
"""simple docstring"""




from typing import Any

import numpy as np




class 	UpperCamelCase      :

				"""simple docstring"""



				def __init__(							self						,      lowercase__						,      lowercase__     )       ->       None:
									"""simple docstring"""







									if np.any(tableau[:, -1]						,      where=tableau[:, -1] < 0     ):
														raise ValueError('RHS must be > 0'     )

									SCREAMING_SNAKE_CASE           =      tableau
									SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE           =      tableau.shape

									# Number of decision variables x1, x2, x3...
									SCREAMING_SNAKE_CASE           =      n_vars

									# Number of artificial variables to be minimised
									SCREAMING_SNAKE_CASE           =      len(np.where(tableau[self.n_vars : -1] == -1     )[0]     )

									# 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
									SCREAMING_SNAKE_CASE           =      (self.n_art_vars > 0) + 1

									# Number of slack variables added to make inequalities into equalities
									SCREAMING_SNAKE_CASE           =      self.n_rows - self.n_stages

									# Objectives for each stage
									SCREAMING_SNAKE_CASE           =      ['max']

									# In two stage simplex, first minimise then maximise
									if self.n_art_vars:
														self.objectives.append('min'     )

									SCREAMING_SNAKE_CASE           =      ['']

									# Index of current pivot row and column
									SCREAMING_SNAKE_CASE           =      None
									SCREAMING_SNAKE_CASE           =      None

									# Does objective row only contain (non)-negative values?
									SCREAMING_SNAKE_CASE           =      False


				@staticmethod
				def 				A   (							*lowercase__     )       ->       list[str]:
									"""simple docstring"""







									if len(lowercase__     ) != 3:
														raise ValueError('Must provide n_vars, n_slack, and n_art_vars'     )

									if not all(x >= 0 and isinstance(lowercase__						,      lowercase__     ) for x in args     ):
														raise ValueError('All arguments must be non-negative integers'     )

									# decision | slack | artificial
									SCREAMING_SNAKE_CASE           =      ['x', 's', 'a']
									SCREAMING_SNAKE_CASE           =      []
									for i in range(3     ):
														for j in range(args[i]     ):
																			titles.append(string_starts[i] + str(j + 1     )     )
									titles.append('RHS'     )
									return titles


				def 				A   (							self						,      lowercase__     )       ->       tuple[Any, Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      self.objectives[-1]

									# Find entries of highest magnitude in objective rows
									SCREAMING_SNAKE_CASE           =      (objective == 'min') - (objective == 'max')
									SCREAMING_SNAKE_CASE           =      np.argmax(sign * tableau[0, : self.n_vars]     )

									# Choice is only valid if below 0 for maximise, and above for minimise
									if sign * self.tableau[0, col_idx] <= 0:
														SCREAMING_SNAKE_CASE           =      True
														return 0, 0

									# Pivot row is chosen as having the lowest quotient when elements of
									# the pivot column divide the right-hand side

									# Slice excluding the objective rows
									SCREAMING_SNAKE_CASE           =      slice(self.n_stages						,      self.n_rows     )

									# RHS
									SCREAMING_SNAKE_CASE           =      tableau[s, -1]

									# Elements of pivot column within slice
									SCREAMING_SNAKE_CASE           =      tableau[s, col_idx]

									# Array filled with nans
									SCREAMING_SNAKE_CASE           =      np.full(self.n_rows - self.n_stages						,      np.nan     )

									# If element in pivot column is greater than zeron_stages, return
									# quotient or nan otherwise
									SCREAMING_SNAKE_CASE           =      np.divide(lowercase__						,      lowercase__						,      out=lowercase__						,      where=divisor > 0     )

									# Arg of minimum quotient excluding the nan values. n_stages is added
									# to compensate for earlier exclusion of objective columns
									SCREAMING_SNAKE_CASE           =      np.nanargmin(lowercase__     ) + self.n_stages
									return row_idx, col_idx


				def 				A   (							self						,      lowercase__						,      lowercase__						,      lowercase__     )       ->       np.ndarray:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      tableau[row_idx].copy()

									SCREAMING_SNAKE_CASE           =      piv_row[col_idx]

									# Entry becomes 1
									piv_row *= 1 / piv_val

									# Variable in pivot column becomes basic, ie the only non-zero entry
									for idx, coeff in enumerate(tableau[:, col_idx]     ):
														tableau[idx] += -coeff * piv_row
									SCREAMING_SNAKE_CASE           =      piv_row
									return tableau


				def 				A   (							self						,      lowercase__     )       ->       np.ndarray:
									"""simple docstring"""







									self.objectives.pop()

									if not self.objectives:
														return tableau

									# Slice containing ids for artificial columns
									SCREAMING_SNAKE_CASE           =      slice(-self.n_art_vars - 1						,      -1     )

									# Delete the artificial variable columns
									SCREAMING_SNAKE_CASE           =      np.delete(lowercase__						,      lowercase__						,      axis=1     )

									# Delete the objective row of the first stage
									SCREAMING_SNAKE_CASE           =      np.delete(lowercase__						,      0						,      axis=0     )

									SCREAMING_SNAKE_CASE           =      1
									self.n_rows -= 1
									SCREAMING_SNAKE_CASE           =      0
									SCREAMING_SNAKE_CASE           =      False
									return tableau


				def 				A   (							self     )       ->       dict[Any, Any]:
									"""simple docstring"""







									for _ in range(100     ):
														# Completion of each stage removes an objective. If both stages
														# are complete, then no objectives are left
														if not self.objectives:
																			SCREAMING_SNAKE_CASE           =      self.generate_col_titles(
																			    self.n_vars						,      self.n_slack						,      self.n_art_vars     )

																			# Find the values of each variable at optimal solution
																			return self.interpret_tableau(self.tableau						,      self.col_titles     )

														SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE           =      self.find_pivot(self.tableau     )

														# If there are no more negative values in objective row
														if self.stop_iter:
																			# Delete artificial variable columns and rows. Update attributes
																			SCREAMING_SNAKE_CASE           =      self.change_stage(self.tableau     )
														else:
																			SCREAMING_SNAKE_CASE           =      self.pivot(self.tableau						,      lowercase__						,      lowercase__     )
									return {}




				def 				A   (							self						,      lowercase__						,      lowercase__     )       ->       dict[str, float]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      {'P': abs(tableau[0, -1]     )}

									for i in range(self.n_vars     ):
														# Gives ids of nonzero entries in the ith column
														SCREAMING_SNAKE_CASE           =      np.nonzero(tableau[:, i]     )
														SCREAMING_SNAKE_CASE           =      len(nonzero[0]     )

														# First entry in the nonzero ids
														SCREAMING_SNAKE_CASE           =      nonzero[0][0]
														SCREAMING_SNAKE_CASE           =      tableau[nonzero_rowidx, i]

														# If there is only one nonzero value in column, which is one
														if n_nonzero == nonzero_val == 1:
																			SCREAMING_SNAKE_CASE           =      tableau[nonzero_rowidx, -1]
																			SCREAMING_SNAKE_CASE           =      rhs_val

        # Check for basic variables
									for title in col_titles:
														# Don't add RHS or slack variables to output dict
														if title[0] not in "R-s-a":
																			output_dict.setdefault(lowercase__						,      0     )
									return output_dict


if __name__ == "__main__":
	import doctest

	doctest.testmod()


"""simple docstring"""




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_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_=None     ):
					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(SCREAMING_SNAKE_CASE_,	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(SCREAMING_SNAKE_CASE_,	dtype=array.dtype,	mode='w+',	shape=array.shape     )
					SCREAMING_SNAKE_CASE           =      array[:]
					file_array.flush()
					return index

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					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(SCREAMING_SNAKE_CASE_,	dtype=SCREAMING_SNAKE_CASE_,	shape=SCREAMING_SNAKE_CASE_,	mode='r'     )

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

					return weight

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					if index is None or len(SCREAMING_SNAKE_CASE_     ) == 0:
										# Nothing to save
										return

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

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

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

					# Update index
					save_offload_index(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     )




class 	UpperCamelCase      (			__magic_name__ ):

				"""simple docstring"""



				def __init__(							self						,      lowercase__						,      lowercase__     )       ->       Union[str, Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      dataset
									SCREAMING_SNAKE_CASE           =      prefix


				def __getitem__(							self						,      lowercase__     )       ->       Dict:
									"""simple docstring"""







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


				def __iter__(							self     )       ->       List[str]:
									"""simple docstring"""







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




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







									return len(self.dataset     )






class 	UpperCamelCase      (			__magic_name__ ):

				"""simple docstring"""



				def __init__(							self						,      lowercase__ = None						,      lowercase__ = None						,      lowercase__ = None						,      lowercase__=None						,      )       ->       Optional[Any]:
									"""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(lowercase__						,      'index.json'     )     ) as f:
																			SCREAMING_SNAKE_CASE           =      json.load(lowercase__     )
									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						,      lowercase__     )       ->       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=lowercase__     ) as f:
																			SCREAMING_SNAKE_CASE           =      f.get_tensor(weight_info.get('weight_name'						,      lowercase__     )     )

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

									SCREAMING_SNAKE_CASE           =      os.path.join(self.save_folder						,      f'''{key}.dat'''     )
									return load_offloaded_weight(lowercase__						,      lowercase__     )


				def __iter__(							self     )       ->       Any:
									"""simple docstring"""







									return iter(self.all_keys     )




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







									return len(self.all_keys     )






def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					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
