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      __lowercase  (		lowerCamelCase      :  Union[str, Any]						,						lowerCamelCase      :  Tuple						,						lowerCamelCase      :  Tuple						,						lowerCamelCase      :  Optional[Any]=None			):
	UpperCamelCase_      :		Tuple				=      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.
		UpperCamelCase_      :		str				=      weight.view(torch.intaa			)
		UpperCamelCase_      :		Union[str, Any]				=      'bfloat16'
	UpperCamelCase_      :		List[str]				=      weight.cpu().numpy()
	UpperCamelCase_      :		List[Any]				=      os.path.join(lowercase__						,						F"{weight_name}.dat"			)
	if index is not None:
		if dtype is None:
			UpperCamelCase_      :		Union[str, Any]				=      str(array.dtype			)
		UpperCamelCase_      :		str				=      {'dtype': dtype, 'shape': list(array.shape			)}
	if array.ndim == 0:
		UpperCamelCase_      :		Tuple				=      array[None]
	UpperCamelCase_      :		Union[str, Any]				=      np.memmap(lowercase__						,						dtype=array.dtype						,						mode='w+'						,						shape=array.shape			)
	UpperCamelCase_      :		str				=      array[:]
	file_array.flush()
	return index





def      __lowercase  (		lowerCamelCase      :  Tuple						,						lowerCamelCase      :  int			):
	UpperCamelCase_      :		Tuple				=      tuple(weight_info['shape']			)
	if shape == ():
		# NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
		UpperCamelCase_      :		Any				=      (1,)

	UpperCamelCase_      :		Union[str, Any]				=      weight_info['dtype']
	if dtype == "bfloat16":
		# NumPy does not support bfloat16 so this was saved as a int16
		UpperCamelCase_      :		List[Any]				=      'int16'

	UpperCamelCase_      :		int				=      np.memmap(lowercase__						,						dtype=lowercase__						,						shape=lowercase__						,						mode='r'			)

	if len(weight_info['shape']			) == 0:
		UpperCamelCase_      :		Union[str, Any]				=      weight[0]
	UpperCamelCase_      :		List[str]				=      torch.tensor(lowercase__			)
	if weight_info["dtype"] == "bfloat16":
		UpperCamelCase_      :		Any				=      weight.view(torch.bfloataa			)

	return weight





def      __lowercase  (		lowerCamelCase      :  Tuple						,						lowerCamelCase      :  Optional[Any]			):







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

	UpperCamelCase_      :		str				=      os.path.join(lowercase__						,						'index.json'			)
	if os.path.isfile(lowercase__			):
		with open(lowercase__						,						'r'						,						encoding='utf-8'			) as f:
			UpperCamelCase_      :		str				=      json.load(lowercase__			)
	else:
		UpperCamelCase_      :		List[Any]				=      {}
	current_index.update(lowercase__			)

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





def      __lowercase  (		lowerCamelCase      :  Dict						,						lowerCamelCase      :  Tuple			):
	os.makedirs(lowercase__						,						exist_ok=lowercase__			)
	UpperCamelCase_      :		Union[str, Any]				=      {}
	for name, parameter in state_dict.items():
		UpperCamelCase_      :		List[str]				=      offload_weight(lowercase__						,						lowercase__						,						lowercase__						,						index=lowercase__			)

	# Update index
	save_offload_index(lowercase__						,						lowercase__			)





class 	_lowercase		(      A_ ):

	def __init__(				self			:							Union[str, Any]     ,   snake_case			:							List[str]     ,   snake_case			:							int    )     ->		Any:

		"""simple docstring"""





		UpperCamelCase_      :		Tuple				=      dataset
		UpperCamelCase_      :		Tuple				=      prefix

	def __getitem__(				self			:							int     ,   snake_case			:							List[str]    )     ->		Any:

		"""simple docstring"""





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

	def __iter__(				self			:							List[Any]    )     ->		str:

		"""simple docstring"""





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


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

		"""simple docstring"""





		return len(self.dataset    )





class 	_lowercase		(      A_ ):

	def __init__(				self			:							List[str]     ,   snake_case			:							Any = None     ,   snake_case			:							Union[str, Any] = None     ,   snake_case			:							Union[str, Any] = None     ,   snake_case			:							Tuple=None     ,   )     ->		str:

		"""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.'    )

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

	def __getitem__(				self			:							Dict     ,   snake_case			:							Optional[Any]    )     ->		List[str]:

		"""simple docstring"""





		if key in self.state_dict:
			return self.state_dict[key]
		UpperCamelCase_      :		List[Any]				=      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

			UpperCamelCase_      :		List[str]				=      'cpu' if self.device is None else self.device
			with safe_open(weight_info['safetensors_file']     ,   framework='pt'     ,   device=snake_case    ) as f:
				UpperCamelCase_      :		Tuple				=      f.get_tensor(weight_info.get('weight_name'     ,   snake_case    )    )

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

		UpperCamelCase_      :		List[Any]				=      os.path.join(self.save_folder     ,   f"{key}.dat"    )
		return load_offloaded_weight(snake_case     ,   snake_case    )

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

		"""simple docstring"""





		return iter(self.all_keys    )


	def __len__(				self			:							Union[str, Any]    )     ->		int:

		"""simple docstring"""





		return len(self.all_keys    )





def      __lowercase  (		lowerCamelCase      :  Tuple						,						lowerCamelCase      :  str			):
	UpperCamelCase_      :		List[Any]				=      {}
	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




from typing import Any

import numpy as np





class 	_lowercase		:

 def __init__(				self			:							Dict     ,   snake_case			:							np.ndarray     ,   snake_case			:							int    )     ->		None:

  """simple docstring"""





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

  UpperCamelCase_      :		List[str]				=      tableau
  UpperCamelCase_,				UpperCamelCase_      :		Tuple				=      tableau.shape

  # Number of decision variables x1, x2, x3...
  UpperCamelCase_      :		Dict				=      n_vars

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

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

  # Number of slack variables added to make inequalities into equalities
  UpperCamelCase_      :		Union[str, Any]				=      self.n_rows - self.n_stages

  # Objectives for each stage
  UpperCamelCase_      :		Dict				=      ['max']

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

  UpperCamelCase_      :		str				=      ['']

  # Index of current pivot row and column
  UpperCamelCase_      :		str				=      None
  UpperCamelCase_      :		Optional[int]				=      None

  # Does objective row only contain (non)-negative values?
  UpperCamelCase_      :		Any				=      False

 @staticmethod
 def 		SCREAMING_SNAKE_CASE__     (				*snake_case			:							int    )     ->		list[str]:

  """simple docstring"""





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

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

  # decision | slack | artificial
  UpperCamelCase_      :		Tuple				=      ['x', 's', 'a']
  UpperCamelCase_      :		Optional[int]				=      []
  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 		SCREAMING_SNAKE_CASE__     (				self			:							Any     ,   snake_case			:							np.ndarray    )     ->		tuple[Any, Any]:

  """simple docstring"""





  UpperCamelCase_      :		Any				=      self.objectives[-1]

  # Find entries of highest magnitude in objective rows
  UpperCamelCase_      :		str				=      (objective == 'min') - (objective == 'max')
  UpperCamelCase_      :		str				=      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:
   UpperCamelCase_      :		Tuple				=      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
  UpperCamelCase_      :		Dict				=      slice(self.n_stages     ,   self.n_rows    )

  # RHS
  UpperCamelCase_      :		str				=      tableau[s, -1]

  # Elements of pivot column within slice
  UpperCamelCase_      :		List[Any]				=      tableau[s, col_idx]

  # Array filled with nans
  UpperCamelCase_      :		Optional[int]				=      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
  UpperCamelCase_      :		int				=      np.divide(snake_case     ,   snake_case     ,   out=snake_case     ,   where=divisor > 0    )

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

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Any     ,   snake_case			:							np.ndarray     ,   snake_case			:							int     ,   snake_case			:							int    )     ->		np.ndarray:

  """simple docstring"""





  UpperCamelCase_      :		Union[str, Any]				=      tableau[row_idx].copy()

  UpperCamelCase_      :		List[Any]				=      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
  UpperCamelCase_      :		Any				=      piv_row
  return tableau

 def 		SCREAMING_SNAKE_CASE__     (				self			:							str     ,   snake_case			:							np.ndarray    )     ->		np.ndarray:

  """simple docstring"""





  self.objectives.pop()

  if not self.objectives:
   return tableau

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

  # Delete the artificial variable columns
  UpperCamelCase_      :		Any				=      np.delete(snake_case     ,   snake_case     ,   axis=1    )

  # Delete the objective row of the first stage
  UpperCamelCase_      :		Dict				=      np.delete(snake_case     ,   0     ,   axis=0    )

  UpperCamelCase_      :		List[Any]				=      1
  self.n_rows -= 1
  UpperCamelCase_      :		int				=      0
  UpperCamelCase_      :		Optional[int]				=      False
  return tableau

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[Any]    )     ->		dict[Any, Any]:

  """simple docstring"""





  for _ in range(1_0_0    ):
   # Completion of each stage removes an objective. If both stages
   # are complete, then no objectives are left
   if not self.objectives:
    UpperCamelCase_      :		Any				=      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    )

   UpperCamelCase_,				UpperCamelCase_      :		Tuple				=      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
    UpperCamelCase_      :		Union[str, Any]				=      self.change_stage(self.tableau    )
   else:
    UpperCamelCase_      :		Union[str, Any]				=      self.pivot(self.tableau     ,   snake_case     ,   snake_case    )
  return {}


 def 		SCREAMING_SNAKE_CASE__     (				self			:							Tuple     ,   snake_case			:							np.ndarray     ,   snake_case			:							list[str]    )     ->		dict[str, float]:

  """simple docstring"""





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

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

   # First entry in the nonzero ids
   UpperCamelCase_      :		Optional[int]				=      nonzero[0][0]
   UpperCamelCase_      :		Union[str, Any]				=      tableau[nonzero_rowidx, i]

   # If there is only one nonzero value in column, which is one
   if n_nonzero == nonzero_val == 1:
    UpperCamelCase_      :		Optional[int]				=      tableau[nonzero_rowidx, -1]
    UpperCamelCase_      :		Union[str, Any]				=      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(snake_case     ,   0    )
  return output_dict


if __name__ == "__main__":
   import doctest

   doctest.testmod()



