




"""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

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


     def  UpperCAmelCase__	(*snake_case__							: Any		,       **snake_case__							: Any   ):
       """simple docstring"""

       pass






     A_      =    False


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


def  UpperCAmelCase__	(snake_case__							: Namespace   ):
  """simple docstring"""

  _snake_case							:		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(snake_case__		,       args.host		,       args.port		,       args.workers   )






class  lowercase(					__a			):







 '''simple docstring'''





 lowercase__			 = 42







class  lowercase(					__a			):







 '''simple docstring'''





 lowercase__			 = 42
 lowercase__			 = 42







class  lowercase(					__a			):







 '''simple docstring'''





 lowercase__			 = 42







class  lowercase(					__a			):







 '''simple docstring'''





 lowercase__			 = 42







class  lowercase(					__a			):







 '''simple docstring'''





 @staticmethod
 def 			UpperCamelCase_       (    a_:		ArgumentParser		):




   '''simple docstring'''



   _snake_case							:		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=8_888,				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:		Union[str, Any],				a_:		Pipeline,				a_:		str,				a_:		int,				a_:		int		):




   '''simple docstring'''



   _snake_case							:		List[str]								=  pipeline

   _snake_case							:		Any								=  host
   _snake_case							:		Optional[Any]								=  port
   _snake_case							:		Optional[int]								=  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}"		)
     _snake_case							:		List[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 			UpperCamelCase_       (    self:		str		):




   '''simple docstring'''



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


 def 			UpperCamelCase_       (    self:		Tuple		):




   '''simple docstring'''



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


 def 			UpperCamelCase_       (    self:		Union[str, Any],				a_:		str = Body(a_,				embed=a_		),				a_:		bool = Body(a_,				embed=a_		)		):




   '''simple docstring'''



   try:
     _snake_case							:		Union[str, Any]								=  self._pipeline.tokenizer.tokenize(a_		)

     if return_ids:
       _snake_case							:		Union[str, Any]								=  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 			UpperCamelCase_       (    self:		Any,				a_:		List[int] = Body(a_,				embed=a_		),				a_:		bool = Body(a_,				embed=a_		),				a_:		bool = Body(a_,				embed=a_		),				):




   '''simple docstring'''



   try:
     _snake_case							:		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 			UpperCamelCase_       (    self:		Tuple,				a_:		Optional[Any]=Body(a_,				embed=a_		)		):




   '''simple docstring'''



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

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








"""simple docstring"""


import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
A_      =    logging.get_logger()






@dataclass
class  lowercase:







 '''simple docstring'''





 lowercase__			 = 42
 lowercase__			 = field(default_factory=__a			)
 lowercase__			 = field(default_factory=__a			)


 def 			UpperCamelCase_       (    self:		Optional[Any],				a_:		Union[str, Any],				a_:		Tensor,				a_:		Tensor		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  len(list(m.modules()		)		) == 1 or isinstance(a_,				nn.Convad		) or isinstance(a_,				nn.BatchNormad		)
   if has_not_submodules:
     self.traced.append(a_		)


 def __call__(    self:		List[Any],				a_:		Tensor		):




   '''simple docstring'''



   for m in self.module.modules():
     self.handles.append(m.register_forward_hook(self._forward_hook		)		)
   self.module(a_		)
   [x.remove() for x in self.handles]
   return self


 @property
 def 			UpperCamelCase_       (    self:		Union[str, Any]		):




   '''simple docstring'''



   return list(filter(lambda a_		: len(list(x.state_dict().keys()		)		) > 0,				self.traced		)		)







@dataclass
class  lowercase:







 '''simple docstring'''





 lowercase__			 = 42
 lowercase__			 = 42
 lowercase__			 = 0
 lowercase__			 = field(default_factory=__a			)
 lowercase__			 = field(default_factory=__a			)


 def __call__(    self:		Dict,				a_:		Tensor		):




   '''simple docstring'''



   _snake_case							:		Tuple								=  Tracker(self.dest		)(a_		).parametrized
   _snake_case							:		int								=  Tracker(self.src		)(a_		).parametrized

   _snake_case							:		Tuple								=  list(filter(lambda a_		: type(a_		) not in self.src_skip,				a_		)		)
   _snake_case							:		Union[str, Any]								=  list(filter(lambda a_		: type(a_		) not in self.dest_skip,				a_		)		)

   if len(a_		) != len(a_		):
     raise Exception(
         f"Numbers of operations are different. Source module has {len(a_		)} operations while"
         f" destination module has {len(a_		)}."		)

   for dest_m, src_m in zip(a_,				a_		):
     dest_m.load_state_dict(src_m.state_dict()		)
     if self.verbose == 1:
       print(f"Transfered from={src_m} to={dest_m}"		)







def  UpperCAmelCase__	(snake_case__							: str		,       snake_case__							: ResNetConfig		,       snake_case__							: Path		,       snake_case__							: bool = True   ):
  """simple docstring"""

  print(F"Converting {name}..."   )
  with torch.no_grad():
    _snake_case							:		Dict								=  timm.create_model(snake_case__		,       pretrained=snake_case__   ).eval()
    _snake_case							:		List[Any]								=  ResNetForImageClassification(snake_case__   ).eval()
    _snake_case							:		List[str]								=  ModuleTransfer(src=snake_case__		,       dest=snake_case__   )
    _snake_case							:		Optional[Any]								=  torch.randn((1, 3, 2_24, 2_24)   )
    module_transfer(snake_case__   )

  assert torch.allclose(from_model(snake_case__   )		,       our_model(snake_case__   ).logits   ), "The model logits don't match the original one."

  _snake_case							:		Optional[int]								=  F"resnet{'-'.join(name.split('resnet'   )   )}"
  print(snake_case__   )

  if push_to_hub:
    our_model.push_to_hub(
        repo_path_or_name=save_directory / checkpoint_name		,       commit_message="""Add model"""		,       use_temp_dir=snake_case__		,       )

    # we can use the convnext one
    _snake_case							:		Union[str, Any]								=  AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""   )
    image_processor.push_to_hub(
        repo_path_or_name=save_directory / checkpoint_name		,       commit_message="""Add image processor"""		,       use_temp_dir=snake_case__		,       )

    print(F"Pushed {checkpoint_name}"   )


def  UpperCAmelCase__	(snake_case__							: Path		,       snake_case__							: str = None		,       snake_case__							: bool = True   ):
  """simple docstring"""

  _snake_case							:		Optional[Any]								=  """imagenet-1k-id2label.json"""
  _snake_case							:		Optional[Any]								=  10_00
  _snake_case							:		str								=  (1, num_labels)

  _snake_case							:		List[Any]								=  """huggingface/label-files"""
  _snake_case							:		Union[str, Any]								=  num_labels
  _snake_case							:		Optional[int]								=  json.load(open(hf_hub_download(snake_case__		,       snake_case__		,       repo_type="""dataset"""   )		,       """r"""   )   )
  _snake_case							:		Union[str, Any]								=  {int(snake_case__   ): v for k, v in idalabel.items()}

  _snake_case							:		str								=  idalabel
  _snake_case							:		Union[str, Any]								=  {v: k for k, v in idalabel.items()}

  _snake_case							:		Tuple								=  partial(snake_case__		,       num_labels=snake_case__		,       idalabel=snake_case__		,       labelaid=snake_case__   )

  _snake_case							:		Optional[int]								=  {
      """resnet18""": ImageNetPreTrainedConfig(
          depths=[2, 2, 2, 2]		,       hidden_sizes=[64, 1_28, 2_56, 5_12]		,       layer_type="""basic"""   ),
      """resnet26""": ImageNetPreTrainedConfig(
          depths=[2, 2, 2, 2]		,       hidden_sizes=[2_56, 5_12, 10_24, 20_48]		,       layer_type="""bottleneck"""   ),
      """resnet34""": ImageNetPreTrainedConfig(
          depths=[3, 4, 6, 3]		,       hidden_sizes=[64, 1_28, 2_56, 5_12]		,       layer_type="""basic"""   ),
      """resnet50""": ImageNetPreTrainedConfig(
          depths=[3, 4, 6, 3]		,       hidden_sizes=[2_56, 5_12, 10_24, 20_48]		,       layer_type="""bottleneck"""   ),
      """resnet101""": ImageNetPreTrainedConfig(
          depths=[3, 4, 23, 3]		,       hidden_sizes=[2_56, 5_12, 10_24, 20_48]		,       layer_type="""bottleneck"""   ),
      """resnet152""": ImageNetPreTrainedConfig(
          depths=[3, 8, 36, 3]		,       hidden_sizes=[2_56, 5_12, 10_24, 20_48]		,       layer_type="""bottleneck"""   ),
  }

  if model_name:
    convert_weight_and_push(snake_case__		,       names_to_config[model_name]		,       snake_case__		,       snake_case__   )
  else:
    for model_name, config in names_to_config.items():
      convert_weight_and_push(snake_case__		,       snake_case__		,       snake_case__		,       snake_case__   )
  return config, expected_shape


if __name__ == "__main__":
     A_      =    argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--model_name''',
         default=None,
         type=str,
         help=(
             '''The name of the model you wish to convert, it must be one of the supported resnet* architecture,'''
             ''' currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted.'''
         ),
     )
     parser.add_argument(
         '''--pytorch_dump_folder_path''',
         default=None,
         type=Path,
         required=True,
         help='''Path to the output PyTorch model directory.''',
     )
     parser.add_argument(
         '''--push_to_hub''',
         default=True,
         type=bool,
         required=False,
         help='''If True, push model and image processor to the hub.''',
     )

     A_      =    parser.parse_args()
     A_      =    args.pytorch_dump_folder_path
     pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
     convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)


