

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


_a	 =     get_logger(__name__)







def _a				( 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					,      )					->					int:

	"""simple docstring"""






	for i, state in enumerate(SCREAMING_SNAKE_CASE	):
		__lowerCAmelCase:     Any     			=   f'''{MODEL_NAME}.bin''' if i == 0 else f'''{MODEL_NAME}_{i}.bin'''
		__lowerCAmelCase:     Optional[Any]     			=   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	):
		__lowerCAmelCase:     List[str]     			=   opt.state_dict()
		__lowerCAmelCase:     Dict     			=   f'''{OPTIMIZER_NAME}.bin''' if i == 0 else f'''{OPTIMIZER_NAME}_{i}.bin'''
		__lowerCAmelCase:     Union[str, Any]     			=   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	):
		__lowerCAmelCase:     str     			=   scheduler.state_dict()
		__lowerCAmelCase:     Union[str, Any]     			=   f'''{SCHEDULER_NAME}.bin''' if i == 0 else f'''{SCHEDULER_NAME}_{i}.bin'''
		__lowerCAmelCase:     Optional[int]     			=   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:
		__lowerCAmelCase:     str     			=   scaler.state_dict()
		__lowerCAmelCase:     List[Any]     			=   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
	__lowerCAmelCase:     Any     			=   {}
	__lowerCAmelCase:     str     			=   f'''{RNG_STATE_NAME}_{process_index}.pkl'''
	__lowerCAmelCase:     List[str]     			=   random.getstate()
	__lowerCAmelCase:     Optional[Any]     			=   np.random.get_state()
	__lowerCAmelCase:     Optional[int]     			=   torch.get_rng_state()
	if is_xpu_available():
		__lowerCAmelCase:     List[Any]     			=   torch.xpu.get_rng_state_all()
	else:
		__lowerCAmelCase:     Optional[Any]     			=   torch.cuda.get_rng_state_all()
	if is_tpu_available():
		__lowerCAmelCase:     Any     			=   xm.get_rng_state()
	__lowerCAmelCase:     Tuple     			=   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 _a				( SCREAMING_SNAKE_CASE	:    Optional[Any]					,      SCREAMING_SNAKE_CASE	:    Optional[int]					,      SCREAMING_SNAKE_CASE	:    Dict					,      SCREAMING_SNAKE_CASE	:    Optional[Any]					,      SCREAMING_SNAKE_CASE	:    List[str]					,      SCREAMING_SNAKE_CASE	:    Tuple=None					,      SCREAMING_SNAKE_CASE	:    str=None					,      **SCREAMING_SNAKE_CASE	:    Dict					,      )					->					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:
		__lowerCAmelCase:     Any     			=   'cpu'
	elif map_location == "on_device":
		__lowerCAmelCase:     Dict     			=   PartialState().device
	# Model states
	for i, model in enumerate(SCREAMING_SNAKE_CASE	):
		__lowerCAmelCase:     int     			=   f'''{MODEL_NAME}.bin''' if i == 0 else f'''{MODEL_NAME}_{i}.bin'''
		__lowerCAmelCase:     Optional[Any]     			=   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	):
		__lowerCAmelCase:     Optional[Any]     			=   f'''{OPTIMIZER_NAME}.bin''' if i == 0 else f'''{OPTIMIZER_NAME}_{i}.bin'''
		__lowerCAmelCase:     Tuple     			=   os.path.join(SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE	)
		__lowerCAmelCase:     Optional[Any]     			=   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	):
		__lowerCAmelCase:     Union[str, Any]     			=   f'''{SCHEDULER_NAME}.bin''' if i == 0 else f'''{SCHEDULER_NAME}_{i}.bin'''
		__lowerCAmelCase:     str     			=   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:
		__lowerCAmelCase:     Optional[int]     			=   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:
		__lowerCAmelCase:     Optional[int]     			=   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 _a				( SCREAMING_SNAKE_CASE	:    Tuple					,      SCREAMING_SNAKE_CASE	:    str					,      SCREAMING_SNAKE_CASE	:    int = 0	)					->					Dict:

	"""simple docstring"""






	__lowerCAmelCase:     Any     			=   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 _a				( SCREAMING_SNAKE_CASE	:    List[str]					,      SCREAMING_SNAKE_CASE	:    Optional[Any]					,      SCREAMING_SNAKE_CASE	:    int = 0	)					->					Tuple:

	"""simple docstring"""






	__lowerCAmelCase:     Optional[int]     			=   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'	)	)








# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


_a	 =     {
    '''configuration_tvlt''': ['''TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TvltConfig'''],
    '''feature_extraction_tvlt''': ['''TvltFeatureExtractor'''],
    '''processing_tvlt''': ['''TvltProcessor'''],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a	 =     [
      '''TVLT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TvltModel''',
      '''TvltForPreTraining''',
      '''TvltForAudioVisualClassification''',
      '''TvltPreTrainedModel''',
  ]

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a	 =     ['''TvltImageProcessor''']


if TYPE_CHECKING:
  from .configuration_tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig
  from .processing_tvlt import TvltProcessor
  from .feature_extraction_tvlt import TvltFeatureExtractor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tvlt import (
        TVLT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TvltForAudioVisualClassification,
        TvltForPreTraining,
        TvltModel,
        TvltPreTrainedModel,
    )

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_tvlt import TvltImageProcessor


else:
  import sys

  _a	 =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





