


'''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 MobileNetVaImageProcessor



class      a    (		unittest.TestCase	):


     """simple docstring"""






     def __init__(    self					:		Any							,			snake_case					:		List[str]							,			snake_case					:		List[Any]=7							,			snake_case					:		List[Any]=3							,			snake_case					:		Any=18							,			snake_case					:		Optional[int]=30							,			snake_case					:		Any=400							,			snake_case					:		Dict=True							,			snake_case					:		Any=None							,			snake_case					:		Optional[Any]=True							,			snake_case					:		Union[str, Any]=None							,			)						->							Optional[int]:
          __UpperCAmelCase     :			Union[str, Any]  							=      size if size is not None else {'''shortest_edge''': 20}
          __UpperCAmelCase     :			Optional[int]  							=      crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
          __UpperCAmelCase     :			Optional[Any]  							=      parent
          __UpperCAmelCase     :			Any  							=      batch_size
          __UpperCAmelCase     :			Any  							=      num_channels
          __UpperCAmelCase     :			Any  							=      image_size
          __UpperCAmelCase     :			Union[str, Any]  							=      min_resolution
          __UpperCAmelCase     :			Dict  							=      max_resolution
          __UpperCAmelCase     :			List[Any]  							=      do_resize
          __UpperCAmelCase     :			str  							=      size
          __UpperCAmelCase     :			Dict  							=      do_center_crop
          __UpperCAmelCase     :			Optional[int]  							=      crop_size






     def 			lowerCamelCase__			(    self					:		Dict      )						->							int:
          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      a    (		_a     ,  unittest.TestCase	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			Tuple							       =       MobileNetVaImageProcessor if is_vision_available() else None

     def 			lowerCamelCase__			(    self					:		Any      )						->							str:
          __UpperCAmelCase     :			List[str]  							=      MobileNetVaImageProcessingTester(self      )

     @property
     def 			lowerCamelCase__			(    self					:		Union[str, Any]      )						->							str:
          return self.image_processor_tester.prepare_image_processor_dict()

     def 			lowerCamelCase__			(    self					:		int      )						->							List[Any]:
          __UpperCAmelCase     :			Any  							=      self.image_processing_class(**self.image_processor_dict      )
          self.assertTrue(hasattr(snake_case							,			'''do_resize'''      )      )
          self.assertTrue(hasattr(snake_case							,			'''size'''      )      )
          self.assertTrue(hasattr(snake_case							,			'''do_center_crop'''      )      )
          self.assertTrue(hasattr(snake_case							,			'''crop_size'''      )      )

     def 			lowerCamelCase__			(    self					:		List[str]      )						->							List[str]:
          __UpperCAmelCase     :			Dict  							=      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}      )

          __UpperCAmelCase     :			List[str]  							=      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 			lowerCamelCase__			(    self					:		Any      )						->							List[Any]:
          pass

     def 			lowerCamelCase__			(    self					:		List[str]      )						->							Any:
          # Initialize image_processing
          __UpperCAmelCase     :			List[Any]  							=      self.image_processing_class(**self.image_processor_dict      )
          # create random PIL images
          __UpperCAmelCase     :			Union[str, Any]  							=      prepare_image_inputs(self.image_processor_tester							,			equal_resolution=snake_case      )
          for image in image_inputs:
               self.assertIsInstance(snake_case							,			Image.Image      )

          # Test not batched input
          __UpperCAmelCase     :			int  							=      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     :			Tuple  							=      image_processing(snake_case							,			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 			lowerCamelCase__			(    self					:		Optional[int]      )						->							Optional[Any]:
          # Initialize image_processing
          __UpperCAmelCase     :			Optional[int]  							=      self.image_processing_class(**self.image_processor_dict      )
          # create random numpy tensors
          __UpperCAmelCase     :			int  							=      prepare_image_inputs(self.image_processor_tester							,			equal_resolution=snake_case							,			numpify=snake_case      )
          for image in image_inputs:
               self.assertIsInstance(snake_case							,			np.ndarray      )

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

          # Test not batched input
          __UpperCAmelCase     :			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
          __UpperCAmelCase     :			List[Any]  							=      image_processing(snake_case							,			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_tf_available, is_torch_available


__UpperCAmelCase      :Union[str, Any]			    =       {
    "configuration_rag": ["RagConfig"],
    "retrieval_rag": ["RagRetriever"],
    "tokenization_rag": ["RagTokenizer"],
}

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

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase      :Dict			    =       [
          "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

      __UpperCAmelCase      :Any			    =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)