


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


lowerCamelCase		      = logging.get_logger(__name__)

lowerCamelCase		      = {
    '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 A						(     UpperCamelCase_  ):
      UpperCamelCase__							:						Optional[int]   ='reformer'
      UpperCamelCase__							:						List[str]   =['past_buckets_states']
      UpperCamelCase__							:						int   ={}






      def __init__(      self		:  Tuple    ,     lowercase_		:  Union[str, Any]=64    ,     lowercase_		:  Optional[Any]=["local", "lsh", "local", "lsh", "local", "lsh"]    ,     lowercase_		:  int=1.0    ,     lowercase_		:  Any=True    ,     lowercase_		:  Tuple=[64, 64]    ,     lowercase_		:  List[Any]=[64, 192]    ,     lowercase_		:  Optional[int]=0    ,     lowercase_		:  str=2    ,     lowercase_		:  Any=512    ,     lowercase_		:  List[str]=None    ,     lowercase_		:  List[str]="relu"    ,     lowercase_		:  Union[str, Any]=0.05    ,     lowercase_		:  Any=256    ,     lowercase_		:  List[str]=0.02    ,     lowercase_		:  List[str]=False    ,     lowercase_		:  Dict=1E-12    ,     lowercase_		:  Dict=1    ,     lowercase_		:  List[Any]=0    ,     lowercase_		:  List[Any]=0.05    ,     lowercase_		:  int=64    ,     lowercase_		:  str=64    ,     lowercase_		:  Optional[Any]=0.0    ,     lowercase_		:  Optional[Any]=1    ,     lowercase_		:  Optional[Any]=0    ,     lowercase_		:  str=4096    ,     lowercase_		:  Dict=12    ,     lowercase_		:  Union[str, Any]=None    ,     lowercase_		:  Optional[int]=1    ,     lowercase_		:  str=0    ,     lowercase_		:  Dict=320    ,     lowercase_		:  List[Any]=False    ,     lowercase_		:  Optional[Any]=True    ,     lowercase_		:  Optional[int]=None    ,     **lowercase_		:  List[Any]    ,     )      ->		str:
             """simple docstring"""




             _lowerCamelCase :	Dict						=hash_seed
             _lowerCamelCase :	List[str]						=vocab_size
             _lowerCamelCase :	int						=attention_head_size
             _lowerCamelCase :	List[Any]						=hidden_size
             _lowerCamelCase :	int						=num_attention_heads
             _lowerCamelCase :	Tuple						=num_hashes
             _lowerCamelCase :	Optional[int]						=len(lowercase_				)
             _lowerCamelCase :	List[str]						=tuple(lowercase_				) if isinstance(lowercase_    ,     lowercase_				) else num_buckets
             _lowerCamelCase :	List[str]						=lsh_attn_chunk_length
             _lowerCamelCase :	Dict						=local_attn_chunk_length
             _lowerCamelCase :	List[Any]						=lsh_num_chunks_after
             _lowerCamelCase :	Any						=lsh_num_chunks_before
             _lowerCamelCase :	Union[str, Any]						=local_num_chunks_after
             _lowerCamelCase :	Dict						=local_num_chunks_before
             _lowerCamelCase :	Union[str, Any]						=hidden_act
             _lowerCamelCase :	Dict						=feed_forward_size
             _lowerCamelCase :	List[Any]						=hidden_dropout_prob
             _lowerCamelCase :	Optional[int]						=lsh_attention_probs_dropout_prob
             _lowerCamelCase :	str						=local_attention_probs_dropout_prob
             _lowerCamelCase :	List[Any]						=max_position_embeddings
             _lowerCamelCase :	str						=initializer_range
             _lowerCamelCase :	Union[str, Any]						=layer_norm_eps
             _lowerCamelCase :	Tuple						=axial_pos_embds
             _lowerCamelCase :	List[str]						=tuple(lowercase_				)
             _lowerCamelCase :	Any						=tuple(lowercase_				)
             _lowerCamelCase :	Optional[int]						=axial_norm_std
             _lowerCamelCase :	int						=chunk_size_lm_head
             _lowerCamelCase :	Any						=attn_layers
             _lowerCamelCase :	Union[str, Any]						=use_cache
             _lowerCamelCase :	Optional[Any]						=classifier_dropout
             super().__init__(
                 pad_token_id=lowercase_    ,     eos_token_id=lowercase_    ,     is_decoder=lowercase_    ,     tie_word_embeddings=lowercase_    ,     **lowercase_    ,     )






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 A						(     unittest.TestCase  ):






      def __init__(      self		:  Optional[Any]    ,     lowercase_		:  str    ,     lowercase_		:  Tuple=7    ,     lowercase_		:  int=3    ,     lowercase_		:  Union[str, Any]=18    ,     lowercase_		:  Optional[int]=30    ,     lowercase_		:  Tuple=400    ,     lowercase_		:  str=True    ,     lowercase_		:  Dict=None    ,     lowercase_		:  Tuple=0.875    ,     lowercase_		:  List[Any]=True    ,     lowercase_		:  Union[str, Any]=[0.5, 0.5, 0.5]    ,     lowercase_		:  List[str]=[0.5, 0.5, 0.5]    ,     )      ->		Any:
             """simple docstring"""




             _lowerCamelCase :	Optional[Any]						=size if size is not None else {'shortest_edge': 20}
             _lowerCamelCase :	Optional[Any]						=parent
             _lowerCamelCase :	Union[str, Any]						=batch_size
             _lowerCamelCase :	Dict						=num_channels
             _lowerCamelCase :	Union[str, Any]						=image_size
             _lowerCamelCase :	Optional[int]						=min_resolution
             _lowerCamelCase :	Tuple						=max_resolution
             _lowerCamelCase :	Any						=do_resize
             _lowerCamelCase :	Dict						=size
             _lowerCamelCase :	int						=crop_pct
             _lowerCamelCase :	Dict						=do_normalize
             _lowerCamelCase :	int						=image_mean
             _lowerCamelCase :	Union[str, Any]						=image_std






      def  lowerCamelCase (      self		:  List[str]				)      ->		int:
             """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_pct": self.crop_pct,
             }




@require_torch
@require_vision
class A						(     UpperCamelCase_					,							unittest.TestCase  ):
      UpperCamelCase__							:						List[Any]   =ConvNextImageProcessor if is_vision_available() else None






      def  lowerCamelCase (      self		:  List[str]				)      ->		Tuple:
             """simple docstring"""




             _lowerCamelCase :	Optional[Any]						=ConvNextImageProcessingTester(self				)






      @property
      def  lowerCamelCase (      self		:  List[str]				)      ->		int:
             """simple docstring"""




             return self.image_processor_tester.prepare_image_processor_dict()






      def  lowerCamelCase (      self		:  Optional[int]				)      ->		str:
             """simple docstring"""




             _lowerCamelCase :	Dict						=self.image_processing_class(**self.image_processor_dict				)
             self.assertTrue(hasattr(lowercase_    ,     'do_resize'				)				)
             self.assertTrue(hasattr(lowercase_    ,     'size'				)				)
             self.assertTrue(hasattr(lowercase_    ,     'crop_pct'				)				)
             self.assertTrue(hasattr(lowercase_    ,     'do_normalize'				)				)
             self.assertTrue(hasattr(lowercase_    ,     'image_mean'				)				)
             self.assertTrue(hasattr(lowercase_    ,     'image_std'				)				)






      def  lowerCamelCase (      self		:  Union[str, Any]				)      ->		List[Any]:
             """simple docstring"""




             _lowerCamelCase :	Tuple						=self.image_processing_class.from_dict(self.image_processor_dict				)
             self.assertEqual(image_processor.size    ,     {'shortest_edge': 20}				)

             _lowerCamelCase :	Tuple						=self.image_processing_class.from_dict(self.image_processor_dict    ,     size=42				)
             self.assertEqual(image_processor.size    ,     {'shortest_edge': 42}				)






      def  lowerCamelCase (      self		:  Optional[Any]				)      ->		int:
             """simple docstring"""




             pass






      def  lowerCamelCase (      self		:  Dict				)      ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase :	Optional[Any]						=self.image_processing_class(**self.image_processor_dict				)
             # create random PIL images
             _lowerCamelCase :	Any						=prepare_image_inputs(self.image_processor_tester    ,     equal_resolution=lowercase_				)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_    ,     Image.Image				)

             # Test not batched input
             _lowerCamelCase :	Tuple						=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
             _lowerCamelCase :	List[str]						=image_processing(lowercase_    ,     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  lowerCamelCase (      self		:  int				)      ->		Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase :	Optional[int]						=self.image_processing_class(**self.image_processor_dict				)
             # create random numpy tensors
             _lowerCamelCase :	List[Any]						=prepare_image_inputs(self.image_processor_tester    ,     equal_resolution=lowercase_    ,     numpify=lowercase_				)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_    ,     np.ndarray				)

             # Test not batched input
             _lowerCamelCase :	Optional[Any]						=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
             _lowerCamelCase :	Any						=image_processing(lowercase_    ,     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  lowerCamelCase (      self		:  str				)      ->		Optional[int]:
             """simple docstring"""




             _lowerCamelCase :	List[Any]						=self.image_processing_class(**self.image_processor_dict				)
             # create random PyTorch tensors
             _lowerCamelCase :	Any						=prepare_image_inputs(self.image_processor_tester    ,     equal_resolution=lowercase_    ,     torchify=lowercase_				)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_    ,     torch.Tensor				)

             # Test not batched input
             _lowerCamelCase :	Optional[Any]						=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
             _lowerCamelCase :	Union[str, Any]						=image_processing(lowercase_    ,     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'],
                 )    ,     )


