


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 							UpperCAmelCase				(     UpperCAmelCase=None				,UpperCAmelCase=None      )->		Optional[Any]:







					'''simple docstring'''



					return field(default_factory=lambda: default				,metadata=UpperCAmelCase      )

@dataclass
class 						snake_case			:
			'''simple docstring'''

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







def 							UpperCAmelCase				(     UpperCAmelCase      )->		List[Any]:







					'''simple docstring'''



					try:
										int(UpperCAmelCase      )
										return True
					except ValueError:
										return False







def 							UpperCAmelCase				(     UpperCAmelCase      )->		Tuple:







					'''simple docstring'''



					try:
										float(UpperCAmelCase      )
										return True
					except ValueError:
										return False

class 						snake_case			:
			'''simple docstring'''

			def __init__(	self     :     str							,							lowerCAmelCase_     :     str					)					->					Optional[Any]:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       args
								SCREAMING_SNAKE_CASE_	      =       defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}					)

								with open(self.args.csv_file							,							newline=''''''					) as csv_file:
													SCREAMING_SNAKE_CASE_	      =       csv.DictReader(lowerCAmelCase_					)
													for row in reader:
																		SCREAMING_SNAKE_CASE_	      =       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
																							SCREAMING_SNAKE_CASE_	      =       int(row['''result''']					)
																		elif can_convert_to_float(row['''result''']					):
																							# value is not None
																							SCREAMING_SNAKE_CASE_	      =       float(row['''result''']					)




			def _lowercase (	self     :     int					)					->					Any:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       plt.subplots()
								SCREAMING_SNAKE_CASE_	      =       '''Time usage''' if self.args.is_time else '''Memory usage'''
								SCREAMING_SNAKE_CASE_	      =       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()					):
													SCREAMING_SNAKE_CASE_	      =       sorted(set(self.result_dict[model_name]['''bsz''']					)					)
													SCREAMING_SNAKE_CASE_	      =       sorted(set(self.result_dict[model_name]['''seq_len''']					)					)
													SCREAMING_SNAKE_CASE_	      =       self.result_dict[model_name]['''result''']

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

													SCREAMING_SNAKE_CASE_	      =       (
													    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:
																							SCREAMING_SNAKE_CASE_	      =       np.asarray(
																							    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]							,							dtype=lowerCAmelCase_							,							)
																		else:
																							SCREAMING_SNAKE_CASE_	      =       np.asarray(
																							    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]							,							dtype=np.floataa							,							)

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

																		SCREAMING_SNAKE_CASE_	      =       np.asarray(lowerCAmelCase_							,							lowerCAmelCase_					)[: len(lowerCAmelCase_					)]
																		plt.scatter(
																		    lowerCAmelCase_							,							lowerCAmelCase_							,							label=F'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''					)
																		plt.plot(lowerCAmelCase_							,							lowerCAmelCase_							,							'''--'''					)

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

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

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

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







def 							UpperCAmelCase				(     )->		str:







					'''simple docstring'''



					SCREAMING_SNAKE_CASE_	      =       HfArgumentParser(UpperCAmelCase      )
					SCREAMING_SNAKE_CASE_	      =       parser.parse_args_into_dataclasses()[0]
					SCREAMING_SNAKE_CASE_	      =       Plot(args=UpperCAmelCase      )
					plot.plot()


if __name__ == "__main__":
		main()






import json
import os
from collections.abc import Mapping
from typing import Dict, List, Optional, Union

import numpy as np
import torch

from .imports import is_safetensors_available







def 							UpperCAmelCase				(     UpperCAmelCase				,UpperCAmelCase				,UpperCAmelCase				,UpperCAmelCase=None      )->		Dict:







					'''simple docstring'''



					SCREAMING_SNAKE_CASE_	      =       None
					# Check the string instead of the dtype to be compatible with versions of PyTorch that don't have bfloat16.
					if str(weight.dtype      ) == "torch.bfloat16":
										# Need to reinterpret the underlined data as int16 since NumPy does not handle bfloat16s.
										SCREAMING_SNAKE_CASE_	      =       weight.view(torch.intaa      )
										SCREAMING_SNAKE_CASE_	      =       '''bfloat16'''
					SCREAMING_SNAKE_CASE_	      =       weight.cpu().numpy()
					SCREAMING_SNAKE_CASE_	      =       os.path.join(UpperCAmelCase				,f'''{weight_name}.dat'''      )
					if index is not None:
										if dtype is None:
															SCREAMING_SNAKE_CASE_	      =       str(array.dtype      )
										SCREAMING_SNAKE_CASE_	      =       {'''dtype''': dtype, '''shape''': list(array.shape      )}
					if array.ndim == 0:
										SCREAMING_SNAKE_CASE_	      =       array[None]
					SCREAMING_SNAKE_CASE_	      =       np.memmap(UpperCAmelCase				,dtype=array.dtype				,mode='''w+'''				,shape=array.shape      )
					SCREAMING_SNAKE_CASE_	      =       array[:]
					file_array.flush()
					return index







def 							UpperCAmelCase				(     UpperCAmelCase				,UpperCAmelCase      )->		List[str]:







					'''simple docstring'''



					SCREAMING_SNAKE_CASE_	      =       tuple(weight_info['''shape''']      )
					if shape == ():
										# NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
										SCREAMING_SNAKE_CASE_	      =       (1,)

					SCREAMING_SNAKE_CASE_	      =       weight_info['''dtype''']
					if dtype == "bfloat16":
										# NumPy does not support bfloat16 so this was saved as a int16
										SCREAMING_SNAKE_CASE_	      =       '''int16'''

					SCREAMING_SNAKE_CASE_	      =       np.memmap(UpperCAmelCase				,dtype=UpperCAmelCase				,shape=UpperCAmelCase				,mode='''r'''      )

					if len(weight_info['''shape''']      ) == 0:
										SCREAMING_SNAKE_CASE_	      =       weight[0]
					SCREAMING_SNAKE_CASE_	      =       torch.tensor(UpperCAmelCase      )
					if weight_info["dtype"] == "bfloat16":
										SCREAMING_SNAKE_CASE_	      =       weight.view(torch.bfloataa      )

					return weight







def 							UpperCAmelCase				(     UpperCAmelCase				,UpperCAmelCase      )->		str:







					'''simple docstring'''



					if index is None or len(UpperCAmelCase      ) == 0:
										# Nothing to save
										return

					SCREAMING_SNAKE_CASE_	      =       os.path.join(UpperCAmelCase				,'''index.json'''      )
					if os.path.isfile(UpperCAmelCase      ):
										with open(UpperCAmelCase				,'''r'''				,encoding='''utf-8'''      ) as f:
															SCREAMING_SNAKE_CASE_	      =       json.load(UpperCAmelCase      )
					else:
										SCREAMING_SNAKE_CASE_	      =       {}
					current_index.update(UpperCAmelCase      )

					with open(UpperCAmelCase				,'''w'''				,encoding='''utf-8'''      ) as f:
										json.dump(UpperCAmelCase				,UpperCAmelCase				,indent=2      )







def 							UpperCAmelCase				(     UpperCAmelCase				,UpperCAmelCase      )->		Optional[Any]:







					'''simple docstring'''



					os.makedirs(UpperCAmelCase				,exist_ok=UpperCAmelCase      )
					SCREAMING_SNAKE_CASE_	      =       {}
					for name, parameter in state_dict.items():
										SCREAMING_SNAKE_CASE_	      =       offload_weight(UpperCAmelCase				,UpperCAmelCase				,UpperCAmelCase				,index=UpperCAmelCase      )

					# Update index
					save_offload_index(UpperCAmelCase				,UpperCAmelCase      )

class 						snake_case			(       lowerCAmelCase__	):
			'''simple docstring'''

			def __init__(	self     :     int							,							lowerCAmelCase_     :     Mapping							,							lowerCAmelCase_     :     str					)					->					Dict:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       dataset
								SCREAMING_SNAKE_CASE_	      =       prefix

			def __getitem__(	self     :     Optional[int]							,							lowerCAmelCase_     :     Optional[int]					)					->					Union[str, Any]:




								"""simple docstring"""






								return self.dataset[F'''{self.prefix}{key}''']

			def __iter__(	self     :     Tuple					)					->					str:




								"""simple docstring"""






								return iter([key for key in self.dataset if key.startswith(self.prefix					)]					)




			def __len__(	self     :     Dict					)					->					Optional[int]:




								"""simple docstring"""






								return len(self.dataset					)

class 						snake_case			(       lowerCAmelCase__	):
			'''simple docstring'''

			def __init__(	self     :     int							,							lowerCAmelCase_     :     Dict[str, torch.Tensor] = None							,							lowerCAmelCase_     :     Optional[Union[str, os.PathLike]] = None							,							lowerCAmelCase_     :     Mapping = None							,							lowerCAmelCase_     :     int=None							,							)					->					Tuple:




								"""simple docstring"""






								if state_dict is None and save_folder is None:
													raise ValueError('''Need either a `state_dict` or a `save_folder` containing offloaded weights.'''					)

								SCREAMING_SNAKE_CASE_	      =       {} if state_dict is None else state_dict
								SCREAMING_SNAKE_CASE_	      =       save_folder
								if index is None and save_folder is not None:
													with open(os.path.join(lowerCAmelCase_							,							'''index.json'''					)					) as f:
																		SCREAMING_SNAKE_CASE_	      =       json.load(lowerCAmelCase_					)
								SCREAMING_SNAKE_CASE_	      =       {} if index is None else index
								SCREAMING_SNAKE_CASE_	      =       list(self.state_dict.keys()					)
								self.all_keys.extend([key for key in self.index if key not in self.all_keys]					)
								SCREAMING_SNAKE_CASE_	      =       device

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




								"""simple docstring"""






								if key in self.state_dict:
													return self.state_dict[key]
								SCREAMING_SNAKE_CASE_	      =       self.index[key]
								if weight_info.get('''safetensors_file'''					) is not None:
													if not is_safetensors_available():
																		raise ImportError('''These offloaded weights require the use of safetensors: `pip install safetensors`.'''					)

													from safetensors import safe_open

													SCREAMING_SNAKE_CASE_	      =       '''cpu''' if self.device is None else self.device
													with safe_open(weight_info['''safetensors_file''']							,							framework='''pt'''							,							device=lowerCAmelCase_					) as f:
																		SCREAMING_SNAKE_CASE_	      =       f.get_tensor(weight_info.get('''weight_name'''							,							lowerCAmelCase_					)					)

													if "dtype" in weight_info:
																		return tensor.to(getattr(lowerCAmelCase_							,							weight_info['''dtype''']					)					)
													else:
																		return tensor

								SCREAMING_SNAKE_CASE_	      =       os.path.join(self.save_folder							,							F'''{key}.dat'''					)
								return load_offloaded_weight(lowerCAmelCase_							,							lowerCAmelCase_					)

			def __iter__(	self     :     str					)					->					Optional[int]:




								"""simple docstring"""






								return iter(self.all_keys					)




			def __len__(	self     :     Optional[Any]					)					->					Optional[Any]:




								"""simple docstring"""






								return len(self.all_keys					)







def 							UpperCAmelCase				(     UpperCAmelCase				,UpperCAmelCase      )->		List[str]:







					'''simple docstring'''



					SCREAMING_SNAKE_CASE_	      =       {}
					for module_name in submodule_names:
										# We want to catch module_name parameter (module_name.xxx) or potentially module_name, but not any of the
										# submodules that could being like module_name (transformers.h.1 and transformers.h.10 for instance)
										result.update(
										    {
										        key: param
										        for key, param in state_dict.items()
										        if key == module_name or key.startswith(module_name + '''.'''      )
										    }      )
					return result


