





'''simple docstring'''





import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class 							_UpperCamelCase					(	lowercase__  ,						unittest.TestCase     ):
       _UpperCamelCase				:					Dict         =   DiTPipeline
       _UpperCamelCase				:					Tuple         =   CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
       _UpperCamelCase				:					Tuple         =   PipelineTesterMixin.required_optional_params - {
           """latents""",
           """num_images_per_prompt""",
           """callback""",
           """callback_steps""",
       }
       _UpperCamelCase				:					List[str]         =   CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
       _UpperCamelCase				:					int         =   False

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




              """simple docstring"""


              torch.manual_seed(0		)
              UpperCamelCase_          =  TransformeraDModel(
                  sample_size=16					,   num_layers=2					,   patch_size=4					,   attention_head_dim=8					,   num_attention_heads=2					,   in_channels=4					,   out_channels=8					,   attention_bias=__lowercase					,   activation_fn="gelu-approximate"					,   num_embeds_ada_norm=1000					,   norm_type="ada_norm_zero"					,   norm_elementwise_affine=__lowercase					,   )
              UpperCamelCase_          =  AutoencoderKL()
              UpperCamelCase_          =  DDIMScheduler()
              UpperCamelCase_          =  {"transformer": transformer.eval(), "vae": vae.eval(), "scheduler": scheduler}
              return components

       def lowercase (		self: Optional[int]					,   _SCREAMING_SNAKE_CASE: Dict					,   _SCREAMING_SNAKE_CASE: Optional[int]=0		)       -> int:




              """simple docstring"""


              if str(__lowercase		).startswith("mps"		):
                     UpperCamelCase_          =  torch.manual_seed(__lowercase		)
              else:
                     UpperCamelCase_          =  torch.Generator(device=__lowercase		).manual_seed(__lowercase		)
              UpperCamelCase_          =  {
                  "class_labels": [1],
                  "generator": generator,
                  "num_inference_steps": 2,
                  "output_type": "numpy",
              }
              return inputs

       def lowercase (		self: List[Any]		)       -> int:




              """simple docstring"""


              UpperCamelCase_          =  "cpu"

              UpperCamelCase_          =  self.get_dummy_components()
              UpperCamelCase_          =  self.pipeline_class(**__lowercase		)
              pipe.to(__lowercase		)
              pipe.set_progress_bar_config(disable=__lowercase		)

              UpperCamelCase_          =  self.get_dummy_inputs(__lowercase		)
              UpperCamelCase_          =  pipe(**__lowercase		).images
              UpperCamelCase_          =  image[0, -3:, -3:, -1]

              self.assertEqual(image.shape					,   (1, 16, 16, 3)		)
              UpperCamelCase_          =  np.array([0.29_46, 0.66_01, 0.43_29, 0.32_96, 0.41_44, 0.53_19, 0.72_73, 0.50_13, 0.44_57]		)
              UpperCamelCase_          =  np.abs(image_slice.flatten() - expected_slice		).max()
              self.assertLessEqual(__lowercase					,   1e-3		)

       def lowercase (		self: Optional[int]		)       -> Union[str, Any]:




              """simple docstring"""


              self._test_inference_batch_single_identical(relax_max_difference=__lowercase					,   expected_max_diff=1e-3		)



       @unittest.skipIf(
           torch_device != "cuda" or not is_xformers_available()					,   reason="XFormers attention is only available with CUDA and `xformers` installed"					,   )
       def lowercase (		self: List[Any]		)       -> List[Any]:




              """simple docstring"""


              self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3		)



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

       def lowercase (		self: List[Any]		)       -> List[str]:




              """simple docstring"""


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

       def lowercase (		self: Any		)       -> Optional[int]:




              """simple docstring"""


              UpperCamelCase_          =  torch.manual_seed(0		)

              UpperCamelCase_          =  DiTPipeline.from_pretrained("facebook/DiT-XL-2-256"		)
              pipe.to("cuda"		)

              UpperCamelCase_          =  ["vase", "umbrella", "white shark", "white wolf"]
              UpperCamelCase_          =  pipe.get_label_ids(__lowercase		)

              UpperCamelCase_          =  pipe(__lowercase					,   generator=__lowercase					,   num_inference_steps=40					,   output_type="np"		).images

              for word, image in zip(__lowercase					,   __lowercase		):
                     UpperCamelCase_          =  load_numpy(
                         f'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy'''		)
                     assert np.abs((expected_image - image).max()		) < 1e-2



       def lowercase (		self: Dict		)       -> Dict:




              """simple docstring"""


              UpperCamelCase_          =  DiTPipeline.from_pretrained("facebook/DiT-XL-2-512"		)
              UpperCamelCase_          =  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config		)
              pipe.to("cuda"		)

              UpperCamelCase_          =  ["vase", "umbrella"]
              UpperCamelCase_          =  pipe.get_label_ids(__lowercase		)

              UpperCamelCase_          =  torch.manual_seed(0		)
              UpperCamelCase_          =  pipe(__lowercase					,   generator=__lowercase					,   num_inference_steps=25					,   output_type="np"		).images

              for word, image in zip(__lowercase					,   __lowercase		):
                     UpperCamelCase_          =  load_numpy(
                         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                         f'''/dit/{word}_512.npy'''		)

                     assert np.abs((expected_image - image).max()		) < 1e-1








_UpperCAmelCase        =				{
    'a': 'AAAAA',
    'b': 'AAAAB',
    'c': 'AAABA',
    'd': 'AAABB',
    'e': 'AABAA',
    'f': 'AABAB',
    'g': 'AABBA',
    'h': 'AABBB',
    'i': 'ABAAA',
    'j': 'BBBAA',
    'k': 'ABAAB',
    'l': 'ABABA',
    'm': 'ABABB',
    'n': 'ABBAA',
    'o': 'ABBAB',
    'p': 'ABBBA',
    'q': 'ABBBB',
    'r': 'BAAAA',
    's': 'BAAAB',
    't': 'BAABA',
    'u': 'BAABB',
    'v': 'BBBAB',
    'w': 'BABAA',
    'x': 'BABAB',
    'y': 'BABBA',
    'z': 'BABBB',
    ' ': ' ',
}


_UpperCAmelCase        =				{value: key for key, value in encode_dict.items()}




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		str:
       UpperCamelCase_          =  ""
       for letter in word.lower():
              if letter.isalpha() or letter == " ":
                     encoded += encode_dict[letter]
              else:
                     raise Exception("encode() accepts only letters of the alphabet and spaces"  )
       return encoded




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		str:
       if set(UpperCamelCase_  ) - {"A", "B", " "} != set():
              raise Exception("decode() accepts only 'A', 'B' and spaces"  )
       UpperCamelCase_          =  ""
       for word in coded.split():
              while len(UpperCamelCase_  ) != 0:
                     decoded += decode_dict[word[:5]]
                     UpperCamelCase_          =  word[5:]
              decoded += " "
       return decoded.strip()


if __name__ == "__main__":
     from doctest import testmod

     testmod()

