





"""simple docstring"""







from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFImageClassifierOutputWithNoAttention,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_resnet import ResNetConfig


SCREAMING_SNAKE_CASE_      =					logging.get_logger(__name__)

# General docstring
SCREAMING_SNAKE_CASE_      =					'ResNetConfig'

# Base docstring
SCREAMING_SNAKE_CASE_      =					'microsoft/resnet-50'
SCREAMING_SNAKE_CASE_      =					[1, 2048, 7, 7]

# Image classification docstring
SCREAMING_SNAKE_CASE_      =					'microsoft/resnet-50'
SCREAMING_SNAKE_CASE_      =					'tiger cat'

SCREAMING_SNAKE_CASE_      =					[
    'microsoft/resnet-50',
    # See all resnet models at https://huggingface.co/models?filter=resnet
]

class 							snake_case_ (				tf.keras.layers.Layer ):







   """simple docstring"""




   def __init__(		self					,				lowerCamelCase_					,				lowerCamelCase_ = 3					,				lowerCamelCase_ = 1					,				lowerCamelCase_ = "relu"					,				**lowerCamelCase_)							->      None:
       super().__init__(**lowercase_)
       UpperCamelCase										=  kernel_size // 2
       UpperCamelCase										=  tf.keras.layers.ConvaD(
           lowercase_					,				kernel_size=lowercase_					,				strides=lowercase_					,				padding='''valid'''					,				use_bias=lowercase_					,				name='''convolution''')
       # Use same default momentum and epsilon as PyTorch equivalent
       UpperCamelCase										=  tf.keras.layers.BatchNormalization(epsilon=1e-5					,				momentum=0.9					,				name='''normalization''')
       UpperCamelCase										=  ACTaFN[activation] if activation is not None else tf.keras.layers.Activation('''linear''')







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_)							->      tf.Tensor:
       UpperCamelCase										=  (self.pad_value, self.pad_value)
       UpperCamelCase										=  tf.pad(lowercase_					,				[(0, 0), height_pad, width_pad, (0, 0)])
       UpperCamelCase										=  self.conv(lowercase_)
       return hidden_state







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_ = False)							->      tf.Tensor:
       UpperCamelCase										=  self.convolution(lowercase_)
       UpperCamelCase										=  self.normalization(lowercase_					,				training=lowercase_)
       UpperCamelCase										=  self.activation(lowercase_)
       return hidden_state

class 							snake_case_ (				tf.keras.layers.Layer ):







   """simple docstring"""




   def __init__(		self					,				lowerCamelCase_					,				**lowerCamelCase_)							->      None:
       super().__init__(**lowercase_)
       UpperCamelCase										=  TFResNetConvLayer(
           config.embedding_size					,				kernel_size=7					,				stride=2					,				activation=config.hidden_act					,				name='''embedder'''					,				)
       UpperCamelCase										=  tf.keras.layers.MaxPoolaD(pool_size=3					,				strides=2					,				padding='''valid'''					,				name='''pooler''')
       UpperCamelCase										=  config.num_channels







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_ = False)							->      tf.Tensor:
       UpperCamelCase										=  shape_list(lowercase_)
       if tf.executing_eagerly() and num_channels != self.num_channels:
           raise ValueError(
               '''Make sure that the channel dimension of the pixel values match with the one set in the configuration.''')
       UpperCamelCase										=  pixel_values
       UpperCamelCase										=  self.embedder(lowercase_)
       UpperCamelCase										=  tf.pad(lowercase_					,				[[0, 0], [1, 1], [1, 1], [0, 0]])
       UpperCamelCase										=  self.pooler(lowercase_)
       return hidden_state

class 							snake_case_ (				tf.keras.layers.Layer ):







   """simple docstring"""




   def __init__(		self					,				lowerCamelCase_					,				lowerCamelCase_ = 2					,				**lowerCamelCase_)							->      None:
       super().__init__(**lowercase_)
       UpperCamelCase										=  tf.keras.layers.ConvaD(
           lowercase_					,				kernel_size=1					,				strides=lowercase_					,				use_bias=lowercase_					,				name='''convolution''')
       # Use same default momentum and epsilon as PyTorch equivalent
       UpperCamelCase										=  tf.keras.layers.BatchNormalization(epsilon=1e-5					,				momentum=0.9					,				name='''normalization''')







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_ = False)							->      tf.Tensor:
       UpperCamelCase										=  x
       UpperCamelCase										=  self.convolution(lowercase_)
       UpperCamelCase										=  self.normalization(lowercase_					,				training=lowercase_)
       return hidden_state

class 							snake_case_ (				tf.keras.layers.Layer ):







   """simple docstring"""




   def __init__(		self					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_ = 1					,				lowerCamelCase_ = "relu"					,				**lowerCamelCase_)							->      None:
       super().__init__(**lowercase_)
       UpperCamelCase										=  in_channels != out_channels or stride != 1
       UpperCamelCase										=  TFResNetConvLayer(lowercase_					,				stride=lowercase_					,				name='''layer.0''')
       UpperCamelCase										=  TFResNetConvLayer(lowercase_					,				activation=lowercase_					,				name='''layer.1''')
       UpperCamelCase										=  (
           TFResNetShortCut(lowercase_					,				stride=lowercase_					,				name='''shortcut''')
           if should_apply_shortcut
           else tf.keras.layers.Activation('''linear'''					,				name='''shortcut''')
       )
       UpperCamelCase										=  ACTaFN[activation]







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_ = False)							->      tf.Tensor:
       UpperCamelCase										=  hidden_state
       UpperCamelCase										=  self.conva(lowercase_					,				training=lowercase_)
       UpperCamelCase										=  self.conva(lowercase_					,				training=lowercase_)
       UpperCamelCase										=  self.shortcut(lowercase_					,				training=lowercase_)
       hidden_state += residual
       UpperCamelCase										=  self.activation(lowercase_)
       return hidden_state

class 							snake_case_ (				tf.keras.layers.Layer ):







   """simple docstring"""




   def __init__(		self					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_ = 1					,				lowerCamelCase_ = "relu"					,				lowerCamelCase_ = 4					,				**lowerCamelCase_					,				)							->      None:
       super().__init__(**lowercase_)
       UpperCamelCase										=  in_channels != out_channels or stride != 1
       UpperCamelCase										=  out_channels // reduction
       UpperCamelCase										=  TFResNetConvLayer(lowercase_					,				kernel_size=1					,				name='''layer.0''')
       UpperCamelCase										=  TFResNetConvLayer(lowercase_					,				stride=lowercase_					,				name='''layer.1''')
       UpperCamelCase										=  TFResNetConvLayer(lowercase_					,				kernel_size=1					,				activation=lowercase_					,				name='''layer.2''')
       UpperCamelCase										=  (
           TFResNetShortCut(lowercase_					,				stride=lowercase_					,				name='''shortcut''')
           if should_apply_shortcut
           else tf.keras.layers.Activation('''linear'''					,				name='''shortcut''')
       )
       UpperCamelCase										=  ACTaFN[activation]







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_ = False)							->      tf.Tensor:
       UpperCamelCase										=  hidden_state
       UpperCamelCase										=  self.conva(lowercase_					,				training=lowercase_)
       UpperCamelCase										=  self.conva(lowercase_					,				training=lowercase_)
       UpperCamelCase										=  self.conva(lowercase_					,				training=lowercase_)
       UpperCamelCase										=  self.shortcut(lowercase_					,				training=lowercase_)
       hidden_state += residual
       UpperCamelCase										=  self.activation(lowercase_)
       return hidden_state

class 							snake_case_ (				tf.keras.layers.Layer ):







   """simple docstring"""




   def __init__(		self					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_ = 2					,				lowerCamelCase_ = 2					,				**lowerCamelCase_)							->      None:
       super().__init__(**lowercase_)

       UpperCamelCase										=  TFResNetBottleNeckLayer if config.layer_type == '''bottleneck''' else TFResNetBasicLayer

       UpperCamelCase										=  [layer(lowercase_					,				lowercase_					,				stride=lowercase_					,				activation=config.hidden_act					,				name='''layers.0''')]
       layers += [
           layer(lowercase_					,				lowercase_					,				activation=config.hidden_act					,				name=F'layers.{i + 1}')
           for i in range(depth - 1)
       ]
       UpperCamelCase										=  layers







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_ = False)							->      tf.Tensor:

       for layer in self.stage_layers:
           UpperCamelCase										=  layer(lowercase_					,				training=lowercase_)
       return hidden_state

class 							snake_case_ (				tf.keras.layers.Layer ):







   """simple docstring"""




   def __init__(		self					,				lowerCamelCase_					,				**lowerCamelCase_)							->      None:
       super().__init__(**lowercase_)
       # based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
       UpperCamelCase										=  [
           TFResNetStage(
               lowercase_					,				config.embedding_size					,				config.hidden_sizes[0]					,				stride=2 if config.downsample_in_first_stage else 1					,				depth=config.depths[0]					,				name='''stages.0'''					,				)
       ]
       for i, (in_channels, out_channels, depth) in enumerate(
           zip(config.hidden_sizes					,				config.hidden_sizes[1:]					,				config.depths[1:])):
           self.stages.append(TFResNetStage(lowercase_					,				lowercase_					,				lowercase_					,				depth=lowercase_					,				name=F'stages.{i + 1}'))







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_ = False					,				lowerCamelCase_ = True					,				lowerCamelCase_ = False					,				)							->      TFBaseModelOutputWithNoAttention:
       UpperCamelCase										=  () if output_hidden_states else None

       for stage_module in self.stages:
           if output_hidden_states:
               UpperCamelCase										=  hidden_states + (hidden_state,)

           UpperCamelCase										=  stage_module(lowercase_					,				training=lowercase_)

       if output_hidden_states:
           UpperCamelCase										=  hidden_states + (hidden_state,)

       if not return_dict:
           return tuple(v for v in [hidden_state, hidden_states] if v is not None)

       return TFBaseModelOutputWithNoAttention(last_hidden_state=lowercase_					,				hidden_states=lowercase_)

class 							snake_case_ (				__UpperCAmelCase ):







   """simple docstring"""




   A_										=					ResNetConfig
   A_										=					'''resnet'''
   A_										=					'''pixel_values'''







   @property
   def      UpperCAmelCase__ (		self)							->      str:
       return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_2_4, 2_2_4)					,				dtype=tf.floataa)}
SCREAMING_SNAKE_CASE_      =					R'\n    This model is a TensorFlow\n    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a\n    regular TensorFlow Module and refer to the TensorFlow documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n'


SCREAMING_SNAKE_CASE_      =					R'\n    Args:\n        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConvNextImageProcessor.__call__`] for details.\n\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'

@keras_serializable
class 							snake_case_ (				tf.keras.layers.Layer ):







   """simple docstring"""




   A_										=					ResNetConfig







   def __init__(		self					,				lowerCamelCase_					,				**lowerCamelCase_)							->      None:
       super().__init__(**lowercase_)
       UpperCamelCase										=  config
       UpperCamelCase										=  TFResNetEmbeddings(lowercase_					,				name='''embedder''')
       UpperCamelCase										=  TFResNetEncoder(lowercase_					,				name='''encoder''')
       UpperCamelCase										=  tf.keras.layers.GlobalAveragePoolingaD(keepdims=lowercase_)







   @unpack_inputs
   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_ = None					,				lowerCamelCase_ = None					,				lowerCamelCase_ = False					,				)							->      Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:
       UpperCamelCase										=  (
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       UpperCamelCase										=  return_dict if return_dict is not None else self.config.use_return_dict

       # TF 2.0 image layers can't use NCHW format when running on CPU.
       # We transpose to NHWC format and then transpose back after the full forward pass.
       # (batch_size, num_channels, height, width) -> (batch_size, height, width, num_channels)
       UpperCamelCase										=  tf.transpose(lowercase_					,				perm=[0, 2, 3, 1])
       UpperCamelCase										=  self.embedder(lowercase_					,				training=lowercase_)

       UpperCamelCase										=  self.encoder(
           lowercase_					,				output_hidden_states=lowercase_					,				return_dict=lowercase_					,				training=lowercase_)

       UpperCamelCase										=  encoder_outputs[0]

       UpperCamelCase										=  self.pooler(lowercase_)

       # Transpose all the outputs to the NCHW format
       # (batch_size, height, width, num_channels) -> (batch_size, num_channels, height, width)
       UpperCamelCase										=  tf.transpose(lowercase_					,				(0, 3, 1, 2))
       UpperCamelCase										=  tf.transpose(lowercase_					,				(0, 3, 1, 2))
       UpperCamelCase										=  ()
       for hidden_state in encoder_outputs[1:]:
           UpperCamelCase										=  hidden_states + tuple(tf.transpose(lowercase_					,				(0, 3, 1, 2)) for h in hidden_state)

       if not return_dict:
           return (last_hidden_state, pooled_output) + hidden_states

       UpperCamelCase										=  hidden_states if output_hidden_states else None

       return TFBaseModelOutputWithPoolingAndNoAttention(
           last_hidden_state=lowercase_					,				pooler_output=lowercase_					,				hidden_states=lowercase_					,				)

@add_start_docstrings(
    '''The bare ResNet model outputting raw features without any specific head on top.'''  ,   __UpperCAmelCase  ,   )
class 							snake_case_ (				__UpperCAmelCase ):







   """simple docstring"""




   def __init__(		self					,				lowerCamelCase_					,				**lowerCamelCase_)							->      None:
       super().__init__(lowercase_					,				**lowercase_)
       UpperCamelCase										=  TFResNetMainLayer(config=lowercase_					,				name='''resnet''')







   @add_start_docstrings_to_model_forward(lowercase_)
   @add_code_sample_docstrings(
       checkpoint=_CHECKPOINT_FOR_DOC					,				output_type=lowercase_					,				config_class=_CONFIG_FOR_DOC					,				modality='''vision'''					,				expected_output=_EXPECTED_OUTPUT_SHAPE					,				)
   @unpack_inputs
   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_ = None					,				lowerCamelCase_ = None					,				lowerCamelCase_ = False					,				)							->      Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:
       UpperCamelCase										=  (
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       UpperCamelCase										=  return_dict if return_dict is not None else self.config.use_return_dict

       UpperCamelCase										=  self.resnet(
           pixel_values=lowercase_					,				output_hidden_states=lowercase_					,				return_dict=lowercase_					,				training=lowercase_					,				)
       return resnet_outputs

@add_start_docstrings(
    '''
    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    '''  ,   __UpperCAmelCase  ,   )
class 							snake_case_ (				__UpperCAmelCase  ,   __UpperCAmelCase ):







   """simple docstring"""




   def __init__(		self					,				lowerCamelCase_					,				**lowerCamelCase_)							->      None:
       super().__init__(lowercase_					,				**lowercase_)
       UpperCamelCase										=  config.num_labels
       UpperCamelCase										=  TFResNetMainLayer(lowercase_					,				name='''resnet''')
       # classification head
       UpperCamelCase										=  (
           tf.keras.layers.Dense(config.num_labels					,				name='''classifier.1''')
           if config.num_labels > 0
           else tf.keras.layers.Activation('''linear'''					,				name='''classifier.1''')
       )







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_)							->      tf.Tensor:
       UpperCamelCase										=  tf.keras.layers.Flatten()(lowercase_)
       UpperCamelCase										=  self.classifier_layer(lowercase_)
       return logits







   @add_start_docstrings_to_model_forward(lowercase_)
   @add_code_sample_docstrings(
       checkpoint=_IMAGE_CLASS_CHECKPOINT					,				output_type=lowercase_					,				config_class=_CONFIG_FOR_DOC					,				expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT					,				)
   @unpack_inputs
   def      UpperCAmelCase__ (		self					,				lowerCamelCase_ = None					,				lowerCamelCase_ = None					,				lowerCamelCase_ = None					,				lowerCamelCase_ = None					,				lowerCamelCase_ = False					,				)							->      Union[Tuple[tf.Tensor], TFImageClassifierOutputWithNoAttention]:
       UpperCamelCase										=  return_dict if return_dict is not None else self.config.use_return_dict

       UpperCamelCase										=  self.resnet(
           lowercase_					,				output_hidden_states=lowercase_					,				return_dict=lowercase_					,				training=lowercase_)

       UpperCamelCase										=  outputs.pooler_output if return_dict else outputs[1]

       UpperCamelCase										=  self.classifier(lowercase_)

       UpperCamelCase										=  None if labels is None else self.hf_compute_loss(lowercase_					,				lowercase_)

       if not return_dict:
           UpperCamelCase										=  (logits,) + outputs[2:]
           return (loss,) + output if loss is not None else output

       return TFImageClassifierOutputWithNoAttention(loss=lowercase_					,				logits=lowercase_					,				hidden_states=outputs.hidden_states)





"""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 							snake_case_ (				unittest.TestCase ):







   """simple docstring"""




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

       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      UpperCAmelCase__ (		self)							->      Tuple:
       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 ):







   """simple docstring"""




   A_										=					VideoMAEImageProcessor if is_vision_available() else None







   def      UpperCAmelCase__ (		self)							->      Optional[Any]:
       UpperCamelCase										=  VideoMAEImageProcessingTester(self)







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







   def      UpperCAmelCase__ (		self)							->      List[Any]:
       UpperCamelCase										=  self.image_processing_class(**self.image_processor_dict)
       self.assertTrue(hasattr(lowerCamelCase_					,				'''image_mean'''))
       self.assertTrue(hasattr(lowerCamelCase_					,				'''image_std'''))
       self.assertTrue(hasattr(lowerCamelCase_					,				'''do_normalize'''))
       self.assertTrue(hasattr(lowerCamelCase_					,				'''do_resize'''))
       self.assertTrue(hasattr(lowerCamelCase_					,				'''do_center_crop'''))
       self.assertTrue(hasattr(lowerCamelCase_					,				'''size'''))







   def      UpperCAmelCase__ (		self)							->      Dict:
       UpperCamelCase										=  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})

       UpperCamelCase										=  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[str]:
       pass







   def      UpperCAmelCase__ (		self)							->      Dict:
       # Initialize image_processing
       UpperCamelCase										=  self.image_processing_class(**self.image_processor_dict)
       # create random PIL videos
       UpperCamelCase										=  prepare_video_inputs(self.image_processor_tester					,				equal_resolution=lowerCamelCase_)
       for video in video_inputs:
           self.assertIsInstance(lowerCamelCase_					,				lowerCamelCase_)
           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(lowerCamelCase_					,				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)							->      Dict:
       # Initialize image_processing
       UpperCamelCase										=  self.image_processing_class(**self.image_processor_dict)
       # create random numpy tensors
       UpperCamelCase										=  prepare_video_inputs(self.image_processor_tester					,				equal_resolution=lowerCamelCase_					,				numpify=lowerCamelCase_)
       for video in video_inputs:
           self.assertIsInstance(lowerCamelCase_					,				lowerCamelCase_)
           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(lowerCamelCase_					,				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)							->      Optional[int]:
       # Initialize image_processing
       UpperCamelCase										=  self.image_processing_class(**self.image_processor_dict)
       # create random PyTorch tensors
       UpperCamelCase										=  prepare_video_inputs(self.image_processor_tester					,				equal_resolution=lowerCamelCase_					,				torchify=lowerCamelCase_)
       for video in video_inputs:
           self.assertIsInstance(lowerCamelCase_					,				lowerCamelCase_)
           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(lowerCamelCase_					,				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'''],
           )					,				)