

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


lowerCAmelCase__						=	get_logger(__name__)


def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							str		,    SCREAMING_SNAKE_CASE_:							List[dict]		,    SCREAMING_SNAKE_CASE_:							list		,    SCREAMING_SNAKE_CASE_:							list		,    SCREAMING_SNAKE_CASE_:							int		,    SCREAMING_SNAKE_CASE_:							GradScaler = None		,    )     -> Optional[int]:




				'''simple docstring'''





				for i, state in enumerate(SCREAMING_SNAKE_CASE__ ):
								A__            =     F'{MODEL_NAME}.bin' if i == 0 else F'{MODEL_NAME}_{i}.bin'
								A__            =     os.path.join(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__ )
								save(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__ )
								logger.info(F'Model weights saved in {output_model_file}' )
				# Optimizer states
				for i, opt in enumerate(SCREAMING_SNAKE_CASE__ ):
								A__            =     opt.state_dict()
								A__            =     F'{OPTIMIZER_NAME}.bin' if i == 0 else F'{OPTIMIZER_NAME}_{i}.bin'
								A__            =     os.path.join(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__ )
								save(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__ )
								logger.info(F'Optimizer state saved in {output_optimizer_file}' )
				# Scheduler states
				for i, scheduler in enumerate(SCREAMING_SNAKE_CASE__ ):
								A__            =     scheduler.state_dict()
								A__            =     F'{SCHEDULER_NAME}.bin' if i == 0 else F'{SCHEDULER_NAME}_{i}.bin'
								A__            =     os.path.join(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__ )
								save(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__ )
								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(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__ )
								torch.save(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__ )
								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(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__ )
				torch.save(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__ )
				logger.info(F'Random states saved in {output_states_file}' )
				return output_dir


def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Optional[Any]		,    SCREAMING_SNAKE_CASE_:							List[Any]		,    SCREAMING_SNAKE_CASE_:							List[Any]		,    SCREAMING_SNAKE_CASE_:							List[Any]		,    SCREAMING_SNAKE_CASE_:							Tuple		,    SCREAMING_SNAKE_CASE_:							Dict=None		,    SCREAMING_SNAKE_CASE_:							Optional[Any]=None		,    **SCREAMING_SNAKE_CASE_:							str		,    )     -> Any:




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

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

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

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

				# Random states
				try:
								A__            =     torch.load(os.path.join(SCREAMING_SNAKE_CASE__		,    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__							(		SCREAMING_SNAKE_CASE_:							str		,    SCREAMING_SNAKE_CASE_:							List[Any]		,    SCREAMING_SNAKE_CASE_:							int = 0 )     -> Dict:




				'''simple docstring'''





				A__            =     Path(SCREAMING_SNAKE_CASE__ ) / F'custom_checkpoint_{index}.pkl'
				logger.info(F'Saving the state of {get_pretty_name(SCREAMING_SNAKE_CASE__ )} to {save_location}' )
				torch.save(obj.state_dict()		,    SCREAMING_SNAKE_CASE__ )





def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Any		,    SCREAMING_SNAKE_CASE_:							Tuple		,    SCREAMING_SNAKE_CASE_:							int = 0 )     -> Dict:




				'''simple docstring'''





				A__            =     F'{path}/custom_checkpoint_{index}.pkl'
				logger.info(F'Loading the state of {get_pretty_name(SCREAMING_SNAKE_CASE__ )} from {load_location}' )
				obj.load_state_dict(torch.load(SCREAMING_SNAKE_CASE__		,    map_location="cpu" ) )




from typing import Optional

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


lowerCAmelCase__						=	[
    """AutomaticSpeechRecognition""",
    """AudioClassification""",
    """ImageClassification""",
    """LanguageModeling""",
    """QuestionAnsweringExtractive""",
    """Summarization""",
    """TaskTemplate""",
    """TextClassification""",
]

lowerCAmelCase__						=	get_logger(__name__)


lowerCAmelCase__						=	{
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}



def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							dict )     -> Optional[TaskTemplate]:




				'''simple docstring'''





				A__            =     task_template_dict.get("task" )
				if task_name is None:
								logger.warning(F'Couldn\'t find template for task \'{task_name}\'. Available templates: {list(SCREAMING_SNAKE_CASE_ )}' )
								return None
				A__            =     NAMEaTEMPLATE.get(SCREAMING_SNAKE_CASE_ )
				return template.from_dict(SCREAMING_SNAKE_CASE_ )

