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							   =      get_logger(__name__)







def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_ = None						,      )	->	Optional[int]:
     # Model states
     for i, state in enumerate(lowerCAmelCase_       ):
          a  :    List[str]				=						f"""{MODEL_NAME}.bin""" if i == 0 else f"""{MODEL_NAME}_{i}.bin"""
          a  :    Union[str, Any]				=						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_       ):
          a  :    List[str]				=						opt.state_dict()
          a  :    List[Any]				=						f"""{OPTIMIZER_NAME}.bin""" if i == 0 else f"""{OPTIMIZER_NAME}_{i}.bin"""
          a  :    Optional[int]				=						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_       ):
          a  :    Tuple				=						scheduler.state_dict()
          a  :    Tuple				=						f"""{SCHEDULER_NAME}.bin""" if i == 0 else f"""{SCHEDULER_NAME}_{i}.bin"""
          a  :    Any				=						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:
          a  :    Optional[Any]				=						scaler.state_dict()
          a  :    Tuple				=						os.path.join(lowerCAmelCase_						,      lowerCAmelCase_       )
          torch.save(lowerCAmelCase_						,      lowerCAmelCase_       )
          logger.info(f"""Gradient scaler state saved in {output_scaler_file}"""       )
     # Random number generator states
     a  :    str				=						{}
     a  :    Optional[Any]				=						f"""{RNG_STATE_NAME}_{process_index}.pkl"""
     a  :    Tuple				=						random.getstate()
     a  :    Union[str, 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  :    str				=						torch.cuda.get_rng_state_all()
     if is_tpu_available():
          a  :    int				=						xm.get_rng_state()
     a  :    str				=						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_						,      )	->	Any:
     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  :    Any				=						'''cpu'''
     elif map_location == "on_device":
          a  :    Any				=						PartialState().device
     # Model states
     for i, model in enumerate(lowerCAmelCase_       ):
          a  :    Dict				=						f"""{MODEL_NAME}.bin""" if i == 0 else f"""{MODEL_NAME}_{i}.bin"""
          a  :    Tuple				=						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_       ):
          a  :    Dict				=						f"""{OPTIMIZER_NAME}.bin""" if i == 0 else f"""{OPTIMIZER_NAME}_{i}.bin"""
          a  :    str				=						os.path.join(lowerCAmelCase_						,      lowerCAmelCase_       )
          a  :    Union[str, Any]				=						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_       ):
          a  :    Union[str, Any]				=						f"""{SCHEDULER_NAME}.bin""" if i == 0 else f"""{SCHEDULER_NAME}_{i}.bin"""
          a  :    int				=						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:
          a  :    Any				=						os.path.join(lowerCAmelCase_						,      lowerCAmelCase_       )
          scaler.load_state_dict(torch.load(lowerCAmelCase_       )       )
          logger.info('''GradScaler state loaded successfully'''       )

     # Random states
     try:
          a  :    str				=						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       )	->	int:
     a  :    Tuple				=						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       )	->	Any:
     a  :    Optional[Any]				=						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'''       )       )
from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase							   =      logging.get_logger(__name__)

UpperCAmelCase							   =      {
    # pcqm4mv1 now deprecated
    """graphormer-base""": """https://huggingface.co/clefourrier/graphormer-base-pcqm4mv2/resolve/main/config.json""",
    # See all Graphormer models at https://huggingface.co/models?filter=graphormer
}






class 							_SCREAMING_SNAKE_CASE(   snake_case__	):
   UpperCamelCase_				:						int 			=   'graphormer'
   UpperCamelCase_				:						Tuple 			=   ['past_key_values']
   def __init__(    self							,     lowerCAmelCase_ = 1							,     lowerCAmelCase_ = 5_12 * 9							,     lowerCAmelCase_ = 5_12 * 3							,     lowerCAmelCase_ = 5_12							,     lowerCAmelCase_ = 5_12							,     lowerCAmelCase_ = 5_12							,     lowerCAmelCase_ = 1_28							,     lowerCAmelCase_ = 5							,     lowerCAmelCase_ = 10_24							,     lowerCAmelCase_ = "multi_hop"							,     lowerCAmelCase_ = 5_12							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = 12							,     lowerCAmelCase_ = 7_68							,     lowerCAmelCase_ = 7_68							,     lowerCAmelCase_ = 32							,     lowerCAmelCase_ = 0.1							,     lowerCAmelCase_ = 0.1							,     lowerCAmelCase_ = 0.0							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = "gelu"							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = 0							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = 0.0							,     lowerCAmelCase_ = 8							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = True							,     lowerCAmelCase_ = True							,     lowerCAmelCase_=0							,     lowerCAmelCase_=1							,     lowerCAmelCase_=2							,     **lowerCAmelCase_							,     )	->Optional[int]:
        a  :    List[Any]				=						num_classes
        a  :    Any				=						num_atoms
        a  :    Dict				=						num_in_degree
        a  :    int				=						num_out_degree
        a  :    Dict				=						num_edges
        a  :    Tuple				=						num_spatial
        a  :    Dict				=						num_edge_dis
        a  :    Optional[int]				=						edge_type
        a  :    List[Any]				=						multi_hop_max_dist
        a  :    Optional[int]				=						spatial_pos_max
        a  :    str				=						max_nodes
        a  :    Optional[Any]				=						num_hidden_layers
        a  :    Any				=						embedding_dim
        a  :    Dict				=						embedding_dim
        a  :    Dict				=						ffn_embedding_dim
        a  :    List[Any]				=						num_attention_heads
        a  :    List[str]				=						dropout
        a  :    int				=						attention_dropout
        a  :    Optional[Any]				=						layerdrop
        a  :    str				=						encoder_normalize_before
        a  :    Optional[int]				=						pre_layernorm
        a  :    Tuple				=						apply_graphormer_init
        a  :    int				=						activation_fn
        a  :    Any				=						embed_scale
        a  :    Optional[int]				=						freeze_embeddings
        a  :    Tuple				=						num_trans_layers_to_freeze
        a  :    List[str]				=						share_input_output_embed
        a  :    List[Any]				=						traceable
        a  :    Optional[Any]				=						q_noise
        a  :    Dict				=						qn_block_size

        # These parameters are here for future extensions
        # atm, the model only supports self attention
        a  :    List[Any]				=						kdim
        a  :    List[str]				=						vdim
        a  :    int				=						self_attention
        a  :    List[Any]				=						bias

        super().__init__(
            pad_token_id=lowerCAmelCase_							,     bos_token_id=lowerCAmelCase_							,     eos_token_id=lowerCAmelCase_							,     **lowerCAmelCase_							,     )