




import gc
import unittest

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

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


UpperCAmelCase_		:					Dict          =  False




class 		__A   (      unittest.TestCase      ):

						def      A__    (		self :str			):
										'''simple docstring'''

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

						@property
						def      A__    (		self :Optional[int]			):
										'''simple docstring'''

										return 12

						@property
						def      A__    (		self :Dict			):
										'''simple docstring'''

										return 12

						@property
						def      A__    (		self :List[str]			):
										'''simple docstring'''

										return 32

						@property
						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										torch.manual_seed(0			)
										__magic_name__						:						str										=VQModel(
										    block_out_channels=[32, 64]     ,			in_channels=3     ,			out_channels=3     ,			down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]     ,			up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]     ,			latent_channels=3     ,			num_vq_embeddings=self.num_embed     ,			vq_embed_dim=3     ,			)
										return model

						@property
						def      A__    (		self :Optional[int]			):
										'''simple docstring'''

										__magic_name__						:						str										=CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""			)
										return tokenizer

						@property
						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										torch.manual_seed(0			)
										__magic_name__						:						Optional[int]										=CLIPTextConfig(
										    bos_token_id=0     ,			eos_token_id=2     ,			hidden_size=self.text_embedder_hidden_size     ,			intermediate_size=37     ,			layer_norm_eps=1E-05     ,			num_attention_heads=4     ,			num_hidden_layers=5     ,			pad_token_id=1     ,			vocab_size=10_00     ,			)
										return CLIPTextModel(__snake_case			)

						@property
						def      A__    (		self :int			):
										'''simple docstring'''

										torch.manual_seed(0			)

										__magic_name__						:						Optional[int]										=12
										__magic_name__						:						str										=12

										__magic_name__						:						Optional[int]										={
										    """attention_bias""": True,
										    """cross_attention_dim""": 32,
										    """attention_head_dim""": height * width,
										    """num_attention_heads""": 1,
										    """num_vector_embeds""": self.num_embed,
										    """num_embeds_ada_norm""": self.num_embeds_ada_norm,
										    """norm_num_groups""": 32,
										    """sample_size""": width,
										    """activation_fn""": """geglu-approximate""",
										}

										__magic_name__						:						int										=TransformeraDModel(**__snake_case			)
										return model

						def      A__    (		self :Any			):
										'''simple docstring'''

										__magic_name__						:						Any										="""cpu"""

										__magic_name__						:						List[str]										=self.dummy_vqvae
										__magic_name__						:						List[Any]										=self.dummy_text_encoder
										__magic_name__						:						List[Any]										=self.dummy_tokenizer
										__magic_name__						:						List[Any]										=self.dummy_transformer
										__magic_name__						:						Tuple										=VQDiffusionScheduler(self.num_embed			)
										__magic_name__						:						Optional[int]										=LearnedClassifierFreeSamplingEmbeddings(learnable=__snake_case			)

										__magic_name__						:						List[str]										=VQDiffusionPipeline(
										    vqvae=__snake_case     ,			text_encoder=__snake_case     ,			tokenizer=__snake_case     ,			transformer=__snake_case     ,			scheduler=__snake_case     ,			learned_classifier_free_sampling_embeddings=__snake_case     ,			)
										__magic_name__						:						int										=pipe.to(__snake_case			)
										pipe.set_progress_bar_config(disable=__snake_case			)

										__magic_name__						:						Any										="""teddy bear playing in the pool"""

										__magic_name__						:						List[str]										=torch.Generator(device=__snake_case			).manual_seed(0			)
										__magic_name__						:						Optional[Any]										=pipe([prompt]     ,			generator=__snake_case     ,			num_inference_steps=2     ,			output_type="""np"""			)
										__magic_name__						:						Any										=output.images

										__magic_name__						:						List[str]										=torch.Generator(device=__snake_case			).manual_seed(0			)
										__magic_name__						:						Dict										=pipe(
										    [prompt]     ,			generator=__snake_case     ,			output_type="""np"""     ,			return_dict=__snake_case     ,			num_inference_steps=2			)[0]

										__magic_name__						:						List[str]										=image[0, -3:, -3:, -1]
										__magic_name__						:						int										=image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 24, 24, 3)

										__magic_name__						:						Optional[int]										=np.array([0.6551, 0.6168, 0.5008, 0.5676, 0.5659, 0.4295, 0.6073, 0.5599, 0.4992]			)

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


						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						Optional[int]										="""cpu"""

										__magic_name__						:						Union[str, Any]										=self.dummy_vqvae
										__magic_name__						:						str										=self.dummy_text_encoder
										__magic_name__						:						Dict										=self.dummy_tokenizer
										__magic_name__						:						Optional[Any]										=self.dummy_transformer
										__magic_name__						:						Tuple										=VQDiffusionScheduler(self.num_embed			)
										__magic_name__						:						int										=LearnedClassifierFreeSamplingEmbeddings(
										    learnable=__snake_case     ,			hidden_size=self.text_embedder_hidden_size     ,			length=tokenizer.model_max_length			)

										__magic_name__						:						int										=VQDiffusionPipeline(
										    vqvae=__snake_case     ,			text_encoder=__snake_case     ,			tokenizer=__snake_case     ,			transformer=__snake_case     ,			scheduler=__snake_case     ,			learned_classifier_free_sampling_embeddings=__snake_case     ,			)
										__magic_name__						:						Dict										=pipe.to(__snake_case			)
										pipe.set_progress_bar_config(disable=__snake_case			)

										__magic_name__						:						Optional[Any]										="""teddy bear playing in the pool"""

										__magic_name__						:						Tuple										=torch.Generator(device=__snake_case			).manual_seed(0			)
										__magic_name__						:						Tuple										=pipe([prompt]     ,			generator=__snake_case     ,			num_inference_steps=2     ,			output_type="""np"""			)
										__magic_name__						:						List[str]										=output.images

										__magic_name__						:						Optional[Any]										=torch.Generator(device=__snake_case			).manual_seed(0			)
										__magic_name__						:						str										=pipe(
										    [prompt]     ,			generator=__snake_case     ,			output_type="""np"""     ,			return_dict=__snake_case     ,			num_inference_steps=2			)[0]

										__magic_name__						:						Any										=image[0, -3:, -3:, -1]
										__magic_name__						:						Tuple										=image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 24, 24, 3)

										__magic_name__						:						Union[str, Any]										=np.array([0.6693, 0.6075, 0.4959, 0.5701, 0.5583, 0.4333, 0.6171, 0.5684, 0.4988]			)

										assert np.abs(image_slice.flatten() - expected_slice			).max() < 2.0
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1E-2




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

						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

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


						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=load_numpy(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
										    """/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy"""			)

										__magic_name__						:						int										=VQDiffusionPipeline.from_pretrained("""microsoft/vq-diffusion-ithq"""			)
										__magic_name__						:						Tuple										=pipeline.to(__snake_case			)
										pipeline.set_progress_bar_config(disable=__snake_case			)

										# requires GPU generator for gumbel softmax
										# don't use GPU generator in tests though
										__magic_name__						:						int										=torch.Generator(device=__snake_case			).manual_seed(0			)
										__magic_name__						:						Optional[Any]										=pipeline(
										    """teddy bear playing in the pool"""     ,			num_images_per_prompt=1     ,			generator=__snake_case     ,			output_type="""np"""     ,			)

										__magic_name__						:						Dict										=output.images[0]

										assert image.shape == (2_56, 2_56, 3)
										assert np.abs(expected_image - image			).max() < 2.0










from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
SCREAMING_SNAKE_CASE:     Optional[int]									= 2_9_9_7_9_2_4_5_8

# Symbols
SCREAMING_SNAKE_CASE				,					SCREAMING_SNAKE_CASE				,					SCREAMING_SNAKE_CASE				,					SCREAMING_SNAKE_CASE:     Tuple									= symbols('''ct x y z''')


def 					_a							(					lowerCAmelCase )->	float:
		if velocity > c:
				raise ValueError('Speed must not exceed light speed 299,792,458 [m/s]!' )
		elif velocity < 1:
				# Usually the speed should be much higher than 1 (c order of magnitude)
				raise ValueError('Speed must be greater than or equal to 1!' )

		return velocity / c


def 					_a							(					lowerCAmelCase )->	float:
		return 1 / sqrt(1 - beta(lowerCAmelCase ) ** 2 )


def 					_a							(					lowerCAmelCase )->	np.ndarray:
		return np.array(
		    [
		        [gamma(lowerCAmelCase ), -gamma(lowerCAmelCase ) * beta(lowerCAmelCase ), 0, 0],
		        [-gamma(lowerCAmelCase ) * beta(lowerCAmelCase ), gamma(lowerCAmelCase ), 0, 0],
		        [0, 0, 1, 0],
		        [0, 0, 0, 1],
		    ] )


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase = None )->	np.ndarray:
		# Ensure event is not empty
		if event is None:
				SCREAMING_SNAKE_CASE_					      =							np.array([ct, x, y, z] )  # Symbolic four vector
		else:
				event[0] *= c  # x0 is ct (speed of light * time)

		return transformation_matrix(lowerCAmelCase ) @ event


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						# Example of symbolic vector:
						SCREAMING_SNAKE_CASE:     int									= transform(2_9_9_7_9_2_4_5)
						print('''Example of four vector: ''')
						print(f"""ct' = {four_vector[0]}""")
						print(f"""x' = {four_vector[1]}""")
						print(f"""y' = {four_vector[2]}""")
						print(f"""z' = {four_vector[3]}""")

						# Substitute symbols with numerical values
						SCREAMING_SNAKE_CASE:     List[Any]									= {ct: c, x: 1, y: 1, z: 1}
						SCREAMING_SNAKE_CASE:     Optional[Any]									= [four_vector[i].subs(sub_dict) for i in range(4)]

						print(f"""\n{numerical_vector}""")