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 ViTImageProcessor

class 					_lowercase    (     unittest.TestCase			):






    '''simple docstring'''




    def __init__(    self       ,						lowerCamelCase__       ,						lowerCamelCase__=13       ,						lowerCamelCase__=3       ,						lowerCamelCase__=224       ,						lowerCamelCase__=30       ,						lowerCamelCase__=400       ,						lowerCamelCase__=True       ,						lowerCamelCase__=None       ,						lowerCamelCase__=True       ,						lowerCamelCase__=[0.5, 0.5, 0.5]       ,						lowerCamelCase__=[0.5, 0.5, 0.5]       ,						):
       lowerCAmelCase_:							Optional[int]       =	size if size is not None else {"height": 18, "width": 18}
       lowerCAmelCase_:							Any       =	parent
       lowerCAmelCase_:							List[Any]       =	batch_size
       lowerCAmelCase_:							Dict       =	num_channels
       lowerCAmelCase_:							List[str]       =	image_size
       lowerCAmelCase_:							Optional[int]       =	min_resolution
       lowerCAmelCase_:							int       =	max_resolution
       lowerCAmelCase_:							int       =	do_resize
       lowerCAmelCase_:							Union[str, Any]       =	size
       lowerCAmelCase_:							Any       =	do_normalize
       lowerCAmelCase_:							List[Any]       =	image_mean
       lowerCAmelCase_:							List[str]       =	image_std




    def      _a    (    self ):
       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
       }





@require_torch
@require_vision
class 					_lowercase    (     UpperCAmelCase__ ,  unittest.TestCase			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				Optional[Any]          =    ViTImageProcessor if is_vision_available() else None




    def      _a    (    self ):
       lowerCAmelCase_:							Optional[int]       =	EfficientFormerImageProcessorTester(self )




    @property
    def      _a    (    self ):
       return self.image_proc_tester.prepare_image_processor_dict()




    def      _a    (    self ):
       lowerCAmelCase_:							Optional[int]       =	self.image_processing_class(**self.image_processor_dict )
       self.assertTrue(hasattr(lowerCamelCase__       ,						"image_mean" ) )
       self.assertTrue(hasattr(lowerCamelCase__       ,						"image_std" ) )
       self.assertTrue(hasattr(lowerCamelCase__       ,						"do_normalize" ) )
       self.assertTrue(hasattr(lowerCamelCase__       ,						"do_resize" ) )
       self.assertTrue(hasattr(lowerCamelCase__       ,						"size" ) )




    def      _a    (    self ):
       pass




    def      _a    (    self ):
       # Initialize image_processor
       lowerCAmelCase_:							List[Any]       =	self.image_processing_class(**self.image_processor_dict )
       # create random PIL images
       lowerCAmelCase_:							Optional[int]       =	prepare_image_inputs(self.image_proc_tester       ,						equal_resolution=lowerCamelCase__ )
       for image in image_inputs:
          self.assertIsInstance(lowerCamelCase__       ,						Image.Image )

       # Test not batched input
       lowerCAmelCase_:							int       =	image_processor(image_inputs[0]       ,						return_tensors="pt" ).pixel_values
       self.assertEqual(
           encoded_images.shape       ,						(
               1,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )       ,						)

       # Test batched
       lowerCAmelCase_:							str       =	image_processor(lowerCamelCase__       ,						return_tensors="pt" ).pixel_values
       self.assertEqual(
           encoded_images.shape       ,						(
               self.image_proc_tester.batch_size,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )       ,						)




    def      _a    (    self ):
       # Initialize image_processor
       lowerCAmelCase_:							str       =	self.image_processing_class(**self.image_processor_dict )
       # create random numpy tensors
       lowerCAmelCase_:							str       =	prepare_image_inputs(self.image_proc_tester       ,						equal_resolution=lowerCamelCase__       ,						numpify=lowerCamelCase__ )
       for image in image_inputs:
          self.assertIsInstance(lowerCamelCase__       ,						np.ndarray )

       # Test not batched input
       lowerCAmelCase_:							int       =	image_processor(image_inputs[0]       ,						return_tensors="pt" ).pixel_values
       self.assertEqual(
           encoded_images.shape       ,						(
               1,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )       ,						)

       # Test batched
       lowerCAmelCase_:							Optional[int]       =	image_processor(lowerCamelCase__       ,						return_tensors="pt" ).pixel_values
       self.assertEqual(
           encoded_images.shape       ,						(
               self.image_proc_tester.batch_size,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )       ,						)




    def      _a    (    self ):
       # Initialize image_processor
       lowerCAmelCase_:							List[str]       =	self.image_processing_class(**self.image_processor_dict )
       # create random PyTorch tensors
       lowerCAmelCase_:							List[Any]       =	prepare_image_inputs(self.image_proc_tester       ,						equal_resolution=lowerCamelCase__       ,						torchify=lowerCamelCase__ )
       for image in image_inputs:
          self.assertIsInstance(lowerCamelCase__       ,						torch.Tensor )

       # Test not batched input
       lowerCAmelCase_:							Dict       =	image_processor(image_inputs[0]       ,						return_tensors="pt" ).pixel_values
       self.assertEqual(
           encoded_images.shape       ,						(
               1,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )       ,						)

       # Test batched
       lowerCAmelCase_:							str       =	image_processor(lowerCamelCase__       ,						return_tensors="pt" ).pixel_values
       self.assertEqual(
           encoded_images.shape       ,						(
               self.image_proc_tester.batch_size,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )       ,						)
from typing import TYPE_CHECKING

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


a  :      int            =    {
    """configuration_bridgetower""": [
        """BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BridgeTowerConfig""",
        """BridgeTowerTextConfig""",
        """BridgeTowerVisionConfig""",
    ],
    """processing_bridgetower""": ["""BridgeTowerProcessor"""],
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a  :      Any            =    ["""BridgeTowerImageProcessor"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a  :      str            =    [
     """BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """BridgeTowerForContrastiveLearning""",
     """BridgeTowerForImageAndTextRetrieval""",
     """BridgeTowerForMaskedLM""",
     """BridgeTowerModel""",
     """BridgeTowerPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_bridgetower import (
     BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
     BridgeTowerConfig,
     BridgeTowerTextConfig,
     BridgeTowerVisionConfig,
 )
 from .processing_bridgetower import BridgeTowerProcessor

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .image_processing_bridgetower import BridgeTowerImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_bridgetower import (
      BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
      BridgeTowerForContrastiveLearning,
      BridgeTowerForImageAndTextRetrieval,
      BridgeTowerForMaskedLM,
      BridgeTowerModel,
      BridgeTowerPreTrainedModel,
  )


else:
 import sys

 a  :      List[Any]            =    _LazyModule(__name__, globals()["""__file__"""], _import_structure)