



'''simple docstring'''



from collections import OrderedDict
from typing import Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


lowerCamelCase	 =       logging.get_logger(__name__)

lowerCamelCase	 =       {
    """deepmind/language-perceiver""": """https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json""",
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}





class _UpperCamelCase		(     A		):





    '''simple docstring'''





    lowerCAmelCase__					    =    """perceiver"""

    def __init__(							self  :  Optional[Any]  ,       _lowerCAmelCase  :  str=2_5_6  ,       _lowerCAmelCase  :  Optional[int]=1_2_8_0  ,       _lowerCAmelCase  :  Optional[int]=7_6_8  ,       _lowerCAmelCase  :  Union[str, Any]=1  ,       _lowerCAmelCase  :  int=2_6  ,       _lowerCAmelCase  :  Any=8  ,       _lowerCAmelCase  :  Union[str, Any]=8  ,       _lowerCAmelCase  :  Any=None  ,       _lowerCAmelCase  :  List[Any]=None  ,       _lowerCAmelCase  :  List[str]="kv"  ,       _lowerCAmelCase  :  str=1  ,       _lowerCAmelCase  :  List[str]=1  ,       _lowerCAmelCase  :  Dict="gelu"  ,       _lowerCAmelCase  :  int=0.1  ,       _lowerCAmelCase  :  List[Any]=0.02  ,       _lowerCAmelCase  :  Optional[Any]=1e-12  ,       _lowerCAmelCase  :  Dict=True  ,       _lowerCAmelCase  :  Any=2_6_2  ,       _lowerCAmelCase  :  List[str]=2_0_4_8  ,       _lowerCAmelCase  :  Dict=5_6  ,       _lowerCAmelCase  :  Tuple=[3_6_8, 4_9_6]  ,       _lowerCAmelCase  :  int=1_6  ,       _lowerCAmelCase  :  Any=1_9_2_0  ,       _lowerCAmelCase  :  List[Any]=1_6  ,       _lowerCAmelCase  :  Dict=[1, 1_6, 2_2_4, 2_2_4]  ,       **_lowerCAmelCase  :  str  ,       ):







           '''simple docstring'''

           super().__init__(**_lowerCAmelCase)

           __lowercase       	=num_latents
           __lowercase       	=d_latents
           __lowercase       	=d_model
           __lowercase       	=num_blocks
           __lowercase       	=num_self_attends_per_block
           __lowercase       	=num_self_attention_heads
           __lowercase       	=num_cross_attention_heads
           __lowercase       	=qk_channels
           __lowercase       	=v_channels
           __lowercase       	=cross_attention_shape_for_attention
           __lowercase       	=self_attention_widening_factor
           __lowercase       	=cross_attention_widening_factor
           __lowercase       	=hidden_act
           __lowercase       	=attention_probs_dropout_prob
           __lowercase       	=initializer_range
           __lowercase       	=layer_norm_eps
           __lowercase       	=use_query_residual
           # masked language modeling attributes
           __lowercase       	=vocab_size
           __lowercase       	=max_position_embeddings
           # image classification attributes
           __lowercase       	=image_size
           # flow attributes
           __lowercase       	=train_size
           # multimodal autoencoding attributes
           __lowercase       	=num_frames
           __lowercase       	=audio_samples_per_frame
           __lowercase       	=samples_per_patch
           __lowercase       	=output_shape





class _UpperCamelCase		(     A		):





    '''simple docstring'''





    @property
    def 				__lowerCamelCase     (							self  :  Any):







           '''simple docstring'''

           if self.task == "multiple-choice":
                  __lowercase       	={0: 'batch', 1: 'choice', 2: 'sequence'}
           else:
                  __lowercase       	={0: 'batch', 1: 'sequence'}
           return OrderedDict(
               [
                   ('inputs', dynamic_axis),
                   ('attention_mask', dynamic_axis),
               ])

    @property
    def 				__lowerCamelCase     (							self  :  List[str]):







           '''simple docstring'''

           return 1e-4

    def 				__lowerCamelCase     (							self  :  Optional[Any]  ,       _lowerCAmelCase  :  Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]  ,       _lowerCAmelCase  :  int = -1  ,       _lowerCAmelCase  :  int = -1  ,       _lowerCAmelCase  :  int = -1  ,       _lowerCAmelCase  :  bool = False  ,       _lowerCAmelCase  :  Optional[TensorType] = None  ,       _lowerCAmelCase  :  int = 3  ,       _lowerCAmelCase  :  int = 4_0  ,       _lowerCAmelCase  :  int = 4_0  ,       ):







           '''simple docstring'''

           if isinstance(_lowerCAmelCase  ,       _lowerCAmelCase):
                  # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
                  __lowercase       	=compute_effective_axis_dimension(
                      _lowerCAmelCase  ,       fixed_dimension=OnnxConfig.default_fixed_batch  ,       num_token_to_add=0)
                  # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
                  __lowercase       	=preprocessor.num_special_tokens_to_add(_lowerCAmelCase)
                  __lowercase       	=compute_effective_axis_dimension(
                      _lowerCAmelCase  ,       fixed_dimension=OnnxConfig.default_fixed_sequence  ,       num_token_to_add=_lowerCAmelCase)
                  # Generate dummy inputs according to compute batch and sequence
                  __lowercase       	=[' '.join(['a']) * seq_length] * batch_size
                  __lowercase       	=dict(preprocessor(_lowerCAmelCase  ,       return_tensors=_lowerCAmelCase))
                  __lowercase       	=inputs.pop('input_ids')
                  return inputs
           elif isinstance(_lowerCAmelCase  ,       _lowerCAmelCase) and preprocessor.model_input_names[0] == "pixel_values":
                  # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
                  __lowercase       	=compute_effective_axis_dimension(_lowerCAmelCase  ,       fixed_dimension=OnnxConfig.default_fixed_batch)
                  __lowercase       	=self._generate_dummy_images(_lowerCAmelCase  ,       _lowerCAmelCase  ,       _lowerCAmelCase  ,       _lowerCAmelCase)
                  __lowercase       	=dict(preprocessor(images=_lowerCAmelCase  ,       return_tensors=_lowerCAmelCase))
                  __lowercase       	=inputs.pop('pixel_values')
                  return inputs
           else:
                  raise ValueError(
                      'Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.')








'''simple docstring'''



import string



def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



							__lowercase       	=''
							for i in sequence:
														__lowercase       	=ord(_lowerCAmelCase					)
														if 65 <= extract <= 90:
																					output += chr(155 - extract					)
														elif 97 <= extract <= 122:
																					output += chr(219 - extract					)
														else:
																					output += i
							return output



def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



							__lowercase       	=string.ascii_letters
							__lowercase       	=string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
							return "".join(
							    letters_reversed[letters.index(_lowerCAmelCase					)] if c in letters else c for c in sequence					)



def   _A       (   ):



							"""simple docstring"""



							from timeit import timeit

							print('Running performance benchmarks...'					)
							__lowercase       	='from string import printable ; from __main__ import atbash, atbash_slow'
							print(f"""> atbash_slow(): {timeit('atbash_slow(printable)'  ,   setup=_lowerCAmelCase					)} seconds"""					)
							print(f""">      atbash(): {timeit('atbash(printable)'  ,   setup=_lowerCAmelCase					)} seconds"""					)


if __name__ == "__main__":
		for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
				print(f"{example} encrypted in atbash: {atbash(example)}")
		benchmark()



