

'''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_    =   get_logger(__name__)

def   lowerCamelCase__       (      a__			,      a__			,      a__			,      a__			,      a__			,      a__ = None			,      ):



    """simple docstring"""







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

def   lowerCamelCase__       (      a__			,      a__			,      a__			,      a__			,      a__			,      a__=None			,      a__=None			,      **a__			,      ):



    """simple docstring"""







    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:
        _snake_case : Dict												=		'''cpu'''
    elif map_location == "on_device":
        _snake_case : Tuple												=		PartialState().device
    # Model states
    for i, model in enumerate(__a):
        _snake_case : List[Any]												=		F"""{MODEL_NAME}.bin""" if i == 0 else F"""{MODEL_NAME}_{i}.bin"""
        _snake_case : str												=		os.path.join(__a			,      __a)
        models[i].load_state_dict(torch.load(__a			,      map_location=__a)			,      **__a)
    logger.info('All model weights loaded successfully')

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

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

    # GradScaler state
    if scaler is not None:
        _snake_case : List[str]												=		os.path.join(__a			,      __a)
        scaler.load_state_dict(torch.load(__a))
        logger.info('GradScaler state loaded successfully')

    # Random states
    try:
        _snake_case : List[Any]												=		torch.load(os.path.join(__a			,      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   lowerCamelCase__       (      a__			,      a__			,      a__ = 0):



    """simple docstring"""







    _snake_case : str												=		Path(__a) / F"""custom_checkpoint_{index}.pkl"""
    logger.info(F"""Saving the state of {get_pretty_name(__a)} to {save_location}""")
    torch.save(obj.state_dict()			,      __a)




def   lowerCamelCase__       (      a__			,      a__			,      a__ = 0):



    """simple docstring"""







    _snake_case : Optional[Any]												=		F"""{path}/custom_checkpoint_{index}.pkl"""
    logger.info(F"""Loading the state of {get_pretty_name(__a)} from {load_location}""")
    obj.load_state_dict(torch.load(__a			,      map_location='cpu'))






'''simple docstring'''



import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_    =   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_    =   {}

class 			SCREAMING_SNAKE_CASE					(							lowercase_					):






      '''simple docstring'''






      SCREAMING_SNAKE_CASE__ :				Optional[Any]    =				'''fsmt_decoder'''




      def __init__(					self :		Any     ,					snake_case :		int=0     ,					snake_case :		Dict=0   ):



          """simple docstring"""





          super().__init__()
          _snake_case : int												=		vocab_size
          _snake_case : Dict												=		bos_token_id



class 			SCREAMING_SNAKE_CASE					(							lowercase_					):






      '''simple docstring'''






      SCREAMING_SNAKE_CASE__ :				Union[str, Any]    =				'''fsmt'''
      SCREAMING_SNAKE_CASE__ :				Union[str, Any]    =				{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}




      def __init__(					self :		Optional[int]     ,					snake_case :		Tuple=["en", "de"]     ,					snake_case :		Optional[int]=42024     ,					snake_case :		List[str]=42024     ,					snake_case :		Union[str, Any]="relu"     ,					snake_case :		Optional[Any]=1024     ,					snake_case :		Union[str, Any]=200     ,					snake_case :		int=1024     ,					snake_case :		str=4096     ,					snake_case :		Dict=12     ,					snake_case :		str=16     ,					snake_case :		List[str]=0.0     ,					snake_case :		List[Any]=4096     ,					snake_case :		str=12     ,					snake_case :		str=16     ,					snake_case :		Optional[int]=0.0     ,					snake_case :		Optional[int]=0.0     ,					snake_case :		str=0.1     ,					snake_case :		Tuple=0.0     ,					snake_case :		Optional[int]=0.02     ,					snake_case :		List[str]=2     ,					snake_case :		int=True     ,					snake_case :		Any=True     ,					snake_case :		str=False     ,					snake_case :		Union[str, Any]=5     ,					snake_case :		Dict=1.0     ,					snake_case :		Union[str, Any]=False     ,					snake_case :		Optional[Any]=True     ,					snake_case :		Optional[int]=1     ,					snake_case :		int=0     ,					snake_case :		Optional[int]=2     ,					snake_case :		Optional[Any]=2     ,					**snake_case :		Any     ,					):



          """simple docstring"""





          _snake_case : int												=		langs
          _snake_case : str												=		src_vocab_size
          _snake_case : List[Any]												=		tgt_vocab_size
          _snake_case : Optional[int]												=		d_model  # encoder_embed_dim and decoder_embed_dim

          _snake_case : Optional[Any]												=		encoder_ffn_dim
          _snake_case : Dict												=		encoder_layers
          _snake_case : List[Any]												=		encoder_attention_heads
          _snake_case : Any												=		encoder_layerdrop
          _snake_case : List[str]												=		decoder_layerdrop
          _snake_case : str												=		decoder_ffn_dim
          _snake_case : Union[str, Any]												=		decoder_layers
          _snake_case : Tuple												=		decoder_attention_heads
          _snake_case : str												=		max_position_embeddings
          _snake_case : Dict												=		init_std  # Normal(0, this parameter)
          _snake_case : List[str]												=		activation_function

          _snake_case : Any												=		DecoderConfig(vocab_size=snake_case     ,					bos_token_id=snake_case   )
          if "decoder" in common_kwargs:
              del common_kwargs["decoder"]

          _snake_case : str												=		scale_embedding  # scale factor will be sqrt(d_model) if True

          # 3 Types of Dropout
          _snake_case : int												=		attention_dropout
          _snake_case : List[Any]												=		activation_dropout
          _snake_case : Optional[int]												=		dropout

          _snake_case : Union[str, Any]												=		use_cache
          super().__init__(
              pad_token_id=snake_case     ,					bos_token_id=snake_case     ,					eos_token_id=snake_case     ,					decoder_start_token_id=snake_case     ,					is_encoder_decoder=snake_case     ,					tie_word_embeddings=snake_case     ,					forced_eos_token_id=snake_case     ,					max_length=snake_case     ,					num_beams=snake_case     ,					length_penalty=snake_case     ,					early_stopping=snake_case     ,					**snake_case     ,					)




      def 	__UpperCAmelCase			(					self :		Optional[int]   ):



          """simple docstring"""





          _snake_case : Any												=		copy.deepcopy(self.__dict__   )
          _snake_case : str												=		self.decoder.to_dict()
          _snake_case : Dict												=		self.__class__.model_type
          return output



