


"""simple docstring"""







import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
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 						_A  (  _a   ,unittest.TestCase  ):







    """simple docstring"""




    UpperCAmelCase	:							List[Any]     =	KandinskyVaaImgaImgPipeline
    UpperCAmelCase	:							Dict     =	["""image_embeds""", """negative_image_embeds""", """image"""]
    UpperCAmelCase	:							Dict     =	[
        """image_embeds""",
        """negative_image_embeds""",
        """image""",
    ]
    UpperCAmelCase	:							Dict     =	[
        """generator""",
        """height""",
        """width""",
        """strength""",
        """guidance_scale""",
        """num_inference_steps""",
        """return_dict""",
        """guidance_scale""",
        """num_images_per_prompt""",
        """output_type""",
        """return_dict""",
    ]
    UpperCAmelCase	:							List[Any]     =	False






    @property
    def      __snake_case							( self    :   Tuple):
      return 32






    @property
    def      __snake_case							( self    :   Union[str, Any]):
      return 32






    @property
    def      __snake_case							( self    :   List[Any]):
      return self.time_input_dim






    @property
    def      __snake_case							( self    :   List[str]):
      return self.time_input_dim * 4






    @property
    def      __snake_case							( self    :   List[str]):
      return 100






    @property
    def      __snake_case							( self    :   Any):
      torch.manual_seed(0)

      a						:		Optional[int]											=      {
          "in_channels": 4,
          # Out channels is double in channels because predicts mean and variance
          "out_channels": 8,
          "addition_embed_type": "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": "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(**__UpperCAmelCase)
      return model






    @property
    def      __snake_case							( self    :   Union[str, 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      __snake_case							( self    :   Dict):
      torch.manual_seed(0)
      a						:		Optional[int]											=      VQModel(**self.dummy_movq_kwargs)
      return model






    def      __snake_case							( self    :   Union[str, Any]):
      a						:		Union[str, Any]											=      self.dummy_unet
      a						:		int											=      self.dummy_movq

      a						:		Dict											=      {
          "num_train_timesteps": 1000,
          "beta_schedule": "linear",
          "beta_start": 0.00_085,
          "beta_end": 0.012,
          "clip_sample": False,
          "set_alpha_to_one": False,
          "steps_offset": 0,
          "prediction_type": "epsilon",
          "thresholding": False,
      }

      a						:		List[str]											=      DDIMScheduler(**__UpperCAmelCase)

      a						:		int											=      {
          "unet": unet,
          "scheduler": scheduler,
          "movq": movq,
      }

      return components






    def      __snake_case							( self    :   Tuple    ,			__UpperCAmelCase    :   Any    ,			__UpperCAmelCase    :   List[str]=0):
      a						:		Union[str, Any]											=      floats_tensor((1, self.text_embedder_hidden_size)    ,			rng=random.Random(__UpperCAmelCase)).to(__UpperCAmelCase)
      a						:		List[str]											=      floats_tensor((1, self.text_embedder_hidden_size)    ,			rng=random.Random(seed + 1)).to(
          __UpperCAmelCase)
      # create init_image
      a						:		Union[str, Any]											=      floats_tensor((1, 3, 64, 64)    ,			rng=random.Random(__UpperCAmelCase)).to(__UpperCAmelCase)
      a						:		Tuple											=      image.cpu().permute(0    ,			2    ,			3    ,			1)[0]
      a						:		Tuple											=      Image.fromarray(np.uinta(__UpperCAmelCase)).convert("RGB").resize((256, 256))

      if str(__UpperCAmelCase).startswith("mps"):
        a						:		Dict											=      torch.manual_seed(__UpperCAmelCase)
      else:
        a						:		Any											=      torch.Generator(device=__UpperCAmelCase).manual_seed(__UpperCAmelCase)
      a						:		Dict											=      {
          "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      __snake_case							( self    :   Tuple):
      a						:		int											=      "cpu"

      a						:		Union[str, Any]											=      self.get_dummy_components()

      a						:		List[str]											=      self.pipeline_class(**__UpperCAmelCase)
      a						:		Optional[Any]											=      pipe.to(__UpperCAmelCase)

      pipe.set_progress_bar_config(disable=__UpperCAmelCase)

      a						:		Optional[int]											=      pipe(**self.get_dummy_inputs(__UpperCAmelCase))
      a						:		List[str]											=      output.images

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

      a						:		Optional[int]											=      image[0, -3:, -3:, -1]
      a						:		str											=      image_from_tuple[0, -3:, -3:, -1]

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

      a						:		List[str]											=      np.array(
          [0.6_199_778, 0.63_984_406, 0.46_145_785, 0.62_944_984, 0.5_622_215, 0.47_306_132, 0.47_441_456, 0.4_607_606, 0.48_719_263])
      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 						_A  (  unittest.TestCase  ):







    """simple docstring"""




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






    def      __snake_case							( self    :   List[str]):
      a						:		Optional[int]											=      load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/kandinskyv22/kandinskyv22_img2img_frog.npy")

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

      a						:		List[Any]											=      KandinskyVaaPriorPipeline.from_pretrained(
          "kandinsky-community/kandinsky-2-2-prior"    ,			torch_dtype=torch.floataa)
      pipe_prior.to(__UpperCAmelCase)

      a						:		Dict											=      KandinskyVaaImgaImgPipeline.from_pretrained(
          "kandinsky-community/kandinsky-2-2-decoder"    ,			torch_dtype=torch.floataa)
      a						:		Any											=      pipeline.to(__UpperCAmelCase)

      pipeline.set_progress_bar_config(disable=__UpperCAmelCase)

      a						:		str											=      torch.Generator(device="cpu").manual_seed(0)
      a						:		Tuple											=      pipe_prior(
          __UpperCAmelCase    ,			generator=__UpperCAmelCase    ,			num_inference_steps=5    ,			negative_prompt=""    ,			).to_tuple()

      a						:		Any											=      pipeline(
          image=__UpperCAmelCase    ,			image_embeds=__UpperCAmelCase    ,			negative_image_embeds=__UpperCAmelCase    ,			generator=__UpperCAmelCase    ,			num_inference_steps=100    ,			height=768    ,			width=768    ,			strength=0.2    ,			output_type="np"    ,			)

      a						:		Optional[Any]											=      output.images[0]

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

      assert_mean_pixel_difference(__UpperCAmelCase    ,			__UpperCAmelCase)










"""simple docstring"""







import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
 import torch






class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase	:							str     =	"""char"""
    UpperCAmelCase	:							Optional[Any]     =	"""bpe"""
    UpperCAmelCase	:							Optional[Any]     =	"""wp"""






__lowercase    =				(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)






class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase	:							Optional[Any]     =	["""image_processor""", """char_tokenizer"""]
    UpperCAmelCase	:							Optional[Any]     =	"""ViTImageProcessor"""
    UpperCAmelCase	:							List[Any]     =	"""MgpstrTokenizer"""






    def __init__( self    :   List[Any]    ,			__UpperCAmelCase    :   Union[str, Any]=None    ,			__UpperCAmelCase    :   Dict=None    ,			**__UpperCAmelCase    :   str):
      a						:		Tuple											=      None
      if "feature_extractor" in kwargs:
        warnings.warn(
            "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
            " instead."    ,			__UpperCAmelCase    ,			)
        a						:		List[str]											=      kwargs.pop("feature_extractor")

      a						:		Dict											=      image_processor if image_processor is not None else feature_extractor
      if image_processor is None:
        raise ValueError("You need to specify an `image_processor`.")
      if tokenizer is None:
        raise ValueError("You need to specify a `tokenizer`.")

      a						:		Union[str, Any]											=      tokenizer
      a						:		int											=      AutoTokenizer.from_pretrained("gpt2")
      a						:		str											=      AutoTokenizer.from_pretrained("bert-base-uncased")

      super().__init__(__UpperCAmelCase    ,			__UpperCAmelCase)






    def __call__( self    :   str    ,			__UpperCAmelCase    :   Dict=None    ,			__UpperCAmelCase    :   Tuple=None    ,			__UpperCAmelCase    :   Union[str, Any]=None    ,			**__UpperCAmelCase    :   int):
      if images is None and text is None:
        raise ValueError("You need to specify either an `images` or `text` input to process.")

      if images is not None:
        a						:		List[str]											=      self.image_processor(__UpperCAmelCase    ,			return_tensors=__UpperCAmelCase    ,			**__UpperCAmelCase)
      if text is not None:
        a						:		Optional[Any]											=      self.char_tokenizer(__UpperCAmelCase    ,			return_tensors=__UpperCAmelCase    ,			**__UpperCAmelCase)

      if text is None:
        return inputs
      elif images is None:
        return encodings
      else:
        a						:		Any											=      encodings["input_ids"]
        return inputs






    def      __snake_case							( self    :   List[Any]    ,			__UpperCAmelCase    :   List[str]):
      a							,		a							,		a						:		Tuple											=      sequences
      a						:		Optional[int]											=      char_preds.size(0)

      a							,		a						:		Dict											=      self._decode_helper(__UpperCAmelCase    ,			"char")
      a							,		a						:		Dict											=      self._decode_helper(__UpperCAmelCase    ,			"bpe")
      a							,		a						:		Union[str, Any]											=      self._decode_helper(__UpperCAmelCase    ,			"wp")

      a						:		Any											=      []
      a						:		Union[str, Any]											=      []
      for i in range(__UpperCAmelCase):
        a						:		Any											=      [char_scores[i], bpe_scores[i], wp_scores[i]]
        a						:		Optional[Any]											=      [char_strs[i], bpe_strs[i], wp_strs[i]]
        a						:		List[str]											=      scores.index(max(__UpperCAmelCase))
        final_strs.append(strs[max_score_index])
        final_scores.append(scores[max_score_index])

      a						:		Dict											=      {}
      a						:		List[str]											=      final_strs
      a						:		str											=      final_scores
      a						:		int											=      char_strs
      a						:		int											=      bpe_strs
      a						:		Tuple											=      wp_strs
      return out






    def      __snake_case							( self    :   Any    ,			__UpperCAmelCase    :   List[str]    ,			__UpperCAmelCase    :   List[str]):
      if format == DecodeType.CHARACTER:
        a						:		int											=      self.char_decode
        a						:		int											=      1
        a						:		Dict											=      "[s]"
      elif format == DecodeType.BPE:
        a						:		List[str]											=      self.bpe_decode
        a						:		List[str]											=      2
        a						:		int											=      "#"
      elif format == DecodeType.WORDPIECE:
        a						:		Union[str, Any]											=      self.wp_decode
        a						:		List[str]											=      102
        a						:		int											=      "[SEP]"
      else:
        raise ValueError(f'''Format {format} is not supported.''')

      a							,		a						:		str											=      [], []
      a						:		Optional[int]											=      pred_logits.size(0)
      a						:		List[str]											=      pred_logits.size(1)
      a							,		a						:		Tuple											=      pred_logits.topk(1    ,			dim=-1    ,			largest=__UpperCAmelCase    ,			sorted=__UpperCAmelCase)
      a						:		List[str]											=      preds_index.view(-1    ,			__UpperCAmelCase)[:, 1:]
      a						:		Any											=      decoder(__UpperCAmelCase)
      a							,		a						:		Union[str, Any]											=      torch.nn.functional.softmax(__UpperCAmelCase    ,			dim=2).max(dim=2)
      a						:		Union[str, Any]											=      preds_max_prob[:, 1:]

      for index in range(__UpperCAmelCase):
        a						:		str											=      preds_str[index].find(__UpperCAmelCase)
        a						:		Optional[Any]											=      preds_str[index][:pred_eos]
        a						:		Optional[int]											=      preds_index[index].cpu().tolist()
        a						:		Optional[int]											=      pred_index.index(__UpperCAmelCase) if eos_token in pred_index else -1
        a						:		List[str]											=      preds_max_prob[index][: pred_eos_index + 1]
        a						:		int											=      pred_max_prob.cumprod(dim=0)[-1] if pred_max_prob.nelement() != 0 else 0.0
        dec_strs.append(__UpperCAmelCase)
        conf_scores.append(__UpperCAmelCase)

      return dec_strs, conf_scores






    def      __snake_case							( self    :   Optional[int]    ,			__UpperCAmelCase    :   Any):
      a						:		Dict											=      [seq.replace(" "    ,			"") for seq in self.char_tokenizer.batch_decode(__UpperCAmelCase)]
      return decode_strs






    def      __snake_case							( self    :   Optional[int]    ,			__UpperCAmelCase    :   List[str]):
      return self.bpe_tokenizer.batch_decode(__UpperCAmelCase)






    def      __snake_case							( self    :   Union[str, Any]    ,			__UpperCAmelCase    :   int):
      a						:		Any											=      [seq.replace(" "    ,			"") for seq in self.wp_tokenizer.batch_decode(__UpperCAmelCase)]
      return decode_strs






