



'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase											=		logging.get_logger(__name__)

_UpperCamelCase											=		{
    'microsoft/swinv2-tiny-patch4-window8-256': (
        'https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json'
    ),
}






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  a_				   ="""swinv2"""

  a_				   ={
      """num_attention_heads""": """num_heads""",
      """num_hidden_layers""": """num_layers""",
  }




  def __init__(					self :      Optional[int]     ,      _a :      int=224     ,      _a :      Union[str, Any]=4     ,      _a :      Tuple=3     ,      _a :      List[str]=96     ,      _a :      Dict=[2, 2, 6, 2]     ,      _a :      Optional[int]=[3, 6, 12, 24]     ,      _a :      List[Any]=7     ,      _a :      str=4.0     ,      _a :      Optional[Any]=True     ,      _a :      List[Any]=0.0     ,      _a :      Optional[Any]=0.0     ,      _a :      Dict=0.1     ,      _a :      Tuple="gelu"     ,      _a :      int=False     ,      _a :      List[Any]=0.02     ,      _a :      int=1e-5     ,      _a :      Dict=32     ,      **_a :      List[str]     ,      )   ->   Tuple:
    super().__init__(**_a  )

    __lowerCamelCase			:			Union[str, Any]       			=					image_size
    __lowerCamelCase			:			Union[str, Any]       			=					patch_size
    __lowerCamelCase			:			Tuple       			=					num_channels
    __lowerCamelCase			:			List[Any]       			=					embed_dim
    __lowerCamelCase			:			Tuple       			=					depths
    __lowerCamelCase			:			Union[str, Any]       			=					len(_a  )
    __lowerCamelCase			:			List[Any]       			=					num_heads
    __lowerCamelCase			:			List[Any]       			=					window_size
    __lowerCamelCase			:			List[str]       			=					mlp_ratio
    __lowerCamelCase			:			int       			=					qkv_bias
    __lowerCamelCase			:			int       			=					hidden_dropout_prob
    __lowerCamelCase			:			str       			=					attention_probs_dropout_prob
    __lowerCamelCase			:			List[Any]       			=					drop_path_rate
    __lowerCamelCase			:			Union[str, Any]       			=					hidden_act
    __lowerCamelCase			:			Any       			=					use_absolute_embeddings
    __lowerCamelCase			:			Dict       			=					layer_norm_eps
    __lowerCamelCase			:			Tuple       			=					initializer_range
    __lowerCamelCase			:			Tuple       			=					encoder_stride
    # we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
    # this indicates the channel dimension after the last stage of the model
    __lowerCamelCase			:			int       			=					int(embed_dim * 2 ** (len(_a  ) - 1)  )
    __lowerCamelCase			:			int       			=					(0, 0, 0, 0)











'''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

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

   def 			a_	(				*_lowerCAmelCase			,**_lowerCAmelCase			) -> Union[str, Any]:
     pass


   _UpperCamelCase											=		False


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

def 			a_	(				_lowerCAmelCase			) -> Optional[int]:
  __lowerCamelCase			:			List[Any]       			=					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_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  a_				   =42






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  a_				   =42
  a_				   =42






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  a_				   =42






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  a_				   =42






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




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




  def __init__(					self :      int     ,      _a :      Pipeline     ,      _a :      str     ,      _a :      int     ,      _a :      int  )   ->   Optional[int]:
    __lowerCamelCase			:			int       			=					pipeline

    __lowerCamelCase			:			List[Any]       			=					host
    __lowerCamelCase			:			int       			=					port
    __lowerCamelCase			:			Tuple       			=					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			:			Optional[Any]       			=					FastAPI(
          routes=[
              APIRoute(
                  '/'     ,      self.model_info     ,      response_model=_a     ,      response_class=_a     ,      methods=['GET']     ,      ),
              APIRoute(
                  '/tokenize'     ,      self.tokenize     ,      response_model=_a     ,      response_class=_a     ,      methods=['POST']     ,      ),
              APIRoute(
                  '/detokenize'     ,      self.detokenize     ,      response_model=_a     ,      response_class=_a     ,      methods=['POST']     ,      ),
              APIRoute(
                  '/forward'     ,      self.forward     ,      response_model=_a     ,      response_class=_a     ,      methods=['POST']     ,      ),
          ]     ,      timeout=600     ,      )




  def 		_lowercase	(					self :      Optional[Any]  )   ->   List[str]:
    run(self._app     ,      host=self.host     ,      port=self.port     ,      workers=self.workers  )




  def 		_lowercase	(					self :      Tuple  )   ->   int:
    return ServeModelInfoResult(infos=vars(self._pipeline.model.config  )  )




  def 		_lowercase	(					self :      str     ,      _a :      str = Body(_a     ,      embed=_a  )     ,      _a :      bool = Body(_a     ,      embed=_a  )  )   ->   Optional[int]:
    try:
      __lowerCamelCase			:			Dict       			=					self._pipeline.tokenizer.tokenize(_a  )

      if return_ids:
        __lowerCamelCase			:			int       			=					self._pipeline.tokenizer.convert_tokens_to_ids(_a  )
        return ServeTokenizeResult(tokens=_a     ,      tokens_ids=_a  )
      else:
        return ServeTokenizeResult(tokens=_a  )

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




  def 		_lowercase	(					self :      Optional[Any]     ,      _a :      List[int] = Body(_a     ,      embed=_a  )     ,      _a :      bool = Body(_a     ,      embed=_a  )     ,      _a :      bool = Body(_a     ,      embed=_a  )     ,      )   ->   List[Any]:
    try:
      __lowerCamelCase			:			Dict       			=					self._pipeline.tokenizer.decode(_a     ,      _a     ,      _a  )
      return ServeDeTokenizeResult(model=''     ,      text=_a  )
    except Exception as e:
      raise HTTPException(status_code=500     ,      detail={'model': '', 'error': str(_a  )}  )




  async def 		_lowercase	(					self :      Dict     ,      _a :      Any=Body(_a     ,      embed=_a  )  )   ->   Optional[int]:

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

    try:
      # Forward through the model
      __lowerCamelCase			:			Union[str, Any]       			=					self._pipeline(_a  )
      return ServeForwardResult(output=_a  )
    except Exception as e:
      raise HTTPException(500     ,      {'error': str(_a  )}  )






