





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 PoolFormerImageProcessor

class 		snake_case_   (							unittest.TestCase						):







	'''simple docstring'''




	def __init__(						self							:     str					,      __lowerCamelCase							:     Dict					,      __lowerCamelCase							:     Tuple=7					,      __lowerCamelCase							:     Dict=3					,      __lowerCamelCase							:     Optional[Any]=30					,      __lowerCamelCase							:     Optional[int]=400					,      __lowerCamelCase							:     str=True					,      __lowerCamelCase							:     str=None					,      __lowerCamelCase							:     Tuple=0.9					,      __lowerCamelCase							:     Any=None					,      __lowerCamelCase							:     Dict=True					,      __lowerCamelCase							:     Union[str, Any]=[0.5, 0.5, 0.5]					,      __lowerCamelCase							:     str=[0.5, 0.5, 0.5]					,      )       -> List[Any]:

			'''simple docstring'''




			__lowercase											=     size if size is not None else {'shortest_edge': 30}
			__lowercase											=     crop_size if crop_size is not None else {'height': 30, 'width': 30}
			__lowercase											=     parent
			__lowercase											=     batch_size
			__lowercase											=     num_channels
			__lowercase											=     min_resolution
			__lowercase											=     max_resolution
			__lowercase											=     do_resize_and_center_crop
			__lowercase											=     size
			__lowercase											=     crop_pct
			__lowercase											=     crop_size
			__lowercase											=     do_normalize
			__lowercase											=     image_mean
			__lowercase											=     image_std






	def    UpperCAmelCase			(						self							:     Union[str, Any]   )       -> Optional[int]:

			'''simple docstring'''




			return {
			    "size": self.size,
			    "do_resize_and_center_crop": self.do_resize_and_center_crop,
			    "crop_pct": self.crop_pct,
			    "crop_size": self.crop_size,
			    "do_normalize": self.do_normalize,
			    "image_mean": self.image_mean,
			    "image_std": self.image_std,
			}





@require_torch
@require_vision
class 		snake_case_   (							a_     ,     unittest.TestCase						):







	'''simple docstring'''
	__UpperCamelCase   =		PoolFormerImageProcessor if is_vision_available() else None




	def    UpperCAmelCase			(						self							:     Any   )       -> List[str]:

			'''simple docstring'''




			__lowercase											=     PoolFormerImageProcessingTester(self   )




	@property
	def    UpperCAmelCase			(						self							:     Union[str, Any]   )       -> str:

			'''simple docstring'''




			return self.image_processor_tester.prepare_image_processor_dict()




	def    UpperCAmelCase			(						self							:     int   )       -> Union[str, Any]:

			'''simple docstring'''




			__lowercase											=     self.image_processing_class(**self.image_processor_dict   )
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE					,      'do_resize_and_center_crop'   )   )
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE					,      'size'   )   )
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE					,      'crop_pct'   )   )
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE					,      'do_normalize'   )   )
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE					,      'image_mean'   )   )
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE					,      'image_std'   )   )




	def    UpperCAmelCase			(						self							:     List[Any]   )       -> str:

			'''simple docstring'''




			__lowercase											=     self.image_processing_class.from_dict(self.image_processor_dict   )
			self.assertEqual(image_processor.size					,      {'shortest_edge': 30}   )
			self.assertEqual(image_processor.crop_size					,      {'height': 30, 'width': 30}   )

			__lowercase											=     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							:     int   )       -> Any:

			'''simple docstring'''




			pass




	def    UpperCAmelCase			(						self							:     Union[str, Any]   )       -> List[Any]:

			'''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=_SCREAMING_SNAKE_CASE   )
			for image in image_inputs:
					self.assertIsInstance(_SCREAMING_SNAKE_CASE					,      Image.Image   )

			# Test not batched input
			__lowercase											=     image_processing(image_inputs[0]					,      return_tensors='pt'   ).pixel_values

			self.assertEqual(
			    encoded_images.shape					,      (
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    )					,      )

			# Test batched
			__lowercase											=     image_processing(_SCREAMING_SNAKE_CASE					,      return_tensors='pt'   ).pixel_values
			self.assertEqual(
			    encoded_images.shape					,      (
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    )					,      )




	def    UpperCAmelCase			(						self							:     List[str]   )       -> Dict:

			'''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=_SCREAMING_SNAKE_CASE					,      numpify=_SCREAMING_SNAKE_CASE   )
			for image in image_inputs:
					self.assertIsInstance(_SCREAMING_SNAKE_CASE					,      np.ndarray   )

			# Test not batched input
			__lowercase											=     image_processing(image_inputs[0]					,      return_tensors='pt'   ).pixel_values
			self.assertEqual(
			    encoded_images.shape					,      (
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    )					,      )

			# Test batched
			__lowercase											=     image_processing(_SCREAMING_SNAKE_CASE					,      return_tensors='pt'   ).pixel_values
			self.assertEqual(
			    encoded_images.shape					,      (
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    )					,      )






	def    UpperCAmelCase			(						self							:     Union[str, Any]   )       -> List[Any]:

			'''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=_SCREAMING_SNAKE_CASE					,      torchify=_SCREAMING_SNAKE_CASE   )
			for image in image_inputs:
					self.assertIsInstance(_SCREAMING_SNAKE_CASE					,      torch.Tensor   )

			# Test not batched input
			__lowercase											=     image_processing(image_inputs[0]					,      return_tensors='pt'   ).pixel_values
			self.assertEqual(
			    encoded_images.shape					,      (
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    )					,      )

			# Test batched
			__lowercase											=     image_processing(_SCREAMING_SNAKE_CASE					,      return_tensors='pt'   ).pixel_values
			self.assertEqual(
			    encoded_images.shape					,      (
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    )					,      )







'''simple docstring'''



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


_UpperCamelCase						:  Optional[int]							       =				logging.get_logger(__name__)

_UpperCamelCase						:  Tuple							       =				{
    "google/canine-s": "https://huggingface.co/google/canine-s/resolve/main/config.json",
    # See all CANINE models at https://huggingface.co/models?filter=canine
}



class 				_snake_case					(   a_			):
  SCREAMING_SNAKE_CASE			:       List[Any]				=     '''canine'''

  def __init__(				self      , _SCREAMING_SNAKE_CASE=7_68      , _SCREAMING_SNAKE_CASE=12      , _SCREAMING_SNAKE_CASE=12      , _SCREAMING_SNAKE_CASE=30_72      , _SCREAMING_SNAKE_CASE="gelu"      , _SCREAMING_SNAKE_CASE=0.1      , _SCREAMING_SNAKE_CASE=0.1      , _SCREAMING_SNAKE_CASE=1_63_84      , _SCREAMING_SNAKE_CASE=16      , _SCREAMING_SNAKE_CASE=0.02      , _SCREAMING_SNAKE_CASE=1e-12      , _SCREAMING_SNAKE_CASE=0      , _SCREAMING_SNAKE_CASE=0Xe0_00      , _SCREAMING_SNAKE_CASE=0Xe0_01      , _SCREAMING_SNAKE_CASE=4      , _SCREAMING_SNAKE_CASE=4      , _SCREAMING_SNAKE_CASE=8      , _SCREAMING_SNAKE_CASE=1_63_84      , _SCREAMING_SNAKE_CASE=1_28      , **_SCREAMING_SNAKE_CASE      , ):





      '''simple docstring'''

      super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE      , bos_token_id=_SCREAMING_SNAKE_CASE      , eos_token_id=_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							max_position_embeddings
      lowerCAmelCase 						=							hidden_size
      lowerCAmelCase 						=							num_hidden_layers
      lowerCAmelCase 						=							num_attention_heads
      lowerCAmelCase 						=							intermediate_size
      lowerCAmelCase 						=							hidden_act
      lowerCAmelCase 						=							hidden_dropout_prob
      lowerCAmelCase 						=							attention_probs_dropout_prob
      lowerCAmelCase 						=							initializer_range
      lowerCAmelCase 						=							type_vocab_size
      lowerCAmelCase 						=							layer_norm_eps

      # Character config:
      lowerCAmelCase 						=							downsampling_rate
      lowerCAmelCase 						=							upsampling_kernel_size
      lowerCAmelCase 						=							num_hash_functions
      lowerCAmelCase 						=							num_hash_buckets
      lowerCAmelCase 						=							local_transformer_stride





