




'''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        __lowerCAmelCase (UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_ = 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        __lowerCAmelCase (UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_=None   ,    UpperCAmelCase_=None   ,    **UpperCAmelCase_   ,    ):
    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        __lowerCAmelCase (UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_ = 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        __lowerCAmelCase (UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_ = 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"""		)		)












import warnings
from dataclasses import dataclass, field
from typing import Optional, Tuple

from .training_args import TrainingArguments
from .utils import cached_property, is_tf_available, logging, requires_backends


SCREAMING_SNAKE_CASE_						:					Optional[Any]			   =       logging.get_logger(__name__)

if is_tf_available():
 import tensorflow as tf







@dataclass
class 		__UpperCAmelCase(		lowercase__ ):





     """simple docstring"""



     __UpperCAmelCase	: int						=    """tf"""
     __UpperCAmelCase	: Optional[str]						=    field(
         default=lowercase__       ,	metadata={"""help""": """Name of TPU"""}       ,	)

     __UpperCAmelCase	: Optional[str]						=    field(
         default=lowercase__       ,	metadata={"""help""": """Zone of TPU"""}       ,	)

     __UpperCAmelCase	: Optional[str]						=    field(
         default=lowercase__       ,	metadata={"""help""": """Name of Cloud TPU-enabled project"""}       ,	)

     __UpperCAmelCase	: float						=    field(
         default=1.0       ,	metadata={"""help""": """Power for the Polynomial decay LR scheduler."""}       ,	)

     __UpperCAmelCase	: bool						=    field(default=lowercase__       ,	metadata={"""help""": """Whether to activate the XLA compilation or not"""} )

     @cached_property
     def 				__SCREAMING_SNAKE_CASE		(      self :       Dict    )			->			Tuple["tf.distribute.Strategy", int]:






         """simple docstring"""
         requires_backends(self						,				["""tf"""]    )
         logger.info("""Tensorflow: setting up strategy"""    )

         a__      =       tf.config.list_physical_devices("""GPU"""    )

         # Set to float16 at first
         if self.fpaa:
             tf.keras.mixed_precision.set_global_policy("""mixed_float16"""    )

         if self.no_cuda:
             a__      =       tf.distribute.OneDeviceStrategy(device="""/cpu:0"""    )
         else:
             try:
                 if self.tpu_name:
                     a__      =       tf.distribute.cluster_resolver.TPUClusterResolver(
                         self.tpu_name						,				zone=self.tpu_zone						,				project=self.gcp_project    )
                 else:
                     a__      =       tf.distribute.cluster_resolver.TPUClusterResolver()
             except ValueError:
                 if self.tpu_name:
                     raise RuntimeError(f"""Couldn't connect to TPU {self.tpu_name}!"""    )
                 else:
                     a__      =       None

             if tpu:
                 # Set to bfloat16 in case of TPU
                 if self.fpaa:
                     tf.keras.mixed_precision.set_global_policy("""mixed_bfloat16"""    )

                 tf.config.experimental_connect_to_cluster(A    )
                 tf.tpu.experimental.initialize_tpu_system(A    )

                 a__      =       tf.distribute.TPUStrategy(A    )

             elif len(A    ) == 0:
                 a__      =       tf.distribute.OneDeviceStrategy(device="""/cpu:0"""    )
             elif len(A    ) == 1:
                 a__      =       tf.distribute.OneDeviceStrategy(device="""/gpu:0"""    )
             elif len(A    ) > 1:
                 # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
                 a__      =       tf.distribute.MirroredStrategy()
             else:
                 raise ValueError("""Cannot find the proper strategy, please check your environment properties."""    )

         return strategy

     @property
     def 				__SCREAMING_SNAKE_CASE		(      self :       Tuple    )			->			"tf.distribute.Strategy":






         """simple docstring"""
         requires_backends(self						,				["""tf"""]    )
         return self._setup_strategy

     @property
     def 				__SCREAMING_SNAKE_CASE		(      self :       Dict    )			->			int:






         """simple docstring"""
         requires_backends(self						,				["""tf"""]    )
         return self._setup_strategy.num_replicas_in_sync

     @property
     def 				__SCREAMING_SNAKE_CASE		(      self :       Dict    )			->			List[Any]:






         """simple docstring"""
         return False  # TF Logging is handled by Keras not the Trainer

     @property
     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[Any]    )			->			int:






         """simple docstring"""
         if self.per_gpu_train_batch_size:
             logger.warning(
                 """Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future """
                 """version. Using `--per_device_train_batch_size` is preferred."""    )
         a__      =       self.per_gpu_train_batch_size or self.per_device_train_batch_size
         return per_device_batch_size * self.n_replicas

     @property
     def 				__SCREAMING_SNAKE_CASE		(      self :       List[str]    )			->			int:






         """simple docstring"""
         if self.per_gpu_eval_batch_size:
             logger.warning(
                 """Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future """
                 """version. Using `--per_device_eval_batch_size` is preferred."""    )
         a__      =       self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
         return per_device_batch_size * self.n_replicas






     @property
     def 				__SCREAMING_SNAKE_CASE		(      self :       str    )			->			int:






         """simple docstring"""
         requires_backends(self						,				["""tf"""]    )
         warnings.warn(
             """The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead."""						,				A						,				)
         return self._setup_strategy.num_replicas_in_sync






