



"""simple docstring"""
import unittest

import numpy as np

from transformers import BertConfig, 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():
						from transformers.models.bert.modeling_flax_bert import (
						    FlaxBertForMaskedLM,
						    FlaxBertForMultipleChoice,
						    FlaxBertForNextSentencePrediction,
						    FlaxBertForPreTraining,
						    FlaxBertForQuestionAnswering,
						    FlaxBertForSequenceClassification,
						    FlaxBertForTokenClassification,
						    FlaxBertModel,
						)
class 				lowerCamelCase  (			unittest.TestCase				):






					'''simple docstring'''





					def __init__(       self:							List[Any]						,     snake_case:							List[str]						,     snake_case:							Optional[Any]=13						,     snake_case:							List[str]=7						,     snake_case:							Dict=True						,     snake_case:							List[str]=True						,     snake_case:							Optional[int]=True						,     snake_case:							Any=True						,     snake_case:							Optional[Any]=99						,     snake_case:							Tuple=32						,     snake_case:							Tuple=5						,     snake_case:							Dict=4						,     snake_case:							Optional[Any]=37						,     snake_case:							Union[str, Any]="gelu"						,     snake_case:							Tuple=0.1						,     snake_case:							List[Any]=0.1						,     snake_case:							List[str]=512						,     snake_case:							Optional[int]=16						,     snake_case:							int=2						,     snake_case:							List[Any]=0.0_2						,     snake_case:							Union[str, Any]=4						,     )   ->	List[str]:
											snake_case_ :Dict						=			parent
											snake_case_ :Any						=			batch_size
											snake_case_ :Any						=			seq_length
											snake_case_ :List[str]						=			is_training
											snake_case_ :Optional[Any]						=			use_attention_mask
											snake_case_ :Dict						=			use_token_type_ids
											snake_case_ :Union[str, Any]						=			use_labels
											snake_case_ :str						=			vocab_size
											snake_case_ :int						=			hidden_size
											snake_case_ :List[str]						=			num_hidden_layers
											snake_case_ :Dict						=			num_attention_heads
											snake_case_ :Any						=			intermediate_size
											snake_case_ :Tuple						=			hidden_act
											snake_case_ :int						=			hidden_dropout_prob
											snake_case_ :Optional[Any]						=			attention_probs_dropout_prob
											snake_case_ :Any						=			max_position_embeddings
											snake_case_ :Union[str, Any]						=			type_vocab_size
											snake_case_ :Optional[int]						=			type_sequence_label_size
											snake_case_ :Union[str, Any]						=			initializer_range
											snake_case_ :Tuple						=			num_choices






					def    lowerCAmelCase_				(       self:							Tuple						)   ->	str:
											snake_case_ :Optional[Any]						=			ids_tensor([self.batch_size, self.seq_length]						,     self.vocab_size						)

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

											snake_case_ :Any						=			None
											if self.use_token_type_ids:
																	snake_case_ :List[str]						=			ids_tensor([self.batch_size, self.seq_length]						,     self.type_vocab_size						)

											snake_case_ :int						=			BertConfig(
											    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=snake_case						,     initializer_range=self.initializer_range						,     )

											return config, input_ids, token_type_ids, attention_mask






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






					def    lowerCAmelCase_				(       self:							Optional[Any]						)   ->	Any:
											snake_case_ :int						=			self.prepare_config_and_inputs()
											snake_case_,       snake_case_,       snake_case_,       snake_case_ :Dict						=			config_and_inputs

											snake_case_ :Union[str, Any]						=			True
											snake_case_ :Optional[int]						=			floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
											snake_case_ :Tuple						=			ids_tensor([self.batch_size, self.seq_length]						,     vocab_size=2						)

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

@require_flax
class 				lowerCamelCase  (			_lowerCAmelCase    ,						unittest.TestCase				):






					'''simple docstring'''





					_A		:			List[str]						=     True

					_A		:			Dict						=     (
					    (
					        FlaxBertModel,
					        FlaxBertForPreTraining,
					        FlaxBertForMaskedLM,
					        FlaxBertForMultipleChoice,
					        FlaxBertForQuestionAnswering,
					        FlaxBertForNextSentencePrediction,
					        FlaxBertForSequenceClassification,
					        FlaxBertForTokenClassification,
					        FlaxBertForQuestionAnswering,
					    )
					    if is_flax_available()
					    else ()
					)






					def    lowerCAmelCase_				(       self:							int						)   ->	List[str]:
											snake_case_ :Any						=			FlaxBertModelTester(self						)






					@slow
					def    lowerCAmelCase_				(       self:							List[str]						)   ->	Dict:
											# Only check this for base model, not necessary for all model classes.
											# This will also help speed-up tests.
											snake_case_ :Dict						=			FlaxBertModel.from_pretrained("""bert-base-cased"""						)
											snake_case_ :Dict						=			model(np.ones((1, 1)						)						)
											self.assertIsNotNone(snake_case						)




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__  (				lowercase__						,    unittest.TestCase			):
      # TODO: is there an appropriate internal test set?
      snake_case__				:     Any        =						'''ssube/stable-diffusion-x4-upscaler-onnx'''
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       int=0     )		->			Tuple:
             a_					:		Union[str, Any]							=  floats_tensor((1, 3, 1_2_8, 1_2_8)     ,							rng=random.Random(SCREAMING_SNAKE_CASE__     )     )
             a_					:		Dict							=  torch.manual_seed(SCREAMING_SNAKE_CASE__     )
             a_					:		Union[str, Any]							=  {
                 '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        SCREAMING_SNAKE_CASE							(       self				:       List[str]     )		->			Dict:
             a_					:		Dict							=  OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint     ,							provider='CPUExecutionProvider'     )
             pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__     )

             a_					:		Optional[int]							=  self.get_dummy_inputs()
             a_					:		int							=  pipe(**SCREAMING_SNAKE_CASE__     ).images
             a_					:		Tuple							=  image[0, -3:, -3:, -1].flatten()

             # started as 128, should now be 512
             assert image.shape == (1, 5_1_2, 5_1_2, 3)
             a_					:		List[Any]							=  np.array(
                 [0.6974782, 0.68902093, 0.70135885, 0.7583618, 0.7804545, 0.7854912, 0.78667426, 0.78743863, 0.78070223]     )
             assert np.abs(image_slice - expected_slice     ).max() < 1E-1
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			Dict:
             a_					:		Tuple							=  OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint     ,							provider='CPUExecutionProvider'     )
             a_					:		int							=  PNDMScheduler.from_config(pipe.scheduler.config     ,							skip_prk_steps=SCREAMING_SNAKE_CASE__     )
             pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__     )

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

             assert image.shape == (1, 5_1_2, 5_1_2, 3)
             a_					:		str							=  np.array(
                 [0.6898892, 0.59240556, 0.52499527, 0.58866215, 0.52258235, 0.52572715, 0.62414473, 0.6174387, 0.6214964]     )
             assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-1
      def        SCREAMING_SNAKE_CASE							(       self				:       Any     )		->			Dict:
             a_					:		Optional[Any]							=  OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint     ,							provider='CPUExecutionProvider'     )
             a_					:		Optional[Any]							=  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config     )
             pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__     )

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

             assert image.shape == (1, 5_1_2, 5_1_2, 3)
             a_					:		Optional[Any]							=  np.array(
                 [0.7659278, 0.76437664, 0.75579107, 0.7691116, 0.77666986, 0.7727672, 0.7758664, 0.7812226, 0.76942515]     )

             assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-1
      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     )		->			int:
             a_					:		List[str]							=  OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint     ,							provider='CPUExecutionProvider'     )
             a_					:		int							=  EulerDiscreteScheduler.from_config(pipe.scheduler.config     )
             pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__     )

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

             assert image.shape == (1, 5_1_2, 5_1_2, 3)
             a_					:		int							=  np.array(
                 [0.6974782, 0.68902093, 0.70135885, 0.7583618, 0.7804545, 0.7854912, 0.78667426, 0.78743863, 0.78070223]     )
             assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-1

      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			Optional[Any]:
             a_					:		Tuple							=  OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint     ,							provider='CPUExecutionProvider'     )
             a_					:		Optional[int]							=  EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config     )
             pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__     )

             a_					:		Union[str, Any]							=  self.get_dummy_inputs()
             a_					:		List[str]							=  pipe(**SCREAMING_SNAKE_CASE__     ).images
             a_					:		List[str]							=  image[0, -3:, -3:, -1]

             assert image.shape == (1, 5_1_2, 5_1_2, 3)
             a_					:		Union[str, Any]							=  np.array(
                 [0.77424496, 0.773601, 0.7645288, 0.7769598, 0.7772739, 0.7738688, 0.78187233, 0.77879584, 0.767043]     )

             assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-1
@nightly
@require_onnxruntime
@require_torch_gpu
class  SCREAMING_SNAKE_CASE__  (				unittest.TestCase			):
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     )		->			Union[str, Any]:
             return (
                 "CUDAExecutionProvider",
                 {
                     "gpu_mem_limit": "15000000000",  # 15GB
                     "arena_extend_strategy": "kSameAsRequested",
                 },
             )
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     )		->			List[str]:
             a_					:		List[str]							=  ort.SessionOptions()
             a_					:		int							=  False
             return options
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     )		->			Tuple:
             a_					:		str							=  load_image(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                 '/img2img/sketch-mountains-input.jpg'     )
             a_					:		int							=  init_image.resize((1_2_8, 1_2_8)     )
             # using the PNDM scheduler by default
             a_					:		Dict							=  OnnxStableDiffusionUpscalePipeline.from_pretrained(
                 'ssube/stable-diffusion-x4-upscaler-onnx'     ,							provider=self.gpu_provider     ,							sess_options=self.gpu_options     ,							)
             pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__     )

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

             a_					:		str							=  torch.manual_seed(0     )
             a_					:		List[str]							=  pipe(
                 prompt=SCREAMING_SNAKE_CASE__     ,							image=SCREAMING_SNAKE_CASE__     ,							guidance_scale=7.5     ,							num_inference_steps=1_0     ,							generator=SCREAMING_SNAKE_CASE__     ,							output_type='np'     ,							)
             a_					:		Dict							=  output.images
             a_					:		Any							=  images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

             assert images.shape == (1, 5_1_2, 5_1_2, 3)
             a_					:		str							=  np.array([0.4883, 0.4947, 0.4980, 0.4975, 0.4982, 0.4980, 0.5000, 0.5006, 0.4972]     )
             # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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

      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     )		->			List[str]:
             a_					:		Dict							=  load_image(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                 '/img2img/sketch-mountains-input.jpg'     )
             a_					:		List[str]							=  init_image.resize((1_2_8, 1_2_8)     )
             a_					:		Dict							=  LMSDiscreteScheduler.from_pretrained(
                 'ssube/stable-diffusion-x4-upscaler-onnx'     ,							subfolder='scheduler'     )
             a_					:		Union[str, Any]							=  OnnxStableDiffusionUpscalePipeline.from_pretrained(
                 'ssube/stable-diffusion-x4-upscaler-onnx'     ,							scheduler=SCREAMING_SNAKE_CASE__     ,							provider=self.gpu_provider     ,							sess_options=self.gpu_options     ,							)
             pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__     )

             a_					:		Any							=  'A fantasy landscape, trending on artstation'

             a_					:		Tuple							=  torch.manual_seed(0     )
             a_					:		Optional[Any]							=  pipe(
                 prompt=SCREAMING_SNAKE_CASE__     ,							image=SCREAMING_SNAKE_CASE__     ,							guidance_scale=7.5     ,							num_inference_steps=2_0     ,							generator=SCREAMING_SNAKE_CASE__     ,							output_type='np'     ,							)
             a_					:		str							=  output.images
             a_					:		List[Any]							=  images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

             assert images.shape == (1, 5_1_2, 5_1_2, 3)
             a_					:		Tuple							=  np.array(
                 [0.50173753, 0.50223356, 0.502039, 0.50233036, 0.5023725, 0.5022601, 0.5018758, 0.50234085, 0.50241566]     )
             # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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


