






'''simple docstring'''


from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np



class 					lowercase						(  a_ ):


							def 						_snake_case							(			self    ,			_snake_case)							->				float:
								return 0.0







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase  )    ->    List[Any]:
	UpperCAmelCase_		:      Optional[int]       	=					min([-2_0, np.min(fft_results[1 : samplerate // 2 - 1]  )]  )
	UpperCAmelCase_		:      Optional[int]       	=					max([2_0, np.max(fft_results[1 : samplerate // 2 - 1]  )]  )
	return lowest, highest







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase  )    ->    str:
	UpperCAmelCase_		:      Tuple       	=					5_1_2
	UpperCAmelCase_		:      List[Any]       	=					[1] + [0] * (size - 1)
	UpperCAmelCase_		:      List[Any]       	=					[filter_type.process(UpperCamelCase  ) for item in inputs]

	UpperCAmelCase_		:      Dict       	=					[0] * (samplerate - size)  # zero-padding
	outputs += filler
	UpperCAmelCase_		:      str       	=					np.abs(np.fft.fft(UpperCamelCase  )  )
	UpperCAmelCase_		:      Optional[Any]       	=					2_0 * np.logaa(UpperCamelCase  )

	# Frequencies on log scale from 24 to nyquist frequency
	plt.xlim(2_4   ,samplerate / 2 - 1  )
	plt.xlabel('Frequency (Hz)'  )
	plt.xscale('log'  )

	# Display within reasonable bounds
	UpperCAmelCase_		:      List[str]       	=					get_bounds(UpperCamelCase   ,UpperCamelCase  )
	plt.ylim(max([-8_0, bounds[0]]  )   ,min([8_0, bounds[1]]  )  )
	plt.ylabel('Gain (dB)'  )

	plt.plot(UpperCamelCase  )
	plt.show()







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase  )    ->    List[str]:
	UpperCAmelCase_		:      Any       	=					5_1_2
	UpperCAmelCase_		:      Optional[int]       	=					[1] + [0] * (size - 1)
	UpperCAmelCase_		:      str       	=					[filter_type.process(UpperCamelCase  ) for item in inputs]

	UpperCAmelCase_		:      Optional[int]       	=					[0] * (samplerate - size)  # zero-padding
	outputs += filler
	UpperCAmelCase_		:      List[Any]       	=					np.angle(np.fft.fft(UpperCamelCase  )  )

	# Frequencies on log scale from 24 to nyquist frequency
	plt.xlim(2_4   ,samplerate / 2 - 1  )
	plt.xlabel('Frequency (Hz)'  )
	plt.xscale('log'  )

	plt.ylim(-2 * pi   ,2 * pi  )
	plt.ylabel('Phase shift (Radians)'  )
	plt.plot(np.unwrap(UpperCamelCase   ,-2 * pi  )  )
	plt.show()









'''simple docstring'''


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


lowerCAmelCase__										= logging.get_logger(__name__)

lowerCAmelCase__										= {
    "tanreinama/GPTSAN-2.8B-spout_is_uniform": (
        "https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json"
    ),
}



class 					lowercase						(  a_ ):
							_lowerCamelCase		:      Any=   "gptsan-japanese"
							_lowerCamelCase		:      List[Any]=   [
							    "past_key_values",
							]
							_lowerCamelCase		:      Tuple=   {
							    "hidden_size": "d_model",
							    "num_attention_heads": "num_heads",
							    "num_hidden_layers": "num_layers",
							}


							def __init__(			self    ,			_snake_case=3_6000    ,			_snake_case=1280    ,			_snake_case=1024    ,			_snake_case=8192    ,			_snake_case=4096    ,			_snake_case=128    ,			_snake_case=10    ,			_snake_case=0    ,			_snake_case=16    ,			_snake_case=16    ,			_snake_case=128    ,			_snake_case=0.0    ,			_snake_case=1e-5    ,			_snake_case=False    ,			_snake_case=0.0    ,			_snake_case="float32"    ,			_snake_case=False    ,			_snake_case=False    ,			_snake_case=False    ,			_snake_case=0.002    ,			_snake_case=False    ,			_snake_case=True    ,			_snake_case=3_5998    ,			_snake_case=3_5995    ,			_snake_case=3_5999    ,			**_snake_case    ,			)							->				Optional[int]:
								UpperCAmelCase_		:      Dict       	=					vocab_size
								UpperCAmelCase_		:      Optional[int]       	=					max_position_embeddings
								UpperCAmelCase_		:      Any       	=					d_model
								UpperCAmelCase_		:      int       	=					d_ff
								UpperCAmelCase_		:      Tuple       	=					d_ext
								UpperCAmelCase_		:      Tuple       	=					d_spout
								UpperCAmelCase_		:      List[Any]       	=					num_switch_layers
								UpperCAmelCase_		:      Tuple       	=					num_ext_layers
								UpperCAmelCase_		:      Optional[int]       	=					num_switch_layers + num_ext_layers
								UpperCAmelCase_		:      Tuple       	=					num_heads
								UpperCAmelCase_		:      List[str]       	=					num_experts
								UpperCAmelCase_		:      List[str]       	=					expert_capacity
								UpperCAmelCase_		:      Optional[int]       	=					dropout_rate
								UpperCAmelCase_		:      List[Any]       	=					layer_norm_epsilon
								UpperCAmelCase_		:      Any       	=					router_bias
								UpperCAmelCase_		:      Optional[Any]       	=					router_jitter_noise
								UpperCAmelCase_		:      str       	=					router_dtype
								UpperCAmelCase_		:      List[str]       	=					router_ignore_padding_tokens
								UpperCAmelCase_		:      Optional[int]       	=					output_hidden_states
								UpperCAmelCase_		:      Union[str, Any]       	=					output_attentions
								UpperCAmelCase_		:      Optional[int]       	=					initializer_factor
								UpperCAmelCase_		:      Optional[int]       	=					output_router_logits
								UpperCAmelCase_		:      List[str]       	=					use_cache

								super().__init__(
								    separator_token_id=_snake_case    ,			pad_token_id=_snake_case    ,			eos_token_id=_snake_case    ,			**_snake_case    ,			)






