
# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_A     :   Dict						      =				{
    "configuration_cpmant": ["CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CpmAntConfig"],
    "tokenization_cpmant": ["CpmAntTokenizer"],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A     :   List[Any]						      =				[
       "CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "CpmAntForCausalLM",
       "CpmAntModel",
       "CpmAntPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
   from .tokenization_cpmant import CpmAntTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_cpmant import (
          CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
          CpmAntForCausalLM,
          CpmAntModel,
          CpmAntPreTrainedModel,
      )


else:
   import sys

   _A     :   int						      =				_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 diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    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   lowerCamelCase	(						A_							,				unittest.TestCase						):
     UpperCAmelCase__       :							Any     =	KandinskyVaaControlnetImgaImgPipeline
     UpperCAmelCase__       :							Optional[Any]     =	["image_embeds", "negative_image_embeds", "image", "hint"]
     UpperCAmelCase__       :							List[Any]     =	["image_embeds", "negative_image_embeds", "image", "hint"]
     UpperCAmelCase__       :							List[str]     =	[
         "generator",
         "height",
         "width",
         "strength",
         "guidance_scale",
         "num_inference_steps",
         "return_dict",
         "guidance_scale",
         "num_images_per_prompt",
         "output_type",
         "return_dict",
     ]
     UpperCAmelCase__       :							List[str]     =	False





     @property
     def 	UpperCAmelCase(self   :					Optional[int]     )		->		Dict:
         return 3_2





     @property
     def 	UpperCAmelCase(self   :					Union[str, Any]     )		->		Optional[int]:
         return 3_2





     @property
     def 	UpperCAmelCase(self   :					Optional[Any]     )		->		Union[str, Any]:
         return self.time_input_dim





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





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





     @property
     def 	UpperCAmelCase(self   :					int     )		->		Optional[Any]:
         torch.manual_seed(0     )

         snake_case				  =      {
             "in_channels": 8,
             # Out channels is double in channels because predicts mean and variance
             "out_channels": 8,
             "addition_embed_type": "image_hint",
             "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,
         }

         snake_case				  =      UNetaDConditionModel(**_A     )
         return model





     @property
     def 	UpperCAmelCase(self   :					Any     )		->		List[str]:
         return {
             "block_out_channels": [3_2, 3_2, 6_4, 6_4],
             "down_block_types": [
                 "DownEncoderBlock2D",
                 "DownEncoderBlock2D",
                 "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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
             "vq_embed_dim": 4,
         }





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





     def 	UpperCAmelCase(self   :					Any     )		->		Optional[Any]:
         snake_case				  =      self.dummy_unet
         snake_case				  =      self.dummy_movq

         snake_case				  =      {
             "num_train_timesteps": 1_0_0_0,
             "beta_schedule": "linear",
             "beta_start": 0.0_00_85,
             "beta_end": 0.0_12,
             "clip_sample": False,
             "set_alpha_to_one": False,
             "steps_offset": 0,
             "prediction_type": "epsilon",
             "thresholding": False,
         }

         snake_case				  =      DDIMScheduler(**_A     )

         snake_case				  =      {
             "unet": unet,
             "scheduler": scheduler,
             "movq": movq,
         }

         return components





     def 	UpperCAmelCase(self   :					Any       ,					_A   :					Any       ,					_A   :					Optional[Any]=0     )		->		List[Any]:
         snake_case				  =      floats_tensor((1, self.text_embedder_hidden_size)       ,					rng=random.Random(_A     )     ).to(_A     )
         snake_case				  =      floats_tensor((1, self.text_embedder_hidden_size)       ,					rng=random.Random(seed + 1     )     ).to(
             _A     )
         # create init_image
         snake_case				  =      floats_tensor((1, 3, 6_4, 6_4)       ,					rng=random.Random(_A     )     ).to(_A     )
         snake_case				  =      image.cpu().permute(0       ,					2       ,					3       ,					1     )[0]
         snake_case				  =      Image.fromarray(np.uinta(_A     )     ).convert("RGB"     ).resize((2_5_6, 2_5_6)     )
         # create hint
         snake_case				  =      floats_tensor((1, 3, 6_4, 6_4)       ,					rng=random.Random(_A     )     ).to(_A     )

         if str(_A     ).startswith("mps"     ):
             snake_case				  =      torch.manual_seed(_A     )
         else:
             snake_case				  =      torch.Generator(device=_A     ).manual_seed(_A     )
         snake_case				  =      {
             "image": init_image,
             "image_embeds": image_embeds,
             "negative_image_embeds": negative_image_embeds,
             "hint": hint,
             "generator": generator,
             "height": 6_4,
             "width": 6_4,
             "num_inference_steps": 1_0,
             "guidance_scale": 7.0,
             "strength": 0.2,
             "output_type": "np",
         }
         return inputs





     def 	UpperCAmelCase(self   :					str     )		->		Union[str, Any]:
         snake_case				  =      "cpu"

         snake_case				  =      self.get_dummy_components()

         snake_case				  =      self.pipeline_class(**_A     )
         snake_case				  =      pipe.to(_A     )

         pipe.set_progress_bar_config(disable=_A     )

         snake_case				  =      pipe(**self.get_dummy_inputs(_A     )     )
         snake_case				  =      output.images

         snake_case				  =      pipe(
             **self.get_dummy_inputs(_A     )       ,					return_dict=_A       ,					)[0]

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

         assert image.shape == (1, 6_4, 6_4, 3)

         snake_case				  =      np.array(
             [0.54_98_50_34, 0.55_50_93_65, 0.52_56_15_04, 0.5_57_04_94, 0.5_59_38_18, 0.5_26_39_79, 0.50_28_56_43, 0.5_06_98_46, 0.51_19_67_36]     )
         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   lowerCamelCase	(						unittest.TestCase						):





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





     def 	UpperCAmelCase(self   :					int     )		->		Tuple:
         snake_case				  =      load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy"     )

         snake_case				  =      load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"     )
         snake_case				  =      init_image.resize((5_1_2, 5_1_2)     )

         snake_case				  =      load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/kandinskyv22/hint_image_cat.png"     )
         snake_case				  =      torch.from_numpy(np.array(_A     )     ).float() / 2_55.0
         snake_case				  =      hint.permute(2       ,					0       ,					1     ).unsqueeze(0     )

         snake_case				  =      "A robot, 4k photo"

         snake_case				  =      KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
             "kandinsky-community/kandinsky-2-2-prior"       ,					torch_dtype=torch.floataa     )
         pipe_prior.to(_A     )

         snake_case				  =      KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
             "kandinsky-community/kandinsky-2-2-controlnet-depth"       ,					torch_dtype=torch.floataa     )
         snake_case				  =      pipeline.to(_A     )

         pipeline.set_progress_bar_config(disable=_A     )

         snake_case				  =      torch.Generator(device="cpu"     ).manual_seed(0     )

         snake_case  ,							snake_case				  =      pipe_prior(
             _A       ,					image=_A       ,					strength=0.85       ,					generator=_A       ,					negative_prompt=""       ,					).to_tuple()

         snake_case				  =      pipeline(
             image=_A       ,					image_embeds=_A       ,					negative_image_embeds=_A       ,					hint=_A       ,					generator=_A       ,					num_inference_steps=1_0_0       ,					height=5_1_2       ,					width=5_1_2       ,					strength=0.5       ,					output_type="np"       ,					)

         snake_case				  =      output.images[0]

         assert image.shape == (5_1_2, 5_1_2, 3)

         assert_mean_pixel_difference(_A       ,					_A     )

