





"""simple docstring"""





import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin






class    _lowerCAmelCase  (	a			):
       """simple docstring"""




       __magic_name__	:List[Any]						    =   None
       __magic_name__	:Union[str, Any]						    =   None






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return self.feat_extract_tester.prepare_feat_extract_dict()






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[int]											=				self.feature_extraction_class(**self.feat_extract_dict )
         self.assertTrue(hasattr(__UpperCAmelCase       ,					'feature_size' ) )
         self.assertTrue(hasattr(__UpperCAmelCase       ,					'sampling_rate' ) )
         self.assertTrue(hasattr(__UpperCAmelCase       ,					'padding_value' ) )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				self.feat_extract_tester.prepare_inputs_for_common()
         lowerCAmelCase__     :Optional[int]											=				self.feature_extraction_class(**self.feat_extract_dict )
         lowerCAmelCase__     :Union[str, Any]											=				feat_extract.model_input_names[0]

         lowerCAmelCase__     :List[Any]											=				BatchFeature({input_name: speech_inputs} )

         self.assertTrue(all(len(__UpperCAmelCase ) == len(__UpperCAmelCase ) for x, y in zip(__UpperCAmelCase       ,					processed_features[input_name] ) ) )

         lowerCAmelCase__     :Optional[Any]											=				self.feat_extract_tester.prepare_inputs_for_common(equal_length=__UpperCAmelCase )
         lowerCAmelCase__     :Optional[Any]											=				BatchFeature({input_name: speech_inputs}       ,					tensor_type='np' )

         lowerCAmelCase__     :Tuple											=				processed_features[input_name]

         if len(batch_features_input.shape ) < 3:
           lowerCAmelCase__     :Tuple											=				batch_features_input[:, :, None]

         self.assertTrue(
             batch_features_input.shape
             == (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )






       @require_torch
       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Dict											=				self.feat_extract_tester.prepare_inputs_for_common(equal_length=__UpperCAmelCase )
         lowerCAmelCase__     :Dict											=				self.feature_extraction_class(**self.feat_extract_dict )
         lowerCAmelCase__     :Tuple											=				feat_extract.model_input_names[0]

         lowerCAmelCase__     :int											=				BatchFeature({input_name: speech_inputs}       ,					tensor_type='pt' )

         lowerCAmelCase__     :List[Any]											=				processed_features[input_name]

         if len(batch_features_input.shape ) < 3:
           lowerCAmelCase__     :Dict											=				batch_features_input[:, :, None]

         self.assertTrue(
             batch_features_input.shape
             == (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )






       @require_tf
       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :List[str]											=				self.feat_extract_tester.prepare_inputs_for_common(equal_length=__UpperCAmelCase )
         lowerCAmelCase__     :Optional[Any]											=				self.feature_extraction_class(**self.feat_extract_dict )
         lowerCAmelCase__     :List[Any]											=				feat_extract.model_input_names[0]

         lowerCAmelCase__     :str											=				BatchFeature({input_name: speech_inputs}       ,					tensor_type='tf' )

         lowerCAmelCase__     :Any											=				processed_features[input_name]

         if len(batch_features_input.shape ) < 3:
           lowerCAmelCase__     :Union[str, Any]											=				batch_features_input[:, :, None]

         self.assertTrue(
             batch_features_input.shape
             == (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )






       def 	snake_case		(       self       ,					__UpperCAmelCase=False ):



         '''simple docstring'''

         def _inputs_have_equal_length(__UpperCAmelCase ):
           lowerCAmelCase__     :int											=				len(input[0] )
           for input_slice in input[1:]:
             if len(__UpperCAmelCase ) != length:
               return False
           return True

         def _inputs_are_equal(__UpperCAmelCase       ,					__UpperCAmelCase ):
           if len(__UpperCAmelCase ) != len(__UpperCAmelCase ):
             return False

           for input_slice_a, input_slice_a in zip(__UpperCAmelCase       ,					__UpperCAmelCase ):
             if not np.allclose(np.asarray(__UpperCAmelCase )       ,					np.asarray(__UpperCAmelCase )       ,					atol=1E-3 ):
               return False
           return True

         lowerCAmelCase__     :Union[str, Any]											=				self.feature_extraction_class(**self.feat_extract_dict )
         lowerCAmelCase__     :int											=				self.feat_extract_tester.prepare_inputs_for_common(numpify=__UpperCAmelCase )
         lowerCAmelCase__     :List[Any]											=				feat_extract.model_input_names[0]

         lowerCAmelCase__     :List[Any]											=				BatchFeature({input_name: speech_inputs} )

         lowerCAmelCase__     :List[Any]											=				self.feat_extract_tester.seq_length_diff
         lowerCAmelCase__     :List[str]											=				self.feat_extract_tester.max_seq_length + pad_diff
         lowerCAmelCase__     :Tuple											=				self.feat_extract_tester.min_seq_length
         lowerCAmelCase__     :Optional[Any]											=				self.feat_extract_tester.batch_size
         lowerCAmelCase__     :List[str]											=				self.feat_extract_tester.feature_size

         # test padding for List[int] + numpy
         lowerCAmelCase__     :Tuple											=				feat_extract.pad(__UpperCAmelCase       ,					padding=__UpperCAmelCase )
         lowerCAmelCase__     :Tuple											=				input_a[input_name]

         lowerCAmelCase__     :List[str]											=				feat_extract.pad(__UpperCAmelCase       ,					padding='longest' )
         lowerCAmelCase__     :Dict											=				input_a[input_name]

         lowerCAmelCase__     :Optional[int]											=				feat_extract.pad(__UpperCAmelCase       ,					padding='max_length'       ,					max_length=len(speech_inputs[-1] ) )
         lowerCAmelCase__     :Union[str, Any]											=				input_a[input_name]

         lowerCAmelCase__     :Any											=				feat_extract.pad(__UpperCAmelCase       ,					padding='longest'       ,					return_tensors='np' )
         lowerCAmelCase__     :Dict											=				input_a[input_name]

         # max_length parameter has to be provided when setting `padding="max_length"`
         with self.assertRaises(__UpperCAmelCase ):
           feat_extract.pad(__UpperCAmelCase       ,					padding='max_length' )[input_name]

         lowerCAmelCase__     :Any											=				feat_extract.pad(
             __UpperCAmelCase       ,					padding='max_length'       ,					max_length=__UpperCAmelCase       ,					return_tensors='np' )
         lowerCAmelCase__     :str											=				input_a[input_name]

         self.assertFalse(_inputs_have_equal_length(__UpperCAmelCase ) )
         self.assertTrue(_inputs_have_equal_length(__UpperCAmelCase ) )
         self.assertTrue(_inputs_have_equal_length(__UpperCAmelCase ) )
         self.assertTrue(_inputs_are_equal(__UpperCAmelCase       ,					__UpperCAmelCase ) )
         self.assertTrue(len(input_a[0] ) == pad_min_length )
         self.assertTrue(len(input_a[1] ) == pad_min_length + pad_diff )
         self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0] )) )
         self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length) )

         if feature_size > 1:
           self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size )

         # test padding for `pad_to_multiple_of` for List[int] + numpy
         lowerCAmelCase__     :Any											=				feat_extract.pad(__UpperCAmelCase       ,					pad_to_multiple_of=1_0 )
         lowerCAmelCase__     :List[str]											=				input_a[input_name]

         lowerCAmelCase__     :str											=				feat_extract.pad(__UpperCAmelCase       ,					padding='longest'       ,					pad_to_multiple_of=1_0 )
         lowerCAmelCase__     :Tuple											=				input_a[input_name]

         lowerCAmelCase__     :List[str]											=				feat_extract.pad(
             __UpperCAmelCase       ,					padding='max_length'       ,					pad_to_multiple_of=1_0       ,					max_length=__UpperCAmelCase )
         lowerCAmelCase__     :Any											=				input_a[input_name]

         lowerCAmelCase__     :Union[str, Any]											=				feat_extract.pad(
             __UpperCAmelCase       ,					padding='max_length'       ,					pad_to_multiple_of=1_0       ,					max_length=__UpperCAmelCase       ,					return_tensors='np'       ,					)
         lowerCAmelCase__     :Union[str, Any]											=				input_a[input_name]

         self.assertTrue(all(len(__UpperCAmelCase ) % 1_0 == 0 for x in input_a ) )
         self.assertTrue(_inputs_are_equal(__UpperCAmelCase       ,					__UpperCAmelCase ) )

         lowerCAmelCase__     :Any											=				pad_max_length if pad_max_length % 1_0 == 0 else (pad_max_length // 1_0 + 1) * 1_0
         self.assertTrue(all(len(__UpperCAmelCase ) == expected_mult_pad_length for x in input_a ) )
         self.assertEqual(input_a.shape[:2]       ,					(batch_size, expected_mult_pad_length) )

         if feature_size > 1:
           self.assertTrue(input_a.shape[2] == feature_size )

         # Check padding value is correct
         lowerCAmelCase__     :List[Any]											=				(np.ones(self.feat_extract_tester.feature_size ) * feat_extract.padding_value).sum()
         self.assertTrue(
             abs(np.asarray(input_a[0] )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) )
             < 1E-3 )
         self.assertTrue(
             abs(
                 np.asarray(input_a[1] )[pad_min_length + pad_diff :].sum()
                 - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff) )
             < 1E-3 )
         self.assertTrue(
             abs(
                 np.asarray(input_a[2] )[pad_min_length + 2 * pad_diff :].sum()
                 - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff) )
             < 1E-3 )
         self.assertTrue(
             abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) ) < 1E-3 )
         self.assertTrue(
             abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length) )
             < 1E-3 )






       def 	snake_case		(       self       ,					__UpperCAmelCase=False ):



         '''simple docstring'''

         def _inputs_have_equal_length(__UpperCAmelCase ):
           lowerCAmelCase__     :List[str]											=				len(input[0] )
           for input_slice in input[1:]:
             if len(__UpperCAmelCase ) != length:
               return False
           return True

         def _inputs_are_equal(__UpperCAmelCase       ,					__UpperCAmelCase ):
           if len(__UpperCAmelCase ) != len(__UpperCAmelCase ):
             return False

           for input_slice_a, input_slice_a in zip(__UpperCAmelCase       ,					__UpperCAmelCase ):
             if not np.allclose(np.asarray(__UpperCAmelCase )       ,					np.asarray(__UpperCAmelCase )       ,					atol=1E-3 ):
               return False
           return True

         lowerCAmelCase__     :Any											=				self.feature_extraction_class(**self.feat_extract_dict )
         lowerCAmelCase__     :Union[str, Any]											=				self.feat_extract_tester.prepare_inputs_for_common(numpify=__UpperCAmelCase )
         lowerCAmelCase__     :Union[str, Any]											=				feat_extract.model_input_names[0]

         lowerCAmelCase__     :str											=				BatchFeature({input_name: speech_inputs} )

         # truncate to smallest
         lowerCAmelCase__     :Union[str, Any]											=				feat_extract.pad(
             __UpperCAmelCase       ,					padding='max_length'       ,					max_length=len(speech_inputs[0] )       ,					truncation=__UpperCAmelCase )
         lowerCAmelCase__     :Optional[int]											=				input_a[input_name]

         lowerCAmelCase__     :Optional[Any]											=				feat_extract.pad(__UpperCAmelCase       ,					padding='max_length'       ,					max_length=len(speech_inputs[0] ) )
         lowerCAmelCase__     :Any											=				input_a[input_name]

         self.assertTrue(_inputs_have_equal_length(__UpperCAmelCase ) )
         self.assertFalse(_inputs_have_equal_length(__UpperCAmelCase ) )

         # truncate to smallest with np
         lowerCAmelCase__     :Any											=				feat_extract.pad(
             __UpperCAmelCase       ,					padding='max_length'       ,					max_length=len(speech_inputs[0] )       ,					return_tensors='np'       ,					truncation=__UpperCAmelCase       ,					)
         lowerCAmelCase__     :List[Any]											=				input_a[input_name]

         lowerCAmelCase__     :Any											=				feat_extract.pad(
             __UpperCAmelCase       ,					padding='max_length'       ,					max_length=len(speech_inputs[0] )       ,					return_tensors='np' )
         lowerCAmelCase__     :Tuple											=				input_a[input_name]

         self.assertTrue(_inputs_have_equal_length(__UpperCAmelCase ) )
         self.assertTrue(input_a.shape[1] == len(speech_inputs[0] ) )

         # since truncation forces padding to be smaller than longest input
         # function can't return `np.ndarray`, but has to return list
         self.assertFalse(_inputs_have_equal_length(__UpperCAmelCase ) )

         # truncate to middle
         lowerCAmelCase__     :Union[str, Any]											=				feat_extract.pad(
             __UpperCAmelCase       ,					padding='max_length'       ,					max_length=len(speech_inputs[1] )       ,					truncation=__UpperCAmelCase       ,					return_tensors='np'       ,					)
         lowerCAmelCase__     :str											=				input_a[input_name]

         lowerCAmelCase__     :Dict											=				feat_extract.pad(
             __UpperCAmelCase       ,					padding='max_length'       ,					max_length=len(speech_inputs[1] )       ,					truncation=__UpperCAmelCase )
         lowerCAmelCase__     :Optional[int]											=				input_a[input_name]

         lowerCAmelCase__     :Union[str, Any]											=				feat_extract.pad(
             __UpperCAmelCase       ,					padding='max_length'       ,					max_length=len(speech_inputs[1] )       ,					return_tensors='np' )
         lowerCAmelCase__     :Union[str, Any]											=				input_a[input_name]

         self.assertTrue(input_a.shape[1] == len(speech_inputs[1] ) )
         self.assertTrue(_inputs_have_equal_length(__UpperCAmelCase ) )
         self.assertTrue(_inputs_have_equal_length(__UpperCAmelCase ) )
         self.assertTrue(_inputs_are_equal(__UpperCAmelCase       ,					__UpperCAmelCase ) )

         # since truncation forces padding to be smaller than longest input
         # function can't return `np.ndarray`, but has to return list
         self.assertFalse(_inputs_have_equal_length(__UpperCAmelCase ) )
         self.assertTrue(len(input_a[-1] ) == len(speech_inputs[-1] ) )

         # padding has to be max_length when setting `truncation=True`
         with self.assertRaises(__UpperCAmelCase ):
           feat_extract.pad(__UpperCAmelCase       ,					truncation=__UpperCAmelCase )[input_name]

         # padding has to be max_length when setting `truncation=True`
         with self.assertRaises(__UpperCAmelCase ):
           feat_extract.pad(__UpperCAmelCase       ,					padding='longest'       ,					truncation=__UpperCAmelCase )[input_name]

         # padding has to be max_length when setting `truncation=True`
         with self.assertRaises(__UpperCAmelCase ):
           feat_extract.pad(__UpperCAmelCase       ,					padding='longest'       ,					truncation=__UpperCAmelCase )[input_name]

         # max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
         with self.assertRaises(__UpperCAmelCase ):
           feat_extract.pad(__UpperCAmelCase       ,					padding='max_length'       ,					truncation=__UpperCAmelCase )[input_name]

         # test truncation for `pad_to_multiple_of` for List[int] + numpy
         lowerCAmelCase__     :Dict											=				1_2
         lowerCAmelCase__     :Any											=				feat_extract.pad(
             __UpperCAmelCase       ,					padding='max_length'       ,					max_length=len(speech_inputs[0] )       ,					pad_to_multiple_of=__UpperCAmelCase       ,					truncation=__UpperCAmelCase       ,					)
         lowerCAmelCase__     :Dict											=				input_a[input_name]

         lowerCAmelCase__     :Dict											=				feat_extract.pad(
             __UpperCAmelCase       ,					padding='max_length'       ,					max_length=len(speech_inputs[0] )       ,					pad_to_multiple_of=__UpperCAmelCase       ,					)
         lowerCAmelCase__     :List[Any]											=				input_a[input_name]

         # retrieve expected_length as multiple of pad_to_multiple_of
         lowerCAmelCase__     :Optional[Any]											=				len(speech_inputs[0] )
         if expected_length % pad_to_multiple_of != 0:
           lowerCAmelCase__     :Optional[int]											=				((len(speech_inputs[0] ) // pad_to_multiple_of) + 1) * pad_to_multiple_of

         self.assertTrue(len(input_a[0] ) == expected_length )
         self.assertTrue(_inputs_have_equal_length(__UpperCAmelCase ) )
         self.assertFalse(_inputs_have_equal_length(__UpperCAmelCase ) )






       def 	snake_case		(       self ):



         '''simple docstring'''

         self._check_padding(numpify=__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         self._check_padding(numpify=__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         self._check_truncation(numpify=__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         self._check_truncation(numpify=__UpperCAmelCase )






       @require_torch
       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :int											=				self.feature_extraction_class(**self.feat_extract_dict )
         lowerCAmelCase__     :Optional[Any]											=				self.feat_extract_tester.prepare_inputs_for_common()
         lowerCAmelCase__     :Optional[int]											=				feat_extract.model_input_names[0]

         lowerCAmelCase__     :Optional[Any]											=				BatchFeature({input_name: speech_inputs} )

         lowerCAmelCase__     :int											=				feat_extract.pad(__UpperCAmelCase       ,					padding='longest'       ,					return_tensors='np' )[input_name]
         lowerCAmelCase__     :Tuple											=				feat_extract.pad(__UpperCAmelCase       ,					padding='longest'       ,					return_tensors='pt' )[input_name]

         self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1E-2 )






       @require_tf
       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Tuple											=				self.feature_extraction_class(**self.feat_extract_dict )
         lowerCAmelCase__     :Optional[Any]											=				self.feat_extract_tester.prepare_inputs_for_common()
         lowerCAmelCase__     :Union[str, Any]											=				feat_extract.model_input_names[0]

         lowerCAmelCase__     :int											=				BatchFeature({input_name: speech_inputs} )

         lowerCAmelCase__     :Optional[int]											=				feat_extract.pad(__UpperCAmelCase       ,					padding='longest'       ,					return_tensors='np' )[input_name]
         lowerCAmelCase__     :Tuple											=				feat_extract.pad(__UpperCAmelCase       ,					padding='longest'       ,					return_tensors='tf' )[input_name]

         self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_tf.numpy().astype(np.floataa ).sum() ) < 1E-2 )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :List[Any]											=				self.feat_extract_dict
         lowerCAmelCase__     :Optional[int]											=				True
         lowerCAmelCase__     :List[str]											=				self.feature_extraction_class(**__UpperCAmelCase )
         lowerCAmelCase__     :Dict											=				self.feat_extract_tester.prepare_inputs_for_common()
         lowerCAmelCase__     :int											=				[len(__UpperCAmelCase ) for x in speech_inputs]
         lowerCAmelCase__     :List[Any]											=				feat_extract.model_input_names[0]

         lowerCAmelCase__     :Optional[int]											=				BatchFeature({input_name: speech_inputs} )

         lowerCAmelCase__     :Dict											=				feat_extract.pad(__UpperCAmelCase       ,					padding='longest'       ,					return_tensors='np' )
         self.assertIn('attention_mask'       ,					__UpperCAmelCase )
         self.assertListEqual(list(processed.attention_mask.shape )       ,					list(processed[input_name].shape[:2] ) )
         self.assertListEqual(processed.attention_mask.sum(-1 ).tolist()       ,					__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Tuple											=				self.feat_extract_dict
         lowerCAmelCase__     :int											=				True
         lowerCAmelCase__     :Any											=				self.feature_extraction_class(**__UpperCAmelCase )
         lowerCAmelCase__     :Any											=				self.feat_extract_tester.prepare_inputs_for_common()
         lowerCAmelCase__     :List[str]											=				[len(__UpperCAmelCase ) for x in speech_inputs]
         lowerCAmelCase__     :List[Any]											=				feat_extract.model_input_names[0]

         lowerCAmelCase__     :int											=				BatchFeature({input_name: speech_inputs} )
         lowerCAmelCase__     :int											=				min(__UpperCAmelCase )

         lowerCAmelCase__     :List[Any]											=				feat_extract.pad(
             __UpperCAmelCase       ,					padding='max_length'       ,					max_length=__UpperCAmelCase       ,					truncation=__UpperCAmelCase       ,					return_tensors='np' )
         self.assertIn('attention_mask'       ,					__UpperCAmelCase )
         self.assertListEqual(
             list(processed_pad.attention_mask.shape )       ,					[processed_pad[input_name].shape[0], max_length] )
         self.assertListEqual(
             processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist()       ,					[max_length for x in speech_inputs] )







"""simple docstring"""





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




       __magic_name__	:int						    =   StableUnCLIPPipeline
       __magic_name__	:int						    =   TEXT_TO_IMAGE_PARAMS
       __magic_name__	:List[str]						    =   TEXT_TO_IMAGE_BATCH_PARAMS
       __magic_name__	:Optional[int]						    =   TEXT_TO_IMAGE_IMAGE_PARAMS
       __magic_name__	:Union[str, Any]						    =   TEXT_TO_IMAGE_IMAGE_PARAMS

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






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Dict											=				3_2
         lowerCAmelCase__     :List[Any]											=				embedder_hidden_size

         # prior components

         torch.manual_seed(0 )
         lowerCAmelCase__     :Any											=				CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

         torch.manual_seed(0 )
         lowerCAmelCase__     :Any											=				CLIPTextModelWithProjection(
             CLIPTextConfig(
                 bos_token_id=0       ,					eos_token_id=2       ,					hidden_size=__UpperCAmelCase       ,					projection_dim=__UpperCAmelCase       ,					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       ,					) )

         torch.manual_seed(0 )
         lowerCAmelCase__     :int											=				PriorTransformer(
             num_attention_heads=2       ,					attention_head_dim=1_2       ,					embedding_dim=__UpperCAmelCase       ,					num_layers=1       ,					)

         torch.manual_seed(0 )
         lowerCAmelCase__     :Optional[int]											=				DDPMScheduler(
             variance_type='fixed_small_log'       ,					prediction_type='sample'       ,					num_train_timesteps=1_0_0_0       ,					clip_sample=__UpperCAmelCase       ,					clip_sample_range=5.0       ,					beta_schedule='squaredcos_cap_v2'       ,					)

         # regular denoising components

         torch.manual_seed(0 )
         lowerCAmelCase__     :Dict											=				StableUnCLIPImageNormalizer(embedding_dim=__UpperCAmelCase )
         lowerCAmelCase__     :List[Any]											=				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=__UpperCAmelCase       ,					projection_dim=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       ,					) )

         torch.manual_seed(0 )
         lowerCAmelCase__     :Optional[int]											=				UNetaDConditionModel(
             sample_size=3_2       ,					in_channels=4       ,					out_channels=4       ,					down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D')       ,					up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D')       ,					block_out_channels=(3_2, 6_4)       ,					attention_head_dim=(2, 4)       ,					class_embed_type='projection'       ,					projection_class_embeddings_input_dim=embedder_projection_dim * 2       ,					cross_attention_dim=__UpperCAmelCase       ,					layers_per_block=1       ,					upcast_attention=__UpperCAmelCase       ,					use_linear_projection=__UpperCAmelCase       ,					)

         torch.manual_seed(0 )
         lowerCAmelCase__     :Tuple											=				DDIMScheduler(
             beta_schedule='scaled_linear'       ,					beta_start=0.0_00_85       ,					beta_end=0.0_12       ,					prediction_type='v_prediction'       ,					set_alpha_to_one=__UpperCAmelCase       ,					steps_offset=1       ,					)

         torch.manual_seed(0 )
         lowerCAmelCase__     :Optional[int]											=				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       ,					__UpperCAmelCase       ,					__UpperCAmelCase=0 ):



         '''simple docstring'''

         if str(__UpperCAmelCase ).startswith('mps' ):
           lowerCAmelCase__     :str											=				torch.manual_seed(__UpperCAmelCase )
         else:
           lowerCAmelCase__     :Optional[int]											=				torch.Generator(device=__UpperCAmelCase ).manual_seed(__UpperCAmelCase )
         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__     :Optional[int]											=				torch_device == 'cpu'

         self._test_attention_slicing_forward_pass(test_max_difference=__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :str											=				torch_device in ['cpu', 'mps']

         self._test_inference_batch_single_identical(test_max_difference=__UpperCAmelCase )






@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 ):



         '''simple docstring'''

         lowerCAmelCase__     :Tuple											=				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__     :List[Any]											=				StableUnCLIPPipeline.from_pretrained('fusing/stable-unclip-2-1-l'       ,					torch_dtype=torch.floataa )
         pipe.to(__UpperCAmelCase )
         pipe.set_progress_bar_config(disable=__UpperCAmelCase )
         # 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[str]											=				torch.Generator(device='cpu' ).manual_seed(0 )
         lowerCAmelCase__     :List[str]											=				pipe('anime turle'       ,					generator=__UpperCAmelCase       ,					output_type='np' )

         lowerCAmelCase__     :Union[str, Any]											=				output.images[0]

         assert image.shape == (7_6_8, 7_6_8, 3)

         assert_mean_pixel_difference(__UpperCAmelCase       ,					__UpperCAmelCase )






       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__     :List[str]											=				pipe.to(__UpperCAmelCase )
         pipe.set_progress_bar_config(disable=__UpperCAmelCase )
         pipe.enable_attention_slicing()
         pipe.enable_sequential_cpu_offload()

         lowerCAmelCase__     :Tuple											=				pipe(
             'anime turtle'       ,					prior_num_inference_steps=2       ,					num_inference_steps=2       ,					output_type='np'       ,					)

         lowerCAmelCase__     :str											=				torch.cuda.max_memory_allocated()
         # make sure that less than 7 GB is allocated
         assert mem_bytes < 7 * 1_0**9
