
"""simple docstring"""


import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLPipeline,
    UNetaDConditionModel,
)
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 PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class 	lowerCamelCase__      (      __magic_name__				,			__magic_name__				,			unittest.TestCase       ):





			'''simple docstring'''
			lowerCamelCase						      =	StableDiffusionXLPipeline
			lowerCamelCase						      =	TEXT_TO_IMAGE_PARAMS
			lowerCamelCase						      =	TEXT_TO_IMAGE_BATCH_PARAMS
			lowerCamelCase						      =	TEXT_TO_IMAGE_IMAGE_PARAMS
			lowerCamelCase						      =	TEXT_TO_IMAGE_IMAGE_PARAMS



			def       _lowerCAmelCase	(							self						)				->  Dict:
				torch.manual_seed(0						)
				_lowerCAmelCase			=UNetaDConditionModel(
				    block_out_channels=(32, 64)  ,  layers_per_block=2  ,  sample_size=32  ,  in_channels=4  ,  out_channels=4  ,  down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")  ,  up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")  ,  attention_head_dim=(2, 4)  ,  use_linear_projection=__UpperCAmelCase  ,  addition_embed_type="""text_time"""  ,  addition_time_embed_dim=8  ,  transformer_layers_per_block=(1, 2)  ,  projection_class_embeddings_input_dim=80  ,  cross_attention_dim=64  ,  )
				_lowerCAmelCase			=EulerDiscreteScheduler(
				    beta_start=0.0_0_0_8_5  ,  beta_end=0.0_1_2  ,  steps_offset=1  ,  beta_schedule="""scaled_linear"""  ,  timestep_spacing="""leading"""  ,  )
				torch.manual_seed(0						)
				_lowerCAmelCase			=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=1_28  ,  )
				torch.manual_seed(0						)
				_lowerCAmelCase			=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=10_00  ,  hidden_act="""gelu"""  ,  projection_dim=32  ,  )
				_lowerCAmelCase			=CLIPTextModel(__UpperCAmelCase						)
				_lowerCAmelCase			=CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""  ,  local_files_only=__UpperCAmelCase						)

				_lowerCAmelCase			=CLIPTextModelWithProjection(__UpperCAmelCase						)
				_lowerCAmelCase			=CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""  ,  local_files_only=__UpperCAmelCase						)

				_lowerCAmelCase			={
				    """unet""": unet,
				    """scheduler""": scheduler,
				    """vae""": vae,
				    """text_encoder""": text_encoder,
				    """tokenizer""": tokenizer,
				    """text_encoder_2""": text_encoder_a,
				    """tokenizer_2""": tokenizer_a,
				    # "safety_checker": None,
				    # "feature_extractor": None,
				}
				return components



			def       _lowerCAmelCase	(							self  ,  __UpperCAmelCase  ,  __UpperCAmelCase=0						)				->  Optional[int]:
				if str(__UpperCAmelCase						).startswith("""mps"""						):
					_lowerCAmelCase			=torch.manual_seed(__UpperCAmelCase						)
				else:
					_lowerCAmelCase			=torch.Generator(device=__UpperCAmelCase						).manual_seed(__UpperCAmelCase						)
				_lowerCAmelCase			={
				    """prompt""": """A painting of a squirrel eating a burger""",
				    """generator""": generator,
				    """num_inference_steps""": 2,
				    """guidance_scale""": 5.0,
				    """output_type""": """numpy""",
				}
				return inputs



			def       _lowerCAmelCase	(							self						)				->  Any:
				_lowerCAmelCase			="""cpu"""  # ensure determinism for the device-dependent torch.Generator
				_lowerCAmelCase			=self.get_dummy_components()
				_lowerCAmelCase			=StableDiffusionXLPipeline(**__UpperCAmelCase						)
				_lowerCAmelCase			=sd_pipe.to(__UpperCAmelCase						)
				sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase						)

				_lowerCAmelCase			=self.get_dummy_inputs(__UpperCAmelCase						)
				_lowerCAmelCase			=sd_pipe(**__UpperCAmelCase						).images
				_lowerCAmelCase			=image[0, -3:, -3:, -1]

				assert image.shape == (1, 64, 64, 3)
				_lowerCAmelCase			=np.array([0.5_8_7_3, 0.6_1_2_8, 0.4_7_9_7, 0.5_1_2_2, 0.5_6_7_4, 0.4_6_3_9, 0.5_2_2_7, 0.5_1_4_9, 0.4_7_4_7]						)

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



			def       _lowerCAmelCase	(							self						)				->  List[Any]:
				_lowerCAmelCase			=self.get_dummy_components()
				_lowerCAmelCase			=StableDiffusionXLPipeline(**__UpperCAmelCase						)
				_lowerCAmelCase			=sd_pipe.to(__UpperCAmelCase						)
				_lowerCAmelCase			=sd_pipe.to(__UpperCAmelCase						)
				sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase						)

				# forward without prompt embeds
				_lowerCAmelCase			=self.get_dummy_inputs(__UpperCAmelCase						)
				_lowerCAmelCase			=3 * ["""this is a negative prompt"""]
				_lowerCAmelCase			=negative_prompt
				_lowerCAmelCase			=3 * [inputs["""prompt"""]]

				_lowerCAmelCase			=sd_pipe(**__UpperCAmelCase						)
				_lowerCAmelCase			=output.images[0, -3:, -3:, -1]

				# forward with prompt embeds
				_lowerCAmelCase			=self.get_dummy_inputs(__UpperCAmelCase						)
				_lowerCAmelCase			=3 * ["""this is a negative prompt"""]
				_lowerCAmelCase			=3 * [inputs.pop("""prompt"""						)]

				(
				    (
				    _lowerCAmelCase							
				)		,							(
				    _lowerCAmelCase							
				)		,							(
				    _lowerCAmelCase							
				)		,							(
				    _lowerCAmelCase							
				)		,														
				)			=sd_pipe.encode_prompt(__UpperCAmelCase  ,  negative_prompt=__UpperCAmelCase						)

				_lowerCAmelCase			=sd_pipe(
				    **__UpperCAmelCase  ,  prompt_embeds=__UpperCAmelCase  ,  negative_prompt_embeds=__UpperCAmelCase  ,  pooled_prompt_embeds=__UpperCAmelCase  ,  negative_pooled_prompt_embeds=__UpperCAmelCase  ,  )
				_lowerCAmelCase			=output.images[0, -3:, -3:, -1]

				# make sure that it's equal
				assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()						).max() < 1e-4



			def       _lowerCAmelCase	(							self						)				->  Optional[Any]:
				super().test_attention_slicing_forward_pass(expected_max_diff=3e-3						)



			def       _lowerCAmelCase	(							self						)				->  Union[str, Any]:
				super().test_inference_batch_single_identical(expected_max_diff=3e-3						)







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





			'''simple docstring'''



			def       _lowerCAmelCase	(							self						)				->  Optional[int]:
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()



			def       _lowerCAmelCase	(							self  ,  __UpperCAmelCase  ,  __UpperCAmelCase="cpu"  ,  __UpperCAmelCase=torch.floataa  ,  __UpperCAmelCase=0						)				->  List[Any]:
				_lowerCAmelCase			=torch.Generator(device=__UpperCAmelCase						).manual_seed(__UpperCAmelCase						)
				_lowerCAmelCase			=np.random.RandomState(__UpperCAmelCase						).standard_normal((1, 4, 64, 64)						)
				_lowerCAmelCase			=torch.from_numpy(__UpperCAmelCase						).to(device=__UpperCAmelCase  ,  dtype=__UpperCAmelCase						)
				_lowerCAmelCase			={
				    """prompt""": """a photograph of an astronaut riding a horse""",
				    """latents""": latents,
				    """generator""": generator,
				    """num_inference_steps""": 3,
				    """guidance_scale""": 7.5,
				    """output_type""": """numpy""",
				}
				return inputs



			def       _lowerCAmelCase	(							self						)				->  List[Any]:
				_lowerCAmelCase			=DiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-base"""						)
				pipe.to(__UpperCAmelCase						)
				pipe.set_progress_bar_config(disable=__UpperCAmelCase						)

				_lowerCAmelCase			=self.get_inputs(__UpperCAmelCase						)
				_lowerCAmelCase			=pipe(**__UpperCAmelCase						).images
				_lowerCAmelCase			=image[0, -3:, -3:, -1].flatten()

				assert image.shape == (1, 5_12, 5_12, 3)
				_lowerCAmelCase			=np.array([0.4_9_4_9_3, 0.4_7_8_9_6, 0.4_0_7_9_8, 0.5_4_2_1_4, 0.5_3_2_1_2, 0.4_8_2_0_2, 0.4_7_6_5_6, 0.4_6_3_2_9, 0.4_8_5_0_6]						)
				assert np.abs(image_slice - expected_slice						).max() < 7e-3





"""simple docstring"""


class 	lowerCamelCase__      :





			'''simple docstring'''



			def __init__(							self  ,  __UpperCAmelCase=None						)				->  List[Any]:
				_lowerCAmelCase			=data
				_lowerCAmelCase			=None



			def __repr__(							self						)				->  int:
				_lowerCAmelCase			=""""""
				_lowerCAmelCase			=self
				while temp:
					string_rep += f'''<{temp.data}> ---> '''
					_lowerCAmelCase			=temp.next
				string_rep += "<END>"
				return string_rep







def 					_lowerCamelCase(__UpperCamelCase )       ->				str:

	# if elements_list is empty
	if not elements_list:
		raise Exception("""The Elements List is empty""" )

	# Set first element as Head
	_lowerCAmelCase			=Node(elements_list[0] )
	_lowerCAmelCase			=head
	# Loop through elements from position 1
	for data in elements_list[1:]:
		_lowerCAmelCase			=Node(__UpperCamelCase )
		_lowerCAmelCase			=current.next
	return head


__A 		= [1, 3, 5, 32, 44, 12, 43]
print(F"""List: {list_data}""")
print('Creating Linked List from List.')
__A 		= make_linked_list(list_data)
print('Linked List:')

print(linked_list)



