


'''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 							__UpperCamelCase				(		lowerCAmelCase_		,      unittest.TestCase					):
	A_				=    KandinskyImgaImgPipeline
	A_				=    ["prompt", "image_embeds", "negative_image_embeds", "image"]
	A_				=    [
	    "prompt",
	    "negative_prompt",
	    "image_embeds",
	    "negative_image_embeds",
	    "image",
	]
	A_				=    [
	    "generator",
	    "height",
	    "width",
	    "strength",
	    "guidance_scale",
	    "negative_prompt",
	    "num_inference_steps",
	    "return_dict",
	    "guidance_scale",
	    "num_images_per_prompt",
	    "output_type",
	    "return_dict",
	]
	A_				=    False




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return 32




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return 32




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return self.time_input_dim




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return self.time_input_dim * 4




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return 100




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Optional[int]					    =  XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'							)
						return tokenizer




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						torch.manual_seed(0							)
						__a			:       Tuple					    =  MCLIPConfig(
						    numDims=self.cross_attention_dim						,				transformerDimensions=self.text_embedder_hidden_size						,				hidden_size=self.text_embedder_hidden_size						,				intermediate_size=37						,				num_attention_heads=4						,				num_hidden_layers=5						,				vocab_size=1005						,				)

						__a			:       Any					    =  MultilingualCLIP(__a							)
						__a			:       int					    =  text_encoder.eval()

						return text_encoder




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						torch.manual_seed(0							)

						__a			:       Dict					    =  {
						    '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,
						}

						__a			:       str					    =  UNetaDConditionModel(**__a							)
						return model




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return {
						    "block_out_channels": [32, 64],
						    "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": 12,
						    "out_channels": 3,
						    "up_block_types": [
						        "AttnUpDecoderBlock2D",
						        "UpDecoderBlock2D",
						    ],
						    "vq_embed_dim": 4,
						}




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						torch.manual_seed(0							)
						__a			:       Union[str, Any]					    =  VQModel(**self.dummy_movq_kwargs							)
						return model




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Any					    =  self.dummy_text_encoder
						__a			:       Any					    =  self.dummy_tokenizer
						__a			:       Union[str, Any]					    =  self.dummy_unet
						__a			:       List[Any]					    =  self.dummy_movq

						__a			:       Dict					    =  {
						    'num_train_timesteps': 1000,
						    '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,
						}

						__a			:       Dict					    =  DDIMScheduler(**__a							)

						__a			:       Optional[int]					    =  {
						    'text_encoder': text_encoder,
						    'tokenizer': tokenizer,
						    'unet': unet,
						    'scheduler': scheduler,
						    'movq': movq,
						}

						return components




	def       __UpperCAmelCase							(   self						,				__a						,				__a=0							):

						'''simple docstring'''





						__a			:       Optional[Any]					    =  floats_tensor((1, self.cross_attention_dim)						,				rng=random.Random(__a							)							).to(__a							)
						__a			:       Union[str, Any]					    =  floats_tensor((1, self.cross_attention_dim)						,				rng=random.Random(seed + 1							)							).to(__a							)
						# create init_image
						__a			:       Optional[Any]					    =  floats_tensor((1, 3, 64, 64)						,				rng=random.Random(__a							)							).to(__a							)
						__a			:       Optional[Any]					    =  image.cpu().permute(0						,				2						,				3						,				1							)[0]
						__a			:       List[Any]					    =  Image.fromarray(np.uinta(__a							)							).convert('RGB'							).resize((256, 256)							)

						if str(__a							).startswith('mps'							):
											__a			:       str					    =  torch.manual_seed(__a							)
						else:
											__a			:       Union[str, Any]					    =  torch.Generator(device=__a							).manual_seed(__a							)
						__a			:       Optional[int]					    =  {
						    'prompt': 'horse',
						    'image': init_image,
						    'image_embeds': image_embeds,
						    'negative_image_embeds': negative_image_embeds,
						    'generator': generator,
						    'height': 64,
						    'width': 64,
						    'num_inference_steps': 10,
						    'guidance_scale': 7.0,
						    'strength': 0.2,
						    'output_type': 'np',
						}
						return inputs





	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       int					    =  'cpu'

						__a			:       Any					    =  self.get_dummy_components()

						__a			:       List[Any]					    =  self.pipeline_class(**__a							)
						__a			:       Optional[int]					    =  pipe.to(__a							)

						pipe.set_progress_bar_config(disable=__a							)

						__a			:       Tuple					    =  pipe(**self.get_dummy_inputs(__a							)							)
						__a			:       int					    =  output.images

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

						__a			:       Optional[Any]					    =  image[0, -3:, -3:, -1]
						__a			:       List[Any]					    =  image_from_tuple[0, -3:, -3:, -1]

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

						__a			:       int					    =  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 							__UpperCamelCase				(		unittest.TestCase					):




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





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





	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Tuple					    =  load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/kandinsky/kandinsky_img2img_frog.npy'							)

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

						__a			:       int					    =  KandinskyPriorPipeline.from_pretrained(
						    'kandinsky-community/kandinsky-2-1-prior'						,				torch_dtype=torch.floataa							)
						pipe_prior.to(__a							)

						__a			:       Union[str, Any]					    =  KandinskyImgaImgPipeline.from_pretrained(
						    'kandinsky-community/kandinsky-2-1'						,				torch_dtype=torch.floataa							)
						__a			:       Optional[int]					    =  pipeline.to(__a							)

						pipeline.set_progress_bar_config(disable=__a							)

						__a			:       List[Any]					    =  torch.Generator(device='cpu'							).manual_seed(0							)
						__a    ,				__a			:       List[str]					    =  pipe_prior(
						    __a						,				generator=__a						,				num_inference_steps=5						,				negative_prompt=''						,				).to_tuple()

						__a			:       Union[str, Any]					    =  pipeline(
						    __a						,				image=__a						,				image_embeds=__a						,				negative_image_embeds=__a						,				generator=__a						,				num_inference_steps=100						,				height=768						,				width=768						,				strength=0.2						,				output_type='np'						,				)

						__a			:       Optional[int]					    =  output.images[0]

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

						assert_mean_pixel_difference(__a						,				__a							)










"""simple docstring"""





from typing import TYPE_CHECKING

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


__A											=		{
    """configuration_data2vec_audio""": ["""DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Data2VecAudioConfig"""],
    """configuration_data2vec_text""": [
        """DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Data2VecTextConfig""",
        """Data2VecTextOnnxConfig""",
    ],
    """configuration_data2vec_vision""": [
        """DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Data2VecVisionConfig""",
        """Data2VecVisionOnnxConfig""",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=		[
      """DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """Data2VecAudioForAudioFrameClassification""",
      """Data2VecAudioForCTC""",
      """Data2VecAudioForSequenceClassification""",
      """Data2VecAudioForXVector""",
      """Data2VecAudioModel""",
      """Data2VecAudioPreTrainedModel""",
  ]
  __A											=		[
      """DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """Data2VecTextForCausalLM""",
      """Data2VecTextForMaskedLM""",
      """Data2VecTextForMultipleChoice""",
      """Data2VecTextForQuestionAnswering""",
      """Data2VecTextForSequenceClassification""",
      """Data2VecTextForTokenClassification""",
      """Data2VecTextModel""",
      """Data2VecTextPreTrainedModel""",
  ]
  __A											=		[
      """DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """Data2VecVisionForImageClassification""",
      """Data2VecVisionForMaskedImageModeling""",
      """Data2VecVisionForSemanticSegmentation""",
      """Data2VecVisionModel""",
      """Data2VecVisionPreTrainedModel""",
  ]

if is_tf_available():
  __A											=		[
      """TFData2VecVisionForImageClassification""",
      """TFData2VecVisionForSemanticSegmentation""",
      """TFData2VecVisionModel""",
      """TFData2VecVisionPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
  from .configuration_dataavec_text import (
      DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      DataaVecTextConfig,
      DataaVecTextOnnxConfig,
  )
  from .configuration_dataavec_vision import (
      DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
      DataaVecVisionConfig,
      DataaVecVisionOnnxConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_dataavec_audio import (
        DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
        DataaVecAudioForAudioFrameClassification,
        DataaVecAudioForCTC,
        DataaVecAudioForSequenceClassification,
        DataaVecAudioForXVector,
        DataaVecAudioModel,
        DataaVecAudioPreTrainedModel,
    )
    from .modeling_dataavec_text import (
        DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
        DataaVecTextForCausalLM,
        DataaVecTextForMaskedLM,
        DataaVecTextForMultipleChoice,
        DataaVecTextForQuestionAnswering,
        DataaVecTextForSequenceClassification,
        DataaVecTextForTokenClassification,
        DataaVecTextModel,
        DataaVecTextPreTrainedModel,
    )
    from .modeling_dataavec_vision import (
        DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
        DataaVecVisionForImageClassification,
        DataaVecVisionForMaskedImageModeling,
        DataaVecVisionForSemanticSegmentation,
        DataaVecVisionModel,
        DataaVecVisionPreTrainedModel,
    )
  if is_tf_available():
    from .modeling_tf_dataavec_vision import (
        TFDataaVecVisionForImageClassification,
        TFDataaVecVisionForSemanticSegmentation,
        TFDataaVecVisionModel,
        TFDataaVecVisionPreTrainedModel,
    )

else:
  import sys

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