






"""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							,		_lowerCamelCase							,		_lowerCamelCase=9_9							,		_lowerCamelCase=1_3							,		_lowerCamelCase=7							,		_lowerCamelCase=9							,		_lowerCamelCase=True							,		_lowerCamelCase=True							,		_lowerCamelCase=False							,		_lowerCamelCase=3_2							,		_lowerCamelCase=5							,		_lowerCamelCase=4							,		_lowerCamelCase=3_7							,		_lowerCamelCase=8							,		_lowerCamelCase=0.1							,		_lowerCamelCase=0.0_0_2							,		_lowerCamelCase=1							,		_lowerCamelCase=0							,		_lowerCamelCase=0							,		_lowerCamelCase=None							,		_lowerCamelCase=None							,		):
      lowercase							    =       parent
      lowercase							    =       batch_size
      lowercase							    =       encoder_seq_length
      lowercase							    =       decoder_seq_length
      # For common tests
      lowercase							    =       self.decoder_seq_length
      lowercase							    =       is_training
      lowercase							    =       use_attention_mask
      lowercase							    =       use_labels
      lowercase							    =       vocab_size
      lowercase							    =       hidden_size
      lowercase							    =       num_hidden_layers
      lowercase							    =       num_attention_heads
      lowercase							    =       d_ff
      lowercase							    =       relative_attention_num_buckets
      lowercase							    =       dropout_rate
      lowercase							    =       initializer_factor
      lowercase							    =       eos_token_id
      lowercase							    =       pad_token_id
      lowercase							    =       decoder_start_token_id
      lowercase							    =       None
      lowercase							    =       decoder_layers







    def 			UpperCamelCase_      (			self       ):
      return TaConfig.from_pretrained('google/umt5-base'       )







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase=None							,		_lowerCamelCase=None							,		_lowerCamelCase=None							,		_lowerCamelCase=None							,		_lowerCamelCase=None							,		):





      if attention_mask is None:
        lowercase							    =       input_ids.ne(config.pad_token_id       )
      if decoder_attention_mask is None:
        lowercase							    =       decoder_input_ids.ne(config.pad_token_id       )
      if head_mask is None:
        lowercase							    =       torch.ones(config.num_hidden_layers							,		config.num_attention_heads							,		device=__A       )
      if decoder_head_mask is None:
        lowercase							    =       torch.ones(config.num_decoder_layers							,		config.num_attention_heads							,		device=__A       )
      if cross_attn_head_mask is None:
        lowercase							    =       torch.ones(
            config.num_decoder_layers							,		config.num_attention_heads							,		device=__A       )
      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       ):
      lowercase							    =       ids_tensor([self.batch_size, self.encoder_seq_length]							,		self.vocab_size       )
      lowercase							    =       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
      lowercase							    =       input_ids.clamp(self.pad_token_id + 1       )
      lowercase							    =       decoder_input_ids.clamp(self.pad_token_id + 1       )

      lowercase							    =       self.get_config()
      lowercase							    =       config.num_attention_heads
      lowercase							    =       self.prepare_inputs_dict(__A							,		__A							,		__A       )
      return config, input_dict







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.prepare_config_and_inputs()
      return config, inputs_dict







    def 			UpperCamelCase_      (			self       ):
      return TaConfig(
          vocab_size=1_6_6							,		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       ):
      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							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		):
      lowercase							    =       UMTaModel(config=__A       )
      model.to(__A       )
      model.eval()
      lowercase							    =       model(
          input_ids=__A							,		decoder_input_ids=__A							,		attention_mask=__A							,		decoder_attention_mask=__A							,		)
      lowercase							    =       model(input_ids=__A							,		decoder_input_ids=__A       )
      lowercase							    =       result.last_hidden_state
      lowercase							    =       result.past_key_values
      lowercase							    =       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(__A       )							,		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							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		):
      lowercase							    =       UMTaModel(config=__A       ).get_decoder().to(__A       ).eval()
      # first forward pass
      lowercase							    =       model(__A							,		use_cache=__A       )
      lowercase							    =       model(__A       )
      lowercase							    =       model(__A							,		use_cache=__A       )

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

      lowercase							    =       outputs.to_tuple()

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

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

      lowercase							    =       model(__A       )["last_hidden_state"]
      lowercase							    =       model(__A							,		past_key_values=__A       )["last_hidden_state"]

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

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







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase							,		):
      lowercase							    =       UMTaModel(config=__A       ).to(__A       ).half().eval()
      lowercase							    =       model(**__A       )["last_hidden_state"]
      self.parent.assertFalse(torch.isnan(__A       ).any().item()       )


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







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       UMTaModelTester(self       )







    @unittest.skip('Test has a segmentation fault on torch 1.8.0'       )
    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.model_tester.prepare_config_and_inputs()
      lowercase							    =       UMTaModel(config_and_inputs[0]       ).to(__A       )
      with tempfile.TemporaryDirectory() as tmpdirname:
        torch.onnx.export(
            __A							,		(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])							,		F'{tmpdirname}/t5_test.onnx'							,		export_params=__A							,		opset_version=9							,		input_names=['input_ids', 'decoder_input_ids']							,		)







    @unittest.skipIf(torch_device == 'cpu'							,		'Cant do half precision'       )
    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model_fpaa_forward(*__A       )







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       ["encoder_attentions", "decoder_attentions", "cross_attentions"]
      lowercase							    =       self.model_tester.prepare_config_and_inputs()
      lowercase							    =       config_and_inputs[0]
      lowercase							    =       UMTaForConditionalGeneration(__A       ).eval()
      model.to(__A       )

      lowercase							    =       {
          "head_mask": torch.zeros(config.num_layers							,		config.num_heads							,		device=__A       ),
          "decoder_head_mask": torch.zeros(config.num_decoder_layers							,		config.num_heads							,		device=__A       ),
          "cross_attn_head_mask": torch.zeros(config.num_decoder_layers							,		config.num_heads							,		device=__A       ),
      }

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

        lowercase							    =       model.generate(
            config_and_inputs[1]['input_ids']							,		num_beams=1							,		max_length=3							,		output_attentions=__A							,		return_dict_in_generate=__A							,		**__A							,		)
        # We check the state of decoder_attentions and cross_attentions just from the last step
        lowercase							    =       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       ):
      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       ):
      lowercase							    =       UMTaForConditionalGeneration.from_pretrained('google/umt5-small'							,		return_dict=__A       ).to(__A       )
      lowercase							    =       AutoTokenizer.from_pretrained('google/umt5-small'							,		use_fast=__A							,		legacy=__A       )
      lowercase							    =       [
          "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>.",
      ]
      lowercase							    =       tokenizer(__A							,		return_tensors='pt'							,		padding=__A       ).input_ids
      # fmt: off
      lowercase							    =       torch.tensor(
          [
              [ 3_8_5_3_0, 2_1_0_7_0_3, 2_5_6_2_9_9, 1_4_1_0, 2_5_6_2_9_8, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
              [   8_2_6, 3_2_1, 6_7_1, 2_5_9_2_2, 2_5_6_2_9_9, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
              [  1_4_6_0, 3_3_9, 3_1_2, 1_9_0_1_4, 1_0_6_2_0, 7_5_8, 2_5_6_2_9_9, 2_3_5_5,2_7_4, 1, 0, 0, 0, 0, 0, 0,0, 0],
              [   5_1_7, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 3_0_1, 2_5_6_2_9_8, 2_7_5, 1_1_9_9_8_3,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
              [   3_2_0, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 2_2_3_4, 2_8_9, 2_2_7_5, 3_3_3,6_1_3_9_1, 2_8_9, 2_5_6_2_9_8, 5_4_3, 2_5_6_2_9_7, 1_6_8_7_1_4, 3_2_9, 2_5_6_2_9_6,2_7_4, 1],
          ]       )
      # fmt: on
      torch.testing.assert_allclose(__A							,		__A       )

      lowercase							    =       model.generate(input_ids.to(__A       )       )
      lowercase							    =       [
          "<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>",
      ]
      lowercase							    =       tokenizer.batch_decode(__A       )
      self.assertEqual(__A							,		__A       )






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_					(					UpperCamelCase		, UpperCamelCase		, UpperCamelCase		, unittest.TestCase    ):

   '''simple docstring'''




   __A :      Tuple     		=		StableUnCLIPPipeline
   __A :      Optional[int]     		=		TEXT_TO_IMAGE_PARAMS
   __A :      str     		=		TEXT_TO_IMAGE_BATCH_PARAMS
   __A :      int     		=		TEXT_TO_IMAGE_IMAGE_PARAMS
   __A :      Tuple     		=		TEXT_TO_IMAGE_IMAGE_PARAMS

   # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
   __A :      Union[str, Any]     		=		False






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					List[str]    =				32
     lowerCamelCase :					Dict    =				embedder_hidden_size

     # prior components

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

     torch.manual_seed(0 )
     lowerCamelCase :					Optional[int]    =				CLIPTextModelWithProjection(
         CLIPTextConfig(
             bos_token_id=0						,				eos_token_id=2						,				hidden_size=__A						,				projection_dim=__A						,				intermediate_size=37						,				layer_norm_eps=1e-05						,				num_attention_heads=4						,				num_hidden_layers=5						,				pad_token_id=1						,				vocab_size=1000						,				) )

     torch.manual_seed(0 )
     lowerCamelCase :					List[Any]    =				PriorTransformer(
         num_attention_heads=2						,				attention_head_dim=12						,				embedding_dim=__A						,				num_layers=1						,				)

     torch.manual_seed(0 )
     lowerCamelCase :					Dict    =				DDPMScheduler(
         variance_type="fixed_small_log"						,				prediction_type="sample"						,				num_train_timesteps=1000						,				clip_sample=__A						,				clip_sample_range=5.0						,				beta_schedule="squaredcos_cap_v2"						,				)

     # regular denoising components

     torch.manual_seed(0 )
     lowerCamelCase :					Optional[int]    =				StableUnCLIPImageNormalizer(embedding_dim=__A )
     lowerCamelCase :					Tuple    =				DDPMScheduler(beta_schedule="squaredcos_cap_v2" )

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

     torch.manual_seed(0 )
     lowerCamelCase :					str    =				CLIPTextModel(
         CLIPTextConfig(
             bos_token_id=0						,				eos_token_id=2						,				hidden_size=__A						,				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=1000						,				) )

     torch.manual_seed(0 )
     lowerCamelCase :					Any    =				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=__A						,				layers_per_block=1						,				upcast_attention=__A						,				use_linear_projection=__A						,				)

     torch.manual_seed(0 )
     lowerCamelCase :					int    =				DDIMScheduler(
         beta_schedule="scaled_linear"						,				beta_start=0.00085						,				beta_end=0.012						,				prediction_type="v_prediction"						,				set_alpha_to_one=__A						,				steps_offset=1						,				)

     torch.manual_seed(0 )
     lowerCamelCase :					Optional[Any]    =				AutoencoderKL()

     lowerCamelCase :					Optional[int]    =				{
         # 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						,				__A						,				__A=0 ):






     """simple docstring"""





     if str(__A ).startswith("mps" ):
       lowerCamelCase :					Optional[int]    =				torch.manual_seed(__A )
     else:
       lowerCamelCase :					Optional[Any]    =				torch.Generator(device=__A ).manual_seed(__A )
     lowerCamelCase :					Tuple    =				{
         "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 ):






     """simple docstring"""





     lowerCamelCase :					Dict    =				torch_device == "cpu"

     self._test_attention_slicing_forward_pass(test_max_difference=__A )






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					List[Any]    =				torch_device in ["cpu", "mps"]

     self._test_inference_batch_single_identical(test_max_difference=__A )



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

   '''simple docstring'''




   def _snake_case    (		self ):






     """simple docstring"""





     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Optional[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" )

     lowerCamelCase :					str    =				StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"						,				torch_dtype=torch.floataa )
     pipe.to(__A )
     pipe.set_progress_bar_config(disable=__A )
     # 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()

     lowerCamelCase :					List[Any]    =				torch.Generator(device="cpu" ).manual_seed(0 )
     lowerCamelCase :					Dict    =				pipe("anime turle"						,				generator=__A						,				output_type="np" )

     lowerCamelCase :					Dict    =				output.images[0]

     assert image.shape == (768, 768, 3)

     assert_mean_pixel_difference(__A						,				__A )






   def _snake_case    (		self ):






     """simple docstring"""





     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()
     torch.cuda.reset_peak_memory_stats()

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

     lowerCamelCase :					Any    =				pipe(
         "anime turtle"						,				prior_num_inference_steps=2						,				num_inference_steps=2						,				output_type="np"						,				)

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



