from dataclasses import asdict, dataclass
from typing import Optional

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


_lowerCamelCase					:   int							=		logging.get_logger(__name__)

# TODO Update this
_lowerCamelCase					:   int							=		{
    '''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 						lowercase    (     a     ):
							lowercase__						:       Tuple								=							"""esm"""




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



												'''simple docstring'''

												super().__init__(pad_token_id=_UpperCamelCase	,							mask_token_id=_UpperCamelCase	,							**_UpperCamelCase      )

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





							def 	__snake_case(						self   :					int      )					->				Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       super().to_dict()
												if isinstance(self.esmfold_config	,							_UpperCamelCase      ):
																	SCREAMING_SNAKE_CASE    			=       self.esmfold_config.to_dict()
												return output



@dataclass
class 						lowercase    :
							lowercase__						:       str								=							None
							lowercase__						:       bool								=							True
							lowercase__						:       bool								=							False
							lowercase__						:       bool								=							False
							lowercase__						:       bool								=							False
							lowercase__						:       float								=							0

							lowercase__						:       bool								=							True
							lowercase__						:       bool								=							False

							lowercase__						:       int								=							128
							lowercase__						:       "TrunkConfig"								=							None




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



												'''simple docstring'''

												if self.trunk is None:
																	SCREAMING_SNAKE_CASE    			=       TrunkConfig()
												elif isinstance(self.trunk	,							_UpperCamelCase      ):
																	SCREAMING_SNAKE_CASE    			=       TrunkConfig(**self.trunk      )





							def 	__snake_case(						self   :					Optional[Any]      )					->				Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       asdict(self      )
												SCREAMING_SNAKE_CASE    			=       self.trunk.to_dict()
												return output



@dataclass
class 						lowercase    :
							lowercase__						:       int								=							48
							lowercase__						:       int								=							1_024
							lowercase__						:       int								=							128
							lowercase__						:       int								=							32
							lowercase__						:       int								=							32
							lowercase__						:       int								=							32
							lowercase__						:       float								=							0
							lowercase__						:       float								=							0
							lowercase__						:       bool								=							False
							lowercase__						:       int								=							4
							lowercase__						:       Optional[int]								=							128
							lowercase__						:       "StructureModuleConfig"								=							None




							def 	__snake_case(						self   :					Any      )					->				Tuple:



												'''simple docstring'''

												if self.structure_module is None:
																	SCREAMING_SNAKE_CASE    			=       StructureModuleConfig()
												elif isinstance(self.structure_module	,							_UpperCamelCase      ):
																	SCREAMING_SNAKE_CASE    			=       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}."      )

												SCREAMING_SNAKE_CASE    			=       self.sequence_state_dim // self.sequence_head_width
												SCREAMING_SNAKE_CASE    			=       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 	__snake_case(						self   :					Dict      )					->				List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       asdict(self      )
												SCREAMING_SNAKE_CASE    			=       self.structure_module.to_dict()
												return output



@dataclass
class 						lowercase    :
							lowercase__						:       int								=							384
							lowercase__						:       int								=							128
							lowercase__						:       int								=							16
							lowercase__						:       int								=							128
							lowercase__						:       int								=							12
							lowercase__						:       int								=							4
							lowercase__						:       int								=							8
							lowercase__						:       float								=							0.1
							lowercase__						:       int								=							8
							lowercase__						:       int								=							1
							lowercase__						:       int								=							2
							lowercase__						:       int								=							7
							lowercase__						:       int								=							10
							lowercase__						:       float								=							1e-8
							lowercase__						:       float								=							1e5




							def 	__snake_case(						self   :					int      )					->				str:



												'''simple docstring'''

												return asdict(self      )





def 	__lowerCamelCase							():
					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>",
					)





import numpy as np





def 	__lowerCamelCase							(UpperCAmelCase__     :							np.ndarray      , UpperCAmelCase__     :							np.ndarray      , UpperCAmelCase__     :							float = 1e-12      , UpperCAmelCase__     :							int = 1_0_0      , ):
					assert np.shape(UpperCAmelCase__  )[0] == np.shape(UpperCAmelCase__  )[1]
					# Ensure proper dimensionality.
					assert np.shape(UpperCAmelCase__  )[0] == np.shape(UpperCAmelCase__  )[0]
					# Ensure inputs are either both complex or both real
					assert np.iscomplexobj(UpperCAmelCase__  ) == np.iscomplexobj(UpperCAmelCase__  )
					SCREAMING_SNAKE_CASE    			=       np.iscomplexobj(UpperCAmelCase__  )
					if is_complex:
										# Ensure complex input_matrix is Hermitian
										assert np.array_equal(UpperCAmelCase__      , input_matrix.conj().T  )

					# Set convergence to False. Will define convergence when we exceed max_iterations
					# or when we have small changes from one iteration to next.

					SCREAMING_SNAKE_CASE    			=       False
					SCREAMING_SNAKE_CASE    			=       0
					SCREAMING_SNAKE_CASE    			=       0
					SCREAMING_SNAKE_CASE    			=       1e12

					while not convergence:
										# Multiple matrix by the vector.
										SCREAMING_SNAKE_CASE    			=       np.dot(UpperCAmelCase__      , UpperCAmelCase__  )
										# Normalize the resulting output vector.
										SCREAMING_SNAKE_CASE    			=       w / np.linalg.norm(UpperCAmelCase__  )
										# Find rayleigh quotient
										# (faster than usual b/c we know vector is normalized already)
										SCREAMING_SNAKE_CASE    			=       vector.conj().T if is_complex else vector.T
										SCREAMING_SNAKE_CASE    			=       np.dot(UpperCAmelCase__      , np.dot(UpperCAmelCase__      , UpperCAmelCase__  )  )

										# Check convergence.
										SCREAMING_SNAKE_CASE    			=       np.abs(lambda_ - lambda_previous  ) / lambda_
										iterations += 1

										if error <= error_tol or iterations >= max_iterations:
															SCREAMING_SNAKE_CASE    			=       True

										SCREAMING_SNAKE_CASE    			=       lambda_

					if is_complex:
										SCREAMING_SNAKE_CASE    			=       np.real(lambda_  )

					return lambda_, vector





def 	__lowerCamelCase							():
					SCREAMING_SNAKE_CASE    			=       np.array([[4_1, 4, 2_0], [4, 2_6, 3_0], [2_0, 3_0, 5_0]]  )
					SCREAMING_SNAKE_CASE    			=       np.array([4_1, 4, 2_0]  )
					SCREAMING_SNAKE_CASE    			=       real_input_matrix.astype(np.complexaaa  )
					SCREAMING_SNAKE_CASE    			=       np.triu(1j * complex_input_matrix      , 1  )
					complex_input_matrix += imag_matrix
					complex_input_matrix += -1 * imag_matrix.T
					SCREAMING_SNAKE_CASE    			=       np.array([4_1, 4, 2_0]  ).astype(np.complexaaa  )

					for problem_type in ["real", "complex"]:
										if problem_type == "real":
															SCREAMING_SNAKE_CASE    			=       real_input_matrix
															SCREAMING_SNAKE_CASE    			=       real_vector
										elif problem_type == "complex":
															SCREAMING_SNAKE_CASE    			=       complex_input_matrix
															SCREAMING_SNAKE_CASE    			=       complex_vector

										# Our implementation.
										SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       power_iteration(UpperCAmelCase__      , UpperCAmelCase__  )

										# Numpy implementation.

										# Get eigenvalues and eigenvectors using built-in numpy
										# eigh (eigh used for symmetric or hermetian matrices).
										SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       np.linalg.eigh(UpperCAmelCase__  )
										# Last eigenvalue is the maximum one.
										SCREAMING_SNAKE_CASE    			=       eigen_values[-1]
										# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
										SCREAMING_SNAKE_CASE    			=       eigen_vectors[:, -1]

										# Check our implementation and numpy gives close answers.
										assert np.abs(eigen_value - eigen_value_max  ) <= 1e-6
										# Take absolute values element wise of each eigenvector.
										# as they are only unique to a minus sign.
										assert np.linalg.norm(np.abs(UpperCAmelCase__  ) - np.abs(UpperCAmelCase__  )  ) <= 1e-6


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							test_power_iteration()




