
"""simple docstring"""

import os

import torch

from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version


if is_torch_version(">=", FSDP_PYTORCH_VERSION):
 import torch.distributed.checkpoint as dist_cp
 from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner
 from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
 from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
 from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType


UpperCamelCase			:       int									=				get_logger(__name__)






def 							A		(       snake_case   :Optional[Any]       ,      snake_case   :Optional[int]       ,      snake_case   :Tuple       ,      snake_case   :List[str]       ,      snake_case   :Any=0	)   ->			Union[str, Any]:
 os.makedirs(snake_case       ,      exist_ok=snake_case	)
 with FSDP.state_dict_type(
     snake_case       ,      fsdp_plugin.state_dict_type       ,      fsdp_plugin.state_dict_config       ,      fsdp_plugin.optim_state_dict_config	):
  __UpperCamelCase       						= model.state_dict()
  if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
   __UpperCamelCase       						= f'{MODEL_NAME}.bin' if model_index == 0 else f'{MODEL_NAME}_{model_index}.bin'
   __UpperCamelCase       						= os.path.join(snake_case       ,      snake_case	)
   if accelerator.process_index == 0:
    logger.info(f'Saving model to {output_model_file}'	)
    torch.save(snake_case       ,      snake_case	)
    logger.info(f'Model saved to {output_model_file}'	)
  elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
   __UpperCamelCase       						= (
       f'{MODEL_NAME}_rank{accelerator.process_index}.bin'
       if model_index == 0
       else f'{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'
   )
   __UpperCamelCase       						= os.path.join(snake_case       ,      snake_case	)
   logger.info(f'Saving model to {output_model_file}'	)
   torch.save(snake_case       ,      snake_case	)
   logger.info(f'Model saved to {output_model_file}'	)
  elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
   __UpperCamelCase       						= os.path.join(snake_case       ,      f'{MODEL_NAME}_{model_index}'	)
   os.makedirs(snake_case       ,      exist_ok=snake_case	)
   logger.info(f'Saving model to {ckpt_dir}'	)
   __UpperCamelCase       						= {'model': state_dict}

   dist_cp.save_state_dict(
       state_dict=snake_case       ,      storage_writer=dist_cp.FileSystemWriter(snake_case	)       ,      planner=DefaultSavePlanner()       ,      )
   logger.info(f'Model saved to {ckpt_dir}'	)






def 							A		(       snake_case   :Any       ,      snake_case   :str       ,      snake_case   :List[str]       ,      snake_case   :List[str]       ,      snake_case   :List[str]=0	)   ->			List[Any]:
 accelerator.wait_for_everyone()
 with FSDP.state_dict_type(
     snake_case       ,      fsdp_plugin.state_dict_type       ,      fsdp_plugin.state_dict_config       ,      fsdp_plugin.optim_state_dict_config	):
  if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
   if type(snake_case	) != FSDP and accelerator.process_index != 0:
    if not fsdp_plugin.sync_module_states:
     raise ValueError(
         'Set the `sync_module_states` flag to `True` so that model states are synced across processes when '
         'initializing FSDP object'	)
    return
   __UpperCamelCase       						= f'{MODEL_NAME}.bin' if model_index == 0 else f'{MODEL_NAME}_{model_index}.bin'
   __UpperCamelCase       						= os.path.join(snake_case       ,      snake_case	)
   logger.info(f'Loading model from {input_model_file}'	)
   __UpperCamelCase       						= torch.load(snake_case	)
   logger.info(f'Model loaded from {input_model_file}'	)
  elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
   __UpperCamelCase       						= (
       f'{MODEL_NAME}_rank{accelerator.process_index}.bin'
       if model_index == 0
       else f'{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'
   )
   __UpperCamelCase       						= os.path.join(snake_case       ,      snake_case	)
   logger.info(f'Loading model from {input_model_file}'	)
   __UpperCamelCase       						= torch.load(snake_case	)
   logger.info(f'Model loaded from {input_model_file}'	)
  elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
   __UpperCamelCase       						= (
       os.path.join(snake_case       ,      f'{MODEL_NAME}_{model_index}'	)
       if f'{MODEL_NAME}' not in input_dir
       else input_dir
   )
   logger.info(f'Loading model from {ckpt_dir}'	)
   __UpperCamelCase       						= {'model': model.state_dict()}
   dist_cp.load_state_dict(
       state_dict=snake_case       ,      storage_reader=dist_cp.FileSystemReader(snake_case	)       ,      planner=DefaultLoadPlanner()       ,      )
   __UpperCamelCase       						= state_dict['model']
   logger.info(f'Model loaded from {ckpt_dir}'	)
  model.load_state_dict(snake_case	)






def 							A		(       snake_case   :Tuple       ,      snake_case   :Dict       ,      snake_case   :List[str]       ,      snake_case   :str       ,      snake_case   :Tuple       ,      snake_case   :Union[str, Any]=0	)   ->			Union[str, Any]:
 os.makedirs(snake_case       ,      exist_ok=snake_case	)
 with FSDP.state_dict_type(
     snake_case       ,      fsdp_plugin.state_dict_type       ,      fsdp_plugin.state_dict_config       ,      fsdp_plugin.optim_state_dict_config	):
  __UpperCamelCase       						= FSDP.optim_state_dict(snake_case       ,      snake_case	)
  if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
   if accelerator.process_index == 0:
    __UpperCamelCase       						= (
        f'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else f'{OPTIMIZER_NAME}_{optimizer_index}.bin'
    )
    __UpperCamelCase       						= os.path.join(snake_case       ,      snake_case	)
    logger.info(f'Saving Optimizer state to {output_optimizer_file}'	)
    torch.save(snake_case       ,      snake_case	)
    logger.info(f'Optimizer state saved in {output_optimizer_file}'	)
  else:
   __UpperCamelCase       						= os.path.join(snake_case       ,      f'{OPTIMIZER_NAME}_{optimizer_index}'	)
   os.makedirs(snake_case       ,      exist_ok=snake_case	)
   logger.info(f'Saving Optimizer state to {ckpt_dir}'	)
   dist_cp.save_state_dict(
       state_dict={'optimizer': optim_state}       ,      storage_writer=dist_cp.FileSystemWriter(snake_case	)       ,      planner=DefaultSavePlanner()       ,      )
   logger.info(f'Optimizer state saved in {ckpt_dir}'	)






def 							A		(       snake_case   :Any       ,      snake_case   :str       ,      snake_case   :str       ,      snake_case   :Optional[Any]       ,      snake_case   :List[Any]       ,      snake_case   :int=0	)   ->			Dict:
 accelerator.wait_for_everyone()
 with FSDP.state_dict_type(
     snake_case       ,      fsdp_plugin.state_dict_type       ,      fsdp_plugin.state_dict_config       ,      fsdp_plugin.optim_state_dict_config	):
  if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
   __UpperCamelCase       						= None
   # below check should work but currently it isn't working (mostly opytorch issue),
   # in the meantime disabling it at the cost of excess memory usage
   # if accelerator.process_index == 0 or not fsdp_plugin.optim_state_dict_config.rank0_only:
   __UpperCamelCase       						= (
       f'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else f'{OPTIMIZER_NAME}_{optimizer_index}.bin'
   )
   __UpperCamelCase       						= os.path.join(snake_case       ,      snake_case	)
   logger.info(f'Loading Optimizer state from {input_optimizer_file}'	)
   __UpperCamelCase       						= torch.load(snake_case	)
   logger.info(f'Optimizer state loaded from {input_optimizer_file}'	)
  else:
   __UpperCamelCase       						= (
       os.path.join(snake_case       ,      f'{OPTIMIZER_NAME}_{optimizer_index}'	)
       if f'{OPTIMIZER_NAME}' not in input_dir
       else input_dir
   )
   logger.info(f'Loading Optimizer from {ckpt_dir}'	)
   __UpperCamelCase       						= load_sharded_optimizer_state_dict(
       model_state_dict=model.state_dict()       ,      optimizer_key='optimizer'       ,      storage_reader=dist_cp.FileSystemReader(snake_case	)       ,      )
   __UpperCamelCase       						= optim_state['optimizer']
   logger.info(f'Optimizer loaded from {ckpt_dir}'	)
  __UpperCamelCase       						= FSDP.optim_state_dict_to_load(snake_case       ,      snake_case       ,      snake_case	)
  optimizer.load_state_dict(snake_case	)





"""simple docstring"""

from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


UpperCamelCase			:       Optional[int]									=				logging.get_logger(__name__)

UpperCamelCase			:       int									=				{
    "EleutherAI/gpt-j-6B": "https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}

class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):
     lowercase		     =		"gptj"
     lowercase		     =		{
         "max_position_embeddings": "n_positions",
         "hidden_size": "n_embd",
         "num_attention_heads": "n_head",
         "num_hidden_layers": "n_layer",
     }





     def __init__(			self			,  __UpperCAmelCase=5_0400			,  __UpperCAmelCase=2048			,  __UpperCAmelCase=4096			,  __UpperCAmelCase=28			,  __UpperCAmelCase=16			,  __UpperCAmelCase=64			,  __UpperCAmelCase=None			,  __UpperCAmelCase="gelu_new"			,  __UpperCAmelCase=0.0			,  __UpperCAmelCase=0.0			,  __UpperCAmelCase=0.0			,  __UpperCAmelCase=1E-5			,  __UpperCAmelCase=0.0_2			,  __UpperCAmelCase=True			,  __UpperCAmelCase=5_0256			,  __UpperCAmelCase=5_0256			,  __UpperCAmelCase=False			,  **__UpperCAmelCase			,  ):

      '''simple docstring'''




      __UpperCamelCase       						= vocab_size
      __UpperCamelCase       						= n_positions
      __UpperCamelCase       						= n_embd
      __UpperCamelCase       						= n_layer
      __UpperCamelCase       						= n_head
      __UpperCamelCase       						= n_inner
      __UpperCamelCase       						= rotary_dim
      __UpperCamelCase       						= activation_function
      __UpperCamelCase       						= resid_pdrop
      __UpperCamelCase       						= embd_pdrop
      __UpperCamelCase       						= attn_pdrop
      __UpperCamelCase       						= layer_norm_epsilon
      __UpperCamelCase       						= initializer_range
      __UpperCamelCase       						= use_cache

      __UpperCamelCase       						= bos_token_id
      __UpperCamelCase       						= eos_token_id

      super().__init__(
          bos_token_id=__UpperCAmelCase			,  eos_token_id=__UpperCAmelCase			,  tie_word_embeddings=__UpperCAmelCase			,  **__UpperCAmelCase   )


class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):





     def __init__(			self			,  __UpperCAmelCase			,  __UpperCAmelCase = "default"			,  __UpperCAmelCase = None			,  __UpperCAmelCase = False			,  ):

      '''simple docstring'''




      super().__init__(__UpperCAmelCase			,  task=__UpperCAmelCase			,  patching_specs=__UpperCAmelCase			,  use_past=__UpperCAmelCase   )
      if not getattr(self._config			,  'pad_token_id'			,  __UpperCAmelCase   ):
       # TODO: how to do that better?
       __UpperCamelCase       						= 0





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}   )
      if self.use_past:
       self.fill_with_past_key_values_(__UpperCAmelCase			,  direction='inputs'   )
       __UpperCamelCase       						= {0: 'batch', 1: 'past_sequence + sequence'}
      else:
       __UpperCamelCase       						= {0: 'batch', 1: 'sequence'}

      return common_inputs





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return self._config.n_layer





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return self._config.n_head





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase = -1			,  __UpperCAmelCase = -1			,  __UpperCAmelCase = False			,  __UpperCAmelCase = None			,  ):

      '''simple docstring'''




      __UpperCamelCase       						= super(__UpperCAmelCase			,  self   ).generate_dummy_inputs(
          __UpperCAmelCase			,  batch_size=__UpperCAmelCase			,  seq_length=__UpperCAmelCase			,  is_pair=__UpperCAmelCase			,  framework=__UpperCAmelCase   )

      # We need to order the input in the way they appears in the forward()
      __UpperCamelCase       						= OrderedDict({'input_ids': common_inputs['input_ids']}   )

      # Need to add the past_keys
      if self.use_past:
       if not is_torch_available():
        raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'   )
       else:
        import torch

        __UpperCamelCase						,			__UpperCamelCase       						= common_inputs['input_ids'].shape
        # Not using the same length for past_key_values
        __UpperCamelCase       						= seqlen + 2
        __UpperCamelCase       						= (
            batch,
            self.num_attention_heads,
            past_key_values_length,
            self._config.hidden_size // self.num_attention_heads,
        )
        __UpperCamelCase       						= [
            (torch.zeros(__UpperCAmelCase   ), torch.zeros(__UpperCAmelCase   )) for _ in range(self.num_layers   )
        ]

      __UpperCamelCase       						= common_inputs['attention_mask']
      if self.use_past:
       __UpperCamelCase       						= ordered_inputs['attention_mask'].dtype
       __UpperCamelCase       						= torch.cat(
           [ordered_inputs['attention_mask'], torch.ones(__UpperCAmelCase			,  __UpperCAmelCase			,  dtype=__UpperCAmelCase   )]			,  dim=1   )

      return ordered_inputs







     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return 13



