


"""simple docstring"""






import copy
import os
from typing import Union

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


UpperCamelCase__				:Dict			   =	logging.get_logger(__name__)

UpperCamelCase__				:List[str]			   =	{
    """google/pix2struct-textcaps-base""": (
        """https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json"""
    ),
}






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		A    =							"pix2struct_text_model"
		A    =							["past_key_values"]
		A    =							{
		    "hidden_size": "hidden_size",
		    "num_attention_heads": "num_heads",
		    "num_hidden_layers": "num_layers",
		}
		def __init__( self					,				SCREAMING_SNAKE_CASE__=5_02_44					,				SCREAMING_SNAKE_CASE__=7_68					,				SCREAMING_SNAKE_CASE__=64					,				SCREAMING_SNAKE_CASE__=20_48					,				SCREAMING_SNAKE_CASE__=12					,				SCREAMING_SNAKE_CASE__=12					,				SCREAMING_SNAKE_CASE__=32					,				SCREAMING_SNAKE_CASE__=1_28					,				SCREAMING_SNAKE_CASE__=0.1					,				SCREAMING_SNAKE_CASE__=1E-6					,				SCREAMING_SNAKE_CASE__=1.0					,				SCREAMING_SNAKE_CASE__="gelu_new"					,				SCREAMING_SNAKE_CASE__=0					,				SCREAMING_SNAKE_CASE__=False					,				SCREAMING_SNAKE_CASE__=0					,				SCREAMING_SNAKE_CASE__=1					,				SCREAMING_SNAKE_CASE__=False					,				SCREAMING_SNAKE_CASE__=True					,				**SCREAMING_SNAKE_CASE__					,				)      ->	Dict:

					"""simple docstring"""



					_UpperCamelCase					:Tuple					     =	vocab_size
					_UpperCamelCase					:Optional[int]					     =	hidden_size
					_UpperCamelCase					:str					     =	d_kv
					_UpperCamelCase					:Optional[int]					     =	d_ff
					_UpperCamelCase					:List[Any]					     =	num_layers
					_UpperCamelCase					:List[str]					     =	num_heads
					_UpperCamelCase					:Union[str, Any]					     =	relative_attention_num_buckets
					_UpperCamelCase					:str					     =	relative_attention_max_distance
					_UpperCamelCase					:Any					     =	dropout_rate
					_UpperCamelCase					:List[Any]					     =	layer_norm_epsilon
					_UpperCamelCase					:List[Any]					     =	initializer_factor
					_UpperCamelCase					:Any					     =	use_cache

					_UpperCamelCase					:Tuple					     =	eos_token_id
					_UpperCamelCase					:Tuple					     =	decoder_start_token_id

					# for backwards compatibility
					_UpperCamelCase					:Tuple					     =	dense_act_fn

					super().__init__(
					    pad_token_id=SCREAMING_SNAKE_CASE__					,				eos_token_id=SCREAMING_SNAKE_CASE__					,				decoder_start_token_id=SCREAMING_SNAKE_CASE__					,				tie_word_embeddings=SCREAMING_SNAKE_CASE__					,				is_decoder=SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__					,				)





		@classmethod
		def   _UpperCamelCase( cls					,				SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )      ->	"PretrainedConfig":

					"""simple docstring"""



					cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase     ,      _UpperCamelCase					:List[str]					     =	cls.get_config_dict(SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )

					# get the text config dict if we are loading from Pix2StructConfig
					if config_dict.get('''model_type'''   ) == "pix2struct":
								_UpperCamelCase					:List[Any]					     =	config_dict['''text_config''']

					if "model_type" in config_dict and hasattr(cls					,				'''model_type'''   ) and config_dict["model_type"] != cls.model_type:
								logger.warning(
								    f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
								    f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."   )

					return cls.from_dict(SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		A    =							"pix2struct_vision_model"
		def __init__( self					,				SCREAMING_SNAKE_CASE__=7_68					,				SCREAMING_SNAKE_CASE__=7_68					,				SCREAMING_SNAKE_CASE__=20_48					,				SCREAMING_SNAKE_CASE__=64					,				SCREAMING_SNAKE_CASE__=12					,				SCREAMING_SNAKE_CASE__=12					,				SCREAMING_SNAKE_CASE__="gelu_new"					,				SCREAMING_SNAKE_CASE__=1E-6					,				SCREAMING_SNAKE_CASE__=0.0					,				SCREAMING_SNAKE_CASE__=0.0					,				SCREAMING_SNAKE_CASE__=1E-10					,				SCREAMING_SNAKE_CASE__=1.0					,				SCREAMING_SNAKE_CASE__=40_96					,				SCREAMING_SNAKE_CASE__=32					,				SCREAMING_SNAKE_CASE__=1_28					,				**SCREAMING_SNAKE_CASE__					,				)      ->	Optional[int]:

					"""simple docstring"""



					super().__init__(**SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:str					     =	hidden_size
					_UpperCamelCase					:List[Any]					     =	patch_embed_hidden_size
					_UpperCamelCase					:Union[str, Any]					     =	d_ff
					_UpperCamelCase					:Union[str, Any]					     =	dropout_rate
					_UpperCamelCase					:int					     =	num_hidden_layers
					_UpperCamelCase					:List[Any]					     =	num_attention_heads
					_UpperCamelCase					:Any					     =	initializer_range
					_UpperCamelCase					:Tuple					     =	initializer_factor
					_UpperCamelCase					:str					     =	attention_dropout
					_UpperCamelCase					:str					     =	layer_norm_eps
					_UpperCamelCase					:Dict					     =	dense_act_fn
					_UpperCamelCase					:Optional[int]					     =	seq_len
					_UpperCamelCase					:str					     =	relative_attention_num_buckets
					_UpperCamelCase					:Optional[int]					     =	relative_attention_max_distance
					_UpperCamelCase					:List[str]					     =	d_kv





		@classmethod
		def   _UpperCamelCase( cls					,				SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )      ->	"PretrainedConfig":

					"""simple docstring"""



					cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase     ,      _UpperCamelCase					:str					     =	cls.get_config_dict(SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )

					# get the vision config dict if we are loading from Pix2StructConfig
					if config_dict.get('''model_type'''   ) == "pix2struct":
								_UpperCamelCase					:Union[str, Any]					     =	config_dict['''vision_config''']

					if "model_type" in config_dict and hasattr(cls					,				'''model_type'''   ) and config_dict["model_type"] != cls.model_type:
								logger.warning(
								    f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
								    f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."   )

					return cls.from_dict(SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		A    =							"pix2struct"
		A    =							True
		def __init__( self					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=1.0					,				SCREAMING_SNAKE_CASE__=0.0_2					,				SCREAMING_SNAKE_CASE__=False					,				SCREAMING_SNAKE_CASE__=False					,				SCREAMING_SNAKE_CASE__=True					,				**SCREAMING_SNAKE_CASE__					,				)      ->	int:

					"""simple docstring"""



					super().__init__(tie_word_embeddings=SCREAMING_SNAKE_CASE__					,				is_encoder_decoder=SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )

					if text_config is None:
								_UpperCamelCase					:Tuple					     =	{}
								logger.info('''text_config is None. Initializing the Pix2StructTextConfig with default values.'''   )

					if vision_config is None:
								_UpperCamelCase					:List[Any]					     =	{}
								logger.info('''vision_config is None. Initializing the Pix2StructVisionConfig with default values.'''   )

					_UpperCamelCase					:str					     =	PixaStructTextConfig(**SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Tuple					     =	PixaStructVisionConfig(**SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:List[str]					     =	self.text_config.decoder_start_token_id
					_UpperCamelCase					:int					     =	self.text_config.pad_token_id
					_UpperCamelCase					:Any					     =	self.text_config.eos_token_id

					_UpperCamelCase					:Optional[Any]					     =	initializer_factor
					_UpperCamelCase					:Optional[Any]					     =	initializer_range

					_UpperCamelCase					:str					     =	self.initializer_range
					_UpperCamelCase					:Tuple					     =	self.initializer_range

					_UpperCamelCase					:Optional[Any]					     =	is_vqa
		@classmethod
		def   _UpperCamelCase( cls					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )      ->	Optional[Any]:

					"""simple docstring"""



					return cls(text_config=text_config.to_dict()					,				vision_config=vision_config.to_dict()					,				**SCREAMING_SNAKE_CASE__   )





		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					_UpperCamelCase					:List[Any]					     =	copy.deepcopy(self.__dict__   )
					_UpperCamelCase					:Tuple					     =	self.text_config.to_dict()
					_UpperCamelCase					:Tuple					     =	self.vision_config.to_dict()
					_UpperCamelCase					:Tuple					     =	self.__class__.model_type
					return output





"""simple docstring"""






from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	float:

					"""simple docstring"""



					return 0.0





def 				A_       ( snake_case__       ,					snake_case__		)					->  tuple[int | float, int | float]:
			_UpperCamelCase					:Union[str, Any]					     =	min([-20, np.min(fft_results[1 : samplerate // 2 - 1]		)]		)
			_UpperCamelCase					:Optional[int]					     =	max([20, np.max(fft_results[1 : samplerate // 2 - 1]		)]		)
			return lowest, highest





def 				A_       ( snake_case__       ,					snake_case__		)					->  None:
			_UpperCamelCase					:List[str]					     =	5_12
			_UpperCamelCase					:int					     =	[1] + [0] * (size - 1)
			_UpperCamelCase					:Union[str, Any]					     =	[filter_type.process(snake_case__		) for item in inputs]

			_UpperCamelCase					:Any					     =	[0] * (samplerate - size)  # zero-padding
			outputs += filler
			_UpperCamelCase					:int					     =	np.abs(np.fft.fft(snake_case__		)		)
			_UpperCamelCase					:str					     =	20 * np.logaa(snake_case__		)

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

			# Display within reasonable bounds
			_UpperCamelCase					:Tuple					     =	get_bounds(snake_case__       ,					snake_case__		)
			plt.ylim(max([-80, bounds[0]]		)       ,					min([80, bounds[1]]		)		)
			plt.ylabel('''Gain (dB)'''		)

			plt.plot(snake_case__		)
			plt.show()





def 				A_       ( snake_case__       ,					snake_case__		)					->  None:
			_UpperCamelCase					:Tuple					     =	5_12
			_UpperCamelCase					:Union[str, Any]					     =	[1] + [0] * (size - 1)
			_UpperCamelCase					:List[Any]					     =	[filter_type.process(snake_case__		) for item in inputs]

			_UpperCamelCase					:str					     =	[0] * (samplerate - size)  # zero-padding
			outputs += filler
			_UpperCamelCase					:Tuple					     =	np.angle(np.fft.fft(snake_case__		)		)

			# Frequencies on log scale from 24 to nyquist frequency
			plt.xlim(24       ,					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(snake_case__       ,					-2 * pi		)		)
			plt.show()

