
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List

from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql  # noqa F401
from .text import text



def 		lowercase_    (     A__	)							->							str:
    """simple docstring"""

    snake_case				  =      []
    for line in lines:
        snake_case				  =      re.sub(r"#.*"				,	""				,	A__	)  # remove comments
        if line:
            filtered_lines.append(A__	)
    snake_case				  =      "\n".join(A__	)

    # Make a hash from all this code
    snake_case				  =      full_str.encode("utf-8"	)
    return shaaaa(A__	).hexdigest()


# get importable module names and hash for caching
_A     :   Optional[int]						      =				{
    "csv": (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
    "json": (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
    "pandas": (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
    "parquet": (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
    "arrow": (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
    "text": (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
    "imagefolder": (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
    "audiofolder": (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}

# Used to infer the module to use based on the data files extensions
_A     :   str						      =				{
    ".csv": ("csv", {}),
    ".tsv": ("csv", {"sep": "\t"}),
    ".json": ("json", {}),
    ".jsonl": ("json", {}),
    ".parquet": ("parquet", {}),
    ".arrow": ("arrow", {}),
    ".txt": ("text", {}),
}
_EXTENSION_TO_MODULE.update({ext: ("imagefolder", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("imagefolder", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ("audiofolder", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("audiofolder", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_A     :   List[str]						      =				{"imagefolder", "audiofolder"}

# Used to filter data files based on extensions given a module name
_A     :   Tuple						      =				{}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
   _MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)

_MODULE_TO_EXTENSIONS["imagefolder"].append(".zip")





_MODULE_TO_EXTENSIONS["audiofolder"].append(".zip")



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     )

