





"""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

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


       def 			UpperCAmelCase__							(  *SCREAMING_SNAKE_CASE     :						List[str]							,						**SCREAMING_SNAKE_CASE     :						int			):







             '''simple docstring'''




             pass






       SCREAMING_SNAKE_CASE__					=   False


SCREAMING_SNAKE_CASE__					=   logging.get_logger("transformers-cli/serving")


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Namespace			):







      '''simple docstring'''




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

class 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      42







class 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      42
      _SCREAMING_SNAKE_CASE					=      42







class 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      42







class 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      42







class 		lowercase					(					_UpperCAmelCase					):





      @staticmethod
      def     _snake_case			(       lowercase )		->     Any:
            lowerCAmelCase  						=       parser.add_parser(
                """serve"""      ,						help="""CLI tool to run inference requests through REST and GraphQL endpoints.""" )
            serve_parser.add_argument(
                """--task"""      ,						type=lowercase      ,						choices=get_supported_tasks()      ,						help="""The task to run the pipeline on"""      ,						)
            serve_parser.add_argument("""--host"""      ,						type=lowercase      ,						default="""localhost"""      ,						help="""Interface the server will listen on.""" )
            serve_parser.add_argument("""--port"""      ,						type=lowercase      ,						default=8_888      ,						help="""Port the serving will listen to.""" )
            serve_parser.add_argument("""--workers"""      ,						type=lowercase      ,						default=1      ,						help="""Number of http workers""" )
            serve_parser.add_argument("""--model"""      ,						type=lowercase      ,						help="""Model's name or path to stored model.""" )
            serve_parser.add_argument("""--config"""      ,						type=lowercase      ,						help="""Model's config name or path to stored model.""" )
            serve_parser.add_argument("""--tokenizer"""      ,						type=lowercase      ,						help="""Tokenizer name to use.""" )
            serve_parser.add_argument(
                """--device"""      ,						type=lowercase      ,						default=-1      ,						help="""Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)"""      ,						)
            serve_parser.set_defaults(func=lowercase )





      def __init__(       self      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase )		->     Tuple:
            lowerCAmelCase  						=       pipeline

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





      def     _snake_case			(       self )		->     str:
            run(self._app      ,						host=self.host      ,						port=self.port      ,						workers=self.workers )





      def     _snake_case			(       self )		->     List[Any]:
            return ServeModelInfoResult(infos=vars(self._pipeline.model.config ) )





      def     _snake_case			(       self      ,						lowercase = Body(lowercase      ,						embed=lowercase )      ,						lowercase = Body(lowercase      ,						embed=lowercase ) )		->     Dict:
            try:
                  lowerCAmelCase  						=       self._pipeline.tokenizer.tokenize(lowercase )

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

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





      def     _snake_case			(       self      ,						lowercase = Body(lowercase      ,						embed=lowercase )      ,						lowercase = Body(lowercase      ,						embed=lowercase )      ,						lowercase = Body(lowercase      ,						embed=lowercase )      ,						)		->     Any:
            try:
                  lowerCAmelCase  						=       self._pipeline.tokenizer.decode(lowercase      ,						lowercase      ,						lowercase )
                  return ServeDeTokenizeResult(model=""""""      ,						text=lowercase )
            except Exception as e:
                  raise HTTPException(status_code=500      ,						detail={"""model""": """""", """error""": str(lowercase )} )





      async def     _snake_case			(       self      ,						lowercase=Body(lowercase      ,						embed=lowercase ) )		->     str:

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

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










from .constants import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SAFE_WEIGHTS_INDEX_NAME,
    SAFE_WEIGHTS_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    TORCH_LAUNCH_PARAMS,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
)
from .dataclasses import (
    BnbQuantizationConfig,
    ComputeEnvironment,
    CustomDtype,
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    DynamoBackend,
    FPaRecipeKwargs,
    FullyShardedDataParallelPlugin,
    GradientAccumulationPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    KwargsHandler,
    LoggerType,
    MegatronLMPlugin,
    PrecisionType,
    ProjectConfiguration,
    RNGType,
    SageMakerDistributedType,
    TensorInformation,
    TorchDynamoPlugin,
)
from .environment import get_int_from_env, parse_choice_from_env, parse_flag_from_env
from .imports import (
    get_ccl_version,
    is_abit_bnb_available,
    is_abit_bnb_available,
    is_aim_available,
    is_bfaa_available,
    is_bnb_available,
    is_botoa_available,
    is_ccl_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_fpa_available,
    is_ipex_available,
    is_megatron_lm_available,
    is_mlflow_available,
    is_mps_available,
    is_npu_available,
    is_rich_available,
    is_safetensors_available,
    is_sagemaker_available,
    is_tensorboard_available,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)
from .modeling import (
    check_device_map,
    check_tied_parameters_in_config,
    check_tied_parameters_on_same_device,
    compute_module_sizes,
    convert_file_size_to_int,
    dtype_byte_size,
    find_tied_parameters,
    get_balanced_memory,
    get_max_layer_size,
    get_max_memory,
    get_mixed_precision_context_manager,
    id_tensor_storage,
    infer_auto_device_map,
    load_checkpoint_in_model,
    load_offloaded_weights,
    load_state_dict,
    named_module_tensors,
    retie_parameters,
    set_module_tensor_to_device,
    shard_checkpoint,
)
from .offload import (
    OffloadedWeightsLoader,
    PrefixedDataset,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
    save_offload_index,
)
from .operations import (
    broadcast,
    broadcast_object_list,
    concatenate,
    convert_outputs_to_fpaa,
    convert_to_fpaa,
    find_batch_size,
    find_device,
    gather,
    gather_object,
    get_data_structure,
    honor_type,
    initialize_tensors,
    is_namedtuple,
    is_tensor_information,
    is_torch_tensor,
    listify,
    pad_across_processes,
    recursively_apply,
    reduce,
    send_to_device,
    slice_tensors,
)
from .versions import compare_versions, is_torch_version


if is_deepspeed_available():
    from .deepspeed import (
        DeepSpeedEngineWrapper,
        DeepSpeedOptimizerWrapper,
        DeepSpeedSchedulerWrapper,
        DummyOptim,
        DummyScheduler,
        HfDeepSpeedConfig,
    )

from .bnb import has_abit_bnb_layers, load_and_quantize_model
from .fsdp_utils import load_fsdp_model, load_fsdp_optimizer, save_fsdp_model, save_fsdp_optimizer
from .launch import (
    PrepareForLaunch,
    _filter_args,
    prepare_deepspeed_cmd_env,
    prepare_multi_gpu_env,
    prepare_sagemager_args_inputs,
    prepare_simple_launcher_cmd_env,
    prepare_tpu,
)
from .megatron_lm import (
    AbstractTrainStep,
    BertTrainStep,
    GPTTrainStep,
    MegatronEngine,
    MegatronLMDummyDataLoader,
    MegatronLMDummyScheduler,
    MegatronLMOptimizerWrapper,
    MegatronLMSchedulerWrapper,
    TaTrainStep,
    avg_losses_across_data_parallel_group,
    gather_across_data_parallel_groups,
)
from .megatron_lm import initialize as megatron_lm_initialize
from .megatron_lm import prepare_data_loader as megatron_lm_prepare_data_loader
from .megatron_lm import prepare_model as megatron_lm_prepare_model
from .megatron_lm import prepare_optimizer as megatron_lm_prepare_optimizer
from .megatron_lm import prepare_scheduler as megatron_lm_prepare_scheduler
from .memory import find_executable_batch_size, release_memory
from .other import (
    extract_model_from_parallel,
    get_pretty_name,
    is_port_in_use,
    merge_dicts,
    patch_environment,
    save,
    wait_for_everyone,
    write_basic_config,
)
from .random import set_seed, synchronize_rng_state, synchronize_rng_states
from .torch_xla import install_xla
from .tqdm import tqdm
from .transformer_engine import convert_model, has_transformer_engine_layers


