

'''simple docstring'''


import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest




class     a			(				UpperCamelCase_       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    (DEISMultistepScheduler,)
						__UpperCAmelCase			     =    (("""num_inference_steps""", 25),)







						def       __magic_name__							(							self					:      List[str]			,			**snake_case_					:      Tuple  ):



								'''simple docstring'''



								snake_case__						:       Dict       						=  {
								    '''num_train_timesteps''': 1_0_0_0,
								    '''beta_start''': 0.0_0_0_1,
								    '''beta_end''': 0.0_2,
								    '''beta_schedule''': '''linear''',
								    '''solver_order''': 2,
								}

								config.update(**snake_case_  )
								return config







						def       __magic_name__							(							self					:      Optional[int]			,			snake_case_					:      Optional[int]=0			,			**snake_case_					:      str  ):



								'''simple docstring'''



								snake_case__						:       str       						=  dict(self.forward_default_kwargs  )
								snake_case__						:       Optional[int]       						=  kwargs.pop('''num_inference_steps'''			,			snake_case_  )
								snake_case__						:       str       						=  self.dummy_sample
								snake_case__						:       str       						=  0.1 * sample
								snake_case__						:       List[str]       						=  [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

								for scheduler_class in self.scheduler_classes:
										snake_case__						:       str       						=  self.get_scheduler_config(**snake_case_  )
										snake_case__						:       Union[str, Any]       						=  scheduler_class(**snake_case_  )
										scheduler.set_timesteps(snake_case_  )
										# copy over dummy past residuals
										snake_case__						:       Optional[Any]       						=  dummy_past_residuals[: scheduler.config.solver_order]

										with tempfile.TemporaryDirectory() as tmpdirname:
												scheduler.save_config(snake_case_  )
												snake_case__						:       Optional[int]       						=  scheduler_class.from_pretrained(snake_case_  )
												new_scheduler.set_timesteps(snake_case_  )
												# copy over dummy past residuals
												snake_case__						:       Tuple       						=  dummy_past_residuals[: new_scheduler.config.solver_order]

										snake_case__						:       List[Any]       						=  sample, sample
										for t in range(snake_case_			,			time_step + scheduler.config.solver_order + 1  ):
												snake_case__						:       Optional[int]       						=  scheduler.step(snake_case_			,			snake_case_			,			snake_case_			,			**snake_case_  ).prev_sample
												snake_case__						:       str       						=  new_scheduler.step(snake_case_			,			snake_case_			,			snake_case_			,			**snake_case_  ).prev_sample

												assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"







						def       __magic_name__							(							self					:      Any  ):



								'''simple docstring'''



								pass







						def       __magic_name__							(							self					:      Optional[int]			,			snake_case_					:      List[str]=0			,			**snake_case_					:      Optional[Any]  ):



								'''simple docstring'''



								snake_case__						:       str       						=  dict(self.forward_default_kwargs  )
								snake_case__						:       List[str]       						=  kwargs.pop('''num_inference_steps'''			,			snake_case_  )
								snake_case__						:       Union[str, Any]       						=  self.dummy_sample
								snake_case__						:       int       						=  0.1 * sample
								snake_case__						:       int       						=  [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

								for scheduler_class in self.scheduler_classes:
										snake_case__						:       Tuple       						=  self.get_scheduler_config()
										snake_case__						:       Union[str, Any]       						=  scheduler_class(**snake_case_  )
										scheduler.set_timesteps(snake_case_  )

										# copy over dummy past residuals (must be after setting timesteps)
										snake_case__						:       List[Any]       						=  dummy_past_residuals[: scheduler.config.solver_order]

										with tempfile.TemporaryDirectory() as tmpdirname:
												scheduler.save_config(snake_case_  )
												snake_case__						:       List[Any]       						=  scheduler_class.from_pretrained(snake_case_  )
												# copy over dummy past residuals
												new_scheduler.set_timesteps(snake_case_  )

												# copy over dummy past residual (must be after setting timesteps)
												snake_case__						:       Dict       						=  dummy_past_residuals[: new_scheduler.config.solver_order]

										snake_case__						:       Tuple       						=  scheduler.step(snake_case_			,			snake_case_			,			snake_case_			,			**snake_case_  ).prev_sample
										snake_case__						:       Union[str, Any]       						=  new_scheduler.step(snake_case_			,			snake_case_			,			snake_case_			,			**snake_case_  ).prev_sample

										assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"







						def       __magic_name__							(							self					:      List[str]			,			snake_case_					:      str=None			,			**snake_case_					:      Tuple  ):



								'''simple docstring'''



								if scheduler is None:
										snake_case__						:       Dict       						=  self.scheduler_classes[0]
										snake_case__						:       Optional[Any]       						=  self.get_scheduler_config(**snake_case_  )
										snake_case__						:       int       						=  scheduler_class(**snake_case_  )

								snake_case__						:       Optional[int]       						=  self.scheduler_classes[0]
								snake_case__						:       str       						=  self.get_scheduler_config(**snake_case_  )
								snake_case__						:       Any       						=  scheduler_class(**snake_case_  )

								snake_case__						:       Dict       						=  1_0
								snake_case__						:       Optional[int]       						=  self.dummy_model()
								snake_case__						:       Any       						=  self.dummy_sample_deter
								scheduler.set_timesteps(snake_case_  )

								for i, t in enumerate(scheduler.timesteps  ):
										snake_case__						:       List[str]       						=  model(snake_case_			,			snake_case_  )
										snake_case__						:       Any       						=  scheduler.step(snake_case_			,			snake_case_			,			snake_case_  ).prev_sample

								return sample







						def       __magic_name__							(							self					:      str  ):



								'''simple docstring'''



								snake_case__						:       List[Any]       						=  dict(self.forward_default_kwargs  )

								snake_case__						:       Dict       						=  kwargs.pop('''num_inference_steps'''			,			snake_case_  )

								for scheduler_class in self.scheduler_classes:
										snake_case__						:       Optional[Any]       						=  self.get_scheduler_config()
										snake_case__						:       List[Any]       						=  scheduler_class(**snake_case_  )

										snake_case__						:       str       						=  self.dummy_sample
										snake_case__						:       List[str]       						=  0.1 * sample

										if num_inference_steps is not None and hasattr(snake_case_			,			'''set_timesteps'''  ):
												scheduler.set_timesteps(snake_case_  )
										elif num_inference_steps is not None and not hasattr(snake_case_			,			'''set_timesteps'''  ):
												snake_case__						:       List[str]       						=  num_inference_steps

										# copy over dummy past residuals (must be done after set_timesteps)
										snake_case__						:       Tuple       						=  [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
										snake_case__						:       List[str]       						=  dummy_past_residuals[: scheduler.config.solver_order]

										snake_case__						:       Dict       						=  scheduler.timesteps[5]
										snake_case__						:       List[str]       						=  scheduler.timesteps[6]

										snake_case__						:       List[str]       						=  scheduler.step(snake_case_			,			snake_case_			,			snake_case_			,			**snake_case_  ).prev_sample
										snake_case__						:       Optional[int]       						=  scheduler.step(snake_case_			,			snake_case_			,			snake_case_			,			**snake_case_  ).prev_sample

										self.assertEqual(output_a.shape			,			sample.shape  )
										self.assertEqual(output_a.shape			,			output_a.shape  )







						def       __magic_name__							(							self					:      int  ):



								'''simple docstring'''



								snake_case__						:       str       						=  DEISMultistepScheduler(**self.get_scheduler_config()  )
								snake_case__						:       List[str]       						=  self.full_loop(scheduler=snake_case_  )
								snake_case__						:       Tuple       						=  torch.mean(torch.abs(snake_case_  )  )

								assert abs(result_mean.item() - 0.2_3_9_1_6  ) < 1e-3

								snake_case__						:       int       						=  DPMSolverSinglestepScheduler.from_config(scheduler.config  )
								snake_case__						:       int       						=  DPMSolverMultistepScheduler.from_config(scheduler.config  )
								snake_case__						:       str       						=  UniPCMultistepScheduler.from_config(scheduler.config  )
								snake_case__						:       str       						=  DEISMultistepScheduler.from_config(scheduler.config  )

								snake_case__						:       Optional[int]       						=  self.full_loop(scheduler=snake_case_  )
								snake_case__						:       Tuple       						=  torch.mean(torch.abs(snake_case_  )  )

								assert abs(result_mean.item() - 0.2_3_9_1_6  ) < 1e-3







						def       __magic_name__							(							self					:      Optional[int]  ):



								'''simple docstring'''



								for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
										self.check_over_configs(num_train_timesteps=snake_case_  )







						def       __magic_name__							(							self					:      Any  ):



								'''simple docstring'''



								self.check_over_configs(thresholding=snake_case_  )
								for order in [1, 2, 3]:
										for solver_type in ["logrho"]:
												for threshold in [0.5, 1.0, 2.0]:
														for prediction_type in ["epsilon", "sample"]:
																self.check_over_configs(
																    thresholding=snake_case_			,			prediction_type=snake_case_			,			sample_max_value=snake_case_			,			algorithm_type='''deis'''			,			solver_order=snake_case_			,			solver_type=snake_case_			,			)







						def       __magic_name__							(							self					:      Tuple  ):



								'''simple docstring'''



								for prediction_type in ["epsilon", "v_prediction"]:
										self.check_over_configs(prediction_type=snake_case_  )







						def       __magic_name__							(							self					:      Dict  ):



								'''simple docstring'''



								for algorithm_type in ["deis"]:
										for solver_type in ["logrho"]:
												for order in [1, 2, 3]:
														for prediction_type in ["epsilon", "sample"]:
																self.check_over_configs(
																    solver_order=snake_case_			,			solver_type=snake_case_			,			prediction_type=snake_case_			,			algorithm_type=snake_case_			,			)
																snake_case__						:       List[Any]       						=  self.full_loop(
																    solver_order=snake_case_			,			solver_type=snake_case_			,			prediction_type=snake_case_			,			algorithm_type=snake_case_			,			)
																assert not torch.isnan(snake_case_  ).any(), "Samples have nan numbers"







						def       __magic_name__							(							self					:      Union[str, Any]  ):



								'''simple docstring'''



								self.check_over_configs(lower_order_final=snake_case_  )
								self.check_over_configs(lower_order_final=snake_case_  )







						def       __magic_name__							(							self					:      List[Any]  ):



								'''simple docstring'''



								for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
										self.check_over_forward(num_inference_steps=snake_case_			,			time_step=0  )







						def       __magic_name__							(							self					:      Dict  ):



								'''simple docstring'''



								snake_case__						:       List[Any]       						=  self.full_loop()
								snake_case__						:       Optional[Any]       						=  torch.mean(torch.abs(snake_case_  )  )

								assert abs(result_mean.item() - 0.2_3_9_1_6  ) < 1e-3







						def       __magic_name__							(							self					:      Optional[int]  ):



								'''simple docstring'''



								snake_case__						:       List[Any]       						=  self.full_loop(prediction_type='''v_prediction'''  )
								snake_case__						:       str       						=  torch.mean(torch.abs(snake_case_  )  )

								assert abs(result_mean.item() - 0.0_9_1  ) < 1e-3







						def       __magic_name__							(							self					:      Tuple  ):



								'''simple docstring'''



								snake_case__						:       Union[str, Any]       						=  self.scheduler_classes[0]
								snake_case__						:       Tuple       						=  self.get_scheduler_config(thresholding=snake_case_			,			dynamic_thresholding_ratio=0  )
								snake_case__						:       str       						=  scheduler_class(**snake_case_  )

								snake_case__						:       List[Any]       						=  1_0
								snake_case__						:       str       						=  self.dummy_model()
								snake_case__						:       List[str]       						=  self.dummy_sample_deter.half()
								scheduler.set_timesteps(snake_case_  )

								for i, t in enumerate(scheduler.timesteps  ):
										snake_case__						:       int       						=  model(snake_case_			,			snake_case_  )
										snake_case__						:       Dict       						=  scheduler.step(snake_case_			,			snake_case_			,			snake_case_  ).prev_sample

								assert sample.dtype == torch.floataa






'''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

					lowerCAmelCase__						:       Any										=						True
except (ImportError, AttributeError):
					lowerCAmelCase__						:       Dict										=						object




					def 	_a       (				*__lowerCAmelCase		:      List[str]   ,							**__lowerCAmelCase		:      Tuple     ):

							"""simple docstring"""

							pass





					lowerCAmelCase__						:       str										=						False


lowerCAmelCase__						:       List[str]										=						logging.get_logger("""transformers-cli/serving""")




def 	_a       (				__lowerCAmelCase		:      Namespace     ):

		"""simple docstring"""

		snake_case__						:       Union[str, 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     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    42





class     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    42
						__UpperCAmelCase			     =    42





class     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    42





class     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    42





class     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						@staticmethod
						def       __magic_name__							(							snake_case_					:      ArgumentParser  ):



								'''simple docstring'''



								snake_case__						:       Optional[Any]       						=  parser.add_parser(
								    '''serve'''			,			help='''CLI tool to run inference requests through REST and GraphQL endpoints.'''  )
								serve_parser.add_argument(
								    '''--task'''			,			type=snake_case_			,			choices=get_supported_tasks()			,			help='''The task to run the pipeline on'''			,			)
								serve_parser.add_argument('''--host'''			,			type=snake_case_			,			default='''localhost'''			,			help='''Interface the server will listen on.'''  )
								serve_parser.add_argument('''--port'''			,			type=snake_case_			,			default=8_8_8_8			,			help='''Port the serving will listen to.'''  )
								serve_parser.add_argument('''--workers'''			,			type=snake_case_			,			default=1			,			help='''Number of http workers'''  )
								serve_parser.add_argument('''--model'''			,			type=snake_case_			,			help='''Model\'s name or path to stored model.'''  )
								serve_parser.add_argument('''--config'''			,			type=snake_case_			,			help='''Model\'s config name or path to stored model.'''  )
								serve_parser.add_argument('''--tokenizer'''			,			type=snake_case_			,			help='''Tokenizer name to use.'''  )
								serve_parser.add_argument(
								    '''--device'''			,			type=snake_case_			,			default=-1			,			help='''Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)'''			,			)
								serve_parser.set_defaults(func=snake_case_  )







						def __init__(							self					:      Union[str, Any]			,			snake_case_					:      Pipeline			,			snake_case_					:      str			,			snake_case_					:      int			,			snake_case_					:      int  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  pipeline

								snake_case__						:       Tuple       						=  host
								snake_case__						:       Optional[Any]       						=  port
								snake_case__						:       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}"""  )
										snake_case__						:       str       						=  FastAPI(
										    routes=[
										        APIRoute(
										            '''/'''			,			self.model_info			,			response_model=snake_case_			,			response_class=snake_case_			,			methods=['''GET''']			,			),
										        APIRoute(
										            '''/tokenize'''			,			self.tokenize			,			response_model=snake_case_			,			response_class=snake_case_			,			methods=['''POST''']			,			),
										        APIRoute(
										            '''/detokenize'''			,			self.detokenize			,			response_model=snake_case_			,			response_class=snake_case_			,			methods=['''POST''']			,			),
										        APIRoute(
										            '''/forward'''			,			self.forward			,			response_model=snake_case_			,			response_class=snake_case_			,			methods=['''POST''']			,			),
										    ]			,			timeout=6_0_0			,			)







						def       __magic_name__							(							self					:      str  ):



								'''simple docstring'''



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







						def       __magic_name__							(							self					:      Optional[Any]  ):



								'''simple docstring'''



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







						def       __magic_name__							(							self					:      List[str]			,			snake_case_					:      str = Body(snake_case_			,			embed=snake_case_  )			,			snake_case_					:      bool = Body(snake_case_			,			embed=snake_case_  )  ):



								'''simple docstring'''



								try:
										snake_case__						:       Optional[Any]       						=  self._pipeline.tokenizer.tokenize(snake_case_  )

										if return_ids:
												snake_case__						:       Optional[int]       						=  self._pipeline.tokenizer.convert_tokens_to_ids(snake_case_  )
												return ServeTokenizeResult(tokens=snake_case_			,			tokens_ids=snake_case_  )
										else:
												return ServeTokenizeResult(tokens=snake_case_  )

								except Exception as e:
										raise HTTPException(status_code=5_0_0			,			detail={'''model''': '''''', '''error''': str(snake_case_  )}  )







						def       __magic_name__							(							self					:      List[Any]			,			snake_case_					:      List[int] = Body(snake_case_			,			embed=snake_case_  )			,			snake_case_					:      bool = Body(snake_case_			,			embed=snake_case_  )			,			snake_case_					:      bool = Body(snake_case_			,			embed=snake_case_  )			,			):



								'''simple docstring'''



								try:
										snake_case__						:       Optional[int]       						=  self._pipeline.tokenizer.decode(snake_case_			,			snake_case_			,			snake_case_  )
										return ServeDeTokenizeResult(model=''''''			,			text=snake_case_  )
								except Exception as e:
										raise HTTPException(status_code=5_0_0			,			detail={'''model''': '''''', '''error''': str(snake_case_  )}  )







						async def       __magic_name__							(							self					:      Tuple			,			snake_case_					:      List[str]=Body(snake_case_			,			embed=snake_case_  )  ):



								'''simple docstring'''



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

								try:
										# Forward through the model
										snake_case__						:       Tuple       						=  self._pipeline(snake_case_  )
										return ServeForwardResult(output=snake_case_  )
								except Exception as e:
										raise HTTPException(5_0_0			,			{'''error''': str(snake_case_  )}  )



