

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_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import GLPNImageProcessor





class 					__lowerCAmelCase	(   unittest.TestCase				):




  def __init__(							self				,  lowerCAmelCase				,  lowerCAmelCase=7				,  lowerCAmelCase=3				,  lowerCAmelCase=18				,  lowerCAmelCase=30				,  lowerCAmelCase=400				,  lowerCAmelCase=True				,  lowerCAmelCase=32				,  lowerCAmelCase=True				,  )    ->					List[str]:
        '''simple docstring'''






        _lowercase      			=parent
        _lowercase      			=batch_size
        _lowercase      			=num_channels
        _lowercase      			=image_size
        _lowercase      			=min_resolution
        _lowercase      			=max_resolution
        _lowercase      			=do_resize
        _lowercase      			=size_divisor
        _lowercase      			=do_rescale




  def     A__	(							self   )    ->					str:
        '''simple docstring'''






        return {
            "do_resize": self.do_resize,
            "size_divisor": self.size_divisor,
            "do_rescale": self.do_rescale,
        }





@require_torch
@require_vision
class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE     ,     unittest.TestCase				):
  _a		     =    GLPNImageProcessor if is_vision_available() else None




  def     A__	(							self   )    ->					int:
        '''simple docstring'''






        _lowercase      			=GLPNImageProcessingTester(self   )




  @property
  def     A__	(							self   )    ->					str:
        '''simple docstring'''






        return self.image_processor_tester.prepare_image_processor_dict()




  def     A__	(							self   )    ->					List[Any]:
        '''simple docstring'''






        _lowercase      			=self.image_processing_class(**self.image_processor_dict   )
        self.assertTrue(hasattr(lowerCAmelCase				,  'do_resize'   )   )
        self.assertTrue(hasattr(lowerCAmelCase				,  'size_divisor'   )   )
        self.assertTrue(hasattr(lowerCAmelCase				,  'resample'   )   )
        self.assertTrue(hasattr(lowerCAmelCase				,  'do_rescale'   )   )




  def     A__	(							self   )    ->					Optional[int]:
        '''simple docstring'''






        pass




  def     A__	(							self   )    ->					Optional[int]:
        '''simple docstring'''






        _lowercase      			=self.image_processing_class(**self.image_processor_dict   )
        # create random PIL images
        _lowercase      			=prepare_image_inputs(self.image_processor_tester				,  equal_resolution=lowerCAmelCase   )
        for image in image_inputs:
              self.assertIsInstance(lowerCAmelCase				,  Image.Image   )

        # Test not batched input (GLPNImageProcessor doesn't support batching)
        _lowercase      			=image_processing(image_inputs[0]				,  return_tensors='pt'   ).pixel_values
        self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0   )
        self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0   )




  def     A__	(							self   )    ->					Optional[Any]:
        '''simple docstring'''






        _lowercase      			=self.image_processing_class(**self.image_processor_dict   )
        # create random numpy tensors
        _lowercase      			=prepare_image_inputs(self.image_processor_tester				,  equal_resolution=lowerCAmelCase				,  numpify=lowerCAmelCase   )
        for image in image_inputs:
              self.assertIsInstance(lowerCAmelCase				,  np.ndarray   )

        # Test not batched input (GLPNImageProcessor doesn't support batching)
        _lowercase      			=image_processing(image_inputs[0]				,  return_tensors='pt'   ).pixel_values
        self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0   )
        self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0   )




  def     A__	(							self   )    ->					str:
        '''simple docstring'''






        _lowercase      			=self.image_processing_class(**self.image_processor_dict   )
        # create random PyTorch tensors
        _lowercase      			=prepare_image_inputs(self.image_processor_tester				,  equal_resolution=lowerCAmelCase				,  torchify=lowerCAmelCase   )
        for image in image_inputs:
              self.assertIsInstance(lowerCAmelCase				,  torch.Tensor   )

        # Test not batched input (GLPNImageProcessor doesn't support batching)
        _lowercase      			=image_processing(image_inputs[0]				,  return_tensors='pt'   ).pixel_values
        self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0   )
        self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0   )





from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_   							=  logging.get_logger(__name__)

lowercase_   							=  {
    'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}





class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):
  _a		     =    """timesformer"""




  def __init__(							self				,  lowerCAmelCase=224				,  lowerCAmelCase=16				,  lowerCAmelCase=3				,  lowerCAmelCase=8				,  lowerCAmelCase=768				,  lowerCAmelCase=12				,  lowerCAmelCase=12				,  lowerCAmelCase=3_072				,  lowerCAmelCase="gelu"				,  lowerCAmelCase=0.0				,  lowerCAmelCase=0.0				,  lowerCAmelCase=0.02				,  lowerCAmelCase=1e-6				,  lowerCAmelCase=True				,  lowerCAmelCase="divided_space_time"				,  lowerCAmelCase=0				,  **lowerCAmelCase				,  )    ->					Any:
        '''simple docstring'''






        super().__init__(**lowerCAmelCase   )

        _lowercase      			=image_size
        _lowercase      			=patch_size
        _lowercase      			=num_channels
        _lowercase      			=num_frames

        _lowercase      			=hidden_size
        _lowercase      			=num_hidden_layers
        _lowercase      			=num_attention_heads
        _lowercase      			=intermediate_size
        _lowercase      			=hidden_act
        _lowercase      			=hidden_dropout_prob
        _lowercase      			=attention_probs_dropout_prob
        _lowercase      			=initializer_range
        _lowercase      			=layer_norm_eps
        _lowercase      			=qkv_bias

        _lowercase      			=attention_type
        _lowercase      			=drop_path_rate


