

from typing import Callable, List, Optional, Union

import PIL
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPTextModel,
    CLIPTokenizer,
)

from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging


A_		:     Optional[Any]        =     logging.get_logger(__name__)  # pylint: disable=invalid-name



class 		_lowercase		(       UpperCamelCase_	):

   def __init__(      self			:       str							,   __lowerCAmelCase			:       Union[str, Any]							,   __lowerCAmelCase			:       Tuple							,   __lowerCAmelCase			:       Union[str, Any]							,   __lowerCAmelCase			:       List[str]							,   __lowerCAmelCase			:       Optional[int]							,   __lowerCAmelCase			:       Optional[Any]							,   __lowerCAmelCase			:       Union[str, Any]							,   __lowerCAmelCase			:       Dict							,   __lowerCAmelCase			:       Any							,   )       ->				List[Any]:


    """simple docstring"""






    super().__init__()

    if hasattr(scheduler.config							,   "steps_offset"							) and scheduler.config.steps_offset != 1:
     a						=		(
         f"""The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"""
         f""" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure """
         """to update the config accordingly as leaving `steps_offset` might led to incorrect results"""
         """ in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"""
         """ it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"""
         """ file"""
     )
     deprecate("steps_offset!=1"							,   "1.0.0"							,   _a							,   standard_warn=_a							)
     a						=		dict(scheduler.config							)
     a						=		1
     a						=		FrozenDict(_a							)

    if hasattr(scheduler.config							,   "skip_prk_steps"							) and scheduler.config.skip_prk_steps is False:
     a						=		(
         f"""The configuration file of this scheduler: {scheduler} has not set the configuration"""
         """ `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"""
         """ sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to"""
         """ incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face"""
         """ Hub, it would be very nice if you could open a Pull request for the"""
         """ `scheduler/scheduler_config.json` file"""
     )
     deprecate("skip_prk_steps not set"							,   "1.0.0"							,   _a							,   standard_warn=_a							)
     a						=		dict(scheduler.config							)
     a						=		True
     a						=		FrozenDict(_a							)

    if safety_checker is None:
     logger.warning(
         f"""You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"""
         " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
         " results in services or applications open to the public. Both the diffusers team and Hugging Face"
         " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
         " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
         " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."							)

    self.register_modules(
        segmentation_model=_a							,   segmentation_processor=_a							,   vae=_a							,   text_encoder=_a							,   tokenizer=_a							,   unet=_a							,   scheduler=_a							,   safety_checker=_a							,   feature_extractor=_a							,   )

   def 		A							(      self			:       Any							,   __lowerCAmelCase			:       Any = "auto"							)       ->				Optional[int]:


    """simple docstring"""






    if slice_size == "auto":
     # half the attention head size is usually a good trade-off between
     # speed and memory
     a						=		self.unet.config.attention_head_dim // 2
    self.unet.set_attention_slice(_a							)

   def 		A							(      self			:       Any							)       ->				List[Any]:


    """simple docstring"""






    self.enable_attention_slicing(_a							)

   def 		A							(      self			:       str							)       ->				Optional[Any]:


    """simple docstring"""






    if is_accelerate_available():
     from accelerate import cpu_offload
    else:
     raise ImportError("Please install accelerate via `pip install accelerate`"							)

    a						=		torch.device("cuda"							)

    for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
     if cpu_offloaded_model is not None:
      cpu_offload(_a							,   _a							)

   @property
   # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
   def 		A							(      self			:       str							)       ->				Optional[int]:


    """simple docstring"""






    if self.device != torch.device("meta"							) or not hasattr(self.unet							,   "_hf_hook"							):
     return self.device
    for module in self.unet.modules():
     if (
         hasattr(_a							,   "_hf_hook"							)
         and hasattr(module._hf_hook							,   "execution_device"							)
         and module._hf_hook.execution_device is not None
     ):
      return torch.device(module._hf_hook.execution_device							)
    return self.device




   @torch.no_grad()
   def __call__(      self			:       Tuple							,   __lowerCAmelCase			:       Any							,   __lowerCAmelCase			:       str							,   __lowerCAmelCase			:       Any							,   __lowerCAmelCase			:       int = 512							,   __lowerCAmelCase			:       str = 512							,   __lowerCAmelCase			:       Optional[Any] = 50							,   __lowerCAmelCase			:       Union[str, Any] = 7.5							,   __lowerCAmelCase			:       List[Any] = None							,   __lowerCAmelCase			:       Tuple = 1							,   __lowerCAmelCase			:       Any = 0.0							,   __lowerCAmelCase			:       Tuple = None							,   __lowerCAmelCase			:       Tuple = None							,   __lowerCAmelCase			:       Any = "pil"							,   __lowerCAmelCase			:       List[Any] = True							,   __lowerCAmelCase			:       Union[str, Any] = None							,   __lowerCAmelCase			:       Tuple = 1							,   **__lowerCAmelCase			:       Union[str, Any]							,   )       ->				List[str]:


    """simple docstring"""






    a						=		self.segmentation_processor(
        text=[text]							,   images=[image]							,   padding="max_length"							,   return_tensors="pt"							).to(self.device							)
    a						=		self.segmentation_model(**_a							)
    a						=		torch.sigmoid(outputs.logits							).cpu().detach().unsqueeze(-1							).numpy()
    a						=		self.numpy_to_pil(_a							)[0].resize(image.size							)

    # Run inpainting pipeline with the generated mask
    a						=		StableDiffusionInpaintPipeline(
        vae=self.vae							,   text_encoder=self.text_encoder							,   tokenizer=self.tokenizer							,   unet=self.unet							,   scheduler=self.scheduler							,   safety_checker=self.safety_checker							,   feature_extractor=self.feature_extractor							,   )
    return inpainting_pipeline(
        prompt=_a							,   image=_a							,   mask_image=_a							,   height=_a							,   width=_a							,   num_inference_steps=_a							,   guidance_scale=_a							,   negative_prompt=_a							,   num_images_per_prompt=_a							,   eta=_a							,   generator=_a							,   latents=_a							,   output_type=_a							,   return_dict=_a							,   callback=_a							,   callback_steps=_a							,   )






import inspect
import unittest

from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
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 ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
     from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor



class 		_lowercase		:

   def __init__(      self			:       List[str]							,   __lowerCAmelCase			:       Optional[int]							,   __lowerCAmelCase			:       Optional[int]=13							,   __lowerCAmelCase			:       str=32							,   __lowerCAmelCase			:       str=3							,   __lowerCAmelCase			:       int=4							,   __lowerCAmelCase			:       List[str]=[10, 20, 30, 40]							,   __lowerCAmelCase			:       Any=[2, 2, 3, 2]							,   __lowerCAmelCase			:       Any=True							,   __lowerCAmelCase			:       int=True							,   __lowerCAmelCase			:       str=37							,   __lowerCAmelCase			:       List[Any]="gelu"							,   __lowerCAmelCase			:       int=10							,   __lowerCAmelCase			:       str=0.0_2							,   __lowerCAmelCase			:       int=["stage2", "stage3", "stage4"]							,   __lowerCAmelCase			:       List[str]=[2, 3, 4]							,   __lowerCAmelCase			:       str=None							,   )       ->				Optional[Any]:


    """simple docstring"""






    a						=		parent
    a						=		batch_size
    a						=		image_size
    a						=		num_channels
    a						=		num_stages
    a						=		hidden_sizes
    a						=		depths
    a						=		is_training
    a						=		use_labels
    a						=		intermediate_size
    a						=		hidden_act
    a						=		num_labels
    a						=		initializer_range
    a						=		out_features
    a						=		out_indices
    a						=		scope

   def 		A							(      self			:       Optional[Any]							)       ->				int:


    """simple docstring"""






    a						=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

    a						=		self.get_config()

    return config, pixel_values, labels

   def 		A							(      self			:       Union[str, Any]							)       ->				Optional[int]:


    """simple docstring"""






    return ConvNextVaConfig(
        num_channels=self.num_channels							,   hidden_sizes=self.hidden_sizes							,   depths=self.depths							,   num_stages=self.num_stages							,   hidden_act=self.hidden_act							,   is_decoder=__lowerCAmelCase							,   initializer_range=self.initializer_range							,   out_features=self.out_features							,   out_indices=self.out_indices							,   num_labels=self.num_labels							,   )

   def 		A							(      self			:       Any							,   __lowerCAmelCase			:       List[str]							,   __lowerCAmelCase			:       Optional[Any]							,   __lowerCAmelCase			:       Dict							)       ->				Optional[int]:


    """simple docstring"""






    a						=		ConvNextVaModel(config=__lowerCAmelCase							)
    model.to(__lowerCAmelCase							)
    model.eval()
    a						=		model(__lowerCAmelCase							)
    # expected last hidden states: B, C, H // 32, W // 32
    self.parent.assertEqual(
        result.last_hidden_state.shape							,   (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)							,   )

   def 		A							(      self			:       int							,   __lowerCAmelCase			:       Dict							,   __lowerCAmelCase			:       Dict							,   __lowerCAmelCase			:       Union[str, Any]							)       ->				Dict:


    """simple docstring"""






    a						=		ConvNextVaForImageClassification(__lowerCAmelCase							)
    model.to(__lowerCAmelCase							)
    model.eval()
    a						=		model(__lowerCAmelCase							,   labels=__lowerCAmelCase							)
    self.parent.assertEqual(result.logits.shape							,   (self.batch_size, self.num_labels)							)

   def 		A							(      self			:       Union[str, Any]							,   __lowerCAmelCase			:       Optional[Any]							,   __lowerCAmelCase			:       Any							,   __lowerCAmelCase			:       Optional[int]							)       ->				int:


    """simple docstring"""






    a						=		ConvNextVaBackbone(config=__lowerCAmelCase							)
    model.to(__lowerCAmelCase							)
    model.eval()
    a						=		model(__lowerCAmelCase							)

    # verify hidden states
    self.parent.assertEqual(len(result.feature_maps							)							,   len(config.out_features							)							)
    self.parent.assertListEqual(list(result.feature_maps[0].shape							)							,   [self.batch_size, self.hidden_sizes[1], 4, 4]							)

    # verify channels
    self.parent.assertEqual(len(model.channels							)							,   len(config.out_features							)							)
    self.parent.assertListEqual(model.channels							,   config.hidden_sizes[1:]							)

    # verify backbone works with out_features=None
    a						=		None
    a						=		ConvNextVaBackbone(config=__lowerCAmelCase							)
    model.to(__lowerCAmelCase							)
    model.eval()
    a						=		model(__lowerCAmelCase							)

    # verify feature maps
    self.parent.assertEqual(len(result.feature_maps							)							,   1							)
    self.parent.assertListEqual(list(result.feature_maps[0].shape							)							,   [self.batch_size, self.hidden_sizes[-1], 1, 1]							)

    # verify channels
    self.parent.assertEqual(len(model.channels							)							,   1							)
    self.parent.assertListEqual(model.channels							,   [config.hidden_sizes[-1]]							)

   def 		A							(      self			:       Union[str, Any]							)       ->				Dict:


    """simple docstring"""






    a						=		self.prepare_config_and_inputs()
    a		,  a		,  a						=		config_and_inputs
    a						=		{"pixel_values": pixel_values}
    return config, inputs_dict




   def 		A							(      self			:       Dict							)       ->				Optional[int]:


    """simple docstring"""






    a						=		self.prepare_config_and_inputs()
    a		,  a		,  a						=		config_and_inputs
    a						=		{"pixel_values": pixel_values, "labels": labels}
    return config, inputs_dict



@require_torch
class 		_lowercase		(       UpperCAmelCase__,   UpperCAmelCase__,   unittest.TestCase	):
   _UpperCAmelCase     	=			(
       (
           ConvNextVaModel,
           ConvNextVaForImageClassification,
           ConvNextVaBackbone,
       )
       if is_torch_available()
       else ()
   )
   _UpperCAmelCase     	=			(
       {'''feature-extraction''': ConvNextVaModel, '''image-classification''': ConvNextVaForImageClassification}
       if is_torch_available()
       else {}
   )

   _UpperCAmelCase     	=			False
   _UpperCAmelCase     	=			False
   _UpperCAmelCase     	=			False
   _UpperCAmelCase     	=			False
   _UpperCAmelCase     	=			False

   def 		A							(      self			:       List[str]							)       ->				List[Any]:


    """simple docstring"""






    a						=		ConvNextVaModelTester(self							)
    a						=		ConfigTester(self							,   config_class=__lowerCAmelCase							,   has_text_modality=__lowerCAmelCase							,   hidden_size=37							)

   def 		A							(      self			:       Tuple							)       ->				Dict:


    """simple docstring"""






    self.create_and_test_config_common_properties()
    self.config_tester.create_and_test_config_to_json_string()
    self.config_tester.create_and_test_config_to_json_file()
    self.config_tester.create_and_test_config_from_and_save_pretrained()
    self.config_tester.create_and_test_config_with_num_labels()
    self.config_tester.check_config_can_be_init_without_params()
    self.config_tester.check_config_arguments_init()

   def 		A							(      self			:       List[Any]							)       ->				List[Any]:


    """simple docstring"""






    return

   @unittest.skip(reason="ConvNextV2 does not use inputs_embeds"							)
   def 		A							(      self			:       List[Any]							)       ->				List[Any]:


    """simple docstring"""






    pass

   @unittest.skip(reason="ConvNextV2 does not support input and output embeddings"							)
   def 		A							(      self			:       int							)       ->				List[Any]:


    """simple docstring"""






    pass

   @unittest.skip(reason="ConvNextV2 does not use feedforward chunking"							)
   def 		A							(      self			:       Optional[int]							)       ->				Dict:


    """simple docstring"""






    pass

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


    """simple docstring"""






    if not self.model_tester.is_training:
     return

    for model_class in self.all_model_classes:
     a		,  a						=		self.model_tester.prepare_config_and_inputs_with_labels()
     a						=		True

     if model_class.__name__ in [
         *get_values(__lowerCAmelCase							),
         *get_values(__lowerCAmelCase							),
     ]:
      continue

     a						=		model_class(__lowerCAmelCase							)
     model.to(__lowerCAmelCase							)
     model.train()
     a						=		self._prepare_for_class(__lowerCAmelCase							,   __lowerCAmelCase							,   return_labels=__lowerCAmelCase							)
     a						=		model(**__lowerCAmelCase							).loss
     loss.backward()

   def 		A							(      self			:       Optional[int]							)       ->				List[str]:


    """simple docstring"""






    if not self.model_tester.is_training:
     return

    for model_class in self.all_model_classes:
     a		,  a						=		self.model_tester.prepare_config_and_inputs_with_labels()
     a						=		False
     a						=		True

     if (
         model_class.__name__
         in [*get_values(__lowerCAmelCase							), *get_values(__lowerCAmelCase							)]
         or not model_class.supports_gradient_checkpointing
     ):
      continue

     a						=		model_class(__lowerCAmelCase							)
     model.to(__lowerCAmelCase							)
     model.gradient_checkpointing_enable()
     model.train()
     a						=		self._prepare_for_class(__lowerCAmelCase							,   __lowerCAmelCase							,   return_labels=__lowerCAmelCase							)
     a						=		model(**__lowerCAmelCase							).loss
     loss.backward()

   def 		A							(      self			:       List[Any]							)       ->				Any:


    """simple docstring"""






    a		,  a						=		self.model_tester.prepare_config_and_inputs_for_common()

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

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

   def 		A							(      self			:       Dict							)       ->				Dict:


    """simple docstring"""






    a						=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*__lowerCAmelCase							)

   def 		A							(      self			:       Tuple							)       ->				List[Any]:


    """simple docstring"""






    def check_hidden_states_output(__lowerCAmelCase			:       Union[str, Any]							,   __lowerCAmelCase			:       Dict							,   __lowerCAmelCase			:       Tuple							):
     a						=		model_class(__lowerCAmelCase							)
     model.to(__lowerCAmelCase							)
     model.eval()

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

     a						=		outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

     a						=		self.model_tester.num_stages
     self.assertEqual(len(__lowerCAmelCase							)							,   expected_num_stages + 1							)

     # ConvNextV2's feature maps are of shape (batch_size, num_channels, height, width)
     self.assertListEqual(
         list(hidden_states[0].shape[-2:]							)							,   [self.model_tester.image_size // 4, self.model_tester.image_size // 4]							,   )

    a		,  a						=		self.model_tester.prepare_config_and_inputs_for_common()

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

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

     check_hidden_states_output(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							)

   def 		A							(      self			:       Optional[Any]							)       ->				Optional[Any]:


    """simple docstring"""






    a						=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase							)




   @slow
   def 		A							(      self			:       Tuple							)       ->				List[str]:


    """simple docstring"""






    for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     a						=		ConvNextVaModel.from_pretrained(__lowerCAmelCase							)
     self.assertIsNotNone(__lowerCAmelCase							)


def 	UpperCAmelCase__      (      ):






 '''simple docstring'''

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




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

   @cached_property
   def 		A							(      self			:       Optional[int]							)       ->				str:


    """simple docstring"""






    return AutoImageProcessor.from_pretrained("facebook/convnextv2-tiny-1k-224"							) if is_vision_available() else None




   @slow
   def 		A							(      self			:       List[str]							)       ->				Union[str, Any]:


    """simple docstring"""






    a						=		ConvNextVaForImageClassification.from_pretrained("facebook/convnextv2-tiny-1k-224"							).to(__lowerCAmelCase							)

    a						=		self.default_image_processor
    a						=		prepare_img()
    a						=		preprocessor(images=__lowerCAmelCase							,   return_tensors="pt"							).to(__lowerCAmelCase							)

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

    # verify the logits
    a						=		torch.Size((1, 1000)							)
    self.assertEqual(outputs.logits.shape							,   __lowerCAmelCase							)

    a						=		torch.tensor([0.9_9_9_6, 0.1_9_6_6, -0.4_3_8_6]							).to(__lowerCAmelCase							)
    self.assertTrue(torch.allclose(outputs.logits[0, :3]							,   __lowerCAmelCase							,   atol=1E-4							)							)

