






"""simple docstring"""







from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional

from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


try:
 from fastapi import Body, FastAPI, HTTPException
 from fastapi.routing import APIRoute
 from pydantic import BaseModel
 from starlette.responses import JSONResponse
 from uvicorn import run

 lowerCAmelCase_						:     List[Any]							  =   True
except (ImportError, AttributeError):
 lowerCAmelCase_						:     List[str]							  =   object






 def    _lowerCAmelCase							(   *lowerCAmelCase						,   **lowerCAmelCase		):







        '''simple docstring'''
        pass





 lowerCAmelCase_						:     Union[str, Any]							  =   False


lowerCAmelCase_						:     Optional[Any]							  =   logging.get_logger('''transformers-cli/serving''')






def    _lowerCAmelCase							(   lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase						     =      pipeline(
           task=args.task						,   model=args.model if args.model else None						,   config=args.config						,   tokenizer=args.tokenizer						,   device=args.device						,   )
       return ServeCommand(A__						,   args.host						,   args.port						,   args.workers		)





class 	UpperCamelCase_			(							_SCREAMING_SNAKE_CASE	):
  _A							:       Optional[int]					=						42





class 	UpperCamelCase_			(							_SCREAMING_SNAKE_CASE	):
  _A							:       Optional[Any]					=						42
  _A							:       Union[str, Any]					=						42





class 	UpperCamelCase_			(							_SCREAMING_SNAKE_CASE	):
  _A							:       List[Any]					=						42





class 	UpperCamelCase_			(							_SCREAMING_SNAKE_CASE	):
  _A							:       str					=						42





class 	UpperCamelCase_			(							_SCREAMING_SNAKE_CASE	):



  @staticmethod
  def 		UpperCamelCase_					(   snake_case__					)      ->      Dict:



         """simple docstring"""





         UpperCAmelCase						     =      parser.add_parser(
             """serve"""			,		help="""CLI tool to run inference requests through REST and GraphQL endpoints."""					)
         serve_parser.add_argument(
             """--task"""			,		type=snake_case__			,		choices=get_supported_tasks()			,		help="""The task to run the pipeline on"""			,		)
         serve_parser.add_argument("""--host"""			,		type=snake_case__			,		default="""localhost"""			,		help="""Interface the server will listen on."""					)
         serve_parser.add_argument("""--port"""			,		type=snake_case__			,		default=88_88			,		help="""Port the serving will listen to."""					)
         serve_parser.add_argument("""--workers"""			,		type=snake_case__			,		default=1			,		help="""Number of http workers"""					)
         serve_parser.add_argument("""--model"""			,		type=snake_case__			,		help="""Model's name or path to stored model."""					)
         serve_parser.add_argument("""--config"""			,		type=snake_case__			,		help="""Model's config name or path to stored model."""					)
         serve_parser.add_argument("""--tokenizer"""			,		type=snake_case__			,		help="""Tokenizer name to use."""					)
         serve_parser.add_argument(
             """--device"""			,		type=snake_case__			,		default=-1			,		help="""Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)"""			,		)
         serve_parser.set_defaults(func=snake_case__					)



  def __init__(   self			,		snake_case__			,		snake_case__			,		snake_case__			,		snake_case__					)      ->      Optional[Any]:



         """simple docstring"""





         UpperCAmelCase						     =      pipeline

         UpperCAmelCase						     =      host
         UpperCAmelCase						     =      port
         UpperCAmelCase						     =      workers

         if not _serve_dependencies_installed:
                raise RuntimeError(
                    """Using serve command requires FastAPI and uvicorn. """
                    """Please install transformers with [serving]: pip install \"transformers[serving]\"."""
                    """Or install FastAPI and uvicorn separately."""					)
         else:
                logger.info(f'''Serving model over {host}:{port}'''					)
                UpperCAmelCase						     =      FastAPI(
                    routes=[
                        APIRoute(
                            """/"""			,		self.model_info			,		response_model=snake_case__			,		response_class=snake_case__			,		methods=["""GET"""]			,		),
                        APIRoute(
                            """/tokenize"""			,		self.tokenize			,		response_model=snake_case__			,		response_class=snake_case__			,		methods=["""POST"""]			,		),
                        APIRoute(
                            """/detokenize"""			,		self.detokenize			,		response_model=snake_case__			,		response_class=snake_case__			,		methods=["""POST"""]			,		),
                        APIRoute(
                            """/forward"""			,		self.forward			,		response_model=snake_case__			,		response_class=snake_case__			,		methods=["""POST"""]			,		),
                    ]			,		timeout=6_00			,		)



  def 		UpperCamelCase_					(   self					)      ->      Any:



         """simple docstring"""





         run(self._app			,		host=self.host			,		port=self.port			,		workers=self.workers					)



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



         """simple docstring"""





         return ServeModelInfoResult(infos=vars(self._pipeline.model.config					)					)



  def 		UpperCamelCase_					(   self			,		snake_case__ = Body(snake_case__			,		embed=snake_case__					)			,		snake_case__ = Body(snake_case__			,		embed=snake_case__					)					)      ->      Dict:



         """simple docstring"""





         try:
                UpperCAmelCase						     =      self._pipeline.tokenizer.tokenize(snake_case__					)

                if return_ids:
                       UpperCAmelCase						     =      self._pipeline.tokenizer.convert_tokens_to_ids(snake_case__					)
                       return ServeTokenizeResult(tokens=snake_case__			,		tokens_ids=snake_case__					)
                else:
                       return ServeTokenizeResult(tokens=snake_case__					)

         except Exception as e:
                raise HTTPException(status_code=5_00			,		detail={"""model""": """""", """error""": str(snake_case__					)}					)



  def 		UpperCamelCase_					(   self			,		snake_case__ = Body(snake_case__			,		embed=snake_case__					)			,		snake_case__ = Body(snake_case__			,		embed=snake_case__					)			,		snake_case__ = Body(snake_case__			,		embed=snake_case__					)			,		)      ->      Optional[Any]:



         """simple docstring"""





         try:
                UpperCAmelCase						     =      self._pipeline.tokenizer.decode(snake_case__			,		snake_case__			,		snake_case__					)
                return ServeDeTokenizeResult(model=""""""			,		text=snake_case__					)
         except Exception as e:
                raise HTTPException(status_code=5_00			,		detail={"""model""": """""", """error""": str(snake_case__					)}					)



  async def 		UpperCamelCase_					(   self			,		snake_case__=Body(snake_case__			,		embed=snake_case__					)					)      ->      Any:



         """simple docstring"""





         if len(snake_case__					) == 0:
                return ServeForwardResult(output=[]			,		attention=[]					)

         try:
                # Forward through the model
                UpperCAmelCase						     =      self._pipeline(snake_case__					)
                return ServeForwardResult(output=snake_case__					)
         except Exception as e:
                raise HTTPException(5_00			,		{"""error""": str(snake_case__					)}					)




'''simple docstring'''






import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


__A   :    str											= logging.getLogger(__name__)






@dataclass
class   __snake_case			:







    """simple docstring"""



    lowercase  					=   field(
        metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'})
    lowercase  					=   field(
        default=_SCREAMING_SNAKE_CASE      ,metadata={'help': 'Pretrained config name or path if not the same as model_name'})
    lowercase  					=   field(
        default=_SCREAMING_SNAKE_CASE      ,metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'})
    lowercase  					=   field(
        default=_SCREAMING_SNAKE_CASE      ,metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}      ,)
    lowercase  					=   field(default=_SCREAMING_SNAKE_CASE      ,metadata={'help': 'Whether tp freeze the encoder.'})
    lowercase  					=   field(default=_SCREAMING_SNAKE_CASE      ,metadata={'help': 'Whether  to freeze the embeddings.'})







@dataclass
class   __snake_case			:







    """simple docstring"""



    lowercase  					=   field(
        metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'})
    lowercase  					=   field(
        default='summarization'      ,metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'}      ,)
    lowercase  					=   field(
        default=10_24      ,metadata={
            'help': (
                'The maximum total input sequence length after tokenization. Sequences longer '
                'than this will be truncated, sequences shorter will be padded.'
            )
        }      ,)
    lowercase  					=   field(
        default=1_28      ,metadata={
            'help': (
                'The maximum total sequence length for target text after tokenization. Sequences longer '
                'than this will be truncated, sequences shorter will be padded.'
            )
        }      ,)
    lowercase  					=   field(
        default=1_42      ,metadata={
            'help': (
                'The maximum total sequence length for validation target text after tokenization. Sequences longer '
                'than this will be truncated, sequences shorter will be padded. '
                'This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '
                'during ``evaluate`` and ``predict``.'
            )
        }      ,)
    lowercase  					=   field(
        default=1_42      ,metadata={
            'help': (
                'The maximum total sequence length for test target text after tokenization. Sequences longer '
                'than this will be truncated, sequences shorter will be padded.'
            )
        }      ,)
    lowercase  					=   field(default=-1      ,metadata={'help': '# training examples. -1 means use all.'})
    lowercase  					=   field(default=-1      ,metadata={'help': '# validation examples. -1 means use all.'})
    lowercase  					=   field(default=-1      ,metadata={'help': '# test examples. -1 means use all.'})
    lowercase  					=   field(default=_SCREAMING_SNAKE_CASE      ,metadata={'help': 'Source language id for translation.'})
    lowercase  					=   field(default=_SCREAMING_SNAKE_CASE      ,metadata={'help': 'Target language id for translation.'})
    lowercase  					=   field(default=_SCREAMING_SNAKE_CASE      ,metadata={'help': '# num_beams to use for evaluation.'})
    lowercase  					=   field(
        default=_SCREAMING_SNAKE_CASE      ,metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'}      ,)







def 							UpperCamelCase_     (       A__   :  Tuple				,       A__   :  List[str]				,       A__   :  str   ):
  '''simple docstring'''





  logger.info(f'***** {split} metrics *****'   )
  for key in sorted(metrics.keys()   ):
    logger.info(f'  {key} = {metrics[key]}'   )
  save_json(A__				,       os.path.join(A__				,       f'{split}_results.json'   )   )







def 							UpperCamelCase_     (       ):
  '''simple docstring'''





  lowerCAmelCase_     : Dict     =  HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)   )

  if len(sys.argv   ) == 2 and sys.argv[1].endswith(""".json"""   ):
    # If we pass only one argument to the script and it's the path to a json file,
    # let's parse it to get our arguments.
    lowerCAmelCase_,				lowerCAmelCase_,				lowerCAmelCase_     : Optional[int]     =  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
  else:
    lowerCAmelCase_,				lowerCAmelCase_,				lowerCAmelCase_     : Union[str, Any]     =  parser.parse_args_into_dataclasses()

  check_output_dir(A__   )

  # Setup logging
  logging.basicConfig(
      format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""				,       datefmt="""%m/%d/%Y %H:%M:%S"""				,       level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN				,       )
  logger.warning(
      """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"""				,       training_args.local_rank				,       training_args.device				,       training_args.n_gpu				,       bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED   )				,       training_args.fpaa				,       )
  transformers.utils.logging.enable_default_handler()
  transformers.utils.logging.enable_explicit_format()
  # Set the verbosity to info of the Transformers logger (on main process only):
  if is_main_process(training_args.local_rank   ):
    transformers.utils.logging.set_verbosity_info()
  logger.info("""Training/evaluation parameters %s"""				,       A__   )

  # Set seed
  set_seed(training_args.seed   )

  # Load pretrained model and tokenizer
  #
  # Distributed training:
  # The .from_pretrained methods guarantee that only one local process can concurrently
  # download model & vocab.

  lowerCAmelCase_     : List[Any]     =  AutoConfig.from_pretrained(
      model_args.config_name if model_args.config_name else model_args.model_name_or_path				,       cache_dir=model_args.cache_dir				,       )

  lowerCAmelCase_     : List[Any]     =  ("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
  for p in extra_model_params:
    if getattr(A__				,       A__				,       A__   ):
      assert hasattr(A__				,       A__   ), f'({config.__class__.__name__}) doesn\'t have a `{p}` attribute'
      setattr(A__				,       A__				,       getattr(A__				,       A__   )   )

  lowerCAmelCase_     : Any     =  AutoTokenizer.from_pretrained(
      model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path				,       cache_dir=model_args.cache_dir				,       )
  lowerCAmelCase_     : Union[str, Any]     =  AutoModelForSeqaSeqLM.from_pretrained(
      model_args.model_name_or_path				,       from_tf=""".ckpt""" in model_args.model_name_or_path				,       config=A__				,       cache_dir=model_args.cache_dir				,       )

  # use task specific params
  use_task_specific_params(A__				,       data_args.task   )

  # set num_beams for evaluation
  if data_args.eval_beams is None:
    lowerCAmelCase_     : List[Any]     =  model.config.num_beams

  # set decoder_start_token_id for MBart
  if model.config.decoder_start_token_id is None and isinstance(A__				,       (MBartTokenizer, MBartTokenizerFast)   ):
    assert (
        data_args.tgt_lang is not None and data_args.src_lang is not None
    ), "mBart requires --tgt_lang and --src_lang"
    if isinstance(A__				,       A__   ):
      lowerCAmelCase_     : List[str]     =  tokenizer.lang_code_to_id[data_args.tgt_lang]
    else:
      lowerCAmelCase_     : int     =  tokenizer.convert_tokens_to_ids(data_args.tgt_lang   )

  if model_args.freeze_embeds:
    freeze_embeds(A__   )
  if model_args.freeze_encoder:
    freeze_params(model.get_encoder()   )
    assert_all_frozen(model.get_encoder()   )

  lowerCAmelCase_     : Any     =  SeqaSeqDataset

  # Get datasets
  lowerCAmelCase_     : List[str]     =  (
      dataset_class(
          A__				,       type_path="""train"""				,       data_dir=data_args.data_dir				,       n_obs=data_args.n_train				,       max_target_length=data_args.max_target_length				,       max_source_length=data_args.max_source_length				,       prefix=model.config.prefix or """"""				,       )
      if training_args.do_train
      else None
  )
  lowerCAmelCase_     : List[Any]     =  (
      dataset_class(
          A__				,       type_path="""val"""				,       data_dir=data_args.data_dir				,       n_obs=data_args.n_val				,       max_target_length=data_args.val_max_target_length				,       max_source_length=data_args.max_source_length				,       prefix=model.config.prefix or """"""				,       )
      if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
      else None
  )
  lowerCAmelCase_     : List[Any]     =  (
      dataset_class(
          A__				,       type_path="""test"""				,       data_dir=data_args.data_dir				,       n_obs=data_args.n_test				,       max_target_length=data_args.test_max_target_length				,       max_source_length=data_args.max_source_length				,       prefix=model.config.prefix or """"""				,       )
      if training_args.do_predict
      else None
  )

  # Initialize our Trainer
  lowerCAmelCase_     : Dict     =  (
      build_compute_metrics_fn(data_args.task				,       A__   ) if training_args.predict_with_generate else None
  )
  lowerCAmelCase_     : str     =  SeqaSeqTrainer(
      model=A__				,       args=A__				,       data_args=A__				,       train_dataset=A__				,       eval_dataset=A__				,       data_collator=SeqaSeqDataCollator(
          A__				,       A__				,       model.config.decoder_start_token_id				,       training_args.tpu_num_cores   )				,       compute_metrics=A__				,       tokenizer=A__				,       )

  lowerCAmelCase_     : Dict     =  {}
  # Training
  if training_args.do_train:
    logger.info("""*** Train ***"""   )

    lowerCAmelCase_     : Optional[int]     =  trainer.train(
        model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path   ) else None   )
    lowerCAmelCase_     : Union[str, Any]     =  train_result.metrics
    lowerCAmelCase_     : Optional[int]     =  data_args.n_train

    trainer.save_model()  # this also saves the tokenizer

    if trainer.is_world_process_zero():
      handle_metrics("""train"""				,       A__				,       training_args.output_dir   )
      all_metrics.update(A__   )

      # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
      trainer.state.save_to_json(os.path.join(training_args.output_dir				,       """trainer_state.json"""   )   )

      # For convenience, we also re-save the tokenizer to the same directory,
      # so that you can share your model easily on huggingface.co/models =)
      tokenizer.save_pretrained(training_args.output_dir   )

    # Evaluation
  if training_args.do_eval:
    logger.info("""*** Evaluate ***"""   )

    lowerCAmelCase_     : Any     =  trainer.evaluate(metric_key_prefix="""val"""   )
    lowerCAmelCase_     : Optional[int]     =  data_args.n_val
    lowerCAmelCase_     : Any     =  round(metrics["""val_loss"""]				,       4   )

    if trainer.is_world_process_zero():
      handle_metrics("""val"""				,       A__				,       training_args.output_dir   )
      all_metrics.update(A__   )

  if training_args.do_predict:
    logger.info("""*** Predict ***"""   )

    lowerCAmelCase_     : Union[str, Any]     =  trainer.predict(test_dataset=A__				,       metric_key_prefix="""test"""   )
    lowerCAmelCase_     : Optional[int]     =  test_output.metrics
    lowerCAmelCase_     : List[Any]     =  data_args.n_test

    if trainer.is_world_process_zero():
      lowerCAmelCase_     : int     =  round(metrics["""test_loss"""]				,       4   )
      handle_metrics("""test"""				,       A__				,       training_args.output_dir   )
      all_metrics.update(A__   )

      if training_args.predict_with_generate:
        lowerCAmelCase_     : int     =  tokenizer.batch_decode(
            test_output.predictions				,       skip_special_tokens=A__				,       clean_up_tokenization_spaces=A__   )
        lowerCAmelCase_     : List[Any]     =  lmap(str.strip				,       A__   )
        write_txt_file(A__				,       os.path.join(training_args.output_dir				,       """test_generations.txt"""   )   )

  if trainer.is_world_process_zero():
    save_json(A__				,       os.path.join(training_args.output_dir				,       """all_results.json"""   )   )

  return all_metrics







def 							UpperCamelCase_     (       A__   :  Optional[int]   ):
  '''simple docstring'''





  main()


if __name__ == "__main__":
  main()



