import gc
import random
import unittest

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

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, 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__    (		__SCREAMING_SNAKE_CASE		,	unittest.TestCase					):



  """simple docstring"""






  lowerCAmelCase__		:	Optional[int]    		= KandinskyInpaintPipeline
  lowerCAmelCase__		:	int    		= ['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
  lowerCAmelCase__		:	Dict    		= [
      'prompt',
      'negative_prompt',
      'image_embeds',
      'negative_image_embeds',
      'image',
      'mask_image',
  ]
  lowerCAmelCase__		:	Tuple    		= [
      'generator',
      'height',
      'width',
      'latents',
      'guidance_scale',
      'negative_prompt',
      'num_inference_steps',
      'return_dict',
      'guidance_scale',
      'num_images_per_prompt',
      'output_type',
      'return_dict',
  ]
  lowerCAmelCase__		:	Optional[Any]    		= False






  @property
  def 	_UpperCAmelCase				(				self:			Tuple   ) -> Tuple:




    '''simple docstring'''




    return 32






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




    '''simple docstring'''




    return 32






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




    '''simple docstring'''




    return self.time_input_dim






  @property
  def 	_UpperCAmelCase				(				self:			str   ) -> Optional[int]:




    '''simple docstring'''




    return self.time_input_dim * 4






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




    '''simple docstring'''




    return 100






  @property
  def 	_UpperCAmelCase				(				self:			List[Any]   ) -> Dict:




    '''simple docstring'''




    __UpperCAmelCase						    =       XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"   )
    return tokenizer






  @property
  def 	_UpperCAmelCase				(				self:			Union[str, Any]   ) -> int:




    '''simple docstring'''




    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=1_005				,				)

    __UpperCAmelCase						    =       MultilingualCLIP(__snake_case   )
    __UpperCAmelCase						    =       text_encoder.eval()

    return text_encoder






  @property
  def 	_UpperCAmelCase				(				self:			str   ) -> Dict:




    '''simple docstring'''




    torch.manual_seed(0   )

    __UpperCAmelCase						    =       {
        '''in_channels''': 9,
        # 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(**__snake_case   )
    return model






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




    '''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:			Union[str, Any]   ) -> Union[str, Any]:




    '''simple docstring'''




    torch.manual_seed(0   )
    __UpperCAmelCase						    =       VQModel(**self.dummy_movq_kwargs   )
    return model






  def 	_UpperCAmelCase				(				self:			Optional[Any]   ) -> Union[str, Any]:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.dummy_text_encoder
    __UpperCAmelCase						    =       self.dummy_tokenizer
    __UpperCAmelCase						    =       self.dummy_unet
    __UpperCAmelCase						    =       self.dummy_movq

    __UpperCAmelCase						    =       DDIMScheduler(
        num_train_timesteps=1_000				,				beta_schedule="linear"				,				beta_start=0.00085				,				beta_end=0.012				,				clip_sample=__snake_case				,				set_alpha_to_one=__snake_case				,				steps_offset=1				,				prediction_type="epsilon"				,				thresholding=__snake_case				,				)

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

    return components






  def 	_UpperCAmelCase				(				self:			List[Any]				,				__lowerCAmelCase:			List[Any]				,				__lowerCAmelCase:			str=0   ) -> Tuple:




    '''simple docstring'''




    __UpperCAmelCase						    =       floats_tensor((1, self.cross_attention_dim)				,				rng=random.Random(__snake_case   )   ).to(__snake_case   )
    __UpperCAmelCase						    =       floats_tensor((1, self.cross_attention_dim)				,				rng=random.Random(seed + 1   )   ).to(__snake_case   )
    # create init_image
    __UpperCAmelCase						    =       floats_tensor((1, 3, 64, 64)				,				rng=random.Random(__snake_case   )   ).to(__snake_case   )
    __UpperCAmelCase						    =       image.cpu().permute(0				,				2				,				3				,				1   )[0]
    __UpperCAmelCase						    =       Image.fromarray(np.uinta(__snake_case   )   ).convert("RGB"   ).resize((256, 256)   )
    # create mask
    __UpperCAmelCase						    =       np.ones((64, 64)				,				dtype=np.floataa   )
    __UpperCAmelCase						    =       0

    if str(__snake_case   ).startswith("mps"   ):
      __UpperCAmelCase						    =       torch.manual_seed(__snake_case   )
    else:
      __UpperCAmelCase						    =       torch.Generator(device=__snake_case   ).manual_seed(__snake_case   )
    __UpperCAmelCase						    =       {
        '''prompt''': '''horse''',
        '''image''': init_image,
        '''mask_image''': mask,
        '''image_embeds''': image_embeds,
        '''negative_image_embeds''': negative_image_embeds,
        '''generator''': generator,
        '''height''': 64,
        '''width''': 64,
        '''num_inference_steps''': 2,
        '''guidance_scale''': 4.0,
        '''output_type''': '''np''',
    }
    return inputs






  def 	_UpperCAmelCase				(				self:			str   ) -> int:




    '''simple docstring'''




    __UpperCAmelCase						    =       '''cpu'''

    __UpperCAmelCase						    =       self.get_dummy_components()

    __UpperCAmelCase						    =       self.pipeline_class(**__snake_case   )
    __UpperCAmelCase						    =       pipe.to(__snake_case   )

    pipe.set_progress_bar_config(disable=__snake_case   )

    __UpperCAmelCase						    =       pipe(**self.get_dummy_inputs(__snake_case   )   )
    __UpperCAmelCase						    =       output.images

    __UpperCAmelCase						    =       pipe(
        **self.get_dummy_inputs(__snake_case   )				,				return_dict=__snake_case				,				)[0]

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

    print(F'''image.shape {image.shape}'''   )

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

    __UpperCAmelCase						    =       np.array(
        [0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786]   )

    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()}'''






  def 	_UpperCAmelCase				(				self:			Dict   ) -> List[Any]:




    '''simple docstring'''




    super().test_inference_batch_single_identical(expected_max_diff=3E-3   )






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



  """simple docstring"""






  def 	_UpperCAmelCase				(				self:			Dict   ) -> Optional[Any]:




    '''simple docstring'''




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






  def 	_UpperCAmelCase				(				self:			Optional[Any]   ) -> Tuple:




    '''simple docstring'''




    __UpperCAmelCase						    =       load_numpy(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        "/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"   )

    __UpperCAmelCase						    =       load_image(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"   )
    __UpperCAmelCase						    =       np.ones((768, 768)				,				dtype=np.floataa   )
    __UpperCAmelCase						    =       0

    __UpperCAmelCase						    =       '''a hat'''

    __UpperCAmelCase						    =       KandinskyPriorPipeline.from_pretrained(
        "kandinsky-community/kandinsky-2-1-prior"				,				torch_dtype=torch.floataa   )
    pipe_prior.to(__snake_case   )

    __UpperCAmelCase						    =       KandinskyInpaintPipeline.from_pretrained(
        "kandinsky-community/kandinsky-2-1-inpaint"				,				torch_dtype=torch.floataa   )
    __UpperCAmelCase						    =       pipeline.to(__snake_case   )
    pipeline.set_progress_bar_config(disable=__snake_case   )

    __UpperCAmelCase						    =       torch.Generator(device="cpu"   ).manual_seed(0   )
    __UpperCAmelCase						    =       pipe_prior(
        __snake_case				,				generator=__snake_case				,				num_inference_steps=5				,				negative_prompt=""				,				).to_tuple()

    __UpperCAmelCase						    =       pipeline(
        __snake_case				,				image=__snake_case				,				mask_image=__snake_case				,				image_embeds=__snake_case				,				negative_image_embeds=__snake_case				,				generator=__snake_case				,				num_inference_steps=100				,				height=768				,				width=768				,				output_type="np"				,				)

    __UpperCAmelCase						    =       output.images[0]

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

    assert_mean_pixel_difference(__snake_case				,				__snake_case   )




from typing import TYPE_CHECKING

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


a_							=      {
    """configuration_squeezebert""": [
        """SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SqueezeBertConfig""",
        """SqueezeBertOnnxConfig""",
    ],
    """tokenization_squeezebert""": ["""SqueezeBertTokenizer"""],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_							=      ["""SqueezeBertTokenizerFast"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_							=      [
           """SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """SqueezeBertForMaskedLM""",
           """SqueezeBertForMultipleChoice""",
           """SqueezeBertForQuestionAnswering""",
           """SqueezeBertForSequenceClassification""",
           """SqueezeBertForTokenClassification""",
           """SqueezeBertModel""",
           """SqueezeBertModule""",
           """SqueezeBertPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_squeezebert import (
           SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
           SqueezeBertConfig,
           SqueezeBertOnnxConfig,
       )
       from .tokenization_squeezebert import SqueezeBertTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_squeezebert import (
                  SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  SqueezeBertForMaskedLM,
                  SqueezeBertForMultipleChoice,
                  SqueezeBertForQuestionAnswering,
                  SqueezeBertForSequenceClassification,
                  SqueezeBertForTokenClassification,
                  SqueezeBertModel,
                  SqueezeBertModule,
                  SqueezeBertPreTrainedModel,
              )

else:
       import sys

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



