"""simple docstring"""






import inspect
import unittest

from transformers import ViTMSNConfig
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 torch import nn

       from transformers import ViTMSNForImageClassification, ViTMSNModel
       from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import ViTImageProcessor


class     UpperCAmelCase						:



    """simple docstring"""

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

       # in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
       lowercase__:   Any					=      (image_size // patch_size) ** 2
       lowercase__:   Any					=      num_patches + 1






    def _snake_case			(   self							):
       lowercase__:   Dict					=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

       lowercase__:   int					=      None
       if self.use_labels:
          lowercase__:   Optional[int]					=      ids_tensor([self.batch_size]    ,			self.type_sequence_label_size							)

       lowercase__:   List[Any]					=      self.get_config()

       return config, pixel_values, labels






    def _snake_case			(   self							):
       return ViTMSNConfig(
           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    ,			initializer_range=self.initializer_range    ,			)






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):
       lowercase__:   Any					=      ViTMSNModel(config=_UpperCAmelCase							)
       model.to(_UpperCAmelCase							)
       model.eval()
       lowercase__:   str					=      model(_UpperCAmelCase							)
       self.parent.assertEqual(result.last_hidden_state.shape    ,			(self.batch_size, self.seq_length, self.hidden_size)							)






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):
       lowercase__:   Any					=      self.type_sequence_label_size
       lowercase__:   Optional[int]					=      ViTMSNForImageClassification(_UpperCAmelCase							)
       model.to(_UpperCAmelCase							)
       model.eval()
       lowercase__:   Dict					=      model(_UpperCAmelCase    ,			labels=_UpperCAmelCase							)
       print('''Pixel and labels shape: {pixel_values.shape}, {labels.shape}'''							)
       print('''Labels: {labels}'''							)
       self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.type_sequence_label_size)							)

       # test greyscale images
       lowercase__:   int					=      1
       lowercase__:   str					=      ViTMSNForImageClassification(_UpperCAmelCase							)
       model.to(_UpperCAmelCase							)
       model.eval()

       lowercase__:   List[Any]					=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
       lowercase__:   Tuple					=      model(_UpperCAmelCase							)
       self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.type_sequence_label_size)							)






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



@require_torch
class     UpperCAmelCase						(_UpperCAmelCase    ,_UpperCAmelCase    ,unittest.TestCase  ):



    """simple docstring"""

    _UpperCAmelCase	:Optional[int]						    = (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
    _UpperCAmelCase	:List[str]						    = (
        {"feature-extraction": ViTMSNModel, "image-classification": ViTMSNForImageClassification}
        if is_torch_available()
        else {}
    )

    _UpperCAmelCase	:Any						    = False
    _UpperCAmelCase	:Union[str, Any]						    = False
    _UpperCAmelCase	:str						    = False
    _UpperCAmelCase	:int						    = False






    def _snake_case			(   self							):
       lowercase__:   Optional[int]					=      ViTMSNModelTester(self							)
       lowercase__:   Optional[Any]					=      ConfigTester(self    ,			config_class=_UpperCAmelCase    ,			has_text_modality=_UpperCAmelCase    ,			hidden_size=37							)






    def _snake_case			(   self							):
       self.config_tester.run_common_tests()






    @unittest.skip(reason='''ViTMSN does not use inputs_embeds'''							)
    def _snake_case			(   self							):
       pass






    def _snake_case			(   self							):
       lowercase__, lowercase__:   Optional[Any]					=      self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
          lowercase__:   Tuple					=      model_class(_UpperCAmelCase							)
          self.assertIsInstance(model.get_input_embeddings()    ,			(nn.Module)							)
          lowercase__:   Optional[int]					=      model.get_output_embeddings()
          self.assertTrue(x is None or isinstance(_UpperCAmelCase    ,			nn.Linear							)							)






    def _snake_case			(   self							):
       lowercase__, lowercase__:   int					=      self.model_tester.prepare_config_and_inputs_for_common()

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

          lowercase__:   Dict					=      ['''pixel_values''']
          self.assertListEqual(arg_names[:1]    ,			_UpperCAmelCase							)






    def _snake_case			(   self							):
       lowercase__:   Optional[int]					=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_UpperCAmelCase							)






    def _snake_case			(   self							):
       lowercase__:   Dict					=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase							)






    @slow
    def _snake_case			(   self							):
       for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowercase__:   int					=      ViTMSNModel.from_pretrained(_UpperCAmelCase							)
          self.assertIsNotNone(_UpperCAmelCase							)



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




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



    """simple docstring"""

    @cached_property
    def _snake_case			(   self							):
       return ViTImageProcessor.from_pretrained('''facebook/vit-msn-small'''							) if is_vision_available() else None






    @slow
    def _snake_case			(   self							):
       torch.manual_seed(2							)
       lowercase__:   List[Any]					=      ViTMSNForImageClassification.from_pretrained('''facebook/vit-msn-small'''							).to(_UpperCAmelCase							)

       lowercase__:   Union[str, Any]					=      self.default_image_processor
       lowercase__:   List[Any]					=      prepare_img()
       lowercase__:   Dict					=      image_processor(images=_UpperCAmelCase    ,			return_tensors='''pt'''							).to(_UpperCAmelCase							)

       # forward pass
       with torch.no_grad():
          lowercase__:   Optional[Any]					=      model(**_UpperCAmelCase							)

       # verify the logits
       lowercase__:   Optional[int]					=      torch.Size((1, 1000)							)
       self.assertEqual(outputs.logits.shape    ,			_UpperCAmelCase							)

       lowercase__:   Union[str, Any]					=      torch.tensor([-0.0_803, -0.4_454, -0.2_375]							).to(_UpperCAmelCase							)

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

"""simple docstring"""






import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


__A			   = False


class     UpperCAmelCase						(unittest.TestCase  ):



    """simple docstring"""

    pass



@nightly
@require_torch_gpu
class     UpperCAmelCase						(unittest.TestCase  ):



    """simple docstring"""

    def _snake_case			(   self							):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






    def _snake_case			(   self							):
       lowercase__:   Dict					=      VersatileDiffusionTextToImagePipeline.from_pretrained('''shi-labs/versatile-diffusion'''							)
       # remove text_unet
       pipe.remove_unused_weights()
       pipe.to(_UpperCAmelCase							)
       pipe.set_progress_bar_config(disable=_UpperCAmelCase							)

       lowercase__:   List[str]					=      '''A painting of a squirrel eating a burger '''
       lowercase__:   str					=      torch.manual_seed(0							)
       lowercase__:   Union[str, Any]					=      pipe(
           prompt=_UpperCAmelCase    ,			generator=_UpperCAmelCase    ,			guidance_scale=7.5    ,			num_inference_steps=2    ,			output_type='''numpy'''							).images

       with tempfile.TemporaryDirectory() as tmpdirname:
          pipe.save_pretrained(_UpperCAmelCase							)
          lowercase__:   Optional[Any]					=      VersatileDiffusionTextToImagePipeline.from_pretrained(_UpperCAmelCase							)
       pipe.to(_UpperCAmelCase							)
       pipe.set_progress_bar_config(disable=_UpperCAmelCase							)

       lowercase__:   Optional[int]					=      generator.manual_seed(0							)
       lowercase__:   List[str]					=      pipe(
           prompt=_UpperCAmelCase    ,			generator=_UpperCAmelCase    ,			guidance_scale=7.5    ,			num_inference_steps=2    ,			output_type='''numpy'''							).images

       assert np.abs(image - new_image							).sum() < 1e-5, "Models don't have the same forward pass"






    def _snake_case			(   self							):
       lowercase__:   Dict					=      VersatileDiffusionTextToImagePipeline.from_pretrained(
           '''shi-labs/versatile-diffusion'''    ,			torch_dtype=torch.floataa							)
       pipe.to(_UpperCAmelCase							)
       pipe.set_progress_bar_config(disable=_UpperCAmelCase							)

       lowercase__:   Tuple					=      '''A painting of a squirrel eating a burger '''
       lowercase__:   Optional[Any]					=      torch.manual_seed(0							)
       lowercase__:   Tuple					=      pipe(
           prompt=_UpperCAmelCase    ,			generator=_UpperCAmelCase    ,			guidance_scale=7.5    ,			num_inference_steps=50    ,			output_type='''numpy'''							).images

       lowercase__:   Union[str, Any]					=      image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       lowercase__:   Any					=      np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778]							)

       assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2
