





import inspect
import os
import re

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_snake_case  :    Optional[int]						    =  'src/transformers'


# This is to make sure the transformers module imported is the one in the repo.
_snake_case  :    List[str]						    =  direct_transformers_import(PATH_TO_TRANSFORMERS)

_snake_case  :    str						    =  transformers.models.auto.configuration_auto.CONFIG_MAPPING

_snake_case  :    str						    =  {
    # used to compute the property `self.chunk_length`
    'EncodecConfig': ['overlap'],
    # used as `self.bert_model = BertModel(config, ...)`
    'DPRConfig': True,
    # not used in modeling files, but it's an important information
    'FSMTConfig': ['langs'],
    # used internally in the configuration class file
    'GPTNeoConfig': ['attention_types'],
    # used internally in the configuration class file
    'EsmConfig': ['is_folding_model'],
    # used during training (despite we don't have training script for these models yet)
    'Mask2FormerConfig': ['ignore_value'],
    # `ignore_value` used during training (despite we don't have training script for these models yet)
    # `norm` used in conversion script (despite not using in the modeling file)
    'OneFormerConfig': ['ignore_value', 'norm'],
    # used during preprocessing and collation, see `collating_graphormer.py`
    'GraphormerConfig': ['spatial_pos_max'],
    # used internally in the configuration class file
    'T5Config': ['feed_forward_proj'],
    # used internally in the configuration class file
    # `tokenizer_class` get default value `T5Tokenizer` intentionally
    'MT5Config': ['feed_forward_proj', 'tokenizer_class'],
    'UMT5Config': ['feed_forward_proj', 'tokenizer_class'],
    # used internally in the configuration class file
    'LongT5Config': ['feed_forward_proj'],
    # used internally in the configuration class file
    'SwitchTransformersConfig': ['feed_forward_proj'],
    # having default values other than `1e-5` - we can't fix them without breaking
    'BioGptConfig': ['layer_norm_eps'],
    # having default values other than `1e-5` - we can't fix them without breaking
    'GLPNConfig': ['layer_norm_eps'],
    # having default values other than `1e-5` - we can't fix them without breaking
    'SegformerConfig': ['layer_norm_eps'],
    # having default values other than `1e-5` - we can't fix them without breaking
    'CvtConfig': ['layer_norm_eps'],
    # having default values other than `1e-5` - we can't fix them without breaking
    'PerceiverConfig': ['layer_norm_eps'],
    # used internally to calculate the feature size
    'InformerConfig': ['num_static_real_features', 'num_time_features'],
    # used internally to calculate the feature size
    'TimeSeriesTransformerConfig': ['num_static_real_features', 'num_time_features'],
    # used internally to calculate the feature size
    'AutoformerConfig': ['num_static_real_features', 'num_time_features'],
    # used internally to calculate `mlp_dim`
    'SamVisionConfig': ['mlp_ratio'],
    # For (head) training, but so far not implemented
    'ClapAudioConfig': ['num_classes'],
    # Not used, but providing useful information to users
    'SpeechT5HifiGanConfig': ['sampling_rate'],
}


# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
    {
        'CLIPSegConfig': True,
        'DeformableDetrConfig': True,
        'DetaConfig': True,
        'DinatConfig': True,
        'DonutSwinConfig': True,
        'EfficientFormerConfig': True,
        'FSMTConfig': True,
        'JukeboxConfig': True,
        'LayoutLMv2Config': True,
        'MaskFormerSwinConfig': True,
        'MT5Config': True,
        'NatConfig': True,
        'OneFormerConfig': True,
        'PerceiverConfig': True,
        'RagConfig': True,
        'SpeechT5Config': True,
        'SwinConfig': True,
        'Swin2SRConfig': True,
        'Swinv2Config': True,
        'SwitchTransformersConfig': True,
        'TableTransformerConfig': True,
        'TapasConfig': True,
        'TransfoXLConfig': True,
        'UniSpeechConfig': True,
        'UniSpeechSatConfig': True,
        'WavLMConfig': True,
        'WhisperConfig': True,
        # TODO: @Arthur (for `alignment_head` and `alignment_layer`)
        'JukeboxPriorConfig': True,
        # TODO: @Younes (for `is_decoder`)
        'Pix2StructTextConfig': True,
    }
)

def   a_    ( lowerCAmelCase_      :			int,	lowerCAmelCase_      :			Any,	lowerCAmelCase_      :			int,	lowerCAmelCase_      :			Optional[int]	):
   __lowerCAmelCase								=	False
   for attribute in attributes:
      for modeling_source in source_strings:
         # check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
         if (
             F"""config.{attribute}""" in modeling_source
             or F"""getattr(config, \"{attribute}\"""" in modeling_source
             or F"""getattr(self.config, \"{attribute}\"""" in modeling_source
         ):
            __lowerCAmelCase								=	True
         # Deal with multi-line cases
         elif (
             re.search(
                 RF"""getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*\"{attribute}\"""",	__a,	)
             is not None
         ):
            __lowerCAmelCase								=	True
         # `SequenceSummary` is called with `SequenceSummary(config)`
         elif attribute in [
             "summary_type",
             "summary_use_proj",
             "summary_activation",
             "summary_last_dropout",
             "summary_proj_to_labels",
             "summary_first_dropout",
         ]:
            if "SequenceSummary" in modeling_source:
               __lowerCAmelCase								=	True
         if attribute_used:
            break
      if attribute_used:
         break

    # common and important attributes, even if they do not always appear in the modeling files
   __lowerCAmelCase								=	[
       'bos_index',
       'eos_index',
       'pad_index',
       'unk_index',
       'mask_index',
       'image_size',
       'use_cache',
       'out_features',
       'out_indices',
   ]
   __lowerCAmelCase								=	['encoder_no_repeat_ngram_size']

   # Special cases to be allowed
   __lowerCAmelCase								=	True
   if not attribute_used:
      __lowerCAmelCase								=	False
      for attribute in attributes:
         # Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
         if attribute in ["is_encoder_decoder"] and default_value is True:
            __lowerCAmelCase								=	True
         elif attribute in ["tie_word_embeddings"] and default_value is False:
            __lowerCAmelCase								=	True

         # Allow cases without checking the default value in the configuration class
         elif attribute in attributes_to_allow + attributes_used_in_generation:
            __lowerCAmelCase								=	True
         elif attribute.endswith('_token_id'	):
            __lowerCAmelCase								=	True

         # configuration class specific cases
         if not case_allowed:
            __lowerCAmelCase								=	SPECIAL_CASES_TO_ALLOW.get(config_class.__name__,	[]	)
            __lowerCAmelCase								=	allowed_cases is True or attribute in allowed_cases

   return attribute_used or case_allowed

def   a_    ( lowerCAmelCase_      :			str	):
   __lowerCAmelCase								=	dict(inspect.signature(config_class.__init__	).parameters	)
   __lowerCAmelCase								=	[x for x in list(signature.keys()	) if x not in ['self', 'kwargs']]
   __lowerCAmelCase								=	[signature[param].default for param in parameter_names]

   # If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
   # as one variant is used, the test should pass
   __lowerCAmelCase								=	{}
   if len(config_class.attribute_map	) > 0:
      __lowerCAmelCase								=	{v: k for k, v in config_class.attribute_map.items()}

   # Get the path to modeling source files
   __lowerCAmelCase								=	inspect.getsourcefile(__a	)
   __lowerCAmelCase								=	os.path.dirname(__a	)
   # Let's check against all frameworks: as long as one framework uses an attribute, we are good.
   __lowerCAmelCase								=	[os.path.join(__a,	__a	) for fn in os.listdir(__a	) if fn.startswith('modeling_'	)]

   # Get the source code strings
   __lowerCAmelCase								=	[]
   for path in modeling_paths:
      if os.path.isfile(__a	):
         with open(__a	) as fp:
            modeling_sources.append(fp.read()	)

   __lowerCAmelCase								=	[]
   for config_param, default_value in zip(__a,	__a	):
      # `attributes` here is all the variant names for `config_param`
      __lowerCAmelCase								=	[config_param]
      # some configuration classes have non-empty `attribute_map`, and both names could be used in the
      # corresponding modeling files. As long as one of them appears, it is fine.
      if config_param in reversed_attribute_map:
         attributes.append(reversed_attribute_map[config_param]	)

      if not check_attribute_being_used(__a,	__a,	__a,	__a	):
         unused_attributes.append(attributes[0]	)

   return sorted(__a	)

def   a_    ( ):
   __lowerCAmelCase								=	{}
   for _config_class in list(CONFIG_MAPPING.values()	):
      # Skip deprecated models
      if "models.deprecated" in _config_class.__module__:
         continue
      # Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
      __lowerCAmelCase								=	[
          cls
          for name, cls in inspect.getmembers(
              inspect.getmodule(_config_class	),	lambda lowerCAmelCase_	: inspect.isclass(__a	)
              and issubclass(__a,	__a	)
              and inspect.getmodule(__a	) == inspect.getmodule(_config_class	),	)
      ]
      for config_class in config_classes_in_module:
         __lowerCAmelCase								=	check_config_attributes_being_used(__a	)
         if len(__a	) > 0:
            __lowerCAmelCase								=	unused_attributes

   if len(__a	) > 0:
      __lowerCAmelCase								=	'The following configuration classes contain unused attributes in the corresponding modeling files:\n'
      for name, attributes in configs_with_unused_attributes.items():
         error += F"""{name}: {attributes}\n"""

      raise ValueError(__a	)


if __name__ == "__main__":
     check_config_attributes()



'''simple docstring'''







from typing import Any, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler

from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
    Adafactor,
    AdamW,
    get_constant_schedule,
    get_constant_schedule_with_warmup,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available


if is_fairscale_available():
  from fairscale.optim import OSS


__lowerCAmelCase         =    logging.get_logger(__name__)

__lowerCAmelCase         =    {
    """linear""": get_linear_schedule_with_warmup,
    """cosine""": get_cosine_schedule_with_warmup,
    """cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
    """polynomial""": get_polynomial_decay_schedule_with_warmup,
    """constant""": get_constant_schedule,
    """constant_w_warmup""": get_constant_schedule_with_warmup,
}







class UpperCAmelCase__	( lowercase__	):





  """simple docstring"""

  def __init__(		self      :     Optional[int]							,_a      :     Optional[Any]=None							,_a      :     Dict=None							,*_a      :     int							,**_a      :     str       ):


     '''simple docstring'''






     super().__init__(*_a							,**_a       )

     if config is None:
        assert isinstance(self.model							,_a       ), (
            "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
            F""" {self.model.__class__}"""
        )
        _a				:	List[Any]			=   self.model.config
     else:
        _a				:	Optional[int]			=   config

     _a				:	List[str]			=   data_args
     _a				:	List[Any]			=   self.config.tgt_vocab_size if isinstance(self.config							,_a       ) else self.config.vocab_size

     if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
        assert self.config.pad_token_id is not None, (
            "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
            " calculation or doing label smoothing."
        )

     if self.config.pad_token_id is None and self.config.eos_token_id is not None:
        logger.warning(
            F"""The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for"""
            ' padding..'       )

     if self.args.label_smoothing == 0:
        _a				:	List[str]			=   torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id       )
     else:
        # dynamically import label_smoothed_nll_loss
        from utils import label_smoothed_nll_loss

        _a				:	Tuple			=   label_smoothed_nll_loss


  def   __lowercase		(		self      :     List[str]							,_a      :     int       ):


     '''simple docstring'''






     if self.optimizer is None:
        _a				:	Union[str, Any]			=   ['bias', 'LayerNorm.weight']
        _a				:	Tuple			=   [
            {
                'params': [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay       )],
                'weight_decay': self.args.weight_decay,
            },
            {
                'params': [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay       )],
                'weight_decay': 0.0,
            },
        ]
        _a				:	Optional[int]			=   Adafactor if self.args.adafactor else AdamW
        if self.args.adafactor:
           _a				:	Any			=   Adafactor
           _a				:	Dict			=   {'scale_parameter': False, 'relative_step': False}
        else:
           _a				:	Union[str, Any]			=   AdamW
           _a				:	str			=   {
               'betas': (self.args.adam_betaa, self.args.adam_betaa),
               'eps': self.args.adam_epsilon,
           }
        _a				:	Union[str, Any]			=   self.args.learning_rate
        if self.sharded_ddp:
           _a				:	str			=   OSS(
               params=_a							,optim=_a							,**_a							,)
        else:
           _a				:	Tuple			=   optimizer_cls(_a							,**_a       )

     if self.lr_scheduler is None:
        _a				:	List[Any]			=   self._get_lr_scheduler(_a       )
     else:  # ignoring --lr_scheduler
        logger.warning('scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.'       )


  def   __lowercase		(		self      :     List[Any]							,_a      :     List[Any]       ):


     '''simple docstring'''






     _a				:	str			=   arg_to_scheduler[self.args.lr_scheduler]
     if self.args.lr_scheduler == "constant":
        _a				:	int			=   schedule_func(self.optimizer       )
     elif self.args.lr_scheduler == "constant_w_warmup":
        _a				:	List[str]			=   schedule_func(self.optimizer							,num_warmup_steps=self.args.warmup_steps       )
     else:
        _a				:	Optional[int]			=   schedule_func(
            self.optimizer							,num_warmup_steps=self.args.warmup_steps							,num_training_steps=_a       )
     return scheduler


  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     if isinstance(self.train_dataset							,torch.utils.data.IterableDataset       ):
        return None
     elif is_torch_tpu_available():
        return get_tpu_sampler(self.train_dataset       )
     else:
        if self.args.sortish_sampler:
           self.train_dataset.make_sortish_sampler(
               self.args.per_device_train_batch_size							,distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED)							,)

        return (
            RandomSampler(self.train_dataset       )
            if self.args.local_rank == -1
            else DistributedSampler(self.train_dataset       )
        )


  def   __lowercase		(		self      :     Dict							,_a      :     Dict							,_a      :     Any							,_a      :     Dict       ):


     '''simple docstring'''






     if self.args.label_smoothing == 0:
        if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
           # force training to ignore pad token
           _a				:	List[Any]			=   model(**_a							,use_cache=_a       )[0]
           _a				:	Union[str, Any]			=   self.loss_fn(logits.view(-1							,logits.shape[-1]       )							,labels.view(-1       )       )
        else:
           # compute usual loss via models
           _a,  _a				:	Union[str, Any]			=   model(**_a							,labels=_a							,use_cache=_a       )[:2]
     else:
        # compute label smoothed loss
        _a				:	List[Any]			=   model(**_a							,use_cache=_a       )[0]
        _a				:	Any			=   torch.nn.functional.log_softmax(_a							,dim=-1       )
        _a,  _a				:	List[str]			=   self.loss_fn(_a							,_a							,self.args.label_smoothing							,ignore_index=self.config.pad_token_id       )
     return loss, logits


  def   __lowercase		(		self      :     Optional[int]							,_a      :     Union[str, Any]							,_a      :     List[Any]       ):


     '''simple docstring'''






     _a				:	Optional[int]			=   inputs.pop('labels'       )
     _a,  _a				:	int			=   self._compute_loss(_a							,_a							,_a       )
     return loss


  def   __lowercase		(		self      :     Optional[Any]							,_a      :     nn.Module							,_a      :     Dict[str, Union[torch.Tensor, Any]]							,_a      :     bool							,_a      :     Optional[List[str]] = None							,):


     '''simple docstring'''






     _a				:	int			=   self._prepare_inputs(_a       )

     _a				:	Any			=   {
         'max_length': self.data_args.val_max_target_length
         if self.data_args is not None
         else self.config.max_length,
         'num_beams': self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
     }

     if self.args.predict_with_generate and not self.args.prediction_loss_only:
        _a				:	int			=   self.model.generate(
            inputs['input_ids']							,attention_mask=inputs['attention_mask']							,**_a							,)
        # in case the batch is shorter than max length, the output should be padded
        if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
           _a				:	int			=   self._pad_tensors_to_max_len(_a							,gen_kwargs['max_length']       )

     _a				:	Union[str, Any]			=   inputs.pop('labels'       )
     with torch.no_grad():
        # compute loss on predict data
        _a,  _a				:	Optional[int]			=   self._compute_loss(_a							,_a							,_a       )

     _a				:	Optional[Any]			=   loss.mean().detach()
     if self.args.prediction_loss_only:
        return (loss, None, None)

     _a				:	Optional[Any]			=   generated_tokens if self.args.predict_with_generate else logits

     if labels.shape[-1] < gen_kwargs["max_length"]:
        _a				:	Dict			=   self._pad_tensors_to_max_len(_a							,gen_kwargs['max_length']       )

     return (loss, logits, labels)


  def   __lowercase		(		self      :     str							,_a      :     Tuple							,_a      :     Tuple       ):


     '''simple docstring'''






     _a				:	List[Any]			=   self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id

     if pad_token_id is None:
        raise ValueError(
            'Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be'
            F""" padded to `max_length`={max_length}"""       )

     _a				:	int			=   pad_token_id * torch.ones(
         (tensor.shape[0], max_length)							,dtype=tensor.dtype							,device=tensor.device       )
     _a				:	Union[str, Any]			=   tensor
     return padded_tensor



