"""simple docstring"""


from __future__ import annotations

import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers import TFViTForImageClassification, TFViTModel


if is_vision_available():
    from PIL import Image

    from transformers import ViTImageProcessor


class        a				:


    def __init__(					self	:  Tuple   ,				__lowerCAmelCase	:  Any   ,				__lowerCAmelCase	:  Optional[int]=13   ,				__lowerCAmelCase	:  Optional[Any]=30   ,				__lowerCAmelCase	:  Optional[Any]=2   ,				__lowerCAmelCase	:  List[str]=3   ,				__lowerCAmelCase	:  List[Any]=True   ,				__lowerCAmelCase	:  List[str]=True   ,				__lowerCAmelCase	:  List[str]=32   ,				__lowerCAmelCase	:  Any=2   ,				__lowerCAmelCase	:  Optional[int]=4   ,				__lowerCAmelCase	:  Optional[Any]=37   ,				__lowerCAmelCase	:  str="gelu"   ,				__lowerCAmelCase	:  Optional[Any]=0.1   ,				__lowerCAmelCase	:  Optional[Any]=0.1   ,				__lowerCAmelCase	:  int=10   ,				__lowerCAmelCase	:  List[Any]=0.02   ,				__lowerCAmelCase	:  Optional[Any]=3   ,				__lowerCAmelCase	:  int=None   ,				):
      _UpperCAmelCase						 =      parent
      _UpperCAmelCase						 =      batch_size
      _UpperCAmelCase						 =      image_size
      _UpperCAmelCase						 =      patch_size
      _UpperCAmelCase						 =      num_channels
      _UpperCAmelCase						 =      is_training
      _UpperCAmelCase						 =      use_labels
      _UpperCAmelCase						 =      hidden_size
      _UpperCAmelCase						 =      num_hidden_layers
      _UpperCAmelCase						 =      num_attention_heads
      _UpperCAmelCase						 =      intermediate_size
      _UpperCAmelCase						 =      hidden_act
      _UpperCAmelCase						 =      hidden_dropout_prob
      _UpperCAmelCase						 =      attention_probs_dropout_prob
      _UpperCAmelCase						 =      type_sequence_label_size
      _UpperCAmelCase						 =      initializer_range
      _UpperCAmelCase						 =      scope

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


    def      lowerCAmelCase_  (					self	:  Optional[Any]					):
      _UpperCAmelCase						 =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

      _UpperCAmelCase						 =      None
      if self.use_labels:
        _UpperCAmelCase						 =      ids_tensor([self.batch_size]   ,				self.type_sequence_label_size					)

      _UpperCAmelCase						 =      self.get_config()

      return config, pixel_values, labels


    def      lowerCAmelCase_  (					self	:  Optional[Any]					):
      return ViTConfig(
          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   ,				is_decoder=__lowerCAmelCase   ,				initializer_range=self.initializer_range   ,				)


    def      lowerCAmelCase_  (					self	:  str   ,				__lowerCAmelCase	:  List[str]   ,				__lowerCAmelCase	:  List[Any]   ,				__lowerCAmelCase	:  Optional[int]					):
      _UpperCAmelCase						 =      TFViTModel(config=__lowerCAmelCase					)
      _UpperCAmelCase						 =      model(__lowerCAmelCase   ,				training=__lowerCAmelCase					)
      self.parent.assertEqual(result.last_hidden_state.shape   ,				(self.batch_size, self.seq_length, self.hidden_size)					)

      # Test with an image with different size than the one specified in config.
      _UpperCAmelCase						 =      self.image_size // 2
      _UpperCAmelCase						 =      pixel_values[:, :, :image_size, :image_size]
      _UpperCAmelCase						 =      model(__lowerCAmelCase   ,				interpolate_pos_encoding=__lowerCAmelCase   ,				training=__lowerCAmelCase					)
      _UpperCAmelCase						 =      (image_size // self.patch_size) ** 2 + 1
      self.parent.assertEqual(result.last_hidden_state.shape   ,				(self.batch_size, seq_length, self.hidden_size)					)


    def      lowerCAmelCase_  (					self	:  Dict   ,				__lowerCAmelCase	:  Dict   ,				__lowerCAmelCase	:  int   ,				__lowerCAmelCase	:  Union[str, Any]					):
      _UpperCAmelCase						 =      self.type_sequence_label_size
      _UpperCAmelCase						 =      TFViTForImageClassification(__lowerCAmelCase					)
      _UpperCAmelCase						 =      model(__lowerCAmelCase   ,				labels=__lowerCAmelCase   ,				training=__lowerCAmelCase					)
      self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.type_sequence_label_size)					)

      # Test with an image with different size than the one specified in config.
      _UpperCAmelCase						 =      self.image_size // 2
      _UpperCAmelCase						 =      pixel_values[:, :, :image_size, :image_size]
      _UpperCAmelCase						 =      model(__lowerCAmelCase   ,				interpolate_pos_encoding=__lowerCAmelCase   ,				training=__lowerCAmelCase					)
      self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.type_sequence_label_size)					)

      # test greyscale images
      _UpperCAmelCase						 =      1
      _UpperCAmelCase						 =      TFViTForImageClassification(__lowerCAmelCase					)
      _UpperCAmelCase						 =      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
      _UpperCAmelCase						 =      model(__lowerCAmelCase					)
      self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.type_sequence_label_size)					)


    def      lowerCAmelCase_  (					self	:  Any					):
      _UpperCAmelCase						 =      self.prepare_config_and_inputs()
      _UpperCAmelCase ,				_UpperCAmelCase ,				_UpperCAmelCase						 =      config_and_inputs
      _UpperCAmelCase						 =      {"""pixel_values""": pixel_values}
      return config, inputs_dict



@require_tf
class        a				(		lowerCAmelCase_	,							lowerCAmelCase_	,							unittest.TestCase   ):
    _snake_case     :  List[Any]     							=							(TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
    _snake_case     :  int     							=							(
        {'feature-extraction': TFViTModel, 'image-classification': TFViTForImageClassification}
        if is_tf_available()
        else {}
    )

    _snake_case     :  Optional[int]     							=							False
    _snake_case     :  Optional[Any]     							=							False
    _snake_case     :  Union[str, Any]     							=							False


    def      lowerCAmelCase_  (					self	:  int					):
      _UpperCAmelCase						 =      TFViTModelTester(self					)
      _UpperCAmelCase						 =      ConfigTester(self   ,				config_class=__lowerCAmelCase   ,				has_text_modality=__lowerCAmelCase   ,				hidden_size=37					)


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


    @unittest.skip(reason="""ViT does not use inputs_embeds"""					)
    def      lowerCAmelCase_  (					self	:  Union[str, Any]					):
      pass


    @unittest.skip(reason="""ViT does not use inputs_embeds"""					)
    def      lowerCAmelCase_  (					self	:  Union[str, Any]					):
      pass


    def      lowerCAmelCase_  (					self	:  Any					):
      _UpperCAmelCase ,				_UpperCAmelCase						 =      self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        _UpperCAmelCase						 =      model_class(__lowerCAmelCase					)
        self.assertIsInstance(model.get_input_embeddings()   ,				(tf.keras.layers.Layer)					)
        _UpperCAmelCase						 =      model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(__lowerCAmelCase   ,				tf.keras.layers.Layer					)					)


    def      lowerCAmelCase_  (					self	:  List[str]					):
      _UpperCAmelCase ,				_UpperCAmelCase						 =      self.model_tester.prepare_config_and_inputs_for_common()

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

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


    def      lowerCAmelCase_  (					self	:  Any					):
      _UpperCAmelCase						 =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  Any					):
      _UpperCAmelCase						 =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase					)


    @slow
    def      lowerCAmelCase_  (					self	:  Tuple					):
      _UpperCAmelCase						 =      TFViTModel.from_pretrained("""google/vit-base-patch16-224"""					)
      self.assertIsNotNone(__lowerCAmelCase					)






def     __UpperCAmelCase					(			):







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


@require_tf
@require_vision
class        a				(		unittest.TestCase   ):


    @cached_property
    def      lowerCAmelCase_  (					self	:  Optional[int]					):
      return ViTImageProcessor.from_pretrained("""google/vit-base-patch16-224"""					) if is_vision_available() else None


    @slow
    def      lowerCAmelCase_  (					self	:  Tuple					):
      _UpperCAmelCase						 =      TFViTForImageClassification.from_pretrained("""google/vit-base-patch16-224"""					)

      _UpperCAmelCase						 =      self.default_image_processor
      _UpperCAmelCase						 =      prepare_img()
      _UpperCAmelCase						 =      image_processor(images=__lowerCAmelCase   ,				return_tensors="""tf"""					)

      # forward pass
      _UpperCAmelCase						 =      model(**__lowerCAmelCase					)

      # verify the logits
      _UpperCAmelCase						 =      tf.TensorShape((1, 1000)					)
      self.assertEqual(outputs.logits.shape   ,				__lowerCAmelCase					)

      _UpperCAmelCase						 =      tf.constant([-0.2_744, 0.8_215, -0.0_836]					)

      tf.debugging.assert_near(outputs.logits[0, :3]   ,				__lowerCAmelCase   ,				atol=1e-4					)

"""simple docstring"""


import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()


class        a				(		lowerCAmelCase_	,							lowerCAmelCase_	,							lowerCAmelCase_	,							unittest.TestCase   ):
    _snake_case     :  Optional[int]     							=							StableUnCLIPPipeline
    _snake_case     :  List[str]     							=							TEXT_TO_IMAGE_PARAMS
    _snake_case     :  Tuple     							=							TEXT_TO_IMAGE_BATCH_PARAMS
    _snake_case     :  List[Any]     							=							TEXT_TO_IMAGE_IMAGE_PARAMS
    _snake_case     :  str     							=							TEXT_TO_IMAGE_IMAGE_PARAMS

    # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
    _snake_case     :  str     							=							False


    def      lowerCAmelCase_  (					self	:  Dict					):
      _UpperCAmelCase						 =      32
      _UpperCAmelCase						 =      embedder_hidden_size

      # prior components

      torch.manual_seed(0					)
      _UpperCAmelCase						 =      CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""					)

      torch.manual_seed(0					)
      _UpperCAmelCase						 =      CLIPTextModelWithProjection(
          CLIPTextConfig(
              bos_token_id=0   ,				eos_token_id=2   ,				hidden_size=__lowerCAmelCase   ,				projection_dim=__lowerCAmelCase   ,				intermediate_size=37   ,				layer_norm_eps=1e-0_5   ,				num_attention_heads=4   ,				num_hidden_layers=5   ,				pad_token_id=1   ,				vocab_size=1000   ,				)					)

      torch.manual_seed(0					)
      _UpperCAmelCase						 =      PriorTransformer(
          num_attention_heads=2   ,				attention_head_dim=12   ,				embedding_dim=__lowerCAmelCase   ,				num_layers=1   ,				)

      torch.manual_seed(0					)
      _UpperCAmelCase						 =      DDPMScheduler(
          variance_type="""fixed_small_log"""   ,				prediction_type="""sample"""   ,				num_train_timesteps=1000   ,				clip_sample=__lowerCAmelCase   ,				clip_sample_range=5.0   ,				beta_schedule="""squaredcos_cap_v2"""   ,				)

      # regular denoising components

      torch.manual_seed(0					)
      _UpperCAmelCase						 =      StableUnCLIPImageNormalizer(embedding_dim=__lowerCAmelCase					)
      _UpperCAmelCase						 =      DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""					)

      torch.manual_seed(0					)
      _UpperCAmelCase						 =      CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""					)

      torch.manual_seed(0					)
      _UpperCAmelCase						 =      CLIPTextModel(
          CLIPTextConfig(
              bos_token_id=0   ,				eos_token_id=2   ,				hidden_size=__lowerCAmelCase   ,				projection_dim=32   ,				intermediate_size=37   ,				layer_norm_eps=1e-0_5   ,				num_attention_heads=4   ,				num_hidden_layers=5   ,				pad_token_id=1   ,				vocab_size=1000   ,				)					)

      torch.manual_seed(0					)
      _UpperCAmelCase						 =      UNetaDConditionModel(
          sample_size=32   ,				in_channels=4   ,				out_channels=4   ,				down_block_types=("""CrossAttnDownBlock2D""", """DownBlock2D""")   ,				up_block_types=("""UpBlock2D""", """CrossAttnUpBlock2D""")   ,				block_out_channels=(32, 64)   ,				attention_head_dim=(2, 4)   ,				class_embed_type="""projection"""   ,				projection_class_embeddings_input_dim=embedder_projection_dim * 2   ,				cross_attention_dim=__lowerCAmelCase   ,				layers_per_block=1   ,				upcast_attention=__lowerCAmelCase   ,				use_linear_projection=__lowerCAmelCase   ,				)

      torch.manual_seed(0					)
      _UpperCAmelCase						 =      DDIMScheduler(
          beta_schedule="""scaled_linear"""   ,				beta_start=0.00_085   ,				beta_end=0.012   ,				prediction_type="""v_prediction"""   ,				set_alpha_to_one=__lowerCAmelCase   ,				steps_offset=1   ,				)

      torch.manual_seed(0					)
      _UpperCAmelCase						 =      AutoencoderKL()

      _UpperCAmelCase						 =      {
          # prior components
          """prior_tokenizer""": prior_tokenizer,
          """prior_text_encoder""": prior_text_encoder,
          """prior""": prior,
          """prior_scheduler""": prior_scheduler,
          # image noising components
          """image_normalizer""": image_normalizer,
          """image_noising_scheduler""": image_noising_scheduler,
          # regular denoising components
          """tokenizer""": tokenizer,
          """text_encoder""": text_encoder,
          """unet""": unet,
          """scheduler""": scheduler,
          """vae""": vae,
      }

      return components


    def      lowerCAmelCase_  (					self	:  List[Any]   ,				__lowerCAmelCase	:  Any   ,				__lowerCAmelCase	:  int=0					):
      if str(__lowerCAmelCase					).startswith("""mps"""					):
        _UpperCAmelCase						 =      torch.manual_seed(__lowerCAmelCase					)
      else:
        _UpperCAmelCase						 =      torch.Generator(device=__lowerCAmelCase					).manual_seed(__lowerCAmelCase					)
      _UpperCAmelCase						 =      {
          """prompt""": """A painting of a squirrel eating a burger""",
          """generator""": generator,
          """num_inference_steps""": 2,
          """prior_num_inference_steps""": 2,
          """output_type""": """numpy""",
      }
      return inputs


    def      lowerCAmelCase_  (					self	:  List[str]					):
      _UpperCAmelCase						 =      torch_device == """cpu"""

      self._test_attention_slicing_forward_pass(test_max_difference=__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  Any					):
      _UpperCAmelCase						 =      torch_device in ["""cpu""", """mps"""]

      self._test_inference_batch_single_identical(test_max_difference=__lowerCAmelCase					)



@slow
@require_torch_gpu
class        a				(		unittest.TestCase   ):


    def      lowerCAmelCase_  (					self	:  List[str]					):
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()


    def      lowerCAmelCase_  (					self	:  List[str]					):
      _UpperCAmelCase						 =      load_numpy(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"""					)

      _UpperCAmelCase						 =      StableUnCLIPPipeline.from_pretrained("""fusing/stable-unclip-2-1-l"""   ,				torch_dtype=torch.floataa					)
      pipe.to(__lowerCAmelCase					)
      pipe.set_progress_bar_config(disable=__lowerCAmelCase					)
      # stable unclip will oom when integration tests are run on a V100,
      # so turn on memory savings
      pipe.enable_attention_slicing()
      pipe.enable_sequential_cpu_offload()

      _UpperCAmelCase						 =      torch.Generator(device="""cpu"""					).manual_seed(0					)
      _UpperCAmelCase						 =      pipe("""anime turle"""   ,				generator=__lowerCAmelCase   ,				output_type="""np"""					)

      _UpperCAmelCase						 =      output.images[0]

      assert image.shape == (768, 768, 3)

      assert_mean_pixel_difference(__lowerCAmelCase   ,				__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  List[str]					):
      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()
      torch.cuda.reset_peak_memory_stats()

      _UpperCAmelCase						 =      StableUnCLIPPipeline.from_pretrained("""fusing/stable-unclip-2-1-l"""   ,				torch_dtype=torch.floataa					)
      _UpperCAmelCase						 =      pipe.to(__lowerCAmelCase					)
      pipe.set_progress_bar_config(disable=__lowerCAmelCase					)
      pipe.enable_attention_slicing()
      pipe.enable_sequential_cpu_offload()

      _UpperCAmelCase						 =      pipe(
          """anime turtle"""   ,				prior_num_inference_steps=2   ,				num_inference_steps=2   ,				output_type="""np"""   ,				)

      _UpperCAmelCase						 =      torch.cuda.max_memory_allocated()
      # make sure that less than 7 GB is allocated
      assert mem_bytes < 7 * 10**9
