


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  __A       (  a      ,   a      ,   a      ,   unittest.TestCase      ):
				__A								=			AltDiffusionPipeline
				__A								=			TEXT_TO_IMAGE_PARAMS
				__A								=			TEXT_TO_IMAGE_BATCH_PARAMS
				__A								=			TEXT_TO_IMAGE_IMAGE_PARAMS
				__A								=			TEXT_TO_IMAGE_IMAGE_PARAMS


				def _snake_case    (						self							):
								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""")				,	cross_attention_dim=32				,	)
								lowerCamelCase			=DDIMScheduler(
								    beta_start=0.0_0_0_8_5				,	beta_end=0.0_1_2				,	beta_schedule="""scaled_linear"""				,	clip_sample=UpperCAmelCase_				,	set_alpha_to_one=UpperCAmelCase_				,	)
								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				,	)

								# 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							)
								lowerCamelCase			=CLIPTextConfig(
								    bos_token_id=0				,	eos_token_id=2				,	hidden_size=32				,	projection_dim=32				,	intermediate_size=37				,	layer_norm_eps=1E-05				,	num_attention_heads=4				,	num_hidden_layers=5				,	pad_token_id=1				,	vocab_size=5002				,	)
								lowerCamelCase			=CLIPTextModel(UpperCAmelCase_							)

								lowerCamelCase			=XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""							)
								lowerCamelCase			=77

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


				def _snake_case    (						self				,	UpperCAmelCase_				,	UpperCAmelCase_=0							):
								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""": 6.0,
								    """output_type""": """numpy""",
								}
								return inputs


				def _snake_case    (						self							):
								super().test_attention_slicing_forward_pass(expected_max_diff=3E-3							)


				def _snake_case    (						self							):
								super().test_inference_batch_single_identical(expected_max_diff=3E-3							)


				def _snake_case    (						self							):
								lowerCamelCase			="""cpu"""  # ensure determinism for the device-dependent torch.Generator

								lowerCamelCase			=self.get_dummy_components()
								torch.manual_seed(0							)
								lowerCamelCase			=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				,	)
								# TODO: remove after fixing the non-deterministic text encoder
								lowerCamelCase			=RobertaSeriesModelWithTransformation(UpperCAmelCase_							)
								lowerCamelCase			=text_encoder

								lowerCamelCase			=AltDiffusionPipeline(**UpperCAmelCase_							)
								lowerCamelCase			=alt_pipe.to(UpperCAmelCase_							)
								alt_pipe.set_progress_bar_config(disable=UpperCAmelCase_							)

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

								assert image.shape == (1, 64, 64, 3)
								lowerCamelCase			=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 _snake_case    (						self							):
								lowerCamelCase			="""cpu"""  # ensure determinism for the device-dependent torch.Generator

								lowerCamelCase			=self.get_dummy_components()
								lowerCamelCase			=PNDMScheduler(skip_prk_steps=UpperCAmelCase_							)
								torch.manual_seed(0							)
								lowerCamelCase			=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				,	)
								# TODO: remove after fixing the non-deterministic text encoder
								lowerCamelCase			=RobertaSeriesModelWithTransformation(UpperCAmelCase_							)
								lowerCamelCase			=text_encoder
								lowerCamelCase			=AltDiffusionPipeline(**UpperCAmelCase_							)
								lowerCamelCase			=alt_pipe.to(UpperCAmelCase_							)
								alt_pipe.set_progress_bar_config(disable=UpperCAmelCase_							)

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

								assert image.shape == (1, 64, 64, 3)
								lowerCamelCase			=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  __A       (  unittest.TestCase      ):


				def _snake_case    (						self							):
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()


				def _snake_case    (						self							):
								# make sure here that pndm scheduler skips prk
								lowerCamelCase			=AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion"""				,	safety_checker=UpperCAmelCase_							)
								lowerCamelCase			=alt_pipe.to(UpperCAmelCase_							)
								alt_pipe.set_progress_bar_config(disable=UpperCAmelCase_							)

								lowerCamelCase			="""A painting of a squirrel eating a burger"""
								lowerCamelCase			=torch.manual_seed(0							)
								lowerCamelCase			=alt_pipe([prompt]				,	generator=UpperCAmelCase_				,	guidance_scale=6.0				,	num_inference_steps=20				,	output_type="""np"""							)

								lowerCamelCase			=output.images

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

								assert image.shape == (1, 512, 512, 3)
								lowerCamelCase			=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 _snake_case    (						self							):
								lowerCamelCase			=DDIMScheduler.from_pretrained("""BAAI/AltDiffusion"""				,	subfolder="""scheduler"""							)

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

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

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

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

								assert image.shape == (1, 512, 512, 3)
								lowerCamelCase			=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












import qiskit





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase			)  ->					qiskit.result.counts.Counts:
				lowerCamelCase			=qiskit.Aer.get_backend("""aer_simulator"""			)

				# Create a Quantum Circuit acting on the q register
				lowerCamelCase			=qiskit.QuantumCircuit(_UpperCAmelCase     , _UpperCAmelCase			)

				# Map the quantum measurement to the classical bits
				circuit.measure([0]     , [0]			)

				# Execute the circuit on the simulator
				lowerCamelCase			=qiskit.execute(_UpperCAmelCase     , _UpperCAmelCase     , shots=10_00			)

				# Return the histogram data of the results of the experiment.
				return job.result().get_counts(_UpperCAmelCase			)


if __name__ == "__main__":
							print(F"Total count for various states are: {single_qubit_measure(1, 1)}")





