




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				(		__lowercase       ,     unittest.TestCase     ):
		__a					    =       VideoToVideoSDPipeline
		__a					    =       TEXT_GUIDED_IMAGE_VARIATION_PARAMS.union({"""video"""}     ) - {"""image""", """width""", """height"""}
		__a					    =       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""video"""}     ) - {"""image"""}
		__a					    =       PipelineTesterMixin.required_optional_params - {"""latents"""}
		__a					    =       False

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






		def 				UpperCAmelCase (    self			):
								torch.manual_seed(0			)
								_UpperCAmelCase      =			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      =			DDIMScheduler(
								    beta_start=0.0_0085						,		beta_end=0.012						,		beta_schedule="""scaled_linear"""						,		clip_sample=_SCREAMING_SNAKE_CASE						,		set_alpha_to_one=_SCREAMING_SNAKE_CASE						,		)
								torch.manual_seed(0			)
								_UpperCAmelCase      =			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      =			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						,		)
								_UpperCAmelCase      =			CLIPTextModel(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""			)

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






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=0			):
								# 3 frames
								_UpperCAmelCase      =			floats_tensor((1, 3, 3, 32, 32)						,		rng=random.Random(_SCREAMING_SNAKE_CASE			)			).to(_SCREAMING_SNAKE_CASE			)

								if str(_SCREAMING_SNAKE_CASE			).startswith("""mps"""			):
														_UpperCAmelCase      =			torch.manual_seed(_SCREAMING_SNAKE_CASE			)
								else:
														_UpperCAmelCase      =			torch.Generator(device=_SCREAMING_SNAKE_CASE			).manual_seed(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			{
								    """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 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			"""cpu"""  # ensure determinism for the device-dependent torch.Generator
								_UpperCAmelCase      =			self.get_dummy_components()
								_UpperCAmelCase      =			VideoToVideoSDPipeline(**_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			sd_pipe.to(_SCREAMING_SNAKE_CASE			)
								sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE			)

								_UpperCAmelCase      =			self.get_dummy_inputs(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			"""np"""
								_UpperCAmelCase      =			sd_pipe(**_SCREAMING_SNAKE_CASE			).frames
								_UpperCAmelCase      =			frames[0][-3:, -3:, -1]

								assert frames[0].shape == (32, 32, 3)
								_UpperCAmelCase      =			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 				UpperCAmelCase (    self			):
								self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=_SCREAMING_SNAKE_CASE						,		expected_max_diff=5e-3			)






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






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






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






		def 				UpperCAmelCase (    self			):
								return super().test_progress_bar()




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






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			VideoToVideoSDPipeline.from_pretrained("""cerspense/zeroscope_v2_XL"""						,		torch_dtype=torch.floataa			)
								pipe.enable_model_cpu_offload()

								# 10 frames
								_UpperCAmelCase      =			torch.Generator(device="""cpu"""			).manual_seed(0			)
								_UpperCAmelCase      =			torch.randn((1, 10, 3, 1024, 576)						,		generator=_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			video.to("""cuda"""			)

								_UpperCAmelCase      =			"""Spiderman is surfing"""

								_UpperCAmelCase      =			pipe(_SCREAMING_SNAKE_CASE						,		video=_SCREAMING_SNAKE_CASE						,		generator=_SCREAMING_SNAKE_CASE						,		num_inference_steps=3						,		output_type="""pt"""			).frames

								_UpperCAmelCase      =			np.array([-1.045_8984, -1.127_9297, -0.966_3086, -0.9150_3906, -0.7509_7656]			)
								assert np.abs(video_frames.cpu().numpy()[0, 0, 0, 0, -5:] - expected_array			).sum() < 1e-2





def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				bool:
						_UpperCAmelCase      =			len(snake_case       ) + 1
						_UpperCAmelCase      =			len(snake_case       ) + 1

						# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
						# length i of input_string matches with prefix string of length j of
						# given pattern.
						# "dp" stands for dynamic programming.
						_UpperCAmelCase      =			[[0 for i in range(snake_case       )] for j in range(snake_case       )]

						# since string of zero length match pattern of zero length
						_UpperCAmelCase      =			1

						# since pattern of zero length will never match with string of non-zero length
						for i in range(1			,     snake_case       ):
												_UpperCAmelCase      =			0

						# since string of zero length will match with pattern where there
						# is at least one * alternatively
						for j in range(1			,     snake_case       ):
												_UpperCAmelCase      =			dp[0][j - 2] if pattern[j - 1] == """*""" else 0

						# now using bottom-up approach to find for all remaining lengths
						for i in range(1			,     snake_case       ):
												for j in range(1			,     snake_case       ):
																		if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
																								_UpperCAmelCase      =			dp[i - 1][j - 1]

																		elif pattern[j - 1] == "*":
																								if dp[i][j - 2] == 1:
																														_UpperCAmelCase      =			1
																								elif pattern[j - 2] in (input_string[i - 1], "."):
																														_UpperCAmelCase      =			dp[i - 1][j]
																								else:
																														_UpperCAmelCase      =			0
																		else:
																								_UpperCAmelCase      =			0

						return bool(dp[-1][-1]       )


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				# inputing the strings
				# input_string = input("input a string :")
				# pattern = input("input a pattern :")

				a													=			"aab"
				a													=			"c*a*b"

				# using function to check whether given string matches the given pattern
				if match_pattern(input_string, pattern):
								print(F'{input_string} matches the given pattern {pattern}')
				else:
								print(F'{input_string} does not match with the given pattern {pattern}')