






'''simple docstring'''




import argparse

# utilities to import the model weights and config file
import os
import pickle as pkl

# PyTorch + new model classes
import torch
from torch import nn

from transformers import AutoTokenizer, MegaConfig, MegaForMaskedLM


# import the EncoderLayer class used to pretrain
# !! NOTE !! this requires the version of fairseq that is built when you install the Mega source
try:
    from fairseq.modules.mega_layer import MegaEncoderLayer
except ImportError:
    raise ImportError('''You need to install the version of fairseq from the Mega repo!''')

class      _UpperCAmelCase    (  nn.Module		):




   """simple docstring"""

   def __init__(	self				:			int   ,   __UpperCAmelCase				:			int   ,   __UpperCAmelCase				:			Union[str, Any]   ,   __UpperCAmelCase				:			Any	):





         '''simple docstring'''





         super().__init__()
         _A      =			mega_args
         _A      =			nn.Embedding(__UpperCAmelCase   ,   self.mega_args.encoder_embed_dim	)
         _A      =			nn.ModuleList([MegaEncoderLayer(self.mega_args	) for _ in range(__UpperCAmelCase	)]	)
         _A      =			depth

   def 		lowerCAmelCase				(	self				:			Dict   ,   __UpperCAmelCase				:			Tuple   ,   __UpperCAmelCase				:			Optional[int]   ,   __UpperCAmelCase				:			int=True   ,   __UpperCAmelCase				:			Tuple=0	):





         '''simple docstring'''





         if batch_first:
               _A      =			input_ids.T

         # to make things more confusing, Mega expects the attention mask to
         # be (batch, time), but with values of 0 (normal token) and 1 (ignore token)
         # which is the opposite of what HF returns
         if ignore_mask_value == 0:
               _A      =			1 - attention_mask

         # get token embeddings from IDs
         _A      =			self.embedding_layer(__UpperCAmelCase	)

         # pass through the Mega layers
         # input is (time, batch, encoder dim) and output is the same
         for encoder in self.encoders:
               _A      =			encoder(__UpperCAmelCase   ,   __UpperCAmelCase	)

         # return according to the shape specified
         if batch_first:
               # (T, B, H) --> (B, T, H)
               return torch.transpose(__UpperCAmelCase   ,   0   ,   1	)
         else:
               return embeds





class      _UpperCAmelCase    (  nn.Module		):




   """simple docstring"""

   def __init__(	self				:			Any   ,   __UpperCAmelCase				:			Dict   ,   __UpperCAmelCase				:			Optional[Any]   ,   __UpperCAmelCase				:			Tuple	):





         '''simple docstring'''





         super().__init__()
         _A      =			MegaLM(__UpperCAmelCase   ,   __UpperCAmelCase   ,   __UpperCAmelCase	)
         _A      =			nn.Linear(mega_args.encoder_embed_dim   ,   __UpperCAmelCase	)
         _A      =			nn.Dropout(p=0.1	)

   def 		lowerCAmelCase				(	self				:			List[str]   ,   __UpperCAmelCase				:			str   ,   __UpperCAmelCase				:			List[Any]   ,   __UpperCAmelCase				:			Dict=True   ,   __UpperCAmelCase				:			List[Any]=0	):





         '''simple docstring'''





         _A      =			self.mega(__UpperCAmelCase   ,   __UpperCAmelCase   ,   __UpperCAmelCase   ,   __UpperCAmelCase	)
         return self.mlm_head(self.dropout(__UpperCAmelCase	)	)





def       __lowercase		(    __lowercase     ,						__lowercase     ,						__lowercase ) ->		Optional[Any]:

      '''simple docstring'''


      with open(os.path.join(__lowercase     ,						"model_args.pkl" )     ,						"rb" ) as f:
            _A      =			pkl.load(__lowercase )

      # load the original encoder
      _A      =			OriginalMegaForMaskedLM(**__lowercase ).eval()

      # load its weights
      print(
          "Original Mega encoder:"     ,						original_mlm.mega.load_state_dict(
              torch.load(os.path.join(__lowercase     ,						"encoder_weights.pt" )     ,						map_location="cpu" ) )     ,						)
      print(
          "Original Mega MLM layer:"     ,						original_mlm.mlm_head.load_state_dict(
              torch.load(os.path.join(__lowercase     ,						"mlm_head_weights.pt" )     ,						map_location="cpu" ) )     ,						)

      # create a new config from the old one
      _A      =			MegaConfig(
          num_hidden_layers=mega_original_args["depth"]     ,						vocab_size=mega_original_args["vocab_size"]     ,						hidden_size=mega_original_args["mega_args"].encoder_embed_dim     ,						shared_representation_size=mega_original_args["mega_args"].encoder_z_dim     ,						intermediate_size=mega_original_args["mega_args"].encoder_hidden_dim     ,						ema_projection_size=mega_original_args["mega_args"].encoder_n_dim     ,						dropout_prob=mega_original_args["mega_args"].dropout     ,						attention_probs_dropout_prob=mega_original_args["mega_args"].attention_dropout     ,						hidden_dropout_prob=mega_original_args["mega_args"].hidden_dropout     ,						activation=mega_original_args["mega_args"].activation_fn     ,						attention_activation=mega_original_args["mega_args"].attention_activation_fn     ,						bidirectional=mega_original_args["mega_args"].bidirectional     ,						use_chunking=mega_original_args["mega_args"].encoder_chunk_size > 0     ,						chunk_size=mega_original_args["mega_args"].encoder_chunk_size     ,						truncation=mega_original_args["mega_args"].truncation_length     ,						normalization_type=mega_original_args["mega_args"].normalization_type     ,						normalize_before_mega=__lowercase     ,						norm_affine=__lowercase     ,						use_feature_dropout=mega_original_args["mega_args"].feature_dropout     ,						relative_positional_bias=mega_original_args["mega_args"].rel_pos_bias     ,						max_positions=mega_original_args["mega_args"].max_source_positions     ,						nffn_hidden_size=mega_original_args["mega_args"].encoder_ffn_embed_dim     ,						normalize_before_ffn=mega_original_args["mega_args"].normalize_before     ,						nffn_activation_dropout_prob=0.0     ,						add_token_type_embeddings=__lowercase     ,						add_lm_hidden_dense_layer=__lowercase     ,						)

      _A      =			MegaForMaskedLM(__lowercase ).eval()

      # the originl checkpoint just uses nn.Embedding for the word embeddings
      # we use a wrapper module for embeddings to add support for positional embeddings
      _A      =			original_mlm.mega.embedding_layer.weight

      # modify the state dictionary of the original checkpoint to account for naming issues in the Hugging Face
      # ecosystem -- any names containing "beta" or "gamma" aren't safe to use and are renamed upon _load_pretrained,
      # also renaming previously confusing parameter names
      _A      =			original_mlm.mega.encoders.state_dict()
      _A      =			{}
      for module_name in original_state_dict.keys():
            _A      =			None
            # have to handle gamma, beta, and alpha differently due to their use
            # in multiple modules within the original repository;
            # beta is used in EMA, MovingAverageGatedAttention, and RotaryRelativePositionalBias, and must be renamed due to flax/tf weights
            # the EMA sublayer was renamed from "move" to "ema_gate" for readability, so that is also done here
            if "beta" in module_name:
                  # EMA sub-layers were always called "move" in the original repo
                  if "move.beta" in module_name:
                        _A      =			module_name.replace("move.beta"     ,						"ema_gate.ema_expansion_matrix" )
                  elif "mega_layer.beta" in module_name:
                        _A      =			module_name.replace("beta"     ,						"qk_bias" )
                  else:
                        _A      =			module_name.replace("beta"     ,						"b_param" )
        # beta is used in EMA and MovingAverageGatedAttention, and must be renamed due to flax/tf weights
            elif "gamma" in module_name:
                  if "move.gamma" in module_name:
                        _A      =			module_name.replace("move.gamma"     ,						"ema_gate.kernel_projection_matrix" )
                  elif "mega_layer.gamma" in module_name:
                        _A      =			module_name.replace("gamma"     ,						"qk_weight" )
                  else:
                        _A      =			module_name.replace("gamma"     ,						"g_param" )
        # alpha is used in EMA and positional bias; renaming to improve readability
            elif "move.alpha" in module_name:
                  _A      =			module_name.replace("move.alpha"     ,						"ema_gate.decay_factor" )
            # delta is only used in EMA; renaming to improve readability
            elif "move.delta" in module_name:
                  _A      =			module_name.replace("move.delta"     ,						"ema_gate.damping_factor" )
            # omega is only used in EMA; renaming to improve readability
            elif "omega" in module_name:
                  _A      =			module_name.replace("move.omega"     ,						"ema_gate.residual_weight" )

            if new_module_name:
                  _A      =			new_module_name

      if len(__lowercase ) != 0:
            print(F'''Renaming these keys: {updated_keys.keys()}''' )
      else:
            print("No need to rename state dict entries" )
      for old, new in updated_keys.items():
            _A      =			original_state_dict.pop(__lowercase )

      # now attempt to load the state dictionary with updated names
      # note that we now call it `mega.layers` instead of `mega.encoders` due to hugging face style
      print("HF Mega encoder:"     ,						hf_mlm.mega.layers.load_state_dict(__lowercase ) )

      # load the MLM head weights directly
      print(
          "HF Mega MLM layer:"     ,						hf_mlm.mlm_head.load_state_dict(
              torch.load(os.path.join(__lowercase     ,						"mlm_head_weights.pt" )     ,						map_location="cpu" ) )     ,						)

      # test on a randomly generated input sequence
      _A      =			torch.randint(0     ,						hf_config.vocab_size     ,						size=(4, 256) )
      _A      =			torch.ones_like(__lowercase )
      # mask a few tokens to make sure masking is applied appropriately :)
      _A      =			0

      # run forward passes
      _A      =			original_mlm(__lowercase     ,						__lowercase     ,						batch_first=__lowercase     ,						ignore_mask_value=0 )
      _A      =			hf_mlm(__lowercase     ,						__lowercase )[0]

      # print shapes and diff
      print(F'''original output {original_output.shape}''' )
      print(F'''hf output {hf_output.shape}''' )
      print(F'''max diff: {(original_output - hf_output).max()}''' )  # 0.0
      _A      =			torch.allclose(__lowercase     ,						__lowercase     ,						atol=1e-3 )

      if success:
            print("Yay!" )
            hf_mlm.save_pretrained(__lowercase )
      else:
            raise RuntimeError(F'''Something\'s broken :(\nOriginal:\n{original_output}\n\nHF\n{hf_output}\n{hf_mlm}''' )

      if includes_tokenizer:
            print("Transferring tokenizer" )
            _A      =			AutoTokenizer.from_pretrained(__lowercase )
            tokenizer.save_pretrained(__lowercase )


if __name__ == "__main__":
    lowerCamelCase_							      =      argparse.ArgumentParser()

    parser.add_argument(
        '''--pretrained_checkpoint_path''',
        default=None,
        type=str,
        required=True,
        help='''Point to the directory containing your model weights using the official Mega repo''',
    )

    parser.add_argument(
        '''--output_path''', default=None, type=str, required=True, help='''Location to save the Hugging Face version'''
    )

    parser.add_argument(
        '''--includes_tokenizer''',
        action='''store_true''',
        help='''Use this flag if there is a Hugging Face tokenizer in the original checkpoint repo''',
    )

    lowerCamelCase_							      =      parser.parse_args()

    convert_checkpoint_to_huggingface(args.pretrained_checkpoint_path, args.output_path, args.includes_tokenizer)













'''simple docstring'''




import importlib.util
import weakref
from functools import partialmethod

from .dependency_versions_check import dep_version_check
from .utils import is_accelerate_available, is_torch_available, logging


if is_torch_available():
    import torch

lowerCamelCase_							      =      logging.get_logger(__name__)

def       __lowercase		(    ) ->		Optional[Any]:

      '''simple docstring'''


      return importlib.util.find_spec("deepspeed" ) is not None


if is_accelerate_available() and is_deepspeed_available():
    from accelerate.utils.deepspeed import HfDeepSpeedConfig as DeepSpeedConfig
else:
    # Inherits from a dummy `object` if accelerate is not available, so that python succeeds to import this file.
    # Deepspeed glue code will never inherit this dummy object as it checks if accelerate is available.
    from builtins import object as DeepSpeedConfig

class      _UpperCAmelCase    (  snake_case_		):




   """simple docstring"""

   def __init__(	self				:			Dict   ,   __UpperCAmelCase				:			Tuple	):





         '''simple docstring'''





         set_hf_deepspeed_config(self	)
         dep_version_check("accelerate"	)
         dep_version_check("deepspeed"	)
         super().__init__(__UpperCAmelCase	)





class      _UpperCAmelCase    (  snake_case_		):




   """simple docstring"""

   def __init__(	self				:			List[str]   ,   __UpperCAmelCase				:			List[str]	):





         '''simple docstring'''





         super().__init__(__UpperCAmelCase	)
         _A      =			None
         _A      =			[]

   def 		lowerCAmelCase				(	self				:			Optional[int]	):





         '''simple docstring'''





         if self._dtype is None:
               raise ValueError("trainer_config_process() wasn't called yet to tell dtype"	)
         return self._dtype

   def 		lowerCAmelCase				(	self				:			Dict   ,   __UpperCAmelCase				:			Dict	):





         '''simple docstring'''





         _A      =			self.get_value(__UpperCAmelCase	)
         if val is None:
               return False
         else:
               return val == "auto"

   def 		lowerCAmelCase				(	self				:			List[Any]   ,   __UpperCAmelCase				:			int   ,   __UpperCAmelCase				:			Optional[int]   ,   __UpperCAmelCase				:			Any=None   ,   __UpperCAmelCase				:			Union[str, Any]=True	):





         '''simple docstring'''





         _A  ,			_A      =			self.find_config_node(__UpperCAmelCase	)
         if config is None:
               return

         if config.get(__UpperCAmelCase	) == "auto":
               _A      =			hf_val
               return

         if not must_match:
               return

         _A      =			config.get(__UpperCAmelCase	)
         if ds_val is not None and ds_val != hf_val:
               self.mismatches.append(f'''- ds {ds_key_long}={ds_val} vs hf {hf_key}={hf_val}'''	)

   snake_case					       =		partialmethod(snake_case_							,					must_match=snake_case_		)

   def 		lowerCAmelCase				(	self				:			str   ,   __UpperCAmelCase				:			Any	):





         '''simple docstring'''





         _A      =			args.world_size * args.per_device_train_batch_size * args.gradient_accumulation_steps
         self.fill_match(
             "train_micro_batch_size_per_gpu"   ,   args.per_device_train_batch_size   ,   "per_device_train_batch_size"	)
         self.fill_match("gradient_accumulation_steps"   ,   args.gradient_accumulation_steps   ,   "gradient_accumulation_steps"	)
         self.fill_match("train_batch_size"   ,   __UpperCAmelCase   ,   "train_batch_size (calculated)"	)
         self.fill_match("gradient_clipping"   ,   args.max_grad_norm   ,   "max_grad_norm"	)

         self.fill_match("optimizer.params.lr"   ,   args.learning_rate   ,   "learning_rate"	)
         self.fill_match("optimizer.params.betas"   ,   [args.adam_betaa, args.adam_betaa]   ,   "adam_beta1+adam_beta2"	)
         self.fill_match("optimizer.params.eps"   ,   args.adam_epsilon   ,   "adam_epsilon"	)
         self.fill_match("optimizer.params.weight_decay"   ,   args.weight_decay   ,   "weight_decay"	)

         self.fill_only("scheduler.params.warmup_min_lr"   ,   0	)  # not a trainer arg
         self.fill_match("scheduler.params.warmup_max_lr"   ,   args.learning_rate   ,   "learning_rate"	)
         # total_num_steps - will get set in trainer_config_finalize

         # fp16
         if args.fpaa or args.fpaa_full_eval:
               _A      =			"apex" if args.fpaa_backend == "apex" else "amp"
         else:
               _A      =			None

         if args.save_on_each_node:
               # deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
               _A      =			self.config.get("checkpoint"   ,   {}	)
               _A      =			args.save_on_each_node

         # amp: similar to the pytorch native amp - it has a bunch of optional params but we won't set
         # any here unless the user did the work
         self.fill_match(
             "fp16.enabled"   ,   ((args.fpaa or args.fpaa_full_eval) and fpaa_backend == "amp")   ,   "fp16|fp16_full_eval+fp16_backend(amp)"   ,   )

         # apex: delegates amp work to apex (which needs to be available), but it cannot be used with any
         # ZeRO features
         self.fill_match("amp.enabled"   ,   fpaa_backend == "apex"   ,   "fp16+fp16_backend(apex)"	)
         self.fill_match("amp.opt_level"   ,   args.fpaa_opt_level   ,   "fp16_opt_level"	)

         self.fill_match("bf16.enabled"   ,   (args.bfaa or args.bfaa_full_eval)   ,   "bf16|bf16_full_eval"	)

         # deepspeed's default mode is fp16 unless there is a config that says differently
         if self.is_true("bf16.enabled"	):
               _A      =			torch.bfloataa
         elif self.is_false("fp16.enabled"	):
               _A      =			torch.floataa
         else:
               _A      =			torch.floataa

   def 		lowerCAmelCase				(	self				:			Optional[int]   ,   __UpperCAmelCase				:			Dict   ,   __UpperCAmelCase				:			str   ,   __UpperCAmelCase				:			Tuple	):





         '''simple docstring'''





         _A      =			[
             "zero_optimization.reduce_bucket_size",
             "zero_optimization.stage3_prefetch_bucket_size",
             "zero_optimization.stage3_param_persistence_threshold",
         ]
         _A      =			[x for x in hidden_size_based_keys if self.is_auto(__UpperCAmelCase	)]

         if len(__UpperCAmelCase	) > 0:
               if hasattr(model.config   ,   "hidden_size"	):
                     _A      =			model.config.hidden_size
               elif hasattr(model.config   ,   "hidden_sizes"	):
                     # if there are many hidden sizes pick the largest one
                     _A      =			max(model.config.hidden_sizes	)
               else:
                     raise ValueError(
                         "The model's config file has neither `hidden_size` nor `hidden_sizes` entry, "
                         "therefore it's not possible to automatically fill out the following `auto` entries "
                         f'''in the DeepSpeed config file: {hidden_size_auto_keys}. You can fix that by replacing '''
                         "`auto` values for these keys with an integer value of your choice."	)

               self.fill_only("zero_optimization.reduce_bucket_size"   ,   hidden_size * hidden_size	)
               if self.is_zeroa():
                     # automatically assign the optimal config values based on model config
                     self.fill_only("zero_optimization.stage3_prefetch_bucket_size"   ,   0.9 * hidden_size * hidden_size	)
                     self.fill_only("zero_optimization.stage3_param_persistence_threshold"   ,   10 * hidden_size	)

        # scheduler
         self.fill_match("scheduler.params.total_num_steps"   ,   __UpperCAmelCase   ,   "num_training_steps (calculated)"	)
         self.fill_match("scheduler.params.warmup_num_steps"   ,   args.get_warmup_steps(__UpperCAmelCase	)   ,   "warmup_steps"	)

         if len(self.mismatches	) > 0:
               _A      =			"\n".join(self.mismatches	)
               raise ValueError(
                   "Please correct the following DeepSpeed config values that mismatch TrainingArguments"
                   f''' values:\n{mismatches}\nThe easiest method is to set these DeepSpeed config values to \'auto\'.'''	)





lowerCamelCase_							      =      None

def       __lowercase		(    __lowercase ) ->		Optional[Any]:

      '''simple docstring'''


      global _hf_deepspeed_config_weak_ref
      # will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
      _A      =			weakref.ref(__lowercase )

def       __lowercase		(    ) ->		str:

      '''simple docstring'''


      global _hf_deepspeed_config_weak_ref
      _A      =			None

def       __lowercase		(    ) ->		Optional[int]:

      '''simple docstring'''


      if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
            return _hf_deepspeed_config_weak_ref().is_zeroa()
      else:
            return False

def       __lowercase		(    ) ->		str:

      '''simple docstring'''


      if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
            return _hf_deepspeed_config_weak_ref().config
      else:
            return None

def       __lowercase		(    __lowercase     ,						__lowercase     ,						__lowercase     ,						__lowercase     ,						__lowercase ) ->		int:

      '''simple docstring'''


      from accelerate.utils import DummyOptim, DummyScheduler

      _A      =			hf_deepspeed_config.config

      # Optimizer + Scheduler
      # Currently supported combos:
      # 1. DS scheduler + DS optimizer: Yes
      # 2. HF scheduler + HF optimizer: Yes
      # 3. DS scheduler + HF optimizer: Yes
      # 4. HF scheduler + DS optimizer: No
      #
      # Unless Offload is enabled in which case it's:
      # 1. DS scheduler + DS optimizer: Yes
      # 2. HF scheduler + HF optimizer: Mostly*
      # 3. DS scheduler + HF optimizer: Mostly*
      # 4. HF scheduler + DS optimizer: No
      #
      # Mostly*: All non-native DeepSpeed optimizers that have both CPU and GPU implementation should work (except LAMB)

      _A      =			None
      if "optimizer" in config:
            if args.adafactor:
                  raise ValueError(
                      "--adafactor was passed, but also found `optimizer` configured in the DeepSpeed config. "
                      "Only one optimizer can be configured." )
            _A      =			DummyOptim(params=__lowercase )
      else:
            if hf_deepspeed_config.is_offload():
                  logger.info(
                      "Detected ZeRO Offload and non-DeepSpeed optimizers: This combination should work as long as the"
                      " custom optimizer has both CPU and GPU implementation (except LAMB)" )

            # ds supports Adam, OneBitAdam, and Lamb optimizers and can import other optimizers from torch.
            # But trainer uses AdamW by default.
            _A      =			trainer.create_optimizer()
            # To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
            _A      =			True

      _A      =			None
      if "scheduler" in config:
            _A      =			DummyScheduler(__lowercase )
      else:
            if isinstance(__lowercase     ,						__lowercase ):
                  raise ValueError(
                      "Found `optimizer` configured in the DeepSpeed config, but no `scheduler`. "
                      "Please configure a scheduler in the DeepSpeed config." )
            _A      =			trainer.create_scheduler(num_training_steps=__lowercase     ,						optimizer=__lowercase )

      return optimizer, lr_scheduler

def       __lowercase		(    __lowercase     ,						__lowercase     ,						__lowercase=False ) ->		Tuple:

      '''simple docstring'''


      from deepspeed.utils import logger as ds_logger

      _A      =			trainer.model
      _A      =			trainer.args

      _A      =			trainer.accelerator.state.deepspeed_plugin.hf_ds_config

      # resume config update - some bits like `model` and `num_training_steps` only become available during train
      hf_deepspeed_config.trainer_config_finalize(__lowercase     ,						__lowercase     ,						__lowercase )

      # set the Deepspeed log level consistent with the Trainer
      ds_logger.setLevel(args.get_process_log_level() )

      if inference:
            # only Z3 makes sense for the inference
            if not hf_deepspeed_config.is_zeroa():
                  raise ValueError("ZeRO inference only makes sense with ZeRO Stage 3 - please adjust your config" )

            # in case the training config is re-used for inference
            hf_deepspeed_config.del_config_sub_tree("optimizer" )
            hf_deepspeed_config.del_config_sub_tree("lr_scheduler" )
            _A  ,			_A      =			None, None
            _A      =			None
      else:
            _A      =			None  # important for when deepspeed_init is used as re-init
            _A      =			list(filter(lambda __lowercase : p.requires_grad     ,						model.parameters() ) )
            _A  ,			_A      =			deepspeed_optim_sched(
                __lowercase     ,						__lowercase     ,						__lowercase     ,						__lowercase     ,						__lowercase )

      # keep for quick debug:
      # from pprint import pprint; pprint(config)

      return optimizer, lr_scheduler


def       __lowercase		(    __lowercase     ,						__lowercase ) ->		Union[str, Any]:

      '''simple docstring'''


      import glob

      _A      =			sorted(glob.glob(F'''{checkpoint_path}/global_step*''' ) )

      if len(__lowercase ) > 0:
            logger.info(F'''Attempting to resume from {checkpoint_path}''' )
            # this magically updates self.optimizer and self.lr_scheduler
            _A  ,			_A      =			deepspeed_engine.load_checkpoint(
                __lowercase     ,						load_optimizer_states=__lowercase     ,						load_lr_scheduler_states=__lowercase )
            if load_path is None:
                  raise ValueError(F'''[deepspeed] failed to resume from checkpoint {checkpoint_path}''' )
      else:
            raise ValueError(F'''Can\'t find a valid checkpoint at {checkpoint_path}''' )





