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


__UpperCamelCase		:       Dict									=     logging.get_logger(__name__)

__UpperCamelCase		:       Optional[int]									=     {
    '''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 					lowercase__						(   __lowerCamelCase):
						UpperCamelCase_     =		"perceiver"


						def __init__(       self	:				Any    ,		UpperCamelCase__	:				Any=256    ,		UpperCamelCase__	:				Tuple=1280    ,		UpperCamelCase__	:				Dict=768    ,		UpperCamelCase__	:				Optional[Any]=1    ,		UpperCamelCase__	:				str=26    ,		UpperCamelCase__	:				Optional[Any]=8    ,		UpperCamelCase__	:				Dict=8    ,		UpperCamelCase__	:				Any=None    ,		UpperCamelCase__	:				Tuple=None    ,		UpperCamelCase__	:				Optional[int]="kv"    ,		UpperCamelCase__	:				Optional[int]=1    ,		UpperCamelCase__	:				Any=1    ,		UpperCamelCase__	:				Any="gelu"    ,		UpperCamelCase__	:				Dict=0.1    ,		UpperCamelCase__	:				Any=0.02    ,		UpperCamelCase__	:				int=1E-12    ,		UpperCamelCase__	:				Optional[int]=True    ,		UpperCamelCase__	:				Optional[Any]=262    ,		UpperCamelCase__	:				List[Any]=2048    ,		UpperCamelCase__	:				List[str]=56    ,		UpperCamelCase__	:				Optional[int]=[368, 496]    ,		UpperCamelCase__	:				List[Any]=16    ,		UpperCamelCase__	:				Optional[int]=1920    ,		UpperCamelCase__	:				Optional[int]=16    ,		UpperCamelCase__	:				Dict=[1, 16, 224, 224]    ,		**UpperCamelCase__	:				Union[str, Any]    ,		):





												'''simple docstring'''





												super().__init__(**__A						)

												SCREAMING_SNAKE_CASE							:    str							=							num_latents
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							d_latents
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							d_model
												SCREAMING_SNAKE_CASE							:    Any							=							num_blocks
												SCREAMING_SNAKE_CASE							:    Dict							=							num_self_attends_per_block
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							num_self_attention_heads
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							num_cross_attention_heads
												SCREAMING_SNAKE_CASE							:    int							=							qk_channels
												SCREAMING_SNAKE_CASE							:    List[str]							=							v_channels
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							cross_attention_shape_for_attention
												SCREAMING_SNAKE_CASE							:    List[Any]							=							self_attention_widening_factor
												SCREAMING_SNAKE_CASE							:    List[str]							=							cross_attention_widening_factor
												SCREAMING_SNAKE_CASE							:    Dict							=							hidden_act
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							attention_probs_dropout_prob
												SCREAMING_SNAKE_CASE							:    int							=							initializer_range
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							layer_norm_eps
												SCREAMING_SNAKE_CASE							:    Tuple							=							use_query_residual
												# masked language modeling attributes
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							vocab_size
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							max_position_embeddings
												# image classification attributes
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							image_size
												# flow attributes
												SCREAMING_SNAKE_CASE							:    Any							=							train_size
												# multimodal autoencoding attributes
												SCREAMING_SNAKE_CASE							:    int							=							num_frames
												SCREAMING_SNAKE_CASE							:    List[str]							=							audio_samples_per_frame
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							samples_per_patch
												SCREAMING_SNAKE_CASE							:    List[Any]							=							output_shape






class 					lowercase__						(   __lowerCamelCase):


						@property
						def        __A       (       self	:				Union[str, Any]						):





												'''simple docstring'''





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


						@property
						def        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												return 1E-4



						def        __A       (       self	:				Any    ,		UpperCamelCase__	:				Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]    ,		UpperCamelCase__	:				int = -1    ,		UpperCamelCase__	:				int = -1    ,		UpperCamelCase__	:				int = -1    ,		UpperCamelCase__	:				bool = False    ,		UpperCamelCase__	:				Optional[TensorType] = None    ,		UpperCamelCase__	:				int = 3    ,		UpperCamelCase__	:				int = 40    ,		UpperCamelCase__	:				int = 40    ,		):





												'''simple docstring'''





												if isinstance(__A    ,		__A						):
																		# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							compute_effective_axis_dimension(
																		    __A    ,		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
																		SCREAMING_SNAKE_CASE							:    List[str]							=							preprocessor.num_special_tokens_to_add(__A						)
																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							compute_effective_axis_dimension(
																		    __A    ,		fixed_dimension=OnnxConfig.default_fixed_sequence    ,		num_token_to_add=__A						)
																		# Generate dummy inputs according to compute batch and sequence
																		SCREAMING_SNAKE_CASE							:    Any							=							[''' '''.join(['''a''']						) * seq_length] * batch_size
																		SCREAMING_SNAKE_CASE							:    Any							=							dict(preprocessor(__A    ,		return_tensors=__A						)						)
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							inputs.pop('''input_ids'''						)
																		return inputs
												elif isinstance(__A    ,		__A						) 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
																		SCREAMING_SNAKE_CASE							:    Any							=							compute_effective_axis_dimension(__A    ,		fixed_dimension=OnnxConfig.default_fixed_batch						)
																		SCREAMING_SNAKE_CASE							:    int							=							self._generate_dummy_images(__A    ,		__A    ,		__A    ,		__A						)
																		SCREAMING_SNAKE_CASE							:    Dict							=							dict(preprocessor(images=__A    ,		return_tensors=__A						)						)
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							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'''
a__							:							Optional[Any] =256
# Modulus to hash a string
a__							:							Dict =1_000_003
def 				lowercase__   (   __lowercase						:  str				,  __lowercase						:  str     ) ->      bool:



       """simple docstring"""






       __UpperCamelCase       =  len(__lowercase     )
       __UpperCamelCase       =  len(__lowercase     )
       if p_len > t_len:
              return False

       __UpperCamelCase       =  0
       __UpperCamelCase       =  0
       __UpperCamelCase       =  1

       # Calculating the hash of pattern and substring of text
       for i in range(__lowercase     ):
              __UpperCamelCase       =  (ord(pattern[i]     ) + p_hash * alphabet_size) % modulus
              __UpperCamelCase       =  (ord(text[i]     ) + text_hash * alphabet_size) % modulus
              if i == p_len - 1:
                     continue
              __UpperCamelCase       =  (modulus_power * alphabet_size) % modulus

       for i in range(0				,  t_len - p_len + 1     ):
              if text_hash == p_hash and text[i : i + p_len] == pattern:
                     return True
              if i == t_len - p_len:
                     continue
              # Calculate the https://en.wikipedia.org/wiki/Rolling_hash
              __UpperCamelCase       =  (
                  (text_hash - ord(text[i]     ) * modulus_power) * alphabet_size
                  + ord(text[i + p_len]     )
              ) % modulus
       return False
def 				lowercase__   (   ) ->      None:



       """simple docstring"""






       __UpperCamelCase       =  'abc1abc12'
       __UpperCamelCase       =  'alskfjaldsabc1abc1abc12k23adsfabcabc'
       __UpperCamelCase       =  'alskfjaldsk23adsfabcabc'
       assert rabin_karp(__lowercase				,  __lowercase     ) and not rabin_karp(__lowercase				,  __lowercase     )

       # Test 2)
       __UpperCamelCase       =  'ABABX'
       __UpperCamelCase       =  'ABABZABABYABABX'
       assert rabin_karp(__lowercase				,  __lowercase     )

       # Test 3)
       __UpperCamelCase       =  'AAAB'
       __UpperCamelCase       =  'ABAAAAAB'
       assert rabin_karp(__lowercase				,  __lowercase     )

       # Test 4)
       __UpperCamelCase       =  'abcdabcy'
       __UpperCamelCase       =  'abcxabcdabxabcdabcdabcy'
       assert rabin_karp(__lowercase				,  __lowercase     )

       # Test 5)
       __UpperCamelCase       =  'Lü'
       __UpperCamelCase       =  'Lüsai'
       assert rabin_karp(__lowercase				,  __lowercase     )
       __UpperCamelCase       =  'Lue'
       assert not rabin_karp(__lowercase				,  __lowercase     )
       print('Success.'     )


if __name__ == "__main__":
    test_rabin_karp()
