






"""simple docstring"""
from dataclasses import asdict, dataclass
from typing import Optional

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


lowerCamelCase_       			=   logging.get_logger(__name__)

# TODO Update this
lowerCamelCase_       			=   {
    '''facebook/esm-1b''': '''https://huggingface.co/facebook/esm-1b/resolve/main/config.json''',
    # See all ESM models at https://huggingface.co/models?filter=esm
}







class     UpperCamelCase_ (__A  ):
	__magic_name__			   =       '''esm'''





	def __init__(   self      :				List[Any]			,			lowerCAmelCase_      :				Optional[Any]=None			,			lowerCAmelCase_      :				Optional[int]=None			,			lowerCAmelCase_      :				Tuple=None			,			lowerCAmelCase_      :				Any=768			,			lowerCAmelCase_      :				List[str]=12			,			lowerCAmelCase_      :				Tuple=12			,			lowerCAmelCase_      :				Any=3_072			,			lowerCAmelCase_      :				str=0.1			,			lowerCAmelCase_      :				Tuple=0.1			,			lowerCAmelCase_      :				List[Any]=1_026			,			lowerCAmelCase_      :				str=0.0_2			,			lowerCAmelCase_      :				Union[str, Any]=1e-12			,			lowerCAmelCase_      :				List[Any]="absolute"			,			lowerCAmelCase_      :				str=True			,			lowerCAmelCase_      :				Tuple=None			,			lowerCAmelCase_      :				Optional[int]=False			,			lowerCAmelCase_      :				Optional[Any]=False			,			lowerCAmelCase_      :				Dict=None			,			lowerCAmelCase_      :				Tuple=None			,			**lowerCAmelCase_      :				int			,			)		->    Union[str, Any]:
				super().__init__(pad_token_id=lowerCAmelCase_			,			mask_token_id=lowerCAmelCase_			,			**lowerCAmelCase_		)

				UpperCAmelCase_  :				Optional[int]      =      vocab_size
				UpperCAmelCase_  :				Tuple      =      hidden_size
				UpperCAmelCase_  :				List[Any]      =      num_hidden_layers
				UpperCAmelCase_  :				Tuple      =      num_attention_heads
				UpperCAmelCase_  :				Union[str, Any]      =      intermediate_size
				UpperCAmelCase_  :				Tuple      =      hidden_dropout_prob
				UpperCAmelCase_  :				Any      =      attention_probs_dropout_prob
				UpperCAmelCase_  :				Union[str, Any]      =      max_position_embeddings
				UpperCAmelCase_  :				Dict      =      initializer_range
				UpperCAmelCase_  :				Union[str, Any]      =      layer_norm_eps
				UpperCAmelCase_  :				Optional[Any]      =      position_embedding_type
				UpperCAmelCase_  :				Tuple      =      use_cache
				UpperCAmelCase_  :				int      =      emb_layer_norm_before
				UpperCAmelCase_  :				List[Any]      =      token_dropout
				UpperCAmelCase_  :				Optional[int]      =      is_folding_model
				if is_folding_model:
							if esmfold_config is None:
										logger.info("No esmfold_config supplied for folding model, using default values."		)
										UpperCAmelCase_  :				List[str]      =      EsmFoldConfig()
							elif isinstance(lowerCAmelCase_			,			lowerCAmelCase_		):
										UpperCAmelCase_  :				Dict      =      EsmFoldConfig(**lowerCAmelCase_		)
							UpperCAmelCase_  :				Optional[int]      =      esmfold_config
							if vocab_list is None:
										logger.warning("No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!"		)
										UpperCAmelCase_  :				Optional[int]      =      get_default_vocab_list()
							else:
										UpperCAmelCase_  :				str      =      vocab_list
				else:
							UpperCAmelCase_  :				Dict      =      None
							UpperCAmelCase_  :				List[str]      =      None
				if self.esmfold_config is not None and getattr(self.esmfold_config			,			"use_esm_attn_map"			,			lowerCAmelCase_		):
							raise ValueError("The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!"		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Any		)		->    Optional[Any]:
				UpperCAmelCase_  :				int      =      super().to_dict()
				if isinstance(self.esmfold_config			,			lowerCAmelCase_		):
							UpperCAmelCase_  :				Dict      =      self.esmfold_config.to_dict()
				return output







@dataclass
class     UpperCamelCase_ :
	__magic_name__			   =       None
	__magic_name__			   =       True
	__magic_name__			   =       False
	__magic_name__			   =       False
	__magic_name__			   =       False
	__magic_name__			   =       0

	__magic_name__			   =       True
	__magic_name__			   =       False

	__magic_name__			   =       1_28
	__magic_name__			   =       None





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]		)		->    int:
				if self.trunk is None:
							UpperCAmelCase_  :				List[Any]      =      TrunkConfig()
				elif isinstance(self.trunk			,			lowerCAmelCase_		):
							UpperCAmelCase_  :				str      =      TrunkConfig(**self.trunk		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    Union[str, Any]:
				UpperCAmelCase_  :				Optional[Any]      =      asdict(self		)
				UpperCAmelCase_  :				Tuple      =      self.trunk.to_dict()
				return output







@dataclass
class     UpperCamelCase_ :
	__magic_name__			   =       48
	__magic_name__			   =       10_24
	__magic_name__			   =       1_28
	__magic_name__			   =       32
	__magic_name__			   =       32
	__magic_name__			   =       32
	__magic_name__			   =       0
	__magic_name__			   =       0
	__magic_name__			   =       False
	__magic_name__			   =       4
	__magic_name__			   =       1_28
	__magic_name__			   =       None





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]		)		->    List[Any]:
				if self.structure_module is None:
							UpperCAmelCase_  :				Any      =      StructureModuleConfig()
				elif isinstance(self.structure_module			,			lowerCAmelCase_		):
							UpperCAmelCase_  :				int      =      StructureModuleConfig(**self.structure_module		)

				if self.max_recycles <= 0:
							raise ValueError(f"""`max_recycles` should be positive, got {self.max_recycles}."""		)
				if self.sequence_state_dim % self.sequence_state_dim != 0:
							raise ValueError(
							    "`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got"
							    f""" {self.sequence_state_dim} and {self.sequence_state_dim}."""		)
				if self.pairwise_state_dim % self.pairwise_state_dim != 0:
							raise ValueError(
							    "`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got"
							    f""" {self.pairwise_state_dim} and {self.pairwise_state_dim}."""		)

				UpperCAmelCase_  :				Any      =      self.sequence_state_dim // self.sequence_head_width
				UpperCAmelCase_  :				Optional[int]      =      self.pairwise_state_dim // self.pairwise_head_width

				if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
							raise ValueError(
							    "`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got"
							    f""" {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}."""		)
				if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
							raise ValueError(
							    "`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got"
							    f""" {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}."""		)
				if self.pairwise_state_dim % 2 != 0:
							raise ValueError(f"""`pairwise_state_dim` should be even, got {self.pairwise_state_dim}."""		)

				if self.dropout >= 0.4:
							raise ValueError(f"""`dropout` should not be greater than 0.4, got {self.dropout}."""		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    str:
				UpperCAmelCase_  :				List[Any]      =      asdict(self		)
				UpperCAmelCase_  :				Dict      =      self.structure_module.to_dict()
				return output







@dataclass
class     UpperCamelCase_ :
	__magic_name__			   =       3_84
	__magic_name__			   =       1_28
	__magic_name__			   =       16
	__magic_name__			   =       1_28
	__magic_name__			   =       12
	__magic_name__			   =       4
	__magic_name__			   =       8
	__magic_name__			   =       0.1
	__magic_name__			   =       8
	__magic_name__			   =       1
	__magic_name__			   =       2
	__magic_name__			   =       7
	__magic_name__			   =       10
	__magic_name__			   =       1e-8
	__magic_name__			   =       1e5





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]		)		->    Optional[Any]:
				return asdict(self		)





def        snake_case   (     ):
			return (
			    "<cls>",
			    "<pad>",
			    "<eos>",
			    "<unk>",
			    "L",
			    "A",
			    "G",
			    "V",
			    "S",
			    "E",
			    "R",
			    "T",
			    "I",
			    "D",
			    "P",
			    "K",
			    "Q",
			    "N",
			    "F",
			    "Y",
			    "M",
			    "H",
			    "W",
			    "C",
			    "X",
			    "B",
			    "U",
			    "Z",
			    "O",
			    ".",
			    "-",
			    "<null_1>",
			    "<mask>",
			)











"""simple docstring"""
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey

def        snake_case   (     A__			,A__			,A__			,A__			,A__			,A__		):

			# prepare kernel
			# the kernel size have to be odd
			if (ksize % 2) == 0:
						UpperCAmelCase_  :				List[Any]      =      ksize + 1
			UpperCAmelCase_  :				Optional[Any]      =      np.zeros((ksize, ksize)			,dtype=np.floataa		)

			# each value
			for y in range(A__		):
						for x in range(A__		):
									# distance from center
									UpperCAmelCase_  :				Tuple      =      x - ksize // 2
									UpperCAmelCase_  :				Any      =      y - ksize // 2

									# degree to radiant
									UpperCAmelCase_  :				int      =      theta / 1_80 * np.pi
									UpperCAmelCase_  :				Optional[int]      =      np.cos(_theta		)
									UpperCAmelCase_  :				Union[str, Any]      =      np.sin(_theta		)

									# get kernel x
									UpperCAmelCase_  :				Tuple      =      cos_theta * px + sin_theta * py

									# get kernel y
									UpperCAmelCase_  :				List[str]      =      -sin_theta * px + cos_theta * py

									# fill kernel
									UpperCAmelCase_  :				Dict      =      np.exp(
									    -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)		) * np.cos(2 * np.pi * _x / lambd + psi		)

			return gabor


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			# read original image
			lowerCamelCase_       			=   imread('''../image_data/lena.jpg''')
			# turn image in gray scale value
			lowerCamelCase_       			=   cvtColor(img, COLOR_BGR2GRAY)

			# Apply multiple Kernel to detect edges
			lowerCamelCase_       			=   np.zeros(gray.shape[:2])
			for theta in [0, 30, 60, 90, 120, 150]:
						lowerCamelCase_       			=   gabor_filter_kernel(10, 8, theta, 10, 0, 0)
						out += filteraD(gray, CV_8UC3, kernel_aa)
			lowerCamelCase_       			=   out / out.max() * 255
			lowerCamelCase_       			=   out.astype(np.uinta)

			imshow('''Original''', gray)
			imshow('''Gabor filter with 20x20 mask and 6 directions''', out)

			waitKey(0)



