




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__	:				Optional[Any]    =       get_logger(__name__)

def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE = None			,)     ->			Dict:







       for i, state in enumerate(_UpperCAmelCase   ):
              lowerCamelCase       :				Union[str, Any]      			= f'''{MODEL_NAME}.bin''' if i == 0 else f'''{MODEL_NAME}_{i}.bin'''
              lowerCamelCase       :				Any      			= 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   ):
              lowerCamelCase       :				List[str]      			= opt.state_dict()
              lowerCamelCase       :				Union[str, Any]      			= f'''{OPTIMIZER_NAME}.bin''' if i == 0 else f'''{OPTIMIZER_NAME}_{i}.bin'''
              lowerCamelCase       :				int      			= 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   ):
              lowerCamelCase       :				List[Any]      			= scheduler.state_dict()
              lowerCamelCase       :				Tuple      			= f'''{SCHEDULER_NAME}.bin''' if i == 0 else f'''{SCHEDULER_NAME}_{i}.bin'''
              lowerCamelCase       :				int      			= 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:
              lowerCamelCase       :				Dict      			= scaler.state_dict()
              lowerCamelCase       :				List[Any]      			= os.path.join(_UpperCAmelCase			,_UpperCAmelCase   )
              torch.save(_UpperCAmelCase			,_UpperCAmelCase   )
              logger.info(f'''Gradient scaler state saved in {output_scaler_file}'''   )
       # Random number generator states
       lowerCamelCase       :				Tuple      			= {}
       lowerCamelCase       :				Any      			= f'''{RNG_STATE_NAME}_{process_index}.pkl'''
       lowerCamelCase       :				List[Any]      			= random.getstate()
       lowerCamelCase       :				List[str]      			= np.random.get_state()
       lowerCamelCase       :				Tuple      			= torch.get_rng_state()
       if is_xpu_available():
              lowerCamelCase       :				Union[str, Any]      			= torch.xpu.get_rng_state_all()
       else:
              lowerCamelCase       :				int      			= torch.cuda.get_rng_state_all()
       if is_tpu_available():
              lowerCamelCase       :				Dict      			= xm.get_rng_state()
       lowerCamelCase       :				int      			= os.path.join(_UpperCAmelCase			,_UpperCAmelCase   )
       torch.save(_UpperCAmelCase			,_UpperCAmelCase   )
       logger.info(f'''Random states saved in {output_states_file}'''   )
       return output_dir

def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE=None			,_SCREAMING_SNAKE_CASE=None			,**_SCREAMING_SNAKE_CASE			,)     ->			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:
              lowerCamelCase       :				int      			= "cpu"
       elif map_location == "on_device":
              lowerCamelCase       :				Optional[int]      			= PartialState().device
       # Model states
       for i, model in enumerate(_UpperCAmelCase   ):
              lowerCamelCase       :				Any      			= f'''{MODEL_NAME}.bin''' if i == 0 else f'''{MODEL_NAME}_{i}.bin'''
              lowerCamelCase       :				Dict      			= 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   ):
              lowerCamelCase       :				int      			= f'''{OPTIMIZER_NAME}.bin''' if i == 0 else f'''{OPTIMIZER_NAME}_{i}.bin'''
              lowerCamelCase       :				Any      			= os.path.join(_UpperCAmelCase			,_UpperCAmelCase   )
              lowerCamelCase       :				Optional[Any]      			= 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   ):
              lowerCamelCase       :				Optional[Any]      			= f'''{SCHEDULER_NAME}.bin''' if i == 0 else f'''{SCHEDULER_NAME}_{i}.bin'''
              lowerCamelCase       :				Optional[Any]      			= 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:
              lowerCamelCase       :				Union[str, Any]      			= os.path.join(_UpperCAmelCase			,_UpperCAmelCase   )
              scaler.load_state_dict(torch.load(_UpperCAmelCase   )   )
              logger.info("GradScaler state loaded successfully"   )

       # Random states
       try:
              lowerCamelCase       :				Dict      			= 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 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE = 0   )     ->			int:
       lowerCamelCase       :				str      			= 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 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE = 0   )     ->			Tuple:
       lowerCamelCase       :				Tuple      			= 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"   )   )












# this script builds a small sample spm file tests/fixtures/test_sentencepiece_no_bos.model, with features needed by pegasus

# 1. pip install sentencepiece
#
# 2. wget https://raw.githubusercontent.com/google/sentencepiece/master/data/botchan.txt

# 3. build
import sentencepiece as spm

# pegasus:
# 1. no bos
# 2. eos_id is 1
# 3. unk_id is 2
# build a sample spm file accordingly
spm.SentencePieceTrainer.train(
    '--input=botchan.txt --model_prefix=test_sentencepiece_no_bos --bos_id=-1 --unk_id=2  --eos_id=1  --vocab_size=1000'
)

# 4. now update the fixture
# mv test_sentencepiece_no_bos.model ../../tests/fixtures/






