

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      A__					(			__A  :						List[str]			)					->str:
      __A    =[]
      for line in lines:
            __A    =re.sub(r'''#.*'''     ,				''''''     ,				__A			)  # remove comments
            if line:
                  filtered_lines.append(__A			)
      __A    ='''\n'''.join(__A			)

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


# get importable module names and hash for caching
_lowerCamelCase      :					Dict					    =      {
    '''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
_lowerCamelCase      :					int					    =      {
    '''.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})
_lowerCamelCase      :					List[str]					    =      {'''imagefolder''', '''audiofolder'''}

# Used to filter data files based on extensions given a module name
_lowerCamelCase      :					Dict[str, List[str]]					    =      {}
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 diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, 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__  (					__magic_name__				,							unittest.TestCase			):



       '''simple docstring'''







       lowercase_						=    KandinskyVaaPipeline
       lowercase_						=    [
           """image_embeds""",
           """negative_image_embeds""",
       ]
       lowercase_						=    ["""image_embeds""", """negative_image_embeds"""]
       lowercase_						=    [
           """generator""",
           """height""",
           """width""",
           """latents""",
           """guidance_scale""",
           """num_inference_steps""",
           """return_dict""",
           """guidance_scale""",
           """num_images_per_prompt""",
           """output_type""",
           """return_dict""",
       ]
       lowercase_						=    False

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return 3_2

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return 3_2

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return self.time_input_dim

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return self.time_input_dim * 4

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return 1_0_0

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             torch.manual_seed(0     )

             __A    ={
                 '''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    =UNetaDConditionModel(**lowercase__     )
             return model

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             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     ):







             '''simple docstring'''



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

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =self.dummy_unet
             __A    =self.dummy_movq

             __A    =DDIMScheduler(
                 num_train_timesteps=1_0_0_0 ,   beta_schedule='''linear''' ,   beta_start=0.0_0085 ,   beta_end=0.012 ,   clip_sample=lowercase__ ,   set_alpha_to_one=lowercase__ ,   steps_offset=1 ,   prediction_type='''epsilon''' ,   thresholding=lowercase__ ,   )

             __A    ={
                 '''unet''': unet,
                 '''scheduler''': scheduler,
                 '''movq''': movq,
             }
             return components

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__=0     ):







             '''simple docstring'''



             __A    =floats_tensor((1, self.text_embedder_hidden_size) ,   rng=random.Random(lowercase__     )     ).to(lowercase__     )
             __A    =floats_tensor((1, self.text_embedder_hidden_size) ,   rng=random.Random(seed + 1     )     ).to(
                 lowercase__     )
             if str(lowercase__     ).startswith('''mps'''     ):
                   __A    =torch.manual_seed(lowercase__     )
             else:
                   __A    =torch.Generator(device=lowercase__     ).manual_seed(lowercase__     )
             __A    ={
                 '''image_embeds''': image_embeds,
                 '''negative_image_embeds''': negative_image_embeds,
                 '''generator''': generator,
                 '''height''': 6_4,
                 '''width''': 6_4,
                 '''guidance_scale''': 4.0,
                 '''num_inference_steps''': 2,
                 '''output_type''': '''np''',
             }
             return inputs


       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    ='''cpu'''

             __A    =self.get_dummy_components()

             __A    =self.pipeline_class(**lowercase__     )
             __A    =pipe.to(lowercase__     )

             pipe.set_progress_bar_config(disable=lowercase__     )

             __A    =pipe(**self.get_dummy_inputs(lowercase__     )     )
             __A    =output.images

             __A    =pipe(
                 **self.get_dummy_inputs(lowercase__     ) ,   return_dict=lowercase__ ,   )[0]

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

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

             __A    =np.array(
                 [0.623_7976, 1.0, 0.3644_1332, 1.0, 0.7063_9634, 0.2987_7186, 0.8565_2125, 0.521_6843, 0.5445_4046]     )

             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			):



       '''simple docstring'''







       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



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


       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =load_numpy(
                 '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                 '''/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy'''     )

             __A    =KandinskyVaaPriorPipeline.from_pretrained(
                 '''kandinsky-community/kandinsky-2-2-prior''' ,   torch_dtype=torch.floataa     )
             pipe_prior.to(lowercase__     )

             __A    =KandinskyVaaPipeline.from_pretrained(
                 '''kandinsky-community/kandinsky-2-2-decoder''' ,   torch_dtype=torch.floataa     )
             __A    =pipeline.to(lowercase__     )
             pipeline.set_progress_bar_config(disable=lowercase__     )

             __A    ='''red cat, 4k photo'''

             __A    =torch.Generator(device='''cuda'''     ).manual_seed(0     )
             __A    ,		__A    =pipe_prior(
                 lowercase__ ,   generator=lowercase__ ,   num_inference_steps=5 ,   negative_prompt='''''' ,   ).to_tuple()

             __A    =torch.Generator(device='''cuda'''     ).manual_seed(0     )
             __A    =pipeline(
                 image_embeds=lowercase__ ,   negative_image_embeds=lowercase__ ,   generator=lowercase__ ,   num_inference_steps=1_0_0 ,   output_type='''np''' ,   )

             __A    =output.images[0]

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

             assert_mean_pixel_difference(lowercase__ ,   lowercase__     )


