


'''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 						a__							(				unittest.TestCase    ):





							'''simple docstring'''






							def __init__(						self   ,       lowerCamelCase_   ,       lowerCamelCase_=13   ,       lowerCamelCase_=7   ,       lowerCamelCase_=True   ,       lowerCamelCase_=True   ,       lowerCamelCase_=True   ,       lowerCamelCase_=True   ,       lowerCamelCase_=99   ,       lowerCamelCase_=32   ,       lowerCamelCase_=5   ,       lowerCamelCase_=4   ,       lowerCamelCase_=37   ,       lowerCamelCase_="gelu"   ,       lowerCamelCase_=0.1   ,       lowerCamelCase_=0.1   ,       lowerCamelCase_=5_12   ,       lowerCamelCase_=16   ,       lowerCamelCase_=2   ,       lowerCamelCase_=0.02   ,       lowerCamelCase_=4   ,       )			->				List[Any]:
														lowerCAmelCase__			  =      parent
														lowerCAmelCase__			  =      batch_size
														lowerCAmelCase__			  =      seq_length
														lowerCAmelCase__			  =      is_training
														lowerCAmelCase__			  =      use_attention_mask
														lowerCAmelCase__			  =      use_token_type_ids
														lowerCAmelCase__			  =      use_labels
														lowerCAmelCase__			  =      vocab_size
														lowerCAmelCase__			  =      hidden_size
														lowerCAmelCase__			  =      num_hidden_layers
														lowerCAmelCase__			  =      num_attention_heads
														lowerCAmelCase__			  =      intermediate_size
														lowerCAmelCase__			  =      hidden_act
														lowerCAmelCase__			  =      hidden_dropout_prob
														lowerCAmelCase__			  =      attention_probs_dropout_prob
														lowerCAmelCase__			  =      max_position_embeddings
														lowerCAmelCase__			  =      type_vocab_size
														lowerCAmelCase__			  =      type_sequence_label_size
														lowerCAmelCase__			  =      initializer_range
														lowerCAmelCase__			  =      num_choices







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				str:
														lowerCAmelCase__			  =      ids_tensor([self.batch_size, self.seq_length]   ,       self.vocab_size  )

														lowerCAmelCase__			  =      None
														if self.use_attention_mask:
																					lowerCAmelCase__			  =      random_attention_mask([self.batch_size, self.seq_length]  )

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

														lowerCAmelCase__			  =      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=lowerCamelCase_   ,       initializer_range=self.initializer_range   ,       )

														return config, input_ids, token_type_ids, attention_mask







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														lowerCAmelCase__			  =      self.prepare_config_and_inputs()
														lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__			  =      config_and_inputs
														lowerCAmelCase__			  =      {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
														return config, inputs_dict







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Tuple:
														lowerCAmelCase__			  =      self.prepare_config_and_inputs()
														lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__			  =      config_and_inputs

														lowerCAmelCase__			  =      True
														lowerCAmelCase__			  =      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]  )
														lowerCAmelCase__			  =      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 						a__							(				a__							,							unittest.TestCase    ):





							'''simple docstring'''






							lowercase__     :  Dict      		= True

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







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Tuple:
														lowerCAmelCase__			  =      FlaxBertModelTester(self  )







							@slow
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				str:
														# Only check this for base model, not necessary for all model classes.
														# This will also help speed-up tests.
														lowerCAmelCase__			  =      FlaxBertModel.from_pretrained('''bert-base-cased'''  )
														lowerCAmelCase__			  =      model(np.ones((1, 1)  )  )
														self.assertIsNotNone(lowerCamelCase_  )



'''simple docstring'''






import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class 						a__							(				a__							,							a__							,							a__							,							unittest.TestCase    ):





							'''simple docstring'''






							lowercase__     :  Optional[Any]      		= AltDiffusionPipeline
							lowercase__     :  Dict      		= TEXT_TO_IMAGE_PARAMS
							lowercase__     :  str      		= TEXT_TO_IMAGE_BATCH_PARAMS
							lowercase__     :  Any      		= TEXT_TO_IMAGE_IMAGE_PARAMS
							lowercase__     :  List[str]      		= TEXT_TO_IMAGE_IMAGE_PARAMS







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				str:
														torch.manual_seed(0  )
														lowerCAmelCase__			  =      UNetaDConditionModel(
														    block_out_channels=(32, 64)   ,       layers_per_block=2   ,       sample_size=32   ,       in_channels=4   ,       out_channels=4   ,       down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')   ,       up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')   ,       cross_attention_dim=32   ,       )
														lowerCAmelCase__			  =      DDIMScheduler(
														    beta_start=0.00_085   ,       beta_end=0.012   ,       beta_schedule='''scaled_linear'''   ,       clip_sample=lowerCamelCase_   ,       set_alpha_to_one=lowerCamelCase_   ,       )
														torch.manual_seed(0  )
														lowerCAmelCase__			  =      AutoencoderKL(
														    block_out_channels=[32, 64]   ,       in_channels=3   ,       out_channels=3   ,       down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']   ,       up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']   ,       latent_channels=4   ,       )

														# TODO: address the non-deterministic text encoder (fails for save-load tests)
														# torch.manual_seed(0)
														# text_encoder_config = RobertaSeriesConfig(
														#     hidden_size=32,
														#     project_dim=32,
														#     intermediate_size=37,
														#     layer_norm_eps=1e-05,
														#     num_attention_heads=4,
														#     num_hidden_layers=5,
														#     vocab_size=5002,
														# )
														# text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

														torch.manual_seed(0  )
														lowerCAmelCase__			  =      CLIPTextConfig(
														    bos_token_id=0   ,       eos_token_id=2   ,       hidden_size=32   ,       projection_dim=32   ,       intermediate_size=37   ,       layer_norm_eps=1e-05   ,       num_attention_heads=4   ,       num_hidden_layers=5   ,       pad_token_id=1   ,       vocab_size=50_02   ,       )
														lowerCAmelCase__			  =      CLIPTextModel(lowerCamelCase_  )

														lowerCAmelCase__			  =      XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''  )
														lowerCAmelCase__			  =      77

														lowerCAmelCase__			  =      {
														    '''unet''': unet,
														    '''scheduler''': scheduler,
														    '''vae''': vae,
														    '''text_encoder''': text_encoder,
														    '''tokenizer''': tokenizer,
														    '''safety_checker''': None,
														    '''feature_extractor''': None,
														}
														return components







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_=0  )			->				List[str]:
														if str(lowerCamelCase_  ).startswith('''mps'''  ):
																					lowerCAmelCase__			  =      torch.manual_seed(lowerCamelCase_  )
														else:
																					lowerCAmelCase__			  =      torch.Generator(device=lowerCamelCase_  ).manual_seed(lowerCamelCase_  )
														lowerCAmelCase__			  =      {
														    '''prompt''': '''A painting of a squirrel eating a burger''',
														    '''generator''': generator,
														    '''num_inference_steps''': 2,
														    '''guidance_scale''': 6.0,
														    '''output_type''': '''numpy''',
														}
														return inputs







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Union[str, Any]:
														super().test_attention_slicing_forward_pass(expected_max_diff=3e-3  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														super().test_inference_batch_single_identical(expected_max_diff=3e-3  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[str]:
														lowerCAmelCase__			  =      '''cpu'''  # ensure determinism for the device-dependent torch.Generator

														lowerCAmelCase__			  =      self.get_dummy_components()
														torch.manual_seed(0  )
														lowerCAmelCase__			  =      RobertaSeriesConfig(
														    hidden_size=32   ,       project_dim=32   ,       intermediate_size=37   ,       layer_norm_eps=1e-05   ,       num_attention_heads=4   ,       num_hidden_layers=5   ,       vocab_size=50_02   ,       )
														# TODO: remove after fixing the non-deterministic text encoder
														lowerCAmelCase__			  =      RobertaSeriesModelWithTransformation(lowerCamelCase_  )
														lowerCAmelCase__			  =      text_encoder

														lowerCAmelCase__			  =      AltDiffusionPipeline(**lowerCamelCase_  )
														lowerCAmelCase__			  =      alt_pipe.to(lowerCamelCase_  )
														alt_pipe.set_progress_bar_config(disable=lowerCamelCase_  )

														lowerCAmelCase__			  =      self.get_dummy_inputs(lowerCamelCase_  )
														lowerCAmelCase__			  =      '''A photo of an astronaut'''
														lowerCAmelCase__			  =      alt_pipe(**lowerCamelCase_  )
														lowerCAmelCase__			  =      output.images
														lowerCAmelCase__			  =      image[0, -3:, -3:, -1]

														assert image.shape == (1, 64, 64, 3)
														lowerCAmelCase__			  =      np.array(
														    [0.5_748_162, 0.60_447_145, 0.48_821_217, 0.50_100_636, 0.5_431_185, 0.45_763_683, 0.49_657_696, 0.48_132_733, 0.47_573_093]  )

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







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[Any]:
														lowerCAmelCase__			  =      '''cpu'''  # ensure determinism for the device-dependent torch.Generator

														lowerCAmelCase__			  =      self.get_dummy_components()
														lowerCAmelCase__			  =      PNDMScheduler(skip_prk_steps=lowerCamelCase_  )
														torch.manual_seed(0  )
														lowerCAmelCase__			  =      RobertaSeriesConfig(
														    hidden_size=32   ,       project_dim=32   ,       intermediate_size=37   ,       layer_norm_eps=1e-05   ,       num_attention_heads=4   ,       num_hidden_layers=5   ,       vocab_size=50_02   ,       )
														# TODO: remove after fixing the non-deterministic text encoder
														lowerCAmelCase__			  =      RobertaSeriesModelWithTransformation(lowerCamelCase_  )
														lowerCAmelCase__			  =      text_encoder
														lowerCAmelCase__			  =      AltDiffusionPipeline(**lowerCamelCase_  )
														lowerCAmelCase__			  =      alt_pipe.to(lowerCamelCase_  )
														alt_pipe.set_progress_bar_config(disable=lowerCamelCase_  )

														lowerCAmelCase__			  =      self.get_dummy_inputs(lowerCamelCase_  )
														lowerCAmelCase__			  =      alt_pipe(**lowerCamelCase_  )
														lowerCAmelCase__			  =      output.images
														lowerCAmelCase__			  =      image[0, -3:, -3:, -1]

														assert image.shape == (1, 64, 64, 3)
														lowerCAmelCase__			  =      np.array(
														    [0.51_605_093, 0.5_707_241, 0.47_365_507, 0.50_578_886, 0.5_633_877, 0.4_642_503, 0.5_182_081, 0.48_763_484, 0.49_084_237]  )

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




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





							'''simple docstring'''






							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Tuple:
														# clean up the VRAM after each test
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[Any]:
														# make sure here that pndm scheduler skips prk
														lowerCAmelCase__			  =      AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion'''   ,       safety_checker=lowerCamelCase_  )
														lowerCAmelCase__			  =      alt_pipe.to(lowerCamelCase_  )
														alt_pipe.set_progress_bar_config(disable=lowerCamelCase_  )

														lowerCAmelCase__			  =      '''A painting of a squirrel eating a burger'''
														lowerCAmelCase__			  =      torch.manual_seed(0  )
														lowerCAmelCase__			  =      alt_pipe([prompt]   ,       generator=lowerCamelCase_   ,       guidance_scale=6.0   ,       num_inference_steps=20   ,       output_type='''np'''  )

														lowerCAmelCase__			  =      output.images

														lowerCAmelCase__			  =      image[0, -3:, -3:, -1]

														assert image.shape == (1, 5_12, 5_12, 3)
														lowerCAmelCase__			  =      np.array([0.1_010, 0.0_800, 0.0_794, 0.0_885, 0.0_843, 0.0_762, 0.0_769, 0.0_729, 0.0_586]  )

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







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Any:
														lowerCAmelCase__			  =      DDIMScheduler.from_pretrained('''BAAI/AltDiffusion'''   ,       subfolder='''scheduler'''  )

														lowerCAmelCase__			  =      AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion'''   ,       scheduler=lowerCamelCase_   ,       safety_checker=lowerCamelCase_  )
														lowerCAmelCase__			  =      alt_pipe.to(lowerCamelCase_  )
														alt_pipe.set_progress_bar_config(disable=lowerCamelCase_  )

														lowerCAmelCase__			  =      '''A painting of a squirrel eating a burger'''
														lowerCAmelCase__			  =      torch.manual_seed(0  )

														lowerCAmelCase__			  =      alt_pipe([prompt]   ,       generator=lowerCamelCase_   ,       num_inference_steps=2   ,       output_type='''numpy'''  )
														lowerCAmelCase__			  =      output.images

														lowerCAmelCase__			  =      image[0, -3:, -3:, -1]

														assert image.shape == (1, 5_12, 5_12, 3)
														lowerCAmelCase__			  =      np.array([0.4_019, 0.4_052, 0.3_810, 0.4_119, 0.3_916, 0.3_982, 0.4_651, 0.4_195, 0.5_323]  )

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