



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  __snake_case      (   SCREAMING_SNAKE_CASE			,SCREAMING_SNAKE_CASE			,SCREAMING_SNAKE_CASE			,unittest.TestCase):







							'''simple docstring'''






							UpperCamelCase__				:      Dict         =					AltDiffusionPipeline
							UpperCamelCase__				:      Tuple         =					TEXT_TO_IMAGE_PARAMS
							UpperCamelCase__				:      str         =					TEXT_TO_IMAGE_BATCH_PARAMS
							UpperCamelCase__				:      int         =					TEXT_TO_IMAGE_IMAGE_PARAMS
							UpperCamelCase__				:      str         =					TEXT_TO_IMAGE_IMAGE_PARAMS


							def _a      (							self		):
											torch.manual_seed(0		)
											a__				      =		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  ,  )
											a__				      =		DDIMScheduler(
											    beta_start=0.00_085  ,  beta_end=0.012  ,  beta_schedule="""scaled_linear"""  ,  clip_sample=a_  ,  set_alpha_to_one=a_  ,  )
											torch.manual_seed(0		)
											a__				      =		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		)
											a__				      =		CLIPTextConfig(
											    bos_token_id=0  ,  eos_token_id=2  ,  hidden_size=32  ,  projection_dim=32  ,  intermediate_size=37  ,  layer_norm_eps=1E-0_5  ,  num_attention_heads=4  ,  num_hidden_layers=5  ,  pad_token_id=1  ,  vocab_size=5_002  ,  )
											a__				      =		CLIPTextModel(a_		)

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

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


							def _a      (							self  ,  a_  ,  a_=0		):
											if str(a_		).startswith("""mps"""		):
															a__				      =		torch.manual_seed(a_		)
											else:
															a__				      =		torch.Generator(device=a_		).manual_seed(a_		)
											a__				      =		{
											    """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 _a      (							self		):
											super().test_attention_slicing_forward_pass(expected_max_diff=3E-3		)


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


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

											a__				      =		self.get_dummy_components()
											torch.manual_seed(0		)
											a__				      =		RobertaSeriesConfig(
											    hidden_size=32  ,  project_dim=32  ,  intermediate_size=37  ,  layer_norm_eps=1E-0_5  ,  num_attention_heads=4  ,  num_hidden_layers=5  ,  vocab_size=5_002  ,  )
											# TODO: remove after fixing the non-deterministic text encoder
											a__				      =		RobertaSeriesModelWithTransformation(a_		)
											a__				      =		text_encoder

											a__				      =		AltDiffusionPipeline(**a_		)
											a__				      =		alt_pipe.to(a_		)
											alt_pipe.set_progress_bar_config(disable=a_		)

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

											assert image.shape == (1, 64, 64, 3)
											a__				      =		np.array(
											    [0.5_748_162, 0.60_447_145, 0.48_821_217, 0.50_100_636, 0.5_431_185, 0.45_763_683, 0.49_657_696, 0.48_132_733, 0.47_573_093]		)

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







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

											a__				      =		self.get_dummy_components()
											a__				      =		PNDMScheduler(skip_prk_steps=a_		)
											torch.manual_seed(0		)
											a__				      =		RobertaSeriesConfig(
											    hidden_size=32  ,  project_dim=32  ,  intermediate_size=37  ,  layer_norm_eps=1E-0_5  ,  num_attention_heads=4  ,  num_hidden_layers=5  ,  vocab_size=5_002  ,  )
											# TODO: remove after fixing the non-deterministic text encoder
											a__				      =		RobertaSeriesModelWithTransformation(a_		)
											a__				      =		text_encoder
											a__				      =		AltDiffusionPipeline(**a_		)
											a__				      =		alt_pipe.to(a_		)
											alt_pipe.set_progress_bar_config(disable=a_		)

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

											assert image.shape == (1, 64, 64, 3)
											a__				      =		np.array(
											    [0.51_605_093, 0.5_707_241, 0.47_365_507, 0.50_578_886, 0.5_633_877, 0.4_642_503, 0.5_182_081, 0.48_763_484, 0.49_084_237]		)

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




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







							'''simple docstring'''






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


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

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

											a__				      =		output.images

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

											assert image.shape == (1, 512, 512, 3)
											a__				      =		np.array([0.1_010, 0.0_800, 0.0_794, 0.0_885, 0.0_843, 0.0_762, 0.0_769, 0.0_729, 0.0_586]		)

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







							def _a      (							self		):
											a__				      =		DDIMScheduler.from_pretrained("""BAAI/AltDiffusion"""  ,  subfolder="""scheduler"""		)

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

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

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

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

											assert image.shape == (1, 512, 512, 3)
											a__				      =		np.array([0.4_019, 0.4_052, 0.3_810, 0.4_119, 0.3_916, 0.3_982, 0.4_651, 0.4_195, 0.5_323]		)

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






def 			A_      (	__a     :				str = "The quick brown fox jumps over the lazy dog" ,		):

				"""simple docstring"""
				a__				      =		set()

				# Replace all the whitespace in our sentence
				a__				      =		input_str.replace(""" """ ,		""""""		)
				for alpha in input_str:
								if "a" <= alpha.lower() <= "z":
												frequency.add(alpha.lower()		)
				return len(__a		) == 26

def 			A_      (	__a     :				str = "The quick brown fox jumps over the lazy dog" ,		):

				"""simple docstring"""
				a__				      =		[False] * 26
				for char in input_str:
								if char.islower():
												a__				      =		True
								elif char.isupper():
												a__				      =		True
				return all(__a		)

def 			A_      (	__a     :				str = "The quick brown fox jumps over the lazy dog" ,		):

				"""simple docstring"""
				return len({char for char in input_str.lower() if char.isalpha()}		) == 26

def 			A_      (	):

				"""simple docstring"""
				from timeit import timeit

				a__				      =		"""from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"""
				print(timeit("""is_pangram()""" ,		setup=__a		)		)
				print(timeit("""is_pangram_faster()""" ,		setup=__a		)		)
				print(timeit("""is_pangram_fastest()""" ,		setup=__a		)		)
				# 5.348480500048026, 2.6477354579837993, 1.8470395830227062
				# 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	benchmark()

