



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 		_lowercase		(							unittest.TestCase						):

   def __init__(   self      :							Dict				, __lowerCAmelCase      :							List[str]				, __lowerCAmelCase      :							Optional[Any]=7				, __lowerCAmelCase      :							int=3				, __lowerCAmelCase      :							Optional[int]=18				, __lowerCAmelCase      :							List[str]=30				, __lowerCAmelCase      :							List[Any]=400				, __lowerCAmelCase      :							Any=True				, __lowerCAmelCase      :							str=None				, __lowerCAmelCase      :							Union[str, Any]=True				, __lowerCAmelCase      :							Tuple=None				, )							->					Any:


    """simple docstring"""






    a							=		size if size is not None else {"shortest_edge": 20}
    a							=		crop_size if crop_size is not None else {"height": 18, "width": 18}
    a							=		parent
    a							=		batch_size
    a							=		num_channels
    a							=		image_size
    a							=		min_resolution
    a							=		max_resolution
    a							=		do_resize
    a							=		size
    a							=		do_center_crop
    a							=		crop_size




   def 		A				(   self      :							Optional[int]				)							->					Any:


    """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 		_lowercase		(							UpperCAmelCase__, unittest.TestCase						):
   _UpperCAmelCase  						=      MobileNetVaImageProcessor if is_vision_available() else None

   def 		A				(   self      :							Any				)							->					Optional[int]:


    """simple docstring"""






    a							=		MobileNetVaImageProcessingTester(self				)

   @property
   def 		A				(   self      :							List[str]				)							->					List[str]:


    """simple docstring"""






    return self.image_processor_tester.prepare_image_processor_dict()

   def 		A				(   self      :							Optional[int]				)							->					Union[str, Any]:


    """simple docstring"""






    a							=		self.image_processing_class(**self.image_processor_dict				)
    self.assertTrue(hasattr(__lowerCAmelCase				, "do_resize"				)				)
    self.assertTrue(hasattr(__lowerCAmelCase				, "size"				)				)
    self.assertTrue(hasattr(__lowerCAmelCase				, "do_center_crop"				)				)
    self.assertTrue(hasattr(__lowerCAmelCase				, "center_crop"				)				)

   def 		A				(   self      :							List[str]				)							->					Dict:


    """simple docstring"""






    a							=		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}				)

    a							=		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 		A				(   self      :							Dict				)							->					Tuple:


    """simple docstring"""






    pass

   def 		A				(   self      :							int				)							->					Optional[int]:


    """simple docstring"""






    a							=		self.image_processing_class(**self.image_processor_dict				)
    # create random PIL images
    a							=		prepare_image_inputs(self.image_processor_tester				, equal_resolution=__lowerCAmelCase				)
    for image in image_inputs:
     self.assertIsInstance(__lowerCAmelCase				, Image.Image				)

    # Test not batched input
    a							=		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
    a							=		image_processing(__lowerCAmelCase				, 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      :							List[Any]				)							->					Dict:


    """simple docstring"""






    a							=		self.image_processing_class(**self.image_processor_dict				)
    # create random numpy tensors
    a							=		prepare_image_inputs(self.image_processor_tester				, equal_resolution=__lowerCAmelCase				, numpify=__lowerCAmelCase				)
    for image in image_inputs:
     self.assertIsInstance(__lowerCAmelCase				, np.ndarray				)

    # Test not batched input
    a							=		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
    a							=		image_processing(__lowerCAmelCase				, 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      :							Tuple				)							->					Tuple:


    """simple docstring"""






    a							=		self.image_processing_class(**self.image_processor_dict				)
    # create random PyTorch tensors
    a							=		prepare_image_inputs(self.image_processor_tester				, equal_resolution=__lowerCAmelCase				, torchify=__lowerCAmelCase				)
    for image in image_inputs:
     self.assertIsInstance(__lowerCAmelCase				, torch.Tensor				)

    # Test not batched input
    a							=		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
    a							=		image_processing(__lowerCAmelCase				, 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'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_A      :     Optional[Any]       	={
    '''configuration_realm''': ['''REALM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RealmConfig'''],
    '''tokenization_realm''': ['''RealmTokenizer'''],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A      :     str       	=['''RealmTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A      :     List[Any]       	=[
			    '''REALM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''RealmEmbedder''',
			    '''RealmForOpenQA''',
			    '''RealmKnowledgeAugEncoder''',
			    '''RealmPreTrainedModel''',
			    '''RealmReader''',
			    '''RealmScorer''',
			    '''load_tf_weights_in_realm''',
			]
			_A      :     Union[str, Any]       	=['''RealmRetriever''']


if TYPE_CHECKING:
			from .configuration_realm import REALM_PRETRAINED_CONFIG_ARCHIVE_MAP, RealmConfig
			from .tokenization_realm import RealmTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_realm import RealmTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_realm import (
						    REALM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    RealmEmbedder,
						    RealmForOpenQA,
						    RealmKnowledgeAugEncoder,
						    RealmPreTrainedModel,
						    RealmReader,
						    RealmScorer,
						    load_tf_weights_in_realm,
						)
						from .retrieval_realm import RealmRetriever


else:
			import sys

			_A      :     int       	=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

