





'''simple docstring'''


import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, 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 transformers import MobileNetVaForImageClassification, MobileNetVaModel
       from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import MobileNetVaImageProcessor

class    lowerCAmelCase_		(		__magic_name__		):




 def 							_snake_case   (	self		)    ->							Dict:
     _lowerCAmelCase				=      self.config_class(**self.inputs_dict		)
     self.parent.assertTrue(hasattr(_lowerCAmelCase			,     "tf_padding"		)		)
     self.parent.assertTrue(hasattr(_lowerCAmelCase			,     "depth_multiplier"		)		)







class    lowerCAmelCase_		:




 def __init__(	self			,     _lowerCAmelCase			,     _lowerCAmelCase=13			,     _lowerCAmelCase=3			,     _lowerCAmelCase=32			,     _lowerCAmelCase=0.25			,     _lowerCAmelCase=8			,     _lowerCAmelCase=True			,     _lowerCAmelCase=1024			,     _lowerCAmelCase=32			,     _lowerCAmelCase="relu6"			,     _lowerCAmelCase=0.1			,     _lowerCAmelCase=0.02			,     _lowerCAmelCase=True			,     _lowerCAmelCase=True			,     _lowerCAmelCase=10			,     _lowerCAmelCase=None			,     )    ->							int:
     _lowerCAmelCase				=      parent
     _lowerCAmelCase				=      batch_size
     _lowerCAmelCase				=      num_channels
     _lowerCAmelCase				=      image_size
     _lowerCAmelCase				=      depth_multiplier
     _lowerCAmelCase				=      min_depth
     _lowerCAmelCase				=      tf_padding
     _lowerCAmelCase				=      int(last_hidden_size * depth_multiplier		)
     _lowerCAmelCase				=      output_stride
     _lowerCAmelCase				=      hidden_act
     _lowerCAmelCase				=      classifier_dropout_prob
     _lowerCAmelCase				=      use_labels
     _lowerCAmelCase				=      is_training
     _lowerCAmelCase				=      num_labels
     _lowerCAmelCase				=      initializer_range
     _lowerCAmelCase				=      scope




 def 							_snake_case   (	self		)    ->							Union[str, Any]:
     _lowerCAmelCase				=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

     _lowerCAmelCase				=      None
     _lowerCAmelCase				=      None
     if self.use_labels:
         _lowerCAmelCase				=      ids_tensor([self.batch_size]			,     self.num_labels		)
         _lowerCAmelCase				=      ids_tensor([self.batch_size, self.image_size, self.image_size]			,     self.num_labels		)

     _lowerCAmelCase				=      self.get_config()

     return config, pixel_values, labels, pixel_labels




 def 							_snake_case   (	self		)    ->							Union[str, Any]:
     return MobileNetVaConfig(
         num_channels=self.num_channels			,     image_size=self.image_size			,     depth_multiplier=self.depth_multiplier			,     min_depth=self.min_depth			,     tf_padding=self.tf_padding			,     hidden_act=self.hidden_act			,     classifier_dropout_prob=self.classifier_dropout_prob			,     initializer_range=self.initializer_range			,     )




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							str:
     _lowerCAmelCase				=      MobileNetVaModel(config=_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase				=      model(_lowerCAmelCase		)
     self.parent.assertEqual(
         result.last_hidden_state.shape			,     (
             self.batch_size,
             self.last_hidden_size,
             self.image_size // self.output_stride,
             self.image_size // self.output_stride,
         )			,     )




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							Any:
     _lowerCAmelCase				=      self.num_labels
     _lowerCAmelCase				=      MobileNetVaForImageClassification(_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase				=      model(_lowerCAmelCase			,     labels=_lowerCAmelCase		)
     self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.num_labels)		)





 def 							_snake_case   (	self		)    ->							Dict:
     _lowerCAmelCase				=      self.prepare_config_and_inputs()
     _lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase				=      config_and_inputs
     _lowerCAmelCase				=      {"pixel_values": pixel_values}
     return config, inputs_dict







@require_torch
class    lowerCAmelCase_		(		__magic_name__						,__magic_name__						,unittest.TestCase		):
 __lowerCamelCase						:							Optional[int]         =					(MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
 __lowerCamelCase						:							Union[str, Any]         =					(
     {"feature-extraction": MobileNetVaModel, "image-classification": MobileNetVaForImageClassification}
     if is_torch_available()
     else {}
 )

 __lowerCamelCase						:							List[Any]         =					False
 __lowerCamelCase						:							List[Any]         =					False
 __lowerCamelCase						:							Optional[int]         =					False
 __lowerCamelCase						:							Tuple         =					False




 def 							_snake_case   (	self		)    ->							List[Any]:
     _lowerCAmelCase				=      MobileNetVaModelTester(self		)
     _lowerCAmelCase				=      MobileNetVaConfigTester(self			,     config_class=_lowerCAmelCase			,     has_text_modality=_lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							List[Any]:
     self.config_tester.run_common_tests()




 @unittest.skip(reason="MobileNetV1 does not use inputs_embeds"		)
 def 							_snake_case   (	self		)    ->							Any:
     pass




 @unittest.skip(reason="MobileNetV1 does not support input and output embeddings"		)
 def 							_snake_case   (	self		)    ->							Dict:
     pass




 @unittest.skip(reason="MobileNetV1 does not output attentions"		)
 def 							_snake_case   (	self		)    ->							List[Any]:
     pass




 def 							_snake_case   (	self		)    ->							Optional[Any]:
     _lowerCAmelCase			,			_lowerCAmelCase				=      self.model_tester.prepare_config_and_inputs_for_common()

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

         _lowerCAmelCase				=      ["pixel_values"]
         self.assertListEqual(arg_names[:1]			,     _lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							Tuple:
     _lowerCAmelCase				=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							Optional[int]:
     def check_hidden_states_output(_lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase		):
         _lowerCAmelCase				=      model_class(_lowerCAmelCase		)
         model.to(_lowerCAmelCase		)
         model.eval()

         with torch.no_grad():
             _lowerCAmelCase				=      model(**self._prepare_for_class(_lowerCAmelCase			,     _lowerCAmelCase		)		)

         _lowerCAmelCase				=      outputs.hidden_states

         _lowerCAmelCase				=      26
         self.assertEqual(len(_lowerCAmelCase		)			,     _lowerCAmelCase		)

     _lowerCAmelCase			,			_lowerCAmelCase				=      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         _lowerCAmelCase				=      True
         check_hidden_states_output(_lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase		)

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         _lowerCAmelCase				=      True

         check_hidden_states_output(_lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							str:
     _lowerCAmelCase				=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase		)





 @slow
 def 							_snake_case   (	self		)    ->							Optional[Any]:
     for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _lowerCAmelCase				=      MobileNetVaModel.from_pretrained(_lowerCAmelCase		)
         self.assertIsNotNone(_lowerCAmelCase		)







def 	__a():



    '''simple docstring'''





    _lowerCAmelCase				=      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
    return image





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




 @cached_property
 def 							_snake_case   (	self		)    ->							List[Any]:
     return (
         MobileNetVaImageProcessor.from_pretrained("google/mobilenet_v1_1.0_224"		) if is_vision_available() else None
     )





 @slow
 def 							_snake_case   (	self		)    ->							Tuple:
     _lowerCAmelCase				=      MobileNetVaForImageClassification.from_pretrained("google/mobilenet_v1_1.0_224"		).to(_lowerCAmelCase		)

     _lowerCAmelCase				=      self.default_image_processor
     _lowerCAmelCase				=      prepare_img()
     _lowerCAmelCase				=      image_processor(images=_lowerCAmelCase			,     return_tensors="pt"		).to(_lowerCAmelCase		)

     # forward pass
     with torch.no_grad():
         _lowerCAmelCase				=      model(**_lowerCAmelCase		)

     # verify the logits
     _lowerCAmelCase				=      torch.Size((1, 1001)		)
     self.assertEqual(outputs.logits.shape			,     _lowerCAmelCase		)

     _lowerCAmelCase				=      torch.tensor([-4.1739, -1.1233, 3.1205]		).to(_lowerCAmelCase		)

     self.assertTrue(torch.allclose(outputs.logits[0, :3]			,     _lowerCAmelCase			,     atol=1E-4		)		)









'''simple docstring'''


from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
       from PIL import Image

       from ..image_utils import load_image

if is_torch_available():
       import torch

       from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

_SCREAMING_SNAKE_CASE	 =							logging.get_logger(__name__)

@add_end_docstrings(__magic_name__		)
class    lowerCAmelCase_		(		__magic_name__		):




 def __init__(	self			,     *_lowerCAmelCase			,     **_lowerCAmelCase		)    ->							List[Any]:
     super().__init__(*_lowerCAmelCase			,     **_lowerCAmelCase		)
     requires_backends(self			,     "vision"		)
     self.check_model_type(_lowerCAmelCase		)




 def __call__(	self			,     _lowerCAmelCase			,     **_lowerCAmelCase		)    ->							str:
     return super().__call__(_lowerCAmelCase			,     **_lowerCAmelCase		)




 def 							_snake_case   (	self			,     **_lowerCAmelCase		)    ->							Tuple:
     return {}, {}, {}




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							Union[str, Any]:
     _lowerCAmelCase				=      load_image(_lowerCAmelCase		)
     _lowerCAmelCase				=      image.size
     _lowerCAmelCase				=      self.image_processor(images=_lowerCAmelCase			,     return_tensors=self.framework		)
     return model_inputs




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							Tuple:
     _lowerCAmelCase				=      self.model(**_lowerCAmelCase		)
     return model_outputs





 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							Optional[Any]:
     _lowerCAmelCase				=      model_outputs.predicted_depth
     _lowerCAmelCase				=      torch.nn.functional.interpolate(
         predicted_depth.unsqueeze(1		)			,     size=self.image_size[::-1]			,     mode="bicubic"			,     align_corners=_lowerCAmelCase		)
     _lowerCAmelCase				=      prediction.squeeze().cpu().numpy()
     _lowerCAmelCase				=      (output * 255 / np.max(_lowerCAmelCase		)).astype("uint8"		)
     _lowerCAmelCase				=      Image.fromarray(_lowerCAmelCase		)
     _lowerCAmelCase				=      {}
     _lowerCAmelCase				=      predicted_depth
     _lowerCAmelCase				=      depth
     return output_dict


