'''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


UpperCamelCase       :     Dict			=    get_logger(__name__)






def SCREAMING_SNAKE_CASE__  (				snake_case       :				str				,	snake_case       :				List[dict]				,	snake_case       :				list				,	snake_case       :				list				,	snake_case       :				int				,	snake_case       :				GradScaler = None				,	)   ->				Dict:







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






def SCREAMING_SNAKE_CASE__  (				snake_case       :				int				,	snake_case       :				Any				,	snake_case       :				List[Any]				,	snake_case       :				List[str]				,	snake_case       :				List[str]				,	snake_case       :				int=None				,	snake_case       :				List[Any]=None				,	**snake_case       :				Any				,	)   ->				Tuple:







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

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

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

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

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







     """simple docstring"""
     a					:							List[Any]							= Path(snake_case  ) / F"""custom_checkpoint_{index}.pkl"""
     logger.info(F"""Saving the state of {get_pretty_name(snake_case  )} to {save_location}"""  )
     torch.save(obj.state_dict()				,	snake_case  )






def SCREAMING_SNAKE_CASE__  (				snake_case       :				Any				,	snake_case       :				Dict				,	snake_case       :				int = 0  )   ->				Dict:







     """simple docstring"""
     a					:							Tuple							= F"""{path}/custom_checkpoint_{index}.pkl"""
     logger.info(F"""Loading the state of {get_pretty_name(snake_case  )} from {load_location}"""  )
     obj.load_state_dict(torch.load(snake_case				,	map_location='cpu'  )  )


'''simple docstring'''





import glob
import os

from get_test_info import get_tester_classes


if __name__ == "__main__":
      UpperCamelCase       :     List[Any]			=    []

      UpperCamelCase       :     Tuple			=    os.path.join("""tests""", """models""", """**""", """test_modeling_*.py""")
      UpperCamelCase       :     List[str]			=    glob.glob(pattern)
      # TODO: deal with TF/Flax too
      UpperCamelCase       :     List[Any]			=    [
          x for x in test_files if not (x.startswith("""test_modeling_tf_""") or x.startswith("""test_modeling_flax_"""))
      ]

      for test_file in test_files:
            UpperCamelCase       :     List[str]			=    get_tester_classes(test_file)
            for tester_class in tester_classes:
                  # A few tester classes don't have `parent` parameter in `__init__`.
                  # TODO: deal this better
                  try:
                        UpperCamelCase       :     int			=    tester_class(parent=None)
                  except Exception:
                        continue
                  if hasattr(tester, """get_config"""):
                        UpperCamelCase       :     Union[str, Any]			=    tester.get_config()
                        for k, v in config.to_dict().items():
                              if isinstance(v, int):
                                    UpperCamelCase       :     List[Any]			=    None
                                    if k in ["vocab_size"]:
                                          UpperCamelCase       :     List[Any]			=    100
                                    elif k in ["max_position_embeddings"]:
                                          UpperCamelCase       :     Tuple			=    128
                                    elif k in ["hidden_size", "d_model"]:
                                          UpperCamelCase       :     Any			=    40
                                    elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
                                          UpperCamelCase       :     Tuple			=    5
                                    if target is not None and v > target:
                                          failures.append(
                                              f'''{tester_class.__name__} will produce a `config` of type `{config.__class__.__name__}`'''
                                              f''' with config["{k}"] = {v} which is too large for testing! Set its value to be smaller'''
                                              f''' than {target}.'''
                                          )

      if len(failures) > 0:
            raise Exception(f'''There were {len(failures)} failures:\n''' + """\n""".join(failures))

