import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
      import transformers

def 							UpperCamelCase				(    lowerCAmelCase_   )		->						Tuple:




       '''simple docstring'''






       random.seed(__lowerCAmelCase   )
       np.random.seed(__lowerCAmelCase   )
       torch.manual_seed(__lowerCAmelCase   )
       torch.cuda.manual_seed_all(__lowerCAmelCase   )
       # ^^ safe to call this function even if cuda is not available





class      lowerCAmelCase						:

     '''simple docstring'''





     def __init__(					self				,      lowerCAmelCase__				,      lowerCAmelCase__ = 0.9999				,      lowerCAmelCase__ = 0.0				,      lowerCAmelCase__ = 0				,      lowerCAmelCase__ = False				,      lowerCAmelCase__ = 1.0				,      lowerCAmelCase__ = 2 / 3				,      lowerCAmelCase__ = None				,      lowerCAmelCase__ = None				,      **lowerCAmelCase__				,      ):







            if isinstance(lowerCamelCase__				,      torch.nn.Module       ):
                   _A=	(
                       "Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. "
                       "Please pass the parameters of the module instead."
                   )
                   deprecate(
                       'passing a `torch.nn.Module` to `ExponentialMovingAverage`'				,      '1.0.0'				,      lowerCamelCase__				,      standard_warn=lowerCamelCase__				,      )
                   _A=	parameters.parameters()

                   # set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
                   _A=	True

            if kwargs.get('max_value'				,      lowerCamelCase__       ) is not None:
                   _A=	"The `max_value` argument is deprecated. Please use `decay` instead."
                   deprecate('max_value'				,      '1.0.0'				,      lowerCamelCase__				,      standard_warn=lowerCamelCase__       )
                   _A=	kwargs["max_value"]

            if kwargs.get('min_value'				,      lowerCamelCase__       ) is not None:
                   _A=	"The `min_value` argument is deprecated. Please use `min_decay` instead."
                   deprecate('min_value'				,      '1.0.0'				,      lowerCamelCase__				,      standard_warn=lowerCamelCase__       )
                   _A=	kwargs["min_value"]

            _A=	list(lowerCamelCase__       )
            _A=	[p.clone().detach() for p in parameters]

            if kwargs.get('device'				,      lowerCamelCase__       ) is not None:
                   _A=	"The `device` argument is deprecated. Please use `to` instead."
                   deprecate('device'				,      '1.0.0'				,      lowerCamelCase__				,      standard_warn=lowerCamelCase__       )
                   self.to(device=kwargs['device']       )

            _A=	None

            _A=	decay
            _A=	min_decay
            _A=	update_after_step
            _A=	use_ema_warmup
            _A=	inv_gamma
            _A=	power
            _A=	0
            _A=	None  # set in `step()`

            _A=	model_cls
            _A=	model_config



     @classmethod
     def 			a__  (					cls				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            _A=	model_cls.load_config(lowerCamelCase__				,      return_unused_kwargs=lowerCamelCase__       )
            _A=	model_cls.from_pretrained(lowerCamelCase__       )

            _A=	cls(model.parameters()				,      model_cls=lowerCamelCase__				,      model_config=model.config       )

            ema_model.load_state_dict(lowerCamelCase__       )
            return ema_model



     def 			a__  (					self				,      lowerCAmelCase__       ):







            if self.model_cls is None:
                   raise ValueError('`save_pretrained` can only be used if `model_cls` was defined at __init__.'       )

            if self.model_config is None:
                   raise ValueError('`save_pretrained` can only be used if `model_config` was defined at __init__.'       )

            _A=	self.model_cls.from_config(self.model_config       )
            _A=	self.state_dict()
            state_dict.pop('shadow_params'				,      lowerCamelCase__       )

            model.register_to_config(**lowerCamelCase__       )
            self.copy_to(model.parameters()       )
            model.save_pretrained(lowerCamelCase__       )



     def 			a__  (					self				,      lowerCAmelCase__       ):
            _A=	max(0				,      optimization_step - self.update_after_step - 1       )

            if step <= 0:
                   return 0.0

            if self.use_ema_warmup:
                   _A=	1 - (1 + step / self.inv_gamma) ** -self.power
            else:
                   _A=	(1 + step) / (10 + step)

            _A=	min(lowerCamelCase__				,      self.decay       )
            # make sure decay is not smaller than min_decay
            _A=	max(lowerCamelCase__				,      self.min_decay       )
            return cur_decay_value



     @torch.no_grad()
     def 			a__  (					self				,      lowerCAmelCase__       ):







            if isinstance(lowerCamelCase__				,      torch.nn.Module       ):
                   _A=	(
                       "Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. "
                       "Please pass the parameters of the module instead."
                   )
                   deprecate(
                       'passing a `torch.nn.Module` to `ExponentialMovingAverage.step`'				,      '1.0.0'				,      lowerCamelCase__				,      standard_warn=lowerCamelCase__				,      )
                   _A=	parameters.parameters()

            _A=	list(lowerCamelCase__       )

            self.optimization_step += 1

            # Compute the decay factor for the exponential moving average.
            _A=	self.get_decay(self.optimization_step       )
            _A=	decay
            _A=	1 - decay

            _A=	contextlib.nullcontext
            if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
                   import deepspeed

            for s_param, param in zip(self.shadow_params				,      lowerCamelCase__       ):
                   if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
                          _A=	deepspeed.zero.GatheredParameters(lowerCamelCase__				,      modifier_rank=lowerCamelCase__       )

                   with context_manager():
                          if param.requires_grad:
                                 s_param.sub_(one_minus_decay * (s_param - param)       )
                          else:
                                 s_param.copy_(lowerCamelCase__       )



     def 			a__  (					self				,      lowerCAmelCase__       ):
            _A=	list(lowerCamelCase__       )
            for s_param, param in zip(self.shadow_params				,      lowerCamelCase__       ):
                   param.data.copy_(s_param.to(param.device       ).data       )



     def 			a__  (					self				,      lowerCAmelCase__=None				,      lowerCAmelCase__=None       ):
            _A=	[
                p.to(device=lowerCamelCase__				,      dtype=lowerCamelCase__       ) if p.is_floating_point() else p.to(device=lowerCamelCase__       )
                for p in self.shadow_params
            ]



     def 			a__  (					self       ):
            return {
                "decay": self.decay,
                "min_decay": self.min_decay,
                "optimization_step": self.optimization_step,
                "update_after_step": self.update_after_step,
                "use_ema_warmup": self.use_ema_warmup,
                "inv_gamma": self.inv_gamma,
                "power": self.power,
                "shadow_params": self.shadow_params,
            }



     def 			a__  (					self				,      lowerCAmelCase__       ):
            _A=	[param.detach().cpu().clone() for param in parameters]



     def 			a__  (					self				,      lowerCAmelCase__       ):







            if self.temp_stored_params is None:
                   raise RuntimeError('This ExponentialMovingAverage has no `store()`ed weights ' 'to `restore()`'       )
            for c_param, param in zip(self.temp_stored_params				,      lowerCamelCase__       ):
                   param.data.copy_(c_param.data       )

            # Better memory-wise.
            _A=	None





     def 			a__  (					self				,      lowerCAmelCase__       ):
            _A=	copy.deepcopy(lowerCamelCase__       )

            _A=	state_dict.get('decay'				,      self.decay       )
            if self.decay < 0.0 or self.decay > 1.0:
                   raise ValueError('Decay must be between 0 and 1'       )

            _A=	state_dict.get('min_decay'				,      self.min_decay       )
            if not isinstance(self.min_decay				,      lowerCamelCase__       ):
                   raise ValueError('Invalid min_decay'       )

            _A=	state_dict.get('optimization_step'				,      self.optimization_step       )
            if not isinstance(self.optimization_step				,      lowerCamelCase__       ):
                   raise ValueError('Invalid optimization_step'       )

            _A=	state_dict.get('update_after_step'				,      self.update_after_step       )
            if not isinstance(self.update_after_step				,      lowerCamelCase__       ):
                   raise ValueError('Invalid update_after_step'       )

            _A=	state_dict.get('use_ema_warmup'				,      self.use_ema_warmup       )
            if not isinstance(self.use_ema_warmup				,      lowerCamelCase__       ):
                   raise ValueError('Invalid use_ema_warmup'       )

            _A=	state_dict.get('inv_gamma'				,      self.inv_gamma       )
            if not isinstance(self.inv_gamma				,      (float, int)       ):
                   raise ValueError('Invalid inv_gamma'       )

            _A=	state_dict.get('power'				,      self.power       )
            if not isinstance(self.power				,      (float, int)       ):
                   raise ValueError('Invalid power'       )

            _A=	state_dict.get('shadow_params'				,      lowerCamelCase__       )
            if shadow_params is not None:
                   _A=	shadow_params
                   if not isinstance(self.shadow_params				,      lowerCamelCase__       ):
                          raise ValueError('shadow_params must be a list'       )
                   if not all(isinstance(lowerCamelCase__				,      torch.Tensor       ) for p in self.shadow_params       ):
                          raise ValueError('shadow_params must all be Tensors'       )
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__       )}       )