



'''simple docstring'''






import random
import unittest

import numpy as np
import torch

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionUpscalePipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
      import onnxruntime as ort

class        SCREAMING_SNAKE_CASE(							A__							,	unittest.TestCase    ):







 """simple docstring"""






 lowerCamelCase__    				= """ssube/stable-diffusion-x4-upscaler-onnx"""







 def   A			(		self    :  Any	,    __snake_case    :  Union[str, Any]=0	)    ->	Dict:
        UpperCAmelCase				: Union[str, Any]											=	floats_tensor((1, 3, 128, 128)	,    rng=random.Random(__snake_case	)	)
        UpperCAmelCase				: Optional[Any]											=	torch.manual_seed(__snake_case	)
        UpperCAmelCase				: int											=	{
            '''prompt''': '''A painting of a squirrel eating a burger''',
            '''image''': image,
            '''generator''': generator,
            '''num_inference_steps''': 3,
            '''guidance_scale''': 7.5,
            '''output_type''': '''numpy''',
        }
        return inputs







 def   A			(		self    :  str	)    ->	Optional[Any]:
        UpperCAmelCase				: Optional[int]											=	OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint	,    provider='''CPUExecutionProvider'''	)
        pipe.set_progress_bar_config(disable=__snake_case	)

        UpperCAmelCase				: List[Any]											=	self.get_dummy_inputs()
        UpperCAmelCase				: List[Any]											=	pipe(**__snake_case	).images
        UpperCAmelCase				: List[Any]											=	image[0, -3:, -3:, -1].flatten()

        # started as 128, should now be 512
        assert image.shape == (1, 512, 512, 3)
        UpperCAmelCase				: Union[str, Any]											=	np.array(
            [0.6_97_47_82, 0.68_90_20_93, 0.70_13_58_85, 0.7_58_36_18, 0.7_80_45_45, 0.7_85_49_12, 0.78_66_74_26, 0.78_74_38_63, 0.78_07_02_23]	)
        assert np.abs(image_slice - expected_slice	).max() < 1E-1







 def   A			(		self    :  Union[str, Any]	)    ->	Optional[int]:
        UpperCAmelCase				: str											=	OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint	,    provider='''CPUExecutionProvider'''	)
        UpperCAmelCase				: Optional[Any]											=	PNDMScheduler.from_config(pipe.scheduler.config	,    skip_prk_steps=__snake_case	)
        pipe.set_progress_bar_config(disable=__snake_case	)

        UpperCAmelCase				: str											=	self.get_dummy_inputs()
        UpperCAmelCase				: Dict											=	pipe(**__snake_case	).images
        UpperCAmelCase				: str											=	image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        UpperCAmelCase				: Union[str, Any]											=	np.array(
            [0.6_89_88_92, 0.59_24_05_56, 0.52_49_95_27, 0.58_86_62_15, 0.52_25_82_35, 0.52_57_27_15, 0.62_41_44_73, 0.6_17_43_87, 0.6_21_49_64]	)
        assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-1







 def   A			(		self    :  List[str]	)    ->	str:
        UpperCAmelCase				: Union[str, Any]											=	OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint	,    provider='''CPUExecutionProvider'''	)
        UpperCAmelCase				: Union[str, Any]											=	DPMSolverMultistepScheduler.from_config(pipe.scheduler.config	)
        pipe.set_progress_bar_config(disable=__snake_case	)

        UpperCAmelCase				: Any											=	self.get_dummy_inputs()
        UpperCAmelCase				: Any											=	pipe(**__snake_case	).images
        UpperCAmelCase				: str											=	image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        UpperCAmelCase				: Any											=	np.array(
            [0.7_65_92_78, 0.76_43_76_64, 0.75_57_91_07, 0.7_69_11_16, 0.77_66_69_86, 0.7_72_76_72, 0.7_75_86_64, 0.7_81_22_26, 0.76_94_25_15]	)

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







 def   A			(		self    :  int	)    ->	Optional[int]:
        UpperCAmelCase				: Any											=	OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint	,    provider='''CPUExecutionProvider'''	)
        UpperCAmelCase				: Tuple											=	EulerDiscreteScheduler.from_config(pipe.scheduler.config	)
        pipe.set_progress_bar_config(disable=__snake_case	)

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

        assert image.shape == (1, 512, 512, 3)
        UpperCAmelCase				: Dict											=	np.array(
            [0.6_97_47_82, 0.68_90_20_93, 0.70_13_58_85, 0.7_58_36_18, 0.7_80_45_45, 0.7_85_49_12, 0.78_66_74_26, 0.78_74_38_63, 0.78_07_02_23]	)
        assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-1







 def   A			(		self    :  Optional[int]	)    ->	Optional[int]:
        UpperCAmelCase				: List[Any]											=	OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint	,    provider='''CPUExecutionProvider'''	)
        UpperCAmelCase				: Any											=	EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config	)
        pipe.set_progress_bar_config(disable=__snake_case	)

        UpperCAmelCase				: Optional[int]											=	self.get_dummy_inputs()
        UpperCAmelCase				: Any											=	pipe(**__snake_case	).images
        UpperCAmelCase				: List[str]											=	image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        UpperCAmelCase				: Union[str, Any]											=	np.array(
            [0.77_42_44_96, 0.77_36_01, 0.7_64_52_88, 0.7_76_95_98, 0.7_77_27_39, 0.7_73_86_88, 0.78_18_72_33, 0.77_87_95_84, 0.76_70_43]	)

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

@nightly
@require_onnxruntime
@require_torch_gpu
class        SCREAMING_SNAKE_CASE(							unittest.TestCase    ):







 """simple docstring"""






 @property
 def   A			(		self    :  Tuple	)    ->	str:
        return (
            "CUDAExecutionProvider",
            {
                "gpu_mem_limit": "15000000000",  # 15GB
                "arena_extend_strategy": "kSameAsRequested",
            },
        )







 @property
 def   A			(		self    :  Optional[int]	)    ->	Any:
        UpperCAmelCase				: int											=	ort.SessionOptions()
        UpperCAmelCase				: Tuple											=	False
        return options







 def   A			(		self    :  List[Any]	)    ->	Any:
        UpperCAmelCase				: Optional[int]											=	load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/img2img/sketch-mountains-input.jpg'''	)
        UpperCAmelCase				: Any											=	init_image.resize((128, 128)	)
        # using the PNDM scheduler by default
        UpperCAmelCase				: Optional[int]											=	OnnxStableDiffusionUpscalePipeline.from_pretrained(
            '''ssube/stable-diffusion-x4-upscaler-onnx'''	,    provider=self.gpu_provider	,    sess_options=self.gpu_options	,    )
        pipe.set_progress_bar_config(disable=__snake_case	)

        UpperCAmelCase				: Tuple											=	'''A fantasy landscape, trending on artstation'''

        UpperCAmelCase				: Optional[Any]											=	torch.manual_seed(0	)
        UpperCAmelCase				: Any											=	pipe(
            prompt=__snake_case	,    image=__snake_case	,    guidance_scale=7.5	,    num_inference_steps=10	,    generator=__snake_case	,    output_type='''np'''	,    )
        UpperCAmelCase				: str											=	output.images
        UpperCAmelCase				: Optional[Any]											=	images[0, 255:258, 383:386, -1]

        assert images.shape == (1, 512, 512, 3)
        UpperCAmelCase				: List[str]											=	np.array([0.48_83, 0.49_47, 0.49_80, 0.49_75, 0.49_82, 0.49_80, 0.50_00, 0.50_06, 0.49_72]	)
        # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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







 def   A			(		self    :  str	)    ->	Union[str, Any]:
        UpperCAmelCase				: List[Any]											=	load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/img2img/sketch-mountains-input.jpg'''	)
        UpperCAmelCase				: str											=	init_image.resize((128, 128)	)
        UpperCAmelCase				: Union[str, Any]											=	LMSDiscreteScheduler.from_pretrained(
            '''ssube/stable-diffusion-x4-upscaler-onnx'''	,    subfolder='''scheduler'''	)
        UpperCAmelCase				: Tuple											=	OnnxStableDiffusionUpscalePipeline.from_pretrained(
            '''ssube/stable-diffusion-x4-upscaler-onnx'''	,    scheduler=__snake_case	,    provider=self.gpu_provider	,    sess_options=self.gpu_options	,    )
        pipe.set_progress_bar_config(disable=__snake_case	)

        UpperCAmelCase				: Optional[Any]											=	'''A fantasy landscape, trending on artstation'''

        UpperCAmelCase				: int											=	torch.manual_seed(0	)
        UpperCAmelCase				: List[Any]											=	pipe(
            prompt=__snake_case	,    image=__snake_case	,    guidance_scale=7.5	,    num_inference_steps=20	,    generator=__snake_case	,    output_type='''np'''	,    )
        UpperCAmelCase				: int											=	output.images
        UpperCAmelCase				: Optional[Any]											=	images[0, 255:258, 383:386, -1]

        assert images.shape == (1, 512, 512, 3)
        UpperCAmelCase				: Optional[Any]											=	np.array(
            [0.50_17_37_53, 0.50_22_33_56, 0.50_20_39, 0.50_23_30_36, 0.5_02_37_25, 0.5_02_26_01, 0.5_01_87_58, 0.50_23_40_85, 0.50_24_15_66]	)
        # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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










'''simple docstring'''






from typing import List, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__:     Optional[Any]										=    logging.get_logger(__name__)

UpperCamelCase__:     Tuple										=    {
    "huggingface/time-series-transformer-tourism-monthly": (
        "https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json"
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	lowerCamelCase__    				= """time_series_transformer"""
	lowerCamelCase__    				= {
	    """hidden_size""": """d_model""",
	    """num_attention_heads""": """encoder_attention_heads""",
	    """num_hidden_layers""": """encoder_layers""",
	}







	def __init__(		self    :  str	,    __snake_case    :  Optional[int] = None	,    __snake_case    :  Optional[int] = None	,    __snake_case    :  str = "student_t"	,    __snake_case    :  str = "nll"	,    __snake_case    :  int = 1	,    __snake_case    :  List[int] = [1, 2, 3, 4, 5, 6, 7]	,    __snake_case    :  Optional[Union[str, bool]] = "mean"	,    __snake_case    :  int = 0	,    __snake_case    :  int = 0	,    __snake_case    :  int = 0	,    __snake_case    :  int = 0	,    __snake_case    :  Optional[List[int]] = None	,    __snake_case    :  Optional[List[int]] = None	,    __snake_case    :  int = 32	,    __snake_case    :  int = 32	,    __snake_case    :  int = 2	,    __snake_case    :  int = 2	,    __snake_case    :  int = 2	,    __snake_case    :  int = 2	,    __snake_case    :  bool = True	,    __snake_case    :  str = "gelu"	,    __snake_case    :  int = 64	,    __snake_case    :  float = 0.1	,    __snake_case    :  float = 0.1	,    __snake_case    :  float = 0.1	,    __snake_case    :  float = 0.1	,    __snake_case    :  float = 0.1	,    __snake_case    :  int = 100	,    __snake_case    :  float = 0.02	,    __snake_case    :  Optional[Any]=True	,    **__snake_case    :  List[Any]	,    )    ->	List[str]:
								# time series specific configuration
								UpperCAmelCase				: List[Any]											=	prediction_length
								UpperCAmelCase				: List[Any]											=	context_length or prediction_length
								UpperCAmelCase				: Tuple											=	distribution_output
								UpperCAmelCase				: Optional[Any]											=	loss
								UpperCAmelCase				: Tuple											=	input_size
								UpperCAmelCase				: Optional[int]											=	num_time_features
								UpperCAmelCase				: Dict											=	lags_sequence
								UpperCAmelCase				: Any											=	scaling
								UpperCAmelCase				: Tuple											=	num_dynamic_real_features
								UpperCAmelCase				: Any											=	num_static_real_features
								UpperCAmelCase				: Optional[int]											=	num_static_categorical_features
								if cardinality and num_static_categorical_features > 0:
															if len(__snake_case	) != num_static_categorical_features:
																						raise ValueError(
																						    '''The cardinality should be a list of the same length as `num_static_categorical_features`'''	)
															UpperCAmelCase				: Any											=	cardinality
								else:
															UpperCAmelCase				: Optional[Any]											=	[0]
								if embedding_dimension and num_static_categorical_features > 0:
															if len(__snake_case	) != num_static_categorical_features:
																						raise ValueError(
																						    '''The embedding dimension should be a list of the same length as `num_static_categorical_features`'''	)
															UpperCAmelCase				: Optional[int]											=	embedding_dimension
								else:
															UpperCAmelCase				: Optional[Any]											=	[min(50	,    (cat + 1) // 2	) for cat in self.cardinality]
								UpperCAmelCase				: List[Any]											=	num_parallel_samples

								# Transformer architecture configuration
								UpperCAmelCase				: int											=	input_size * len(__snake_case	) + self._number_of_features
								UpperCAmelCase				: int											=	d_model
								UpperCAmelCase				: str											=	encoder_attention_heads
								UpperCAmelCase				: str											=	decoder_attention_heads
								UpperCAmelCase				: List[str]											=	encoder_ffn_dim
								UpperCAmelCase				: Any											=	decoder_ffn_dim
								UpperCAmelCase				: Any											=	encoder_layers
								UpperCAmelCase				: str											=	decoder_layers

								UpperCAmelCase				: Optional[int]											=	dropout
								UpperCAmelCase				: Union[str, Any]											=	attention_dropout
								UpperCAmelCase				: List[Any]											=	activation_dropout
								UpperCAmelCase				: Optional[int]											=	encoder_layerdrop
								UpperCAmelCase				: Dict											=	decoder_layerdrop

								UpperCAmelCase				: Tuple											=	activation_function
								UpperCAmelCase				: Dict											=	init_std

								UpperCAmelCase				: Optional[int]											=	use_cache

								super().__init__(is_encoder_decoder=__snake_case	,    **__snake_case	)







	@property
	def   A			(		self    :  Union[str, Any]	)    ->	int:
								return (
								    sum(self.embedding_dimension	)
								    + self.num_dynamic_real_features
								    + self.num_time_features
								    + self.num_static_real_features
								    + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
								)





