'''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_video_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import VideoMAEImageProcessor





class 	_lowercase		(      unittest.TestCase ):

 def __init__(				self			:							Union[str, Any]     ,   snake_case			:							Optional[Any]     ,   snake_case			:							Optional[Any]=7     ,   snake_case			:							Optional[int]=3     ,   snake_case			:							Any=1_0     ,   snake_case			:							Tuple=1_8     ,   snake_case			:							List[Any]=3_0     ,   snake_case			:							Optional[Any]=4_0_0     ,   snake_case			:							Optional[Any]=True     ,   snake_case			:							str=None     ,   snake_case			:							int=True     ,   snake_case			:							Tuple=[0.5, 0.5, 0.5]     ,   snake_case			:							Any=[0.5, 0.5, 0.5]     ,   snake_case			:							Dict=None     ,   )     ->		Any:

  """simple docstring"""





  UpperCamelCase_      :		List[str]				=      size if size is not None else {'shortest_edge': 1_8}
  UpperCamelCase_      :		Union[str, Any]				=      crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}

  UpperCamelCase_      :		Dict				=      parent
  UpperCamelCase_      :		Any				=      batch_size
  UpperCamelCase_      :		Optional[int]				=      num_channels
  UpperCamelCase_      :		Tuple				=      num_frames
  UpperCamelCase_      :		Optional[int]				=      image_size
  UpperCamelCase_      :		Tuple				=      min_resolution
  UpperCamelCase_      :		Optional[Any]				=      max_resolution
  UpperCamelCase_      :		Any				=      do_resize
  UpperCamelCase_      :		Optional[Any]				=      size
  UpperCamelCase_      :		Tuple				=      do_normalize
  UpperCamelCase_      :		str				=      image_mean
  UpperCamelCase_      :		List[Any]				=      image_std
  UpperCamelCase_      :		Dict				=      crop_size


 def 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[Any]    )     ->		Tuple:

  """simple docstring"""





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





@require_torch
@require_vision
class 	_lowercase		(      snake_case_		,   unittest.TestCase ):
 lowercase         =     VideoMAEImageProcessor if is_vision_available() else None

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Dict    )     ->		Dict:

  """simple docstring"""





  UpperCamelCase_      :		Optional[int]				=      VideoMAEImageProcessingTester(self    )

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

  """simple docstring"""





  return self.image_processor_tester.prepare_image_processor_dict()

 def 		SCREAMING_SNAKE_CASE__     (				self			:							str    )     ->		Optional[int]:

  """simple docstring"""





  UpperCamelCase_      :		Union[str, Any]				=      self.image_processing_class(**self.image_processor_dict    )
  self.assertTrue(hasattr(snake_case     ,   'image_mean'    )    )
  self.assertTrue(hasattr(snake_case     ,   'image_std'    )    )
  self.assertTrue(hasattr(snake_case     ,   'do_normalize'    )    )
  self.assertTrue(hasattr(snake_case     ,   'do_resize'    )    )
  self.assertTrue(hasattr(snake_case     ,   'do_center_crop'    )    )
  self.assertTrue(hasattr(snake_case     ,   'size'    )    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							int    )     ->		Dict:

  """simple docstring"""





  UpperCamelCase_      :		int				=      self.image_processing_class.from_dict(self.image_processor_dict    )
  self.assertEqual(image_processor.size     ,   {'shortest_edge': 1_8}    )
  self.assertEqual(image_processor.crop_size     ,   {'height': 1_8, 'width': 1_8}    )

  UpperCamelCase_      :		Optional[Any]				=      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 		SCREAMING_SNAKE_CASE__     (				self			:							List[Any]    )     ->		int:

  """simple docstring"""





  pass

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Tuple    )     ->		Tuple:

  """simple docstring"""





  UpperCamelCase_      :		List[Any]				=      self.image_processing_class(**self.image_processor_dict    )
  # create random PIL videos
  UpperCamelCase_      :		List[str]				=      prepare_video_inputs(self.image_processor_tester     ,   equal_resolution=snake_case    )
  for video in video_inputs:
   self.assertIsInstance(snake_case     ,   snake_case    )
   self.assertIsInstance(video[0]     ,   Image.Image    )

  # Test not batched input
  UpperCamelCase_      :		Optional[Any]				=      image_processing(video_inputs[0]     ,   return_tensors='pt'    ).pixel_values
  self.assertEqual(
      encoded_videos.shape     ,   (
          1,
          self.image_processor_tester.num_frames,
          self.image_processor_tester.num_channels,
          self.image_processor_tester.crop_size['height'],
          self.image_processor_tester.crop_size['width'],
      )     ,   )

  # Test batched
  UpperCamelCase_      :		Any				=      image_processing(snake_case     ,   return_tensors='pt'    ).pixel_values
  self.assertEqual(
      encoded_videos.shape     ,   (
          self.image_processor_tester.batch_size,
          self.image_processor_tester.num_frames,
          self.image_processor_tester.num_channels,
          self.image_processor_tester.crop_size['height'],
          self.image_processor_tester.crop_size['width'],
      )     ,   )

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

  """simple docstring"""





  UpperCamelCase_      :		Optional[int]				=      self.image_processing_class(**self.image_processor_dict    )
  # create random numpy tensors
  UpperCamelCase_      :		List[Any]				=      prepare_video_inputs(self.image_processor_tester     ,   equal_resolution=snake_case     ,   numpify=snake_case    )
  for video in video_inputs:
   self.assertIsInstance(snake_case     ,   snake_case    )
   self.assertIsInstance(video[0]     ,   np.ndarray    )

  # Test not batched input
  UpperCamelCase_      :		Any				=      image_processing(video_inputs[0]     ,   return_tensors='pt'    ).pixel_values
  self.assertEqual(
      encoded_videos.shape     ,   (
          1,
          self.image_processor_tester.num_frames,
          self.image_processor_tester.num_channels,
          self.image_processor_tester.crop_size['height'],
          self.image_processor_tester.crop_size['width'],
      )     ,   )

  # Test batched
  UpperCamelCase_      :		int				=      image_processing(snake_case     ,   return_tensors='pt'    ).pixel_values
  self.assertEqual(
      encoded_videos.shape     ,   (
          self.image_processor_tester.batch_size,
          self.image_processor_tester.num_frames,
          self.image_processor_tester.num_channels,
          self.image_processor_tester.crop_size['height'],
          self.image_processor_tester.crop_size['width'],
      )     ,   )


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

  """simple docstring"""





  UpperCamelCase_      :		Optional[int]				=      self.image_processing_class(**self.image_processor_dict    )
  # create random PyTorch tensors
  UpperCamelCase_      :		Union[str, Any]				=      prepare_video_inputs(self.image_processor_tester     ,   equal_resolution=snake_case     ,   torchify=snake_case    )
  for video in video_inputs:
   self.assertIsInstance(snake_case     ,   snake_case    )
   self.assertIsInstance(video[0]     ,   torch.Tensor    )

  # Test not batched input
  UpperCamelCase_      :		int				=      image_processing(video_inputs[0]     ,   return_tensors='pt'    ).pixel_values
  self.assertEqual(
      encoded_videos.shape     ,   (
          1,
          self.image_processor_tester.num_frames,
          self.image_processor_tester.num_channels,
          self.image_processor_tester.crop_size['height'],
          self.image_processor_tester.crop_size['width'],
      )     ,   )

  # Test batched
  UpperCamelCase_      :		Union[str, Any]				=      image_processing(snake_case     ,   return_tensors='pt'    ).pixel_values
  self.assertEqual(
      encoded_videos.shape     ,   (
          self.image_processor_tester.batch_size,
          self.image_processor_tester.num_frames,
          self.image_processor_tester.num_channels,
          self.image_processor_tester.crop_size['height'],
          self.image_processor_tester.crop_size['width'],
      )     ,   )




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a_					      = {
    'configuration_marian': ['MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MarianConfig', 'MarianOnnxConfig'],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_					      = ['MarianTokenizer']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_					      = [
       'MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST',
       'MarianForCausalLM',
       'MarianModel',
       'MarianMTModel',
       'MarianPreTrainedModel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_					      = ['TFMarianModel', 'TFMarianMTModel', 'TFMarianPreTrainedModel']

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_					      = ['FlaxMarianModel', 'FlaxMarianMTModel', 'FlaxMarianPreTrainedModel']

if TYPE_CHECKING:
   from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_marian import MarianTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_marian import (
          MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
          MarianForCausalLM,
          MarianModel,
          MarianMTModel,
          MarianPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
   import sys

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



