


'''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


__SCREAMING_SNAKE_CASE					:				Tuple									=		logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE					:				Optional[Any]									=		{
    '''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 					__lowerCAmelCase   (   lowercase						):

						"""simple docstring"""






						_UpperCAmelCase		:       int   						="perceiver"






						def __init__(  self							:      Dict ,  lowerCAmelCase							:      Union[str, Any]=2_56 ,  lowerCAmelCase							:      List[Any]=12_80 ,  lowerCAmelCase							:      Any=7_68 ,  lowerCAmelCase							:      int=1 ,  lowerCAmelCase							:      str=26 ,  lowerCAmelCase							:      Dict=8 ,  lowerCAmelCase							:      List[str]=8 ,  lowerCAmelCase							:      List[str]=None ,  lowerCAmelCase							:      List[str]=None ,  lowerCAmelCase							:      Dict="kv" ,  lowerCAmelCase							:      Any=1 ,  lowerCAmelCase							:      Optional[Any]=1 ,  lowerCAmelCase							:      Union[str, Any]="gelu" ,  lowerCAmelCase							:      List[Any]=0.1 ,  lowerCAmelCase							:      List[str]=0.0_2 ,  lowerCAmelCase							:      Dict=1e-12 ,  lowerCAmelCase							:      str=True ,  lowerCAmelCase							:      int=2_62 ,  lowerCAmelCase							:      Optional[Any]=20_48 ,  lowerCAmelCase							:      int=56 ,  lowerCAmelCase							:      Union[str, Any]=[3_68, 4_96] ,  lowerCAmelCase							:      Union[str, Any]=16 ,  lowerCAmelCase							:      str=19_20 ,  lowerCAmelCase							:      Any=16 ,  lowerCAmelCase							:      Dict=[1, 16, 2_24, 2_24] ,  **lowerCAmelCase							:      Dict ,  ):
													super().__init__(**lowerCAmelCase	)

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






class 					__lowerCAmelCase   (   lowercase						):

						"""simple docstring"""






						@property
						def 				_UpperCAmelCase       (  self							:      str	):
													if self.task == "multiple-choice":
																				A_			    =		{0: "batch", 1: "choice", 2: "sequence"}
													else:
																				A_			    =		{0: "batch", 1: "sequence"}
													return OrderedDict(
													    [
													        ("inputs", dynamic_axis),
													        ("attention_mask", dynamic_axis),
													    ]	)






						@property
						def 				_UpperCAmelCase       (  self							:      Dict	):
													return 1e-4






						def 				_UpperCAmelCase       (  self							:      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 = 40 ,  lowerCAmelCase							:      int = 40 ,  ):
													# copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

													if isinstance(lowerCAmelCase ,  lowerCAmelCase	):
																				# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
																				A_			    =		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
																				A_			    =		preprocessor.num_special_tokens_to_add(lowerCAmelCase	)
																				A_			    =		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
																				A_			    =		[" ".join(["a"]	) * seq_length] * batch_size
																				A_			    =		dict(preprocessor(lowerCAmelCase ,  return_tensors=lowerCAmelCase	)	)
																				A_			    =		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
																				A_			    =		compute_effective_axis_dimension(lowerCAmelCase ,  fixed_dimension=OnnxConfig.default_fixed_batch	)
																				A_			    =		self._generate_dummy_images(lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase	)
																				A_			    =		dict(preprocessor(images=lowerCAmelCase ,  return_tensors=lowerCAmelCase	)	)
																				A_			    =		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
from math import logaa







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		document.translate(
							    str.maketrans(""						,  ""						,  string.punctuation ) ).replace("\n"						,  "" )
							A_			    =		document_without_punctuation.split(" " )  # word tokenization
							return len([word for word in tokenize_document if word.lower() == term.lower()] )







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		corpus.lower().translate(
							    str.maketrans(""						,  ""						,  string.punctuation ) )  # strip all punctuation and replace it with ''
							A_			    =		corpus_without_punctuation.split("\n" )
							A_			    =		term.lower()
							return (len([doc for doc in docs if term in doc] ), len(UpperCamelCase_ ))







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_=False ):
							if smoothing:
														if n == 0:
																					raise ValueError("log10(0) is undefined." )
														return round(1 + logaa(n / (1 + df) )						,  3 )

							if df == 0:
														raise ZeroDivisionError("df must be > 0" )
							elif n == 0:
														raise ValueError("log10(0) is undefined." )
							return round(logaa(n / df )						,  3 )







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							return round(tf * idf						,  3 )





