
"""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 						a__		:
   def __init__(     self      ,       _a      ,       _a=13      ,       _a=30      ,       _a=2      ,       _a=3      ,       _a=True      ,       _a=True      ,       _a=32      ,       _a=5      ,       _a=4      ,       _a=37      ,       _a="gelu"      ,       _a=0.1      ,       _a=0.1      ,       _a=10      ,       _a=0.0_2      ,       _a=None      ,       ):
          lowercase       :							Dict									=				parent
          lowercase       :							Dict									=				batch_size
          lowercase       :							List[Any]									=				image_size
          lowercase       :							int									=				patch_size
          lowercase       :							List[str]									=				num_channels
          lowercase       :							Any									=				is_training
          lowercase       :							List[Any]									=				use_labels
          lowercase       :							Optional[Any]									=				hidden_size
          lowercase       :							Union[str, Any]									=				num_hidden_layers
          lowercase       :							Optional[Any]									=				num_attention_heads
          lowercase       :							Optional[Any]									=				intermediate_size
          lowercase       :							Any									=				hidden_act
          lowercase       :							Any									=				hidden_dropout_prob
          lowercase       :							List[Any]									=				attention_probs_dropout_prob
          lowercase       :							Union[str, Any]									=				type_sequence_label_size
          lowercase       :							str									=				initializer_range
          lowercase       :							int									=				scope

          # in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
          lowercase       :							Optional[int]									=				(image_size // patch_size) ** 2
          lowercase       :							Dict									=				num_patches + 1
   def      __magic_name__     (     self							):
          lowercase       :							Optional[Any]									=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

          lowercase       :							Optional[Any]									=				None
          if self.use_labels:
                 lowercase       :							List[str]									=				ids_tensor([self.batch_size]      ,       self.type_sequence_label_size							)

          lowercase       :							Union[str, Any]									=				self.get_config()

          return config, pixel_values, labels
   def      __magic_name__     (     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      __magic_name__     (     self      ,       _a      ,       _a      ,       _a							):
          lowercase       :							Dict									=				ViTMSNModel(config=_a							)
          model.to(_a							)
          model.eval()
          lowercase       :							Any									=				model(_a							)
          self.parent.assertEqual(result.last_hidden_state.shape      ,       (self.batch_size, self.seq_length, self.hidden_size)							)
   def      __magic_name__     (     self      ,       _a      ,       _a      ,       _a							):
          lowercase       :							Optional[Any]									=				self.type_sequence_label_size
          lowercase       :							Tuple									=				ViTMSNForImageClassification(_a							)
          model.to(_a							)
          model.eval()
          lowercase       :							List[str]									=				model(_a      ,       labels=_a							)
          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       :							str									=				1
          lowercase       :							Tuple									=				ViTMSNForImageClassification(_a							)
          model.to(_a							)
          model.eval()

          lowercase       :							List[str]									=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
          lowercase       :							Union[str, Any]									=				model(_a							)
          self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.type_sequence_label_size)							)

   def      __magic_name__     (     self							):
          lowercase       :							Optional[int]									=				self.prepare_config_and_inputs()
          lowercase						,       lowercase						,       lowercase       :							List[Any]									=				config_and_inputs
          lowercase       :							Dict									=				{"pixel_values": pixel_values}
          return config, inputs_dict


@require_torch
class 						a__		( a_,     a_,     unittest.TestCase		):
   __lowerCAmelCase				=							(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
   __lowerCAmelCase				=							(
       {"""feature-extraction""": ViTMSNModel, """image-classification""": ViTMSNForImageClassification}
       if is_torch_available()
       else {}
   )

   __lowerCAmelCase				=							False
   __lowerCAmelCase				=							False
   __lowerCAmelCase				=							False
   __lowerCAmelCase				=							False
   def      __magic_name__     (     self							):
          lowercase       :							str									=				ViTMSNModelTester(self							)
          lowercase       :							List[Any]									=				ConfigTester(self      ,       config_class=_a      ,       has_text_modality=_a      ,       hidden_size=37							)
   def      __magic_name__     (     self							):
          self.config_tester.run_common_tests()
   @unittest.skip(reason="ViTMSN does not use inputs_embeds"							)
   def      __magic_name__     (     self							):
          pass
   def      __magic_name__     (     self							):
          lowercase						,       lowercase       :							Optional[int]									=				self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 lowercase       :							List[str]									=				model_class(_a							)
                 self.assertIsInstance(model.get_input_embeddings()      ,       (nn.Module)							)
                 lowercase       :							Optional[int]									=				model.get_output_embeddings()
                 self.assertTrue(x is None or isinstance(_a      ,       nn.Linear							)							)
   def      __magic_name__     (     self							):
          lowercase						,       lowercase       :							Optional[Any]									=				self.model_tester.prepare_config_and_inputs_for_common()

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

                 lowercase       :							Union[str, Any]									=				["pixel_values"]
                 self.assertListEqual(arg_names[:1]      ,       _a							)
   def      __magic_name__     (     self							):
          lowercase       :							Any									=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_a							)
   def      __magic_name__     (     self							):
          lowercase       :							List[str]									=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*_a							)

   @slow
   def      __magic_name__     (     self							):
          for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 lowercase       :							Any									=				ViTMSNModel.from_pretrained(_a							)
                 self.assertIsNotNone(_a							)


def     __magic_name__      ( )					->							List[str]:
       lowercase       :							str									=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
       return image

@require_torch
@require_vision
class 						a__		( unittest.TestCase		):
   @cached_property
   def      __magic_name__     (     self							):
          return ViTImageProcessor.from_pretrained("facebook/vit-msn-small"							) if is_vision_available() else None

   @slow
   def      __magic_name__     (     self							):
          torch.manual_seed(2							)
          lowercase       :							Optional[int]									=				ViTMSNForImageClassification.from_pretrained("facebook/vit-msn-small"							).to(_a							)

          lowercase       :							Optional[int]									=				self.default_image_processor
          lowercase       :							List[str]									=				prepare_img()
          lowercase       :							Optional[int]									=				image_processor(images=_a      ,       return_tensors="pt"							).to(_a							)

          # forward pass
          with torch.no_grad():
                 lowercase       :							List[Any]									=				model(**_a							)

          # verify the logits
          lowercase       :							Dict									=				torch.Size((1, 1_000)							)
          self.assertEqual(outputs.logits.shape      ,       _a							)

          lowercase       :							str									=				torch.tensor([-0.0_8_0_3, -0.4_4_5_4, -0.2_3_7_5]							).to(_a							)

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




"""simple docstring"""




import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class 						a__		( a_,     a_,     unittest.TestCase		):
   __lowerCAmelCase				=							StableDiffusionXLImgaImgPipeline
   __lowerCAmelCase				=							TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
   __lowerCAmelCase				=							PipelineTesterMixin.required_optional_params - {"""latents"""}
   __lowerCAmelCase				=							TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
   __lowerCAmelCase				=							IMAGE_TO_IMAGE_IMAGE_PARAMS
   __lowerCAmelCase				=							IMAGE_TO_IMAGE_IMAGE_PARAMS
   def      __magic_name__     (     self							):
          torch.manual_seed(0							)
          lowercase       :							str									=				UNetaDConditionModel(
              block_out_channels=(32, 64)      ,       layers_per_block=2      ,       sample_size=32      ,       in_channels=4      ,       out_channels=4      ,       down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")      ,       up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")      ,       attention_head_dim=(2, 4)      ,       use_linear_projection=_a      ,       addition_embed_type="text_time"      ,       addition_time_embed_dim=8      ,       transformer_layers_per_block=(1, 2)      ,       projection_class_embeddings_input_dim=80      ,       cross_attention_dim=64      ,       )
          lowercase       :							Optional[Any]									=				EulerDiscreteScheduler(
              beta_start=0.0_0_0_8_5      ,       beta_end=0.0_1_2      ,       steps_offset=1      ,       beta_schedule="scaled_linear"      ,       timestep_spacing="leading"      ,       )
          torch.manual_seed(0							)
          lowercase       :							Any									=				AutoencoderKL(
              block_out_channels=[32, 64]      ,       in_channels=3      ,       out_channels=3      ,       down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]      ,       up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]      ,       latent_channels=4      ,       sample_size=128      ,       )
          torch.manual_seed(0							)
          lowercase       :							Dict									=				CLIPTextConfig(
              bos_token_id=0      ,       eos_token_id=2      ,       hidden_size=32      ,       intermediate_size=37      ,       layer_norm_eps=1E-05      ,       num_attention_heads=4      ,       num_hidden_layers=5      ,       pad_token_id=1      ,       vocab_size=1_000      ,       hidden_act="gelu"      ,       projection_dim=32      ,       )
          lowercase       :							str									=				CLIPTextModel(_a							)
          lowercase       :							List[str]									=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"      ,       local_files_only=_a							)

          lowercase       :							Any									=				CLIPTextModelWithProjection(_a							)
          lowercase       :							Union[str, Any]									=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"      ,       local_files_only=_a							)

          lowercase       :							Any									=				{
              "unet": unet,
              "scheduler": scheduler,
              "vae": vae,
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
              "text_encoder_2": text_encoder_a,
              "tokenizer_2": tokenizer_a,
              # "safety_checker": None,
              # "feature_extractor": None,
          }
          return components
   def      __magic_name__     (     self      ,       _a      ,       _a=0							):
          lowercase       :							Dict									=				floats_tensor((1, 3, 32, 32)      ,       rng=random.Random(_a							)							).to(_a							)
          lowercase       :							List[Any]									=				image / 2 + 0.5
          if str(_a							).startswith("mps"							):
                 lowercase       :							Optional[int]									=				torch.manual_seed(_a							)
          else:
                 lowercase       :							str									=				torch.Generator(device=_a							).manual_seed(_a							)
          lowercase       :							Optional[int]									=				{
              "prompt": "A painting of a squirrel eating a burger",
              "image": image,
              "generator": generator,
              "num_inference_steps": 2,
              "guidance_scale": 5.0,
              "output_type": "numpy",
              "strength": 0.7_5,
          }
          return inputs
   def      __magic_name__     (     self							):
          lowercase       :							Dict									=				"cpu"  # ensure determinism for the device-dependent torch.Generator
          lowercase       :							Optional[int]									=				self.get_dummy_components()
          lowercase       :							str									=				StableDiffusionXLImgaImgPipeline(**_a							)
          lowercase       :							List[Any]									=				sd_pipe.to(_a							)
          sd_pipe.set_progress_bar_config(disable=_a							)

          lowercase       :							Optional[Any]									=				self.get_dummy_inputs(_a							)
          lowercase       :							Tuple									=				sd_pipe(**_a							).images
          lowercase       :							Dict									=				image[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)

          lowercase       :							Tuple									=				np.array([0.4_6_5_6, 0.4_8_4_0, 0.4_4_3_9, 0.6_6_9_8, 0.5_5_7_4, 0.4_5_2_4, 0.5_7_9_9, 0.5_9_4_3, 0.5_1_6_5]							)

          assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2
   def      __magic_name__     (     self							):
          super().test_attention_slicing_forward_pass(expected_max_diff=3E-3							)
   def      __magic_name__     (     self							):
          super().test_inference_batch_single_identical(expected_max_diff=3E-3							)
   def      __magic_name__     (     self							):
          pass

   def      __magic_name__     (     self							):
          lowercase       :							Optional[Any]									=				self.get_dummy_components()
          lowercase       :							Optional[Any]									=				StableDiffusionXLImgaImgPipeline(**_a							)
          lowercase       :							Any									=				sd_pipe.to(_a							)
          lowercase       :							Any									=				sd_pipe.to(_a							)
          sd_pipe.set_progress_bar_config(disable=_a							)

          # forward without prompt embeds
          lowercase       :							Union[str, Any]									=				self.get_dummy_inputs(_a							)
          lowercase       :							int									=				3 * ["this is a negative prompt"]
          lowercase       :							Any									=				negative_prompt
          lowercase       :							List[str]									=				3 * [inputs["prompt"]]

          lowercase       :							Optional[int]									=				sd_pipe(**_a							)
          lowercase       :							List[Any]									=				output.images[0, -3:, -3:, -1]

          # forward with prompt embeds
          lowercase       :							Any									=				self.get_dummy_inputs(_a							)
          lowercase       :							List[str]									=				3 * ["this is a negative prompt"]
          lowercase       :							Optional[Any]									=				3 * [inputs.pop("prompt"							)]

          (
              (
              lowercase
          )						,       (
              lowercase
          )						,       (
              lowercase
          )						,       (
              lowercase
          )						,       
          )       :							List[Any]									=				sd_pipe.encode_prompt(_a      ,       negative_prompt=_a							)

          lowercase       :							int									=				sd_pipe(
              **_a      ,       prompt_embeds=_a      ,       negative_prompt_embeds=_a      ,       pooled_prompt_embeds=_a      ,       negative_pooled_prompt_embeds=_a      ,       )
          lowercase       :							Dict									=				output.images[0, -3:, -3:, -1]

          # make sure that it's equal
          assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()							).max() < 1E-4


@slow
@require_torch_gpu
class 						a__		( unittest.TestCase		):
   def      __magic_name__     (     self							):
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
   def      __magic_name__     (     self      ,       _a      ,       _a="cpu"      ,       _a=torch.floataa      ,       _a=0							):
          lowercase       :							Optional[int]									=				torch.Generator(device=_a							).manual_seed(_a							)
          lowercase       :							Any									=				np.random.RandomState(_a							).standard_normal((1, 4, 64, 64)							)
          lowercase       :							List[str]									=				torch.from_numpy(_a							).to(device=_a      ,       dtype=_a							)
          lowercase       :							List[Any]									=				{
              "prompt": "a photograph of an astronaut riding a horse",
              "latents": latents,
              "generator": generator,
              "num_inference_steps": 3,
              "guidance_scale": 7.5,
              "output_type": "numpy",
          }
          return inputs

   def      __magic_name__     (     self							):
          lowercase       :							List[Any]									=				DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-base"							)
          pipe.to(_a							)
          pipe.set_progress_bar_config(disable=_a							)

          lowercase       :							Any									=				self.get_inputs(_a							)
          lowercase       :							int									=				pipe(**_a							).images
          lowercase       :							Any									=				image[0, -3:, -3:, -1].flatten()

          assert image.shape == (1, 512, 512, 3)
          lowercase       :							Optional[Any]									=				np.array([0.4_9_4_9_3, 0.4_7_8_9_6, 0.4_0_7_9_8, 0.5_4_2_1_4, 0.5_3_2_1_2, 0.4_8_2_0_2, 0.4_7_6_5_6, 0.4_6_3_2_9, 0.4_8_5_0_6]							)
          assert np.abs(image_slice - expected_slice							).max() < 7E-3


