





"""simple docstring"""
import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel







class       a							:



 def __init__(     self				,       _snake_case				,       _snake_case=99				,       _snake_case=13				,       _snake_case=7				,       _snake_case=9				,       _snake_case=True				,       _snake_case=True				,       _snake_case=False				,       _snake_case=32				,       _snake_case=5				,       _snake_case=4				,       _snake_case=37				,       _snake_case=8				,       _snake_case=0.1				,       _snake_case=0.002				,       _snake_case=1				,       _snake_case=0				,       _snake_case=0				,       _snake_case=None				,       _snake_case=None				,       ):







  """simple docstring"""






  lowerCAmelCase    				= parent
  lowerCAmelCase    				= batch_size
  lowerCAmelCase    				= encoder_seq_length
  lowerCAmelCase    				= decoder_seq_length
  # For common tests
  lowerCAmelCase    				= self.decoder_seq_length
  lowerCAmelCase    				= is_training
  lowerCAmelCase    				= use_attention_mask
  lowerCAmelCase    				= use_labels
  lowerCAmelCase    				= vocab_size
  lowerCAmelCase    				= hidden_size
  lowerCAmelCase    				= num_hidden_layers
  lowerCAmelCase    				= num_attention_heads
  lowerCAmelCase    				= d_ff
  lowerCAmelCase    				= relative_attention_num_buckets
  lowerCAmelCase    				= dropout_rate
  lowerCAmelCase    				= initializer_factor
  lowerCAmelCase    				= eos_token_id
  lowerCAmelCase    				= pad_token_id
  lowerCAmelCase    				= decoder_start_token_id
  lowerCAmelCase    				= None
  lowerCAmelCase    				= decoder_layers



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  return TaConfig.from_pretrained('google/umt5-base'     )



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case=None				,       _snake_case=None				,       _snake_case=None				,       _snake_case=None				,       _snake_case=None				,       ):







  """simple docstring"""






  if attention_mask is None:
   lowerCAmelCase    				= input_ids.ne(config.pad_token_id     )
  if decoder_attention_mask is None:
   lowerCAmelCase    				= decoder_input_ids.ne(config.pad_token_id     )
  if head_mask is None:
   lowerCAmelCase    				= torch.ones(config.num_hidden_layers				,       config.num_attention_heads				,       device=_snake_case     )
  if decoder_head_mask is None:
   lowerCAmelCase    				= torch.ones(config.num_decoder_layers				,       config.num_attention_heads				,       device=_snake_case     )
  if cross_attn_head_mask is None:
   lowerCAmelCase    				= torch.ones(
       config.num_decoder_layers				,       config.num_attention_heads				,       device=_snake_case     )
  return {
      "input_ids": input_ids,
      "decoder_input_ids": decoder_input_ids,
      "attention_mask": attention_mask,
      "decoder_attention_mask": decoder_attention_mask,
      "head_mask": head_mask,
      "decoder_head_mask": decoder_head_mask,
      "cross_attn_head_mask": cross_attn_head_mask,
  }



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= ids_tensor([self.batch_size, self.encoder_seq_length]				,       self.vocab_size     )
  lowerCAmelCase    				= ids_tensor([self.batch_size, self.decoder_seq_length]				,       self.vocab_size     )

  # we need to clamp the input ids here to avoid having pad token in between
  # this is because for NllbMoe the position_ids are prepared such that
  # all pad tokens have pos id = 2 and rest are between 2..seq_length
  # and the seq_length here is seq_length - num_pad_tokens
  # but when using past, there is no way of knowing if the past input ids had
  # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
  # position_ids being off by num_pad_tokens in past input
  lowerCAmelCase    				= input_ids.clamp(self.pad_token_id + 1     )
  lowerCAmelCase    				= decoder_input_ids.clamp(self.pad_token_id + 1     )

  lowerCAmelCase    				= self.get_config()
  lowerCAmelCase    				= config.num_attention_heads
  lowerCAmelCase    				= self.prepare_inputs_dict(_snake_case				,       _snake_case				,       _snake_case     )
  return config, input_dict



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase				,lowerCAmelCase    				= self.prepare_config_and_inputs()
  return config, inputs_dict



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  return TaConfig(
      vocab_size=1_66				,       d_model=self.hidden_size				,       d_ff=self.d_ff				,       d_kv=self.hidden_size // self.num_attention_heads				,       num_layers=self.num_hidden_layers				,       num_decoder_layers=self.decoder_layers				,       num_heads=self.num_attention_heads				,       relative_attention_num_buckets=self.relative_attention_num_buckets				,       dropout_rate=self.dropout_rate				,       initializer_factor=self.initializer_factor				,       eos_token_id=self.eos_token_id				,       bos_token_id=self.pad_token_id				,       pad_token_id=self.pad_token_id				,       decoder_start_token_id=self.decoder_start_token_id				,       )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  return TaConfig(
      vocab_size=self.vocab_size				,       d_model=self.hidden_size				,       d_ff=self.d_ff				,       d_kv=self.hidden_size // self.num_attention_heads				,       num_layers=self.num_hidden_layers				,       num_decoder_layers=self.decoder_layers				,       num_heads=self.num_attention_heads				,       relative_attention_num_buckets=self.relative_attention_num_buckets				,       dropout_rate=self.dropout_rate				,       initializer_factor=self.initializer_factor				,       eos_token_id=self.eos_token_id				,       bos_token_id=self.pad_token_id				,       pad_token_id=self.pad_token_id				,       decoder_start_token_id=self.decoder_start_token_id				,       )



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       ):







  """simple docstring"""






  lowerCAmelCase    				= UMTaModel(config=_snake_case     )
  model.to(_snake_case     )
  model.eval()
  lowerCAmelCase    				= model(
      input_ids=_snake_case				,       decoder_input_ids=_snake_case				,       attention_mask=_snake_case				,       decoder_attention_mask=_snake_case				,       )
  lowerCAmelCase    				= model(input_ids=_snake_case				,       decoder_input_ids=_snake_case     )
  lowerCAmelCase    				= result.last_hidden_state
  lowerCAmelCase    				= result.past_key_values
  lowerCAmelCase    				= result.encoder_last_hidden_state

  self.parent.assertEqual(encoder_output.size()				,       (self.batch_size, self.encoder_seq_length, self.hidden_size)     )
  self.parent.assertEqual(decoder_output.size()				,       (self.batch_size, self.decoder_seq_length, self.hidden_size)     )
  # There should be `num_layers` key value embeddings stored in decoder_past
  self.parent.assertEqual(len(_snake_case     )				,       config.num_layers     )
  # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
  self.parent.assertEqual(len(decoder_past[0]     )				,       4     )



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       ):







  """simple docstring"""






  lowerCAmelCase    				= UMTaModel(config=_snake_case     ).get_decoder().to(_snake_case     ).eval()
  # first forward pass
  lowerCAmelCase    				= model(_snake_case				,       use_cache=_snake_case     )
  lowerCAmelCase    				= model(_snake_case     )
  lowerCAmelCase    				= model(_snake_case				,       use_cache=_snake_case     )

  self.parent.assertTrue(len(_snake_case     ) == len(_snake_case     )     )
  self.parent.assertTrue(len(_snake_case     ) == len(_snake_case     ) + 1     )

  lowerCAmelCase				,lowerCAmelCase    				= outputs.to_tuple()

  # create hypothetical next token and extent to next_input_ids
  lowerCAmelCase    				= ids_tensor((self.batch_size, 1)				,       config.vocab_size     )

  # append to next input_ids and
  lowerCAmelCase    				= torch.cat([input_ids, next_tokens]				,       dim=-1     )

  lowerCAmelCase    				= model(_snake_case     )['last_hidden_state']
  lowerCAmelCase    				= model(_snake_case				,       past_key_values=_snake_case     )['last_hidden_state']

  # select random slice
  lowerCAmelCase    				= ids_tensor((1,)				,       output_from_past.shape[-1]     ).item()
  lowerCAmelCase    				= output_from_no_past[:, -1, random_slice_idx].detach()
  lowerCAmelCase    				= output_from_past[:, 0, random_slice_idx].detach()

  # test that outputs are equal for slice
  self.parent.assertTrue(torch.allclose(_snake_case				,       _snake_case				,       atol=1E-3     )     )



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       ):







  """simple docstring"""






  lowerCAmelCase    				= UMTaModel(config=_snake_case     ).to(_snake_case     ).half().eval()
  lowerCAmelCase    				= model(**_snake_case     )['last_hidden_state']
  self.parent.assertFalse(torch.isnan(_snake_case     ).any().item()     )







@require_torch
class       a							(   a__   , a__   , a__   , unittest.TestCase  ):
 snake_case__             =       (
     (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
 )
 snake_case__             =       (UMTaForConditionalGeneration,) if is_torch_available() else ()
 snake_case__             =       (
     {
         '''conversational''': UMTaForConditionalGeneration,
         '''feature-extraction''': UMTaModel,
         '''summarization''': UMTaForConditionalGeneration,
         '''text2text-generation''': UMTaForConditionalGeneration,
         '''translation''': UMTaForConditionalGeneration,
         '''question-answering''': UMTaForQuestionAnswering,
     }
     if is_torch_available()
     else {}
 )
 snake_case__             =       True
 snake_case__             =       False
 snake_case__             =       False
 snake_case__             =       True
 snake_case__             =       True
 # The small UMT5 model needs higher percentages for CPU/MP tests
 snake_case__             =       [0.8, 0.9]



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= UMTaModelTester(self     )



 @unittest.skip('Test has a segmentation fault on torch 1.8.0'     )
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.model_tester.prepare_config_and_inputs()
  lowerCAmelCase    				= UMTaModel(config_and_inputs[0]     ).to(_snake_case     )
  with tempfile.TemporaryDirectory() as tmpdirname:
   torch.onnx.export(
       _snake_case				,       (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])				,       F'{tmpdirname}/t5_test.onnx'				,       export_params=_snake_case				,       opset_version=9				,       input_names=['input_ids', 'decoder_input_ids']				,       )



 @unittest.skipIf(torch_device == 'cpu'				,       'Cant do half precision'     )
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_model_fpaa_forward(*_snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= ['encoder_attentions', 'decoder_attentions', 'cross_attentions']
  lowerCAmelCase    				= self.model_tester.prepare_config_and_inputs()
  lowerCAmelCase    				= config_and_inputs[0]
  lowerCAmelCase    				= UMTaForConditionalGeneration(_snake_case     ).eval()
  model.to(_snake_case     )

  lowerCAmelCase    				= {
      'head_mask': torch.zeros(config.num_layers				,       config.num_heads				,       device=_snake_case     ),
      'decoder_head_mask': torch.zeros(config.num_decoder_layers				,       config.num_heads				,       device=_snake_case     ),
      'cross_attn_head_mask': torch.zeros(config.num_decoder_layers				,       config.num_heads				,       device=_snake_case     ),
  }

  for attn_name, (name, mask) in zip(_snake_case				,       head_masking.items()     ):
   lowerCAmelCase    				= {name: mask}
   # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
   if name == "head_mask":
    lowerCAmelCase    				= torch.ones(
        config.num_decoder_layers				,       config.num_heads				,       device=_snake_case     )

   lowerCAmelCase    				= model.generate(
       config_and_inputs[1]['input_ids']				,       num_beams=1				,       max_length=3				,       output_attentions=_snake_case				,       return_dict_in_generate=_snake_case				,       **_snake_case				,       )
   # We check the state of decoder_attentions and cross_attentions just from the last step
   lowerCAmelCase    				= out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
   self.assertEqual(sum([w.sum().item() for w in attn_weights]     )				,       0.0     )



 @unittest.skip('Does not work on the tiny model as we keep hitting edge cases.'     )
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  pass







@require_torch
@require_sentencepiece
@require_tokenizers
class       a							(   unittest.TestCase  ):



 @slow
 @unittest.skip(
     'Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged'     )
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= UMTaForConditionalGeneration.from_pretrained('google/umt5-small'				,       return_dict=_snake_case     ).to(_snake_case     )
  lowerCAmelCase    				= AutoTokenizer.from_pretrained('google/umt5-small'				,       use_fast=_snake_case				,       legacy=_snake_case     )
  lowerCAmelCase    				= [
      'Bonjour monsieur <extra_id_0> bien <extra_id_1>.',
      'No se como puedo <extra_id_0>.',
      'This is the reason why we <extra_id_0> them.',
      'The <extra_id_0> walks in <extra_id_1>, seats',
      'A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.',
  ]
  lowerCAmelCase    				= tokenizer(_snake_case				,       return_tensors='pt'				,       padding=_snake_case     ).input_ids
  # fmt: off
  lowerCAmelCase    				= torch.tensor(
      [
          [ 3_85_30, 21_07_03, 25_62_99, 14_10, 25_62_98, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
          [   8_26, 3_21, 6_71, 2_59_22, 25_62_99, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
          [  14_60, 3_39, 3_12, 1_90_14, 1_06_20, 7_58, 25_62_99, 23_55,2_74, 1, 0, 0, 0, 0, 0, 0,0, 0],
          [   5_17, 25_62_99, 1_48_69, 2_81, 3_01, 25_62_98, 2_75, 11_99_83,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
          [   3_20, 25_62_99, 1_48_69, 2_81, 22_34, 2_89, 22_75, 3_33,6_13_91, 2_89, 25_62_98, 5_43, 25_62_97, 16_87_14, 3_29, 25_62_96,2_74, 1],
      ]     )
  # fmt: on
  torch.testing.assert_allclose(_snake_case				,       _snake_case     )

  lowerCAmelCase    				= model.generate(input_ids.to(_snake_case     )     )
  lowerCAmelCase    				= [
      '<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>',
      '<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
      '<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
      '<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
      '<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
  ]
  lowerCAmelCase    				= tokenizer.batch_decode(_snake_case     )
  self.assertEqual(_snake_case				,       _snake_case     )












import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

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,
    assert_mean_pixel_difference,
)


enable_full_determinism()
class     UpperCamelCase__					(     lowerCamelCase__				, lowerCamelCase__				, lowerCamelCase__				, unittest.TestCase				):






		'''simple docstring'''






		__a       :       Optional[int]       =	StableUnCLIPPipeline
		__a       :       int       =	TEXT_TO_IMAGE_PARAMS
		__a       :       Optional[Any]       =	TEXT_TO_IMAGE_BATCH_PARAMS
		__a       :       Tuple       =	TEXT_TO_IMAGE_IMAGE_PARAMS
		__a       :       Optional[int]       =	TEXT_TO_IMAGE_IMAGE_PARAMS

		# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
		__a       :       Tuple       =	False






		def 						snake_case__				(							self  )       -> Optional[Any]:






									"""simple docstring"""
									lowercase_	:   int  				=     32
									lowercase_	:   Tuple  				=     embedder_hidden_size

									# prior components

									torch.manual_seed(0  )
									lowercase_	:   Optional[Any]  				=     CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""  )

									torch.manual_seed(0  )
									lowercase_	:   Dict  				=     CLIPTextModelWithProjection(
									    CLIPTextConfig(
									        bos_token_id=0,   eos_token_id=2,   hidden_size=snake_case__,   projection_dim=snake_case__,   intermediate_size=37,   layer_norm_eps=1E-05,   num_attention_heads=4,   num_hidden_layers=5,   pad_token_id=1,   vocab_size=10_00,   )  )

									torch.manual_seed(0  )
									lowercase_	:   Tuple  				=     PriorTransformer(
									    num_attention_heads=2,   attention_head_dim=12,   embedding_dim=snake_case__,   num_layers=1,   )

									torch.manual_seed(0  )
									lowercase_	:   Optional[Any]  				=     DDPMScheduler(
									    variance_type="""fixed_small_log""",   prediction_type="""sample""",   num_train_timesteps=10_00,   clip_sample=snake_case__,   clip_sample_range=5.0,   beta_schedule="""squaredcos_cap_v2""",   )

									# regular denoising components

									torch.manual_seed(0  )
									lowercase_	:   Union[str, Any]  				=     StableUnCLIPImageNormalizer(embedding_dim=snake_case__  )
									lowercase_	:   str  				=     DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""  )

									torch.manual_seed(0  )
									lowercase_	:   str  				=     CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""  )

									torch.manual_seed(0  )
									lowercase_	:   List[Any]  				=     CLIPTextModel(
									    CLIPTextConfig(
									        bos_token_id=0,   eos_token_id=2,   hidden_size=snake_case__,   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=10_00,   )  )

									torch.manual_seed(0  )
									lowercase_	:   Optional[int]  				=     UNetaDConditionModel(
									    sample_size=32,   in_channels=4,   out_channels=4,   down_block_types=("""CrossAttnDownBlock2D""", """DownBlock2D"""),   up_block_types=("""UpBlock2D""", """CrossAttnUpBlock2D"""),   block_out_channels=(32, 64),   attention_head_dim=(2, 4),   class_embed_type="""projection""",   projection_class_embeddings_input_dim=embedder_projection_dim * 2,   cross_attention_dim=snake_case__,   layers_per_block=1,   upcast_attention=snake_case__,   use_linear_projection=snake_case__,   )

									torch.manual_seed(0  )
									lowercase_	:   Dict  				=     DDIMScheduler(
									    beta_schedule="""scaled_linear""",   beta_start=0.00085,   beta_end=0.012,   prediction_type="""v_prediction""",   set_alpha_to_one=snake_case__,   steps_offset=1,   )

									torch.manual_seed(0  )
									lowercase_	:   str  				=     AutoencoderKL()

									lowercase_	:   str  				=     {
									    # prior components
									    """prior_tokenizer""": prior_tokenizer,
									    """prior_text_encoder""": prior_text_encoder,
									    """prior""": prior,
									    """prior_scheduler""": prior_scheduler,
									    # image noising components
									    """image_normalizer""": image_normalizer,
									    """image_noising_scheduler""": image_noising_scheduler,
									    # regular denoising components
									    """tokenizer""": tokenizer,
									    """text_encoder""": text_encoder,
									    """unet""": unet,
									    """scheduler""": scheduler,
									    """vae""": vae,
									}

									return components






		def 						snake_case__				(							self,   snake_case__,   snake_case__=0  )       -> str:






									"""simple docstring"""
									if str(snake_case__  ).startswith("""mps"""  ):
																lowercase_	:   Tuple  				=     torch.manual_seed(snake_case__  )
									else:
																lowercase_	:   int  				=     torch.Generator(device=snake_case__  ).manual_seed(snake_case__  )
									lowercase_	:   Dict  				=     {
									    """prompt""": """A painting of a squirrel eating a burger""",
									    """generator""": generator,
									    """num_inference_steps""": 2,
									    """prior_num_inference_steps""": 2,
									    """output_type""": """numpy""",
									}
									return inputs






		def 						snake_case__				(							self  )       -> List[Any]:






									"""simple docstring"""
									lowercase_	:   int  				=     torch_device == """cpu"""

									self._test_attention_slicing_forward_pass(test_max_difference=snake_case__  )






		def 						snake_case__				(							self  )       -> Any:






									"""simple docstring"""
									lowercase_	:   List[str]  				=     torch_device in ["""cpu""", """mps"""]

									self._test_inference_batch_single_identical(test_max_difference=snake_case__  )




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






		'''simple docstring'''






		def 						snake_case__				(							self  )       -> Any:






									"""simple docstring"""
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()






		def 						snake_case__				(							self  )       -> List[str]:






									"""simple docstring"""
									lowercase_	:   Union[str, Any]  				=     load_numpy(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"""  )

									lowercase_	:   List[Any]  				=     StableUnCLIPPipeline.from_pretrained("""fusing/stable-unclip-2-1-l""",   torch_dtype=torch.floataa  )
									pipe.to(snake_case__  )
									pipe.set_progress_bar_config(disable=snake_case__  )
									# stable unclip will oom when integration tests are run on a V100,
									# so turn on memory savings
									pipe.enable_attention_slicing()
									pipe.enable_sequential_cpu_offload()

									lowercase_	:   Any  				=     torch.Generator(device="""cpu"""  ).manual_seed(0  )
									lowercase_	:   Dict  				=     pipe("""anime turle""",   generator=snake_case__,   output_type="""np"""  )

									lowercase_	:   str  				=     output.images[0]

									assert image.shape == (7_68, 7_68, 3)

									assert_mean_pixel_difference(snake_case__,   snake_case__  )






		def 						snake_case__				(							self  )       -> Dict:






									"""simple docstring"""
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									lowercase_	:   str  				=     StableUnCLIPPipeline.from_pretrained("""fusing/stable-unclip-2-1-l""",   torch_dtype=torch.floataa  )
									lowercase_	:   Optional[int]  				=     pipe.to(snake_case__  )
									pipe.set_progress_bar_config(disable=snake_case__  )
									pipe.enable_attention_slicing()
									pipe.enable_sequential_cpu_offload()

									lowercase_	:   List[Any]  				=     pipe(
									    """anime turtle""",   prior_num_inference_steps=2,   num_inference_steps=2,   output_type="""np""",   )

									lowercase_	:   Optional[int]  				=     torch.cuda.max_memory_allocated()
									# make sure that less than 7 GB is allocated
									assert mem_bytes < 7 * 10**9