



'''simple docstring'''

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 		lowerCamelCase__      (						A	,   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 						__UpperCAmelCase     (  self				:	List[Any]					)  ->       Tuple:



						'''simple docstring'''





						torch.manual_seed(0					)
						_lowercase					:       Union[str, Any]       =		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=UpperCamelCase_     ,  activation_fn='gelu-approximate'     ,  num_embeds_ada_norm=1000     ,  norm_type='ada_norm_zero'     ,  norm_elementwise_affine=UpperCamelCase_     ,  )
						_lowercase					:       Optional[Any]       =		AutoencoderKL()
						_lowercase					:       Tuple       =		DDIMScheduler()
						_lowercase					:       Tuple       =		{'transformer': transformer.eval(), 'vae': vae.eval(), 'scheduler': scheduler}
						return components




					def 						__UpperCAmelCase     (  self				:	Dict     ,  UpperCamelCase_				:	Optional[Any]     ,  UpperCamelCase_				:	List[str]=0					)  ->       List[str]:



						'''simple docstring'''





						if str(UpperCamelCase_					).startswith('mps'					):
							_lowercase					:       Any       =		torch.manual_seed(UpperCamelCase_					)
						else:
							_lowercase					:       Dict       =		torch.Generator(device=UpperCamelCase_					).manual_seed(UpperCamelCase_					)
						_lowercase					:       Any       =		{
						    'class_labels': [1],
						    'generator': generator,
						    'num_inference_steps': 2,
						    'output_type': 'numpy',
						}
						return inputs




					def 						__UpperCAmelCase     (  self				:	List[str]					)  ->       Dict:



						'''simple docstring'''





						_lowercase					:       Union[str, Any]       =		'cpu'

						_lowercase					:       Union[str, Any]       =		self.get_dummy_components()
						_lowercase					:       Tuple       =		self.pipeline_class(**UpperCamelCase_					)
						pipe.to(UpperCamelCase_					)
						pipe.set_progress_bar_config(disable=UpperCamelCase_					)

						_lowercase					:       List[str]       =		self.get_dummy_inputs(UpperCamelCase_					)
						_lowercase					:       Optional[int]       =		pipe(**UpperCamelCase_					).images
						_lowercase					:       Dict       =		image[0, -3:, -3:, -1]

						self.assertEqual(image.shape     ,  (1, 16, 16, 3)					)
						_lowercase					:       Tuple       =		np.array([0.29_46, 0.66_01, 0.43_29, 0.32_96, 0.41_44, 0.53_19, 0.72_73, 0.50_13, 0.44_57]					)
						_lowercase					:       Union[str, Any]       =		np.abs(image_slice.flatten() - expected_slice					).max()
						self.assertLessEqual(UpperCamelCase_     ,  1E-3					)




					def 						__UpperCAmelCase     (  self				:	Dict					)  ->       str:



						'''simple docstring'''





						self._test_inference_batch_single_identical(relax_max_difference=UpperCamelCase_     ,  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 						__UpperCAmelCase     (  self				:	Any					)  ->       Dict:



						'''simple docstring'''





						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3					)






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




					'''simple docstring'''



					def 						__UpperCAmelCase     (  self				:	Optional[int]					)  ->       Dict:



						'''simple docstring'''





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




					def 						__UpperCAmelCase     (  self				:	List[str]					)  ->       List[str]:



						'''simple docstring'''





						_lowercase					:       Union[str, Any]       =		torch.manual_seed(0					)

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

						_lowercase					:       List[str]       =		['vase', 'umbrella', 'white shark', 'white wolf']
						_lowercase					:       List[str]       =		pipe.get_label_ids(UpperCamelCase_					)

						_lowercase					:       Optional[int]       =		pipe(UpperCamelCase_     ,  generator=UpperCamelCase_     ,  num_inference_steps=40     ,  output_type='np'					).images

						for word, image in zip(UpperCamelCase_     ,  UpperCamelCase_					):
							_lowercase					:       List[str]       =		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 						__UpperCAmelCase     (  self				:	Optional[Any]					)  ->       int:



						'''simple docstring'''





						_lowercase					:       List[str]       =		DiTPipeline.from_pretrained('facebook/DiT-XL-2-512'					)
						_lowercase					:       Tuple       =		DPMSolverMultistepScheduler.from_config(pipe.scheduler.config					)
						pipe.to('cuda'					)

						_lowercase					:       Any       =		['vase', 'umbrella']
						_lowercase					:       int       =		pipe.get_label_ids(UpperCamelCase_					)

						_lowercase					:       Optional[int]       =		torch.manual_seed(0					)
						_lowercase					:       int       =		pipe(UpperCamelCase_     ,  generator=UpperCamelCase_     ,  num_inference_steps=25     ,  output_type='np'					).images

						for word, image in zip(UpperCamelCase_     ,  UpperCamelCase_					):
							_lowercase					:       Dict       =		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 (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_A				:    Optional[Any]     						={'''configuration_xlnet''': ['''XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLNetConfig''']}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_A				:    Tuple     						=['''XLNetTokenizer''']

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_A				:    str     						=['''XLNetTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_A				:    Any     						=[
		    '''XLNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''XLNetForMultipleChoice''',
		    '''XLNetForQuestionAnswering''',
		    '''XLNetForQuestionAnsweringSimple''',
		    '''XLNetForSequenceClassification''',
		    '''XLNetForTokenClassification''',
		    '''XLNetLMHeadModel''',
		    '''XLNetModel''',
		    '''XLNetPreTrainedModel''',
		    '''load_tf_weights_in_xlnet''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_A				:    str     						=[
		    '''TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFXLNetForMultipleChoice''',
		    '''TFXLNetForQuestionAnsweringSimple''',
		    '''TFXLNetForSequenceClassification''',
		    '''TFXLNetForTokenClassification''',
		    '''TFXLNetLMHeadModel''',
		    '''TFXLNetMainLayer''',
		    '''TFXLNetModel''',
		    '''TFXLNetPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_xlnet import XLNetTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_xlnet_fast import XLNetTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_xlnet import (
				    XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    XLNetForMultipleChoice,
				    XLNetForQuestionAnswering,
				    XLNetForQuestionAnsweringSimple,
				    XLNetForSequenceClassification,
				    XLNetForTokenClassification,
				    XLNetLMHeadModel,
				    XLNetModel,
				    XLNetPreTrainedModel,
				    load_tf_weights_in_xlnet,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_xlnet import (
				    TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFXLNetForMultipleChoice,
				    TFXLNetForQuestionAnsweringSimple,
				    TFXLNetForSequenceClassification,
				    TFXLNetForTokenClassification,
				    TFXLNetLMHeadModel,
				    TFXLNetMainLayer,
				    TFXLNetModel,
				    TFXLNetPreTrainedModel,
				)

else:
		import sys

		_A				:    str     						=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
