


import os
from pickle import UnpicklingError
from typing import Dict, Tuple

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict

import transformers

from .utils import logging


__lowerCamelCase        =     logging.get_logger(__name__)



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase=False ):
     try:
          import torch  # noqa: F401
     except ImportError:
          logger.error(
              """Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see"""
              """ https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"""
              """ instructions.""" )
          raise

     if not is_sharded:
          a_    :     Optional[int]										=    os.path.abspath(__UpperCamelCase )
          logger.info(F'''Loading PyTorch weights from {pt_path}''' )

          a_    :     Dict										=    torch.load(__UpperCamelCase  ,  map_location="""cpu""" )
          logger.info(F'''PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values() ):,} parameters.''' )

          a_    :     Dict										=    convert_pytorch_state_dict_to_flax(__UpperCamelCase  ,  __UpperCamelCase )
     else:
          # model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
          a_    :     List[Any]										=    convert_pytorch_sharded_state_dict_to_flax(__UpperCamelCase  ,  __UpperCamelCase )
     return flax_state_dict



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  ):

     def is_key_or_prefix_key_in_dict(__UpperCamelCase ) -> bool:
          return len(set(__UpperCamelCase ) & {key, (model_prefix,) + key} ) > 0

     # layer norm
     a_    :     Optional[int]										=    pt_tuple_key[:-1] + ("""scale""",)
     if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(__UpperCamelCase ):
          return renamed_pt_tuple_key, pt_tensor

     # batch norm layer mean
     a_    :     int										=    pt_tuple_key[:-1] + ("""mean""",)
     if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(__UpperCamelCase ):
          return renamed_pt_tuple_key, pt_tensor

     # batch norm layer var
     a_    :     Optional[int]										=    pt_tuple_key[:-1] + ("""var""",)
     if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(__UpperCamelCase ):
          return renamed_pt_tuple_key, pt_tensor

     # embedding
     a_    :     Tuple										=    pt_tuple_key[:-1] + ("""embedding""",)
     if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(__UpperCamelCase ):
          return renamed_pt_tuple_key, pt_tensor

     # conv layer
     a_    :     Optional[Any]										=    pt_tuple_key[:-1] + ("""kernel""",)
     if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(__UpperCamelCase ):
          a_    :     Dict										=    pt_tensor.transpose(2  ,  3  ,  1  ,  0 )
          return renamed_pt_tuple_key, pt_tensor

     # linear layer
     a_    :     Any										=    pt_tuple_key[:-1] + ("""kernel""",)
     if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(__UpperCamelCase ):
          a_    :     List[Any]										=    pt_tensor.T
          return renamed_pt_tuple_key, pt_tensor

     # old PyTorch layer norm weight
     a_    :     Optional[Any]										=    pt_tuple_key[:-1] + ("""weight""",)
     if pt_tuple_key[-1] == "gamma":
          return renamed_pt_tuple_key, pt_tensor

     # old PyTorch layer norm bias
     a_    :     int										=    pt_tuple_key[:-1] + ("""bias""",)
     if pt_tuple_key[-1] == "beta":
          return renamed_pt_tuple_key, pt_tensor

     # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
     a_    :     Any										=    None
     if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
          a_    :     int										=    pt_tuple_key[-2] + """_g"""
     elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
          a_    :     Optional[Any]										=    pt_tuple_key[-2] + """_v"""
     if name is not None:
          a_    :     str										=    pt_tuple_key[:-3] + (name,)
          return renamed_pt_tuple_key, pt_tensor

     return pt_tuple_key, pt_tensor



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase ):
     # convert pytorch tensor to numpy
     a_    :     List[Any]										=    {k: v.numpy() for k, v in pt_state_dict.items()}

     a_    :     List[Any]										=    flax_model.base_model_prefix

     # use params dict if the model contains batch norm layers
     if "params" in flax_model.params:
          a_    :     int										=    flax_model.params["""params"""]
     else:
          a_    :     Optional[Any]										=    flax_model.params
     a_    :     str										=    flatten_dict(__UpperCamelCase )

     # add batch_stats keys,values to dict
     if "batch_stats" in flax_model.params:
          a_    :     str										=    flatten_dict(flax_model.params["""batch_stats"""] )
          random_flax_state_dict.update(__UpperCamelCase )

     a_    :     Any										=    {}

     a_    :     List[str]										=    (model_prefix not in flax_model_params) and (
         model_prefix in {k.split(""".""" )[0] for k in pt_state_dict.keys()}
     )
     a_    :     List[str]										=    (model_prefix in flax_model_params) and (
         model_prefix not in {k.split(""".""" )[0] for k in pt_state_dict.keys()}
     )

     # Need to change some parameters name to match Flax names
     for pt_key, pt_tensor in pt_state_dict.items():
          a_    :     Union[str, Any]										=    tuple(pt_key.split(""".""" ) )

          # remove base model prefix if necessary
          a_    :     Optional[Any]										=    pt_tuple_key[0] == model_prefix
          if load_model_with_head_into_base_model and has_base_model_prefix:
               a_    :     Union[str, Any]										=    pt_tuple_key[1:]

          # Correctly rename weight parameters
          a_			,			a_    :     str										=    rename_key_and_reshape_tensor(
              __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase )

          # add model prefix if necessary
          a_    :     Dict										=    (model_prefix,) + flax_key in random_flax_state_dict
          if load_base_model_into_model_with_head and require_base_model_prefix:
               a_    :     Tuple										=    (model_prefix,) + flax_key

          if flax_key in random_flax_state_dict:
               if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                    raise ValueError(
                        F'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
                        F'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.''' )

        # add batch stats if the model contains batchnorm layers
          if "batch_stats" in flax_model.params:
               if "mean" in flax_key[-1] or "var" in flax_key[-1]:
                    a_    :     List[Any]										=    jnp.asarray(__UpperCamelCase )
                    continue
               # remove num_batches_tracked key
               if "num_batches_tracked" in flax_key[-1]:
                    flax_state_dict.pop(__UpperCamelCase  ,  __UpperCamelCase )
                    continue

               # also add unexpected weight so that warning is thrown
               a_    :     Dict										=    jnp.asarray(__UpperCamelCase )

          else:
               # also add unexpected weight so that warning is thrown
               a_    :     Optional[Any]										=    jnp.asarray(__UpperCamelCase )

     return unflatten_dict(__UpperCamelCase )



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase ):
     import torch

     # Load the index
     a_    :     List[Any]										=    {}
     for shard_file in shard_filenames:
          # load using msgpack utils
          a_    :     List[Any]										=    torch.load(__UpperCamelCase )
          a_    :     int										=    {k: v.numpy() for k, v in pt_state_dict.items()}

          a_    :     Tuple										=    flax_model.base_model_prefix

          # use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
          if "batch_stats" in flax_model.params:
               a_    :     int										=    flax_model.params["""params"""]

               a_    :     str										=    flatten_dict(__UpperCamelCase )
               random_flax_state_dict.update(flatten_dict(flax_model.params["""batch_stats"""] ) )
          else:
               a_    :     Optional[int]										=    flax_model.params
               a_    :     int										=    flatten_dict(__UpperCamelCase )

          a_    :     List[Any]										=    (model_prefix not in flax_model_params) and (
              model_prefix in {k.split(""".""" )[0] for k in pt_state_dict.keys()}
          )
          a_    :     Union[str, Any]										=    (model_prefix in flax_model_params) and (
              model_prefix not in {k.split(""".""" )[0] for k in pt_state_dict.keys()}
          )
          # Need to change some parameters name to match Flax names
          for pt_key, pt_tensor in pt_state_dict.items():
               a_    :     List[Any]										=    tuple(pt_key.split(""".""" ) )

               # remove base model prefix if necessary
               a_    :     List[str]										=    pt_tuple_key[0] == model_prefix
               if load_model_with_head_into_base_model and has_base_model_prefix:
                    a_    :     Optional[int]										=    pt_tuple_key[1:]

               # Correctly rename weight parameters
               a_			,			a_    :     Any										=    rename_key_and_reshape_tensor(
                   __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase )
               # add model prefix if necessary
               a_    :     int										=    (model_prefix,) + flax_key in random_flax_state_dict
               if load_base_model_into_model_with_head and require_base_model_prefix:
                    a_    :     Union[str, Any]										=    (model_prefix,) + flax_key

               if flax_key in random_flax_state_dict:
                    if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                         raise ValueError(
                             F'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
                             F'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.''' )

            # add batch stats if the model contains batchnorm layers
               if "batch_stats" in flax_model.params:
                    if "mean" in flax_key[-1]:
                         a_    :     Optional[int]										=    jnp.asarray(__UpperCamelCase )
                         continue
                    if "var" in flax_key[-1]:
                         a_    :     Union[str, Any]										=    jnp.asarray(__UpperCamelCase )
                         continue
                    # remove num_batches_tracked key
                    if "num_batches_tracked" in flax_key[-1]:
                         flax_state_dict.pop(__UpperCamelCase  ,  __UpperCamelCase )
                         continue

                    # also add unexpected weight so that warning is thrown
                    a_    :     List[Any]										=    jnp.asarray(__UpperCamelCase )

               else:
                    # also add unexpected weight so that warning is thrown
                    a_    :     Tuple										=    jnp.asarray(__UpperCamelCase )
     return unflatten_dict(__UpperCamelCase )



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase ):
     a_    :     Tuple										=    os.path.abspath(__UpperCamelCase )
     logger.info(F'''Loading Flax weights from {flax_checkpoint_path}''' )

     # import correct flax class
     a_    :     int										=    getattr(__UpperCamelCase  ,  """Flax""" + model.__class__.__name__ )

     # load flax weight dict
     with open(__UpperCamelCase  ,  """rb""" ) as state_f:
          try:
               a_    :     Union[str, Any]										=    from_bytes(__UpperCamelCase  ,  state_f.read() )
          except UnpicklingError:
               raise EnvironmentError(F'''Unable to convert {flax_checkpoint_path} to Flax deserializable object. ''' )

     return load_flax_weights_in_pytorch_model(__UpperCamelCase  ,  __UpperCamelCase )



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase ):

     try:
          import torch  # noqa: F401
     except ImportError:
          logger.error(
              """Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see"""
              """ https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"""
              """ instructions.""" )
          raise

     # check if we have bf16 weights
     a_    :     List[str]										=    flatten_dict(jax.tree_util.tree_map(lambda __UpperCamelCase : x.dtype == jnp.bfloataa  ,  __UpperCamelCase ) ).values()
     if any(__UpperCamelCase ):
          # convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
          # and bf16 is not fully supported in PT yet.
          logger.warning(
              """Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` """
              """before loading those in PyTorch model.""" )
          a_    :     int										=    jax.tree_util.tree_map(
              lambda __UpperCamelCase : params.astype(np.floataa ) if params.dtype == jnp.bfloataa else params  ,  __UpperCamelCase )

     a_    :     List[str]										=    flatten_dict(__UpperCamelCase )
     a_    :     List[Any]										=    pt_model.state_dict()

     a_    :     Dict										=    (pt_model.base_model_prefix in flax_state) and (
         pt_model.base_model_prefix not in {k.split(""".""" )[0] for k in pt_model_dict.keys()}
     )
     a_    :     Union[str, Any]										=    (pt_model.base_model_prefix not in flax_state) and (
         pt_model.base_model_prefix in {k.split(""".""" )[0] for k in pt_model_dict.keys()}
     )

     # keep track of unexpected & missing keys
     a_    :     Optional[Any]										=    []
     a_    :     Any										=    set(pt_model_dict.keys() )

     for flax_key_tuple, flax_tensor in flax_state_dict.items():
          a_    :     Tuple										=    flax_key_tuple[0] == pt_model.base_model_prefix
          a_    :     Optional[int]										=    """.""".join((pt_model.base_model_prefix,) + flax_key_tuple ) in pt_model_dict

          # adapt flax_key to prepare for loading from/to base model only
          if load_model_with_head_into_base_model and has_base_model_prefix:
               a_    :     Optional[Any]										=    flax_key_tuple[1:]
          elif load_base_model_into_model_with_head and require_base_model_prefix:
               a_    :     Any										=    (pt_model.base_model_prefix,) + flax_key_tuple

          # rename flax weights to PyTorch format
          if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(__UpperCamelCase ) not in pt_model_dict:
               # conv layer
               a_    :     Dict										=    flax_key_tuple[:-1] + ("""weight""",)
               a_    :     Optional[int]										=    jnp.transpose(__UpperCamelCase  ,  (3, 2, 0, 1) )
          elif flax_key_tuple[-1] == "kernel" and ".".join(__UpperCamelCase ) not in pt_model_dict:
               # linear layer
               a_    :     List[str]										=    flax_key_tuple[:-1] + ("""weight""",)
               a_    :     List[Any]										=    flax_tensor.T
          elif flax_key_tuple[-1] in ["scale", "embedding"]:
               a_    :     Optional[Any]										=    flax_key_tuple[:-1] + ("""weight""",)

          # adding batch stats from flax batch norm to pt
          elif "mean" in flax_key_tuple[-1]:
               a_    :     Optional[int]										=    flax_key_tuple[:-1] + ("""running_mean""",)
          elif "var" in flax_key_tuple[-1]:
               a_    :     int										=    flax_key_tuple[:-1] + ("""running_var""",)

          if "batch_stats" in flax_state:
               a_    :     Optional[int]										=    """.""".join(flax_key_tuple[1:] )  # Remove the params/batch_stats header
          else:
               a_    :     int										=    """.""".join(__UpperCamelCase )

          # We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
          a_    :     Optional[Any]										=    {}
          # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
          for key in pt_model_dict:
               a_    :     Optional[int]										=    key.split(""".""" )
               a_    :     Optional[int]										=    None
               if key_components[-3::2] == ["parametrizations", "original0"]:
                    a_    :     List[str]										=    key_components[-2] + """_g"""
               elif key_components[-3::2] == ["parametrizations", "original1"]:
                    a_    :     Dict										=    key_components[-2] + """_v"""
               if name is not None:
                    a_    :     Optional[int]										=    key_components[:-3] + [name]
                    a_    :     str										=    """.""".join(__UpperCamelCase )
                    a_    :     List[str]										=    key

          if flax_key in special_pt_names:
               a_    :     Tuple										=    special_pt_names[flax_key]

          if flax_key in pt_model_dict:
               if flax_tensor.shape != pt_model_dict[flax_key].shape:
                    raise ValueError(
                        F'''Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '''
                        F'''to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.''' )
               else:
                    # add weight to pytorch dict
                    a_    :     Tuple										=    np.asarray(__UpperCamelCase ) if not isinstance(__UpperCamelCase  ,  np.ndarray ) else flax_tensor
                    a_    :     Union[str, Any]										=    torch.from_numpy(__UpperCamelCase )
                    # remove from missing keys
                    missing_keys.remove(__UpperCamelCase )
          else:
               # weight is not expected by PyTorch model
               unexpected_keys.append(__UpperCamelCase )

     pt_model.load_state_dict(__UpperCamelCase )

     # re-transform missing_keys to list
     a_    :     Union[str, Any]										=    list(__UpperCamelCase )

     if len(__UpperCamelCase ) > 0:
          logger.warning(
              """Some weights of the Flax model were not used when initializing the PyTorch model"""
              F''' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'''
              F''' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'''
              """ (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"""
              F''' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'''
              """ to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"""
              """ FlaxBertForSequenceClassification model).""" )
     else:
          logger.warning(F'''All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n''' )
     if len(__UpperCamelCase ) > 0:
          logger.warning(
              F'''Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'''
              F''' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'''
              """ use it for predictions and inference.""" )
     else:
          logger.warning(
              F'''All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n'''
              """If your task is similar to the task the model of the checkpoint was trained on, """
              F'''you can already use {pt_model.__class__.__name__} for predictions without further training.''' )

     return pt_model







import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


__lowerCamelCase        =     logging.get_logger(__name__)



def  _a			(						__UpperCamelCase=None  ,  __UpperCamelCase=None ):
     return field(default_factory=lambda: default  ,  metadata=__UpperCamelCase )


@dataclass
class   a__	:
       lowerCamelCase__:     List[str]							    =     list_field(
           default=[]						,       metadata={
               """help""": (
                   """Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"""
                   """ of all available models"""
               )
           }						,       )

       lowerCamelCase__:     List[int]							    =     list_field(
           default=[8]						,       metadata={"""help""": """List of batch sizes for which memory and time performance will be evaluated"""}    )

       lowerCamelCase__:     List[int]							    =     list_field(
           default=[8, 32, 128, 512]						,       metadata={"""help""": """List of sequence lengths for which memory and time performance will be evaluated"""}						,       )

       lowerCamelCase__:     bool							    =     field(
           default=lowerCAmelCase_						,       metadata={"""help""": """Whether to benchmark inference of model. Inference can be disabled via --no-inference."""}						,       )
       lowerCamelCase__:     bool							    =     field(
           default=lowerCAmelCase_						,       metadata={"""help""": """Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."""}						,       )
       lowerCamelCase__:     bool							    =     field(
           default=lowerCAmelCase_						,       metadata={"""help""": """Whether to run on available tpu devices. TPU can be disabled via --no-tpu."""}    )
       lowerCamelCase__:     bool							    =     field(default=lowerCAmelCase_						,       metadata={"""help""": """Use FP16 to accelerate inference."""}    )
       lowerCamelCase__:     bool							    =     field(default=lowerCAmelCase_						,       metadata={"""help""": """Benchmark training of model"""}    )
       lowerCamelCase__:     bool							    =     field(default=lowerCAmelCase_						,       metadata={"""help""": """Verbose memory tracing"""}    )
       lowerCamelCase__:     bool							    =     field(
           default=lowerCAmelCase_						,       metadata={"""help""": """Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."""}						,       )
       lowerCamelCase__:     bool							    =     field(
           default=lowerCAmelCase_						,       metadata={
               """help""": """Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"""
           }						,       )
       lowerCamelCase__:     bool							    =     field(default=lowerCAmelCase_						,       metadata={"""help""": """Trace memory line by line"""}    )
       lowerCamelCase__:     bool							    =     field(default=lowerCAmelCase_						,       metadata={"""help""": """Save result to a CSV file"""}    )
       lowerCamelCase__:     bool							    =     field(default=lowerCAmelCase_						,       metadata={"""help""": """Save all print statements in a log file"""}    )
       lowerCamelCase__:     bool							    =     field(default=lowerCAmelCase_						,       metadata={"""help""": """Whether to print environment information"""}    )
       lowerCamelCase__:     bool							    =     field(
           default=lowerCAmelCase_						,       metadata={
               """help""": (
                   """Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"""
                   """ multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"""
                   """ for debugging / testing and on TPU."""
               )
           }						,       )
       lowerCamelCase__:     str							    =     field(
           default=F"""inference_time_{round(time()    )}.csv"""						,       metadata={"""help""": """CSV filename used if saving time results to csv."""}						,       )
       lowerCamelCase__:     str							    =     field(
           default=F"""inference_memory_{round(time()    )}.csv"""						,       metadata={"""help""": """CSV filename used if saving memory results to csv."""}						,       )
       lowerCamelCase__:     str							    =     field(
           default=F"""train_time_{round(time()    )}.csv"""						,       metadata={"""help""": """CSV filename used if saving time results to csv for training."""}						,       )
       lowerCamelCase__:     str							    =     field(
           default=F"""train_memory_{round(time()    )}.csv"""						,       metadata={"""help""": """CSV filename used if saving memory results to csv for training."""}						,       )
       lowerCamelCase__:     str							    =     field(
           default=F"""env_info_{round(time()    )}.csv"""						,       metadata={"""help""": """CSV filename used if saving environment information."""}						,       )
       lowerCamelCase__:     str							    =     field(
           default=F"""log_{round(time()    )}.csv"""						,       metadata={"""help""": """Log filename used if print statements are saved in log."""}						,       )
       lowerCamelCase__:     int							    =     field(default=3						,       metadata={"""help""": """Times an experiment will be run."""}    )
       lowerCamelCase__:     bool							    =     field(
           default=lowerCAmelCase_						,       metadata={
               """help""": (
                   """Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"""
                   """ model weights."""
               )
           }						,       )

       def 						UpperCAmelCase(   self      :     Any    ):
            warnings.warn(
                F'''The class {self.__class__} is deprecated. Hugging Face Benchmarking utils'''
                """ are deprecated in general and it is advised to use external Benchmarking libraries """
                """ to benchmark Transformer models."""      ,							lowerCamelCase_      ,							)

       def 						UpperCAmelCase(   self      :     Union[str, Any]    ):
            return json.dumps(dataclasses.asdict(self    )      ,							indent=2    )

       @property
       def 						UpperCAmelCase(   self      :     List[str]    ):
            if len(self.models    ) <= 0:
                 raise ValueError(
                     """Please make sure you provide at least one model name / model identifier, *e.g.* `--models"""
                     """ bert-base-cased` or `args.models = ['bert-base-cased']."""    )
            return self.models

       @property
       def 						UpperCAmelCase(   self      :     Union[str, Any]    ):
            if not self.multi_process:
                 return False
            elif self.is_tpu:
                 logger.info("""Multiprocessing is currently not possible on TPU."""    )
                 return False
            else:
                 return True



