






"""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_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import ConvNextImageProcessor







class 					lowerCamelCase__					(       unittest.TestCase ):






    def __init__( self	,A	,A=7	,A=3	,A=18	,A=30	,A=400	,A=True	,A=None	,A=0.875	,A=True	,A=[0.5, 0.5, 0.5]	,A=[0.5, 0.5, 0.5]	,):
          UpperCAmelCase							=       size if size is not None else {"""shortest_edge""": 20}
          UpperCAmelCase							=       parent
          UpperCAmelCase							=       batch_size
          UpperCAmelCase							=       num_channels
          UpperCAmelCase							=       image_size
          UpperCAmelCase							=       min_resolution
          UpperCAmelCase							=       max_resolution
          UpperCAmelCase							=       do_resize
          UpperCAmelCase							=       size
          UpperCAmelCase							=       crop_pct
          UpperCAmelCase							=       do_normalize
          UpperCAmelCase							=       image_mean
          UpperCAmelCase							=       image_std






    def 				_UpperCamelCase							( self					):
          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_pct": self.crop_pct,
          }







@require_torch
@require_vision
class 					lowerCamelCase__					(       a_    ,     unittest.TestCase ):
    SCREAMING_SNAKE_CASE		=			ConvNextImageProcessor if is_vision_available() else None






    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       ConvNextImageProcessingTester(self					)






    @property
    def 				_UpperCamelCase							( self					):
          return self.image_processor_tester.prepare_image_processor_dict()






    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       self.image_processing_class(**self.image_processor_dict					)
          self.assertTrue(hasattr(snake_case__	,"""do_resize"""					)					)
          self.assertTrue(hasattr(snake_case__	,"""size"""					)					)
          self.assertTrue(hasattr(snake_case__	,"""crop_pct"""					)					)
          self.assertTrue(hasattr(snake_case__	,"""do_normalize"""					)					)
          self.assertTrue(hasattr(snake_case__	,"""image_mean"""					)					)
          self.assertTrue(hasattr(snake_case__	,"""image_std"""					)					)






    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       self.image_processing_class.from_dict(self.image_processor_dict					)
          self.assertEqual(image_processor.size	,{"""shortest_edge""": 20}					)

          UpperCAmelCase							=       self.image_processing_class.from_dict(self.image_processor_dict	,size=42					)
          self.assertEqual(image_processor.size	,{"""shortest_edge""": 42}					)






    def 				_UpperCamelCase							( self					):
          pass






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

          # Test not batched input
          UpperCAmelCase							=       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.size["""shortest_edge"""],
                  self.image_processor_tester.size["""shortest_edge"""],
              )	,)

          # Test batched
          UpperCAmelCase							=       image_processing(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.size["""shortest_edge"""],
                  self.image_processor_tester.size["""shortest_edge"""],
              )	,)






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

          # Test not batched input
          UpperCAmelCase							=       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.size["""shortest_edge"""],
                  self.image_processor_tester.size["""shortest_edge"""],
              )	,)

          # Test batched
          UpperCAmelCase							=       image_processing(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.size["""shortest_edge"""],
                  self.image_processor_tester.size["""shortest_edge"""],
              )	,)






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

          # Test not batched input
          UpperCAmelCase							=       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.size["""shortest_edge"""],
                  self.image_processor_tester.size["""shortest_edge"""],
              )	,)

          # Test batched
          UpperCAmelCase							=       image_processing(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.size["""shortest_edge"""],
                  self.image_processor_tester.size["""shortest_edge"""],
              )	,)











"""simple docstring"""







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


lowerCAmelCase_		:			str				   =					logging.get_logger(__name__)

lowerCAmelCase_		:			Optional[int]				   =					{
    '''google/reformer-crime-and-punishment''': (
        '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/config.json'''
    ),
    '''google/reformer-enwik8''': '''https://huggingface.co/google/reformer-enwik8/resolve/main/config.json''',
}





class      UpperCamelCase_       (				a_     ):
  _A				:    int =		'reformer'
  _A				:    Any =		['past_buckets_states']
  _A				:    Optional[Any] =		{}



  def __init__(					self       ,						snake_case__=64       ,						snake_case__=["local", "lsh", "local", "lsh", "local", "lsh"]       ,						snake_case__=1.0       ,						snake_case__=True       ,						snake_case__=[64, 64]       ,						snake_case__=[64, 1_92]       ,						snake_case__=0       ,						snake_case__=2       ,						snake_case__=5_12       ,						snake_case__=None       ,						snake_case__="relu"       ,						snake_case__=0.05       ,						snake_case__=2_56       ,						snake_case__=0.02       ,						snake_case__=False       ,						snake_case__=1e-12       ,						snake_case__=1       ,						snake_case__=0       ,						snake_case__=0.05       ,						snake_case__=64       ,						snake_case__=64       ,						snake_case__=0.0       ,						snake_case__=1       ,						snake_case__=0       ,						snake_case__=40_96       ,						snake_case__=12       ,						snake_case__=None       ,						snake_case__=1       ,						snake_case__=0       ,						snake_case__=3_20       ,						snake_case__=False       ,						snake_case__=True       ,						snake_case__=None       ,						**snake_case__       ,						)	->	List[Any]:



         """simple docstring"""





         UpperCAmelCase					=	hash_seed
         UpperCAmelCase					=	vocab_size
         UpperCAmelCase					=	attention_head_size
         UpperCAmelCase					=	hidden_size
         UpperCAmelCase					=	num_attention_heads
         UpperCAmelCase					=	num_hashes
         UpperCAmelCase					=	len(snake_case__ )
         UpperCAmelCase					=	tuple(snake_case__ ) if isinstance(snake_case__       ,						snake_case__ ) else num_buckets
         UpperCAmelCase					=	lsh_attn_chunk_length
         UpperCAmelCase					=	local_attn_chunk_length
         UpperCAmelCase					=	lsh_num_chunks_after
         UpperCAmelCase					=	lsh_num_chunks_before
         UpperCAmelCase					=	local_num_chunks_after
         UpperCAmelCase					=	local_num_chunks_before
         UpperCAmelCase					=	hidden_act
         UpperCAmelCase					=	feed_forward_size
         UpperCAmelCase					=	hidden_dropout_prob
         UpperCAmelCase					=	lsh_attention_probs_dropout_prob
         UpperCAmelCase					=	local_attention_probs_dropout_prob
         UpperCAmelCase					=	max_position_embeddings
         UpperCAmelCase					=	initializer_range
         UpperCAmelCase					=	layer_norm_eps
         UpperCAmelCase					=	axial_pos_embds
         UpperCAmelCase					=	tuple(snake_case__ )
         UpperCAmelCase					=	tuple(snake_case__ )
         UpperCAmelCase					=	axial_norm_std
         UpperCAmelCase					=	chunk_size_lm_head
         UpperCAmelCase					=	attn_layers
         UpperCAmelCase					=	use_cache
         UpperCAmelCase					=	classifier_dropout
         super().__init__(
             pad_token_id=snake_case__       ,						eos_token_id=snake_case__       ,						is_decoder=snake_case__       ,						tie_word_embeddings=snake_case__       ,						**snake_case__       ,						)
