



import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser






def 					__lowerCAmelCase		(  __lowerCamelCase	:    str=None				,   __lowerCamelCase	:    Optional[int]=None	)  ->						Union[str, Any]:
					return field(default_factory=lambda: default				,   metadata=__lowerCamelCase	)






@dataclass
class 						__a :
	SCREAMING_SNAKE_CASE       =       field(
	    metadata={"help": "The csv file to plot."}	,    )
	SCREAMING_SNAKE_CASE       =       field(
	    default=SCREAMING_SNAKE_CASE	,    metadata={"help": "Whether to plot along batch size or sequence length. Defaults to sequence length."}	,    )
	SCREAMING_SNAKE_CASE       =       field(
	    default=SCREAMING_SNAKE_CASE	,    metadata={"help": "Whether the csv file has time results or memory results. Defaults to memory results."}	,    )
	SCREAMING_SNAKE_CASE       =       field(
	    default=SCREAMING_SNAKE_CASE	,    metadata={"help": "Disable logarithmic scale when plotting"}	,    )
	SCREAMING_SNAKE_CASE       =       field(
	    default=SCREAMING_SNAKE_CASE	,    metadata={
	        "help": "Whether the csv file has training results or inference results. Defaults to inference results."
	    }	,    )
	SCREAMING_SNAKE_CASE       =       field(
	    default=SCREAMING_SNAKE_CASE	,    metadata={"help": "Filename under which the plot will be saved. If unused no plot is saved."}	,    )
	SCREAMING_SNAKE_CASE       =       list_field(
	    default=SCREAMING_SNAKE_CASE	,    metadata={"help": "List of model names that are used instead of the ones in the csv file."}				)






def 					__lowerCAmelCase		(  __lowerCamelCase	:    Tuple	)  ->						Tuple:
					try:
										int(__lowerCamelCase	)
										return True
					except ValueError:
										return False






def 					__lowerCAmelCase		(  __lowerCamelCase	:    Optional[Any]	)  ->						List[str]:
					try:
										float(__lowerCamelCase	)
										return True
					except ValueError:
										return False






class 						__a :






	def __init__(						self							:    List[Any]				,	snake_case_							:    List[str])->							List[str]:
						__lowerCAmelCase	   =args
						__lowerCAmelCase	   =defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}})

						with open(self.args.csv_file				,	newline="""""") as csv_file:
											__lowerCAmelCase	   =csv.DictReader(snake_case_)
											for row in reader:
																__lowerCAmelCase	   =row["""model"""]
																self.result_dict[model_name]["bsz"].append(int(row["""batch_size"""]))
																self.result_dict[model_name]["seq_len"].append(int(row["""sequence_length"""]))
																if can_convert_to_int(row["""result"""]):
																					# value is not None
																					__lowerCAmelCase	   =int(row["""result"""])
																elif can_convert_to_float(row["""result"""]):
																					# value is not None
																					__lowerCAmelCase	   =float(row["""result"""])






	def 		UpperCamelCase							(						self							:    str)->							List[str]:
						__lowerCAmelCase   ,	__lowerCAmelCase	   =plt.subplots()
						__lowerCAmelCase	   ="""Time usage""" if self.args.is_time else """Memory usage"""
						__lowerCAmelCase	   =title_str + """ for training""" if self.args.is_train else title_str + """ for inference"""

						if not self.args.no_log_scale:
											# set logarithm scales
											ax.set_xscale("""log""")
											ax.set_yscale("""log""")

						for axis in [ax.xaxis, ax.yaxis]:
											axis.set_major_formatter(ScalarFormatter())

						for model_name_idx, model_name in enumerate(self.result_dict.keys()):
											__lowerCAmelCase	   =sorted(set(self.result_dict[model_name]["""bsz"""]))
											__lowerCAmelCase	   =sorted(set(self.result_dict[model_name]["""seq_len"""]))
											__lowerCAmelCase	   =self.result_dict[model_name]["""result"""]

											((__lowerCAmelCase)   ,	(__lowerCAmelCase))	   =(
											    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
											)

											__lowerCAmelCase	   =(
											    model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
											)

											for inner_loop_value in inner_loop_array:
																if self.args.plot_along_batch:
																					__lowerCAmelCase	   =np.asarray(
																					    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]				,	dtype=snake_case_				,	)
																else:
																					__lowerCAmelCase	   =np.asarray(
																					    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]				,	dtype=np.floataa				,	)

																((__lowerCAmelCase)   ,	(__lowerCAmelCase))	   =(
																    ("""batch_size""", """len""") if self.args.plot_along_batch else ("""in #tokens""", """bsz""")
																)

																__lowerCAmelCase	   =np.asarray(snake_case_				,	snake_case_)[: len(snake_case_)]
																plt.scatter(
																    snake_case_				,	snake_case_				,	label=F"""{label_model_name} - {inner_loop_label}: {inner_loop_value}""")
																plt.plot(snake_case_				,	snake_case_				,	"""--""")

											title_str += F""" {label_model_name} vs."""

						__lowerCAmelCase	   =title_str[:-4]
						__lowerCAmelCase	   ="""Time in s""" if self.args.is_time else """Memory in MB"""

						# plot
						plt.title(snake_case_)
						plt.xlabel(snake_case_)
						plt.ylabel(snake_case_)
						plt.legend()

						if self.args.figure_png_file is not None:
											plt.savefig(self.args.figure_png_file)
						else:
											plt.show()






def 					__lowerCAmelCase		(  )  ->						str:
					__lowerCAmelCase	   =HfArgumentParser(__lowerCamelCase	)
					__lowerCAmelCase	   =parser.parse_args_into_dataclasses()[0]
					__lowerCAmelCase	   =Plot(args=__lowerCamelCase	)
					plot.plot()


if __name__ == "__main__":
				main()









from dataclasses import asdict, dataclass
from typing import Optional

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


lowercase_	       =			logging.get_logger(__name__)

# TODO Update this
lowercase_	       =			{
    '''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 						__a (     SCREAMING_SNAKE_CASE				):
	SCREAMING_SNAKE_CASE       =       "esm"



	def __init__(						self							:    Any				,	snake_case_							:    List[str]=None				,	snake_case_							:    Tuple=None				,	snake_case_							:    str=None				,	snake_case_							:    str=7_68				,	snake_case_							:    Optional[int]=12				,	snake_case_							:    Tuple=12				,	snake_case_							:    Dict=30_72				,	snake_case_							:    Optional[Any]=0.1				,	snake_case_							:    Dict=0.1				,	snake_case_							:    Optional[Any]=10_26				,	snake_case_							:    int=0.0_2				,	snake_case_							:    Any=1e-12				,	snake_case_							:    Union[str, Any]="absolute"				,	snake_case_							:    Optional[int]=True				,	snake_case_							:    Tuple=None				,	snake_case_							:    Optional[int]=False				,	snake_case_							:    int=False				,	snake_case_							:    Optional[int]=None				,	snake_case_							:    List[Any]=None				,	**snake_case_							:    Optional[int]				,	)->							Any:
						super().__init__(pad_token_id=snake_case_				,	mask_token_id=snake_case_				,	**snake_case_)

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



	def 		UpperCamelCase							(						self							:    Dict)->							Union[str, Any]:
						__lowerCAmelCase	   =super().to_dict()
						if isinstance(self.esmfold_config				,	snake_case_):
											__lowerCAmelCase	   =self.esmfold_config.to_dict()
						return output






@dataclass
class 						__a :
	SCREAMING_SNAKE_CASE       =       None
	SCREAMING_SNAKE_CASE       =       True
	SCREAMING_SNAKE_CASE       =       False
	SCREAMING_SNAKE_CASE       =       False
	SCREAMING_SNAKE_CASE       =       False
	SCREAMING_SNAKE_CASE       =       0

	SCREAMING_SNAKE_CASE       =       True
	SCREAMING_SNAKE_CASE       =       False

	SCREAMING_SNAKE_CASE       =       128
	SCREAMING_SNAKE_CASE       =       None



	def 		UpperCamelCase							(						self							:    Dict)->							List[Any]:
						if self.trunk is None:
											__lowerCAmelCase	   =TrunkConfig()
						elif isinstance(self.trunk				,	snake_case_):
											__lowerCAmelCase	   =TrunkConfig(**self.trunk)



	def 		UpperCamelCase							(						self							:    Optional[Any])->							int:
						__lowerCAmelCase	   =asdict(self)
						__lowerCAmelCase	   =self.trunk.to_dict()
						return output






@dataclass
class 						__a :
	SCREAMING_SNAKE_CASE       =       48
	SCREAMING_SNAKE_CASE       =       1024
	SCREAMING_SNAKE_CASE       =       128
	SCREAMING_SNAKE_CASE       =       32
	SCREAMING_SNAKE_CASE       =       32
	SCREAMING_SNAKE_CASE       =       32
	SCREAMING_SNAKE_CASE       =       0
	SCREAMING_SNAKE_CASE       =       0
	SCREAMING_SNAKE_CASE       =       False
	SCREAMING_SNAKE_CASE       =       4
	SCREAMING_SNAKE_CASE       =       128
	SCREAMING_SNAKE_CASE       =       None



	def 		UpperCamelCase							(						self							:    Tuple)->							Optional[int]:
						if self.structure_module is None:
											__lowerCAmelCase	   =StructureModuleConfig()
						elif isinstance(self.structure_module				,	snake_case_):
											__lowerCAmelCase	   =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}.""")

						__lowerCAmelCase	   =self.sequence_state_dim // self.sequence_head_width
						__lowerCAmelCase	   =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 		UpperCamelCase							(						self							:    Dict)->							Any:
						__lowerCAmelCase	   =asdict(self)
						__lowerCAmelCase	   =self.structure_module.to_dict()
						return output






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



	def 		UpperCamelCase							(						self							:    Any)->							Tuple:
						return asdict(self)






def 					__lowerCAmelCase		(  )  ->						List[Any]:
					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>",
					)




