


"""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 ViTImageProcessor






class 						_A  (  unittest.TestCase  ):







    """simple docstring"""




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






    def 	__snake_case							(					self				:    int):
      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 						_A  (  _a    ,unittest.TestCase  ):







    """simple docstring"""




    UpperCAmelCase       :							List[str]	=       ViTImageProcessor if is_vision_available() else None






    def 	__snake_case							(					self				:    Optional[int]):
      a						:		Tuple      							=			EfficientFormerImageProcessorTester(self)






    @property
    def 	__snake_case							(					self				:    int):
      return self.image_proc_tester.prepare_image_processor_dict()






    def 	__snake_case							(					self				:    Tuple):
      a						:		Union[str, Any]      							=			self.image_processing_class(**self.image_processor_dict)
      self.assertTrue(hasattr(__UpperCAmelCase				, "image_mean"))
      self.assertTrue(hasattr(__UpperCAmelCase				, "image_std"))
      self.assertTrue(hasattr(__UpperCAmelCase				, "do_normalize"))
      self.assertTrue(hasattr(__UpperCAmelCase				, "do_resize"))
      self.assertTrue(hasattr(__UpperCAmelCase				, "size"))






    def 	__snake_case							(					self				:    str):
      pass






    def 	__snake_case							(					self				:    Optional[int]):
      # Initialize image_processor
      a						:		int      							=			self.image_processing_class(**self.image_processor_dict)
      # create random PIL images
      a						:		int      							=			prepare_image_inputs(self.image_proc_tester				, equal_resolution=__UpperCAmelCase)
      for image in image_inputs:
        self.assertIsInstance(__UpperCAmelCase				, Image.Image)

      # Test not batched input
      a						:		Tuple      							=			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
      a						:		str      							=			image_processor(__UpperCAmelCase				, 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 	__snake_case							(					self				:    List[str]):
      # Initialize image_processor
      a						:		Dict      							=			self.image_processing_class(**self.image_processor_dict)
      # create random numpy tensors
      a						:		Optional[int]      							=			prepare_image_inputs(self.image_proc_tester				, equal_resolution=__UpperCAmelCase				, numpify=__UpperCAmelCase)
      for image in image_inputs:
        self.assertIsInstance(__UpperCAmelCase				, np.ndarray)

      # Test not batched input
      a						:		Union[str, Any]      							=			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
      a						:		Any      							=			image_processor(__UpperCAmelCase				, 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 	__snake_case							(					self				:    Dict):
      # Initialize image_processor
      a						:		Optional[int]      							=			self.image_processing_class(**self.image_processor_dict)
      # create random PyTorch tensors
      a						:		Union[str, Any]      							=			prepare_image_inputs(self.image_proc_tester				, equal_resolution=__UpperCAmelCase				, torchify=__UpperCAmelCase)
      for image in image_inputs:
        self.assertIsInstance(__UpperCAmelCase				, torch.Tensor)

      # Test not batched input
      a						:		Any      							=			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
      a						:		List[Any]      							=			image_processor(__UpperCAmelCase				, 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"],
          )				, )










"""simple docstring"""







from typing import TYPE_CHECKING

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


__lowercase    					=      {
    """configuration_rag""": ["""RagConfig"""],
    """retrieval_rag""": ["""RagRetriever"""],
    """tokenization_rag""": ["""RagTokenizer"""],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowercase    					=      [
     """RagModel""",
     """RagPreTrainedModel""",
     """RagSequenceForGeneration""",
     """RagTokenForGeneration""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowercase    					=      [
     """TFRagModel""",
     """TFRagPreTrainedModel""",
     """TFRagSequenceForGeneration""",
     """TFRagTokenForGeneration""",
 ]


if TYPE_CHECKING:
 from .configuration_rag import RagConfig
 from .retrieval_rag import RagRetriever
 from .tokenization_rag import RagTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_rag import (
      TFRagModel,
      TFRagPreTrainedModel,
      TFRagSequenceForGeneration,
      TFRagTokenForGeneration,
  )

else:
 import sys

 __lowercase    					=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






