





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    :					List[str] =      True
except (ImportError, AttributeError):
					A    :					int =      object







					def    __lowerCamelCase					(							*__a   :List[str]    ,     **__a   :Tuple    )		->				str:






							"""simple docstring"""

							pass






					A    :					Tuple =      False


A    :					Any =      logging.get_logger('''transformers-cli/serving''')







def    __lowerCamelCase					(							__a   :Namespace    )		->				str:






		"""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(__a    ,     args.host    ,     args.port    ,     args.workers    )


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			__lowerCamelCase	:       dict


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			__lowerCamelCase	:       List[str]
			__lowerCamelCase	:       Optional[List[int]]


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			__lowerCamelCase	:       str


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			__lowerCamelCase	:       Any


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			@staticmethod
			def    a_       (			__lowerCAmelCase   :    ArgumentParser   )	->	List[Any]:







					"""simple docstring"""






					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=88_88							,	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   :    int							,	__lowerCAmelCase   :    Pipeline							,	__lowerCAmelCase   :    str							,	__lowerCAmelCase   :    int							,	__lowerCAmelCase   :    int   )	->	Optional[Any]:







					"""simple docstring"""






					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=6_00							,	)






			def    a_       (			self   :    List[Any]   )	->	Dict:







					"""simple docstring"""






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






			def    a_       (			self   :    Dict   )	->	Optional[Any]:







					"""simple docstring"""






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






			def    a_       (			self   :    int							,	__lowerCAmelCase   :    str = Body(__lowerCAmelCase							,	embed=__lowerCAmelCase   )							,	__lowerCAmelCase   :    bool = Body(__lowerCAmelCase							,	embed=__lowerCAmelCase   )   )	->	int:







					"""simple docstring"""






					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=5_00							,	detail={"""model""": """""", """error""": str(__lowerCAmelCase   )}   )






			def    a_       (			self   :    Optional[Any]							,	__lowerCAmelCase   :    List[int] = Body(__lowerCAmelCase							,	embed=__lowerCAmelCase   )							,	__lowerCAmelCase   :    bool = Body(__lowerCAmelCase							,	embed=__lowerCAmelCase   )							,	__lowerCAmelCase   :    bool = Body(__lowerCAmelCase							,	embed=__lowerCAmelCase   )							,	)	->	Optional[int]:







					"""simple docstring"""






					try:
							A__        =   self._pipeline.tokenizer.decode(__lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase   )
							return ServeDeTokenizeResult(model=""""""							,	text=__lowerCAmelCase   )
					except Exception as e:
							raise HTTPException(status_code=5_00							,	detail={"""model""": """""", """error""": str(__lowerCAmelCase   )}   )







			async def    a_       (			self   :    Dict							,	__lowerCAmelCase   :    str=Body(__lowerCAmelCase							,	embed=__lowerCAmelCase   )   )	->	int:







					"""simple docstring"""






					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(5_00							,	{"""error""": str(__lowerCAmelCase   )}   )










from __future__ import annotations

from PIL import Image

# Define glider example
A    :					Any =      [
    [0, 1, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0, 0],
    [1, 1, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
]

# Define blinker example
A    :					Optional[Any] =      [[0, 1, 0], [0, 1, 0], [0, 1, 0]]







def    __lowerCamelCase					(							__a   :list[list[int]]    )		->				list[list[int]]:






		"""simple docstring"""

		A__        =   []
		for i in range(len(__a    )    ):
				A__        =   []
				for j in range(len(cells[i]    )    ):
						# Get the number of live neighbours
						A__        =   0
						if i > 0 and j > 0:
								neighbour_count += cells[i - 1][j - 1]
						if i > 0:
								neighbour_count += cells[i - 1][j]
						if i > 0 and j < len(cells[i]    ) - 1:
								neighbour_count += cells[i - 1][j + 1]
						if j > 0:
								neighbour_count += cells[i][j - 1]
						if j < len(cells[i]    ) - 1:
								neighbour_count += cells[i][j + 1]
						if i < len(__a    ) - 1 and j > 0:
								neighbour_count += cells[i + 1][j - 1]
						if i < len(__a    ) - 1:
								neighbour_count += cells[i + 1][j]
						if i < len(__a    ) - 1 and j < len(cells[i]    ) - 1:
								neighbour_count += cells[i + 1][j + 1]

						# Rules of the game of life (excerpt from Wikipedia):
						# 1. Any live cell with two or three live neighbours survives.
						# 2. Any dead cell with three live neighbours becomes a live cell.
						# 3. All other live cells die in the next generation.
						#    Similarly, all other dead cells stay dead.
						A__        =   cells[i][j] == 1
						if (
						    (alive and 2 <= neighbour_count <= 3)
						    or not alive
						    and neighbour_count == 3
						):
								next_generation_row.append(1    )
						else:
								next_generation_row.append(0    )

				next_generation.append(__a    )
		return next_generation







def    __lowerCamelCase					(							__a   :list[list[int]]    ,     __a   :int    )		->				list[Image.Image]:






		"""simple docstring"""

		A__        =   []
		for _ in range(__a    ):
				# Create output image
				A__        =   Image.new("""RGB"""    ,     (len(cells[0]    ), len(__a    ))    )
				A__        =   img.load()

				# Save cells to image
				for x in range(len(__a    )    ):
						for y in range(len(cells[0]    )    ):
								A__        =   2_5_5 - cells[y][x] * 2_5_5
								A__        =   (colour, colour, colour)

        # Save image
				images.append(__a    )
				A__        =   new_generation(__a    )
		return images


if __name__ == "__main__":
					A    :					str =      generate_images(GLIDER, 1_6)
					images[0].save('''out.gif''', save_all=True, append_images=images[1:])



