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				):



   """simple docstring"""





   def __init__(							self:    Union[str, Any]			,a:    List[Any]			,a:    List[Any]=7			,a:    Tuple=3			,a:    Union[str, Any]=10			,a:    Tuple=18			,a:    Optional[int]=30			,a:    str=400			,a:    Union[str, Any]=True			,a:    str=None			,a:    Optional[int]=True			,a:    Dict=[0.5, 0.5, 0.5]			,a:    Union[str, Any]=[0.5, 0.5, 0.5]			,a:    Tuple=None			,):
    __UpperCAmelCase         =					size if size is not None else {'''shortest_edge''': 18}
    __UpperCAmelCase         =					crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

    __UpperCAmelCase         =					parent
    __UpperCAmelCase         =					batch_size
    __UpperCAmelCase         =					num_channels
    __UpperCAmelCase         =					num_frames
    __UpperCAmelCase         =					image_size
    __UpperCAmelCase         =					min_resolution
    __UpperCAmelCase         =					max_resolution
    __UpperCAmelCase         =					do_resize
    __UpperCAmelCase         =					size
    __UpperCAmelCase         =					do_normalize
    __UpperCAmelCase         =					image_mean
    __UpperCAmelCase         =					image_std
    __UpperCAmelCase         =					crop_size





   def 			snake_case	(							self:    int	):
    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(     __SCREAMING_SNAKE_CASE   ,unittest.TestCase				):



   """simple docstring"""





   __lowerCamelCase    =  VideoMAEImageProcessor if is_vision_available() else None





   def 			snake_case	(							self:    str	):
    __UpperCAmelCase         =					VideoMAEImageProcessingTester(self	)





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





   def 			snake_case	(							self:    Any	):
    __UpperCAmelCase         =					self.image_processing_class(**self.image_processor_dict	)
    self.assertTrue(hasattr(UpperCamelCase__			,'image_mean'	)	)
    self.assertTrue(hasattr(UpperCamelCase__			,'image_std'	)	)
    self.assertTrue(hasattr(UpperCamelCase__			,'do_normalize'	)	)
    self.assertTrue(hasattr(UpperCamelCase__			,'do_resize'	)	)
    self.assertTrue(hasattr(UpperCamelCase__			,'do_center_crop'	)	)
    self.assertTrue(hasattr(UpperCamelCase__			,'size'	)	)





   def 			snake_case	(							self:    List[str]	):
    __UpperCAmelCase         =					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}	)

    __UpperCAmelCase         =					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 			snake_case	(							self:    Tuple	):
    pass





   def 			snake_case	(							self:    Optional[int]	):
    __UpperCAmelCase         =					self.image_processing_class(**self.image_processor_dict	)
    # create random PIL videos
    __UpperCAmelCase         =					prepare_video_inputs(self.image_processor_tester			,equal_resolution=UpperCamelCase__	)
    for video in video_inputs:
     self.assertIsInstance(UpperCamelCase__			,UpperCamelCase__	)
     self.assertIsInstance(video[0]			,Image.Image	)

    # Test not batched input
    __UpperCAmelCase         =					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         =					image_processing(UpperCamelCase__			,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 			snake_case	(							self:    Any	):
    __UpperCAmelCase         =					self.image_processing_class(**self.image_processor_dict	)
    # create random numpy tensors
    __UpperCAmelCase         =					prepare_video_inputs(self.image_processor_tester			,equal_resolution=UpperCamelCase__			,numpify=UpperCamelCase__	)
    for video in video_inputs:
     self.assertIsInstance(UpperCamelCase__			,UpperCamelCase__	)
     self.assertIsInstance(video[0]			,np.ndarray	)

    # Test not batched input
    __UpperCAmelCase         =					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         =					image_processing(UpperCamelCase__			,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 			snake_case	(							self:    Any	):
    __UpperCAmelCase         =					self.image_processing_class(**self.image_processor_dict	)
    # create random PyTorch tensors
    __UpperCAmelCase         =					prepare_video_inputs(self.image_processor_tester			,equal_resolution=UpperCamelCase__			,torchify=UpperCamelCase__	)
    for video in video_inputs:
     self.assertIsInstance(UpperCamelCase__			,UpperCamelCase__	)
     self.assertIsInstance(video[0]			,torch.Tensor	)

    # Test not batched input
    __UpperCAmelCase         =					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         =					image_processing(UpperCamelCase__			,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'],
        )			,)




'''simple docstring'''




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,
)


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

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

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

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

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		Any       			=		['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

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



