'''simple docstring'''


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

 snake_case__					:      Tuple				      =  True
except (ImportError, AttributeError):
 snake_case__					:      Dict				      =  object


 def  _lowerCamelCase   (				*lowerCamelCase_ :							Dict     ,					**lowerCamelCase_ :							Optional[Any]       ):



      """simple docstring"""





      pass





 snake_case__					:      Union[str, Any]				      =  False


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


def  _lowerCamelCase   (				lowerCamelCase_ :							Namespace       ):



     """simple docstring"""





     UpperCAmelCase_							:       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 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  lowerCamelCase_		:dict







class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  lowerCamelCase_		:List[str]
  lowerCamelCase_		:Optional[List[int]]







class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  lowerCamelCase_		:str







class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  lowerCamelCase_		:Any







class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  @staticmethod
  def _UpperCamelCase					(			snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       List[str]       =				parser.add_parser(
           'serve'				,		help='CLI tool to run inference requests through REST and GraphQL endpoints.'						)
       serve_parser.add_argument(
           '--task'				,		type=snake_case_				,		choices=get_supported_tasks()				,		help='The task to run the pipeline on'				,		)
       serve_parser.add_argument('--host'				,		type=snake_case_				,		default='localhost'				,		help='Interface the server will listen on.'						)
       serve_parser.add_argument('--port'				,		type=snake_case_				,		default=8_8_8_8				,		help='Port the serving will listen to.'						)
       serve_parser.add_argument('--workers'				,		type=snake_case_				,		default=1				,		help='Number of http workers'						)
       serve_parser.add_argument('--model'				,		type=snake_case_				,		help='Model\'s name or path to stored model.'						)
       serve_parser.add_argument('--config'				,		type=snake_case_				,		help='Model\'s config name or path to stored model.'						)
       serve_parser.add_argument('--tokenizer'				,		type=snake_case_				,		help='Tokenizer name to use.'						)
       serve_parser.add_argument(
           '--device'				,		type=snake_case_				,		default=-1				,		help='Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)'				,		)
       serve_parser.set_defaults(func=snake_case_						)


  def __init__(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[int]       =				pipeline

       UpperCAmelCase_							:       Any       =				host
       UpperCAmelCase_							:       int       =				port
       UpperCAmelCase_							:       Union[str, 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}'''						)
            UpperCAmelCase_							:       List[str]       =				FastAPI(
                routes=[
                    APIRoute(
                        '/'				,		self.model_info				,		response_model=snake_case_				,		response_class=snake_case_				,		methods=['GET']				,		),
                    APIRoute(
                        '/tokenize'				,		self.tokenize				,		response_model=snake_case_				,		response_class=snake_case_				,		methods=['POST']				,		),
                    APIRoute(
                        '/detokenize'				,		self.detokenize				,		response_model=snake_case_				,		response_class=snake_case_				,		methods=['POST']				,		),
                    APIRoute(
                        '/forward'				,		self.forward				,		response_model=snake_case_				,		response_class=snake_case_				,		methods=['POST']				,		),
                ]				,		timeout=6_0_0				,		)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

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


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

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


  def _UpperCamelCase					(			self				,		snake_case_ = Body(snake_case_				,		embed=snake_case_						)				,		snake_case_ = Body(snake_case_				,		embed=snake_case_						)						):




       '''simple docstring'''

       try:
            UpperCAmelCase_							:       int       =				self._pipeline.tokenizer.tokenize(snake_case_						)

            if return_ids:
                 UpperCAmelCase_							:       List[str]       =				self._pipeline.tokenizer.convert_tokens_to_ids(snake_case_						)
                 return ServeTokenizeResult(tokens=snake_case_				,		tokens_ids=snake_case_						)
            else:
                 return ServeTokenizeResult(tokens=snake_case_						)

       except Exception as e:
            raise HTTPException(status_code=5_0_0				,		detail={'model': '', 'error': str(snake_case_						)}						)


  def _UpperCamelCase					(			self				,		snake_case_ = Body(snake_case_				,		embed=snake_case_						)				,		snake_case_ = Body(snake_case_				,		embed=snake_case_						)				,		snake_case_ = Body(snake_case_				,		embed=snake_case_						)				,		):




       '''simple docstring'''

       try:
            UpperCAmelCase_							:       Optional[int]       =				self._pipeline.tokenizer.decode(snake_case_				,		snake_case_				,		snake_case_						)
            return ServeDeTokenizeResult(model=''				,		text=snake_case_						)
       except Exception as e:
            raise HTTPException(status_code=5_0_0				,		detail={'model': '', 'error': str(snake_case_						)}						)






  async def _UpperCamelCase					(			self				,		snake_case_=Body(snake_case_				,		embed=snake_case_						)						):




       '''simple docstring'''

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

       try:
            # Forward through the model
            UpperCAmelCase_							:       int       =				self._pipeline(snake_case_						)
            return ServeForwardResult(output=snake_case_						)
       except Exception as e:
            raise HTTPException(5_0_0				,		{'error': str(snake_case_						)}						)







'''simple docstring'''


import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image
else:






 class 					__SCREAMING_SNAKE_CASE						:







   '''simple docstring'''






   @staticmethod
   def _UpperCamelCase					(			*snake_case_				,		**snake_case_						):




        '''simple docstring'''

        pass


def  _lowerCamelCase   (				lowerCamelCase_ :							Image       ):



     """simple docstring"""





     UpperCAmelCase_							:       Dict       =				hashlib.mda(image.tobytes()       )
     return m.hexdigest()






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 					__SCREAMING_SNAKE_CASE						(	unittest.TestCase	):







  '''simple docstring'''






  lowerCamelCase_		:Optional[int]						   =		MODEL_FOR_DEPTH_ESTIMATION_MAPPING


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       str       =				DepthEstimationPipeline(model=snake_case_				,		image_processor=snake_case_						)
       return depth_estimator, [
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
       ]


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Dict       =				depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
       self.assertEqual({'predicted_depth': ANY(torch.Tensor						), 'depth': ANY(Image.Image						)}				,		snake_case_						)
       import datasets

       UpperCAmelCase_							:       Optional[int]       =				datasets.load_dataset('hf-internal-testing/fixtures_image_utils'				,		'image'				,		split='test'						)
       UpperCAmelCase_							:       Union[str, Any]       =				depth_estimator(
           [
               Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						),
               'http://images.cocodataset.org/val2017/000000039769.jpg',
               # RGBA
               dataset[0]['file'],
               # LA
               dataset[1]['file'],
               # L
               dataset[2]['file'],
           ]						)
       self.assertEqual(
           [
               {'predicted_depth': ANY(torch.Tensor						), 'depth': ANY(Image.Image						)},
               {'predicted_depth': ANY(torch.Tensor						), 'depth': ANY(Image.Image						)},
               {'predicted_depth': ANY(torch.Tensor						), 'depth': ANY(Image.Image						)},
               {'predicted_depth': ANY(torch.Tensor						), 'depth': ANY(Image.Image						)},
               {'predicted_depth': ANY(torch.Tensor						), 'depth': ANY(Image.Image						)},
           ]				,		snake_case_				,		)


  @require_tf
  @unittest.skip('Depth estimation is not implemented in TF'						)
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       pass


  @slow
  @require_torch
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Any       =				'Intel/dpt-large'
       UpperCAmelCase_							:       Dict       =				pipeline('depth-estimation'				,		model=snake_case_						)
       UpperCAmelCase_							:       Optional[int]       =				depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'						)
       UpperCAmelCase_							:       Union[str, Any]       =				hashimage(outputs['depth']						)

       # This seems flaky.
       # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
       self.assertEqual(nested_simplify(outputs['predicted_depth'].max().item()						)				,		29.3_04						)
       self.assertEqual(nested_simplify(outputs['predicted_depth'].min().item()						)				,		2.6_62						)






  @require_torch
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       self.skipTest('There is not hf-internal-testing tiny model for either GLPN nor DPT'						)






