

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


lowerCAmelCase__       = {
    '''configuration_trocr''': ['''TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TrOCRConfig'''],
    '''processing_trocr''': ['''TrOCRProcessor'''],
}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowerCAmelCase__       = [
          '''TROCR_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''TrOCRForCausalLM''',
          '''TrOCRPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
      from .processing_trocr import TrOCRProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
      import sys

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






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 						__magic_name__    (       _snake_case	,						unittest.TestCase    ):
 UpperCAmelCase							    =						KandinskyInpaintPipeline
 UpperCAmelCase							    =						["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
 UpperCAmelCase							    =						[
     """prompt""",
     """negative_prompt""",
     """image_embeds""",
     """negative_image_embeds""",
     """image""",
     """mask_image""",
 ]
 UpperCAmelCase							    =						[
     """generator""",
     """height""",
     """width""",
     """latents""",
     """guidance_scale""",
     """negative_prompt""",
     """num_inference_steps""",
     """return_dict""",
     """guidance_scale""",
     """num_images_per_prompt""",
     """output_type""",
     """return_dict""",
 ]
 UpperCAmelCase							    =						False


 @property
 def _UpperCamelCase	(						self		: Union[str, Any]    )    ->      Tuple:
   return 3_2


 @property
 def _UpperCamelCase	(						self		: int    )    ->      List[Any]:
   return 3_2


 @property
 def _UpperCamelCase	(						self		: List[Any]    )    ->      List[Any]:
   return self.time_input_dim


 @property
 def _UpperCamelCase	(						self		: Tuple    )    ->      Tuple:
   return self.time_input_dim * 4


 @property
 def _UpperCamelCase	(						self		: Any    )    ->      Optional[int]:
   return 1_0_0


 @property
 def _UpperCamelCase	(						self		: Optional[int]    )    ->      Optional[int]:
   UpperCAmelCase					      =  XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"    )
   return tokenizer


 @property
 def _UpperCamelCase	(						self		: int    )    ->      Dict:
   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=3_7			,						num_attention_heads=4			,						num_hidden_layers=5			,						vocab_size=1_0_0_5			,						)

   UpperCAmelCase					      =  MultilingualCLIP(lowerCAmelCase__    )
   UpperCAmelCase					      =  text_encoder.eval()

   return text_encoder


 @property
 def _UpperCamelCase	(						self		: Dict    )    ->      Optional[int]:
   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(**lowerCAmelCase__    )
   return model


 @property
 def _UpperCamelCase	(						self		: str    )    ->      Optional[Any]:
   return {
       "block_out_channels": [3_2, 6_4],
       "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": 1_2,
       "out_channels": 3,
       "up_block_types": [
           "AttnUpDecoderBlock2D",
           "UpDecoderBlock2D",
       ],
       "vq_embed_dim": 4,
   }


 @property
 def _UpperCamelCase	(						self		: Dict    )    ->      List[Any]:
   torch.manual_seed(0    )
   UpperCAmelCase					      =  VQModel(**self.dummy_movq_kwargs    )
   return model


 def _UpperCamelCase	(						self		: Tuple    )    ->      Any:
   UpperCAmelCase					      =  self.dummy_text_encoder
   UpperCAmelCase					      =  self.dummy_tokenizer
   UpperCAmelCase					      =  self.dummy_unet
   UpperCAmelCase					      =  self.dummy_movq

   UpperCAmelCase					      =  DDIMScheduler(
       num_train_timesteps=1_0_0_0			,						beta_schedule="linear"			,						beta_start=0.00_085			,						beta_end=0.012			,						clip_sample=lowerCAmelCase__			,						set_alpha_to_one=lowerCAmelCase__			,						steps_offset=1			,						prediction_type="epsilon"			,						thresholding=lowerCAmelCase__			,						)

   UpperCAmelCase					      =  {
       "text_encoder": text_encoder,
       "tokenizer": tokenizer,
       "unet": unet,
       "scheduler": scheduler,
       "movq": movq,
   }

   return components


 def _UpperCamelCase	(						self		: Union[str, Any]			,						lowerCAmelCase__		: Any			,						lowerCAmelCase__		: Tuple=0    )    ->      str:
   UpperCAmelCase					      =  floats_tensor((1, self.cross_attention_dim)			,						rng=random.Random(lowerCAmelCase__    )    ).to(lowerCAmelCase__    )
   UpperCAmelCase					      =  floats_tensor((1, self.cross_attention_dim)			,						rng=random.Random(seed + 1    )    ).to(lowerCAmelCase__    )
   # create init_image
   UpperCAmelCase					      =  floats_tensor((1, 3, 6_4, 6_4)			,						rng=random.Random(lowerCAmelCase__    )    ).to(lowerCAmelCase__    )
   UpperCAmelCase					      =  image.cpu().permute(0			,						2			,						3			,						1    )[0]
   UpperCAmelCase					      =  Image.fromarray(np.uinta(lowerCAmelCase__    )    ).convert("RGB"    ).resize((2_5_6, 2_5_6)    )
   # create mask
   UpperCAmelCase					      =  np.ones((6_4, 6_4)			,						dtype=np.floataa    )
   UpperCAmelCase					      =  0

   if str(lowerCAmelCase__    ).startswith("mps"    ):
     UpperCAmelCase					      =  torch.manual_seed(lowerCAmelCase__    )
   else:
     UpperCAmelCase					      =  torch.Generator(device=lowerCAmelCase__    ).manual_seed(lowerCAmelCase__    )
   UpperCAmelCase					      =  {
       "prompt": "horse",
       "image": init_image,
       "mask_image": mask,
       "image_embeds": image_embeds,
       "negative_image_embeds": negative_image_embeds,
       "generator": generator,
       "height": 6_4,
       "width": 6_4,
       "num_inference_steps": 2,
       "guidance_scale": 4.0,
       "output_type": "np",
   }
   return inputs


 def _UpperCamelCase	(						self		: Dict    )    ->      List[str]:
   UpperCAmelCase					      =  "cpu"

   UpperCAmelCase					      =  self.get_dummy_components()

   UpperCAmelCase					      =  self.pipeline_class(**lowerCAmelCase__    )
   UpperCAmelCase					      =  pipe.to(lowerCAmelCase__    )

   pipe.set_progress_bar_config(disable=lowerCAmelCase__    )

   UpperCAmelCase					      =  pipe(**self.get_dummy_inputs(lowerCAmelCase__    )    )
   UpperCAmelCase					      =  output.images

   UpperCAmelCase					      =  pipe(
       **self.get_dummy_inputs(lowerCAmelCase__    )			,						return_dict=lowerCAmelCase__			,						)[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, 6_4, 6_4, 3)

   UpperCAmelCase					      =  np.array(
       [0.8_326_919, 0.73_790_467, 0.20_918_581, 0.9_309_612, 0.5_511_791, 0.43_713_328, 0.5_513_321, 0.49_922_934, 0.59_497_786]    )

   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		: str    )    ->      Tuple:
   super().test_inference_batch_single_identical(expected_max_diff=3e-3    )






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


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


 def _UpperCamelCase	(						self		: Tuple    )    ->      int:
   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((7_6_8, 7_6_8)			,						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(lowerCAmelCase__    )

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

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

   UpperCAmelCase					      =  pipeline(
       lowerCAmelCase__			,						image=lowerCAmelCase__			,						mask_image=lowerCAmelCase__			,						image_embeds=lowerCAmelCase__			,						negative_image_embeds=lowerCAmelCase__			,						generator=lowerCAmelCase__			,						num_inference_steps=1_0_0			,						height=7_6_8			,						width=7_6_8			,						output_type="np"			,						)

   UpperCAmelCase					      =  output.images[0]

   assert image.shape == (7_6_8, 7_6_8, 3)

   assert_mean_pixel_difference(lowerCAmelCase__			,						lowerCAmelCase__    )



