from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
      import datasets





class      lowerCAmelCase						(					_a			):

     '''simple docstring'''





     @slow
     @require_torch
     def 			a__  (					self       ):
            _A=	EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'				,      'prajjwal1/bert-tiny'       )
            _A=	BertTokenizer.from_pretrained('bert-base-uncased'       )

            _A=	bertabert.config.encoder.vocab_size
            _A=	tokenizer.sep_token_id
            _A=	tokenizer.cls_token_id
            _A=	128

            _A=	datasets.load_dataset('cnn_dailymail'				,      '3.0.0'				,      split='train[:1%]'       )
            _A=	datasets.load_dataset('cnn_dailymail'				,      '3.0.0'				,      split='validation[:1%]'       )

            _A=	train_dataset.select(range(32       )       )
            _A=	val_dataset.select(range(16       )       )

            _A=	4

            def _map_to_encoder_decoder_inputs(lowerCAmelCase__       ):
                   # Tokenizer will automatically set [BOS] <text> [EOS]
                   _A=	tokenizer(batch['article']				,      padding='max_length'				,      truncation=lowerCAmelCase__				,      max_length=512       )
                   _A=	tokenizer(batch['highlights']				,      padding='max_length'				,      truncation=lowerCAmelCase__				,      max_length=128       )
                   _A=	inputs.input_ids
                   _A=	inputs.attention_mask

                   _A=	outputs.input_ids
                   _A=	outputs.input_ids.copy()
                   _A=	[
                       [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
                   ]
                   _A=	outputs.attention_mask

                   assert all(len(lowerCAmelCase__       ) == 512 for x in inputs.input_ids       )
                   assert all(len(lowerCAmelCase__       ) == 128 for x in outputs.input_ids       )

                   return batch

            def _compute_metrics(lowerCAmelCase__       ):
                   _A=	pred.label_ids
                   _A=	pred.predictions

                   # all unnecessary tokens are removed
                   _A=	tokenizer.batch_decode(lowerCAmelCase__				,      skip_special_tokens=lowerCAmelCase__       )
                   _A=	tokenizer.batch_decode(lowerCAmelCase__				,      skip_special_tokens=lowerCAmelCase__       )

                   _A=	sum([int(pred_str[i] == label_str[i]       ) for i in range(len(lowerCAmelCase__       )       )]       ) / len(lowerCAmelCase__       )

                   return {"accuracy": accuracy}

            # map train dataset
            _A=	train_dataset.map(
                _map_to_encoder_decoder_inputs				,      batched=lowerCAmelCase__				,      batch_size=lowerCAmelCase__				,      remove_columns=['article', 'highlights']				,      )
            train_dataset.set_format(
                type='torch'				,      columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']				,      )

            # same for validation dataset
            _A=	val_dataset.map(
                _map_to_encoder_decoder_inputs				,      batched=lowerCAmelCase__				,      batch_size=lowerCAmelCase__				,      remove_columns=['article', 'highlights']				,      )
            val_dataset.set_format(
                type='torch'				,      columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']				,      )

            _A=	self.get_auto_remove_tmp_dir()

            _A=	SeqaSeqTrainingArguments(
                output_dir=lowerCAmelCase__				,      per_device_train_batch_size=lowerCAmelCase__				,      per_device_eval_batch_size=lowerCAmelCase__				,      predict_with_generate=lowerCAmelCase__				,      evaluation_strategy='steps'				,      do_train=lowerCAmelCase__				,      do_eval=lowerCAmelCase__				,      warmup_steps=0				,      eval_steps=2				,      logging_steps=2				,      )

            # instantiate trainer
            _A=	SeqaSeqTrainer(
                model=lowerCAmelCase__				,      args=lowerCAmelCase__				,      compute_metrics=_compute_metrics				,      train_dataset=lowerCAmelCase__				,      eval_dataset=lowerCAmelCase__				,      tokenizer=lowerCAmelCase__				,      )

            # start training
            trainer.train()
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

      UpperCAmelCase_								=							True
except (ImportError, AttributeError):
      UpperCAmelCase_								=							object

      def 							UpperCamelCase				(    *lowerCAmelCase_       ,					**lowerCAmelCase_   )		->						List[Any]:




             '''simple docstring'''






             pass





      UpperCAmelCase_								=							False


UpperCAmelCase_								=							logging.get_logger('''transformers-cli/serving''')

def 							UpperCamelCase				(    lowerCAmelCase_   )		->						int:




       '''simple docstring'''






       _A=	pipeline(
           task=args.task       ,					model=args.model if args.model else None       ,					config=args.config       ,					tokenizer=args.tokenizer       ,					device=args.device       ,					)
       return ServeCommand(lowerCAmelCase_       ,					args.host       ,					args.port       ,					args.workers   )





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     dict





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     List[str]
     _SCREAMING_SNAKE_CASE     :     Optional[List[int]]





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     str





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     Any





class      lowerCAmelCase						(					_a			):



     @staticmethod
     def 			a__  (					lowerCAmelCase__       ):
            _A=	parser.add_parser(
                'serve'				,      help='CLI tool to run inference requests through REST and GraphQL endpoints.'       )
            serve_parser.add_argument(
                '--task'				,      type=lowerCAmelCase__				,      choices=get_supported_tasks()				,      help='The task to run the pipeline on'				,      )
            serve_parser.add_argument('--host'				,      type=lowerCAmelCase__				,      default='localhost'				,      help='Interface the server will listen on.'       )
            serve_parser.add_argument('--port'				,      type=lowerCAmelCase__				,      default=8888				,      help='Port the serving will listen to.'       )
            serve_parser.add_argument('--workers'				,      type=lowerCAmelCase__				,      default=1				,      help='Number of http workers'       )
            serve_parser.add_argument('--model'				,      type=lowerCAmelCase__				,      help='Model\'s name or path to stored model.'       )
            serve_parser.add_argument('--config'				,      type=lowerCAmelCase__				,      help='Model\'s config name or path to stored model.'       )
            serve_parser.add_argument('--tokenizer'				,      type=lowerCAmelCase__				,      help='Tokenizer name to use.'       )
            serve_parser.add_argument(
                '--device'				,      type=lowerCAmelCase__				,      default=-1				,      help='Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)'				,      )
            serve_parser.set_defaults(func=lowerCAmelCase__       )



     def __init__(					self				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            _A=	pipeline

            _A=	host
            _A=	port
            _A=	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}"       )
                   _A=	FastAPI(
                       routes=[
                           APIRoute(
                               '/'				,      self.model_info				,      response_model=lowerCAmelCase__				,      response_class=lowerCAmelCase__				,      methods=['GET']				,      ),
                           APIRoute(
                               '/tokenize'				,      self.tokenize				,      response_model=lowerCAmelCase__				,      response_class=lowerCAmelCase__				,      methods=['POST']				,      ),
                           APIRoute(
                               '/detokenize'				,      self.detokenize				,      response_model=lowerCAmelCase__				,      response_class=lowerCAmelCase__				,      methods=['POST']				,      ),
                           APIRoute(
                               '/forward'				,      self.forward				,      response_model=lowerCAmelCase__				,      response_class=lowerCAmelCase__				,      methods=['POST']				,      ),
                       ]				,      timeout=600				,      )



     def 			a__  (					self       ):
            run(self._app				,      host=self.host				,      port=self.port				,      workers=self.workers       )



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



     def 			a__  (					self				,      lowerCAmelCase__ = Body(lowerCAmelCase__				,      embed=lowerCAmelCase__       )				,      lowerCAmelCase__ = Body(lowerCAmelCase__				,      embed=lowerCAmelCase__       )       ):
            try:
                   _A=	self._pipeline.tokenizer.tokenize(lowerCAmelCase__       )

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

            except Exception as e:
                   raise HTTPException(status_code=500				,      detail={'model': '', 'error': str(lowerCAmelCase__       )}       )



     def 			a__  (					self				,      lowerCAmelCase__ = Body(lowerCAmelCase__				,      embed=lowerCAmelCase__       )				,      lowerCAmelCase__ = Body(lowerCAmelCase__				,      embed=lowerCAmelCase__       )				,      lowerCAmelCase__ = Body(lowerCAmelCase__				,      embed=lowerCAmelCase__       )				,      ):
            try:
                   _A=	self._pipeline.tokenizer.decode(lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       )
                   return ServeDeTokenizeResult(model=''				,      text=lowerCAmelCase__       )
            except Exception as e:
                   raise HTTPException(status_code=500				,      detail={'model': '', 'error': str(lowerCAmelCase__       )}       )





     async def 			a__  (					self				,      lowerCAmelCase__=Body(lowerCAmelCase__				,      embed=lowerCAmelCase__       )       ):

            # Check we don't have empty string
            if len(lowerCAmelCase__       ) == 0:
                   return ServeForwardResult(output=[]				,      attention=[]       )

            try:
                   # Forward through the model
                   _A=	self._pipeline(lowerCAmelCase__       )
                   return ServeForwardResult(output=lowerCAmelCase__       )
            except Exception as e:
                   raise HTTPException(500				,      {'error': str(lowerCAmelCase__       )}       )