



'''simple docstring'''





import argparse
import inspect
import logging
from typing import Tuple

import numpy as np
import torch

from transformers import (
    AutoTokenizer,
    BloomForCausalLM,
    BloomTokenizerFast,
    CTRLLMHeadModel,
    CTRLTokenizer,
    GenerationMixin,
    GPTaLMHeadModel,
    GPTaTokenizer,
    GPTJForCausalLM,
    LlamaForCausalLM,
    LlamaTokenizer,
    OpenAIGPTLMHeadModel,
    OpenAIGPTTokenizer,
    OPTForCausalLM,
    TransfoXLLMHeadModel,
    TransfoXLTokenizer,
    XLMTokenizer,
    XLMWithLMHeadModel,
    XLNetLMHeadModel,
    XLNetTokenizer,
)
from transformers.modeling_outputs import CausalLMOutputWithPast


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
    datefmt="%m/%d/%Y %H:%M:%S",
    level=logging.INFO,
)
lowercase_			=  logging.getLogger(__name__)

lowercase_			=  int(10_000)  # Hardcoded max length to avoid infinite loop

lowercase_			=  {
    "gpt2": (GPTaLMHeadModel, GPTaTokenizer),
    "ctrl": (CTRLLMHeadModel, CTRLTokenizer),
    "openai-gpt": (OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),
    "xlnet": (XLNetLMHeadModel, XLNetTokenizer),
    "transfo-xl": (TransfoXLLMHeadModel, TransfoXLTokenizer),
    "xlm": (XLMWithLMHeadModel, XLMTokenizer),
    "gptj": (GPTJForCausalLM, AutoTokenizer),
    "bloom": (BloomForCausalLM, BloomTokenizerFast),
    "llama": (LlamaForCausalLM, LlamaTokenizer),
    "opt": (OPTForCausalLM, GPTaTokenizer),
}

# Padding text to help Transformer-XL and XLNet with short prompts as proposed by Aman Rusia
# in https://github.com/rusiaaman/XLNet-gen#methodology
# and https://medium.com/@amanrusia/xlnet-speaks-comparison-to-gpt-2-ea1a4e9ba39e
lowercase_			=  "In 1991, the remains of Russian Tsar Nicholas II and his family\n(except for Alexei and Maria) are discovered.\nThe voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the\nremainder of the story. 1883 Western Siberia,\na young Grigori Rasputin is asked by his father and a group of men to perform magic.\nRasputin has a vision and denounces one of the men as a horse thief. Although his\nfather initially slaps him for making such an accusation, Rasputin watches as the\nman is chased outside and beaten. Twenty years later, Rasputin sees a vision of\nthe Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,\nwith people, even a bishop, begging for his blessing. <eod> </s> <eos>"




def lowerCAmelCase  (__A):



     """simple docstring"""



     np.random.seed(args.seed)
     torch.manual_seed(args.seed)
     if args.n_gpu > 0:
          torch.cuda.manual_seed_all(args.seed)




def lowerCAmelCase  (__A  ,     __A  ,     __A  ,     __A):



     """simple docstring"""



     if args.temperature > 0.7:
          logger.info('''CTRL typically works better with lower temperatures (and lower top_k).''')

     _a     =  tokenizer.encode(__A  ,     add_special_tokens=__A)
     if not any(encoded_prompt[0] == x for x in tokenizer.control_codes.values()):
          logger.info('''WARNING! You are not starting your generation from a control code so you won\'t get good results''')
     return prompt_text




def lowerCAmelCase  (__A  ,     __A  ,     __A  ,     __A):



     """simple docstring"""



     _a     =  hasattr(model.config  ,     '''use_lang_emb''') and model.config.use_lang_emb
     if hasattr(model.config  ,     '''lang2id''') and use_lang_emb:
          _a     =  model.config.langaid.keys()
          if args.xlm_language in available_languages:
               _a     =  args.xlm_language
          else:
               _a     =  None
               while language not in available_languages:
                    _a     =  input('''Using XLM. Select language in ''' + str(list(__A)) + ''' >>> ''')

          _a     =  model.config.langaid[language]
          # kwargs["language"] = tokenizer.lang2id[language]

     # TODO fix mask_token_id setup when configurations will be synchronized between models and tokenizers
     # XLM masked-language modeling (MLM) models need masked token
     # is_xlm_mlm = "mlm" in args.model_name_or_path
     # if is_xlm_mlm:
     #     kwargs["mask_token_id"] = tokenizer.mask_token_id

     return prompt_text




def lowerCAmelCase  (__A  ,     __A  ,     __A  ,     __A):



     """simple docstring"""



     _a     =  args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
     _a     =  prefix + prompt_text
     return prompt_text




def lowerCAmelCase  (__A  ,     __A  ,     __A  ,     __A):



     """simple docstring"""



     _a     =  args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
     _a     =  prefix + prompt_text
     return prompt_text


lowercase_			=  {
    "ctrl": prepare_ctrl_input,
    "xlm": prepare_xlm_input,
    "xlnet": prepare_xlnet_input,
    "transfo-xl": prepare_transfoxl_input,
}




def lowerCAmelCase  (__A  ,     __A):



     """simple docstring"""



     if length < 0 and max_sequence_length > 0:
          _a     =  max_sequence_length
     elif 0 < max_sequence_length < length:
          _a     =  max_sequence_length  # No generation bigger than model size
     elif length < 0:
          _a     =  MAX_LENGTH  # avoid infinite loop
     return length




def lowerCAmelCase  (__A):



     """simple docstring"""



     _a     =  None
     if hasattr(__A  ,     '''hidden_size'''):
          _a     =  model_config.hidden_size
     elif hasattr(__A  ,     '''n_embed'''):
          _a     =  model_config.n_embed
     elif hasattr(__A  ,     '''n_embd'''):
          _a     =  model_config.n_embd

     _a     =  None
     if hasattr(__A  ,     '''num_attention_heads'''):
          _a     =  model_config.num_attention_heads
     elif hasattr(__A  ,     '''n_head'''):
          _a     =  model_config.n_head

     if embedding_size is None or num_head is None or num_head == 0:
          raise ValueError('''Check the model config''')

     _a     =  int(embedding_size / num_head)
     if hasattr(__A  ,     '''n_layer'''):
          _a     =  model_config.n_layer
     elif hasattr(__A  ,     '''num_hidden_layers'''):
          _a     =  model_config.num_hidden_layers
     else:
          raise ValueError('''Number of hidden layers couldn\'t be determined from the model config''')

     return num_layer, num_head, num_embedding_size_per_head




def lowerCAmelCase  (__A  ,     __A  ,     __A):



     """simple docstring"""



     _a  ,   _a  ,   _a     =  sparse_model_config(model.config)
     if model.config.model_type == "bloom":
          _a     =  tuple(
              (
                  torch.empty(int(num_attention_heads * batch_size)  ,     __A  ,     __A)
                  .to(model.dtype)
                  .to(model.device),
                  torch.empty(int(num_attention_heads * batch_size)  ,     __A  ,     __A)
                  .to(model.dtype)
                  .to(model.device),
              )
              for _ in range(__A))
     else:
          _a     =  tuple(
              (
                  torch.empty(__A  ,     __A  ,     __A  ,     __A)
                  .to(model.dtype)
                  .to(model.device),
                  torch.empty(__A  ,     __A  ,     __A  ,     __A)
                  .to(model.dtype)
                  .to(model.device),
              )
              for _ in range(__A))
     return past_key_values




def lowerCAmelCase  (__A  ,     __A  ,     __A):



     """simple docstring"""



     _a     =  len(__A)
     _a     =  tokenizer.batch_encode_plus(__A  ,     return_tensors='''pt''')
     _a     =  dummy_input.to(model.device)
     if model.config.use_cache:
          _a     =  generate_past_key_values(__A  ,     __A  ,     1)
     _a     =  torch.cat(
         [
             torch.zeros(dummy_input['''attention_mask'''].shape[0]  ,     1)
             .to(dummy_input['''attention_mask'''].dtype)
             .to(model.device),
             dummy_input['''attention_mask'''],
         ]  ,     -1  ,     )
     return dummy_input






class __A    (  A						):



 '''simple docstring'''







 __lowerCamelCase   :	Tuple										=	('_optimized', '_default')



 def __init__(self			,       A			,       A	)   ->					Optional[int]:


      """simple docstring"""



      _a     =  optimized
      _a     =  default



 def __call__(self			,       *A			,       **A	)   ->					int:


      """simple docstring"""



      if kwargs["past_key_values"] is None and self._default.config.use_cache:
           _a     =  generate_past_key_values(self._default			,       kwargs['''input_ids'''].shape[0]			,       0	)
      kwargs.pop('''position_ids'''			,       A	)
      for k in list(kwargs.keys()	):
           if kwargs[k] is None or isinstance(kwargs[k]			,       A	):
                kwargs.pop(A	)
      _a     =  self._optimized(**A	)
      _a     =  outputs[0]
      _a     =  outputs[1]
      _a     =  CausalLMOutputWithPast(
          loss=A			,       logits=A			,       past_key_values=A			,       hidden_states=A			,       attentions=A			,       )
      return fixed_output



 def __getattr__(self			,       A	)   ->					int:


      """simple docstring"""



      return getattr(self._default			,       A	)



 def  a__  (self			,       A			,       A=None			,       A=None			,       A=None			,       **A	)   ->					List[Any]:


      """simple docstring"""



      return self._default.prepare_inputs_for_generation(
          A			,       past_key_values=A			,       inputs_embeds=A			,       use_cache=A			,       **A	)



 def  a__  (self			,       A			,       A	)   ->					Tuple[Tuple[torch.Tensor]]:


      """simple docstring"""



      return self._default._reorder_cache(A			,       A	)





def lowerCAmelCase  ():



     """simple docstring"""



     _a     =  argparse.ArgumentParser()
     parser.add_argument(
         '''--model_type'''  ,     default=__A  ,     type=__A  ,     required=__A  ,     help='''Model type selected in the list: ''' + ''', '''.join(MODEL_CLASSES.keys())  ,     )
     parser.add_argument(
         '''--model_name_or_path'''  ,     default=__A  ,     type=__A  ,     required=__A  ,     help='''Path to pre-trained model or shortcut name selected in the list: ''' + ''', '''.join(MODEL_CLASSES.keys())  ,     )

     parser.add_argument('''--prompt'''  ,     type=__A  ,     default='''''')
     parser.add_argument('''--length'''  ,     type=__A  ,     default=20)
     parser.add_argument('''--stop_token'''  ,     type=__A  ,     default=__A  ,     help='''Token at which text generation is stopped''')

     parser.add_argument(
         '''--temperature'''  ,     type=__A  ,     default=1.0  ,     help='''temperature of 1.0 has no effect, lower tend toward greedy sampling'''  ,     )
     parser.add_argument(
         '''--repetition_penalty'''  ,     type=__A  ,     default=1.0  ,     help='''primarily useful for CTRL model; in that case, use 1.2''')
     parser.add_argument('''--k'''  ,     type=__A  ,     default=0)
     parser.add_argument('''--p'''  ,     type=__A  ,     default=0.9)

     parser.add_argument('''--prefix'''  ,     type=__A  ,     default=''''''  ,     help='''Text added prior to input.''')
     parser.add_argument('''--padding_text'''  ,     type=__A  ,     default=''''''  ,     help='''Deprecated, the use of `--prefix` is preferred.''')
     parser.add_argument('''--xlm_language'''  ,     type=__A  ,     default=''''''  ,     help='''Optional language when used with the XLM model.''')

     parser.add_argument('''--seed'''  ,     type=__A  ,     default=42  ,     help='''random seed for initialization''')
     parser.add_argument('''--no_cuda'''  ,     action='''store_true'''  ,     help='''Avoid using CUDA when available''')
     parser.add_argument('''--num_return_sequences'''  ,     type=__A  ,     default=1  ,     help='''The number of samples to generate.''')
     parser.add_argument(
         '''--fp16'''  ,     action='''store_true'''  ,     help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'''  ,     )
     parser.add_argument('''--jit'''  ,     action='''store_true'''  ,     help='''Whether or not to use jit trace to accelerate inference''')
     _a     =  parser.parse_args()

     _a     =  torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu''')
     _a     =  0 if args.no_cuda else torch.cuda.device_count()

     logger.warning(F'''device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}''')

     set_seed(__A)

     # Initialize the model and tokenizer
     try:
          _a     =  args.model_type.lower()
          _a  ,   _a     =  MODEL_CLASSES[args.model_type]
     except KeyError:
          raise KeyError('''the model {} you specified is not supported. You are welcome to add it and open a PR :)''')

     _a     =  tokenizer_class.from_pretrained(args.model_name_or_path)
     if tokenizer.pad_token is None:
          _a     =  tokenizer.eos_token
     _a     =  model_class.from_pretrained(args.model_name_or_path)
     model.to(args.device)

     if args.fpaa:
          model.half()
     _a     =  getattr(model.config  ,     '''max_position_embeddings'''  ,     0)
     _a     =  adjust_length_to_model(args.length  ,     max_sequence_length=__A)
     logger.info(__A)

     _a     =  args.prompt if args.prompt else input('''Model prompt >>> ''')

     # Different models need different input formatting and/or extra arguments
     _a     =  args.model_type in PREPROCESSING_FUNCTIONS.keys()
     if requires_preprocessing:
          _a     =  PREPROCESSING_FUNCTIONS.get(args.model_type)
          _a     =  prepare_input(__A  ,     __A  ,     __A  ,     __A)

          if model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
               _a     =  {'''add_space_before_punct_symbol''': True}
          else:
               _a     =  {}

          _a     =  tokenizer.encode(
              __A  ,     add_special_tokens=__A  ,     return_tensors='''pt'''  ,     **__A)
     else:
          _a     =  args.prefix if args.prefix else args.padding_text
          _a     =  tokenizer.encode(prefix + prompt_text  ,     add_special_tokens=__A  ,     return_tensors='''pt''')
     _a     =  encoded_prompt.to(args.device)

     if encoded_prompt.size()[-1] == 0:
          _a     =  None
     else:
          _a     =  encoded_prompt

     if args.jit:
          _a     =  ['''enable jit''']
          _a     =  prepare_jit_inputs(__A  ,     __A  ,     __A)
          torch._C._jit_set_texpr_fuser_enabled(__A)
          _a     =  False
          if hasattr(__A  ,     '''forward'''):
               _a     =  inspect.signature(model.forward)
          else:
               _a     =  inspect.signature(model.__call__)
          _a     =  tuple(jit_inputs[key] for key in sig.parameters if jit_inputs.get(__A  ,     __A) is not None)
          _a     =  torch.jit.trace(__A  ,     __A  ,     strict=__A)
          _a     =  torch.jit.freeze(traced_model.eval())
          traced_model(*__A)
          traced_model(*__A)

          _a     =  _ModelFallbackWrapper(__A  ,     __A)

     _a     =  model.generate(
         input_ids=__A  ,     max_length=args.length + len(encoded_prompt[0])  ,     temperature=args.temperature  ,     top_k=args.k  ,     top_p=args.p  ,     repetition_penalty=args.repetition_penalty  ,     do_sample=__A  ,     num_return_sequences=args.num_return_sequences  ,     )

     # Remove the batch dimension when returning multiple sequences
     if len(output_sequences.shape) > 2:
          output_sequences.squeeze_()

     _a     =  []

     for generated_sequence_idx, generated_sequence in enumerate(__A):
          print(F'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ===''')
          _a     =  generated_sequence.tolist()

          # Decode text
          _a     =  tokenizer.decode(__A  ,     clean_up_tokenization_spaces=__A)

          # Remove all text after the stop token
          _a     =  text[: text.find(args.stop_token) if args.stop_token else None]

          # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
          _a     =  (
              prompt_text + text[len(tokenizer.decode(encoded_prompt[0]  ,     clean_up_tokenization_spaces=__A)) :]
          )

          generated_sequences.append(__A)
          print(__A)

     return generated_sequences


if __name__ == "__main__":
  main()








'''simple docstring'''





import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    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.utils.versions import require_version


lowercase_			=  logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

lowercase_			=  {
    "base": AutoModel,
    "sequence-classification": AutoModelForSequenceClassification,
    "question-answering": AutoModelForQuestionAnswering,
    "pretraining": AutoModelForPreTraining,
    "token-classification": AutoModelForTokenClassification,
    "language-modeling": AutoModelWithLMHead,
    "summarization": AutoModelForSeqaSeqLM,
    "translation": AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
lowercase_			=  {
    "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,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
lowercase_			=  sorted(arg_to_scheduler.keys())
lowercase_			=  "{" + ", ".join(arg_to_scheduler_choices) + "}"






class __A      (  pl.LightningModule						):



 '''simple docstring'''







 def __init__(self			,       A			,       A=None			,       A="base"			,       A=None			,       A=None			,       A=None			,       **A			,       )   ->					int:


      """simple docstring"""



      super().__init__()
      # TODO: move to self.save_hyperparameters()
      # self.save_hyperparameters()
      # can also expand arguments into trainer signature for easier reading

      self.save_hyperparameters(A	)
      _a     =  0
      _a     =  Path(self.hparams.output_dir	)
      _a     =  self.hparams.cache_dir if self.hparams.cache_dir else None
      if config is None:
           _a     =  AutoConfig.from_pretrained(
               self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path			,       **({'''num_labels''': num_labels} if num_labels is not None else {})			,       cache_dir=A			,       **A			,       )
      else:
           _a     =  config

      _a     =  ('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
      for p in extra_model_params:
           if getattr(self.hparams			,       A			,       A	):
                assert hasattr(self.config			,       A	), f'''model config doesn\'t have a `{p}` attribute'''
                setattr(self.config			,       A			,       getattr(self.hparams			,       A	)	)

      if tokenizer is None:
           _a     =  AutoTokenizer.from_pretrained(
               self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path			,       cache_dir=A			,       )
      else:
           _a     =  tokenizer
      _a     =  MODEL_MODES[mode]
      if model is None:
           _a     =  self.model_type.from_pretrained(
               self.hparams.model_name_or_path			,       from_tf=bool('''.ckpt''' in self.hparams.model_name_or_path	)			,       config=self.config			,       cache_dir=A			,       )
      else:
           _a     =  model



 def  a__  (self			,       *A			,       **A	)   ->					List[str]:


      """simple docstring"""



      _a     =  self.model_type.from_pretrained(*A			,       **A	)



 def  a__  (self	)   ->					Dict:


      """simple docstring"""



      _a     =  arg_to_scheduler[self.hparams.lr_scheduler]
      _a     =  get_schedule_func(
          self.opt			,       num_warmup_steps=self.hparams.warmup_steps			,       num_training_steps=self.total_steps()	)
      _a     =  {'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
      return scheduler



 def  a__  (self	)   ->					List[str]:


      """simple docstring"""



      _a     =  self.model
      _a     =  ['''bias''', '''LayerNorm.weight''']
      _a     =  [
          {
              '''params''': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay	)],
              '''weight_decay''': self.hparams.weight_decay,
          },
          {
              '''params''': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay	)],
              '''weight_decay''': 0.0,
          },
      ]
      if self.hparams.adafactor:
           _a     =  Adafactor(
               A			,       lr=self.hparams.learning_rate			,       scale_parameter=A			,       relative_step=A	)

      else:
           _a     =  AdamW(
               A			,       lr=self.hparams.learning_rate			,       eps=self.hparams.adam_epsilon	)
      _a     =  optimizer

      _a     =  self.get_lr_scheduler()

      return [optimizer], [scheduler]



 def  a__  (self			,       A			,       A	)   ->					Tuple:


      """simple docstring"""



      return self.validation_step(A			,       A	)



 def  a__  (self			,       A	)   ->					Any:


      """simple docstring"""



      return self.validation_end(A	)



 def  a__  (self	)   ->					int:


      """simple docstring"""



      _a     =  max(1			,       self.hparams.gpus	)  # TODO: consider num_tpu_cores
      _a     =  self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
      return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs



 def  a__  (self			,       A	)   ->					int:


      """simple docstring"""



      if stage == "test":
           _a     =  len(self.test_dataloader().dataset	)
      else:
           _a     =  self.get_dataloader('''train'''			,       self.hparams.train_batch_size			,       shuffle=A	)
           _a     =  len(self.train_dataloader().dataset	)



 def  a__  (self			,       A			,       A			,       A = False	)   ->					Optional[int]:


      """simple docstring"""



      raise NotImplementedError('''You must implement this for your task'''	)



 def  a__  (self	)   ->					Optional[Any]:


      """simple docstring"""



      return self.train_loader



 def  a__  (self	)   ->					List[Any]:


      """simple docstring"""



      return self.get_dataloader('''dev'''			,       self.hparams.eval_batch_size			,       shuffle=A	)



 def  a__  (self	)   ->					Any:


      """simple docstring"""



      return self.get_dataloader('''test'''			,       self.hparams.eval_batch_size			,       shuffle=A	)



 def  a__  (self			,       A	)   ->					Union[str, Any]:


      """simple docstring"""



      return os.path.join(
          self.hparams.data_dir			,       '''cached_{}_{}_{}'''.format(
              A			,       list(filter(A			,       self.hparams.model_name_or_path.split('''/'''	)	)	).pop()			,       str(self.hparams.max_seq_length	)			,       )			,       )



 @pl.utilities.rank_zero_only
 def  a__  (self			,       A	)   ->					None:


      """simple docstring"""



      _a     =  self.output_dir.joinpath('''best_tfmr'''	)
      _a     =  self.step_count
      self.model.save_pretrained(A	)
      self.tokenizer.save_pretrained(A	)



 @staticmethod
 def  a__  (A			,       A	)   ->					str:


      """simple docstring"""



      parser.add_argument(
          '''--model_name_or_path'''			,       default=A			,       type=A			,       required=A			,       help='''Path to pretrained model or model identifier from huggingface.co/models'''			,       )
      parser.add_argument(
          '''--config_name'''			,       default=''''''			,       type=A			,       help='''Pretrained config name or path if not the same as model_name'''	)
      parser.add_argument(
          '''--tokenizer_name'''			,       default=A			,       type=A			,       help='''Pretrained tokenizer name or path if not the same as model_name'''			,       )
      parser.add_argument(
          '''--cache_dir'''			,       default=''''''			,       type=A			,       help='''Where do you want to store the pre-trained models downloaded from huggingface.co'''			,       )
      parser.add_argument(
          '''--encoder_layerdrop'''			,       type=A			,       help='''Encoder layer dropout probability (Optional). Goes into model.config'''			,       )
      parser.add_argument(
          '''--decoder_layerdrop'''			,       type=A			,       help='''Decoder layer dropout probability (Optional). Goes into model.config'''			,       )
      parser.add_argument(
          '''--dropout'''			,       type=A			,       help='''Dropout probability (Optional). Goes into model.config'''			,       )
      parser.add_argument(
          '''--attention_dropout'''			,       type=A			,       help='''Attention dropout probability (Optional). Goes into model.config'''			,       )
      parser.add_argument('''--learning_rate'''			,       default=5E-5			,       type=A			,       help='''The initial learning rate for Adam.'''	)
      parser.add_argument(
          '''--lr_scheduler'''			,       default='''linear'''			,       choices=A			,       metavar=A			,       type=A			,       help='''Learning rate scheduler'''			,       )
      parser.add_argument('''--weight_decay'''			,       default=0.0			,       type=A			,       help='''Weight decay if we apply some.'''	)
      parser.add_argument('''--adam_epsilon'''			,       default=1E-8			,       type=A			,       help='''Epsilon for Adam optimizer.'''	)
      parser.add_argument('''--warmup_steps'''			,       default=0			,       type=A			,       help='''Linear warmup over warmup_steps.'''	)
      parser.add_argument('''--num_workers'''			,       default=4			,       type=A			,       help='''kwarg passed to DataLoader'''	)
      parser.add_argument('''--num_train_epochs'''			,       dest='''max_epochs'''			,       default=3			,       type=A	)
      parser.add_argument('''--train_batch_size'''			,       default=32			,       type=A	)
      parser.add_argument('''--eval_batch_size'''			,       default=32			,       type=A	)
      parser.add_argument('''--adafactor'''			,       action='''store_true'''	)






class __A      (  pl.Callback						):



 '''simple docstring'''







 def  a__  (self			,       A			,       A	)   ->					str:


      """simple docstring"""



      if (
          trainer.is_global_zero and trainer.global_rank == 0
      ):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
           pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.






class __A      (  pl.Callback						):



 '''simple docstring'''







 def  a__  (self			,       A			,       A	)   ->					List[Any]:


      """simple docstring"""



      _a     =  trainer.lr_schedulers[0]['''scheduler''']
      _a     =  {f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()	)}
      pl_module.logger.log_metrics(A	)



 def  a__  (self			,       A			,       A	)   ->					Tuple:


      """simple docstring"""



      rank_zero_info('''***** Validation results *****'''	)
      _a     =  trainer.callback_metrics
      # Log results
      for key in sorted(A	):
           if key not in ["log", "progress_bar"]:
                rank_zero_info('''{} = {}\n'''.format(A			,       str(metrics[key]	)	)	)



 def  a__  (self			,       A			,       A	)   ->					Dict:


      """simple docstring"""



      rank_zero_info('''***** Test results *****'''	)
      _a     =  trainer.callback_metrics
      # Log and save results to file
      _a     =  os.path.join(pl_module.hparams.output_dir			,       '''test_results.txt'''	)
      with open(A			,       '''w'''	) as writer:
           for key in sorted(A	):
                if key not in ["log", "progress_bar"]:
                     rank_zero_info('''{} = {}\n'''.format(A			,       str(metrics[key]	)	)	)
                     writer.write('''{} = {}\n'''.format(A			,       str(metrics[key]	)	)	)





def lowerCAmelCase  (__A  ,     __A):



     """simple docstring"""



     parser.add_argument(
         '''--output_dir'''  ,     default=__A  ,     type=__A  ,     required=__A  ,     help='''The output directory where the model predictions and checkpoints will be written.'''  ,     )
     parser.add_argument(
         '''--fp16'''  ,     action='''store_true'''  ,     help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'''  ,     )

     parser.add_argument(
         '''--fp16_opt_level'''  ,     type=__A  ,     default='''O2'''  ,     help=(
             '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'''
             '''See details at https://nvidia.github.io/apex/amp.html'''
         )  ,     )
     parser.add_argument('''--n_tpu_cores'''  ,     dest='''tpu_cores'''  ,     type=__A)
     parser.add_argument('''--max_grad_norm'''  ,     dest='''gradient_clip_val'''  ,     default=1.0  ,     type=__A  ,     help='''Max gradient norm''')
     parser.add_argument('''--do_train'''  ,     action='''store_true'''  ,     help='''Whether to run training.''')
     parser.add_argument('''--do_predict'''  ,     action='''store_true'''  ,     help='''Whether to run predictions on the test set.''')
     parser.add_argument(
         '''--gradient_accumulation_steps'''  ,     dest='''accumulate_grad_batches'''  ,     type=__A  ,     default=1  ,     help='''Number of updates steps to accumulate before performing a backward/update pass.'''  ,     )
     parser.add_argument('''--seed'''  ,     type=__A  ,     default=42  ,     help='''random seed for initialization''')
     parser.add_argument(
         '''--data_dir'''  ,     default=__A  ,     type=__A  ,     required=__A  ,     help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''  ,     )




def lowerCAmelCase  (__A  ,     __A  ,     __A=None  ,     __A=True  ,     __A=None  ,     __A=[]  ,     __A=None  ,     __A=None  ,     **__A  ,     ):



     """simple docstring"""



     pl.seed_everything(args.seed)

     # init model
     _a     =  Path(model.hparams.output_dir)
     odir.mkdir(exist_ok=__A)

     # add custom checkpoints
     if checkpoint_callback is None:
          _a     =  pl.callbacks.ModelCheckpoint(
              filepath=args.output_dir  ,     prefix='''checkpoint'''  ,     monitor='''val_loss'''  ,     mode='''min'''  ,     save_top_k=1)
     if early_stopping_callback:
          extra_callbacks.append(__A)
     if logging_callback is None:
          _a     =  LoggingCallback()

     _a     =  {}

     # TODO: remove with PyTorch 1.6 since pl uses native amp
     if args.fpaa:
          _a     =  16
          # train_params["amp_level"] = args.fp16_opt_level

     if args.gpus > 1:
          _a     =  '''auto'''  # "ddp"
          _a     =  '''ddp'''

     _a     =  args.accumulate_grad_batches
     _a     =  None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
     _a     =  '''auto'''

     _a     =  pl.Trainer.from_argparse_args(
         __A  ,     weights_summary=__A  ,     callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]  ,     logger=__A  ,     **__A  ,     )

     if args.do_train:
          trainer.fit(__A)

     return trainer



