





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowerCamelCase :  Any										=  logging.get_logger(__name__)

__lowerCamelCase :  int										=  {
    """google/mobilenet_v1_1.0_224""": """https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v1_0.75_192""": """https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json""",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}



class       A__    ( __snake_case    ):
      _UpperCAmelCase			:List[Any]							=     'mobilenet_v1'





      def __init__(				self ,							A_=3 ,							A_=224 ,							A_=1.0 ,							A_=8 ,							A_="relu6" ,							A_=True ,							A_=0.9_99 ,							A_=0.02 ,							A_=0.0_01 ,							**A_ ,							):
       '''simple docstring'''




       super().__init__(**A_     )

       if depth_multiplier <= 0:
        raise ValueError("depth_multiplier must be greater than zero."     )

       UpperCamelCase			:      List[str] 			=     num_channels
       UpperCamelCase			:      Any 			=     image_size
       UpperCamelCase			:      Optional[Any] 			=     depth_multiplier
       UpperCamelCase			:      Union[str, Any] 			=     min_depth
       UpperCamelCase			:      int 			=     hidden_act
       UpperCamelCase			:      Union[str, Any] 			=     tf_padding
       UpperCamelCase			:      Optional[Any] 			=     classifier_dropout_prob
       UpperCamelCase			:      int 			=     initializer_range
       UpperCamelCase			:      Tuple 			=     layer_norm_eps



class       A__    ( __snake_case    ):
      _UpperCAmelCase			:Any							=     version.parse('1.11'    )





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return OrderedDict([("pixel_values", {0: "batch"})]     )





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       if self.task == "image-classification":
        return OrderedDict([("logits", {0: "batch"})]     )
       else:
        return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]     )





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return 1e-4







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 VivitImageProcessor



class       A__     ( unittest.TestCase    ):





      def __init__(				self ,							A_ ,							A_=7 ,							A_=3 ,							A_=10 ,							A_=18 ,							A_=30 ,							A_=400 ,							A_=True ,							A_=None ,							A_=True ,							A_=[0.5, 0.5, 0.5] ,							A_=[0.5, 0.5, 0.5] ,							A_=None ,							):
       '''simple docstring'''




       UpperCamelCase			:      Optional[int] 			=     size if size is not None else {"shortest_edge": 18}
       UpperCamelCase			:      Tuple 			=     crop_size if crop_size is not None else {"height": 18, "width": 18}

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





      def  __UpperCamelCase(				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       A__     ( __snake_case ,     unittest.TestCase    ):
      _UpperCAmelCase			:List[str]							=     VivitImageProcessor if is_vision_available() else None





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      List[Any] 			=     VivitImageProcessingTester(self     )





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return self.image_processor_tester.prepare_image_processor_dict()





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      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     ):
       '''simple docstring'''




       UpperCamelCase			:      Dict 			=     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			:      Dict 			=     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  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[Any] 			=     self.image_processing_class(**self.image_processor_dict     )
       # create random PIL videos
       UpperCamelCase			:      Union[str, Any] 			=     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
       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			:      str 			=     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     ):
       '''simple docstring'''




       UpperCamelCase			:      List[Any] 			=     self.image_processing_class(**self.image_processor_dict     )
       # create random numpy tensors
       UpperCamelCase			:      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
       UpperCamelCase			:      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
       UpperCamelCase			:      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     ):
       '''simple docstring'''




       UpperCamelCase			:      str 			=     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=A_ ,							torchify=A_     )
       for video in video_inputs:
        self.assertIsInstance(A_ ,							A_     )
        self.assertIsInstance(video[0] ,							torch.Tensor     )

       # Test not batched input
       UpperCamelCase			:      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
       UpperCamelCase			:      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"],
           ) ,							)
