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						:   int         =			True
except (ImportError, AttributeError):
				__SCREAMING_SNAKE_CASE						:   List[str]         =			object
				def  snake_case						(*__lowercase ,      **__lowercase						)	->	Tuple:

										'''simple docstring'''



										pass

				__SCREAMING_SNAKE_CASE						:   Optional[int]         =			False


__SCREAMING_SNAKE_CASE						:   Any         =			logging.get_logger('transformers-cli/serving')
def  snake_case						(__lowercase						)	->	List[Any]:

						'''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(__lowercase ,      args.host ,      args.port ,      args.workers						)



class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		42




class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		42
							_lowerCamelCase						=		42




class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		42




class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		42




class        lowercase_   (					__snake_case		):



							@staticmethod
							def 	UpperCamelCase	(						lowercase_    ):
													_snake_case						:					Any    =   parser.add_parser(
													    "serve"				,    help="CLI tool to run inference requests through REST and GraphQL endpoints."    )
													serve_parser.add_argument(
													    "--task"				,    type=lowercase_				,    choices=get_supported_tasks()				,    help="The task to run the pipeline on"				,    )
													serve_parser.add_argument("--host"				,    type=lowercase_				,    default="localhost"				,    help="Interface the server will listen on."    )
													serve_parser.add_argument("--port"				,    type=lowercase_				,    default=8_888				,    help="Port the serving will listen to."    )
													serve_parser.add_argument("--workers"				,    type=lowercase_				,    default=1				,    help="Number of http workers"    )
													serve_parser.add_argument("--model"				,    type=lowercase_				,    help="Model's name or path to stored model."    )
													serve_parser.add_argument("--config"				,    type=lowercase_				,    help="Model's config name or path to stored model."    )
													serve_parser.add_argument("--tokenizer"				,    type=lowercase_				,    help="Tokenizer name to use."    )
													serve_parser.add_argument(
													    "--device"				,    type=lowercase_				,    default=-1				,    help="Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)"				,    )
													serve_parser.set_defaults(func=lowercase_    )



							def __init__(						self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_    ):
													_snake_case						:					Dict    =   pipeline

													_snake_case						:					Any    =   host
													_snake_case						:					List[Any]    =   port
													_snake_case						:					Optional[Any]    =   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[str]    =   FastAPI(
																			    routes=[
																			        APIRoute(
																			            "/"				,    self.model_info				,    response_model=lowercase_				,    response_class=lowercase_				,    methods=["GET"]				,    ),
																			        APIRoute(
																			            "/tokenize"				,    self.tokenize				,    response_model=lowercase_				,    response_class=lowercase_				,    methods=["POST"]				,    ),
																			        APIRoute(
																			            "/detokenize"				,    self.detokenize				,    response_model=lowercase_				,    response_class=lowercase_				,    methods=["POST"]				,    ),
																			        APIRoute(
																			            "/forward"				,    self.forward				,    response_model=lowercase_				,    response_class=lowercase_				,    methods=["POST"]				,    ),
																			    ]				,    timeout=600				,    )



							def 	UpperCamelCase	(						self    ):
													run(self._app				,    host=self.host				,    port=self.port				,    workers=self.workers    )



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



							def 	UpperCamelCase	(						self				,    lowercase_ = Body(lowercase_				,    embed=lowercase_    )				,    lowercase_ = Body(lowercase_				,    embed=lowercase_    )    ):
													try:
																			_snake_case						:					Any    =   self._pipeline.tokenizer.tokenize(lowercase_    )

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

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



							def 	UpperCamelCase	(						self				,    lowercase_ = Body(lowercase_				,    embed=lowercase_    )				,    lowercase_ = Body(lowercase_				,    embed=lowercase_    )				,    lowercase_ = Body(lowercase_				,    embed=lowercase_    )				,    ):
													try:
																			_snake_case						:					List[Any]    =   self._pipeline.tokenizer.decode(lowercase_				,    lowercase_				,    lowercase_    )
																			return ServeDeTokenizeResult(model=""				,    text=lowercase_    )
													except Exception as e:
																			raise HTTPException(status_code=500				,    detail={"model": "", "error": str(lowercase_    )}    )



							async def 	UpperCamelCase	(						self				,    lowercase_=Body(lowercase_				,    embed=lowercase_    )    ):

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

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

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np



class        lowercase_   :



							def 	UpperCamelCase	(						self    ):
													torch.manual_seed(0    )
													_snake_case						:					Tuple    =   TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"    )

													torch.manual_seed(0    )
													_snake_case						:					List[Any]    =   AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"    )

													torch.manual_seed(0    )
													_snake_case						:					Optional[int]    =   UNetaDConditionModel(
													    sample_size=32				,    layers_per_block=1				,    block_out_channels=[32, 64]				,    down_block_types=[
													        "ResnetDownsampleBlock2D",
													        "SimpleCrossAttnDownBlock2D",
													    ]				,    mid_block_type="UNetMidBlock2DSimpleCrossAttn"				,    up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]				,    in_channels=3				,    out_channels=6				,    cross_attention_dim=32				,    encoder_hid_dim=32				,    attention_head_dim=8				,    addition_embed_type="text"				,    addition_embed_type_num_heads=2				,    cross_attention_norm="group_norm"				,    resnet_time_scale_shift="scale_shift"				,    act_fn="gelu"				,    )
													unet.set_attn_processor(AttnAddedKVProcessor()    )  # For reproducibility tests

													torch.manual_seed(0    )
													_snake_case						:					str    =   DDPMScheduler(
													    num_train_timesteps=1_000				,    beta_schedule="squaredcos_cap_v2"				,    beta_start=0.0_001				,    beta_end=0.02				,    thresholding=lowercase_				,    dynamic_thresholding_ratio=0.95				,    sample_max_value=1.0				,    prediction_type="epsilon"				,    variance_type="learned_range"				,    )

													torch.manual_seed(0    )
													_snake_case						:					int    =   IFWatermarker()

													return {
													    "text_encoder": text_encoder,
													    "tokenizer": tokenizer,
													    "unet": unet,
													    "scheduler": scheduler,
													    "watermarker": watermarker,
													    "safety_checker": None,
													    "feature_extractor": None,
													}



							def 	UpperCamelCase	(						self    ):
													torch.manual_seed(0    )
													_snake_case						:					List[str]    =   TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"    )

													torch.manual_seed(0    )
													_snake_case						:					Tuple    =   AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"    )

													torch.manual_seed(0    )
													_snake_case						:					Any    =   UNetaDConditionModel(
													    sample_size=32				,    layers_per_block=[1, 2]				,    block_out_channels=[32, 64]				,    down_block_types=[
													        "ResnetDownsampleBlock2D",
													        "SimpleCrossAttnDownBlock2D",
													    ]				,    mid_block_type="UNetMidBlock2DSimpleCrossAttn"				,    up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]				,    in_channels=6				,    out_channels=6				,    cross_attention_dim=32				,    encoder_hid_dim=32				,    attention_head_dim=8				,    addition_embed_type="text"				,    addition_embed_type_num_heads=2				,    cross_attention_norm="group_norm"				,    resnet_time_scale_shift="scale_shift"				,    act_fn="gelu"				,    class_embed_type="timestep"				,    mid_block_scale_factor=1.414				,    time_embedding_act_fn="gelu"				,    time_embedding_dim=32				,    )
													unet.set_attn_processor(AttnAddedKVProcessor()    )  # For reproducibility tests

													torch.manual_seed(0    )
													_snake_case						:					Union[str, Any]    =   DDPMScheduler(
													    num_train_timesteps=1_000				,    beta_schedule="squaredcos_cap_v2"				,    beta_start=0.0_001				,    beta_end=0.02				,    thresholding=lowercase_				,    dynamic_thresholding_ratio=0.95				,    sample_max_value=1.0				,    prediction_type="epsilon"				,    variance_type="learned_range"				,    )

													torch.manual_seed(0    )
													_snake_case						:					List[str]    =   DDPMScheduler(
													    num_train_timesteps=1_000				,    beta_schedule="squaredcos_cap_v2"				,    beta_start=0.0_001				,    beta_end=0.02				,    )

													torch.manual_seed(0    )
													_snake_case						:					Dict    =   IFWatermarker()

													return {
													    "text_encoder": text_encoder,
													    "tokenizer": tokenizer,
													    "unet": unet,
													    "scheduler": scheduler,
													    "image_noising_scheduler": image_noising_scheduler,
													    "watermarker": watermarker,
													    "safety_checker": None,
													    "feature_extractor": None,
													}



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					List[Any]    =   self.get_dummy_components()
													_snake_case						:					List[Any]    =   self.pipeline_class(**lowercase_    )
													pipe.to(lowercase_    )
													pipe.set_progress_bar_config(disable=lowercase_    )

													_snake_case						:					Union[str, Any]    =   self.get_dummy_inputs(lowercase_    )

													_snake_case						:					Union[str, Any]    =   inputs["prompt"]
													_snake_case						:					Dict    =   inputs["generator"]
													_snake_case						:					Any    =   inputs["num_inference_steps"]
													_snake_case						:					Union[str, Any]    =   inputs["output_type"]

													if "image" in inputs:
																			_snake_case						:					int    =   inputs["image"]
													else:
																			_snake_case						:					Union[str, Any]    =   None

													if "mask_image" in inputs:
																			_snake_case						:					int    =   inputs["mask_image"]
													else:
																			_snake_case						:					List[str]    =   None

													if "original_image" in inputs:
																			_snake_case						:					Tuple    =   inputs["original_image"]
													else:
																			_snake_case						:					Any    =   None

													_snake_case    ,_snake_case						:					Optional[int]    =   pipe.encode_prompt(lowercase_    )

													# inputs with prompt converted to embeddings
													_snake_case						:					Dict    =   {
													    "prompt_embeds": prompt_embeds,
													    "negative_prompt_embeds": negative_prompt_embeds,
													    "generator": generator,
													    "num_inference_steps": num_inference_steps,
													    "output_type": output_type,
													}

													if image is not None:
																			_snake_case						:					int    =   image

													if mask_image is not None:
																			_snake_case						:					int    =   mask_image

													if original_image is not None:
																			_snake_case						:					Any    =   original_image

													# set all optional components to None
													for optional_component in pipe._optional_components:
																			setattr(lowercase_				,    lowercase_				,    lowercase_    )

													_snake_case						:					Optional[int]    =   pipe(**lowercase_    )[0]

													with tempfile.TemporaryDirectory() as tmpdir:
																			pipe.save_pretrained(lowercase_    )
																			_snake_case						:					Any    =   self.pipeline_class.from_pretrained(lowercase_    )
																			pipe_loaded.to(lowercase_    )
																			pipe_loaded.set_progress_bar_config(disable=lowercase_    )

													pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()    )  # For reproducibility tests

													for optional_component in pipe._optional_components:
																			self.assertTrue(
																			    getattr(lowercase_				,    lowercase_    ) is None				,    f"""`{optional_component}` did not stay set to None after loading."""				,    )

													_snake_case						:					int    =   self.get_dummy_inputs(lowercase_    )

													_snake_case						:					Optional[int]    =   inputs["generator"]
													_snake_case						:					List[Any]    =   inputs["num_inference_steps"]
													_snake_case						:					Tuple    =   inputs["output_type"]

													# inputs with prompt converted to embeddings
													_snake_case						:					int    =   {
													    "prompt_embeds": prompt_embeds,
													    "negative_prompt_embeds": negative_prompt_embeds,
													    "generator": generator,
													    "num_inference_steps": num_inference_steps,
													    "output_type": output_type,
													}

													if image is not None:
																			_snake_case						:					int    =   image

													if mask_image is not None:
																			_snake_case						:					str    =   mask_image

													if original_image is not None:
																			_snake_case						:					int    =   original_image

													_snake_case						:					Optional[Any]    =   pipe_loaded(**lowercase_    )[0]

													_snake_case						:					Dict    =   np.abs(to_np(lowercase_    ) - to_np(lowercase_    )    ).max()
													self.assertLess(lowercase_				,    1e-4    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Tuple    =   self.get_dummy_components()
													_snake_case						:					Any    =   self.pipeline_class(**lowercase_    )
													pipe.to(lowercase_    )
													pipe.set_progress_bar_config(disable=lowercase_    )

													_snake_case						:					int    =   self.get_dummy_inputs(lowercase_    )
													_snake_case						:					List[str]    =   pipe(**lowercase_    )[0]

													with tempfile.TemporaryDirectory() as tmpdir:
																			pipe.save_pretrained(lowercase_    )
																			_snake_case						:					List[Any]    =   self.pipeline_class.from_pretrained(lowercase_    )
																			pipe_loaded.to(lowercase_    )
																			pipe_loaded.set_progress_bar_config(disable=lowercase_    )

													pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()    )  # For reproducibility tests

													_snake_case						:					Optional[Any]    =   self.get_dummy_inputs(lowercase_    )
													_snake_case						:					int    =   pipe_loaded(**lowercase_    )[0]

													_snake_case						:					Tuple    =   np.abs(to_np(lowercase_    ) - to_np(lowercase_    )    ).max()
													self.assertLess(lowercase_				,    1e-4    )