






import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class     SCREAMING_SNAKE_CASE					(			snake_case	,						unittest.TestCase       ):





							"""simple docstring"""

							A_    	=       DiTPipeline
							A_    	=       CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
							A_    	=       PipelineTesterMixin.required_optional_params - {
							    "latents",
							    "num_images_per_prompt",
							    "callback",
							    "callback_steps",
							}
							A_    	=       CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
							A_    	=       False

							def    __A			(	self: Tuple )     ->					str:
												torch.manual_seed(0 )
												_A										=			TransformeraDModel(
												    sample_size=16		,  num_layers=2		,  patch_size=4		,  attention_head_dim=8		,  num_attention_heads=2		,  in_channels=4		,  out_channels=8		,  attention_bias=a_		,  activation_fn='''gelu-approximate'''		,  num_embeds_ada_norm=10_00		,  norm_type='''ada_norm_zero'''		,  norm_elementwise_affine=a_		,  )
												_A										=			AutoencoderKL()
												_A										=			DDIMScheduler()
												_A										=			{'''transformer''': transformer.eval(), '''vae''': vae.eval(), '''scheduler''': scheduler}
												return components

							def    __A			(	self: int		,  __A: List[Any]		,  __A: Dict=0 )     ->					int:
												if str(a_ ).startswith('''mps''' ):
																	_A										=			torch.manual_seed(a_ )
												else:
																	_A										=			torch.Generator(device=a_ ).manual_seed(a_ )
												_A										=			{
												    '''class_labels''': [1],
												    '''generator''': generator,
												    '''num_inference_steps''': 2,
												    '''output_type''': '''numpy''',
												}
												return inputs

							def    __A			(	self: Dict )     ->					Any:
												_A										=			'''cpu'''

												_A										=			self.get_dummy_components()
												_A										=			self.pipeline_class(**a_ )
												pipe.to(a_ )
												pipe.set_progress_bar_config(disable=a_ )

												_A										=			self.get_dummy_inputs(a_ )
												_A										=			pipe(**a_ ).images
												_A										=			image[0, -3:, -3:, -1]

												self.assertEqual(image.shape		,  (1, 16, 16, 3) )
												_A										=			np.array([0.2_946, 0.6_601, 0.4_329, 0.3_296, 0.4_144, 0.5_319, 0.7_273, 0.5_013, 0.4_457] )
												_A										=			np.abs(image_slice.flatten() - expected_slice ).max()
												self.assertLessEqual(a_		,  1e-3 )

							def    __A			(	self: List[str] )     ->					Union[str, Any]:
												self._test_inference_batch_single_identical(relax_max_difference=a_		,  expected_max_diff=1e-3 )



							@unittest.skipIf(
							    torch_device != '''cuda''' or not is_xformers_available()		,  reason='''XFormers attention is only available with CUDA and `xformers` installed'''		,  )
							def    __A			(	self: List[Any] )     ->					int:
												self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )





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





							"""simple docstring"""

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

							def    __A			(	self: Dict )     ->					Tuple:
												_A										=			torch.manual_seed(0 )

												_A										=			DiTPipeline.from_pretrained('''facebook/DiT-XL-2-256''' )
												pipe.to('''cuda''' )

												_A										=			['''vase''', '''umbrella''', '''white shark''', '''white wolf''']
												_A										=			pipe.get_label_ids(a_ )

												_A										=			pipe(a_		,  generator=a_		,  num_inference_steps=40		,  output_type='''np''' ).images

												for word, image in zip(a_		,  a_ ):
																	_A										=			load_numpy(
																	    f"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy""" )
																	assert np.abs((expected_image - image).max() ) < 1e-2



							def    __A			(	self: int )     ->					Any:
												_A										=			DiTPipeline.from_pretrained('''facebook/DiT-XL-2-512''' )
												_A										=			DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
												pipe.to('''cuda''' )

												_A										=			['''vase''', '''umbrella''']
												_A										=			pipe.get_label_ids(a_ )

												_A										=			torch.manual_seed(0 )
												_A										=			pipe(a_		,  generator=a_		,  num_inference_steps=25		,  output_type='''np''' ).images

												for word, image in zip(a_		,  a_ ):
																	_A										=			load_numpy(
																	    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
																	    f"""/dit/{word}_512.npy""" )

																	assert np.abs((expected_image - image).max() ) < 1e-1






'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import _LazyModule


_a					:			Tuple							=    {'processing_wav2vec2_with_lm': ['Wav2Vec2ProcessorWithLM']}


if TYPE_CHECKING:
  from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
  import sys

  _a					:			int							=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


