'''simple docstring'''






import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    UNetaDConditionModel,
    VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xformers_available, skip_mps
from diffusers.utils.testing_utils import enable_full_determinism, slow, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




@skip_mps
class    a (       __lowerCAmelCase						,  unittest.TestCase				):



  """simple docstring"""
  __lowerCAmelCase				      =					VideoToVideoSDPipeline
  __lowerCAmelCase				      =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS.union({"""video"""}				) - {"""image""", """width""", """height"""}
  __lowerCAmelCase				      =					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""video"""}				) - {"""image"""}
  __lowerCAmelCase				      =					PipelineTesterMixin.required_optional_params - {"""latents"""}
  __lowerCAmelCase				      =					False

  # No `output_type`.
  __lowerCAmelCase				      =					frozenset(
      [
          """num_inference_steps""",
          """generator""",
          """latents""",
          """return_dict""",
          """callback""",
          """callback_steps""",
      ]				)







  def    lowercase_  (							self					):

    '''simple docstring'''







    torch.manual_seed(0					)
    __UpperCAmelCase:					Union[str, Any]		= UNetaDConditionModel(
        block_out_channels=(32, 64, 64, 64)   ,    layers_per_block=2   ,    sample_size=32   ,    in_channels=4   ,    out_channels=4   ,    down_block_types=("""CrossAttnDownBlock3D""", """CrossAttnDownBlock3D""", """CrossAttnDownBlock3D""", """DownBlock3D""")   ,    up_block_types=("""UpBlock3D""", """CrossAttnUpBlock3D""", """CrossAttnUpBlock3D""", """CrossAttnUpBlock3D""")   ,    cross_attention_dim=32   ,    attention_head_dim=4   ,    )
    __UpperCAmelCase:					Tuple		= DDIMScheduler(
        beta_start=0.0_0_0_8_5   ,    beta_end=0.0_1_2   ,    beta_schedule="""scaled_linear"""   ,    clip_sample=snake_case_   ,    set_alpha_to_one=snake_case_   ,    )
    torch.manual_seed(0					)
    __UpperCAmelCase:					List[str]		= 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					)
    __UpperCAmelCase:					List[Any]		= CLIPTextConfig(
        bos_token_id=0   ,    eos_token_id=2   ,    hidden_size=32   ,    intermediate_size=37   ,    layer_norm_eps=1e-0_5   ,    num_attention_heads=4   ,    num_hidden_layers=5   ,    pad_token_id=1   ,    vocab_size=1000   ,    hidden_act="""gelu"""   ,    projection_dim=512   ,    )
    __UpperCAmelCase:					str		= CLIPTextModel(snake_case_					)
    __UpperCAmelCase:					Optional[Any]		= CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""					)

    __UpperCAmelCase:					int		= {
        """unet""": unet,
        """scheduler""": scheduler,
        """vae""": vae,
        """text_encoder""": text_encoder,
        """tokenizer""": tokenizer,
    }
    return components







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_=0					):

    '''simple docstring'''







    __UpperCAmelCase:					int		= floats_tensor((1, 3, 3, 32, 32)   ,    rng=random.Random(snake_case_					)					).to(snake_case_					)

    if str(snake_case_					).startswith("""mps"""					):
      __UpperCAmelCase:					List[str]		= torch.manual_seed(snake_case_					)
    else:
      __UpperCAmelCase:					Any		= torch.Generator(device=snake_case_					).manual_seed(snake_case_					)
    __UpperCAmelCase:					List[Any]		= {
        """prompt""": """A painting of a squirrel eating a burger""",
        """video""": video,
        """generator""": generator,
        """num_inference_steps""": 2,
        """guidance_scale""": 6.0,
        """output_type""": """pt""",
    }
    return inputs







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Any		= """cpu"""  # ensure determinism for the device-dependent torch.Generator
    __UpperCAmelCase:					List[Any]		= self.get_dummy_components()
    __UpperCAmelCase:					Any		= VideoToVideoSDPipeline(**snake_case_					)
    __UpperCAmelCase:					Any		= sd_pipe.to(snake_case_					)
    sd_pipe.set_progress_bar_config(disable=snake_case_					)

    __UpperCAmelCase:					Union[str, Any]		= self.get_dummy_inputs(snake_case_					)
    __UpperCAmelCase:					Optional[int]		= """np"""
    __UpperCAmelCase:					List[Any]		= sd_pipe(**snake_case_					).frames
    __UpperCAmelCase:					List[Any]		= frames[0][-3:, -3:, -1]

    assert frames[0].shape == (32, 32, 3)
    __UpperCAmelCase:					Optional[int]		= np.array([106, 117, 113, 174, 137, 112, 148, 151, 131]					)

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







  @unittest.skipIf(
      torch_device != """cuda""" or not is_xformers_available()   ,    reason="""XFormers attention is only available with CUDA and `xformers` installed"""   ,    )
  def    lowercase_  (							self					):

    '''simple docstring'''







    self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=snake_case_   ,    expected_max_diff=5e-3					)







  @unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline."""					)
  def    lowercase_  (							self					):

    '''simple docstring'''







    pass







  @unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline."""					)
  def    lowercase_  (							self					):

    '''simple docstring'''







    pass







  @unittest.skip(reason="""`num_images_per_prompt` argument is not supported for this pipeline."""					)
  def    lowercase_  (							self					):

    '''simple docstring'''







    pass







  def    lowercase_  (							self					):

    '''simple docstring'''







    return super().test_progress_bar()




@slow
@skip_mps
class    a (       unittest.TestCase				):



  """simple docstring"""







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					List[Any]		= VideoToVideoSDPipeline.from_pretrained("""cerspense/zeroscope_v2_XL"""   ,    torch_dtype=torch.floataa					)
    pipe.enable_model_cpu_offload()

    # 10 frames
    __UpperCAmelCase:					Any		= torch.Generator(device="""cpu"""					).manual_seed(0					)
    __UpperCAmelCase:					Optional[Any]		= torch.randn((1, 10, 3, 1024, 576)   ,    generator=snake_case_					)
    __UpperCAmelCase:					str		= video.to("""cuda"""					)

    __UpperCAmelCase:					str		= """Spiderman is surfing"""

    __UpperCAmelCase:					Tuple		= pipe(snake_case_   ,    video=snake_case_   ,    generator=snake_case_   ,    num_inference_steps=3   ,    output_type="""pt"""					).frames

    __UpperCAmelCase:					Dict		= np.array([-1.0_4_5_8_9_8_4, -1.1_2_7_9_2_9_7, -0.9_6_6_3_0_8_6, -0.9_1_5_0_3_9_0_6, -0.7_5_0_9_7_6_5_6]					)
    assert np.abs(video_frames.cpu().numpy()[0, 0, 0, 0, -5:] - expected_array					).sum() < 1e-2
'''simple docstring'''






def  UpperCamelCase__					(					_lowercase    :				List[str]     )							->	Union[str, Any]:
  __UpperCAmelCase:					int		= []
  __UpperCAmelCase:					List[Any]		= []
  __UpperCAmelCase:					List[Any]		= {
      """^""": 3,
      """*""": 2,
      """/""": 2,
      """%""": 2,
      """+""": 1,
      """-""": 1,
  }  # Priority of each operator
  __UpperCAmelCase:					int		= len(_lowercase     ) if (len(_lowercase     ) > 7) else 7

  # Print table header for output
  print(
      """Symbol""".center(8     )   ,    """Stack""".center(_lowercase     )   ,    """Postfix""".center(_lowercase     )   ,    sep=""" | """   ,    )
  print("""-""" * (print_width * 3 + 7)     )

  for x in infix:
    if x.isalpha() or x.isdigit():
      post_fix.append(_lowercase     )  # if x is Alphabet / Digit, add it to Postfix
    elif x == "(":
      stack.append(_lowercase     )  # if x is "(" push to Stack
    elif x == ")":  # if x is ")" pop stack until "(" is encountered
      while stack[-1] != "(":
        post_fix.append(stack.pop()     )  # Pop stack & add the content to Postfix
      stack.pop()
    else:
      if len(_lowercase     ) == 0:
        stack.append(_lowercase     )  # If stack is empty, push x to stack
      else:  # while priority of x is not > priority of element in the stack
        while len(_lowercase     ) > 0 and priority[x] <= priority[stack[-1]]:
          post_fix.append(stack.pop()     )  # pop stack & add to Postfix
        stack.append(_lowercase     )  # push x to stack

    print(
        x.center(8     )   ,    ("""""".join(_lowercase     )).ljust(_lowercase     )   ,    ("""""".join(_lowercase     )).ljust(_lowercase     )   ,    sep=""" | """   ,    )  # Output in tabular format

  while len(_lowercase     ) > 0:  # while stack is not empty
    post_fix.append(stack.pop()     )  # pop stack & add to Postfix
    print(
        """ """.center(8     )   ,    ("""""".join(_lowercase     )).ljust(_lowercase     )   ,    ("""""".join(_lowercase     )).ljust(_lowercase     )   ,    sep=""" | """   ,    )  # Output in tabular format

  return "".join(_lowercase     )  # return Postfix as str






def  UpperCamelCase__					(					_lowercase    :				str     )							->	Tuple:
  __UpperCAmelCase:					Union[str, Any]		= list(infix[::-1]     )  # reverse the infix equation

  for i in range(len(_lowercase     )     ):
    if infix[i] == "(":
      __UpperCAmelCase:					Tuple		= """)"""  # change "(" to ")"
    elif infix[i] == ")":
      __UpperCAmelCase:					Union[str, Any]		= """("""  # change ")" to "("

  return (infix_2_postfix("""""".join(_lowercase     )     ))[
      ::-1
  ]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_	   =			input('\nEnter an Infix Equation = ')  # Input an Infix equation
    SCREAMING_SNAKE_CASE_	   =			''.join(Infix.split())  # Remove spaces from the input
    print('\n\t', Infix, '(Infix) -> ', infix_2_prefix(Infix), '(Prefix)')