

"""simple docstring"""


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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    snake_case     (    _UpperCAmelCase,							unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_       :				Any       				=  KandinskyImgaImgPipeline
				SCREAMING_SNAKE_CASE_       :				Tuple       				=  ['prompt', 'image_embeds', 'negative_image_embeds', 'image']
				SCREAMING_SNAKE_CASE_       :				str       				=  [
				    'prompt',
				    'negative_prompt',
				    'image_embeds',
				    'negative_image_embeds',
				    'image',
				]
				SCREAMING_SNAKE_CASE_       :				Union[str, Any]       				=  [
				    'generator',
				    'height',
				    'width',
				    'strength',
				    'guidance_scale',
				    'negative_prompt',
				    'num_inference_steps',
				    'return_dict',
				    'guidance_scale',
				    'num_images_per_prompt',
				    'output_type',
				    'return_dict',
				]
				SCREAMING_SNAKE_CASE_       :				Dict       				=  False

				@property
				def 							lowercase_					(     self							:    Optional[Any])->       List[str]:



						'''simple docstring'''


						return 3_2

				@property
				def 							lowercase_					(     self							:    Tuple)->       List[str]:



						'''simple docstring'''


						return 3_2

				@property
				def 							lowercase_					(     self							:    int)->       int:



						'''simple docstring'''


						return self.time_input_dim

				@property
				def 							lowercase_					(     self							:    Any)->       List[str]:



						'''simple docstring'''


						return self.time_input_dim * 4

				@property
				def 							lowercase_					(     self							:    List[str])->       int:



						'''simple docstring'''


						return 1_0_0

				@property
				def 							lowercase_					(     self							:    int)->       str:



						'''simple docstring'''


						__lowerCAmelCase:	List[str]									=      XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base")
						return tokenizer

				@property
				def 							lowercase_					(     self							:    List[str])->       Tuple:



						'''simple docstring'''


						torch.manual_seed(0)
						__lowerCAmelCase:	Optional[Any]									=      MCLIPConfig(
						    numDims=self.cross_attention_dim					,  transformerDimensions=self.text_embedder_hidden_size					,  hidden_size=self.text_embedder_hidden_size					,  intermediate_size=3_7					,  num_attention_heads=4					,  num_hidden_layers=5					,  vocab_size=1_0_0_5					,  )

						__lowerCAmelCase:	int									=      MultilingualCLIP(SCREAMING_SNAKE_CASE_)
						__lowerCAmelCase:	Union[str, Any]									=      text_encoder.eval()

						return text_encoder

				@property
				def 							lowercase_					(     self							:    Optional[int])->       Dict:



						'''simple docstring'''


						torch.manual_seed(0)

						__lowerCAmelCase:	List[Any]									=      {
						    """in_channels""": 4,
						    # Out channels is double in channels because predicts mean and variance
						    """out_channels""": 8,
						    """addition_embed_type""": """text_image""",
						    """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""": """text_image_proj""",
						    """cross_attention_dim""": self.cross_attention_dim,
						    """attention_head_dim""": 4,
						    """resnet_time_scale_shift""": """scale_shift""",
						    """class_embed_type""": None,
						}

						__lowerCAmelCase:	Optional[int]									=      UNetaDConditionModel(**SCREAMING_SNAKE_CASE_)
						return model

				@property
				def 							lowercase_					(     self							:    str)->       List[str]:



						'''simple docstring'''


						return {
						    "block_out_channels": [3_2, 6_4],
						    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
						    "in_channels": 3,
						    "latent_channels": 4,
						    "layers_per_block": 1,
						    "norm_num_groups": 8,
						    "norm_type": "spatial",
						    "num_vq_embeddings": 1_2,
						    "out_channels": 3,
						    "up_block_types": [
						        "AttnUpDecoderBlock2D",
						        "UpDecoderBlock2D",
						    ],
						    "vq_embed_dim": 4,
						}

				@property
				def 							lowercase_					(     self							:    Union[str, Any])->       List[Any]:



						'''simple docstring'''


						torch.manual_seed(0)
						__lowerCAmelCase:	Any									=      VQModel(**self.dummy_movq_kwargs)
						return model

				def 							lowercase_					(     self							:    Optional[int])->       Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Optional[int]									=      self.dummy_text_encoder
						__lowerCAmelCase:	Optional[int]									=      self.dummy_tokenizer
						__lowerCAmelCase:	Union[str, Any]									=      self.dummy_unet
						__lowerCAmelCase:	Optional[Any]									=      self.dummy_movq

						__lowerCAmelCase:	List[str]									=      {
						    """num_train_timesteps""": 1_0_0_0,
						    """beta_schedule""": """linear""",
						    """beta_start""": 0.00085,
						    """beta_end""": 0.012,
						    """clip_sample""": False,
						    """set_alpha_to_one""": False,
						    """steps_offset""": 0,
						    """prediction_type""": """epsilon""",
						    """thresholding""": False,
						}

						__lowerCAmelCase:	Optional[Any]									=      DDIMScheduler(**SCREAMING_SNAKE_CASE_)

						__lowerCAmelCase:	Tuple									=      {
						    """text_encoder""": text_encoder,
						    """tokenizer""": tokenizer,
						    """unet""": unet,
						    """scheduler""": scheduler,
						    """movq""": movq,
						}

						return components

				def 							lowercase_					(     self							:    str					,  UpperCamelCase__							:    Union[str, Any]					,  UpperCamelCase__							:    List[str]=0)->       List[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Any									=      floats_tensor((1, self.cross_attention_dim)					,  rng=random.Random(SCREAMING_SNAKE_CASE_)).to(SCREAMING_SNAKE_CASE_)
						__lowerCAmelCase:	Optional[Any]									=      floats_tensor((1, self.cross_attention_dim)					,  rng=random.Random(seed + 1)).to(SCREAMING_SNAKE_CASE_)
						# create init_image
						__lowerCAmelCase:	Dict									=      floats_tensor((1, 3, 6_4, 6_4)					,  rng=random.Random(SCREAMING_SNAKE_CASE_)).to(SCREAMING_SNAKE_CASE_)
						__lowerCAmelCase:	int									=      image.cpu().permute(0					,  2					,  3					,  1)[0]
						__lowerCAmelCase:	Optional[int]									=      Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE_)).convert("RGB").resize((2_5_6, 2_5_6))

						if str(SCREAMING_SNAKE_CASE_).startswith("mps"):
								__lowerCAmelCase:	int									=      torch.manual_seed(SCREAMING_SNAKE_CASE_)
						else:
								__lowerCAmelCase:	int									=      torch.Generator(device=SCREAMING_SNAKE_CASE_).manual_seed(SCREAMING_SNAKE_CASE_)
						__lowerCAmelCase:	Optional[int]									=      {
						    """prompt""": """horse""",
						    """image""": init_image,
						    """image_embeds""": image_embeds,
						    """negative_image_embeds""": negative_image_embeds,
						    """generator""": generator,
						    """height""": 6_4,
						    """width""": 6_4,
						    """num_inference_steps""": 1_0,
						    """guidance_scale""": 7.0,
						    """strength""": 0.2,
						    """output_type""": """np""",
						}
						return inputs




				def 							lowercase_					(     self							:    str)->       Tuple:



						'''simple docstring'''


						__lowerCAmelCase:	Tuple									=      """cpu"""

						__lowerCAmelCase:	Dict									=      self.get_dummy_components()

						__lowerCAmelCase:	Optional[int]									=      self.pipeline_class(**SCREAMING_SNAKE_CASE_)
						__lowerCAmelCase:	List[str]									=      pipe.to(SCREAMING_SNAKE_CASE_)

						pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_)

						__lowerCAmelCase:	Optional[Any]									=      pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE_))
						__lowerCAmelCase:	Optional[Any]									=      output.images

						__lowerCAmelCase:	int									=      pipe(
						    **self.get_dummy_inputs(SCREAMING_SNAKE_CASE_)					,  return_dict=SCREAMING_SNAKE_CASE_					,  )[0]

						__lowerCAmelCase:	Tuple									=      image[0, -3:, -3:, -1]
						__lowerCAmelCase:	str									=      image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 6_4, 6_4, 3)

						__lowerCAmelCase:	Dict									=      np.array(
						    [0.61474943, 0.6073539, 0.43308544, 0.5928269, 0.47493595, 0.46755973, 0.4613838, 0.45368797, 0.50119233])
						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    snake_case     (    unittest.TestCase  ):

				def 							lowercase_					(     self							:    Any)->       Dict:



						'''simple docstring'''


						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()




				def 							lowercase_					(     self							:    Tuple)->       int:



						'''simple docstring'''


						__lowerCAmelCase:	List[str]									=      load_numpy(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/kandinsky/kandinsky_img2img_frog.npy")

						__lowerCAmelCase:	Dict									=      load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png")
						__lowerCAmelCase:	List[Any]									=      """A red cartoon frog, 4k"""

						__lowerCAmelCase:	List[Any]									=      KandinskyPriorPipeline.from_pretrained(
						    "kandinsky-community/kandinsky-2-1-prior"					,  torch_dtype=torch.floataa)
						pipe_prior.to(SCREAMING_SNAKE_CASE_)

						__lowerCAmelCase:	List[Any]									=      KandinskyImgaImgPipeline.from_pretrained(
						    "kandinsky-community/kandinsky-2-1"					,  torch_dtype=torch.floataa)
						__lowerCAmelCase:	List[str]									=      pipeline.to(SCREAMING_SNAKE_CASE_)

						pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_)

						__lowerCAmelCase:	int									=      torch.Generator(device="cpu").manual_seed(0)
						__lowerCAmelCase:	Optional[int]									=      pipe_prior(
						    SCREAMING_SNAKE_CASE_					,  generator=SCREAMING_SNAKE_CASE_					,  num_inference_steps=5					,  negative_prompt=""					,  ).to_tuple()

						__lowerCAmelCase:	Optional[int]									=      pipeline(
						    SCREAMING_SNAKE_CASE_					,  image=SCREAMING_SNAKE_CASE_					,  image_embeds=SCREAMING_SNAKE_CASE_					,  negative_image_embeds=SCREAMING_SNAKE_CASE_					,  generator=SCREAMING_SNAKE_CASE_					,  num_inference_steps=1_0_0					,  height=7_6_8					,  width=7_6_8					,  strength=0.2					,  output_type="np"					,  )

						__lowerCAmelCase:	str									=      output.images[0]

						assert image.shape == (7_6_8, 7_6_8, 3)

						assert_mean_pixel_difference(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)





"""simple docstring"""


from typing import TYPE_CHECKING

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


__A						       =      {
    "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:
			__A						       =      ["SpeechT5Tokenizer"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A						       =      [
			    "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

			__A						       =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


