
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


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import TvltImageProcessor


def     a__			(    snake_case   ,	snake_case=10   ,	snake_case=10   ,	snake_case=False   ,	snake_case=False    ):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE     :					List[str]			=						[]
  for i in range(image_processor_tester.num_frames    ):
    video.append(np.random.randint(255   ,	size=(image_processor_tester.num_channels, width, height)   ,	dtype=np.uinta    )    )

  if not numpify and not torchify:
    # PIL expects the channel dimension as last dimension
    __SCREAMING_SNAKE_CASE     :					List[Any]			=						[Image.fromarray(np.moveaxis(snake_case   ,	0   ,	-1    )    ) for frame in video]

  if torchify:
    __SCREAMING_SNAKE_CASE     :					List[str]			=						[torch.from_numpy(snake_case    ) for frame in video]

  return video


def     a__			(    snake_case   ,	snake_case=False   ,	snake_case=False   ,	snake_case=False    ):





  """simple docstring"""
  assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

  __SCREAMING_SNAKE_CASE     :					str			=						[]
  for i in range(image_processor_tester.batch_size    ):
    if equal_resolution:
      __SCREAMING_SNAKE_CASE     :					Dict			=						image_processor_tester.max_resolution
    else:
      __SCREAMING_SNAKE_CASE     :					Optional[int]			=						np.random.choice(
          np.arange(image_processor_tester.min_resolution   ,	image_processor_tester.max_resolution    )   ,	2    )
      __SCREAMING_SNAKE_CASE     :					List[str]			=						prepare_video(
          image_processor_tester=snake_case   ,	width=snake_case   ,	height=snake_case   ,	numpify=snake_case   ,	torchify=snake_case   ,	)
    video_inputs.append(snake_case    )

  return video_inputs





class   __UpperCamelCase				(    unittest.TestCase   ):






     """simple docstring"""







     def __init__(							self    :    Tuple      ,		_A    :    str      ,		_A    :    Optional[int]=7      ,		_A    :    Tuple=3      ,		_A    :    List[str]=4      ,		_A    :    Union[str, Any]=18      ,		_A    :    Any=30      ,		_A    :    Dict=400      ,		_A    :    Dict=True      ,		_A    :    List[str]=None      ,		_A    :    Optional[Any]=True      ,		_A    :    List[Any]=[0.5, 0.5, 0.5]      ,		_A    :    List[str]=[0.5, 0.5, 0.5]      ,		_A    :    Optional[Any]=True      ,		_A    :    int=None      ,		):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						size if size is not None else {'''shortest_edge''': 18}
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

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




     def 				UpperCAmelCase__    (							self    :    List[str]				):







       """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,
           "do_center_crop": self.do_center_crop,
           "crop_size": self.crop_size,
       }






@require_torch
@require_vision
class   __UpperCamelCase				(    lowerCAmelCase__				,		unittest.TestCase   ):






     """simple docstring"""







     lowerCAmelCase_     =	TvltImageProcessor if is_vision_available() else None



     def 				UpperCAmelCase__    (							self    :    Any				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[Any]			=						TvltImageProcessorTester(self				)



     @property
     def 				UpperCAmelCase__    (							self    :    List[Any]				):







       """simple docstring"""




       return self.image_processor_tester.prepare_image_processor_dict()



     def 				UpperCAmelCase__    (							self    :    List[str]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[Any]			=						self.image_processing_class(**self.image_processor_dict				)
       self.assertTrue(hasattr(_A      ,		'''image_mean'''				)				)
       self.assertTrue(hasattr(_A      ,		'''image_std'''				)				)
       self.assertTrue(hasattr(_A      ,		'''do_normalize'''				)				)
       self.assertTrue(hasattr(_A      ,		'''do_resize'''				)				)
       self.assertTrue(hasattr(_A      ,		'''do_center_crop'''				)				)
       self.assertTrue(hasattr(_A      ,		'''size'''				)				)



     def 				UpperCAmelCase__    (							self    :    Dict				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					int			=						self.image_processing_class(**self.image_processor_dict				)
       # create random PIL videos
       __SCREAMING_SNAKE_CASE     :					Tuple			=						prepare_video_inputs(self.image_processor_tester      ,		equal_resolution=_A				)
       for video in video_inputs:
         self.assertIsInstance(_A      ,		_A				)
         self.assertIsInstance(video[0]      ,		Image.Image				)

       # Test not batched input
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						image_processor(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
       __SCREAMING_SNAKE_CASE     :					List[str]			=						image_processor(_A      ,		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 				UpperCAmelCase__    (							self    :    List[str]				):







       """simple docstring"""




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

       # Test not batched input
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						image_processor(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
       __SCREAMING_SNAKE_CASE     :					Dict			=						image_processor(_A      ,		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 				UpperCAmelCase__    (							self    :    Tuple				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[Any]			=						self.image_processing_class(**self.image_processor_dict				)
       # create random PyTorch tensors
       __SCREAMING_SNAKE_CASE     :					Dict			=						prepare_video_inputs(self.image_processor_tester      ,		equal_resolution=_A      ,		torchify=_A				)
       for video in video_inputs:
         self.assertIsInstance(_A      ,		_A				)
         self.assertIsInstance(video[0]      ,		torch.Tensor				)

       # Test not batched input
       __SCREAMING_SNAKE_CASE     :					Dict			=						image_processor(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
       __SCREAMING_SNAKE_CASE     :					Dict			=						image_processor(_A      ,		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_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase_											=	{
    """configuration_led""": ["""LED_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LEDConfig"""],
    """tokenization_led""": ["""LEDTokenizer"""],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase_											=	["""LEDTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase_											=	[
       """LED_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """LEDForConditionalGeneration""",
       """LEDForQuestionAnswering""",
       """LEDForSequenceClassification""",
       """LEDModel""",
       """LEDPreTrainedModel""",
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase_											=	["""TFLEDForConditionalGeneration""", """TFLEDModel""", """TFLEDPreTrainedModel"""]


if TYPE_CHECKING:
   from .configuration_led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig
   from .tokenization_led import LEDTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_led_fast import LEDTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_led import (
          LED_PRETRAINED_MODEL_ARCHIVE_LIST,
          LEDForConditionalGeneration,
          LEDForQuestionAnswering,
          LEDForSequenceClassification,
          LEDModel,
          LEDPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel

else:
   import sys

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