


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__       (					A_   ,  unittest.TestCase		):



    """simple docstring"""






    UpperCAmelCase__						:		int      =  KandinskyImgaImgPipeline
    UpperCAmelCase__						:		List[Any]      =  ["prompt", "image_embeds", "negative_image_embeds", "image"]
    UpperCAmelCase__						:		str      =  [
        "prompt",
        "negative_prompt",
        "image_embeds",
        "negative_image_embeds",
        "image",
    ]
    UpperCAmelCase__						:		Optional[int]      =  [
        "generator",
        "height",
        "width",
        "strength",
        "guidance_scale",
        "negative_prompt",
        "num_inference_steps",
        "return_dict",
        "guidance_scale",
        "num_images_per_prompt",
        "output_type",
        "return_dict",
    ]
    UpperCAmelCase__						:		Optional[Any]      =  False






    @property
    def _a					(			self    )  ->       Optional[Any]:
         return 32






    @property
    def _a					(			self    )  ->       Dict:
         return 32






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






    @property
    def _a					(			self    )  ->       Union[str, Any]:
         return self.time_input_dim * 4






    @property
    def _a					(			self    )  ->       List[str]:
         return 100






    @property
    def _a					(			self    )  ->       str:
         __UpperCamelCase    							=XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'    )
         return tokenizer






    @property
    def _a					(			self    )  ->       int:
         torch.manual_seed(0    )
         __UpperCamelCase    							=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					,   )

         __UpperCamelCase    							=MultilingualCLIP(A_    )
         __UpperCamelCase    							=text_encoder.eval()

         return text_encoder






    @property
    def _a					(			self    )  ->       int:
         torch.manual_seed(0    )

         __UpperCamelCase    							={
             '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,
         }

         __UpperCamelCase    							=UNetaDConditionModel(**A_    )
         return model






    @property
    def _a					(			self    )  ->       Any:
         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 _a					(			self    )  ->       int:
         torch.manual_seed(0    )
         __UpperCamelCase    							=VQModel(**self.dummy_movq_kwargs    )
         return model






    def _a					(			self    )  ->       Any:
         __UpperCamelCase    							=self.dummy_text_encoder
         __UpperCamelCase    							=self.dummy_tokenizer
         __UpperCamelCase    							=self.dummy_unet
         __UpperCamelCase    							=self.dummy_movq

         __UpperCamelCase    							={
             'num_train_timesteps': 1000,
             'beta_schedule': 'linear',
             'beta_start': 0.0_0085,
             'beta_end': 0.012,
             'clip_sample': False,
             'set_alpha_to_one': False,
             'steps_offset': 0,
             'prediction_type': 'epsilon',
             'thresholding': False,
         }

         __UpperCamelCase    							=DDIMScheduler(**A_    )

         __UpperCamelCase    							={
             'text_encoder': text_encoder,
             'tokenizer': tokenizer,
             'unet': unet,
             'scheduler': scheduler,
             'movq': movq,
         }

         return components






    def _a					(			self					,   A_					,   A_=0    )  ->       int:
         __UpperCamelCase    							=floats_tensor((1, self.cross_attention_dim)					,   rng=random.Random(A_    )    ).to(A_    )
         __UpperCamelCase    							=floats_tensor((1, self.cross_attention_dim)					,   rng=random.Random(seed + 1    )    ).to(A_    )
         # create init_image
         __UpperCamelCase    							=floats_tensor((1, 3, 64, 64)					,   rng=random.Random(A_    )    ).to(A_    )
         __UpperCamelCase    							=image.cpu().permute(0					,   2					,   3					,   1    )[0]
         __UpperCamelCase    							=Image.fromarray(np.uinta(A_    )    ).convert('RGB'    ).resize((256, 256)    )

         if str(A_    ).startswith('mps'    ):
              __UpperCamelCase    							=torch.manual_seed(A_    )
         else:
              __UpperCamelCase    							=torch.Generator(device=A_    ).manual_seed(A_    )
         __UpperCamelCase    							={
             '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 _a					(			self    )  ->       Dict:
         __UpperCamelCase    							='cpu'

         __UpperCamelCase    							=self.get_dummy_components()

         __UpperCamelCase    							=self.pipeline_class(**A_    )
         __UpperCamelCase    							=pipe.to(A_    )

         pipe.set_progress_bar_config(disable=A_    )

         __UpperCamelCase    							=pipe(**self.get_dummy_inputs(A_    )    )
         __UpperCamelCase    							=output.images

         __UpperCamelCase    							=pipe(
             **self.get_dummy_inputs(A_    )					,   return_dict=A_					,   )[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(
             [0.6147_4943, 0.607_3539, 0.4330_8544, 0.592_8269, 0.4749_3595, 0.4675_5973, 0.461_3838, 0.4536_8797, 0.5011_9233]    )
         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		):



    """simple docstring"""






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






    def _a					(			self    )  ->       List[str]:
         __UpperCamelCase    							=load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/kandinsky/kandinsky_img2img_frog.npy'    )

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

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

         __UpperCamelCase    							=KandinskyImgaImgPipeline.from_pretrained(
             'kandinsky-community/kandinsky-2-1'					,   torch_dtype=torch.floataa    )
         __UpperCamelCase    							=pipeline.to(A_    )

         pipeline.set_progress_bar_config(disable=A_    )

         __UpperCamelCase    							=torch.Generator(device='cpu'    ).manual_seed(0    )
         __UpperCamelCase							,      __UpperCamelCase    							=pipe_prior(
             A_					,   generator=A_					,   num_inference_steps=5					,   negative_prompt=''					,   ).to_tuple()

         __UpperCamelCase    							=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'					,   )

         __UpperCamelCase    							=output.images[0]

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

         assert_mean_pixel_difference(A_					,   A_    )









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_A		       =						{
    'configuration_convbert': ['CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvBertConfig', 'ConvBertOnnxConfig'],
    'tokenization_convbert': ['ConvBertTokenizer'],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						['ConvBertTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						[
       'CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'ConvBertForMaskedLM',
       'ConvBertForMultipleChoice',
       'ConvBertForQuestionAnswering',
       'ConvBertForSequenceClassification',
       'ConvBertForTokenClassification',
       'ConvBertLayer',
       'ConvBertModel',
       'ConvBertPreTrainedModel',
       'load_tf_weights_in_convbert',
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						[
       'TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFConvBertForMaskedLM',
       'TFConvBertForMultipleChoice',
       'TFConvBertForQuestionAnswering',
       'TFConvBertForSequenceClassification',
       'TFConvBertForTokenClassification',
       'TFConvBertLayer',
       'TFConvBertModel',
       'TFConvBertPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
   from .tokenization_convbert import ConvBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_convbert_fast import ConvBertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_convbert import (
          CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          ConvBertForMaskedLM,
          ConvBertForMultipleChoice,
          ConvBertForQuestionAnswering,
          ConvBertForSequenceClassification,
          ConvBertForTokenClassification,
          ConvBertLayer,
          ConvBertModel,
          ConvBertPreTrainedModel,
          load_tf_weights_in_convbert,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_convbert import (
          TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFConvBertForMaskedLM,
          TFConvBertForMultipleChoice,
          TFConvBertForQuestionAnswering,
          TFConvBertForSequenceClassification,
          TFConvBertForTokenClassification,
          TFConvBertLayer,
          TFConvBertModel,
          TFConvBertPreTrainedModel,
      )


else:
   import sys

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





