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        lowercase(     *UpperCamelCase_				, **UpperCamelCase_    )						->     str:







      '''simple docstring'''
      pass


     _SCREAMING_SNAKE_CASE      						=	False


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





def        lowercase(     UpperCamelCase_    )						->     Tuple:







 '''simple docstring'''
 UpperCamelCase          =	pipeline(
     task=args.task				, model=args.model if args.model else None				, config=args.config				, tokenizer=args.tokenizer				, device=args.device				, )
 return ServeCommand(UpperCamelCase_				, args.host				, args.port				, args.workers    )
class SCREAMING_SNAKE_CASE_						(	__lowerCAmelCase						):
   __lowerCAmelCase      =							42





class SCREAMING_SNAKE_CASE_						(	__lowerCAmelCase						):
   __lowerCAmelCase      =							42
   __lowerCAmelCase      =							42





class SCREAMING_SNAKE_CASE_						(	__lowerCAmelCase						):
   __lowerCAmelCase      =							42





class SCREAMING_SNAKE_CASE_						(	__lowerCAmelCase						):
   __lowerCAmelCase      =							42





class SCREAMING_SNAKE_CASE_						(	__lowerCAmelCase						):







   @staticmethod
   def 		lowerCamelCase_     (			lowerCamelCase_		:		ArgumentParser							):





    """simple docstring"""


    UpperCamelCase          =	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		:		Any					,					lowerCamelCase_		:		Pipeline					,					lowerCamelCase_		:		str					,					lowerCamelCase_		:		int					,					lowerCamelCase_		:		int							):





    """simple docstring"""


    UpperCamelCase          =	pipeline

    UpperCamelCase          =	host
    UpperCamelCase          =	port
    UpperCamelCase          =	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}"""							)
     UpperCamelCase          =	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 		lowerCamelCase_     (			self		:		Any							):





    """simple docstring"""


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







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





    """simple docstring"""


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







   def 		lowerCamelCase_     (			self		:		List[str]					,					lowerCamelCase_		:		str = Body(_lowerCAmelCase					,					embed=_lowerCAmelCase							)					,					lowerCamelCase_		:		bool = Body(_lowerCAmelCase					,					embed=_lowerCAmelCase							)							):





    """simple docstring"""


    try:
     UpperCamelCase          =	self._pipeline.tokenizer.tokenize(_lowerCAmelCase							)

     if return_ids:
      UpperCamelCase          =	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 		lowerCamelCase_     (			self		:		str					,					lowerCamelCase_		:		List[int] = Body(_lowerCAmelCase					,					embed=_lowerCAmelCase							)					,					lowerCamelCase_		:		bool = Body(_lowerCAmelCase					,					embed=_lowerCAmelCase							)					,					lowerCamelCase_		:		bool = Body(_lowerCAmelCase					,					embed=_lowerCAmelCase							)					,					):





    """simple docstring"""


    try:
     UpperCamelCase          =	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 		lowerCamelCase_     (			self		:		Any					,					lowerCamelCase_		:		Union[str, Any]=Body(_lowerCAmelCase					,					embed=_lowerCAmelCase							)							):





    """simple docstring"""


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

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






import gc
import unittest

import numpy as np
import torch

from diffusers import (
    AudioDiffusionPipeline,
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    DiffusionPipeline,
    Mel,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()




class       __lowerCAmelCase				(							unittest.TestCase):






       def     SCREAMING_SNAKE_CASE			(      self:    Union[str, Any] ):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    int ):
         torch.manual_seed(0 )
         lowercase			:str							=  UNetaDModel(
             sample_size=(32, 64)				,    in_channels=1				,    out_channels=1				,    layers_per_block=2				,    block_out_channels=(1_28, 1_28)				,    down_block_types=("AttnDownBlock2D", "DownBlock2D")				,    up_block_types=("UpBlock2D", "AttnUpBlock2D")				,    )
         return model






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    Any ):
         torch.manual_seed(0 )
         lowercase			:Dict							=  UNetaDConditionModel(
             sample_size=(64, 32)				,    in_channels=1				,    out_channels=1				,    layers_per_block=2				,    block_out_channels=(1_28, 1_28)				,    down_block_types=("CrossAttnDownBlock2D", "DownBlock2D")				,    up_block_types=("UpBlock2D", "CrossAttnUpBlock2D")				,    cross_attention_dim=10				,    )
         return model






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    Union[str, Any] ):
         torch.manual_seed(0 )
         lowercase			:List[str]							=  AutoencoderKL(
             sample_size=(1_28, 64)				,    in_channels=1				,    out_channels=1				,    latent_channels=1				,    layers_per_block=2				,    block_out_channels=(1_28, 1_28)				,    down_block_types=("DownEncoderBlock2D", "DownEncoderBlock2D")				,    up_block_types=("UpDecoderBlock2D", "UpDecoderBlock2D")				,    )
         lowercase			:List[str]							=  UNetaDModel(
             sample_size=(64, 32)				,    in_channels=1				,    out_channels=1				,    layers_per_block=2				,    block_out_channels=(1_28, 1_28)				,    down_block_types=("AttnDownBlock2D", "DownBlock2D")				,    up_block_types=("UpBlock2D", "AttnUpBlock2D")				,    )
         return vqvae, unet






       @slow
       def     SCREAMING_SNAKE_CASE			(      self:    str ):
         lowercase			:Optional[Any]							=  "cpu"  # ensure determinism for the device-dependent torch.Generator
         lowercase			:Optional[int]							=  Mel(
             x_res=self.dummy_unet.config.sample_size[1]				,    y_res=self.dummy_unet.config.sample_size[0]				,    )

         lowercase			:List[str]							=  DDPMScheduler()
         lowercase			:Tuple							=  AudioDiffusionPipeline(vqvae=_lowerCAmelCase				,    unet=self.dummy_unet				,    mel=_lowerCAmelCase				,    scheduler=_lowerCAmelCase )
         lowercase			:Dict							=  pipe.to(_lowerCAmelCase )
         pipe.set_progress_bar_config(disable=_lowerCAmelCase )

         lowercase			:Tuple							=  torch.Generator(device=_lowerCAmelCase ).manual_seed(42 )
         lowercase			:Optional[int]							=  pipe(generator=_lowerCAmelCase				,    steps=4 )
         lowercase			:List[str]							=  output.audios[0]
         lowercase			:List[str]							=  output.images[0]

         lowercase			:List[str]							=  torch.Generator(device=_lowerCAmelCase ).manual_seed(42 )
         lowercase			:Optional[int]							=  pipe(generator=_lowerCAmelCase				,    steps=4				,    return_dict=_lowerCAmelCase )
         lowercase			:int							=  output[0][0]

         assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
         assert (
             image.height == self.dummy_unet.config.sample_size[0]
             and image.width == self.dummy_unet.config.sample_size[1]
         )
         lowercase			:Any							=  np.frombuffer(image.tobytes()				,    dtype="uint8" )[:10]
         lowercase			:Dict							=  np.frombuffer(image_from_tuple.tobytes()				,    dtype="uint8" )[:10]
         lowercase			:List[Any]							=  np.array([69, 2_55, 2_55, 2_55, 0, 0, 77, 1_81, 12, 1_27] )

         assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() == 0

         lowercase			:Optional[Any]							=  Mel(
             x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1]				,    y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0]				,    )

         lowercase			:List[str]							=  DDIMScheduler()
         lowercase			:Tuple							=  self.dummy_vqvae_and_unet
         lowercase			:str							=  AudioDiffusionPipeline(
             vqvae=self.dummy_vqvae_and_unet[0]				,    unet=dummy_vqvae_and_unet[1]				,    mel=_lowerCAmelCase				,    scheduler=_lowerCAmelCase )
         lowercase			:Union[str, Any]							=  pipe.to(_lowerCAmelCase )
         pipe.set_progress_bar_config(disable=_lowerCAmelCase )

         np.random.seed(0 )
         lowercase			:Dict							=  np.random.uniform(-1				,    1				,    ((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,) )
         lowercase			:List[Any]							=  torch.Generator(device=_lowerCAmelCase ).manual_seed(42 )
         lowercase			:Dict							=  pipe(raw_audio=_lowerCAmelCase				,    generator=_lowerCAmelCase				,    start_step=5				,    steps=10 )
         lowercase			:str							=  output.images[0]

         assert (
             image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
             and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
         )
         lowercase			:Optional[Any]							=  np.frombuffer(image.tobytes()				,    dtype="uint8" )[:10]
         lowercase			:List[str]							=  np.array([1_20, 1_17, 1_10, 1_09, 1_38, 1_67, 1_38, 1_48, 1_32, 1_21] )

         assert np.abs(image_slice.flatten() - expected_slice ).max() == 0

         lowercase			:Optional[Any]							=  self.dummy_unet_condition
         lowercase			:Optional[int]							=  AudioDiffusionPipeline(
             vqvae=self.dummy_vqvae_and_unet[0]				,    unet=_lowerCAmelCase				,    mel=_lowerCAmelCase				,    scheduler=_lowerCAmelCase )
         lowercase			:int							=  pipe.to(_lowerCAmelCase )
         pipe.set_progress_bar_config(disable=_lowerCAmelCase )

         np.random.seed(0 )
         lowercase			:List[str]							=  torch.rand((1, 1, 10) )
         lowercase			:Union[str, Any]							=  pipe(generator=_lowerCAmelCase				,    encoding=_lowerCAmelCase )
         lowercase			:Tuple							=  output.images[0]
         lowercase			:Optional[Any]							=  np.frombuffer(image.tobytes()				,    dtype="uint8" )[:10]
         lowercase			:Any							=  np.array([1_07, 1_03, 1_20, 1_27, 1_42, 1_22, 1_13, 1_22, 97, 1_11] )

         assert np.abs(image_slice.flatten() - expected_slice ).max() == 0







@slow
@require_torch_gpu
class       __lowerCAmelCase				(							unittest.TestCase):






       def     SCREAMING_SNAKE_CASE			(      self:    Any ):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






       def     SCREAMING_SNAKE_CASE			(      self:    str ):
         lowercase			:Tuple							=  torch_device

         lowercase			:List[Any]							=  DiffusionPipeline.from_pretrained("teticio/audio-diffusion-ddim-256" )
         lowercase			:Dict							=  pipe.to(_lowerCAmelCase )
         pipe.set_progress_bar_config(disable=_lowerCAmelCase )

         lowercase			:Any							=  torch.Generator(device=_lowerCAmelCase ).manual_seed(42 )
         lowercase			:Optional[int]							=  pipe(generator=_lowerCAmelCase )
         lowercase			:List[str]							=  output.audios[0]
         lowercase			:Any							=  output.images[0]

         assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
         assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
         lowercase			:Dict							=  np.frombuffer(image.tobytes()				,    dtype="uint8" )[:10]
         lowercase			:List[str]							=  np.array([1_51, 1_67, 1_54, 1_44, 1_22, 1_34, 1_21, 1_05, 70, 26] )

         assert np.abs(image_slice.flatten() - expected_slice ).max() == 0



