

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, MobileNetVaForSemanticSegmentation, MobileNetVaModel
     from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import MobileNetVaImageProcessor





class 					__UpperCAmelCase     (       SCREAMING_SNAKE_CASE_						):



   def        __magic_name__    (							self							:     List[str]						):
     UpperCAmelCase       :							str  = self.config_class(**self.inputs_dict						)
     self.parent.assertTrue(hasattr(_a,		'''tf_padding'''						)						)
     self.parent.assertTrue(hasattr(_a,		'''depth_multiplier'''						)						)






class 					__UpperCAmelCase     :



   def __init__(							self							:     Any,		__A							:     List[str],		__A							:     Optional[int]=1_3,		__A							:     List[Any]=3,		__A							:     Dict=3_2,		__A							:     str=0.2_5,		__A							:     str=8,		__A							:     Any=8,		__A							:     Tuple=6,		__A							:     Any=3_2,		__A							:     Any=True,		__A							:     List[Any]=True,		__A							:     Union[str, Any]=True,		__A							:     int="relu6",		__A							:     Optional[Any]=1_2_8_0,		__A							:     Union[str, Any]=0.1,		__A							:     Optional[int]=0.0_2,		__A							:     int=True,		__A							:     Dict=True,		__A							:     List[str]=1_0,		__A							:     Union[str, Any]=None,		):
     UpperCAmelCase       :							str  = parent
     UpperCAmelCase       :							Any  = batch_size
     UpperCAmelCase       :							Optional[int]  = num_channels
     UpperCAmelCase       :							str  = image_size
     UpperCAmelCase       :							Union[str, Any]  = depth_multiplier
     UpperCAmelCase       :							Optional[int]  = depth_divisible_by
     UpperCAmelCase       :							Any  = min_depth
     UpperCAmelCase       :							Optional[Any]  = expand_ratio
     UpperCAmelCase       :							List[Any]  = tf_padding
     UpperCAmelCase       :							Optional[int]  = output_stride
     UpperCAmelCase       :							Union[str, Any]  = first_layer_is_expansion
     UpperCAmelCase       :							Union[str, Any]  = finegrained_output
     UpperCAmelCase       :							int  = hidden_act
     UpperCAmelCase       :							Optional[int]  = last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier						)
     UpperCAmelCase       :							Union[str, Any]  = classifier_dropout_prob
     UpperCAmelCase       :							Optional[Any]  = use_labels
     UpperCAmelCase       :							Dict  = is_training
     UpperCAmelCase       :							List[str]  = num_labels
     UpperCAmelCase       :							str  = initializer_range
     UpperCAmelCase       :							str  = scope



   def        __magic_name__    (							self							:     Optional[int]						):
     UpperCAmelCase       :							Dict  = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

     UpperCAmelCase       :							Optional[int]  = None
     UpperCAmelCase       :							int  = None
     if self.use_labels:
       UpperCAmelCase       :							Any  = ids_tensor([self.batch_size],		self.num_labels						)
       UpperCAmelCase       :							int  = ids_tensor([self.batch_size, self.image_size, self.image_size],		self.num_labels						)

     UpperCAmelCase       :							List[str]  = self.get_config()

     return config, pixel_values, labels, pixel_labels



   def        __magic_name__    (							self							:     Tuple						):
     return MobileNetVaConfig(
         num_channels=self.num_channels,		image_size=self.image_size,		depth_multiplier=self.depth_multiplier,		depth_divisible_by=self.depth_divisible_by,		min_depth=self.min_depth,		expand_ratio=self.expand_ratio,		output_stride=self.output_stride,		first_layer_is_expansion=self.first_layer_is_expansion,		finegrained_output=self.finegrained_output,		hidden_act=self.hidden_act,		tf_padding=self.tf_padding,		classifier_dropout_prob=self.classifier_dropout_prob,		initializer_range=self.initializer_range,		)



   def        __magic_name__    (							self							:     Dict,		__A							:     Optional[int],		__A							:     Tuple,		__A							:     Dict,		__A							:     List[str]						):
     UpperCAmelCase       :							Tuple  = MobileNetVaModel(config=_a						)
     model.to(_a						)
     model.eval()
     UpperCAmelCase       :							Dict  = model(_a						)
     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,
         ),		)
     self.parent.assertEqual(
         result.pooler_output.shape,		(self.batch_size, self.last_hidden_size),		)



   def        __magic_name__    (							self							:     Optional[Any],		__A							:     Tuple,		__A							:     Optional[int],		__A							:     Optional[int],		__A							:     Tuple						):
     UpperCAmelCase       :							Dict  = self.num_labels
     UpperCAmelCase       :							List[str]  = MobileNetVaForImageClassification(_a						)
     model.to(_a						)
     model.eval()
     UpperCAmelCase       :							int  = model(_a,		labels=_a						)
     self.parent.assertEqual(result.logits.shape,		(self.batch_size, self.num_labels)						)



   def        __magic_name__    (							self							:     str,		__A							:     Optional[int],		__A							:     Dict,		__A							:     List[Any],		__A							:     List[str]						):
     UpperCAmelCase       :							Tuple  = self.num_labels
     UpperCAmelCase       :							Union[str, Any]  = MobileNetVaForSemanticSegmentation(_a						)
     model.to(_a						)
     model.eval()
     UpperCAmelCase       :							Any  = model(_a						)
     self.parent.assertEqual(
         result.logits.shape,		(
             self.batch_size,
             self.num_labels,
             self.image_size // self.output_stride,
             self.image_size // self.output_stride,
         ),		)
     UpperCAmelCase       :							List[str]  = model(_a,		labels=_a						)
     self.parent.assertEqual(
         result.logits.shape,		(
             self.batch_size,
             self.num_labels,
             self.image_size // self.output_stride,
             self.image_size // self.output_stride,
         ),		)





   def        __magic_name__    (							self							:     Optional[int]						):
     UpperCAmelCase       :							Optional[int]  = self.prepare_config_and_inputs()
     UpperCAmelCase       :							Optional[Any]  = config_and_inputs
     UpperCAmelCase       :							Any  = {'pixel_values': pixel_values}
     return config, inputs_dict






@require_torch
class 					__UpperCAmelCase     (       SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  unittest.TestCase						):
   UpperCamelCase        =     (
       (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
       if is_torch_available()
       else ()
   )
   UpperCamelCase        =     (
       {
           """feature-extraction""": MobileNetVaModel,
           """image-classification""": MobileNetVaForImageClassification,
           """image-segmentation""": MobileNetVaForSemanticSegmentation,
       }
       if is_torch_available()
       else {}
   )

   UpperCamelCase        =     False
   UpperCamelCase        =     False
   UpperCamelCase        =     False
   UpperCamelCase        =     False



   def        __magic_name__    (							self							:     Dict						):
     UpperCAmelCase       :							str  = MobileNetVaModelTester(self						)
     UpperCAmelCase       :							Optional[int]  = MobileNetVaConfigTester(self,		config_class=_a,		has_text_modality=_a						)



   def        __magic_name__    (							self							:     Tuple						):
     self.config_tester.run_common_tests()



   @unittest.skip(reason='''MobileNetV2 does not use inputs_embeds'''						)
   def        __magic_name__    (							self							:     Any						):
     pass



   @unittest.skip(reason='''MobileNetV2 does not support input and output embeddings'''						)
   def        __magic_name__    (							self							:     List[str]						):
     pass



   @unittest.skip(reason='''MobileNetV2 does not output attentions'''						)
   def        __magic_name__    (							self							:     str						):
     pass



   def        __magic_name__    (							self							:     Dict						):
     UpperCAmelCase       :							Optional[Any]  = self.model_tester.prepare_config_and_inputs_for_common()

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

       UpperCAmelCase       :							str  = ['pixel_values']
       self.assertListEqual(arg_names[:1],		_a						)



   def        __magic_name__    (							self							:     List[Any]						):
     UpperCAmelCase       :							Any  = self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_a						)



   def        __magic_name__    (							self							:     Any						):
     def check_hidden_states_output(__A							:     Optional[Any],		__A							:     List[Any],		__A							:     Union[str, Any]						):
       UpperCAmelCase       :							List[Any]  = model_class(_a						)
       model.to(_a						)
       model.eval()

       with torch.no_grad():
         UpperCAmelCase       :							int  = model(**self._prepare_for_class(_a,		_a						)						)

       UpperCAmelCase       :							int  = outputs.hidden_states

       UpperCAmelCase       :							Optional[Any]  = 1_6
       self.assertEqual(len(_a						),		_a						)

     UpperCAmelCase       :							List[str]  = self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       UpperCAmelCase       :							int  = True
       check_hidden_states_output(_a,		_a,		_a						)

       # check that output_hidden_states also work using config
       del inputs_dict["output_hidden_states"]
       UpperCAmelCase       :							Any  = True

       check_hidden_states_output(_a,		_a,		_a						)



   def        __magic_name__    (							self							:     List[str]						):
     UpperCAmelCase       :							Tuple  = self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*_a						)



   def        __magic_name__    (							self							:     Any						):
     UpperCAmelCase       :							Tuple  = self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_semantic_segmentation(*_a						)





   @slow
   def        __magic_name__    (							self							:     Optional[int]						):
     for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       UpperCAmelCase       :							int  = MobileNetVaModel.from_pretrained(_a						)
       self.assertIsNotNone(_a						)






def 			a__     (		)  ->						Optional[Any]:
  UpperCAmelCase       :							str  = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
  return image






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



   @cached_property
   def        __magic_name__    (							self							:     Dict						):
     return (
         MobileNetVaImageProcessor.from_pretrained('''google/mobilenet_v2_1.0_224'''						) if is_vision_available() else None
     )



   @slow
   def        __magic_name__    (							self							:     Any						):
     UpperCAmelCase       :							int  = MobileNetVaForImageClassification.from_pretrained('''google/mobilenet_v2_1.0_224'''						).to(_a						)

     UpperCAmelCase       :							Optional[Any]  = self.default_image_processor
     UpperCAmelCase       :							Any  = prepare_img()
     UpperCAmelCase       :							List[str]  = image_processor(images=_a,		return_tensors='''pt'''						).to(_a						)

     # forward pass
     with torch.no_grad():
       UpperCAmelCase       :							Dict  = model(**_a						)

     # verify the logits
     UpperCAmelCase       :							List[str]  = torch.Size((1, 1_0_0_1)						)
     self.assertEqual(outputs.logits.shape,		_a						)

     UpperCAmelCase       :							Union[str, Any]  = torch.tensor([0.2_4_4_5, -1.1_9_9_3, 0.1_9_0_5]						).to(_a						)

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





   @slow
   def        __magic_name__    (							self							:     Dict						):
     UpperCAmelCase       :							Tuple  = MobileNetVaForSemanticSegmentation.from_pretrained('''google/deeplabv3_mobilenet_v2_1.0_513'''						)
     UpperCAmelCase       :							Any  = model.to(_a						)

     UpperCAmelCase       :							str  = MobileNetVaImageProcessor.from_pretrained('''google/deeplabv3_mobilenet_v2_1.0_513'''						)

     UpperCAmelCase       :							Optional[Any]  = prepare_img()
     UpperCAmelCase       :							Union[str, Any]  = image_processor(images=_a,		return_tensors='''pt'''						).to(_a						)

     # forward pass
     with torch.no_grad():
       UpperCAmelCase       :							Union[str, Any]  = model(**_a						)
     UpperCAmelCase       :							List[Any]  = outputs.logits

     # verify the logits
     UpperCAmelCase       :							Union[str, Any]  = torch.Size((1, 2_1, 6_5, 6_5)						)
     self.assertEqual(logits.shape,		_a						)

     UpperCAmelCase       :							Optional[Any]  = torch.tensor(
         [
             [[1_7.5_7_9_0, 1_7.7_5_8_1, 1_8.3_3_5_5], [1_8.3_2_5_7, 1_8.4_2_3_0, 1_8.8_9_7_3], [1_8.6_1_6_9, 1_8.8_6_5_0, 1_9.2_1_8_7]],
             [[-2.1_5_9_5, -2.0_9_7_7, -2.3_7_4_1], [-2.4_2_2_6, -2.3_0_2_8, -2.6_8_3_5], [-2.7_8_1_9, -2.5_9_9_1, -2.7_7_0_6]],
             [[4.2_0_5_8, 4.8_3_1_7, 4.7_6_3_8], [4.4_1_3_6, 5.0_3_6_1, 4.9_3_8_3], [4.5_0_2_8, 4.9_6_4_4, 4.8_7_3_4]],
         ],		device=_a,		)

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










'''simple docstring'''





import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

def        a_     (							_lowerCAmelCase       )      ->      str:
  __lowerCamelCase       ,__lowerCamelCase       :       List[Any]           = image.size
  __lowerCamelCase       ,__lowerCamelCase       :       List[str]           = (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
  __lowerCamelCase       :       Optional[Any]           = image.resize((w, h)       ,resample=PIL_INTERPOLATION['lanczos']       )
  __lowerCamelCase       :       List[Any]           = np.array(_lowerCAmelCase       ).astype(np.floataa       ) / 255.0
  __lowerCamelCase       :       Optional[Any]           = image[None].transpose(0       ,3       ,1       ,2       )
  __lowerCamelCase       :       int           = torch.from_numpy(_lowerCAmelCase       )
  return 2.0 * image - 1.0






class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  def __init__( self      :	str			,	_a      :	VQModel			,	_a      :	UNetaDModel			,	_a      :	Union[
          DDIMScheduler,
          PNDMScheduler,
          LMSDiscreteScheduler,
          EulerDiscreteScheduler,
          EulerAncestralDiscreteScheduler,
          DPMSolverMultistepScheduler,
      ]			,	)					->					Optional[Any]:
    super().__init__()
    self.register_modules(vqvae=_a			,	unet=_a			,	scheduler=_a   )




  @torch.no_grad()
  def __call__( self      :	List[Any]			,	_a      :	Union[torch.Tensor, PIL.Image.Image] = None			,	_a      :	Optional[int] = 1			,	_a      :	Optional[int] = 100			,	_a      :	Optional[float] = 0.0			,	_a      :	Optional[Union[torch.Generator, List[torch.Generator]]] = None			,	_a      :	Optional[str] = "pil"			,	_a      :	bool = True			,	)					->					Union[Tuple, ImagePipelineOutput]:
    if isinstance(_a			,	PIL.Image.Image   ):
      __lowerCamelCase       :       Any           = 1
    elif isinstance(_a			,	torch.Tensor   ):
      __lowerCamelCase       :       Any           = image.shape[0]
    else:
      raise ValueError(f'`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(_a   )}'   )

    if isinstance(_a			,	PIL.Image.Image   ):
      __lowerCamelCase       :       List[str]           = preprocess(_a   )

    __lowerCamelCase       ,__lowerCamelCase       :       List[str]           = image.shape[-2:]

    # in_channels should be 6: 3 for latents, 3 for low resolution image
    __lowerCamelCase       :       Union[str, Any]           = (batch_size, self.unet.config.in_channels // 2, height, width)
    __lowerCamelCase       :       Tuple           = next(self.unet.parameters()   ).dtype

    __lowerCamelCase       :       Optional[int]           = randn_tensor(_a			,	generator=_a			,	device=self.device			,	dtype=_a   )

    __lowerCamelCase       :       Optional[int]           = image.to(device=self.device			,	dtype=_a   )

    # set timesteps and move to the correct device
    self.scheduler.set_timesteps(_a			,	device=self.device   )
    __lowerCamelCase       :       Union[str, Any]           = self.scheduler.timesteps

    # scale the initial noise by the standard deviation required by the scheduler
    __lowerCamelCase       :       List[Any]           = latents * self.scheduler.init_noise_sigma

    # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
    # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
    # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
    # and should be between [0, 1]
    __lowerCamelCase       :       int           = 'eta' in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
    __lowerCamelCase       :       List[str]           = {}
    if accepts_eta:
      __lowerCamelCase       :       str           = eta

    for t in self.progress_bar(_a   ):
      # concat latents and low resolution image in the channel dimension.
      __lowerCamelCase       :       str           = torch.cat([latents, image]			,	dim=1   )
      __lowerCamelCase       :       Union[str, Any]           = self.scheduler.scale_model_input(_a			,	_a   )
      # predict the noise residual
      __lowerCamelCase       :       Optional[int]           = self.unet(_a			,	_a   ).sample
      # compute the previous noisy sample x_t -> x_t-1
      __lowerCamelCase       :       List[Any]           = self.scheduler.step(_a			,	_a			,	_a			,	**_a   ).prev_sample

    # decode the image latents with the VQVAE
    __lowerCamelCase       :       Union[str, Any]           = self.vqvae.decode(_a   ).sample
    __lowerCamelCase       :       Union[str, Any]           = torch.clamp(_a			,	-1.0			,	1.0   )
    __lowerCamelCase       :       List[str]           = image / 2 + 0.5
    __lowerCamelCase       :       Optional[Any]           = image.cpu().permute(0			,	2			,	3			,	1   ).numpy()

    if output_type == "pil":
      __lowerCamelCase       :       Tuple           = self.numpy_to_pil(_a   )

    if not return_dict:
      return (image,)

    return ImagePipelineOutput(images=_a   )






