





import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionAttendAndExcitePipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


_UpperCamelCase				=       False




@skip_mps
class  _lowerCamelCase       (			lowercase__       ,      lowercase__       ,      lowercase__       ,      unittest.TestCase			):



      """simple docstring"""






      UpperCAmelCase_  :					str      			=StableDiffusionAttendAndExcitePipeline
      UpperCAmelCase_  :					Tuple      			=False
      UpperCAmelCase_  :					Dict      			=TEXT_TO_IMAGE_PARAMS
      UpperCAmelCase_  :					str      			=TEXT_TO_IMAGE_BATCH_PARAMS.union({"token_indices"}			)
      UpperCAmelCase_  :					int      			=TEXT_TO_IMAGE_IMAGE_PARAMS
      UpperCAmelCase_  :					List[Any]      			=TEXT_TO_IMAGE_IMAGE_PARAMS







      @classmethod
      def   UpperCAmelCase						(       cls      )				->      Optional[Any]:






        '''simple docstring'''






        super().setUpClass()
        torch.use_deterministic_algorithms(SCREAMING_SNAKE_CASE__      )







      @classmethod
      def   UpperCAmelCase						(       cls      )				->      List[str]:






        '''simple docstring'''






        super().tearDownClass()
        torch.use_deterministic_algorithms(SCREAMING_SNAKE_CASE__      )







      def   UpperCAmelCase						(       self      )				->      Union[str, Any]:






        '''simple docstring'''






        torch.manual_seed(0      )
        __snake_case      : List[str]						=				UNetaDConditionModel(
            block_out_channels=(32, 64) ,  layers_per_block=1 ,  sample_size=32 ,  in_channels=4 ,  out_channels=4 ,  down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") ,  up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") ,  cross_attention_dim=32 ,  attention_head_dim=(2, 4) ,  use_linear_projection=SCREAMING_SNAKE_CASE__ ,  )
        __snake_case      : str						=				DDIMScheduler(
            beta_start=0.00_085 ,  beta_end=0.012 ,  beta_schedule="scaled_linear" ,  clip_sample=SCREAMING_SNAKE_CASE__ ,  set_alpha_to_one=SCREAMING_SNAKE_CASE__ ,  )
        torch.manual_seed(0      )
        __snake_case      : Union[str, Any]						=				AutoencoderKL(
            block_out_channels=[32, 64] ,  in_channels=3 ,  out_channels=3 ,  down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] ,  up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] ,  latent_channels=4 ,  sample_size=128 ,  )
        torch.manual_seed(0      )
        __snake_case      : Any						=				CLIPTextConfig(
            bos_token_id=0 ,  eos_token_id=2 ,  hidden_size=32 ,  intermediate_size=37 ,  layer_norm_eps=1E-05 ,  num_attention_heads=4 ,  num_hidden_layers=5 ,  pad_token_id=1 ,  vocab_size=1000 ,  hidden_act="gelu" ,  projection_dim=512 ,  )
        __snake_case      : Optional[int]						=				CLIPTextModel(SCREAMING_SNAKE_CASE__      )
        __snake_case      : List[str]						=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"      )

        __snake_case      : Dict						=				{
            'unet': unet,
            'scheduler': scheduler,
            'vae': vae,
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'safety_checker': None,
            'feature_extractor': None,
        }

        return components







      def   UpperCAmelCase						(       self ,  UpperCAmelCase ,  UpperCAmelCase=0      )				->      Any:






        '''simple docstring'''






        if str(SCREAMING_SNAKE_CASE__      ).startswith("mps"      ):
          __snake_case      : Any						=				torch.manual_seed(SCREAMING_SNAKE_CASE__      )
        else:
          __snake_case      : List[Any]						=				torch.Generator(device=SCREAMING_SNAKE_CASE__      ).manual_seed(SCREAMING_SNAKE_CASE__      )
        __snake_case      : List[str]						=				{
            'prompt': 'a cat and a frog',
            'token_indices': [2, 5],
            'generator': generator,
            'num_inference_steps': 1,
            'guidance_scale': 6.0,
            'output_type': 'numpy',
            'max_iter_to_alter': 2,
            'thresholds': {0: 0.7},
        }
        return inputs







      def   UpperCAmelCase						(       self      )				->      Optional[Any]:






        '''simple docstring'''






        __snake_case      : List[str]						=				'cpu'

        __snake_case      : Optional[Any]						=				self.get_dummy_components()
        __snake_case      : Any						=				self.pipeline_class(**SCREAMING_SNAKE_CASE__      )
        pipe.to(SCREAMING_SNAKE_CASE__      )
        pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__      )

        __snake_case      : str						=				self.get_dummy_inputs(SCREAMING_SNAKE_CASE__      )
        __snake_case      : str						=				pipe(**SCREAMING_SNAKE_CASE__      ).images
        __snake_case      : List[Any]						=				image[0, -3:, -3:, -1]

        self.assertEqual(image.shape ,  (1, 64, 64, 3)      )
        __snake_case      : Any						=				np.array(
            [0.63_905_364, 0.62_897_307, 0.48_599_017, 0.5_133_624, 0.5_550_048, 0.45_769_516, 0.50_326_973, 0.5_023_139, 0.45_384_496]      )
        __snake_case      : Union[str, Any]						=				np.abs(image_slice.flatten() - expected_slice      ).max()
        self.assertLessEqual(SCREAMING_SNAKE_CASE__ ,  1E-3      )







      def   UpperCAmelCase						(       self      )				->      int:






        '''simple docstring'''






        super().test_cpu_offload_forward_pass(expected_max_diff=5E-4      )







      def   UpperCAmelCase						(       self      )				->      int:






        '''simple docstring'''






        self._test_inference_batch_consistent(batch_sizes=[1, 2]      )







      def   UpperCAmelCase						(       self      )				->      Union[str, Any]:






        '''simple docstring'''






        self._test_inference_batch_single_identical(batch_size=2 ,  expected_max_diff=7E-4      )







      def   UpperCAmelCase						(       self      )				->      List[Any]:






        '''simple docstring'''






        super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3      )







      def   UpperCAmelCase						(       self      )				->      str:






        '''simple docstring'''






        super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5E-4      )







      def   UpperCAmelCase						(       self      )				->      Any:






        '''simple docstring'''






        super().test_save_load_local(expected_max_difference=5E-4      )







      def   UpperCAmelCase						(       self      )				->      Tuple:






        '''simple docstring'''






        super().test_save_load_optional_components(expected_max_difference=4E-4      )




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



      """simple docstring"""






      @classmethod
      def   UpperCAmelCase						(       cls      )				->      Union[str, Any]:






        '''simple docstring'''






        super().setUpClass()
        torch.use_deterministic_algorithms(SCREAMING_SNAKE_CASE__      )







      @classmethod
      def   UpperCAmelCase						(       cls      )				->      Tuple:






        '''simple docstring'''






        super().tearDownClass()
        torch.use_deterministic_algorithms(SCREAMING_SNAKE_CASE__      )







      def   UpperCAmelCase						(       self      )				->      Optional[int]:






        '''simple docstring'''






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







      def   UpperCAmelCase						(       self      )				->      Optional[int]:






        '''simple docstring'''






        __snake_case      : str						=				torch.manual_seed(51      )

        __snake_case      : Tuple						=				StableDiffusionAttendAndExcitePipeline.from_pretrained(
            "CompVis/stable-diffusion-v1-4" ,  safety_checker=SCREAMING_SNAKE_CASE__ ,  torch_dtype=torch.floataa      )
        pipe.to("cuda"      )

        __snake_case      : Any						=				'a painting of an elephant with glasses'
        __snake_case      : str						=				[5, 7]

        __snake_case      : Dict						=				pipe(
            prompt=SCREAMING_SNAKE_CASE__ ,  token_indices=SCREAMING_SNAKE_CASE__ ,  guidance_scale=7.5 ,  generator=SCREAMING_SNAKE_CASE__ ,  num_inference_steps=5 ,  max_iter_to_alter=5 ,  output_type="numpy" ,  ).images[0]

        __snake_case      : int						=				load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy"      )
        assert np.abs((expected_image - image).max()      ) < 5E-1





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        SCREAMING_SNAKE_CASE_						(			__A :			List[str]				)	->		str:

       """simple docstring"""

       a_					:		Tuple							=  []
       for line in lines:
              a_					:		Any							=  re.sub(R'#.*'  ,    ''  ,    __A				)  # remove comments
              if line:
                     filtered_lines.append(__A				)
       a_					:		Tuple							=  '\n'.join(__A				)

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


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

# Used to filter data files based on extensions given a module name
UpperCAmelCase_      : 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')


