

"""simple docstring"""



import os
import random
from pathlib import Path
from typing import List

import numpy as np
import torch
from torch.cuda.amp import GradScaler

from .utils import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    get_pretty_name,
    is_tpu_available,
    is_xpu_available,
    save,
)


if is_tpu_available(check_device=False):
 import torch_xla.core.xla_model as xm

from .logging import get_logger
from .state import PartialState


SCREAMING_SNAKE_CASE_       :						Dict					=    get_logger(__name__)





def 			_snake_case  (     UpperCAmelCase_	:       str       , UpperCAmelCase_	:       List[dict]       , UpperCAmelCase_	:       list       , UpperCAmelCase_	:       list       , UpperCAmelCase_	:       int       , UpperCAmelCase_	:       GradScaler = None       , ):
     # Model states
     for i, state in enumerate(UpperCAmelCase_      ):
          A__				      =	F"""{MODEL_NAME}.bin""" if i == 0 else F"""{MODEL_NAME}_{i}.bin"""
          A__				      =	os.path.join(UpperCAmelCase_       , UpperCAmelCase_      )
          save(UpperCAmelCase_       , UpperCAmelCase_      )
          logger.info(F"""Model weights saved in {output_model_file}"""      )
     # Optimizer states
     for i, opt in enumerate(UpperCAmelCase_      ):
          A__				      =	opt.state_dict()
          A__				      =	F"""{OPTIMIZER_NAME}.bin""" if i == 0 else F"""{OPTIMIZER_NAME}_{i}.bin"""
          A__				      =	os.path.join(UpperCAmelCase_       , UpperCAmelCase_      )
          save(UpperCAmelCase_       , UpperCAmelCase_      )
          logger.info(F"""Optimizer state saved in {output_optimizer_file}"""      )
     # Scheduler states
     for i, scheduler in enumerate(UpperCAmelCase_      ):
          A__				      =	scheduler.state_dict()
          A__				      =	F"""{SCHEDULER_NAME}.bin""" if i == 0 else F"""{SCHEDULER_NAME}_{i}.bin"""
          A__				      =	os.path.join(UpperCAmelCase_       , UpperCAmelCase_      )
          save(UpperCAmelCase_       , UpperCAmelCase_      )
          logger.info(F"""Scheduler state saved in {output_scheduler_file}"""      )
     # GradScaler state
     if scaler is not None:
          A__				      =	scaler.state_dict()
          A__				      =	os.path.join(UpperCAmelCase_       , UpperCAmelCase_      )
          torch.save(UpperCAmelCase_       , UpperCAmelCase_      )
          logger.info(F"""Gradient scaler state saved in {output_scaler_file}"""      )
     # Random number generator states
     A__				      =	{}
     A__				      =	F"""{RNG_STATE_NAME}_{process_index}.pkl"""
     A__				      =	random.getstate()
     A__				      =	np.random.get_state()
     A__				      =	torch.get_rng_state()
     if is_xpu_available():
          A__				      =	torch.xpu.get_rng_state_all()
     else:
          A__				      =	torch.cuda.get_rng_state_all()
     if is_tpu_available():
          A__				      =	xm.get_rng_state()
     A__				      =	os.path.join(UpperCAmelCase_       , UpperCAmelCase_      )
     torch.save(UpperCAmelCase_       , UpperCAmelCase_      )
     logger.info(F"""Random states saved in {output_states_file}"""      )
     return output_dir





def 			_snake_case  (     UpperCAmelCase_	:       int       , UpperCAmelCase_	:       Optional[int]       , UpperCAmelCase_	:       str       , UpperCAmelCase_	:       Optional[Any]       , UpperCAmelCase_	:       Optional[int]       , UpperCAmelCase_	:       Any=None       , UpperCAmelCase_	:       List[str]=None       , **UpperCAmelCase_	:       Tuple       , ):
     if map_location not in [None, "cpu", "on_device"]:
          raise TypeError(
              """Unsupported optimizer map location passed, please choose one of `None`, `'cpu'`, or `'on_device'`"""      )
     if map_location is None:
          A__				      =	"""cpu"""
     elif map_location == "on_device":
          A__				      =	PartialState().device
     # Model states
     for i, model in enumerate(UpperCAmelCase_      ):
          A__				      =	F"""{MODEL_NAME}.bin""" if i == 0 else F"""{MODEL_NAME}_{i}.bin"""
          A__				      =	os.path.join(UpperCAmelCase_       , UpperCAmelCase_      )
          models[i].load_state_dict(torch.load(UpperCAmelCase_       , map_location=UpperCAmelCase_      )       , **UpperCAmelCase_      )
     logger.info("""All model weights loaded successfully"""      )

     # Optimizer states
     for i, opt in enumerate(UpperCAmelCase_      ):
          A__				      =	F"""{OPTIMIZER_NAME}.bin""" if i == 0 else F"""{OPTIMIZER_NAME}_{i}.bin"""
          A__				      =	os.path.join(UpperCAmelCase_       , UpperCAmelCase_      )
          A__				      =	torch.load(UpperCAmelCase_       , map_location=UpperCAmelCase_      )
          optimizers[i].load_state_dict(UpperCAmelCase_      )
     logger.info("""All optimizer states loaded successfully"""      )

     # Scheduler states
     for i, scheduler in enumerate(UpperCAmelCase_      ):
          A__				      =	F"""{SCHEDULER_NAME}.bin""" if i == 0 else F"""{SCHEDULER_NAME}_{i}.bin"""
          A__				      =	os.path.join(UpperCAmelCase_       , UpperCAmelCase_      )
          scheduler.load_state_dict(torch.load(UpperCAmelCase_      )      )
     logger.info("""All scheduler states loaded successfully"""      )

     # GradScaler state
     if scaler is not None:
          A__				      =	os.path.join(UpperCAmelCase_       , UpperCAmelCase_      )
          scaler.load_state_dict(torch.load(UpperCAmelCase_      )      )
          logger.info("""GradScaler state loaded successfully"""      )

     # Random states
     try:
          A__				      =	torch.load(os.path.join(UpperCAmelCase_       , F"""{RNG_STATE_NAME}_{process_index}.pkl"""      )      )
          random.setstate(states["""random_state"""]      )
          np.random.set_state(states["""numpy_random_seed"""]      )
          torch.set_rng_state(states["""torch_manual_seed"""]      )
          if is_xpu_available():
               torch.xpu.set_rng_state_all(states["""torch_xpu_manual_seed"""]      )
          else:
               torch.cuda.set_rng_state_all(states["""torch_cuda_manual_seed"""]      )
          if is_tpu_available():
               xm.set_rng_state(states["""xm_seed"""]      )
          logger.info("""All random states loaded successfully"""      )
     except Exception:
          logger.info("""Could not load random states"""      )





def 			_snake_case  (     UpperCAmelCase_	:       Optional[int]       , UpperCAmelCase_	:       List[Any]       , UpperCAmelCase_	:       int = 0      ):
     A__				      =	Path(UpperCAmelCase_      ) / F"""custom_checkpoint_{index}.pkl"""
     logger.info(F"""Saving the state of {get_pretty_name(UpperCAmelCase_      )} to {save_location}"""      )
     torch.save(obj.state_dict()       , UpperCAmelCase_      )





def 			_snake_case  (     UpperCAmelCase_	:       Union[str, Any]       , UpperCAmelCase_	:       int       , UpperCAmelCase_	:       int = 0      ):
     A__				      =	F"""{path}/custom_checkpoint_{index}.pkl"""
     logger.info(F"""Loading the state of {get_pretty_name(UpperCAmelCase_      )} from {load_location}"""      )
     obj.load_state_dict(torch.load(UpperCAmelCase_       , map_location="""cpu"""      )      )









"""simple docstring"""



def 			_snake_case  (     UpperCAmelCase_	:       str       , UpperCAmelCase_	:       str       , UpperCAmelCase_	:       int       , UpperCAmelCase_	:       int       , UpperCAmelCase_	:       int       , UpperCAmelCase_	:       int       , ):
     A__				      =	list(UpperCAmelCase_      )
     A__				      =	list(UpperCAmelCase_      )
     A__				      =	len(UpperCAmelCase_      )
     A__				      =	len(UpperCAmelCase_      )

     A__				      =	[
         [0 for _ in range(len_destination_seq + 1      )] for _ in range(len_source_seq + 1      )
     ]
     A__				      =	[
         ["""0""" for _ in range(len_destination_seq + 1      )] for _ in range(len_source_seq + 1      )
     ]

     for i in range(1       , len_source_seq + 1      ):
          A__				      =	i * delete_cost
          A__				      =	F"""D{source_seq[i - 1]:c}"""

     for i in range(1       , len_destination_seq + 1      ):
          A__				      =	i * insert_cost
          A__				      =	F"""I{destination_seq[i - 1]:c}"""

     for i in range(1       , len_source_seq + 1      ):
          for j in range(1       , len_destination_seq + 1      ):
               if source_seq[i - 1] == destination_seq[j - 1]:
                    A__				      =	costs[i - 1][j - 1] + copy_cost
                    A__				      =	F"""C{source_seq[i - 1]:c}"""
               else:
                    A__				      =	costs[i - 1][j - 1] + replace_cost
                    A__				      =	F"""R{source_seq[i - 1]:c}""" + str(destination_seq[j - 1]      )

               if costs[i - 1][j] + delete_cost < costs[i][j]:
                    A__				      =	costs[i - 1][j] + delete_cost
                    A__				      =	F"""D{source_seq[i - 1]:c}"""

               if costs[i][j - 1] + insert_cost < costs[i][j]:
                    A__				      =	costs[i][j - 1] + insert_cost
                    A__				      =	F"""I{destination_seq[j - 1]:c}"""

     return costs, ops





def 			_snake_case  (     UpperCAmelCase_	:       list[list[str]]       , UpperCAmelCase_	:       int       , UpperCAmelCase_	:       int      ):
     if i == 0 and j == 0:
          return []
     else:
          if ops[i][j][0] == "C" or ops[i][j][0] == "R":
               A__				      =	assemble_transformation(UpperCAmelCase_       , i - 1       , j - 1      )
               seq.append(ops[i][j]      )
               return seq
          elif ops[i][j][0] == "D":
               A__				      =	assemble_transformation(UpperCAmelCase_       , i - 1       , UpperCAmelCase_      )
               seq.append(ops[i][j]      )
               return seq
          else:
               A__				      =	assemble_transformation(UpperCAmelCase_       , UpperCAmelCase_       , j - 1      )
               seq.append(ops[i][j]      )
               return seq


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE_	,SCREAMING_SNAKE_CASE_       :						List[Any]					=    compute_transform_tables('Python', 'Algorithms', -1, 1, 2, 2)

 SCREAMING_SNAKE_CASE_       :						Any					=    len(operations)
 SCREAMING_SNAKE_CASE_       :						Dict					=    len(operations[0])
 SCREAMING_SNAKE_CASE_       :						Any					=    assemble_transformation(operations, m - 1, n - 1)

 SCREAMING_SNAKE_CASE_       :						List[str]					=    list('Python')
 SCREAMING_SNAKE_CASE_       :						Dict					=    0
 SCREAMING_SNAKE_CASE_       :						Optional[int]					=    0

 with open('min_cost.txt', 'w') as file:
  for op in sequence:
   print(''.join(string))

   if op[0] == "C":
    file.write('%-16s' % 'Copy %c' % op[1])
    file.write('\t\t\t' + ''.join(string))
    file.write('\r\n')

    cost -= 1
   elif op[0] == "R":
    SCREAMING_SNAKE_CASE_       :						int					=    op[2]

    file.write('%-16s' % ('Replace %c' % op[1] + ' with ' + str(op[2])))
    file.write('\t\t' + ''.join(string))
    file.write('\r\n')

    cost += 1
   elif op[0] == "D":
    string.pop(i)

    file.write('%-16s' % 'Delete %c' % op[1])
    file.write('\t\t\t' + ''.join(string))
    file.write('\r\n')

    cost += 2
   else:
    string.insert(i, op[1])

    file.write('%-16s' % 'Insert %c' % op[1])
    file.write('\t\t\t' + ''.join(string))
    file.write('\r\n')

    cost += 2

   i += 1

  print(''.join(string))
  print('Cost: ', cost)

  file.write('\r\nMinimum cost: ' + str(cost))






