





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase__    :      List[str]       					={"configuration_t5": ["T5_PRETRAINED_CONFIG_ARCHIVE_MAP", "T5Config", "T5OnnxConfig"]}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__    :      Union[str, Any]       					=["T5Tokenizer"]

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__    :      int       					=["T5TokenizerFast"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__    :      str       					=[
							    "T5_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "T5EncoderModel",
							    "T5ForConditionalGeneration",
							    "T5Model",
							    "T5PreTrainedModel",
							    "load_tf_weights_in_t5",
							    "T5ForQuestionAnswering",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__    :      Union[str, Any]       					=[
							    "TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TFT5EncoderModel",
							    "TFT5ForConditionalGeneration",
							    "TFT5Model",
							    "TFT5PreTrainedModel",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__    :      List[Any]       					=[
							    "FlaxT5EncoderModel",
							    "FlaxT5ForConditionalGeneration",
							    "FlaxT5Model",
							    "FlaxT5PreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_ta import TaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_ta_fast import TaTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_ta import (
														    T5_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TaEncoderModel,
														    TaForConditionalGeneration,
														    TaForQuestionAnswering,
														    TaModel,
														    TaPreTrainedModel,
														    load_tf_weights_in_ta,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_ta import (
														    TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFTaEncoderModel,
														    TFTaForConditionalGeneration,
														    TFTaModel,
														    TFTaPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_ta import (
														    FlaxTaEncoderModel,
														    FlaxTaForConditionalGeneration,
														    FlaxTaModel,
														    FlaxTaPreTrainedModel,
														)


else:
							import sys

							UpperCAmelCase__    :      int       					=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)







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


lowercase__						:Optional[Any]					    =       get_logger(__name__)



def 			UpperCamelCase       (				lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__ = None					,     ):
					'''simple docstring'''
					# Model states
					for i, state in enumerate(lowerCAmelCase__						):
										lowercase		      =		f'{MODEL_NAME}.bin' if i == 0 else f'{MODEL_NAME}_{i}.bin'
										lowercase		      =		os.path.join(lowerCAmelCase__					,     lowerCAmelCase__						)
										save(lowerCAmelCase__					,     lowerCAmelCase__						)
										logger.info(f'Model weights saved in {output_model_file}'						)
					# Optimizer states
					for i, opt in enumerate(lowerCAmelCase__						):
										lowercase		      =		opt.state_dict()
										lowercase		      =		f'{OPTIMIZER_NAME}.bin' if i == 0 else f'{OPTIMIZER_NAME}_{i}.bin'
										lowercase		      =		os.path.join(lowerCAmelCase__					,     lowerCAmelCase__						)
										save(lowerCAmelCase__					,     lowerCAmelCase__						)
										logger.info(f'Optimizer state saved in {output_optimizer_file}'						)
					# Scheduler states
					for i, scheduler in enumerate(lowerCAmelCase__						):
										lowercase		      =		scheduler.state_dict()
										lowercase		      =		f'{SCHEDULER_NAME}.bin' if i == 0 else f'{SCHEDULER_NAME}_{i}.bin'
										lowercase		      =		os.path.join(lowerCAmelCase__					,     lowerCAmelCase__						)
										save(lowerCAmelCase__					,     lowerCAmelCase__						)
										logger.info(f'Scheduler state saved in {output_scheduler_file}'						)
					# GradScaler state
					if scaler is not None:
										lowercase		      =		scaler.state_dict()
										lowercase		      =		os.path.join(lowerCAmelCase__					,     lowerCAmelCase__						)
										torch.save(lowerCAmelCase__					,     lowerCAmelCase__						)
										logger.info(f'Gradient scaler state saved in {output_scaler_file}'						)
					# Random number generator states
					lowercase		      =		{}
					lowercase		      =		f'{RNG_STATE_NAME}_{process_index}.pkl'
					lowercase		      =		random.getstate()
					lowercase		      =		np.random.get_state()
					lowercase		      =		torch.get_rng_state()
					if is_xpu_available():
										lowercase		      =		torch.xpu.get_rng_state_all()
					else:
										lowercase		      =		torch.cuda.get_rng_state_all()
					if is_tpu_available():
										lowercase		      =		xm.get_rng_state()
					lowercase		      =		os.path.join(lowerCAmelCase__					,     lowerCAmelCase__						)
					torch.save(lowerCAmelCase__					,     lowerCAmelCase__						)
					logger.info(f'Random states saved in {output_states_file}'						)
					return output_dir



def 			UpperCamelCase       (				lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__=None					,     lowerCAmelCase__=None					,     **lowerCAmelCase__					,     ):
					'''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:
										lowercase		      =		'''cpu'''
					elif map_location == "on_device":
										lowercase		      =		PartialState().device
					# Model states
					for i, model in enumerate(lowerCAmelCase__						):
										lowercase		      =		f'{MODEL_NAME}.bin' if i == 0 else f'{MODEL_NAME}_{i}.bin'
										lowercase		      =		os.path.join(lowerCAmelCase__					,     lowerCAmelCase__						)
										models[i].load_state_dict(torch.load(lowerCAmelCase__					,     map_location=lowerCAmelCase__						)					,     **lowerCAmelCase__						)
					logger.info('''All model weights loaded successfully'''						)

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

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

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

					# Random states
					try:
										lowercase		      =		torch.load(os.path.join(lowerCAmelCase__					,     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 			UpperCamelCase       (				lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__ = 0						):
					'''simple docstring'''
					lowercase		      =		Path(lowerCAmelCase__						) / f'custom_checkpoint_{index}.pkl'
					logger.info(f'Saving the state of {get_pretty_name(lowerCAmelCase__						)} to {save_location}'						)
					torch.save(obj.state_dict()					,     lowerCAmelCase__						)



def 			UpperCamelCase       (				lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__ = 0						):
					'''simple docstring'''
					lowercase		      =		f'{path}/custom_checkpoint_{index}.pkl'
					logger.info(f'Loading the state of {get_pretty_name(lowerCAmelCase__						)} from {load_location}'						)
					obj.load_state_dict(torch.load(lowerCAmelCase__					,     map_location='''cpu'''						)						)




