from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_torch_dist_unique_port,
    require_torch_multi_gpu,
    require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging


__UpperCamelCase	: Union[str, Any]    		=  logging.get_logger(__name__)


if is_torch_available():
					import torch
					from torch import nn
					from torch.utils.data import Dataset

					from transformers import Trainer





					class       lowercase__		(     UpperCamelCase_):


											def __init__( self			:       Union[str, Any]					,	UpperCamelCase__			:       int = 101		):





																	'''simple docstring'''





																	SCREAMING_SNAKE_CASE							:					Dict           =							length


											def __len__( self			:       Dict		):





																	'''simple docstring'''





																	return self.length



											def __getitem__( self			:       int					,	UpperCamelCase__			:       str		):





																	'''simple docstring'''





																	return i






					class       lowercase__		:


											def __call__( self			:       Dict					,	UpperCamelCase__			:       List[Any]		):





																	'''simple docstring'''





																	return {"input_ids": torch.tensor(UpperCamelCase__		), "labels": torch.tensor(UpperCamelCase__		)}






					class       lowercase__		(     nn.Module):


											def __init__( self			:       List[str]		):





																	'''simple docstring'''





																	super().__init__()
																	# Add some (unused) params otherwise DDP will complain.
																	SCREAMING_SNAKE_CASE							:					Dict           =							nn.Linear(120					,	80		)



											def  __A ( self			:       List[Any]					,	UpperCamelCase__			:       List[Any]					,	UpperCamelCase__			:       Any=None		):





																	'''simple docstring'''





																	if labels is not None:
																							return torch.tensor(0.0					,	device=input_ids.device		), input_ids
																	else:
																							return input_ids





class       lowercase__		(     UpperCamelCase_):


						@require_torch_neuroncore
						def  __A ( self			:       Optional[int]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							f"""--nproc_per_node=2
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
												SCREAMING_SNAKE_CASE							:					Tuple           =							self.get_auto_remove_tmp_dir()
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							f"""--output_dir {output_dir}""".split()
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							['''torchrun'''] + distributed_args + args
												execute_subprocess_async(UpperCamelCase__					,	env=self.get_env()		)
												# successful return here == success - any errors would have caused an error in the sub-call






class       lowercase__		(     UpperCamelCase_):


						@require_torch_multi_gpu
						def  __A ( self			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					int           =							f"""--nproc_per_node={torch.cuda.device_count()}
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
												SCREAMING_SNAKE_CASE							:					Any           =							self.get_auto_remove_tmp_dir()
												SCREAMING_SNAKE_CASE							:					List[Any]           =							f"""--output_dir {output_dir}""".split()
												SCREAMING_SNAKE_CASE							:					str           =							['''torchrun'''] + distributed_args + args
												execute_subprocess_async(UpperCamelCase__					,	env=self.get_env()		)
												# successful return here == success - any errors would have caused an error in the sub-call


if __name__ == "__main__":
					# The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
					#
					# PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py

					__UpperCamelCase	: Any    		=  HfArgumentParser((TrainingArguments,))
					__UpperCamelCase	: Tuple    		=  parser.parse_args_into_dataclasses()[0]

					logger.warning(
					    f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, """
					    f"""distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}"""
					)

					# Essentially, what we want to verify in the distributed case is that we get all samples back,
					# in the right order. (this is crucial for prediction for instance)
					for dataset_length in [101, 40, 7]:
										__UpperCamelCase	: List[str]    		=  DummyDataset(dataset_length)
										def    A			(					_lowercase		):
																SCREAMING_SNAKE_CASE							:					Optional[int]           =							list(range(len(_lowercase		)		)		)
																SCREAMING_SNAKE_CASE							:					List[str]           =							p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
																if not success and training_args.local_rank == 0:
																						logger.warning(
																						    '''Predictions and/or labels do not match expected results:\n  - predictions: '''
																						    f"""{p.predictions.tolist()}\n  - labels: {p.label_ids.tolist()}\n  - expected: {sequential}"""		)
																return {"success": success}

										__UpperCamelCase	: Any    		=  Trainer(
										    model=DummyModel(),
										    args=training_args,
										    data_collator=DummyDataCollator(),
										    eval_dataset=dataset,
										    compute_metrics=compute_metrics,
										)
										__UpperCamelCase	: str    		=  trainer.evaluate()
										logger.info(metrics)
										if metrics["eval_success"] is not True:
															logger.error(metrics)
															exit(1)

										__UpperCamelCase	: Tuple    		=  trainer.predict(dataset)
										logger.info(p.metrics)
										if p.metrics["test_success"] is not True:
															logger.error(p.metrics)
															exit(1)

										__UpperCamelCase	: Any    		=  2

										__UpperCamelCase	: Any    		=  trainer.evaluate()
										logger.info(metrics)
										if metrics["eval_success"] is not True:
															logger.error(metrics)
															exit(1)

										__UpperCamelCase	: Optional[int]    		=  trainer.predict(dataset)
										logger.info(p.metrics)
										if p.metrics["test_success"] is not True:
															logger.error(p.metrics)
															exit(1)



										__UpperCamelCase	: List[str]    		=  None






from dataclasses import asdict, dataclass
from typing import Optional

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


__UpperCamelCase	: Tuple    		=  logging.get_logger(__name__)

# TODO Update this
__UpperCamelCase	: List[str]    		=  {
    '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__		(     UpperCamelCase_):
						UpperCamelCase_  =	"""esm"""


						def __init__( self			:       Tuple					,	UpperCamelCase__			:       Optional[Any]=None					,	UpperCamelCase__			:       int=None					,	UpperCamelCase__			:       List[str]=None					,	UpperCamelCase__			:       Any=768					,	UpperCamelCase__			:       Optional[Any]=12					,	UpperCamelCase__			:       str=12					,	UpperCamelCase__			:       Optional[int]=3072					,	UpperCamelCase__			:       Optional[Any]=0.1					,	UpperCamelCase__			:       Any=0.1					,	UpperCamelCase__			:       Union[str, Any]=1026					,	UpperCamelCase__			:       Dict=0.02					,	UpperCamelCase__			:       Any=1E-12					,	UpperCamelCase__			:       Dict="absolute"					,	UpperCamelCase__			:       Optional[Any]=True					,	UpperCamelCase__			:       int=None					,	UpperCamelCase__			:       Optional[int]=False					,	UpperCamelCase__			:       Tuple=False					,	UpperCamelCase__			:       List[str]=None					,	UpperCamelCase__			:       Optional[Any]=None					,	**UpperCamelCase__			:       Any					,	):





												'''simple docstring'''





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

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





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							super().to_dict()
												if isinstance(self.esmfold_config					,	UpperCamelCase__		):
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							self.esmfold_config.to_dict()
												return output






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

						UpperCamelCase_  =	True
						UpperCamelCase_  =	False

						UpperCamelCase_  =	128
						UpperCamelCase_  =	None


						def  __A ( self			:       Optional[int]		):





												'''simple docstring'''





												if self.trunk is None:
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							TrunkConfig()
												elif isinstance(self.trunk					,	UpperCamelCase__		):
																		SCREAMING_SNAKE_CASE							:					Tuple           =							TrunkConfig(**self.trunk		)



						def  __A ( self			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					str           =							asdict(self		)
												SCREAMING_SNAKE_CASE							:					Tuple           =							self.trunk.to_dict()
												return output






@dataclass
class       lowercase__		:
						UpperCamelCase_  =	48
						UpperCamelCase_  =	1_024
						UpperCamelCase_  =	128
						UpperCamelCase_  =	32
						UpperCamelCase_  =	32
						UpperCamelCase_  =	32
						UpperCamelCase_  =	0
						UpperCamelCase_  =	0
						UpperCamelCase_  =	False
						UpperCamelCase_  =	4
						UpperCamelCase_  =	128
						UpperCamelCase_  =	None


						def  __A ( self			:       Any		):





												'''simple docstring'''





												if self.structure_module is None:
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							StructureModuleConfig()
												elif isinstance(self.structure_module					,	UpperCamelCase__		):
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							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							:					Dict           =							self.sequence_state_dim // self.sequence_head_width
												SCREAMING_SNAKE_CASE							:					Tuple           =							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  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							asdict(self		)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							self.structure_module.to_dict()
												return output






@dataclass
class       lowercase__		:
						UpperCamelCase_  =	384
						UpperCamelCase_  =	128
						UpperCamelCase_  =	16
						UpperCamelCase_  =	128
						UpperCamelCase_  =	12
						UpperCamelCase_  =	4
						UpperCamelCase_  =	8
						UpperCamelCase_  =	0.1
						UpperCamelCase_  =	8
						UpperCamelCase_  =	1
						UpperCamelCase_  =	2
						UpperCamelCase_  =	7
						UpperCamelCase_  =	10
						UpperCamelCase_  =	1E-8
						UpperCamelCase_  =	1E5


						def  __A ( self			:       Dict		):





												'''simple docstring'''





												return asdict(self		)






def    A			(					):
						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>",
						)





