


'''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 PoolFormerImageProcessor


class  lowerCAmelCase_						(       unittest.TestCase      ):





  """simple docstring"""



  def __init__(					self     :							Optional[int]				,							SCREAMING_SNAKE_CASE__     :							List[str]				,							SCREAMING_SNAKE_CASE__     :							str=7				,							SCREAMING_SNAKE_CASE__     :							str=3				,							SCREAMING_SNAKE_CASE__     :							str=3_0				,							SCREAMING_SNAKE_CASE__     :							int=4_0_0				,							SCREAMING_SNAKE_CASE__     :							Optional[int]=True				,							SCREAMING_SNAKE_CASE__     :							List[str]=None				,							SCREAMING_SNAKE_CASE__     :							List[str]=0.9				,							SCREAMING_SNAKE_CASE__     :							int=None				,							SCREAMING_SNAKE_CASE__     :							Tuple=True				,							SCREAMING_SNAKE_CASE__     :							List[str]=[0.5, 0.5, 0.5]				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]=[0.5, 0.5, 0.5]				,							):





       '''simple docstring'''

       __a       =   size if size is not None else {"""shortest_edge""": 3_0}
       __a       =   crop_size if crop_size is not None else {"""height""": 3_0, """width""": 3_0}
       __a       =   parent
       __a       =   batch_size
       __a       =   num_channels
       __a       =   min_resolution
       __a       =   max_resolution
       __a       =   do_resize_and_center_crop
       __a       =   size
       __a       =   crop_pct
       __a       =   crop_size
       __a       =   do_normalize
       __a       =   image_mean
       __a       =   image_std


  def __a						(					self     :							Tuple						):





       '''simple docstring'''

       return {
           "size": self.size,
           "do_resize_and_center_crop": self.do_resize_and_center_crop,
           "crop_pct": self.crop_pct,
           "crop_size": self.crop_size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
       }


@require_torch
@require_vision
class  lowerCAmelCase_						(       snake_case__		,    unittest.TestCase      ):





  """simple docstring"""



  a_       :int												=PoolFormerImageProcessor if is_vision_available() else None

  def __a						(					self     :							int						):





       '''simple docstring'''

       __a       =   PoolFormerImageProcessingTester(self						)

  @property
  def __a						(					self     :							Union[str, Any]						):





       '''simple docstring'''

       return self.image_processor_tester.prepare_image_processor_dict()

  def __a						(					self     :							Union[str, Any]						):





       '''simple docstring'''

       __a       =   self.image_processing_class(**self.image_processor_dict						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__				,							"""do_resize_and_center_crop"""						)						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__				,							"""size"""						)						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__				,							"""crop_pct"""						)						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__				,							"""do_normalize"""						)						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__				,							"""image_mean"""						)						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__				,							"""image_std"""						)						)

  def __a						(					self     :							int						):





       '''simple docstring'''

       __a       =   self.image_processing_class.from_dict(self.image_processor_dict						)
       self.assertEqual(image_processor.size				,							{"""shortest_edge""": 3_0}						)
       self.assertEqual(image_processor.crop_size				,							{"""height""": 3_0, """width""": 3_0}						)

       __a       =   self.image_processing_class.from_dict(self.image_processor_dict				,							size=4_2				,							crop_size=8_4						)
       self.assertEqual(image_processor.size				,							{"""shortest_edge""": 4_2}						)
       self.assertEqual(image_processor.crop_size				,							{"""height""": 8_4, """width""": 8_4}						)

  def __a						(					self     :							Optional[int]						):





       '''simple docstring'''

       pass

  def __a						(					self     :							str						):





       '''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=SCREAMING_SNAKE_CASE__						)
       for image in image_inputs:
            self.assertIsInstance(SCREAMING_SNAKE_CASE__				,							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(SCREAMING_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 __a						(					self     :							Union[str, Any]						):





       '''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=SCREAMING_SNAKE_CASE__				,							numpify=SCREAMING_SNAKE_CASE__						)
       for image in image_inputs:
            self.assertIsInstance(SCREAMING_SNAKE_CASE__				,							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(SCREAMING_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 __a						(					self     :							Any						):





       '''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=SCREAMING_SNAKE_CASE__				,							torchify=SCREAMING_SNAKE_CASE__						)
       for image in image_inputs:
            self.assertIsInstance(SCREAMING_SNAKE_CASE__				,							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(SCREAMING_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


SCREAMING_SNAKE_CASE_      =			{
    'configuration_rag': ['RagConfig'],
    'retrieval_rag': ['RagRetriever'],
    'tokenization_rag': ['RagTokenizer'],
}

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

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_      =			[
     '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

 SCREAMING_SNAKE_CASE_      =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






