





"""simple docstring"""





import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list






@require_torchaudio
@require_sentencepiece
class 					_lowerCAmelCase   (     unittest.TestCase       ):
       """simple docstring"""




       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[Any]											=							'laion/clap-htsat-unfused'
         lowerCAmelCase__			:str											=							tempfile.mkdtemp()






       def      snake_case						(    self    , **__UpperCAmelCase      ):



         '''simple docstring'''

         return RobertaTokenizer.from_pretrained(self.checkpoint    , **__UpperCAmelCase      )






       def      snake_case						(    self    , **__UpperCAmelCase      ):



         '''simple docstring'''

         return ClapFeatureExtractor.from_pretrained(self.checkpoint    , **__UpperCAmelCase      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         shutil.rmtree(self.tmpdirname      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Tuple											=							self.get_tokenizer()
         lowerCAmelCase__			:Tuple											=							self.get_feature_extractor()

         lowerCAmelCase__			:Optional[Any]											=							ClapProcessor(tokenizer=__UpperCAmelCase    , feature_extractor=__UpperCAmelCase      )

         processor.save_pretrained(self.tmpdirname      )
         lowerCAmelCase__			:Any											=							ClapProcessor.from_pretrained(self.tmpdirname      )

         self.assertEqual(processor.tokenizer.get_vocab()    , tokenizer.get_vocab()      )
         self.assertIsInstance(processor.tokenizer    , __UpperCAmelCase      )

         self.assertEqual(processor.feature_extractor.to_json_string()    , feature_extractor.to_json_string()      )
         self.assertIsInstance(processor.feature_extractor    , __UpperCAmelCase      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[Any]											=							ClapProcessor(tokenizer=self.get_tokenizer()    , feature_extractor=self.get_feature_extractor()      )
         processor.save_pretrained(self.tmpdirname      )

         lowerCAmelCase__			:Optional[int]											=							self.get_tokenizer(bos_token='(BOS)'    , eos_token='(EOS)'      )
         lowerCAmelCase__			:List[Any]											=							self.get_feature_extractor(do_normalize=__UpperCAmelCase    , padding_value=1.0      )

         lowerCAmelCase__			:str											=							ClapProcessor.from_pretrained(
             self.tmpdirname    , bos_token='(BOS)'    , eos_token='(EOS)'    , do_normalize=__UpperCAmelCase    , padding_value=1.0      )

         self.assertEqual(processor.tokenizer.get_vocab()    , tokenizer_add_kwargs.get_vocab()      )
         self.assertIsInstance(processor.tokenizer    , __UpperCAmelCase      )

         self.assertEqual(processor.feature_extractor.to_json_string()    , feature_extractor_add_kwargs.to_json_string()      )
         self.assertIsInstance(processor.feature_extractor    , __UpperCAmelCase      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Any											=							self.get_feature_extractor()
         lowerCAmelCase__			:List[str]											=							self.get_tokenizer()

         lowerCAmelCase__			:List[str]											=							ClapProcessor(tokenizer=__UpperCAmelCase    , feature_extractor=__UpperCAmelCase      )

         lowerCAmelCase__			:str											=							floats_list((3, 1_0_0_0)      )

         lowerCAmelCase__			:Optional[Any]											=							feature_extractor(__UpperCAmelCase    , return_tensors='np'      )
         lowerCAmelCase__			:Optional[int]											=							processor(audios=__UpperCAmelCase    , return_tensors='np'      )

         for key in input_feat_extract.keys():
           self.assertAlmostEqual(input_feat_extract[key].sum()    , input_processor[key].sum()    , delta=1E-2      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[Any]											=							self.get_feature_extractor()
         lowerCAmelCase__			:Dict											=							self.get_tokenizer()

         lowerCAmelCase__			:Dict											=							ClapProcessor(tokenizer=__UpperCAmelCase    , feature_extractor=__UpperCAmelCase      )

         lowerCAmelCase__			:Optional[Any]											=							'This is a test string'

         lowerCAmelCase__			:Optional[int]											=							processor(text=__UpperCAmelCase      )

         lowerCAmelCase__			:Union[str, Any]											=							tokenizer(__UpperCAmelCase      )

         for key in encoded_tok.keys():
           self.assertListEqual(encoded_tok[key]    , encoded_processor[key]      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:int											=							self.get_feature_extractor()
         lowerCAmelCase__			:Dict											=							self.get_tokenizer()

         lowerCAmelCase__			:Optional[int]											=							ClapProcessor(tokenizer=__UpperCAmelCase    , feature_extractor=__UpperCAmelCase      )

         lowerCAmelCase__			:Union[str, Any]											=							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         lowerCAmelCase__			:List[Any]											=							processor.batch_decode(__UpperCAmelCase      )
         lowerCAmelCase__			:int											=							tokenizer.batch_decode(__UpperCAmelCase      )

         self.assertListEqual(__UpperCAmelCase    , __UpperCAmelCase      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[str]											=							self.get_feature_extractor()
         lowerCAmelCase__			:List[str]											=							self.get_tokenizer()

         lowerCAmelCase__			:Union[str, Any]											=							ClapProcessor(tokenizer=__UpperCAmelCase    , feature_extractor=__UpperCAmelCase      )

         self.assertListEqual(
             processor.model_input_names[2:]    , feature_extractor.model_input_names    , msg='`processor` and `feature_extractor` model input names do not match'    , )







"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class 					_lowerCAmelCase   (     a  ,   a  ,   unittest.TestCase       ):
       """simple docstring"""




       __magic_name__     :Tuple		  =					StableDiffusionXLImgaImgPipeline
       __magic_name__     :List[Any]		  =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
       __magic_name__     :Optional[Any]		  =					PipelineTesterMixin.required_optional_params - {"""latents"""}
       __magic_name__     :Optional[Any]		  =					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
       __magic_name__     :str		  =					IMAGE_TO_IMAGE_IMAGE_PARAMS
       __magic_name__     :Any		  =					IMAGE_TO_IMAGE_IMAGE_PARAMS






       def      snake_case						(    self      ):



         '''simple docstring'''

         torch.manual_seed(0      )
         lowerCAmelCase__			:Optional[Any]											=							UNetaDConditionModel(
             block_out_channels=(3_2, 6_4)    , layers_per_block=2    , sample_size=3_2    , in_channels=4    , out_channels=4    , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')    , up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')    , attention_head_dim=(2, 4)    , use_linear_projection=__UpperCAmelCase    , addition_embed_type='text_time'    , addition_time_embed_dim=8    , transformer_layers_per_block=(1, 2)    , projection_class_embeddings_input_dim=8_0    , cross_attention_dim=6_4    , )
         lowerCAmelCase__			:str											=							EulerDiscreteScheduler(
             beta_start=0.0_00_85    , beta_end=0.0_12    , steps_offset=1    , beta_schedule='scaled_linear'    , timestep_spacing='leading'    , )
         torch.manual_seed(0      )
         lowerCAmelCase__			:str											=							AutoencoderKL(
             block_out_channels=[3_2, 6_4]    , in_channels=3    , out_channels=3    , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']    , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']    , latent_channels=4    , sample_size=1_2_8    , )
         torch.manual_seed(0      )
         lowerCAmelCase__			:str											=							CLIPTextConfig(
             bos_token_id=0    , eos_token_id=2    , hidden_size=3_2    , intermediate_size=3_7    , layer_norm_eps=1E-05    , num_attention_heads=4    , num_hidden_layers=5    , pad_token_id=1    , vocab_size=1_0_0_0    , hidden_act='gelu'    , projection_dim=3_2    , )
         lowerCAmelCase__			:int											=							CLIPTextModel(__UpperCAmelCase      )
         lowerCAmelCase__			:Tuple											=							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    , local_files_only=__UpperCAmelCase      )

         lowerCAmelCase__			:Any											=							CLIPTextModelWithProjection(__UpperCAmelCase      )
         lowerCAmelCase__			:Optional[Any]											=							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    , local_files_only=__UpperCAmelCase      )

         lowerCAmelCase__			:str											=							{
             'unet': unet,
             'scheduler': scheduler,
             'vae': vae,
             'text_encoder': text_encoder,
             'tokenizer': tokenizer,
             'text_encoder_2': text_encoder_a,
             'tokenizer_2': tokenizer_a,
             # "safety_checker": None,
             # "feature_extractor": None,
         }
         return components






       def      snake_case						(    self    , __UpperCAmelCase    , __UpperCAmelCase=0      ):



         '''simple docstring'''

         lowerCAmelCase__			:Dict											=							floats_tensor((1, 3, 3_2, 3_2)    , rng=random.Random(__UpperCAmelCase      )      ).to(__UpperCAmelCase      )
         lowerCAmelCase__			:Optional[int]											=							image / 2 + 0.5
         if str(__UpperCAmelCase      ).startswith('mps'      ):
           lowerCAmelCase__			:Optional[int]											=							torch.manual_seed(__UpperCAmelCase      )
         else:
           lowerCAmelCase__			:Optional[Any]											=							torch.Generator(device=__UpperCAmelCase      ).manual_seed(__UpperCAmelCase      )
         lowerCAmelCase__			:Dict											=							{
             'prompt': 'A painting of a squirrel eating a burger',
             'image': image,
             'generator': generator,
             'num_inference_steps': 2,
             'guidance_scale': 5.0,
             'output_type': 'numpy',
             'strength': 0.75,
         }
         return inputs






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[Any]											=							'cpu'  # ensure determinism for the device-dependent torch.Generator
         lowerCAmelCase__			:int											=							self.get_dummy_components()
         lowerCAmelCase__			:List[str]											=							StableDiffusionXLImgaImgPipeline(**__UpperCAmelCase      )
         lowerCAmelCase__			:str											=							sd_pipe.to(__UpperCAmelCase      )
         sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase      )

         lowerCAmelCase__			:str											=							self.get_dummy_inputs(__UpperCAmelCase      )
         lowerCAmelCase__			:int											=							sd_pipe(**__UpperCAmelCase      ).images
         lowerCAmelCase__			:int											=							image[0, -3:, -3:, -1]

         assert image.shape == (1, 3_2, 3_2, 3)

         lowerCAmelCase__			:List[str]											=							np.array([0.46_56, 0.48_40, 0.44_39, 0.66_98, 0.55_74, 0.45_24, 0.57_99, 0.59_43, 0.51_65]      )

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






       def      snake_case						(    self      ):



         '''simple docstring'''

         super().test_attention_slicing_forward_pass(expected_max_diff=3E-3      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         super().test_inference_batch_single_identical(expected_max_diff=3E-3      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         pass






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Tuple											=							self.get_dummy_components()
         lowerCAmelCase__			:str											=							StableDiffusionXLImgaImgPipeline(**__UpperCAmelCase      )
         lowerCAmelCase__			:str											=							sd_pipe.to(__UpperCAmelCase      )
         lowerCAmelCase__			:List[str]											=							sd_pipe.to(__UpperCAmelCase      )
         sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase      )

         # forward without prompt embeds
         lowerCAmelCase__			:int											=							self.get_dummy_inputs(__UpperCAmelCase      )
         lowerCAmelCase__			:Optional[int]											=							3 * ['this is a negative prompt']
         lowerCAmelCase__			:Tuple											=							negative_prompt
         lowerCAmelCase__			:str											=							3 * [inputs['prompt']]

         lowerCAmelCase__			:Optional[Any]											=							sd_pipe(**__UpperCAmelCase      )
         lowerCAmelCase__			:List[Any]											=							output.images[0, -3:, -3:, -1]

         # forward with prompt embeds
         lowerCAmelCase__			:Optional[Any]											=							self.get_dummy_inputs(__UpperCAmelCase      )
         lowerCAmelCase__			:Tuple											=							3 * ['this is a negative prompt']
         lowerCAmelCase__			:str											=							3 * [inputs.pop('prompt'      )]

         (
             (
             lowerCAmelCase__
         )							,				(
             lowerCAmelCase__
         )							,				(
             lowerCAmelCase__
         )							,				(
             lowerCAmelCase__
         )							,				
         )			:List[str]											=							sd_pipe.encode_prompt(__UpperCAmelCase    , negative_prompt=__UpperCAmelCase      )

         lowerCAmelCase__			:str											=							sd_pipe(
             **__UpperCAmelCase    , prompt_embeds=__UpperCAmelCase    , negative_prompt_embeds=__UpperCAmelCase    , pooled_prompt_embeds=__UpperCAmelCase    , negative_pooled_prompt_embeds=__UpperCAmelCase    , )
         lowerCAmelCase__			:Optional[Any]											=							output.images[0, -3:, -3:, -1]

         # make sure that it's equal
         assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()      ).max() < 1E-4






@slow
@require_torch_gpu
class 					_lowerCAmelCase   (     unittest.TestCase       ):
       """simple docstring"""




       def      snake_case						(    self      ):



         '''simple docstring'''

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






       def      snake_case						(    self    , __UpperCAmelCase    , __UpperCAmelCase="cpu"    , __UpperCAmelCase=torch.floataa    , __UpperCAmelCase=0      ):



         '''simple docstring'''

         lowerCAmelCase__			:Any											=							torch.Generator(device=__UpperCAmelCase      ).manual_seed(__UpperCAmelCase      )
         lowerCAmelCase__			:Dict											=							np.random.RandomState(__UpperCAmelCase      ).standard_normal((1, 4, 6_4, 6_4)      )
         lowerCAmelCase__			:Optional[int]											=							torch.from_numpy(__UpperCAmelCase      ).to(device=__UpperCAmelCase    , dtype=__UpperCAmelCase      )
         lowerCAmelCase__			:int											=							{
             'prompt': 'a photograph of an astronaut riding a horse',
             'latents': latents,
             'generator': generator,
             'num_inference_steps': 3,
             'guidance_scale': 7.5,
             'output_type': 'numpy',
         }
         return inputs






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[Any]											=							DiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-base'      )
         pipe.to(__UpperCAmelCase      )
         pipe.set_progress_bar_config(disable=__UpperCAmelCase      )

         lowerCAmelCase__			:Tuple											=							self.get_inputs(__UpperCAmelCase      )
         lowerCAmelCase__			:int											=							pipe(**__UpperCAmelCase      ).images
         lowerCAmelCase__			:Union[str, Any]											=							image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         lowerCAmelCase__			:List[str]											=							np.array([0.4_94_93, 0.4_78_96, 0.4_07_98, 0.5_42_14, 0.5_32_12, 0.4_82_02, 0.4_76_56, 0.4_63_29, 0.4_85_06]      )
         assert np.abs(image_slice - expected_slice      ).max() < 7E-3
