





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_												=     True
except (ImportError, AttributeError):
      A_												=     object





      def    __UpperCamelCase      (    *a,     **a) ->List[str]:
           pass







      A_												=     False


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





def    __UpperCamelCase      (    a) ->Union[str, Any]:
     lowerCamelCase__       =       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 SCREAMING_SNAKE_CASE_ (					lowercase_   ):





     """simple docstring"""


     A__				 =    4_2

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





     """simple docstring"""


     A__				 =    4_2
     A__				 =    4_2

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





     """simple docstring"""


     A__				 =    4_2

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





     """simple docstring"""


     A__				 =    4_2

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





     """simple docstring"""


     @staticmethod
     def 					__magic_name__ ( _lowerCAmelCase	):
          lowerCamelCase__       =       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	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	):
          lowerCamelCase__       =       pipeline

          lowerCamelCase__       =       host
          lowerCamelCase__       =       port
          lowerCamelCase__       =       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__       =       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 					__magic_name__ ( self	):
          run(self._app	,  host=self.host	,  port=self.port	,  workers=self.workers	)





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





     def 					__magic_name__ ( self	,  _lowerCAmelCase = Body(_lowerCAmelCase	,  embed=_lowerCAmelCase	)	,  _lowerCAmelCase = Body(_lowerCAmelCase	,  embed=_lowerCAmelCase	)	):
          try:
               lowerCamelCase__       =       self._pipeline.tokenizer.tokenize(_lowerCAmelCase	)

               if return_ids:
                    lowerCamelCase__       =       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 					__magic_name__ ( self	,  _lowerCAmelCase = Body(_lowerCAmelCase	,  embed=_lowerCAmelCase	)	,  _lowerCAmelCase = Body(_lowerCAmelCase	,  embed=_lowerCAmelCase	)	,  _lowerCAmelCase = Body(_lowerCAmelCase	,  embed=_lowerCAmelCase	)	,  ):
          try:
               lowerCamelCase__       =       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 					__magic_name__ ( self	,  _lowerCAmelCase=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__       =       self._pipeline(_lowerCAmelCase	)
               return ServeForwardResult(output=_lowerCAmelCase	)
          except Exception as e:
               raise HTTPException(500	,  {"error": str(_lowerCAmelCase	)}	)








import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
						import torch


if is_vision_available():
						from PIL import Image

						from transformers import MgpstrProcessor, ViTImageProcessor

@require_torch
@require_vision
class SCREAMING_SNAKE_CASE_ (					unittest.TestCase   ):





					"""simple docstring"""


					A__				 =    ViTImageProcessor if is_vision_available() else None





					@property
					def 					__magic_name__ ( self	):
										return self.image_processor_tester.prepare_image_processor_dict()





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       (3, 32, 128)
										lowerCamelCase__       =       tempfile.mkdtemp()

										# fmt: off
										lowerCamelCase__       =       ["[GO]", "[s]", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z"]
										# fmt: on
										lowerCamelCase__       =       dict(zip(_lowerCAmelCase	,  range(len(_lowerCAmelCase	)	)	)	)

										lowerCamelCase__       =       os.path.join(self.tmpdirname	,  VOCAB_FILES_NAMES["vocab_file"]	)
										with open(self.vocab_file	,  "w"	,  encoding="utf-8"	) as fp:
															fp.write(json.dumps(_lowerCAmelCase	) + "\n"	)

										lowerCamelCase__       =       {
										    "do_normalize": False,
										    "do_resize": True,
										    "image_processor_type": "ViTImageProcessor",
										    "resample": 3,
										    "size": {"height": 32, "width": 128},
										}
										lowerCamelCase__       =       os.path.join(self.tmpdirname	,  _lowerCAmelCase	)
										with open(self.image_processor_file	,  "w"	,  encoding="utf-8"	) as fp:
															json.dump(_lowerCAmelCase	,  _lowerCAmelCase	)





					def 					__magic_name__ ( self	,  **_lowerCAmelCase	):
										return MgpstrTokenizer.from_pretrained(self.tmpdirname	,  **_lowerCAmelCase	)





					def 					__magic_name__ ( self	,  **_lowerCAmelCase	):
										return ViTImageProcessor.from_pretrained(self.tmpdirname	,  **_lowerCAmelCase	)





					def 					__magic_name__ ( self	):
										shutil.rmtree(self.tmpdirname	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       np.random.randint(255	,  size=(3, 30, 400)	,  dtype=np.uinta	)

										lowerCamelCase__       =       Image.fromarray(np.moveaxis(_lowerCAmelCase	,  0	,  -1	)	)

										return image_input





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_tokenizer()
										lowerCamelCase__       =       self.get_image_processor()

										lowerCamelCase__       =       MgpstrProcessor(tokenizer=_lowerCAmelCase	,  image_processor=_lowerCAmelCase	)
										processor.save_pretrained(self.tmpdirname	)
										lowerCamelCase__       =       MgpstrProcessor.from_pretrained(self.tmpdirname	,  use_fast=_lowerCAmelCase	)

										self.assertEqual(processor.char_tokenizer.get_vocab()	,  tokenizer.get_vocab()	)
										self.assertIsInstance(processor.char_tokenizer	,  _lowerCAmelCase	)

										self.assertEqual(processor.image_processor.to_json_string()	,  image_processor.to_json_string()	)
										self.assertIsInstance(processor.image_processor	,  _lowerCAmelCase	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_tokenizer()
										lowerCamelCase__       =       self.get_image_processor()

										lowerCamelCase__       =       MgpstrProcessor(tokenizer=_lowerCAmelCase	,  image_processor=_lowerCAmelCase	)
										processor.save_pretrained(self.tmpdirname	)

										lowerCamelCase__       =       self.get_tokenizer(bos_token="(BOS)"	,  eos_token="(EOS)"	)
										lowerCamelCase__       =       self.get_image_processor(do_normalize=_lowerCAmelCase	,  padding_value=1.0	)

										lowerCamelCase__       =       MgpstrProcessor.from_pretrained(
										    self.tmpdirname	,  bos_token="(BOS)"	,  eos_token="(EOS)"	,  do_normalize=_lowerCAmelCase	,  padding_value=1.0	)

										self.assertEqual(processor.char_tokenizer.get_vocab()	,  tokenizer_add_kwargs.get_vocab()	)
										self.assertIsInstance(processor.char_tokenizer	,  _lowerCAmelCase	)

										self.assertEqual(processor.image_processor.to_json_string()	,  image_processor_add_kwargs.to_json_string()	)
										self.assertIsInstance(processor.image_processor	,  _lowerCAmelCase	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_image_processor()
										lowerCamelCase__       =       self.get_tokenizer()

										lowerCamelCase__       =       MgpstrProcessor(tokenizer=_lowerCAmelCase	,  image_processor=_lowerCAmelCase	)

										lowerCamelCase__       =       self.prepare_image_inputs()

										lowerCamelCase__       =       image_processor(_lowerCAmelCase	,  return_tensors="np"	)
										lowerCamelCase__       =       processor(images=_lowerCAmelCase	,  return_tensors="np"	)

										for key in input_image_proc.keys():
															self.assertAlmostEqual(input_image_proc[key].sum()	,  input_processor[key].sum()	,  delta=1E-2	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_image_processor()
										lowerCamelCase__       =       self.get_tokenizer()

										lowerCamelCase__       =       MgpstrProcessor(tokenizer=_lowerCAmelCase	,  image_processor=_lowerCAmelCase	)

										lowerCamelCase__       =       "test"

										lowerCamelCase__       =       processor(text=_lowerCAmelCase	)

										lowerCamelCase__       =       tokenizer(_lowerCAmelCase	)
										for key in encoded_tok.keys():
															self.assertListEqual(encoded_tok[key]	,  encoded_processor[key]	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_image_processor()
										lowerCamelCase__       =       self.get_tokenizer()

										lowerCamelCase__       =       MgpstrProcessor(tokenizer=_lowerCAmelCase	,  image_processor=_lowerCAmelCase	)

										lowerCamelCase__       =       "test"
										lowerCamelCase__       =       self.prepare_image_inputs()

										lowerCamelCase__       =       processor(text=_lowerCAmelCase	,  images=_lowerCAmelCase	)

										self.assertListEqual(list(inputs.keys()	)	,  ["pixel_values", "labels"]	)

										# test if it raises when no input is passed
										with pytest.raises(_lowerCAmelCase	):
															processor()





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_image_processor()
										lowerCamelCase__       =       self.get_tokenizer()

										lowerCamelCase__       =       MgpstrProcessor(tokenizer=_lowerCAmelCase	,  image_processor=_lowerCAmelCase	)

										lowerCamelCase__       =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

										lowerCamelCase__       =       processor.char_decode(_lowerCAmelCase	)
										lowerCamelCase__       =       tokenizer.batch_decode(_lowerCAmelCase	)
										lowerCamelCase__       =       [seq.replace(" "	,  ""	) for seq in decoded_tok]

										self.assertListEqual(_lowerCAmelCase	,  _lowerCAmelCase	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_image_processor()
										lowerCamelCase__       =       self.get_tokenizer()

										lowerCamelCase__       =       MgpstrProcessor(tokenizer=_lowerCAmelCase	,  image_processor=_lowerCAmelCase	)

										lowerCamelCase__       =       None
										lowerCamelCase__       =       self.prepare_image_inputs()

										lowerCamelCase__       =       processor(text=_lowerCAmelCase	,  images=_lowerCAmelCase	)

										self.assertListEqual(list(inputs.keys()	)	,  processor.model_input_names	)






					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_image_processor()
										lowerCamelCase__       =       self.get_tokenizer()

										lowerCamelCase__       =       MgpstrProcessor(tokenizer=_lowerCAmelCase	,  image_processor=_lowerCAmelCase	)

										lowerCamelCase__       =       torch.randn(1	,  27	,  38	)
										lowerCamelCase__       =       torch.randn(1	,  27	,  5_0257	)
										lowerCamelCase__       =       torch.randn(1	,  27	,  3_0522	)

										lowerCamelCase__       =       processor.batch_decode([char_input, bpe_input, wp_input]	)

										self.assertListEqual(list(results.keys()	)	,  ["generated_text", "scores", "char_preds", "bpe_preds", "wp_preds"]	)

