

'''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


_lowercase		:				List[str]	       =		logging.get_logger(__name__)

_lowercase		:				Union[str, 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 					__magic_name__		(					_UpperCAmelCase):
			UpperCamelCase__       = '''perceiver'''







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

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





class 					__magic_name__		(					_UpperCAmelCase):







			@property
			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]					):
										if self.task == "multiple-choice":
																	lowercase_      :    Dict       		= {0: """batch""", 1: """choice""", 2: """sequence"""}
										else:
																	lowercase_      :    int       		= {0: """batch""", 1: """sequence"""}
										return OrderedDict(
										    [
										        ("""inputs""", dynamic_axis),
										        ("""attention_mask""", dynamic_axis),
										    ]					)







			@property
			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]					):
										return 1E-4







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any       ,    lowercase_       :						Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]       ,    lowercase_       :						int = -1       ,    lowercase_       :						int = -1       ,    lowercase_       :						int = -1       ,    lowercase_       :						bool = False       ,    lowercase_       :						Optional[TensorType] = None       ,    lowercase_       :						int = 3       ,    lowercase_       :						int = 40       ,    lowercase_       :						int = 40       ,    ):
										# copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

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




from math import cos, sin, sqrt, tau

from audio_filters.iir_filter import IIRFilter




def 							lowerCamelCase			(   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      float = 1 / sqrt(2				)				)    ->   IIRFilter:
							lowercase_      :    str       		= tau * frequency / samplerate
							lowercase_      :    Tuple       		= sin(UpperCAmelCase__				)
							lowercase_      :    int       		= cos(UpperCAmelCase__				)
							lowercase_      :    Any       		= _sin / (2 * q_factor)

							lowercase_      :    Dict       		= (1 - _cos) / 2
							lowercase_      :    Optional[int]       		= 1 - _cos

							lowercase_      :    Dict       		= 1 + alpha
							lowercase_      :    List[Any]       		= -2 * _cos
							lowercase_      :    Union[str, Any]       		= 1 - alpha

							lowercase_      :    List[Any]       		= IIRFilter(2				)
							filt.set_coefficients([aa, aa, aa]   ,   [ba, ba, ba]				)
							return filt




def 							lowerCamelCase			(   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      float = 1 / sqrt(2				)				)    ->   IIRFilter:
							lowercase_      :    str       		= tau * frequency / samplerate
							lowercase_      :    Optional[int]       		= sin(UpperCAmelCase__				)
							lowercase_      :    Dict       		= cos(UpperCAmelCase__				)
							lowercase_      :    Optional[int]       		= _sin / (2 * q_factor)

							lowercase_      :    Dict       		= (1 + _cos) / 2
							lowercase_      :    str       		= -1 - _cos

							lowercase_      :    Dict       		= 1 + alpha
							lowercase_      :    Optional[Any]       		= -2 * _cos
							lowercase_      :    List[Any]       		= 1 - alpha

							lowercase_      :    Union[str, Any]       		= IIRFilter(2				)
							filt.set_coefficients([aa, aa, aa]   ,   [ba, ba, ba]				)
							return filt




def 							lowerCamelCase			(   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      float = 1 / sqrt(2				)				)    ->   IIRFilter:
							lowercase_      :    int       		= tau * frequency / samplerate
							lowercase_      :    int       		= sin(UpperCAmelCase__				)
							lowercase_      :    Union[str, Any]       		= cos(UpperCAmelCase__				)
							lowercase_      :    str       		= _sin / (2 * q_factor)

							lowercase_      :    str       		= _sin / 2
							lowercase_      :    Any       		= 0
							lowercase_      :    Optional[Any]       		= -ba

							lowercase_      :    Dict       		= 1 + alpha
							lowercase_      :    Union[str, Any]       		= -2 * _cos
							lowercase_      :    Union[str, Any]       		= 1 - alpha

							lowercase_      :    Tuple       		= IIRFilter(2				)
							filt.set_coefficients([aa, aa, aa]   ,   [ba, ba, ba]				)
							return filt




def 							lowerCamelCase			(   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      float = 1 / sqrt(2				)				)    ->   IIRFilter:
							lowercase_      :    List[str]       		= tau * frequency / samplerate
							lowercase_      :    Any       		= sin(UpperCAmelCase__				)
							lowercase_      :    List[Any]       		= cos(UpperCAmelCase__				)
							lowercase_      :    Optional[Any]       		= _sin / (2 * q_factor)

							lowercase_      :    Any       		= 1 - alpha
							lowercase_      :    Optional[Any]       		= -2 * _cos
							lowercase_      :    Optional[int]       		= 1 + alpha

							lowercase_      :    Dict       		= IIRFilter(2				)
							filt.set_coefficients([ba, ba, ba]   ,   [ba, ba, ba]				)
							return filt




def 							lowerCamelCase			(   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      float   ,   UpperCAmelCase__ :      float = 1 / sqrt(2				)   ,   )    ->   IIRFilter:
							lowercase_      :    Dict       		= tau * frequency / samplerate
							lowercase_      :    Tuple       		= sin(UpperCAmelCase__				)
							lowercase_      :    List[Any]       		= cos(UpperCAmelCase__				)
							lowercase_      :    List[Any]       		= _sin / (2 * q_factor)
							lowercase_      :    Any       		= 10 ** (gain_db / 40)

							lowercase_      :    List[str]       		= 1 + alpha * big_a
							lowercase_      :    List[Any]       		= -2 * _cos
							lowercase_      :    Dict       		= 1 - alpha * big_a
							lowercase_      :    str       		= 1 + alpha / big_a
							lowercase_      :    List[str]       		= -2 * _cos
							lowercase_      :    Tuple       		= 1 - alpha / big_a

							lowercase_      :    Any       		= IIRFilter(2				)
							filt.set_coefficients([aa, aa, aa]   ,   [ba, ba, ba]				)
							return filt




def 							lowerCamelCase			(   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      float   ,   UpperCAmelCase__ :      float = 1 / sqrt(2				)   ,   )    ->   IIRFilter:
							lowercase_      :    Dict       		= tau * frequency / samplerate
							lowercase_      :    Union[str, Any]       		= sin(UpperCAmelCase__				)
							lowercase_      :    Any       		= cos(UpperCAmelCase__				)
							lowercase_      :    Any       		= _sin / (2 * q_factor)
							lowercase_      :    Any       		= 10 ** (gain_db / 40)
							lowercase_      :    Any       		= (big_a + 1) - (big_a - 1) * _cos
							lowercase_      :    int       		= (big_a + 1) + (big_a - 1) * _cos
							lowercase_      :    Tuple       		= (big_a - 1) - (big_a + 1) * _cos
							lowercase_      :    Optional[Any]       		= (big_a - 1) + (big_a + 1) * _cos
							lowercase_      :    int       		= 2 * sqrt(UpperCAmelCase__				) * alpha

							lowercase_      :    Tuple       		= big_a * (pmc + aaa)
							lowercase_      :    List[str]       		= 2 * big_a * mpc
							lowercase_      :    Union[str, Any]       		= big_a * (pmc - aaa)
							lowercase_      :    Optional[int]       		= ppmc + aaa
							lowercase_      :    Optional[int]       		= -2 * pmpc
							lowercase_      :    Any       		= ppmc - aaa

							lowercase_      :    Optional[int]       		= IIRFilter(2				)
							filt.set_coefficients([aa, aa, aa]   ,   [ba, ba, ba]				)
							return filt




def 							lowerCamelCase			(   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      float   ,   UpperCAmelCase__ :      float = 1 / sqrt(2				)   ,   )    ->   IIRFilter:
							lowercase_      :    str       		= tau * frequency / samplerate
							lowercase_      :    int       		= sin(UpperCAmelCase__				)
							lowercase_      :    int       		= cos(UpperCAmelCase__				)
							lowercase_      :    Dict       		= _sin / (2 * q_factor)
							lowercase_      :    Union[str, Any]       		= 10 ** (gain_db / 40)
							lowercase_      :    Union[str, Any]       		= (big_a + 1) - (big_a - 1) * _cos
							lowercase_      :    Optional[int]       		= (big_a + 1) + (big_a - 1) * _cos
							lowercase_      :    Any       		= (big_a - 1) - (big_a + 1) * _cos
							lowercase_      :    str       		= (big_a - 1) + (big_a + 1) * _cos
							lowercase_      :    Optional[int]       		= 2 * sqrt(UpperCAmelCase__				) * alpha

							lowercase_      :    Tuple       		= big_a * (ppmc + aaa)
							lowercase_      :    List[Any]       		= -2 * big_a * pmpc
							lowercase_      :    Optional[Any]       		= big_a * (ppmc - aaa)
							lowercase_      :    Optional[Any]       		= pmc + aaa
							lowercase_      :    int       		= 2 * mpc
							lowercase_      :    Tuple       		= pmc - aaa

							lowercase_      :    Union[str, Any]       		= IIRFilter(2				)
							filt.set_coefficients([aa, aa, aa]   ,   [ba, ba, ba]				)
							return filt





