


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 							lowercase  (						_UpperCAmelCase    ,       unittest.TestCase			):
     lowerCamelCase			:			Optional[Any]      = KandinskyImgaImgPipeline
     lowerCamelCase			:			Union[str, Any]      = ['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''']
     lowerCamelCase			:			Optional[Any]      = [
         '''prompt''',
         '''negative_prompt''',
         '''image_embeds''',
         '''negative_image_embeds''',
         '''image''',
     ]
     lowerCamelCase			:			List[Any]      = [
         '''generator''',
         '''height''',
         '''width''',
         '''strength''',
         '''guidance_scale''',
         '''negative_prompt''',
         '''num_inference_steps''',
         '''return_dict''',
         '''guidance_scale''',
         '''num_images_per_prompt''',
         '''output_type''',
         '''return_dict''',
     ]
     lowerCamelCase			:			int      = False







     @property
     def 							lowercase__       (						self :						Any      ):
           return 32







     @property
     def 							lowercase__       (						self :						Dict      ):
           return 32







     @property
     def 							lowercase__       (						self :						int      ):
           return self.time_input_dim







     @property
     def 							lowercase__       (						self :						Optional[int]      ):
           return self.time_input_dim * 4







     @property
     def 							lowercase__       (						self :						List[str]      ):
           return 1_00







     @property
     def 							lowercase__       (						self :						Union[str, Any]      ):
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''      )
           return tokenizer







     @property
     def 							lowercase__       (						self :						Dict      ):
           torch.manual_seed(0      )
           SCREAMING_SNAKE_CASE__       :  str						     =		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=10_05   ,   )

           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		MultilingualCLIP(_lowercase      )
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		text_encoder.eval()

           return text_encoder







     @property
     def 							lowercase__       (						self :						int      ):
           torch.manual_seed(0      )

           SCREAMING_SNAKE_CASE__       :  Tuple						     =		{
               '''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,
           }

           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		UNetaDConditionModel(**_lowercase      )
           return model







     @property
     def 							lowercase__       (						self :						List[str]      ):
           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 							lowercase__       (						self :						Union[str, Any]      ):
           torch.manual_seed(0      )
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		VQModel(**self.dummy_movq_kwargs      )
           return model







     def 							lowercase__       (						self :						int      ):
           SCREAMING_SNAKE_CASE__       :  Dict						     =		self.dummy_text_encoder
           SCREAMING_SNAKE_CASE__       :  str						     =		self.dummy_tokenizer
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		self.dummy_unet
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		self.dummy_movq

           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		{
               '''num_train_timesteps''': 10_00,
               '''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,
           }

           SCREAMING_SNAKE_CASE__       :  Any						     =		DDIMScheduler(**_lowercase      )

           SCREAMING_SNAKE_CASE__       :  str						     =		{
               '''text_encoder''': text_encoder,
               '''tokenizer''': tokenizer,
               '''unet''': unet,
               '''scheduler''': scheduler,
               '''movq''': movq,
           }

           return components







     def 							lowercase__       (						self :						Union[str, Any]   ,   _lowercase :						Union[str, Any]   ,   _lowercase :						Optional[int]=0      ):
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		floats_tensor((1, self.cross_attention_dim)   ,   rng=random.Random(_lowercase      )      ).to(_lowercase      )
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		floats_tensor((1, self.cross_attention_dim)   ,   rng=random.Random(seed + 1      )      ).to(_lowercase      )
           # create init_image
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		floats_tensor((1, 3, 64, 64)   ,   rng=random.Random(_lowercase      )      ).to(_lowercase      )
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		image.cpu().permute(0   ,   2   ,   3   ,   1      )[0]
           SCREAMING_SNAKE_CASE__       :  Any						     =		Image.fromarray(np.uinta(_lowercase      )      ).convert('''RGB'''      ).resize((2_56, 2_56)      )

           if str(_lowercase      ).startswith('''mps'''      ):
                 SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		torch.manual_seed(_lowercase      )
           else:
                 SCREAMING_SNAKE_CASE__       :  int						     =		torch.Generator(device=_lowercase      ).manual_seed(_lowercase      )
           SCREAMING_SNAKE_CASE__       :  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 							lowercase__       (						self :						List[str]      ):
           SCREAMING_SNAKE_CASE__       :  Dict						     =		'''cpu'''

           SCREAMING_SNAKE_CASE__       :  Any						     =		self.get_dummy_components()

           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		self.pipeline_class(**_lowercase      )
           SCREAMING_SNAKE_CASE__       :  int						     =		pipe.to(_lowercase      )

           pipe.set_progress_bar_config(disable=_lowercase      )

           SCREAMING_SNAKE_CASE__       :  Any						     =		pipe(**self.get_dummy_inputs(_lowercase      )      )
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		output.images

           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		pipe(
               **self.get_dummy_inputs(_lowercase      )   ,   return_dict=_lowercase   ,   )[0]

           SCREAMING_SNAKE_CASE__       :  Any						     =		image[0, -3:, -3:, -1]
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		image_from_tuple[0, -3:, -3:, -1]

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

           SCREAMING_SNAKE_CASE__       :  Tuple						     =		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 							lowercase  (						unittest.TestCase			):







     def 							lowercase__       (						self :						Optional[int]      ):
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()







     def 							lowercase__       (						self :						Union[str, Any]      ):
           SCREAMING_SNAKE_CASE__       :  int						     =		load_numpy(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/kandinsky/kandinsky_img2img_frog.npy'''      )

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

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

           SCREAMING_SNAKE_CASE__       :  Dict						     =		KandinskyImgaImgPipeline.from_pretrained(
               '''kandinsky-community/kandinsky-2-1'''   ,   torch_dtype=torch.floataa      )
           SCREAMING_SNAKE_CASE__       :  Dict						     =		pipeline.to(_lowercase      )

           pipeline.set_progress_bar_config(disable=_lowercase      )

           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		torch.Generator(device='''cpu'''      ).manual_seed(0      )
           SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		pipe_prior(
               _lowercase   ,   generator=_lowercase   ,   num_inference_steps=5   ,   negative_prompt=''''''   ,   ).to_tuple()

           SCREAMING_SNAKE_CASE__       :  Dict						     =		pipeline(
               _lowercase   ,   image=_lowercase   ,   image_embeds=_lowercase   ,   negative_image_embeds=_lowercase   ,   generator=_lowercase   ,   num_inference_steps=1_00   ,   height=7_68   ,   width=7_68   ,   strength=0.2   ,   output_type='''np'''   ,   )

           SCREAMING_SNAKE_CASE__       :  Dict						     =		output.images[0]

           assert image.shape == (7_68, 7_68, 3)

           assert_mean_pixel_difference(_lowercase   ,   _lowercase      )










from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE							    =							{
    'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
    'tokenization_tapas': ['TapasTokenizer'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE							    =							[
      'TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TapasForMaskedLM',
      'TapasForQuestionAnswering',
      'TapasForSequenceClassification',
      'TapasModel',
      'TapasPreTrainedModel',
      'load_tf_weights_in_tapas',
  ]
try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE							    =							[
      'TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFTapasForMaskedLM',
      'TFTapasForQuestionAnswering',
      'TFTapasForSequenceClassification',
      'TFTapasModel',
      'TFTapasPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
  from .tokenization_tapas import TapasTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tapas import (
        TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
        TapasForMaskedLM,
        TapasForQuestionAnswering,
        TapasForSequenceClassification,
        TapasModel,
        TapasPreTrainedModel,
        load_tf_weights_in_tapas,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_tapas import (
        TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFTapasForMaskedLM,
        TFTapasForQuestionAnswering,
        TFTapasForSequenceClassification,
        TFTapasModel,
        TFTapasPreTrainedModel,
    )


else:
  import sys

  SCREAMING_SNAKE_CASE							    =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



