
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE    :      Any          =			{'configuration_xglm': ['XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XGLMConfig']}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_SCREAMING_SNAKE_CASE    :      str          =			['XGLMTokenizer']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_SCREAMING_SNAKE_CASE    :      Dict          =			['XGLMTokenizerFast']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_SCREAMING_SNAKE_CASE    :      Optional[int]          =			[
					    'XGLM_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'XGLMForCausalLM',
					    'XGLMModel',
					    'XGLMPreTrainedModel',
					]


try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_SCREAMING_SNAKE_CASE    :      Optional[Any]          =			[
					    'FlaxXGLMForCausalLM',
					    'FlaxXGLMModel',
					    'FlaxXGLMPreTrainedModel',
					]


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_SCREAMING_SNAKE_CASE    :      str          =			[
					    'TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFXGLMForCausalLM',
					    'TFXGLMModel',
					    'TFXGLMPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_xglm import XGLMTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_xglm_fast import XGLMTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_xglm import (
										    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFXGLMForCausalLM,
										    TFXGLMModel,
										    TFXGLMPreTrainedModel,
										)


else:
					import sys

					_SCREAMING_SNAKE_CASE    :      Optional[int]          =			_LazyModule(__name__, globals()['__file__'], _import_structure)

from dataclasses import asdict, dataclass
from typing import Optional

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


_SCREAMING_SNAKE_CASE    :      Dict          =			logging.get_logger(__name__)

# TODO Update this
_SCREAMING_SNAKE_CASE    :      Optional[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  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				"esm"





	def __init__(			self				,       UpperCamelCase__=None				,       UpperCamelCase__=None				,       UpperCamelCase__=None				,       UpperCamelCase__=768				,       UpperCamelCase__=12				,       UpperCamelCase__=12				,       UpperCamelCase__=3072				,       UpperCamelCase__=0.1				,       UpperCamelCase__=0.1				,       UpperCamelCase__=1026				,       UpperCamelCase__=0.0_2				,       UpperCamelCase__=1e-12				,       UpperCamelCase__="absolute"				,       UpperCamelCase__=True				,       UpperCamelCase__=None				,       UpperCamelCase__=False				,       UpperCamelCase__=False				,       UpperCamelCase__=None				,       UpperCamelCase__=None				,       **UpperCamelCase__				,       ):
					super().__init__(pad_token_id=UpperCamelCase__				,       mask_token_id=UpperCamelCase__				,       **UpperCamelCase__		)

					A__  :	Optional[Any]         =							vocab_size
					A__  :	int         =							hidden_size
					A__  :	List[str]         =							num_hidden_layers
					A__  :	Tuple         =							num_attention_heads
					A__  :	str         =							intermediate_size
					A__  :	List[str]         =							hidden_dropout_prob
					A__  :	Optional[Any]         =							attention_probs_dropout_prob
					A__  :	int         =							max_position_embeddings
					A__  :	List[str]         =							initializer_range
					A__  :	List[Any]         =							layer_norm_eps
					A__  :	int         =							position_embedding_type
					A__  :	Optional[Any]         =							use_cache
					A__  :	Optional[int]         =							emb_layer_norm_before
					A__  :	List[str]         =							token_dropout
					A__  :	Tuple         =							is_folding_model
					if is_folding_model:
									if esmfold_config is None:
													logger.info('''No esmfold_config supplied for folding model, using default values.'''		)
													A__  :	List[Any]         =							EsmFoldConfig()
									elif isinstance(UpperCamelCase__				,       UpperCamelCase__		):
													A__  :	Optional[int]         =							EsmFoldConfig(**UpperCamelCase__		)
									A__  :	int         =							esmfold_config
									if vocab_list is None:
													logger.warning('''No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!'''		)
													A__  :	Any         =							get_default_vocab_list()
									else:
													A__  :	Dict         =							vocab_list
					else:
									A__  :	Optional[Any]         =							None
									A__  :	Tuple         =							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		):
					A__  :	Optional[int]         =							super().to_dict()
					if isinstance(self.esmfold_config				,       UpperCamelCase__		):
									A__  :	Dict         =							self.esmfold_config.to_dict()
					return output







@dataclass
class  UpperCamelCase__		:







	'''simple docstring'''
	_lowerCAmelCase							       =				None
	_lowerCAmelCase							       =				True
	_lowerCAmelCase							       =				False
	_lowerCAmelCase							       =				False
	_lowerCAmelCase							       =				False
	_lowerCAmelCase							       =				0

	_lowerCAmelCase							       =				True
	_lowerCAmelCase							       =				False

	_lowerCAmelCase							       =				128
	_lowerCAmelCase							       =				None





	def   __snake_case				(			self		):
					if self.trunk is None:
									A__  :	Tuple         =							TrunkConfig()
					elif isinstance(self.trunk				,       UpperCamelCase__		):
									A__  :	List[Any]         =							TrunkConfig(**self.trunk		)





	def   __snake_case				(			self		):
					A__  :	Optional[int]         =							asdict(self		)
					A__  :	int         =							self.trunk.to_dict()
					return output







@dataclass
class  UpperCamelCase__		:







	'''simple docstring'''
	_lowerCAmelCase							       =				48
	_lowerCAmelCase							       =				1_024
	_lowerCAmelCase							       =				128
	_lowerCAmelCase							       =				32
	_lowerCAmelCase							       =				32
	_lowerCAmelCase							       =				32
	_lowerCAmelCase							       =				0
	_lowerCAmelCase							       =				0
	_lowerCAmelCase							       =				False
	_lowerCAmelCase							       =				4
	_lowerCAmelCase							       =				128
	_lowerCAmelCase							       =				None





	def   __snake_case				(			self		):
					if self.structure_module is None:
									A__  :	str         =							StructureModuleConfig()
					elif isinstance(self.structure_module				,       UpperCamelCase__		):
									A__  :	str         =							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}."		)

					A__  :	Tuple         =							self.sequence_state_dim // self.sequence_head_width
					A__  :	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   __snake_case				(			self		):
					A__  :	List[Any]         =							asdict(self		)
					A__  :	Optional[int]         =							self.structure_module.to_dict()
					return output







@dataclass
class  UpperCamelCase__		:







	'''simple docstring'''
	_lowerCAmelCase							       =				384
	_lowerCAmelCase							       =				128
	_lowerCAmelCase							       =				16
	_lowerCAmelCase							       =				128
	_lowerCAmelCase							       =				12
	_lowerCAmelCase							       =				4
	_lowerCAmelCase							       =				8
	_lowerCAmelCase							       =				0.1
	_lowerCAmelCase							       =				8
	_lowerCAmelCase							       =				1
	_lowerCAmelCase							       =				2
	_lowerCAmelCase							       =				7
	_lowerCAmelCase							       =				10
	_lowerCAmelCase							       =				1e-8
	_lowerCAmelCase							       =				1e5





	def   __snake_case				(			self		):
					return asdict(self		)




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

				"""simple docstring"""







				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>",
				)