



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     lowerCAmelCase_		(  _lowercase	,   unittest.TestCase							):






       """simple docstring"""




       UpperCAmelCase__    : Dict       					=  "ssube/stable-diffusion-x4-upscaler-onnx"
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_=0	)					->		str:
          UpperCamelCase   :  str						     =     floats_tensor((1, 3, 128, 128),      rng=random.Random(__UpperCamelCase	)	)
          UpperCamelCase   :  List[Any]						     =     torch.manual_seed(__UpperCamelCase	)
          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  snake_case_	(      self	)					->		int:
          UpperCamelCase   :  List[str]						     =     OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,      provider='CPUExecutionProvider'	)
          pipe.set_progress_bar_config(disable=__UpperCamelCase	)

          UpperCamelCase   :  Dict						     =     self.get_dummy_inputs()
          UpperCamelCase   :  Optional[int]						     =     pipe(**__UpperCamelCase	).images
          UpperCamelCase   :  Union[str, Any]						     =     image[0, -3:, -3:, -1].flatten()

          # started as 128, should now be 512
          assert image.shape == (1, 512, 512, 3)
          UpperCamelCase   :  str						     =     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  snake_case_	(      self	)					->		Any:
          UpperCamelCase   :  Tuple						     =     OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,      provider='CPUExecutionProvider'	)
          UpperCamelCase   :  Union[str, Any]						     =     PNDMScheduler.from_config(pipe.scheduler.config,      skip_prk_steps=__UpperCamelCase	)
          pipe.set_progress_bar_config(disable=__UpperCamelCase	)

          UpperCamelCase   :  Dict						     =     self.get_dummy_inputs()
          UpperCamelCase   :  Optional[int]						     =     pipe(**__UpperCamelCase	).images
          UpperCamelCase   :  Tuple						     =     image[0, -3:, -3:, -1]

          assert image.shape == (1, 512, 512, 3)
          UpperCamelCase   :  Tuple						     =     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  snake_case_	(      self	)					->		Tuple:
          UpperCamelCase   :  str						     =     OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,      provider='CPUExecutionProvider'	)
          UpperCamelCase   :  int						     =     DPMSolverMultistepScheduler.from_config(pipe.scheduler.config	)
          pipe.set_progress_bar_config(disable=__UpperCamelCase	)

          UpperCamelCase   :  Dict						     =     self.get_dummy_inputs()
          UpperCamelCase   :  List[str]						     =     pipe(**__UpperCamelCase	).images
          UpperCamelCase   :  List[Any]						     =     image[0, -3:, -3:, -1]

          assert image.shape == (1, 512, 512, 3)
          UpperCamelCase   :  Optional[int]						     =     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  snake_case_	(      self	)					->		List[str]:
          UpperCamelCase   :  Optional[Any]						     =     OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,      provider='CPUExecutionProvider'	)
          UpperCamelCase   :  List[Any]						     =     EulerDiscreteScheduler.from_config(pipe.scheduler.config	)
          pipe.set_progress_bar_config(disable=__UpperCamelCase	)

          UpperCamelCase   :  Tuple						     =     self.get_dummy_inputs()
          UpperCamelCase   :  Tuple						     =     pipe(**__UpperCamelCase	).images
          UpperCamelCase   :  Union[str, Any]						     =     image[0, -3:, -3:, -1]

          assert image.shape == (1, 512, 512, 3)
          UpperCamelCase   :  List[str]						     =     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  snake_case_	(      self	)					->		Union[str, Any]:
          UpperCamelCase   :  Union[str, Any]						     =     OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,      provider='CPUExecutionProvider'	)
          UpperCamelCase   :  Union[str, Any]						     =     EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config	)
          pipe.set_progress_bar_config(disable=__UpperCamelCase	)

          UpperCamelCase   :  List[Any]						     =     self.get_dummy_inputs()
          UpperCamelCase   :  Dict						     =     pipe(**__UpperCamelCase	).images
          UpperCamelCase   :  int						     =     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     lowerCAmelCase_		(  unittest.TestCase							):






       """simple docstring"""




       @property
       def  snake_case_	(      self	)					->		Any:
          return (
              "CUDAExecutionProvider",
              {
                  "gpu_mem_limit": "15000000000",  # 15GB
                  "arena_extend_strategy": "kSameAsRequested",
              },
          )
       @property
       def  snake_case_	(      self	)					->		Dict:
          UpperCamelCase   :  int						     =     ort.SessionOptions()
          UpperCamelCase   :  List[Any]						     =     False
          return options
       def  snake_case_	(      self	)					->		Any:
          UpperCamelCase   :  Optional[Any]						     =     load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/img2img/sketch-mountains-input.jpg'	)
          UpperCamelCase   :  List[str]						     =     init_image.resize((128, 128)	)
          # using the PNDM scheduler by default
          UpperCamelCase   :  List[str]						     =     OnnxStableDiffusionUpscalePipeline.from_pretrained(
              'ssube/stable-diffusion-x4-upscaler-onnx',      provider=self.gpu_provider,      sess_options=self.gpu_options,      )
          pipe.set_progress_bar_config(disable=__UpperCamelCase	)

          UpperCamelCase   :  List[str]						     =     'A fantasy landscape, trending on artstation'

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

          assert images.shape == (1, 512, 512, 3)
          UpperCamelCase   :  Any						     =     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  snake_case_	(      self	)					->		str:
          UpperCamelCase   :  List[str]						     =     load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/img2img/sketch-mountains-input.jpg'	)
          UpperCamelCase   :  Optional[int]						     =     init_image.resize((128, 128)	)
          UpperCamelCase   :  Any						     =     LMSDiscreteScheduler.from_pretrained(
              'ssube/stable-diffusion-x4-upscaler-onnx',      subfolder='scheduler'	)
          UpperCamelCase   :  Optional[Any]						     =     OnnxStableDiffusionUpscalePipeline.from_pretrained(
              'ssube/stable-diffusion-x4-upscaler-onnx',      scheduler=__UpperCamelCase,      provider=self.gpu_provider,      sess_options=self.gpu_options,      )
          pipe.set_progress_bar_config(disable=__UpperCamelCase	)

          UpperCamelCase   :  int						     =     'A fantasy landscape, trending on artstation'

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

          assert images.shape == (1, 512, 512, 3)
          UpperCamelCase   :  Dict						     =     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








import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
    import jax.numpy as jnp

    from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
        FlaxRobertaPreLayerNormForCausalLM,
        FlaxRobertaPreLayerNormForMaskedLM,
        FlaxRobertaPreLayerNormForMultipleChoice,
        FlaxRobertaPreLayerNormForQuestionAnswering,
        FlaxRobertaPreLayerNormForSequenceClassification,
        FlaxRobertaPreLayerNormForTokenClassification,
        FlaxRobertaPreLayerNormModel,
    )



class     lowerCAmelCase_		(  unittest.TestCase							):
       def __init__(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_=13,      SCREAMING_SNAKE_CASE_=7,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=99,      SCREAMING_SNAKE_CASE_=32,      SCREAMING_SNAKE_CASE_=5,      SCREAMING_SNAKE_CASE_=4,      SCREAMING_SNAKE_CASE_=37,      SCREAMING_SNAKE_CASE_="gelu",      SCREAMING_SNAKE_CASE_=0.1,      SCREAMING_SNAKE_CASE_=0.1,      SCREAMING_SNAKE_CASE_=512,      SCREAMING_SNAKE_CASE_=16,      SCREAMING_SNAKE_CASE_=2,      SCREAMING_SNAKE_CASE_=0.02,      SCREAMING_SNAKE_CASE_=4,      )					->		Dict:
          UpperCamelCase   :  Optional[int]						     =     parent
          UpperCamelCase   :  Optional[int]						     =     batch_size
          UpperCamelCase   :  Optional[int]						     =     seq_length
          UpperCamelCase   :  Any						     =     is_training
          UpperCamelCase   :  Tuple						     =     use_attention_mask
          UpperCamelCase   :  Dict						     =     use_token_type_ids
          UpperCamelCase   :  Union[str, Any]						     =     use_labels
          UpperCamelCase   :  Any						     =     vocab_size
          UpperCamelCase   :  Any						     =     hidden_size
          UpperCamelCase   :  str						     =     num_hidden_layers
          UpperCamelCase   :  Optional[int]						     =     num_attention_heads
          UpperCamelCase   :  Union[str, Any]						     =     intermediate_size
          UpperCamelCase   :  List[Any]						     =     hidden_act
          UpperCamelCase   :  Any						     =     hidden_dropout_prob
          UpperCamelCase   :  Dict						     =     attention_probs_dropout_prob
          UpperCamelCase   :  int						     =     max_position_embeddings
          UpperCamelCase   :  int						     =     type_vocab_size
          UpperCamelCase   :  Optional[int]						     =     type_sequence_label_size
          UpperCamelCase   :  str						     =     initializer_range
          UpperCamelCase   :  Tuple						     =     num_choices
       def  snake_case_	(      self	)					->		Tuple:
          UpperCamelCase   :  Tuple						     =     ids_tensor([self.batch_size, self.seq_length],      self.vocab_size	)

          UpperCamelCase   :  Dict						     =     None
          if self.use_attention_mask:
             UpperCamelCase   :  Union[str, Any]						     =     random_attention_mask([self.batch_size, self.seq_length]	)

          UpperCamelCase   :  int						     =     None
          if self.use_token_type_ids:
             UpperCamelCase   :  str						     =     ids_tensor([self.batch_size, self.seq_length],      self.type_vocab_size	)

          UpperCamelCase   :  Optional[int]						     =     RobertaPreLayerNormConfig(
              vocab_size=self.vocab_size,      hidden_size=self.hidden_size,      num_hidden_layers=self.num_hidden_layers,      num_attention_heads=self.num_attention_heads,      intermediate_size=self.intermediate_size,      hidden_act=self.hidden_act,      hidden_dropout_prob=self.hidden_dropout_prob,      attention_probs_dropout_prob=self.attention_probs_dropout_prob,      max_position_embeddings=self.max_position_embeddings,      type_vocab_size=self.type_vocab_size,      is_decoder=SCREAMING_SNAKE_CASE_,      initializer_range=self.initializer_range,      )

          return config, input_ids, token_type_ids, attention_mask
       def  snake_case_	(      self	)					->		int:
          UpperCamelCase   :  List[str]						     =     self.prepare_config_and_inputs()
          UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase   :  Any						     =     config_and_inputs
          UpperCamelCase   :  Union[str, Any]						     =     {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
          return config, inputs_dict






       def  snake_case_	(      self	)					->		List[str]:
          UpperCamelCase   :  int						     =     self.prepare_config_and_inputs()
          UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase   :  Optional[int]						     =     config_and_inputs

          UpperCamelCase   :  Dict						     =     True
          UpperCamelCase   :  List[Any]						     =     floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
          UpperCamelCase   :  List[Any]						     =     ids_tensor([self.batch_size, self.seq_length],      vocab_size=2	)

          return (
              config,
              input_ids,
              token_type_ids,
              encoder_hidden_states,
              encoder_attention_mask,
          )






@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class     lowerCAmelCase_		(  a__	,   unittest.TestCase							):
       UpperCAmelCase__    : Union[str, Any]       					=  True

       UpperCAmelCase__    : Optional[Any]       					=  (
           (
               FlaxRobertaPreLayerNormModel,
               FlaxRobertaPreLayerNormForCausalLM,
               FlaxRobertaPreLayerNormForMaskedLM,
               FlaxRobertaPreLayerNormForSequenceClassification,
               FlaxRobertaPreLayerNormForTokenClassification,
               FlaxRobertaPreLayerNormForMultipleChoice,
               FlaxRobertaPreLayerNormForQuestionAnswering,
           )
           if is_flax_available()
           else ()
       )
       def  snake_case_	(      self	)					->		Optional[int]:
          UpperCamelCase   :  int						     =     FlaxRobertaPreLayerNormModelTester(self	)






       @slow
       def  snake_case_	(      self	)					->		List[str]:
          for model_class_name in self.all_model_classes:
             UpperCamelCase   :  List[str]						     =     model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40',      from_pt=SCREAMING_SNAKE_CASE_	)
             UpperCamelCase   :  List[Any]						     =     model(np.ones((1, 1)	)	)
             self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)






@require_flax
class     lowerCAmelCase_		(  unittest.TestCase							):
       @slow
       def  snake_case_	(      self	)					->		Dict:
          UpperCamelCase   :  Union[str, Any]						     =     FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40',      from_pt=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Optional[Any]						     =     np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]],      dtype=jnp.intaa	)
          UpperCamelCase   :  Union[str, Any]						     =     model(SCREAMING_SNAKE_CASE_	)[0]
          UpperCamelCase   :  List[Any]						     =     [1, 11, 5_0265]
          self.assertEqual(list(output.shape	),      SCREAMING_SNAKE_CASE_	)
          # compare the actual values for a slice.
          UpperCamelCase   :  Optional[int]						     =     np.array(
              [[[40.48_80, 18.01_99, -5.23_67], [-1.88_77, -4.08_85, 10.70_85], [-2.26_13, -5.61_10, 7.26_65]]],      dtype=np.floataa	)
          self.assertTrue(np.allclose(output[:, :3, :3],      SCREAMING_SNAKE_CASE_,      atol=1e-4	)	)






       @slow
       def  snake_case_	(      self	)					->		List[Any]:
          UpperCamelCase   :  Optional[int]						     =     FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40',      from_pt=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  List[str]						     =     np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]],      dtype=jnp.intaa	)
          UpperCamelCase   :  Dict						     =     model(SCREAMING_SNAKE_CASE_	)[0]
          # compare the actual values for a slice.
          UpperCamelCase   :  Any						     =     np.array(
              [[[0.02_08, -0.03_56, 0.02_37], [-0.15_69, -0.04_11, -0.26_26], [0.18_79, 0.01_25, -0.00_89]]],      dtype=np.floataa	)
          self.assertTrue(np.allclose(output[:, :3, :3],      SCREAMING_SNAKE_CASE_,      atol=1e-4	)	)



