



"""simple docstring"""






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


_lowercase					:					Dict 		=		logging.get_logger(__name__)

_lowercase					:					Optional[Any] 		=		{
    "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 			__SCREAMING_SNAKE_CASE      ( lowerCAmelCase_  ):


					'''simple docstring'''





					_a       =				'reformer'
					_a       =				['past_buckets_states']
					_a       =				{}



					def __init__(			self				:    Tuple,    lowerCamelCase				:    int=64,    lowerCamelCase				:    Optional[Any]=["local", "lsh", "local", "lsh", "local", "lsh"],    lowerCamelCase				:    Any=1.0,    lowerCamelCase				:    Union[str, Any]=True,    lowerCamelCase				:    str=[64, 64],    lowerCamelCase				:    Tuple=[64, 192],    lowerCamelCase				:    Tuple=0,    lowerCamelCase				:    List[str]=2,    lowerCamelCase				:    Union[str, Any]=512,    lowerCamelCase				:    List[Any]=None,    lowerCamelCase				:    Tuple="relu",    lowerCamelCase				:    Optional[int]=0.05,    lowerCamelCase				:    Optional[int]=256,    lowerCamelCase				:    Dict=0.02,    lowerCamelCase				:    Optional[int]=False,    lowerCamelCase				:    int=1E-12,    lowerCamelCase				:    int=1,    lowerCamelCase				:    List[Any]=0,    lowerCamelCase				:    str=0.05,    lowerCamelCase				:    Optional[int]=64,    lowerCamelCase				:    Tuple=64,    lowerCamelCase				:    List[Any]=0.0,    lowerCamelCase				:    int=1,    lowerCamelCase				:    Any=0,    lowerCamelCase				:    Union[str, Any]=4096,    lowerCamelCase				:    Any=12,    lowerCamelCase				:    Dict=None,    lowerCamelCase				:    Optional[int]=1,    lowerCamelCase				:    List[str]=0,    lowerCamelCase				:    Tuple=320,    lowerCamelCase				:    int=False,    lowerCamelCase				:    Optional[int]=True,    lowerCamelCase				:    List[str]=None,    **lowerCamelCase				:    str,    )->   Union[str, Any]:
								lowerCamelCase__						:						List[Any]						      =hash_seed
								lowerCamelCase__						:						int						      =vocab_size
								lowerCamelCase__						:						List[Any]						      =attention_head_size
								lowerCamelCase__						:						Any						      =hidden_size
								lowerCamelCase__						:						Tuple						      =num_attention_heads
								lowerCamelCase__						:						List[str]						      =num_hashes
								lowerCamelCase__						:						Union[str, Any]						      =len(lowerCamelCase	)
								lowerCamelCase__						:						List[str]						      =tuple(lowerCamelCase	) if isinstance(lowerCamelCase,    lowerCamelCase	) else num_buckets
								lowerCamelCase__						:						Union[str, Any]						      =lsh_attn_chunk_length
								lowerCamelCase__						:						Optional[int]						      =local_attn_chunk_length
								lowerCamelCase__						:						List[str]						      =lsh_num_chunks_after
								lowerCamelCase__						:						List[Any]						      =lsh_num_chunks_before
								lowerCamelCase__						:						int						      =local_num_chunks_after
								lowerCamelCase__						:						Optional[Any]						      =local_num_chunks_before
								lowerCamelCase__						:						List[str]						      =hidden_act
								lowerCamelCase__						:						List[Any]						      =feed_forward_size
								lowerCamelCase__						:						Any						      =hidden_dropout_prob
								lowerCamelCase__						:						Tuple						      =lsh_attention_probs_dropout_prob
								lowerCamelCase__						:						List[str]						      =local_attention_probs_dropout_prob
								lowerCamelCase__						:						List[Any]						      =max_position_embeddings
								lowerCamelCase__						:						Union[str, Any]						      =initializer_range
								lowerCamelCase__						:						Optional[Any]						      =layer_norm_eps
								lowerCamelCase__						:						str						      =axial_pos_embds
								lowerCamelCase__						:						List[str]						      =tuple(lowerCamelCase	)
								lowerCamelCase__						:						int						      =tuple(lowerCamelCase	)
								lowerCamelCase__						:						Union[str, Any]						      =axial_norm_std
								lowerCamelCase__						:						Optional[Any]						      =chunk_size_lm_head
								lowerCamelCase__						:						int						      =attn_layers
								lowerCamelCase__						:						int						      =use_cache
								lowerCamelCase__						:						Union[str, Any]						      =classifier_dropout
								super().__init__(
								    pad_token_id=lowerCamelCase,    eos_token_id=lowerCamelCase,    is_decoder=lowerCamelCase,    tie_word_embeddings=lowerCamelCase,    **lowerCamelCase,    )









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 EfficientNetImageProcessor





class    __lowerCAmelCase					(	unittest.TestCase			):




				def __init__(				self		,							_snake_case		,							_snake_case=13		,							_snake_case=3		,							_snake_case=18		,							_snake_case=30		,							_snake_case=400		,							_snake_case=True		,							_snake_case=None		,							_snake_case=True		,							_snake_case=[0.5, 0.5, 0.5]		,							_snake_case=[0.5, 0.5, 0.5]		,							):



									"""simple docstring"""





									_lowerCAmelCase         =       size if size is not None else {"""height""": 18, """width""": 18}
									_lowerCAmelCase         =       parent
									_lowerCAmelCase         =       batch_size
									_lowerCAmelCase         =       num_channels
									_lowerCAmelCase         =       image_size
									_lowerCAmelCase         =       min_resolution
									_lowerCAmelCase         =       max_resolution
									_lowerCAmelCase         =       do_resize
									_lowerCAmelCase         =       size
									_lowerCAmelCase         =       do_normalize
									_lowerCAmelCase         =       image_mean
									_lowerCAmelCase         =       image_std




				def 				snake_case  (				self						):



									"""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,
									}





@require_torch
@require_vision
class    __lowerCAmelCase					(	lowerCamelCase__			,		unittest.TestCase			):
				__lowerCamelCase					=							EfficientNetImageProcessor if is_vision_available() else None




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       EfficientNetImageProcessorTester(self						)




				@property
				def 				snake_case  (				self						):



									"""simple docstring"""





									return self.image_processor_tester.prepare_image_processor_dict()




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.image_processing_class(**self.image_processor_dict						)
									self.assertTrue(hasattr(_snake_case		,							"""image_mean"""						)						)
									self.assertTrue(hasattr(_snake_case		,							"""image_std"""						)						)
									self.assertTrue(hasattr(_snake_case		,							"""do_normalize"""						)						)
									self.assertTrue(hasattr(_snake_case		,							"""do_resize"""						)						)
									self.assertTrue(hasattr(_snake_case		,							"""size"""						)						)




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.image_processing_class.from_dict(self.image_processor_dict						)
									self.assertEqual(image_processor.size		,							{"""height""": 18, """width""": 18}						)

									_lowerCAmelCase         =       self.image_processing_class.from_dict(self.image_processor_dict		,							size=42						)
									self.assertEqual(image_processor.size		,							{"""height""": 42, """width""": 42}						)




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.image_processing_class(**self.image_processor_dict						)
									# create random PIL images
									_lowerCAmelCase         =       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
									_lowerCAmelCase         =       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["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )		,							)

									# Test batched
									_lowerCAmelCase         =       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["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )		,							)




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.image_processing_class(**self.image_processor_dict						)
									# create random numpy tensors
									_lowerCAmelCase         =       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
									_lowerCAmelCase         =       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["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )		,							)

									# Test batched
									_lowerCAmelCase         =       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["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )		,							)




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.image_processing_class(**self.image_processor_dict						)
									# create random PyTorch tensors
									_lowerCAmelCase         =       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
									_lowerCAmelCase         =       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["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )		,							)

									# Test batched
									_lowerCAmelCase         =       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["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )		,							)



