'''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 lowercase_       (		_UpperCamelCase    ,    unittest.TestCase			):







   """simple docstring"""

   __lowerCAmelCase         =   UnCLIPImageVariationPipeline
   __lowerCAmelCase         =   IMAGE_VARIATION_PARAMS - {"height", "width", "guidance_scale"}
   __lowerCAmelCase         =   IMAGE_VARIATION_BATCH_PARAMS

   __lowerCAmelCase         =   [
       "generator",
       "return_dict",
       "decoder_num_inference_steps",
       "super_res_num_inference_steps",
   ]
   __lowerCAmelCase         =   False
   @property
   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			Union[str, Any]:
        return 32
   @property
   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			List[str]:
        return 32
   @property
   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			Dict:
        return self.time_input_dim
   @property
   def 	__UpperCAmelCase   (      self      :			str     )  ->			Optional[int]:
        return self.time_input_dim * 4
   @property
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			Optional[int]:
        return 1_00
   @property
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			Optional[Any]:
        _A    		=  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )
        return tokenizer
   @property
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			Dict:
        torch.manual_seed(0     )
        _A    		=  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=10_00, )
        return CLIPTextModelWithProjection(UpperCamelCase__     )
   @property
   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			List[str]:
        torch.manual_seed(0     )
        _A    		=  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(UpperCamelCase__     )
   @property
   def 	__UpperCAmelCase   (      self      :			int     )  ->			Dict:
        torch.manual_seed(0     )

        _A    		=  {
            'clip_embeddings_dim': self.text_embedder_hidden_size,
            'time_embed_dim': self.time_embed_dim,
            'cross_attention_dim': self.cross_attention_dim,
        }

        _A    		=  UnCLIPTextProjModel(**UpperCamelCase__     )
        return model
   @property
   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			Any:
        torch.manual_seed(0     )

        _A    		=  {
            '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',
        }

        _A    		=  UNetaDConditionModel(**UpperCamelCase__     )
        return model
   @property
   def 	__UpperCAmelCase   (      self      :			str     )  ->			Any:
        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     )

        _A    		=  UNetaDModel(**self.dummy_super_res_kwargs     )
        return model
   @property
   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			Any:
        # seeded differently to get different unet than `self.dummy_super_res_first`
        torch.manual_seed(1     )

        _A    		=  UNetaDModel(**self.dummy_super_res_kwargs     )
        return model
   def 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			Optional[Any]:
        _A    		=  self.dummy_decoder
        _A    		=  self.dummy_text_proj
        _A    		=  self.dummy_text_encoder
        _A    		=  self.dummy_tokenizer
        _A    		=  self.dummy_super_res_first
        _A    		=  self.dummy_super_res_last

        _A    		=  UnCLIPScheduler(
            variance_type='learned_range', prediction_type='epsilon', num_train_timesteps=10_00, )

        _A    		=  UnCLIPScheduler(
            variance_type='fixed_small_log', prediction_type='epsilon', num_train_timesteps=10_00, )

        _A    		=  CLIPImageProcessor(crop_size=32, size=32     )

        _A    		=  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      :			Tuple, UpperCamelCase__      :			List[Any], UpperCamelCase__      :			Any=0, UpperCamelCase__      :			int=True     )  ->			str:
        _A    		=  floats_tensor((1, 3, 32, 32), rng=random.Random(UpperCamelCase__     )     ).to(UpperCamelCase__     )
        if str(UpperCamelCase__     ).startswith('mps'     ):
             _A    		=  torch.manual_seed(UpperCamelCase__     )
        else:
             _A    		=  torch.Generator(device=UpperCamelCase__     ).manual_seed(UpperCamelCase__     )

        if pil_image:
             _A    		=  input_image * 0.5 + 0.5
             _A    		=  input_image.clamp(0, 1     )
             _A    		=  input_image.cpu().permute(0, 2, 3, 1     ).float().numpy()
             _A    		=  DiffusionPipeline.numpy_to_pil(UpperCamelCase__     )[0]

        return {
            "image": input_image,
            "generator": generator,
            "decoder_num_inference_steps": 2,
            "super_res_num_inference_steps": 2,
            "output_type": "np",
        }
   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			Optional[int]:
        _A    		=  'cpu'

        _A    		=  self.get_dummy_components()

        _A    		=  self.pipeline_class(**UpperCamelCase__     )
        _A    		=  pipe.to(UpperCamelCase__     )

        pipe.set_progress_bar_config(disable=UpperCamelCase__     )

        _A    		=  self.get_dummy_inputs(UpperCamelCase__, pil_image=UpperCamelCase__     )

        _A    		=  pipe(**UpperCamelCase__     )
        _A    		=  output.images

        _A    		=  self.get_dummy_inputs(UpperCamelCase__, pil_image=UpperCamelCase__     )

        _A    		=  pipe(
            **UpperCamelCase__, return_dict=UpperCamelCase__, )[0]

        _A    		=  image[0, -3:, -3:, -1]
        _A    		=  image_from_tuple[0, -3:, -3:, -1]

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

        _A    		=  np.array(
            [
                0.9_997,
                0.0_002,
                0.9_997,
                0.9_997,
                0.9_969,
                0.0_023,
                0.9_997,
                0.9_969,
                0.9_970,
            ]     )

        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      :			Dict     )  ->			Any:
        _A    		=  'cpu'

        _A    		=  self.get_dummy_components()

        _A    		=  self.pipeline_class(**UpperCamelCase__     )
        _A    		=  pipe.to(UpperCamelCase__     )

        pipe.set_progress_bar_config(disable=UpperCamelCase__     )

        _A    		=  self.get_dummy_inputs(UpperCamelCase__, pil_image=UpperCamelCase__     )

        _A    		=  pipe(**UpperCamelCase__     )
        _A    		=  output.images

        _A    		=  self.get_dummy_inputs(UpperCamelCase__, pil_image=UpperCamelCase__     )

        _A    		=  pipe(
            **UpperCamelCase__, return_dict=UpperCamelCase__, )[0]

        _A    		=  image[0, -3:, -3:, -1]
        _A    		=  image_from_tuple[0, -3:, -3:, -1]

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

        _A    		=  np.array([0.9_997, 0.0_003, 0.9_997, 0.9_997, 0.9_970, 0.0_024, 0.9_997, 0.9_971, 0.9_971]     )

        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      :			Optional[Any]     )  ->			Any:
        _A    		=  'cpu'

        _A    		=  self.get_dummy_components()

        _A    		=  self.pipeline_class(**UpperCamelCase__     )
        _A    		=  pipe.to(UpperCamelCase__     )

        pipe.set_progress_bar_config(disable=UpperCamelCase__     )

        _A    		=  self.get_dummy_inputs(UpperCamelCase__, pil_image=UpperCamelCase__     )
        _A    		=  [
            pipeline_inputs['image'],
            pipeline_inputs['image'],
        ]

        _A    		=  pipe(**UpperCamelCase__     )
        _A    		=  output.images

        _A    		=  self.get_dummy_inputs(UpperCamelCase__, pil_image=UpperCamelCase__     )
        _A    		=  [
            tuple_pipeline_inputs['image'],
            tuple_pipeline_inputs['image'],
        ]

        _A    		=  pipe(
            **UpperCamelCase__, return_dict=UpperCamelCase__, )[0]

        _A    		=  image[0, -3:, -3:, -1]
        _A    		=  image_from_tuple[0, -3:, -3:, -1]

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

        _A    		=  np.array(
            [
                0.9_997,
                0.9_989,
                0.0_008,
                0.0_021,
                0.9_960,
                0.0_018,
                0.0_014,
                0.0_002,
                0.9_933,
            ]     )

        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      :			Optional[int]     )  ->			Optional[Any]:
        _A    		=  torch.device('cpu'     )







        class lowercase_       :







           """simple docstring"""

           __lowerCAmelCase         =   1






        _A    		=  self.get_dummy_components()

        _A    		=  self.pipeline_class(**UpperCamelCase__     )
        _A    		=  pipe.to(UpperCamelCase__     )

        pipe.set_progress_bar_config(disable=UpperCamelCase__     )

        _A    		=  torch.Generator(device=UpperCamelCase__     ).manual_seed(0     )
        _A    		=  pipe.decoder.dtype
        _A    		=  1

        _A    		=  (
            batch_size,
            pipe.decoder.config.in_channels,
            pipe.decoder.config.sample_size,
            pipe.decoder.config.sample_size,
        )
        _A    		=  pipe.prepare_latents(
            UpperCamelCase__, dtype=UpperCamelCase__, device=UpperCamelCase__, generator=UpperCamelCase__, latents=UpperCamelCase__, scheduler=DummyScheduler()     )

        _A    		=  (
            batch_size,
            pipe.super_res_first.config.in_channels // 2,
            pipe.super_res_first.config.sample_size,
            pipe.super_res_first.config.sample_size,
        )
        _A    		=  pipe.prepare_latents(
            UpperCamelCase__, dtype=UpperCamelCase__, device=UpperCamelCase__, generator=UpperCamelCase__, latents=UpperCamelCase__, scheduler=DummyScheduler()     )

        _A    		=  self.get_dummy_inputs(UpperCamelCase__, pil_image=UpperCamelCase__     )

        _A    		=  pipe(
            **UpperCamelCase__, decoder_latents=UpperCamelCase__, super_res_latents=UpperCamelCase__     ).images

        _A    		=  self.get_dummy_inputs(UpperCamelCase__, pil_image=UpperCamelCase__     )
        # Don't pass image, instead pass embedding
        _A    		=  pipeline_inputs.pop('image'     )
        _A    		=  pipe.image_encoder(UpperCamelCase__     ).image_embeds

        _A    		=  pipe(
            **UpperCamelCase__, decoder_latents=UpperCamelCase__, super_res_latents=UpperCamelCase__, image_embeddings=UpperCamelCase__, ).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      :			Any     )  ->			Tuple:
        _A    		=  torch_device == 'cpu'

        # Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
        _A    		=  1e-2

        self._test_attention_slicing_forward_pass(
            test_max_difference=UpperCamelCase__, expected_max_diff=UpperCamelCase__     )
   @skip_mps
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			Optional[int]:
        _A    		=  torch_device == 'cpu'
        _A    		=  True
        _A    		=  [
            'decoder_num_inference_steps',
            'super_res_num_inference_steps',
        ]

        self._test_inference_batch_single_identical(
            test_max_difference=UpperCamelCase__, relax_max_difference=UpperCamelCase__, additional_params_copy_to_batched_inputs=UpperCamelCase__, )
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			Dict:
        _A    		=  [
            'decoder_num_inference_steps',
            'super_res_num_inference_steps',
        ]

        if torch_device == "mps":
             # TODO: MPS errors with larger batch sizes
             _A    		=  [2, 3]
             self._test_inference_batch_consistent(
                 batch_sizes=UpperCamelCase__, additional_params_copy_to_batched_inputs=UpperCamelCase__, )
        else:
             self._test_inference_batch_consistent(
                 additional_params_copy_to_batched_inputs=UpperCamelCase__     )
   @skip_mps
   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			Tuple:
        return super().test_dict_tuple_outputs_equivalent()
   @skip_mps
   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			Optional[Any]:
        return super().test_save_load_local()

   @skip_mps
   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			str:
        return super().test_save_load_optional_components()







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







   """simple docstring"""

   def 	__UpperCAmelCase   (      self      :			str     )  ->			Any:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

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

        _A    		=  UnCLIPImageVariationPipeline.from_pretrained(
            'kakaobrain/karlo-v1-alpha-image-variations', torch_dtype=torch.floataa     )
        _A    		=  pipeline.to(UpperCamelCase__     )
        pipeline.set_progress_bar_config(disable=UpperCamelCase__     )

        _A    		=  torch.Generator(device='cpu'     ).manual_seed(0     )
        _A    		=  pipeline(
            UpperCamelCase__, generator=UpperCamelCase__, output_type='np', )

        _A    		=  output.images[0]

        assert image.shape == (2_56, 2_56, 3)

        assert_mean_pixel_difference(UpperCamelCase__, UpperCamelCase__, 15     )




'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_UpperCAmelCase    :     Any         =       {
    '''configuration_maskformer''': ['''MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MaskFormerConfig'''],
    '''configuration_maskformer_swin''': ['''MaskFormerSwinConfig'''],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase    :     str         =       ['''MaskFormerFeatureExtractor''']
   _UpperCAmelCase    :     str         =       ['''MaskFormerImageProcessor''']


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase    :     Tuple         =       [
       '''MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''MaskFormerForInstanceSegmentation''',
       '''MaskFormerModel''',
       '''MaskFormerPreTrainedModel''',
   ]
   _UpperCAmelCase    :     int         =       [
       '''MaskFormerSwinBackbone''',
       '''MaskFormerSwinModel''',
       '''MaskFormerSwinPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
   from .configuration_maskformer_swin import MaskFormerSwinConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_maskformer import MaskFormerFeatureExtractor
      from .image_processing_maskformer import MaskFormerImageProcessor
   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_maskformer import (
          MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          MaskFormerForInstanceSegmentation,
          MaskFormerModel,
          MaskFormerPreTrainedModel,
      )
      from .modeling_maskformer_swin import (
          MaskFormerSwinBackbone,
          MaskFormerSwinModel,
          MaskFormerSwinPreTrainedModel,
      )


else:
   import sys

   _UpperCAmelCase    :     str         =       _LazyModule(__name__, globals()['''__file__'''], _import_structure)



