






import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_				,     unittest.TestCase		):





					"""simple docstring"""






					a_			=	DDIMPipeline
					a_			=	UNCONDITIONAL_IMAGE_GENERATION_PARAMS
					a_			=	PipelineTesterMixin.required_optional_params - {
					    "num_images_per_prompt",
					    "latents",
					    "callback",
					    "callback_steps",
					}
					a_			=	UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
					a_			=	False



					def  _lowercase  (      self  :						Union[str, Any] ):
						torch.manual_seed(0 )
						snake_case__				:   Optional[Any]     =						UNetaDModel(
						    block_out_channels=(3_2, 6_4)							,	layers_per_block=2							,	sample_size=3_2							,	in_channels=3							,	out_channels=3							,	down_block_types=("DownBlock2D", "AttnDownBlock2D")							,	up_block_types=("AttnUpBlock2D", "UpBlock2D")							,	)
						snake_case__				:   Dict     =						DDIMScheduler()
						snake_case__				:   Dict     =						{"unet": unet, "scheduler": scheduler}
						return components



					def  _lowercase  (      self  :						Optional[int]							,	__A  :						Any							,	__A  :						Optional[int]=0 ):
						if str(__A ).startswith("mps" ):
							snake_case__				:   Union[str, Any]     =						torch.manual_seed(__A )
						else:
							snake_case__				:   Union[str, Any]     =						torch.Generator(device=__A ).manual_seed(__A )
						snake_case__				:   Optional[int]     =						{
						    "batch_size": 1,
						    "generator": generator,
						    "num_inference_steps": 2,
						    "output_type": "numpy",
						}
						return inputs



					def  _lowercase  (      self  :						Dict ):
						snake_case__				:   str     =						"cpu"

						snake_case__				:   List[str]     =						self.get_dummy_components()
						snake_case__				:   List[Any]     =						self.pipeline_class(**__A )
						pipe.to(__A )
						pipe.set_progress_bar_config(disable=__A )

						snake_case__				:   int     =						self.get_dummy_inputs(__A )
						snake_case__				:   Any     =						pipe(**__A ).images
						snake_case__				:   Any     =						image[0, -3:, -3:, -1]

						self.assertEqual(image.shape							,	(1, 3_2, 3_2, 3) )
						snake_case__				:   int     =						np.array(
						    [1.0_0_0e0_0, 5.7_1_7e-0_1, 4.7_1_7e-0_1, 1.0_0_0e0_0, 0.0_0_0e0_0, 1.0_0_0e0_0, 3.0_0_0e-0_4, 0.0_0_0e0_0, 9.0_0_0e-0_4] )
						snake_case__				:   Optional[int]     =						np.abs(image_slice.flatten() - expected_slice ).max()
						self.assertLessEqual(__A							,	1e-3 )



					def  _lowercase  (      self  :						List[str] ):
						super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3 )



					def  _lowercase  (      self  :						Union[str, Any] ):
						super().test_save_load_local(expected_max_difference=3e-3 )



					def  _lowercase  (      self  :						int ):
						super().test_save_load_optional_components(expected_max_difference=3e-3 )






					def  _lowercase  (      self  :						Dict ):
						super().test_inference_batch_single_identical(expected_max_diff=3e-3 )



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





					"""simple docstring"""






					def  _lowercase  (      self  :						int ):
						snake_case__				:   Any     =						"google/ddpm-cifar10-32"

						snake_case__				:   Optional[Any]     =						UNetaDModel.from_pretrained(__A )
						snake_case__				:   int     =						DDIMScheduler()

						snake_case__				:   Union[str, Any]     =						DDIMPipeline(unet=__A							,	scheduler=__A )
						ddim.to(__A )
						ddim.set_progress_bar_config(disable=__A )

						snake_case__				:   Tuple     =						torch.manual_seed(0 )
						snake_case__				:   List[Any]     =						ddim(generator=__A							,	eta=0.0							,	output_type="numpy" ).images

						snake_case__				:   Dict     =						image[0, -3:, -3:, -1]

						assert image.shape == (1, 3_2, 3_2, 3)
						snake_case__				:   Tuple     =						np.array([0.1_7_2_3, 0.1_6_1_7, 0.1_6_0_0, 0.1_6_2_6, 0.1_4_9_7, 0.1_5_1_3, 0.1_5_0_5, 0.1_4_4_2, 0.1_4_5_3] )

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






					def  _lowercase  (      self  :						List[str] ):
						snake_case__				:   Optional[int]     =						"google/ddpm-ema-bedroom-256"

						snake_case__				:   List[Any]     =						UNetaDModel.from_pretrained(__A )
						snake_case__				:   Union[str, Any]     =						DDIMScheduler.from_pretrained(__A )

						snake_case__				:   Tuple     =						DDIMPipeline(unet=__A							,	scheduler=__A )
						ddpm.to(__A )
						ddpm.set_progress_bar_config(disable=__A )

						snake_case__				:   Any     =						torch.manual_seed(0 )
						snake_case__				:   Any     =						ddpm(generator=__A							,	output_type="numpy" ).images

						snake_case__				:   Optional[int]     =						image[0, -3:, -3:, -1]

						assert image.shape == (1, 2_5_6, 2_5_6, 3)
						snake_case__				:   str     =						np.array([0.0_0_6_0, 0.0_2_0_1, 0.0_3_4_4, 0.0_0_2_4, 0.0_0_1_8, 0.0_0_0_2, 0.0_0_2_2, 0.0_0_0_0, 0.0_0_6_9] )

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







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__lowerCamelCase      :    Tuple   		=   {
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase      :    Tuple   		=   [
	    """ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """RobertaPreLayerNormForCausalLM""",
	    """RobertaPreLayerNormForMaskedLM""",
	    """RobertaPreLayerNormForMultipleChoice""",
	    """RobertaPreLayerNormForQuestionAnswering""",
	    """RobertaPreLayerNormForSequenceClassification""",
	    """RobertaPreLayerNormForTokenClassification""",
	    """RobertaPreLayerNormModel""",
	    """RobertaPreLayerNormPreTrainedModel""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase      :    Union[str, Any]   		=   [
	    """TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFRobertaPreLayerNormForCausalLM""",
	    """TFRobertaPreLayerNormForMaskedLM""",
	    """TFRobertaPreLayerNormForMultipleChoice""",
	    """TFRobertaPreLayerNormForQuestionAnswering""",
	    """TFRobertaPreLayerNormForSequenceClassification""",
	    """TFRobertaPreLayerNormForTokenClassification""",
	    """TFRobertaPreLayerNormMainLayer""",
	    """TFRobertaPreLayerNormModel""",
	    """TFRobertaPreLayerNormPreTrainedModel""",
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase      :    List[Any]   		=   [
	    """FlaxRobertaPreLayerNormForCausalLM""",
	    """FlaxRobertaPreLayerNormForMaskedLM""",
	    """FlaxRobertaPreLayerNormForMultipleChoice""",
	    """FlaxRobertaPreLayerNormForQuestionAnswering""",
	    """FlaxRobertaPreLayerNormForSequenceClassification""",
	    """FlaxRobertaPreLayerNormForTokenClassification""",
	    """FlaxRobertaPreLayerNormModel""",
	    """FlaxRobertaPreLayerNormPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_roberta_prelayernorm import (
	    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    RobertaPreLayerNormConfig,
	    RobertaPreLayerNormOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_roberta_prelayernorm import (
		    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RobertaPreLayerNormForCausalLM,
		    RobertaPreLayerNormForMaskedLM,
		    RobertaPreLayerNormForMultipleChoice,
		    RobertaPreLayerNormForQuestionAnswering,
		    RobertaPreLayerNormForSequenceClassification,
		    RobertaPreLayerNormForTokenClassification,
		    RobertaPreLayerNormModel,
		    RobertaPreLayerNormPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_roberta_prelayernorm import (
		    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFRobertaPreLayerNormForCausalLM,
		    TFRobertaPreLayerNormForMaskedLM,
		    TFRobertaPreLayerNormForMultipleChoice,
		    TFRobertaPreLayerNormForQuestionAnswering,
		    TFRobertaPreLayerNormForSequenceClassification,
		    TFRobertaPreLayerNormForTokenClassification,
		    TFRobertaPreLayerNormMainLayer,
		    TFRobertaPreLayerNormModel,
		    TFRobertaPreLayerNormPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_roberta_prelayernorm import (
		    FlaxRobertaPreLayerNormForCausalLM,
		    FlaxRobertaPreLayerNormForMaskedLM,
		    FlaxRobertaPreLayerNormForMultipleChoice,
		    FlaxRobertaPreLayerNormForQuestionAnswering,
		    FlaxRobertaPreLayerNormForSequenceClassification,
		    FlaxRobertaPreLayerNormForTokenClassification,
		    FlaxRobertaPreLayerNormModel,
		    FlaxRobertaPreLayerNormPreTrainedModel,
		)

else:
	import sys

	__lowerCamelCase      :    Dict   		=   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



