]	  =      max_resolution
        snake_case   : Optional[int]	  =      do_resize
        snake_case   : Optional[int]	  =      size
        snake_case   : Optional[Any]	  =      do_center_crop
        snake_case   : Optional[int]	  =      crop_size



       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
        }







@require_torch
@require_vision
class      lowerCamelCase	(  SCREAMING_SNAKE_CASE,			unittest.TestCase     ):
       _UpperCamelCase    		=      MobileNetVaImageProcessor if is_vision_available() else None

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : List[str]	  =      MobileNetVaImageProcessingTester(self					)

       @property
       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        return self.image_processor_tester.prepare_image_processor_dict()

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : Union[str, Any]	  =      self.image_processing_class(**self.image_processor_dict					)
        self.assertTrue(hasattr(a__	,						'''do_resize'''					)					)
        self.assertTrue(hasattr(a__	,						'''size'''					)					)
        self.assertTrue(hasattr(a__	,						'''do_center_crop'''					)					)
        self.assertTrue(hasattr(a__	,						'''center_crop'''					)					)

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : str	  =      self.image_processing_class.from_dict(self.image_processor_dict					)
        self.assertEqual(image_processor.size	,						{'''shortest_edge''': 20}					)
        self.assertEqual(image_processor.crop_size	,						{'''height''': 18, '''width''': 18}					)

        snake_case   : Dict	  =      self.image_processing_class.from_dict(self.image_processor_dict	,						size=42	,						crop_size=84					)
        self.assertEqual(image_processor.size	,						{'''shortest_edge''': 42}					)
        self.assertEqual(image_processor.crop_size	,						{'''height''': 84, '''width''': 84}					)

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        pass

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : Union[str, Any]	  =      self.image_processing_class(**self.image_processor_dict					)
        # create random PIL images
        snake_case   : Tuple	  =      prepare_image_inputs(self.image_processor_tester	,						equal_resolution=a__					)
        for image in image_inputs:
         self.assertIsInstance(a__	,						Image.Image					)

        # Test not batched input
        snake_case   : List[str]	  =      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
        snake_case   : List[Any]	  =      image_processing(a__	,						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 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : Optional[int]	  =      self.image_processing_class(**self.image_processor_dict					)
        # create random numpy tensors
        snake_case   : Tuple	  =      prepare_image_inputs(self.image_processor_tester	,						equal_resolution=a__	,						numpify=a__					)
        for image in image_inputs:
         self.assertIsInstance(a__	,						np.ndarray					)

        # Test not batched input
        snake_case   : Dict	  =      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
        snake_case   : List[Any]	  =      image_processing(a__	,						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 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : List[str]	  =      self.image_processing_class(**self.image_processor_dict					)
        # create random PyTorch tensors
        snake_case   : List[Any]	  =      prepare_image_inputs(self.image_processor_tester	,						equal_resolution=a__	,						torchify=a__					)
        for image in image_inputs:
         self.assertIsInstance(a__	,						torch.Tensor					)

        # Test not batched input
        snake_case   : List[str]	  =      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
        snake_case   : Any	  =      image_processing(a__	,						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'''],
            )	,						)










from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
  import torch

a				:							Union[str, Any]					=			logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
  from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):






  def    _UpperCAmelCase	(   cls       )		->str:


   '''simple docstring'''
   return cls






def    _UpperCAmelCase	(   __lowerCamelCase ,     __lowerCamelCase = None ,     __lowerCamelCase = None ,     __lowerCamelCase = None ,     __lowerCamelCase = None ,     )		->Union[str, Any]:


 '''simple docstring'''
 snake_case   : Union[str, Any]	  =      device
 snake_case   : Any	  =      shape[0]

 snake_case   : Any	  =      layout or torch.strided
 snake_case   : Union[str, Any]	  =      device or torch.device('''cpu'''       )

 if generator is not None:
  snake_case   : Any	  =      generator.device.type if not isinstance(__lowerCamelCase ,     __lowerCamelCase       ) else generator[0].device.type
  if gen_device_type != device.type and gen_device_type == "cpu":
   snake_case   : Optional[int]	  =      '''cpu'''
   if device != "mps":
    logger.info(
        f'''The passed generator was created on \'cpu\' even though a tensor on {device} was expected.'''
        f''' Tensors will be created on \'cpu\' and then moved to {device}. Note that one can probably'''
        f''' slighly speed up this function by passing a generator that was created on the {device} device.'''       )
  elif gen_device_type != device.type and gen_device_type == "cuda":
   raise ValueError(f'''Cannot generate a {device} tensor from a generator of type {gen_device_type}.'''       )

 if isinstance(__lowerCamelCase ,     __lowerCamelCase       ):
  snake_case   : Tuple	  =      (1,) + shape[1:]
  snake_case   : List[Any]	  =      [
      torch.randn(__lowerCamelCase ,     generator=generator[i] ,     device=__lowerCamelCase ,     dtype=__lowerCamelCase ,     layout=__lowerCamelCase       )
      for i in range(__lowerCamelCase       )
  ]
  snake_case   : int	  =      torch.cat(__lowerCamelCase ,     dim=0       ).to(__lowerCamelCase       )
 else:
  snake_case   : Union[str, Any]	  =      torch.randn(__lowerCamelCase ,     generator=__lowerCamelCase ,     device=__lowerCamelCase ,     dtype=__lowerCamelCase ,     layout=__lowerCamelCase       ).to(__lowerCamelCase       )

 return latents






def    _UpperCAmelCase	(   __lowerCamelCase       )		->Tuple:


 '''simple docstring'''
 if is_torch_version('''<''' ,     '''2.0.0'''       ) or not hasattr(__lowerCamelCase ,     '''_dynamo'''       ):
  return False
 return isinstance(__lowerCamelCase ,     torch._dynamo.eval_frame.OptimizedModule       )






