"""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

       __SCREAMING_SNAKE_CASE    	=True
except (ImportError, AttributeError):
       __SCREAMING_SNAKE_CASE    	=object



       def 			lowercase__(       *__SCREAMING_SNAKE_CASE							:     List[str]     ,			**__SCREAMING_SNAKE_CASE							:     int      ):
            pass







       __SCREAMING_SNAKE_CASE    	=False


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



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Namespace      ):
     lowercase_	:				int													=	pipeline(
         task=args.task     ,			model=args.model if args.model else None     ,			config=args.config     ,			tokenizer=args.tokenizer     ,			device=args.device     ,			)
     return ServeCommand(__SCREAMING_SNAKE_CASE     ,			args.host     ,			args.port     ,			args.workers      )







class        UpperCamelCase		(			lowercase_  ):
   lowercase							=  42







class        UpperCamelCase		(			lowercase_  ):
   lowercase							=  42
   lowercase							=  42







class        UpperCamelCase		(			lowercase_  ):
   lowercase							=  42







class        UpperCamelCase		(			lowercase_  ):
   lowercase							=  42







class        UpperCamelCase		(			lowercase_  ):
   @staticmethod
   def 			_UpperCAmelCase   (			__UpperCamelCase			)	->      Optional[int]:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	parser.add_parser(
            'serve'     ,help='CLI tool to run inference requests through REST and GraphQL endpoints.'			)
        serve_parser.add_argument(
            '--task'     ,type=__UpperCamelCase     ,choices=get_supported_tasks()     ,help='The task to run the pipeline on'     ,)
        serve_parser.add_argument('--host'     ,type=__UpperCamelCase     ,default='localhost'     ,help='Interface the server will listen on.'			)
        serve_parser.add_argument('--port'     ,type=__UpperCamelCase     ,default=8888     ,help='Port the serving will listen to.'			)
        serve_parser.add_argument('--workers'     ,type=__UpperCamelCase     ,default=1     ,help='Number of http workers'			)
        serve_parser.add_argument('--model'     ,type=__UpperCamelCase     ,help='Model\'s name or path to stored model.'			)
        serve_parser.add_argument('--config'     ,type=__UpperCamelCase     ,help='Model\'s config name or path to stored model.'			)
        serve_parser.add_argument('--tokenizer'     ,type=__UpperCamelCase     ,help='Tokenizer name to use.'			)
        serve_parser.add_argument(
            '--device'     ,type=__UpperCamelCase     ,default=-1     ,help='Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)'     ,)
        serve_parser.set_defaults(func=__UpperCamelCase			)
   def __init__(			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      Tuple:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	pipeline

        lowercase_	:				List[Any]													=	host
        lowercase_	:				List[Any]													=	port
        lowercase_	:				Optional[int]													=	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}'''			)
             lowercase_	:				List[Any]													=	FastAPI(
                 routes=[
                     APIRoute(
                         '/'     ,self.model_info     ,response_model=__UpperCamelCase     ,response_class=__UpperCamelCase     ,methods=['GET']     ,),
                     APIRoute(
                         '/tokenize'     ,self.tokenize     ,response_model=__UpperCamelCase     ,response_class=__UpperCamelCase     ,methods=['POST']     ,),
                     APIRoute(
                         '/detokenize'     ,self.detokenize     ,response_model=__UpperCamelCase     ,response_class=__UpperCamelCase     ,methods=['POST']     ,),
                     APIRoute(
                         '/forward'     ,self.forward     ,response_model=__UpperCamelCase     ,response_class=__UpperCamelCase     ,methods=['POST']     ,),
                 ]     ,timeout=600     ,)
   def 			_UpperCAmelCase   (			self			)	->      Any:


        '''simple docstring'''


        run(self._app     ,host=self.host     ,port=self.port     ,workers=self.workers			)
   def 			_UpperCAmelCase   (			self			)	->      Any:


        '''simple docstring'''


        return ServeModelInfoResult(infos=vars(self._pipeline.model.config			)			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase = Body(__UpperCamelCase     ,embed=__UpperCamelCase			)     ,__UpperCamelCase = Body(__UpperCamelCase     ,embed=__UpperCamelCase			)			)	->      int:


        '''simple docstring'''


        try:
             lowercase_	:				Tuple													=	self._pipeline.tokenizer.tokenize(__UpperCamelCase			)

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

        except Exception as e:
             raise HTTPException(status_code=500     ,detail={'model': '', 'error': str(__UpperCamelCase			)}			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase = Body(__UpperCamelCase     ,embed=__UpperCamelCase			)     ,__UpperCamelCase = Body(__UpperCamelCase     ,embed=__UpperCamelCase			)     ,__UpperCamelCase = Body(__UpperCamelCase     ,embed=__UpperCamelCase			)     ,)	->      Optional[Any]:


        '''simple docstring'''


        try:
             lowercase_	:				Tuple													=	self._pipeline.tokenizer.decode(__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)
             return ServeDeTokenizeResult(model=''     ,text=__UpperCamelCase			)
        except Exception as e:
             raise HTTPException(status_code=500     ,detail={'model': '', 'error': str(__UpperCamelCase			)}			)







   async def 			_UpperCAmelCase   (			self     ,__UpperCamelCase=Body(__UpperCamelCase     ,embed=__UpperCamelCase			)			)	->      Any:


        '''simple docstring'''


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

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


"""simple docstring"""







import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch
       from torch import nn

       from transformers import ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
       from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import ViTImageProcessor







class        UpperCamelCase		:
   def __init__(			self     ,__UpperCamelCase     ,__UpperCamelCase=13     ,__UpperCamelCase=30     ,__UpperCamelCase=2     ,__UpperCamelCase=3     ,__UpperCamelCase=True     ,__UpperCamelCase=True     ,__UpperCamelCase=32     ,__UpperCamelCase=5     ,__UpperCamelCase=4     ,__UpperCamelCase=37     ,__UpperCamelCase="gelu"     ,__UpperCamelCase=0.1     ,__UpperCamelCase=0.1     ,__UpperCamelCase=10     ,__UpperCamelCase=0.02     ,__UpperCamelCase=None     ,__UpperCamelCase=2     ,)	->      List[Any]:


        '''simple docstring'''


        lowercase_	:				Tuple													=	parent
        lowercase_	:				List[Any]													=	batch_size
        lowercase_	:				Optional[int]													=	image_size
        lowercase_	:				List[str]													=	patch_size
        lowercase_	:				int													=	num_channels
        lowercase_	:				List[str]													=	is_training
        lowercase_	:				Union[str, Any]													=	use_labels
        lowercase_	:				str													=	hidden_size
        lowercase_	:				List[str]													=	num_hidden_layers
        lowercase_	:				List[str]													=	num_attention_heads
        lowercase_	:				Dict													=	intermediate_size
        lowercase_	:				Optional[int]													=	hidden_act
        lowercase_	:				Any													=	hidden_dropout_prob
        lowercase_	:				Any													=	attention_probs_dropout_prob
        lowercase_	:				List[str]													=	type_sequence_label_size
        lowercase_	:				Union[str, Any]													=	initializer_range
        lowercase_	:				int													=	scope
        lowercase_	:				Dict													=	encoder_stride

        # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
        lowercase_	:				Any													=	(image_size // patch_size) ** 2
        lowercase_	:				Optional[int]													=	num_patches + 1
   def 			_UpperCAmelCase   (			self			)	->      List[Any]:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

        lowercase_	:				Tuple													=	None
        if self.use_labels:
             lowercase_	:				Dict													=	ids_tensor([self.batch_size]     ,self.type_sequence_label_size			)

        lowercase_	:				Any													=	self.get_config()

        return config, pixel_values, labels
   def 			_UpperCAmelCase   (			self			)	->      Optional[Any]:


        '''simple docstring'''


        return ViTConfig(
            image_size=self.image_size     ,patch_size=self.patch_size     ,num_channels=self.num_channels     ,hidden_size=self.hidden_size     ,num_hidden_layers=self.num_hidden_layers     ,num_attention_heads=self.num_attention_heads     ,intermediate_size=self.intermediate_size     ,hidden_act=self.hidden_act     ,hidden_dropout_prob=self.hidden_dropout_prob     ,attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,is_decoder=__UpperCamelCase     ,initializer_range=self.initializer_range     ,encoder_stride=self.encoder_stride     ,)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      Optional[int]:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	ViTModel(config=__UpperCamelCase			)
        model.to(__UpperCamelCase			)
        model.eval()
        lowercase_	:				Optional[Any]													=	model(__UpperCamelCase			)
        self.parent.assertEqual(result.last_hidden_state.shape     ,(self.batch_size, self.seq_length, self.hidden_size)			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      Any:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	ViTForMaskedImageModeling(config=__UpperCamelCase			)
        model.to(__UpperCamelCase			)
        model.eval()
        lowercase_	:				List[str]													=	model(__UpperCamelCase			)
        self.parent.assertEqual(
            result.reconstruction.shape     ,(self.batch_size, self.num_channels, self.image_size, self.image_size)			)

        # test greyscale images
        lowercase_	:				Any													=	1
        lowercase_	:				List[Any]													=	ViTForMaskedImageModeling(__UpperCamelCase			)
        model.to(__UpperCamelCase			)
        model.eval()

        lowercase_	:				Dict													=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
        lowercase_	:				str													=	model(__UpperCamelCase			)
        self.parent.assertEqual(result.reconstruction.shape     ,(self.batch_size, 1, self.image_size, self.image_size)			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      str:


        '''simple docstring'''


        lowercase_	:				Dict													=	self.type_sequence_label_size
        lowercase_	:				Dict													=	ViTForImageClassification(__UpperCamelCase			)
        model.to(__UpperCamelCase			)
        model.eval()
        lowercase_	:				int													=	model(__UpperCamelCase     ,labels=__UpperCamelCase			)
        self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.type_sequence_label_size)			)

        # test greyscale images
        lowercase_	:				Dict													=	1
        lowercase_	:				Any													=	ViTForImageClassification(__UpperCamelCase			)
        model.to(__UpperCamelCase			)
        model.eval()

        lowercase_	:				Dict													=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
        lowercase_	:				Dict													=	model(__UpperCamelCase			)
        self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.type_sequence_label_size)			)







   def 			_UpperCAmelCase   (			self			)	->      List[str]:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	self.prepare_config_and_inputs()
        (
            (
            lowercase_
        )							,  (
            lowercase_
        )							,  (
            lowercase_
        )							,  
        )	:				Optional[Any]													=	config_and_inputs
        lowercase_	:				str													=	{'pixel_values': pixel_values}
        return config, inputs_dict







@require_torch
class        UpperCamelCase		(			lowercase_						,      lowercase_						,      unittest.TestCase  ):
   lowercase							=  (
       (
           ViTModel,
           ViTForImageClassification,
           ViTForMaskedImageModeling,
       )
       if is_torch_available()
       else ()
   )
   lowercase							=  (
       {'feature-extraction': ViTModel, 'image-classification': ViTForImageClassification}
       if is_torch_available()
       else {}
   )
   lowercase							=  True

   lowercase							=  False
   lowercase							=  False
   lowercase							=  False
   def 			_UpperCAmelCase   (			self			)	->      List[str]:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	ViTModelTester(self			)
        lowercase_	:				Dict													=	ConfigTester(self     ,config_class=__UpperCamelCase     ,has_text_modality=__UpperCamelCase     ,hidden_size=37			)
   def 			_UpperCAmelCase   (			self			)	->      Optional[Any]:


        '''simple docstring'''


        self.config_tester.run_common_tests()
   @unittest.skip(reason='ViT does not use inputs_embeds'			)
   def 			_UpperCAmelCase   (			self			)	->      Any:


        '''simple docstring'''


        pass
   def 			_UpperCAmelCase   (			self			)	->      Dict:


        '''simple docstring'''


        lowercase_							,  lowercase_	:				Any													=	self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             lowercase_	:				str													=	model_class(__UpperCamelCase			)
             self.assertIsInstance(model.get_input_embeddings()     ,(nn.Module)			)
             lowercase_	:				List[Any]													=	model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(__UpperCamelCase     ,nn.Linear			)			)
   def 			_UpperCAmelCase   (			self			)	->      Union[str, Any]:


        '''simple docstring'''


        lowercase_							,  lowercase_	:				Union[str, Any]													=	self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             lowercase_	:				str													=	model_class(__UpperCamelCase			)
             lowercase_	:				List[Any]													=	inspect.signature(model.forward			)
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             lowercase_	:				Tuple													=	[*signature.parameters.keys()]

             lowercase_	:				Tuple													=	['pixel_values']
             self.assertListEqual(arg_names[:1]     ,__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      Any:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      str:


        '''simple docstring'''


        lowercase_	:				str													=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_image_modeling(*__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      str:


        '''simple docstring'''


        lowercase_	:				str													=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase			)







   @slow
   def 			_UpperCAmelCase   (			self			)	->      Tuple:


        '''simple docstring'''


        for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowercase_	:				List[Any]													=	ViTModel.from_pretrained(__UpperCamelCase			)
             self.assertIsNotNone(__UpperCamelCase			)






def 			lowercase__(       ):
     lowercase_	:				Any													=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
     return image







@require_torch
@require_vision
class        UpperCamelCase		(			unittest.TestCase  ):
   @cached_property
   def 			_UpperCAmelCase   (			self			)	->      Any:


        '''simple docstring'''


        return ViTImageProcessor.from_pretrained('google/vit-base-patch16-224'			) if is_vision_available() else None
   @slow
   def 			_UpperCAmelCase   (			self			)	->      int:


        '''simple docstring'''


        lowercase_	:				str													=	ViTForImageClassification.from_pretrained('google/vit-base-patch16-224'			).to(__UpperCamelCase			)

        lowercase_	:				Any													=	self.default_image_processor
        lowercase_	:				Dict													=	prepare_img()
        lowercase_	:				Tuple													=	image_processor(images=__UpperCamelCase     ,return_tensors='pt'			).to(__UpperCamelCase			)

        # forward pass
        with torch.no_grad():
             lowercase_	:				Any													=	model(**__UpperCamelCase			)

        # verify the logits
        lowercase_	:				Optional[Any]													=	torch.Size((1, 1000)			)
        self.assertEqual(outputs.logits.shape     ,__UpperCamelCase			)

        lowercase_	:				Optional[int]													=	torch.tensor([-0.2744, 0.8215, -0.0836]			).to(__UpperCamelCase			)

        self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,__UpperCamelCase     ,atol=1e-4			)			)
   @slow
   def 			_UpperCAmelCase   (			self			)	->      Tuple:


        '''simple docstring'''


        lowercase_	:				str													=	ViTModel.from_pretrained('facebook/dino-vits8'			).to(__UpperCamelCase			)

        lowercase_	:				int													=	ViTImageProcessor.from_pretrained('facebook/dino-vits8'     ,size=480			)
        lowercase_	:				int													=	prepare_img()
        lowercase_	:				Dict													=	image_processor(images=__UpperCamelCase     ,return_tensors='pt'			)
        lowercase_	:				int													=	inputs.pixel_values.to(__UpperCamelCase			)

        # forward pass
        with torch.no_grad():
             lowercase_	:				int													=	model(__UpperCamelCase     ,interpolate_pos_encoding=__UpperCamelCase			)

        # verify the logits
        lowercase_	:				Any													=	torch.Size((1, 3601, 384)			)
        self.assertEqual(outputs.last_hidden_state.shape     ,__UpperCamelCase			)

        lowercase_	:				Any													=	torch.tensor(
            [[4.2340, 4.3906, -6.6692], [4.5463, 1.8928, -6.7257], [4.4429, 0.8496, -5.8585]]			).to(__UpperCamelCase			)

        self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]     ,__UpperCamelCase     ,atol=1e-4			)			)







   @slow
   @require_accelerate
   @require_torch_gpu
   def 			_UpperCAmelCase   (			self			)	->      int:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	ViTModel.from_pretrained('facebook/dino-vits8'     ,torch_dtype=torch.floataa     ,device_map='auto'			)
        lowercase_	:				int													=	self.default_image_processor

        lowercase_	:				Optional[int]													=	prepare_img()
        lowercase_	:				Tuple													=	image_processor(images=__UpperCamelCase     ,return_tensors='pt'			)
        lowercase_	:				Union[str, Any]													=	inputs.pixel_values.to(__UpperCamelCase			)

        # forward pass to make sure inference works in fp16
        with torch.no_grad():
             lowercase_	:				Dict													=	model(__UpperCamelCase			)

