


"""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 snake_case      (  __lowerCAmelCase     ,				unittest.TestCase):
		__UpperCamelCase           =			DiTPipeline
		__UpperCamelCase           =			CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
		__UpperCamelCase           =			PipelineTesterMixin.required_optional_params - {
		    'latents',
		    'num_images_per_prompt',
		    'callback',
		    'callback_steps',
		}
		__UpperCamelCase           =			CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
		__UpperCamelCase           =			False







		def  a_ (					self      :       Any		)       ->						List[str]:





						'''simple docstring'''







						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=_UpperCamelCase    ,   activation_fn="gelu-approximate"    ,   num_embeds_ada_norm=10_00    ,   norm_type="ada_norm_zero"    ,   norm_elementwise_affine=_UpperCamelCase    ,   )
						_A					  =			AutoencoderKL()
						_A					  =			DDIMScheduler()
						_A					  =			{"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
						return components







		def  a_ (					self      :       Optional[int]    ,   a__      :       Optional[int]    ,   a__      :       Dict=0		)       ->						Optional[Any]:





						'''simple docstring'''







						if str(_UpperCamelCase		).startswith("mps"		):
										_A					  =			torch.manual_seed(_UpperCamelCase		)
						else:
										_A					  =			torch.Generator(device=_UpperCamelCase		).manual_seed(_UpperCamelCase		)
						_A					  =			{
						    """class_labels""": [1],
						    """generator""": generator,
						    """num_inference_steps""": 2,
						    """output_type""": """numpy""",
						}
						return inputs







		def  a_ (					self      :       Dict		)       ->						Dict:





						'''simple docstring'''







						_A					  =			"""cpu"""

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

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

						self.assertEqual(image.shape    ,   (1, 16, 16, 3)		)
						_A					  =			np.array([0.2_9_4_6, 0.6_6_0_1, 0.4_3_2_9, 0.3_2_9_6, 0.4_1_4_4, 0.5_3_1_9, 0.7_2_7_3, 0.5_0_1_3, 0.4_4_5_7]		)
						_A					  =			np.abs(image_slice.flatten() - expected_slice		).max()
						self.assertLessEqual(_UpperCamelCase    ,   1E-3		)







		def  a_ (					self      :       List[str]		)       ->						Tuple:





						'''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  a_ (					self      :       str		)       ->						Optional[int]:





						'''simple docstring'''







						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3		)





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







		def  a_ (					self      :       str		)       ->						int:





						'''simple docstring'''







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







		def  a_ (					self      :       List[Any]		)       ->						List[str]:





						'''simple docstring'''







						_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(_UpperCamelCase		)

						_A					  =			pipe(_UpperCamelCase    ,   generator=_UpperCamelCase    ,   num_inference_steps=40    ,   output_type="np"		).images

						for word, image in zip(_UpperCamelCase    ,   _UpperCamelCase		):
										_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		)       ->						Union[str, Any]:





						'''simple docstring'''







						_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(_UpperCamelCase		)

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

						for word, image in zip(_UpperCamelCase    ,   _UpperCamelCase		):
										_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 (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a_       							=    {
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a_       							=    ["RoFormerTokenizerFast"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a_       							=    [
		    "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "RoFormerForCausalLM",
		    "RoFormerForMaskedLM",
		    "RoFormerForMultipleChoice",
		    "RoFormerForQuestionAnswering",
		    "RoFormerForSequenceClassification",
		    "RoFormerForTokenClassification",
		    "RoFormerLayer",
		    "RoFormerModel",
		    "RoFormerPreTrainedModel",
		    "load_tf_weights_in_roformer",
		]


try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a_       							=    [
		    "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TFRoFormerForCausalLM",
		    "TFRoFormerForMaskedLM",
		    "TFRoFormerForMultipleChoice",
		    "TFRoFormerForQuestionAnswering",
		    "TFRoFormerForSequenceClassification",
		    "TFRoFormerForTokenClassification",
		    "TFRoFormerLayer",
		    "TFRoFormerModel",
		    "TFRoFormerPreTrainedModel",
		]


try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a_       							=    [
		    "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "FlaxRoFormerForMaskedLM",
		    "FlaxRoFormerForMultipleChoice",
		    "FlaxRoFormerForQuestionAnswering",
		    "FlaxRoFormerForSequenceClassification",
		    "FlaxRoFormerForTokenClassification",
		    "FlaxRoFormerModel",
		    "FlaxRoFormerPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
		from .tokenization_roformer import RoFormerTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_roformer_fast import RoFormerTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roformer import (
				    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RoFormerForCausalLM,
				    RoFormerForMaskedLM,
				    RoFormerForMultipleChoice,
				    RoFormerForQuestionAnswering,
				    RoFormerForSequenceClassification,
				    RoFormerForTokenClassification,
				    RoFormerLayer,
				    RoFormerModel,
				    RoFormerPreTrainedModel,
				    load_tf_weights_in_roformer,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_roformer import (
				    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFRoFormerForCausalLM,
				    TFRoFormerForMaskedLM,
				    TFRoFormerForMultipleChoice,
				    TFRoFormerForQuestionAnswering,
				    TFRoFormerForSequenceClassification,
				    TFRoFormerForTokenClassification,
				    TFRoFormerLayer,
				    TFRoFormerModel,
				    TFRoFormerPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_roformer import (
				    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    FlaxRoFormerForMaskedLM,
				    FlaxRoFormerForMultipleChoice,
				    FlaxRoFormerForQuestionAnswering,
				    FlaxRoFormerForSequenceClassification,
				    FlaxRoFormerForTokenClassification,
				    FlaxRoFormerModel,
				    FlaxRoFormerPreTrainedModel,
				)


else:
		import sys

		a_       							=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)