


'''simple docstring'''







import gc
import unittest

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

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, 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


enable_full_determinism()


class  lowerCAmelCase_						(       snake_case__		,    snake_case__		,    snake_case__		,    unittest.TestCase      ):





  """simple docstring"""



  a_       :int												=AltDiffusionPipeline
  a_       :Tuple												=TEXT_TO_IMAGE_PARAMS
  a_       :int												=TEXT_TO_IMAGE_BATCH_PARAMS
  a_       :Dict												=TEXT_TO_IMAGE_IMAGE_PARAMS
  a_       :Union[str, Any]												=TEXT_TO_IMAGE_IMAGE_PARAMS

  def __a						(					self     :							Optional[int]						):





       '''simple docstring'''

       torch.manual_seed(0						)
       __a       =   UNetaDConditionModel(
           block_out_channels=(3_2, 6_4)				,							layers_per_block=2				,							sample_size=3_2				,							in_channels=4				,							out_channels=4				,							down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")				,							up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")				,							cross_attention_dim=3_2				,							)
       __a       =   DDIMScheduler(
           beta_start=0.0_0_0_8_5				,							beta_end=0.0_1_2				,							beta_schedule="""scaled_linear"""				,							clip_sample=SCREAMING_SNAKE_CASE__				,							set_alpha_to_one=SCREAMING_SNAKE_CASE__				,							)
       torch.manual_seed(0						)
       __a       =   AutoencoderKL(
           block_out_channels=[3_2, 6_4]				,							in_channels=3				,							out_channels=3				,							down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]				,							up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]				,							latent_channels=4				,							)

       # TODO: address the non-deterministic text encoder (fails for save-load tests)
       # torch.manual_seed(0)
       # text_encoder_config = RobertaSeriesConfig(
       #     hidden_size=32,
       #     project_dim=32,
       #     intermediate_size=37,
       #     layer_norm_eps=1e-05,
       #     num_attention_heads=4,
       #     num_hidden_layers=5,
       #     vocab_size=5002,
       # )
       # text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

       torch.manual_seed(0						)
       __a       =   CLIPTextConfig(
           bos_token_id=0				,							eos_token_id=2				,							hidden_size=3_2				,							projection_dim=3_2				,							intermediate_size=3_7				,							layer_norm_eps=1E-05				,							num_attention_heads=4				,							num_hidden_layers=5				,							pad_token_id=1				,							vocab_size=5_0_0_2				,							)
       __a       =   CLIPTextModel(SCREAMING_SNAKE_CASE__						)

       __a       =   XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""						)
       __a       =   7_7

       __a       =   {
           """unet""": unet,
           """scheduler""": scheduler,
           """vae""": vae,
           """text_encoder""": text_encoder,
           """tokenizer""": tokenizer,
           """safety_checker""": None,
           """feature_extractor""": None,
       }
       return components

  def __a						(					self     :							int				,							SCREAMING_SNAKE_CASE__     :							List[str]				,							SCREAMING_SNAKE_CASE__     :							Dict=0						):





       '''simple docstring'''

       if str(SCREAMING_SNAKE_CASE__						).startswith("""mps"""						):
            __a       =   torch.manual_seed(SCREAMING_SNAKE_CASE__						)
       else:
            __a       =   torch.Generator(device=SCREAMING_SNAKE_CASE__						).manual_seed(SCREAMING_SNAKE_CASE__						)
       __a       =   {
           """prompt""": """A painting of a squirrel eating a burger""",
           """generator""": generator,
           """num_inference_steps""": 2,
           """guidance_scale""": 6.0,
           """output_type""": """numpy""",
       }
       return inputs

  def __a						(					self     :							Dict						):





       '''simple docstring'''

       super().test_attention_slicing_forward_pass(expected_max_diff=3E-3						)

  def __a						(					self     :							int						):





       '''simple docstring'''

       super().test_inference_batch_single_identical(expected_max_diff=3E-3						)

  def __a						(					self     :							str						):





       '''simple docstring'''

       __a       =   """cpu"""  # ensure determinism for the device-dependent torch.Generator

       __a       =   self.get_dummy_components()
       torch.manual_seed(0						)
       __a       =   RobertaSeriesConfig(
           hidden_size=3_2				,							project_dim=3_2				,							intermediate_size=3_7				,							layer_norm_eps=1E-05				,							num_attention_heads=4				,							num_hidden_layers=5				,							vocab_size=5_0_0_2				,							)
       # TODO: remove after fixing the non-deterministic text encoder
       __a       =   RobertaSeriesModelWithTransformation(SCREAMING_SNAKE_CASE__						)
       __a       =   text_encoder

       __a       =   AltDiffusionPipeline(**SCREAMING_SNAKE_CASE__						)
       __a       =   alt_pipe.to(SCREAMING_SNAKE_CASE__						)
       alt_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__						)

       __a       =   self.get_dummy_inputs(SCREAMING_SNAKE_CASE__						)
       __a       =   """A photo of an astronaut"""
       __a       =   alt_pipe(**SCREAMING_SNAKE_CASE__						)
       __a       =   output.images
       __a       =   image[0, -3:, -3:, -1]

       assert image.shape == (1, 6_4, 6_4, 3)
       __a       =   np.array(
           [0.5_7_4_8_1_6_2, 0.6_0_4_4_7_1_4_5, 0.4_8_8_2_1_2_1_7, 0.5_0_1_0_0_6_3_6, 0.5_4_3_1_1_8_5, 0.4_5_7_6_3_6_8_3, 0.4_9_6_5_7_6_9_6, 0.4_8_1_3_2_7_3_3, 0.4_7_5_7_3_0_9_3]						)

       assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2


  def __a						(					self     :							List[str]						):





       '''simple docstring'''

       __a       =   """cpu"""  # ensure determinism for the device-dependent torch.Generator

       __a       =   self.get_dummy_components()
       __a       =   PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE__						)
       torch.manual_seed(0						)
       __a       =   RobertaSeriesConfig(
           hidden_size=3_2				,							project_dim=3_2				,							intermediate_size=3_7				,							layer_norm_eps=1E-05				,							num_attention_heads=4				,							num_hidden_layers=5				,							vocab_size=5_0_0_2				,							)
       # TODO: remove after fixing the non-deterministic text encoder
       __a       =   RobertaSeriesModelWithTransformation(SCREAMING_SNAKE_CASE__						)
       __a       =   text_encoder
       __a       =   AltDiffusionPipeline(**SCREAMING_SNAKE_CASE__						)
       __a       =   alt_pipe.to(SCREAMING_SNAKE_CASE__						)
       alt_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__						)

       __a       =   self.get_dummy_inputs(SCREAMING_SNAKE_CASE__						)
       __a       =   alt_pipe(**SCREAMING_SNAKE_CASE__						)
       __a       =   output.images
       __a       =   image[0, -3:, -3:, -1]

       assert image.shape == (1, 6_4, 6_4, 3)
       __a       =   np.array(
           [0.5_1_6_0_5_0_9_3, 0.5_7_0_7_2_4_1, 0.4_7_3_6_5_5_0_7, 0.5_0_5_7_8_8_8_6, 0.5_6_3_3_8_7_7, 0.4_6_4_2_5_0_3, 0.5_1_8_2_0_8_1, 0.4_8_7_6_3_4_8_4, 0.4_9_0_8_4_2_3_7]						)

       assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2


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





  """simple docstring"""



  def __a						(					self     :							Dict						):





       '''simple docstring'''

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

  def __a						(					self     :							Tuple						):





       '''simple docstring'''

       __a       =   AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion"""				,							safety_checker=SCREAMING_SNAKE_CASE__						)
       __a       =   alt_pipe.to(SCREAMING_SNAKE_CASE__						)
       alt_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__						)

       __a       =   """A painting of a squirrel eating a burger"""
       __a       =   torch.manual_seed(0						)
       __a       =   alt_pipe([prompt]				,							generator=SCREAMING_SNAKE_CASE__				,							guidance_scale=6.0				,							num_inference_steps=2_0				,							output_type="""np"""						)

       __a       =   output.images

       __a       =   image[0, -3:, -3:, -1]

       assert image.shape == (1, 5_1_2, 5_1_2, 3)
       __a       =   np.array([0.1_0_1_0, 0.0_8_0_0, 0.0_7_9_4, 0.0_8_8_5, 0.0_8_4_3, 0.0_7_6_2, 0.0_7_6_9, 0.0_7_2_9, 0.0_5_8_6]						)

       assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2


  def __a						(					self     :							int						):





       '''simple docstring'''

       __a       =   DDIMScheduler.from_pretrained("""BAAI/AltDiffusion"""				,							subfolder="""scheduler"""						)

       __a       =   AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion"""				,							scheduler=SCREAMING_SNAKE_CASE__				,							safety_checker=SCREAMING_SNAKE_CASE__						)
       __a       =   alt_pipe.to(SCREAMING_SNAKE_CASE__						)
       alt_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__						)

       __a       =   """A painting of a squirrel eating a burger"""
       __a       =   torch.manual_seed(0						)

       __a       =   alt_pipe([prompt]				,							generator=SCREAMING_SNAKE_CASE__				,							num_inference_steps=2				,							output_type="""numpy"""						)
       __a       =   output.images

       __a       =   image[0, -3:, -3:, -1]

       assert image.shape == (1, 5_1_2, 5_1_2, 3)
       __a       =   np.array([0.4_0_1_9, 0.4_0_5_2, 0.3_8_1_0, 0.4_1_1_9, 0.3_9_1_6, 0.3_9_8_2, 0.4_6_5_1, 0.4_1_9_5, 0.5_3_2_3]						)

       assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2










'''simple docstring'''







def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					Dict:
     """simple docstring"""




     __a       =   []
     __a       =   set({"""(""", """[""", """{"""}		)
     __a       =   set({""")""", """]""", """}"""}		)
     __a       =   {"""{""": """}""", """[""": """]""", """(""": """)"""}

     for i in range(len(__SCREAMING_SNAKE_CASE		)		):
          if s[i] in open_brackets:
               stack.append(s[i]		)

          elif s[i] in closed_brackets and (
              len(__SCREAMING_SNAKE_CASE		) == 0 or (len(__SCREAMING_SNAKE_CASE		) > 0 and open_to_closed[stack.pop()] != s[i])
          ):
               return False

     return len(__SCREAMING_SNAKE_CASE		) == 0




def        __lowercase						(      )				->					List[str]:
     """simple docstring"""




     __a       =   input("""Enter sequence of brackets: """		)
     if is_balanced(__SCREAMING_SNAKE_CASE		):
          print(__SCREAMING_SNAKE_CASE				,  """is balanced"""		)
     else:
          print(__SCREAMING_SNAKE_CASE				,  """is not balanced"""		)


if __name__ == "__main__":
 main()






