"""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 					UpperCAmelCase_     (   unittest.TestCase):


      def __init__(       self		,      a		,      a=7		,      a=3		,      a=1_0		,      a=1_8		,      a=3_0		,      a=4_0_0		,      a=True		,      a=None		,      a=True		,      a=[0.5, 0.5, 0.5]		,      a=[0.5, 0.5, 0.5]		,      a=None		,      )	-> Optional[Any]:
             lowercase__      :		Optional[Any]          =     size if size is not None else {'shortest_edge': 1_8}
             lowercase__      :		Tuple          =     crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}

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


      def 		_UpperCAmelCase	(       self							)	-> Optional[Any]:
             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 					UpperCAmelCase_     (   _a			,							unittest.TestCase):
      lowerCamelCase__		:   int			  =  VideoMAEImageProcessor if is_vision_available() else None


      def 		_UpperCAmelCase	(       self							)	-> List[Any]:
             lowercase__      :		Optional[int]          =     VideoMAEImageProcessingTester(self							)


      @property
      def 		_UpperCAmelCase	(       self							)	-> Optional[int]:
             return self.image_processor_tester.prepare_image_processor_dict()


      def 		_UpperCAmelCase	(       self							)	-> str:
             lowercase__      :		Tuple          =     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							)	-> List[str]:
             lowercase__      :		List[Any]          =     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}							)

             lowercase__      :		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 		_UpperCAmelCase	(       self							)	-> List[Any]:
             pass


      def 		_UpperCAmelCase	(       self							)	-> Union[str, Any]:
             # Initialize image_processing
             lowercase__      :		List[Any]          =     self.image_processing_class(**self.image_processor_dict							)
             # create random PIL videos
             lowercase__      :		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
             lowercase__      :		Tuple          =     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__      :		int          =     image_processing(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[Any]:
             # Initialize image_processing
             lowercase__      :		Optional[Any]          =     self.image_processing_class(**self.image_processor_dict							)
             # create random numpy tensors
             lowercase__      :		List[Any]          =     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
             lowercase__      :		Optional[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__      :		List[Any]          =     image_processing(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							)	-> str:
             # Initialize image_processing
             lowercase__      :		Any          =     self.image_processing_class(**self.image_processor_dict							)
             # create random PyTorch tensors
             lowercase__      :		Union[str, Any]          =     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
             lowercase__      :		Union[str, 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__      :		Dict          =     image_processing(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'],
                 )		,      )


"""simple docstring"""





from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_UpperCamelCase       :						int     =   {
    "configuration_retribert": ["RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RetriBertConfig"],
    "tokenization_retribert": ["RetriBertTokenizer"],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _UpperCamelCase       :						Tuple     =   ["RetriBertTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _UpperCamelCase       :						int     =   [
           "RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "RetriBertModel",
           "RetriBertPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig
       from .tokenization_retribert import RetriBertTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_retribert_fast import RetriBertTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_retribert import (
                  RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RetriBertModel,
                  RetriBertPreTrainedModel,
              )

else:
       import sys

       _UpperCamelCase       :						Tuple     =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

