

import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class 						__magic_name__					(							snake_case			,      unittest.TestCase				):
     UpperCamelCase_	:Union[str, Any]										=      KandinskyVaaControlnetPipeline
     UpperCamelCase_	:Dict										=      ["""image_embeds""", """negative_image_embeds""", """hint"""]
     UpperCamelCase_	:int										=      ["""image_embeds""", """negative_image_embeds""", """hint"""]
     UpperCamelCase_	:Union[str, Any]										=      [
         """generator""",
         """height""",
         """width""",
         """latents""",
         """guidance_scale""",
         """num_inference_steps""",
         """return_dict""",
         """guidance_scale""",
         """num_images_per_prompt""",
         """output_type""",
         """return_dict""",
     ]
     UpperCamelCase_	:int										=      False






     @property
     def 	UpperCAmelCase_				(				self	)->     int:
         return 32






     @property
     def 	UpperCAmelCase_				(				self	)->     str:
         return 32






     @property
     def 	UpperCAmelCase_				(				self	)->     str:
         return self.time_input_dim






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






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






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

         UpperCamelCase_			  =						{
             "in_channels": 8,
             # Out channels is double in channels because predicts mean and variance
             "out_channels": 8,
             "addition_embed_type": "image_hint",
             "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,
             "encoder_hid_dim": self.text_embedder_hidden_size,
             "encoder_hid_dim_type": "image_proj",
             "cross_attention_dim": self.cross_attention_dim,
             "attention_head_dim": 4,
             "resnet_time_scale_shift": "scale_shift",
             "class_embed_type": None,
         }

         UpperCamelCase_			  =						UNetaDConditionModel(**_lowercase	)
         return model






     @property
     def 	UpperCAmelCase_				(				self	)->     List[Any]:
         return {
             "block_out_channels": [32, 32, 64, 64],
             "down_block_types": [
                 "DownEncoderBlock2D",
                 "DownEncoderBlock2D",
                 "DownEncoderBlock2D",
                 "AttnDownEncoderBlock2D",
             ],
             "in_channels": 3,
             "latent_channels": 4,
             "layers_per_block": 1,
             "norm_num_groups": 8,
             "norm_type": "spatial",
             "num_vq_embeddings": 12,
             "out_channels": 3,
             "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
             "vq_embed_dim": 4,
         }






     @property
     def 	UpperCAmelCase_				(				self	)->     Optional[int]:
         torch.manual_seed(0	)
         UpperCamelCase_			  =						VQModel(**self.dummy_movq_kwargs	)
         return model






     def 	UpperCAmelCase_				(				self	)->     Union[str, Any]:
         UpperCamelCase_			  =						self.dummy_unet
         UpperCamelCase_			  =						self.dummy_movq

         UpperCamelCase_			  =						DDIMScheduler(
             num_train_timesteps=1_000		,						beta_schedule="linear"		,						beta_start=0.00_085		,						beta_end=0.012		,						clip_sample=_lowercase		,						set_alpha_to_one=_lowercase		,						steps_offset=1		,						prediction_type="epsilon"		,						thresholding=_lowercase		,						)

         UpperCamelCase_			  =						{
             "unet": unet,
             "scheduler": scheduler,
             "movq": movq,
         }
         return components






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase=0	)->     List[Any]:
         UpperCamelCase_			  =						floats_tensor((1, self.text_embedder_hidden_size)		,						rng=random.Random(_lowercase	)	).to(_lowercase	)
         UpperCamelCase_			  =						floats_tensor((1, self.text_embedder_hidden_size)		,						rng=random.Random(seed + 1	)	).to(
             _lowercase	)

         # create hint
         UpperCamelCase_			  =						floats_tensor((1, 3, 64, 64)		,						rng=random.Random(_lowercase	)	).to(_lowercase	)

         if str(_lowercase	).startswith("mps"	):
             UpperCamelCase_			  =						torch.manual_seed(_lowercase	)
         else:
             UpperCamelCase_			  =						torch.Generator(device=_lowercase	).manual_seed(_lowercase	)
         UpperCamelCase_			  =						{
             "image_embeds": image_embeds,
             "negative_image_embeds": negative_image_embeds,
             "hint": hint,
             "generator": generator,
             "height": 64,
             "width": 64,
             "guidance_scale": 4.0,
             "num_inference_steps": 2,
             "output_type": "np",
         }
         return inputs






     def 	UpperCAmelCase_				(				self	)->     int:
         UpperCamelCase_			  =						"cpu"

         UpperCamelCase_			  =						self.get_dummy_components()

         UpperCamelCase_			  =						self.pipeline_class(**_lowercase	)
         UpperCamelCase_			  =						pipe.to(_lowercase	)

         pipe.set_progress_bar_config(disable=_lowercase	)

         UpperCamelCase_			  =						pipe(**self.get_dummy_inputs(_lowercase	)	)
         UpperCamelCase_			  =						output.images

         UpperCamelCase_			  =						pipe(
             **self.get_dummy_inputs(_lowercase	)		,						return_dict=_lowercase		,						)[0]

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

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

         UpperCamelCase_			  =						np.array(
             [0.6_959_826, 0.868_279, 0.7_558_092, 0.68_769_467, 0.85_805_804, 0.65_977_496, 0.44_885_302, 0.5_959_111, 0.4_251_595]	)

         assert (
             np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2
         ), F" expected_slice {expected_slice}, but got {image_slice.flatten()}"

         assert (
             np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1e-2
         ), F" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"






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






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






     def 	UpperCAmelCase_				(				self	)->     List[Any]:
         UpperCamelCase_			  =						load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy"	)

         UpperCamelCase_			  =						load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/kandinskyv22/hint_image_cat.png"	)
         UpperCamelCase_			  =						torch.from_numpy(np.array(_lowercase	)	).float() / 255.0
         UpperCamelCase_			  =						hint.permute(2		,						0		,						1	).unsqueeze(0	)

         UpperCamelCase_			  =						KandinskyVaaPriorPipeline.from_pretrained(
             "kandinsky-community/kandinsky-2-2-prior"		,						torch_dtype=torch.floataa	)
         pipe_prior.to(_lowercase	)

         UpperCamelCase_			  =						KandinskyVaaControlnetPipeline.from_pretrained(
             "kandinsky-community/kandinsky-2-2-controlnet-depth"		,						torch_dtype=torch.floataa	)
         UpperCamelCase_			  =						pipeline.to(_lowercase	)
         pipeline.set_progress_bar_config(disable=_lowercase	)

         UpperCamelCase_			  =						"A robot, 4k photo"

         UpperCamelCase_			  =						torch.Generator(device="cuda"	).manual_seed(0	)
         UpperCamelCase_  ,       UpperCamelCase_			  =						pipe_prior(
             _lowercase		,						generator=_lowercase		,						num_inference_steps=5		,						negative_prompt=""		,						).to_tuple()

         UpperCamelCase_			  =						torch.Generator(device="cuda"	).manual_seed(0	)
         UpperCamelCase_			  =						pipeline(
             image_embeds=_lowercase		,						negative_image_embeds=_lowercase		,						hint=_lowercase		,						generator=_lowercase		,						num_inference_steps=100		,						output_type="np"		,						)

         UpperCamelCase_			  =						output.images[0]

         assert image.shape == (512, 512, 3)

         assert_mean_pixel_difference(_lowercase		,						_lowercase	)



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE		:Union[str, Any]						=     {
    """configuration_speecht5""": [
        """SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP""",
        """SpeechT5Config""",
        """SpeechT5HifiGanConfig""",
    ],
    """feature_extraction_speecht5""": ["""SpeechT5FeatureExtractor"""],
    """processing_speecht5""": ["""SpeechT5Processor"""],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:Union[str, Any]						=     ["""SpeechT5Tokenizer"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:Any						=     [
     """SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """SpeechT5ForSpeechToText""",
     """SpeechT5ForSpeechToSpeech""",
     """SpeechT5ForTextToSpeech""",
     """SpeechT5Model""",
     """SpeechT5PreTrainedModel""",
     """SpeechT5HifiGan""",
 ]

if TYPE_CHECKING:
 from .configuration_speechta import (
     SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
     SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
     SpeechTaConfig,
     SpeechTaHifiGanConfig,
 )
 from .feature_extraction_speechta import SpeechTaFeatureExtractor
 from .processing_speechta import SpeechTaProcessor

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_speechta import SpeechTaTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_speechta import (
      SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
      SpeechTaForSpeechToSpeech,
      SpeechTaForSpeechToText,
      SpeechTaForTextToSpeech,
      SpeechTaHifiGan,
      SpeechTaModel,
      SpeechTaPreTrainedModel,
  )

else:
 import sys

 SCREAMING_SNAKE_CASE		:List[str]						=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
