
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase						:      Any							=							{"""configuration_umt5""": ["""UMT5Config""", """UMT5OnnxConfig"""]}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase						:      Optional[Any]							=							[
      """UMT5EncoderModel""",
      """UMT5ForConditionalGeneration""",
      """UMT5ForQuestionAnswering""",
      """UMT5Model""",
      """UMT5PreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_umta import UMTaConfig, UMTaOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_umta import (
        UMTaEncoderModel,
        UMTaForConditionalGeneration,
        UMTaForQuestionAnswering,
        UMTaModel,
        UMTaPreTrainedModel,
    )
else:
  import sys

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


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 __snake_case	(   unittest.TestCase  ):
    def __init__(						self	,snake_case	,snake_case=7	,snake_case=3	,snake_case=10	,snake_case=18	,snake_case=30	,snake_case=400	,snake_case=True	,snake_case=None	,snake_case=True	,snake_case=[0.5, 0.5, 0.5]	,snake_case=[0.5, 0.5, 0.5]	,snake_case=None	,):



           '''simple docstring'''







           lowercase							:  Dict 			=   size if size is not None else {"""shortest_edge""": 18}
           lowercase							:  Tuple 			=   crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

           lowercase							:  Optional[int] 			=   parent
           lowercase							:  Any 			=   batch_size
           lowercase							:  int 			=   num_channels
           lowercase							:  Dict 			=   num_frames
           lowercase							:  Any 			=   image_size
           lowercase							:  str 			=   min_resolution
           lowercase							:  Any 			=   max_resolution
           lowercase							:  Tuple 			=   do_resize
           lowercase							:  Optional[Any] 			=   size
           lowercase							:  str 			=   do_normalize
           lowercase							:  Optional[Any] 			=   image_mean
           lowercase							:  Optional[Any] 			=   image_std
           lowercase							:  Union[str, Any] 			=   crop_size
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''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 __snake_case	(   lowerCAmelCase      ,			unittest.TestCase  ):
    _a	:		Optional[Any]=       VideoMAEImageProcessor if is_vision_available() else None
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Any 			=   VideoMAEImageProcessingTester(self   )
    @property
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           return self.image_processor_tester.prepare_image_processor_dict()
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  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   ):



           '''simple docstring'''







           lowercase							:  List[str] 			=   self.image_processing_class.from_dict(self.image_processor_dict   )
           self.assertEqual(image_processor.size	,{"""shortest_edge""": 18}   )
           self.assertEqual(image_processor.crop_size	,{"""height""": 18, """width""": 18}   )

           lowercase							:  Any 			=   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    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           pass
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Tuple 			=   self.image_processing_class(**self.image_processor_dict   )
           # create random PIL videos
           lowercase							:  Optional[int] 			=   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
           lowercase							:  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
           lowercase							:  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"""],
               )	,)
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Dict 			=   self.image_processing_class(**self.image_processor_dict   )
           # create random numpy tensors
           lowercase							:  Optional[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
           lowercase							:  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
           lowercase							:  Optional[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   ):



           '''simple docstring'''







           lowercase							:  Dict 			=   self.image_processing_class(**self.image_processor_dict   )
           # create random PyTorch tensors
           lowercase							:  List[str] 			=   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
           lowercase							:  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
           lowercase							:  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"""],
               )	,)
