





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

		_lowercase		:  Tuple			       =True
except (ImportError, AttributeError):
		_lowercase		:  int			       =object


		def 		_SCREAMING_SNAKE_CASE     ( *lowerCAmelCase__							,**lowerCAmelCase__						):
				pass





		_lowercase		:  Optional[int]			       =False


_lowercase		:  Dict			       =logging.get_logger("""transformers-cli/serving""")


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__						):
		lowerCamelCase_	:     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(lowerCAmelCase__							,args.host							,args.port							,args.workers						)






class 						UpperCamelCase_  (  snake_case__							):
					_a      :						dict






class 						UpperCamelCase_  (  snake_case__							):
					_a      :						List[str]
					_a      :						Optional[List[int]]






class 						UpperCamelCase_  (  snake_case__							):
					_a      :						str






class 						UpperCamelCase_  (  snake_case__							):
					_a      :						Any






class 						UpperCamelCase_  (  snake_case__							):



					@staticmethod
					def   __a     (		lowerCamelCase     :     ArgumentParser						):
							lowerCamelCase_	:     List[Any] 						=      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     :     str       ,      lowerCamelCase     :     Pipeline       ,      lowerCamelCase     :     str       ,      lowerCamelCase     :     int       ,      lowerCamelCase     :     int						):
							lowerCamelCase_	:     int 						=      pipeline

							lowerCamelCase_	:     str 						=      host
							lowerCamelCase_	:     str 						=      port
							lowerCamelCase_	:     str 						=      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}"						)
									lowerCamelCase_	:     Dict 						=      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     :     Optional[Any]						):
							run(self._app       ,      host=self.host       ,      port=self.port       ,      workers=self.workers						)



					def   __a     (		self     :     Union[str, Any]						):
							return ServeModelInfoResult(infos=vars(self._pipeline.model.config						)						)



					def   __a     (		self     :     Tuple       ,      lowerCamelCase     :     str = Body(lowerCamelCase       ,      embed=lowerCamelCase						)       ,      lowerCamelCase     :     bool = Body(lowerCamelCase       ,      embed=lowerCamelCase						)						):
							try:
									lowerCamelCase_	:     Dict 						=      self._pipeline.tokenizer.tokenize(lowerCamelCase						)

									if return_ids:
											lowerCamelCase_	:     Optional[Any] 						=      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[int]       ,      lowerCamelCase     :     List[int] = Body(lowerCamelCase       ,      embed=lowerCamelCase						)       ,      lowerCamelCase     :     bool = Body(lowerCamelCase       ,      embed=lowerCamelCase						)       ,      lowerCamelCase     :     bool = Body(lowerCamelCase       ,      embed=lowerCamelCase						)       ,      ):
							try:
									lowerCamelCase_	:     Union[str, Any] 						=      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     :     Any       ,      lowerCamelCase     :     Union[str, Any]=Body(lowerCamelCase       ,      embed=lowerCamelCase						)						):

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

							try:
									# Forward through the model
									lowerCamelCase_	:     Tuple 						=      self._pipeline(lowerCamelCase						)
									return ServeForwardResult(output=lowerCamelCase						)
							except Exception as e:
									raise HTTPException(5_00       ,      {'error': str(lowerCamelCase						)}						)







import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_lowercase		:  List[str]			       =5_0000
_lowercase		:  str			       =5000

_lowercase       , _lowercase		:  List[str]			       =os.path.split(__file__)
_lowercase		:  Union[str, Any]			       =os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))


@get_duration
def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__						):
		for i in range(lowerCAmelCase__						):
				lowerCamelCase_	:     Tuple 						=      dataset[i]


@get_duration
def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		for i in range(0							,len(lowerCAmelCase__						)							,lowerCAmelCase__						):
				lowerCamelCase_	:     Tuple 						=      dataset[i : i + batch_size]


@get_duration
def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		with dataset.formatted_as(type=lowerCAmelCase__						):
				for i in range(lowerCAmelCase__						):
						lowerCamelCase_	:     Tuple 						=      dataset[i]


@get_duration
def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		with dataset.formatted_as(type=lowerCAmelCase__						):
				for i in range(0							,lowerCAmelCase__							,lowerCAmelCase__						):
						lowerCamelCase_	:     Optional[int] 						=      dataset[i : i + batch_size]


def 		_SCREAMING_SNAKE_CASE     ( ):
		lowerCamelCase_	:     Any 						=      {'num examples': SPEED_TEST_N_EXAMPLES}
		lowerCamelCase_	:     Optional[Any] 						=      [
		    (read, {'length': SMALL_TEST}),
		    (read, {'length': SPEED_TEST_N_EXAMPLES}),
		    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
		    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_00}),
		    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10_00}),
		    (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
		    (read_formatted, {'type': 'pandas', 'length': SMALL_TEST}),
		    (read_formatted, {'type': 'torch', 'length': SMALL_TEST}),
		    (read_formatted, {'type': 'tensorflow', 'length': SMALL_TEST}),
		    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
		    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10_00}),
		]

		lowerCamelCase_	:     Optional[Any] 						=      [
		    (read, {'length': SMALL_TEST}),
		    (read, {'length': SPEED_TEST_N_EXAMPLES}),
		    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
		    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_00}),
		    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10_00}),
		    (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
		    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
		    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10_00}),
		]
		with tempfile.TemporaryDirectory() as tmp_dir:
				print('generating dataset'						)
				lowerCamelCase_	:     List[Any] 						=      datasets.Features(
				    {'list': datasets.Sequence(datasets.Value('float32'						)						), 'numbers': datasets.Value('float32'						)}						)
				lowerCamelCase_	:     Union[str, Any] 						=      generate_example_dataset(
				    os.path.join(lowerCAmelCase__							,'dataset.arrow'						)							,lowerCAmelCase__							,num_examples=lowerCAmelCase__							,seq_shapes={'list': (1_00,)}							,)
				print('first set of iterations'						)
				for func, kwargs in functions:
						print(func.__name__							,str(lowerCAmelCase__						)						)
						lowerCamelCase_	:     Optional[int] 						=      func(lowerCAmelCase__							,**lowerCAmelCase__						)

				print('shuffling dataset'						)
				lowerCamelCase_	:     int 						=      dataset.shuffle()
				print('Second set of iterations (after shuffling'						)
				for func, kwargs in functions_shuffled:
						print('shuffled '							,func.__name__							,str(lowerCAmelCase__						)						)
						lowerCamelCase_	:     Optional[Any] 						=      func(
						    lowerCAmelCase__							,**lowerCAmelCase__						)

		with open(lowerCAmelCase__							,'wb'						) as f:
				f.write(json.dumps(lowerCAmelCase__						).encode('utf-8'						)						)


if __name__ == "__main__":  # useful to run the profiler
		benchmark_iterating()
