

'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()




class        A_ (				__lowerCAmelCase       ,   unittest.TestCase							):






     '''simple docstring'''



     UpperCAmelCase_						:	Any  		=  UnCLIPImageVariationPipeline
     UpperCAmelCase_						:	Optional[Any]  		=  IMAGE_VARIATION_PARAMS - {"""height""", """width""", """guidance_scale"""}
     UpperCAmelCase_						:	Optional[int]  		=  IMAGE_VARIATION_BATCH_PARAMS

     UpperCAmelCase_						:	Union[str, Any]  		=  [
         """generator""",
         """return_dict""",
         """decoder_num_inference_steps""",
         """super_res_num_inference_steps""",
     ]
     UpperCAmelCase_						:	Tuple  		=  False


     @property
     def    UpperCAmelCase_       (		self :     Tuple       )						->	Union[str, Any]:
      return 32


     @property
     def    UpperCAmelCase_       (		self :     int       )						->	List[str]:
      return 32


     @property
     def    UpperCAmelCase_       (		self :     Optional[Any]       )						->	Optional[Any]:
      return self.time_input_dim


     @property
     def    UpperCAmelCase_       (		self :     Tuple       )						->	List[str]:
      return self.time_input_dim * 4


     @property
     def    UpperCAmelCase_       (		self :     Dict       )						->	Optional[int]:
      return 100


     @property
     def    UpperCAmelCase_       (		self :     str       )						->	Union[str, Any]:
      UpperCAmelCase       :	Union[str, Any]											= CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'       )
      return tokenizer


     @property
     def    UpperCAmelCase_       (		self :     str       )						->	str:
      torch.manual_seed(0       )
      UpperCAmelCase       :	Optional[int]											= CLIPTextConfig(
          bos_token_id=0     ,			eos_token_id=2     ,			hidden_size=self.text_embedder_hidden_size     ,			projection_dim=self.text_embedder_hidden_size     ,			intermediate_size=37     ,			layer_norm_eps=1E-05     ,			num_attention_heads=4     ,			num_hidden_layers=5     ,			pad_token_id=1     ,			vocab_size=1_000     ,			)
      return CLIPTextModelWithProjection(lowercase_       )


     @property
     def    UpperCAmelCase_       (		self :     Dict       )						->	List[Any]:
      torch.manual_seed(0       )
      UpperCAmelCase       :	List[str]											= CLIPVisionConfig(
          hidden_size=self.text_embedder_hidden_size     ,			projection_dim=self.text_embedder_hidden_size     ,			num_hidden_layers=5     ,			num_attention_heads=4     ,			image_size=32     ,			intermediate_size=37     ,			patch_size=1     ,			)
      return CLIPVisionModelWithProjection(lowercase_       )


     @property
     def    UpperCAmelCase_       (		self :     Any       )						->	Any:
      torch.manual_seed(0       )

      UpperCAmelCase       :	List[Any]											= {
          'clip_embeddings_dim': self.text_embedder_hidden_size,
          'time_embed_dim': self.time_embed_dim,
          'cross_attention_dim': self.cross_attention_dim,
      }

      UpperCAmelCase       :	List[Any]											= UnCLIPTextProjModel(**lowercase_       )
      return model


     @property
     def    UpperCAmelCase_       (		self :     Union[str, Any]       )						->	List[Any]:
      torch.manual_seed(0       )

      UpperCAmelCase       :	Any											= {
          'sample_size': 32,
          # RGB in channels
          'in_channels': 3,
          # Out channels is double in channels because predicts mean and variance
          'out_channels': 6,
          'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
          'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
          'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
          'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
          'layers_per_block': 1,
          'cross_attention_dim': self.cross_attention_dim,
          'attention_head_dim': 4,
          'resnet_time_scale_shift': 'scale_shift',
          'class_embed_type': 'identity',
      }

      UpperCAmelCase       :	Optional[Any]											= UNetaDConditionModel(**lowercase_       )
      return model


     @property
     def    UpperCAmelCase_       (		self :     Union[str, Any]       )						->	Dict:
      return {
          "sample_size": 64,
          "layers_per_block": 1,
          "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
          "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
          "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
          "in_channels": 6,
          "out_channels": 3,
      }


     @property
     def    UpperCAmelCase_       (		self :     int       )						->	int:
      torch.manual_seed(0       )

      UpperCAmelCase       :	int											= UNetaDModel(**self.dummy_super_res_kwargs       )
      return model


     @property
     def    UpperCAmelCase_       (		self :     List[str]       )						->	Any:
      torch.manual_seed(1       )

      UpperCAmelCase       :	Any											= UNetaDModel(**self.dummy_super_res_kwargs       )
      return model


     def    UpperCAmelCase_       (		self :     Dict       )						->	Optional[int]:
      UpperCAmelCase       :	List[str]											= self.dummy_decoder
      UpperCAmelCase       :	str											= self.dummy_text_proj
      UpperCAmelCase       :	Dict											= self.dummy_text_encoder
      UpperCAmelCase       :	Dict											= self.dummy_tokenizer
      UpperCAmelCase       :	Optional[int]											= self.dummy_super_res_first
      UpperCAmelCase       :	Dict											= self.dummy_super_res_last

      UpperCAmelCase       :	Tuple											= UnCLIPScheduler(
          variance_type='learned_range'     ,			prediction_type='epsilon'     ,			num_train_timesteps=1_000     ,			)

      UpperCAmelCase       :	Dict											= UnCLIPScheduler(
          variance_type='fixed_small_log'     ,			prediction_type='epsilon'     ,			num_train_timesteps=1_000     ,			)

      UpperCAmelCase       :	Optional[int]											= CLIPImageProcessor(crop_size=32     ,			size=32       )

      UpperCAmelCase       :	int											= self.dummy_image_encoder

      return {
          "decoder": decoder,
          "text_encoder": text_encoder,
          "tokenizer": tokenizer,
          "text_proj": text_proj,
          "feature_extractor": feature_extractor,
          "image_encoder": image_encoder,
          "super_res_first": super_res_first,
          "super_res_last": super_res_last,
          "decoder_scheduler": decoder_scheduler,
          "super_res_scheduler": super_res_scheduler,
      }


     def    UpperCAmelCase_       (		self :     str     ,			lowercase_ :     Optional[Any]     ,			lowercase_ :     Any=0     ,			lowercase_ :     List[str]=True       )						->	Optional[int]:
      UpperCAmelCase       :	Any											= floats_tensor((1, 3, 32, 32)     ,			rng=random.Random(lowercase_       )       ).to(lowercase_       )
      if str(lowercase_       ).startswith('mps'       ):
       UpperCAmelCase       :	Dict											= torch.manual_seed(lowercase_       )
      else:
       UpperCAmelCase       :	str											= torch.Generator(device=lowercase_       ).manual_seed(lowercase_       )

      if pil_image:
       UpperCAmelCase       :	List[Any]											= input_image * 0.5 + 0.5
       UpperCAmelCase       :	Any											= input_image.clamp(0     ,			1       )
       UpperCAmelCase       :	Tuple											= input_image.cpu().permute(0     ,			2     ,			3     ,			1       ).float().numpy()
       UpperCAmelCase       :	Tuple											= DiffusionPipeline.numpy_to_pil(lowercase_       )[0]

      return {
          "image": input_image,
          "generator": generator,
          "decoder_num_inference_steps": 2,
          "super_res_num_inference_steps": 2,
          "output_type": "np",
      }


     def    UpperCAmelCase_       (		self :     int       )						->	Optional[Any]:
      UpperCAmelCase       :	Optional[Any]											= 'cpu'

      UpperCAmelCase       :	Optional[int]											= self.get_dummy_components()

      UpperCAmelCase       :	Dict											= self.pipeline_class(**lowercase_       )
      UpperCAmelCase       :	str											= pipe.to(lowercase_       )

      pipe.set_progress_bar_config(disable=lowercase_       )

      UpperCAmelCase       :	Optional[Any]											= self.get_dummy_inputs(lowercase_     ,			pil_image=lowercase_       )

      UpperCAmelCase       :	str											= pipe(**lowercase_       )
      UpperCAmelCase       :	str											= output.images

      UpperCAmelCase       :	List[str]											= self.get_dummy_inputs(lowercase_     ,			pil_image=lowercase_       )

      UpperCAmelCase       :	Optional[int]											= pipe(
          **lowercase_     ,			return_dict=lowercase_     ,			)[0]

      UpperCAmelCase       :	Optional[Any]											= image[0, -3:, -3:, -1]
      UpperCAmelCase       :	Union[str, Any]											= image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 64, 64, 3)

      UpperCAmelCase       :	List[str]											= np.array(
          [
              0.9997,
              0.0002,
              0.9997,
              0.9997,
              0.9969,
              0.0023,
              0.9997,
              0.9969,
              0.9970,
          ]       )

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


     def    UpperCAmelCase_       (		self :     Any       )						->	Optional[int]:
      UpperCAmelCase       :	str											= 'cpu'

      UpperCAmelCase       :	int											= self.get_dummy_components()

      UpperCAmelCase       :	Union[str, Any]											= self.pipeline_class(**lowercase_       )
      UpperCAmelCase       :	int											= pipe.to(lowercase_       )

      pipe.set_progress_bar_config(disable=lowercase_       )

      UpperCAmelCase       :	int											= self.get_dummy_inputs(lowercase_     ,			pil_image=lowercase_       )

      UpperCAmelCase       :	str											= pipe(**lowercase_       )
      UpperCAmelCase       :	Dict											= output.images

      UpperCAmelCase       :	List[str]											= self.get_dummy_inputs(lowercase_     ,			pil_image=lowercase_       )

      UpperCAmelCase       :	Any											= pipe(
          **lowercase_     ,			return_dict=lowercase_     ,			)[0]

      UpperCAmelCase       :	str											= image[0, -3:, -3:, -1]
      UpperCAmelCase       :	Optional[Any]											= image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 64, 64, 3)

      UpperCAmelCase       :	str											= np.array([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971]       )

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


     def    UpperCAmelCase_       (		self :     List[str]       )						->	Optional[Any]:
      UpperCAmelCase       :	Optional[int]											= 'cpu'

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

      UpperCAmelCase       :	Dict											= self.pipeline_class(**lowercase_       )
      UpperCAmelCase       :	Any											= pipe.to(lowercase_       )

      pipe.set_progress_bar_config(disable=lowercase_       )

      UpperCAmelCase       :	Optional[Any]											= self.get_dummy_inputs(lowercase_     ,			pil_image=lowercase_       )
      UpperCAmelCase       :	str											= [
          pipeline_inputs['image'],
          pipeline_inputs['image'],
      ]

      UpperCAmelCase       :	Any											= pipe(**lowercase_       )
      UpperCAmelCase       :	str											= output.images

      UpperCAmelCase       :	Union[str, Any]											= self.get_dummy_inputs(lowercase_     ,			pil_image=lowercase_       )
      UpperCAmelCase       :	Optional[Any]											= [
          tuple_pipeline_inputs['image'],
          tuple_pipeline_inputs['image'],
      ]

      UpperCAmelCase       :	Tuple											= pipe(
          **lowercase_     ,			return_dict=lowercase_     ,			)[0]

      UpperCAmelCase       :	Optional[Any]											= image[0, -3:, -3:, -1]
      UpperCAmelCase       :	Tuple											= image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (2, 64, 64, 3)

      UpperCAmelCase       :	str											= np.array(
          [
              0.9997,
              0.9989,
              0.0008,
              0.0021,
              0.9960,
              0.0018,
              0.0014,
              0.0002,
              0.9933,
          ]       )

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


     def    UpperCAmelCase_       (		self :     str       )						->	Dict:
      UpperCAmelCase       :	Optional[Any]											= torch.device('cpu'       )




      class        A_ :






           '''simple docstring'''



           UpperCAmelCase_						:	List[Any]  		=  1



      UpperCAmelCase       :	Optional[int]											= self.get_dummy_components()

      UpperCAmelCase       :	List[str]											= self.pipeline_class(**lowercase_       )
      UpperCAmelCase       :	List[Any]											= pipe.to(lowercase_       )

      pipe.set_progress_bar_config(disable=lowercase_       )

      UpperCAmelCase       :	Tuple											= torch.Generator(device=lowercase_       ).manual_seed(0       )
      UpperCAmelCase       :	Any											= pipe.decoder.dtype
      UpperCAmelCase       :	Dict											= 1

      UpperCAmelCase       :	int											= (
          batch_size,
          pipe.decoder.config.in_channels,
          pipe.decoder.config.sample_size,
          pipe.decoder.config.sample_size,
      )
      UpperCAmelCase       :	List[Any]											= pipe.prepare_latents(
          lowercase_     ,			dtype=lowercase_     ,			device=lowercase_     ,			generator=lowercase_     ,			latents=lowercase_     ,			scheduler=DummyScheduler()       )

      UpperCAmelCase       :	str											= (
          batch_size,
          pipe.super_res_first.config.in_channels // 2,
          pipe.super_res_first.config.sample_size,
          pipe.super_res_first.config.sample_size,
      )
      UpperCAmelCase       :	Tuple											= pipe.prepare_latents(
          lowercase_     ,			dtype=lowercase_     ,			device=lowercase_     ,			generator=lowercase_     ,			latents=lowercase_     ,			scheduler=DummyScheduler()       )

      UpperCAmelCase       :	Dict											= self.get_dummy_inputs(lowercase_     ,			pil_image=lowercase_       )

      UpperCAmelCase       :	Optional[Any]											= pipe(
          **lowercase_     ,			decoder_latents=lowercase_     ,			super_res_latents=lowercase_       ).images

      UpperCAmelCase       :	int											= self.get_dummy_inputs(lowercase_     ,			pil_image=lowercase_       )
      # Don't pass image, instead pass embedding
      UpperCAmelCase       :	Tuple											= pipeline_inputs.pop('image'       )
      UpperCAmelCase       :	int											= pipe.image_encoder(lowercase_       ).image_embeds

      UpperCAmelCase       :	Union[str, Any]											= pipe(
          **lowercase_     ,			decoder_latents=lowercase_     ,			super_res_latents=lowercase_     ,			image_embeddings=lowercase_     ,			).images

      # make sure passing text embeddings manually is identical
      assert np.abs(img_out_a - img_out_a       ).max() < 1E-4


     @skip_mps
     def    UpperCAmelCase_       (		self :     Optional[Any]       )						->	int:
      UpperCAmelCase       :	List[Any]											= torch_device == 'cpu'

      # Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
      UpperCAmelCase       :	Any											= 1E-2

      self._test_attention_slicing_forward_pass(
          test_max_difference=lowercase_     ,			expected_max_diff=lowercase_       )


     @skip_mps
     def    UpperCAmelCase_       (		self :     Tuple       )						->	Dict:
      UpperCAmelCase       :	Any											= torch_device == 'cpu'
      UpperCAmelCase       :	Any											= True
      UpperCAmelCase       :	str											= [
          'decoder_num_inference_steps',
          'super_res_num_inference_steps',
      ]

      self._test_inference_batch_single_identical(
          test_max_difference=lowercase_     ,			relax_max_difference=lowercase_     ,			additional_params_copy_to_batched_inputs=lowercase_     ,			)


     def    UpperCAmelCase_       (		self :     Any       )						->	Any:
      UpperCAmelCase       :	int											= [
          'decoder_num_inference_steps',
          'super_res_num_inference_steps',
      ]

      if torch_device == "mps":
       # TODO: MPS errors with larger batch sizes
       UpperCAmelCase       :	Union[str, Any]											= [2, 3]
       self._test_inference_batch_consistent(
           batch_sizes=lowercase_     ,			additional_params_copy_to_batched_inputs=lowercase_     ,			)
      else:
       self._test_inference_batch_consistent(
           additional_params_copy_to_batched_inputs=lowercase_       )


     @skip_mps
     def    UpperCAmelCase_       (		self :     Optional[Any]       )						->	Union[str, Any]:
      return super().test_dict_tuple_outputs_equivalent()


     @skip_mps
     def    UpperCAmelCase_       (		self :     Any       )						->	int:
      return super().test_save_load_local()




     @skip_mps
     def    UpperCAmelCase_       (		self :     Dict       )						->	Optional[Any]:
      return super().test_save_load_optional_components()




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






     '''simple docstring'''



     def    UpperCAmelCase_       (		self :     str       )						->	int:
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()




     def    UpperCAmelCase_       (		self :     Tuple       )						->	List[str]:
      UpperCAmelCase       :	int											= load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png'       )
      UpperCAmelCase       :	int											= load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/unclip/karlo_v1_alpha_cat_variation_fp16.npy'       )

      UpperCAmelCase       :	Optional[Any]											= UnCLIPImageVariationPipeline.from_pretrained(
          'kakaobrain/karlo-v1-alpha-image-variations'     ,			torch_dtype=torch.floataa       )
      UpperCAmelCase       :	Dict											= pipeline.to(lowercase_       )
      pipeline.set_progress_bar_config(disable=lowercase_       )

      UpperCAmelCase       :	Dict											= torch.Generator(device='cpu'       ).manual_seed(0       )
      UpperCAmelCase       :	Union[str, Any]											= pipeline(
          lowercase_     ,			generator=lowercase_     ,			output_type='np'     ,			)

      UpperCAmelCase       :	int											= output.images[0]

      assert image.shape == (256, 256, 3)

      assert_mean_pixel_difference(lowercase_     ,			lowercase_     ,			15       )





"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__					       =	logging.get_logger(__name__)

lowercase__					       =	{
    'MIT/ast-finetuned-audioset-10-10-0.4593': (
        'https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json'
    ),
}




class    __snake_case       (     __lowerCAmelCase				):
      a__        =		"""audio-spectrogram-transformer"""



      def __init__(						self    ,			lowercase=7_68    ,			lowercase=12    ,			lowercase=12    ,			lowercase=30_72    ,			lowercase="gelu"    ,			lowercase=0.0    ,			lowercase=0.0    ,			lowercase=0.02    ,			lowercase=1e-12    ,			lowercase=16    ,			lowercase=True    ,			lowercase=10    ,			lowercase=10    ,			lowercase=10_24    ,			lowercase=1_28    ,			**lowercase    ,			)	->			str:





        '''simple docstring'''





        super().__init__(**lowercase)

        a__:     Any										=	hidden_size
        a__:     int										=	num_hidden_layers
        a__:     Union[str, Any]										=	num_attention_heads
        a__:     Any										=	intermediate_size
        a__:     Union[str, Any]										=	hidden_act
        a__:     int										=	hidden_dropout_prob
        a__:     str										=	attention_probs_dropout_prob
        a__:     str										=	initializer_range
        a__:     Tuple										=	layer_norm_eps
        a__:     Any										=	patch_size
        a__:     int										=	qkv_bias
        a__:     Optional[Any]										=	frequency_stride
        a__:     int										=	time_stride
        a__:     List[str]										=	max_length
        a__:     Tuple										=	num_mel_bins


