


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


__snake_case             =	get_logger(__name__)






def 		_A		(		_lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase = None				,     )				->   Tuple:


     """simple docstring"""


     for i, state in enumerate(lowerCamelCase__   ):
          __UpperCamelCase   = f'''{MODEL_NAME}.bin''' if i == 0 else f'''{MODEL_NAME}_{i}.bin'''
          __UpperCamelCase   = 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__   ):
          __UpperCamelCase   = opt.state_dict()
          __UpperCamelCase   = f'''{OPTIMIZER_NAME}.bin''' if i == 0 else f'''{OPTIMIZER_NAME}_{i}.bin'''
          __UpperCamelCase   = 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__   ):
          __UpperCamelCase   = scheduler.state_dict()
          __UpperCamelCase   = f'''{SCHEDULER_NAME}.bin''' if i == 0 else f'''{SCHEDULER_NAME}_{i}.bin'''
          __UpperCamelCase   = 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:
          __UpperCamelCase   = scaler.state_dict()
          __UpperCamelCase   = os.path.join(lowerCamelCase__				,     lowerCamelCase__   )
          torch.save(lowerCamelCase__				,     lowerCamelCase__   )
          logger.info(f'''Gradient scaler state saved in {output_scaler_file}'''   )
     # Random number generator states
     __UpperCamelCase   = {}
     __UpperCamelCase   = f'''{RNG_STATE_NAME}_{process_index}.pkl'''
     __UpperCamelCase   = random.getstate()
     __UpperCamelCase   = np.random.get_state()
     __UpperCamelCase   = torch.get_rng_state()
     if is_xpu_available():
          __UpperCamelCase   = torch.xpu.get_rng_state_all()
     else:
          __UpperCamelCase   = torch.cuda.get_rng_state_all()
     if is_tpu_available():
          __UpperCamelCase   = xm.get_rng_state()
     __UpperCamelCase   = os.path.join(lowerCamelCase__				,     lowerCamelCase__   )
     torch.save(lowerCamelCase__				,     lowerCamelCase__   )
     logger.info(f'''Random states saved in {output_states_file}'''   )
     return output_dir






def 		_A		(		_lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase=None				,     _lowercase=None				,     **_lowercase				,     )				->   List[str]:


     """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:
          __UpperCamelCase   = 'cpu'
     elif map_location == "on_device":
          __UpperCamelCase   = PartialState().device
     # Model states
     for i, model in enumerate(lowerCamelCase__   ):
          __UpperCamelCase   = f'''{MODEL_NAME}.bin''' if i == 0 else f'''{MODEL_NAME}_{i}.bin'''
          __UpperCamelCase   = 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__   ):
          __UpperCamelCase   = f'''{OPTIMIZER_NAME}.bin''' if i == 0 else f'''{OPTIMIZER_NAME}_{i}.bin'''
          __UpperCamelCase   = os.path.join(lowerCamelCase__				,     lowerCamelCase__   )
          __UpperCamelCase   = 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__   ):
          __UpperCamelCase   = f'''{SCHEDULER_NAME}.bin''' if i == 0 else f'''{SCHEDULER_NAME}_{i}.bin'''
          __UpperCamelCase   = 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:
          __UpperCamelCase   = os.path.join(lowerCamelCase__				,     lowerCamelCase__   )
          scaler.load_state_dict(torch.load(lowerCamelCase__   )   )
          logger.info('GradScaler state loaded successfully'   )

     # Random states
     try:
          __UpperCamelCase   = 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 		_A		(		_lowercase				,     _lowercase				,     _lowercase = 0   )				->   Optional[int]:


     """simple docstring"""


     __UpperCamelCase   = 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 		_A		(		_lowercase				,     _lowercase				,     _lowercase = 0   )				->   List[str]:


     """simple docstring"""


     __UpperCamelCase   = 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'   )   )






import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()






def 		_A		(		_lowercase				,     _lowercase				,     _lowercase   )				->   List[str]:


     """simple docstring"""


     __UpperCamelCase   = json.load(open(_lowercase				,     'r'   )   )
     __UpperCamelCase   = GPTNeoConfig(
         hidden_size=config_json['n_embd']				,     num_layers=config_json['n_layer']				,     num_heads=config_json['n_head']				,     attention_types=config_json['attention_types']				,     max_position_embeddings=config_json['n_positions']				,     resid_dropout=config_json['res_dropout']				,     embed_dropout=config_json['embed_dropout']				,     attention_dropout=config_json['attn_dropout']				,     )
     print(f'''Building PyTorch model from configuration: {config}'''   )
     __UpperCamelCase   = GPTNeoForCausalLM(_lowercase   )

     # Load weights from tf checkpoint
     load_tf_weights_in_gpt_neo(_lowercase				,     _lowercase				,     _lowercase   )

     # Save pytorch-model
     print(f'''Save PyTorch model to {pytorch_dump_path}'''   )
     model.save_pretrained(_lowercase   )


if __name__ == "__main__":
    __snake_case             =	argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
    )
    parser.add_argument(
        '''--config_file''',
        default=None,
        type=str,
        required=True,
        help=(
            '''The config json file corresponding to the pre-trained mesh-tf model. \n'''
            '''This specifies the model architecture.'''
        ),
    )
    parser.add_argument(
        '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
    )
    __snake_case             =	parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


