





"""simple docstring"""




import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class 					_a		( _lowerCAmelCase      ,							unittest.TestCase							):
				UpperCamelCase								=       KandinskyPriorPipeline
				UpperCamelCase								=       ['''prompt''']
				UpperCamelCase								=       ['''prompt''', '''negative_prompt''']
				UpperCamelCase								=       [
				    '''num_images_per_prompt''',
				    '''generator''',
				    '''num_inference_steps''',
				    '''latents''',
				    '''negative_prompt''',
				    '''guidance_scale''',
				    '''output_type''',
				    '''return_dict''',
				]
				UpperCamelCase								=       False






				@property
				def 							snake_case      (					self						:       Optional[Any]				)      -> str:




										'''simple docstring'''



										return 3_2






				@property
				def 							snake_case      (					self						:       Dict				)      -> str:




										'''simple docstring'''



										return 3_2






				@property
				def 							snake_case      (					self						:       Optional[Any]				)      -> Dict:




										'''simple docstring'''



										return self.time_input_dim






				@property
				def 							snake_case      (					self						:       List[Any]				)      -> Any:




										'''simple docstring'''



										return self.time_input_dim * 4






				@property
				def 							snake_case      (					self						:       str				)      -> Dict:




										'''simple docstring'''



										return 1_0_0






				@property
				def 							snake_case      (					self						:       Optional[Any]				)      -> Dict:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[int]      =       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)
										return tokenizer






				@property
				def 							snake_case      (					self						:       Any				)      -> List[str]:




										'''simple docstring'''



										torch.manual_seed(0				)
										_UpperCamelCase     :   Optional[Any]      =       CLIPTextConfig(
										    bos_token_id=0,					eos_token_id=2,					hidden_size=self.text_embedder_hidden_size,					projection_dim=self.text_embedder_hidden_size,					intermediate_size=3_7,					layer_norm_eps=1e-0_5,					num_attention_heads=4,					num_hidden_layers=5,					pad_token_id=1,					vocab_size=1_0_0_0,					)
										return CLIPTextModelWithProjection(lowerCAmelCase__				)






				@property
				def 							snake_case      (					self						:       Tuple				)      -> Tuple:




										'''simple docstring'''



										torch.manual_seed(0				)

										_UpperCamelCase     :   List[Any]      =       {
										    '''num_attention_heads''': 2,
										    '''attention_head_dim''': 1_2,
										    '''embedding_dim''': self.text_embedder_hidden_size,
										    '''num_layers''': 1,
										}

										_UpperCamelCase     :   str      =       PriorTransformer(**lowerCAmelCase__				)
										# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
										_UpperCamelCase     :   str      =       nn.Parameter(torch.ones(model.clip_std.shape				)				)
										return model






				@property
				def 							snake_case      (					self						:       Dict				)      -> List[str]:




										'''simple docstring'''



										torch.manual_seed(0				)
										_UpperCamelCase     :   Tuple      =       CLIPVisionConfig(
										    hidden_size=self.text_embedder_hidden_size,					image_size=2_2_4,					projection_dim=self.text_embedder_hidden_size,					intermediate_size=3_7,					num_attention_heads=4,					num_channels=3,					num_hidden_layers=5,					patch_size=1_4,					)

										_UpperCamelCase     :   int      =       CLIPVisionModelWithProjection(lowerCAmelCase__				)
										return model






				@property
				def 							snake_case      (					self						:       str				)      -> Tuple:




										'''simple docstring'''



										_UpperCamelCase     :   Union[str, Any]      =       CLIPImageProcessor(
										    crop_size=2_2_4,					do_center_crop=lowerCAmelCase__,					do_normalize=lowerCAmelCase__,					do_resize=lowerCAmelCase__,					image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073],					image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711],					resample=3,					size=2_2_4,					)

										return image_processor






				def 							snake_case      (					self						:       int				)      -> Dict:




										'''simple docstring'''



										_UpperCamelCase     :   Union[str, Any]      =       self.dummy_prior
										_UpperCamelCase     :   Optional[int]      =       self.dummy_image_encoder
										_UpperCamelCase     :   Any      =       self.dummy_text_encoder
										_UpperCamelCase     :   Dict      =       self.dummy_tokenizer
										_UpperCamelCase     :   int      =       self.dummy_image_processor

										_UpperCamelCase     :   str      =       UnCLIPScheduler(
										    variance_type='''fixed_small_log''',					prediction_type='''sample''',					num_train_timesteps=1_0_0_0,					clip_sample=lowerCAmelCase__,					clip_sample_range=10.0,					)

										_UpperCamelCase     :   Any      =       {
										    '''prior''': prior,
										    '''image_encoder''': image_encoder,
										    '''text_encoder''': text_encoder,
										    '''tokenizer''': tokenizer,
										    '''scheduler''': scheduler,
										    '''image_processor''': image_processor,
										}

										return components






				def 							snake_case      (					self						:       int,					lowerCAmelCase__						:       Dict,					lowerCAmelCase__						:       List[Any]=0				)      -> Optional[int]:




										'''simple docstring'''



										if str(lowerCAmelCase__				).startswith('''mps'''				):
																_UpperCamelCase     :   Optional[int]      =       torch.manual_seed(lowerCAmelCase__				)
										else:
																_UpperCamelCase     :   str      =       torch.Generator(device=lowerCAmelCase__				).manual_seed(lowerCAmelCase__				)
										_UpperCamelCase     :   Union[str, Any]      =       {
										    '''prompt''': '''horse''',
										    '''generator''': generator,
										    '''guidance_scale''': 4.0,
										    '''num_inference_steps''': 2,
										    '''output_type''': '''np''',
										}
										return inputs






				def 							snake_case      (					self						:       int				)      -> Dict:




										'''simple docstring'''



										_UpperCamelCase     :   List[str]      =       '''cpu'''

										_UpperCamelCase     :   int      =       self.get_dummy_components()

										_UpperCamelCase     :   Any      =       self.pipeline_class(**lowerCAmelCase__				)
										_UpperCamelCase     :   Any      =       pipe.to(lowerCAmelCase__				)

										pipe.set_progress_bar_config(disable=lowerCAmelCase__				)

										_UpperCamelCase     :   List[Any]      =       pipe(**self.get_dummy_inputs(lowerCAmelCase__				)				)
										_UpperCamelCase     :   int      =       output.image_embeds

										_UpperCamelCase     :   Optional[Any]      =       pipe(
										    **self.get_dummy_inputs(lowerCAmelCase__				),					return_dict=lowerCAmelCase__,					)[0]

										_UpperCamelCase     :   Optional[int]      =       image[0, -1_0:]
										_UpperCamelCase     :   List[Any]      =       image_from_tuple[0, -1_0:]

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

										_UpperCamelCase     :   Any      =       np.array(
										    [-0.0_532, 1.7_120, 0.3_656, -1.0_852, -0.8_946, -1.1_756, 0.4_348, 0.2_482, 0.5_146, -0.1_156]				)

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






				@skip_mps
				def 							snake_case      (					self						:       Union[str, Any]				)      -> Optional[int]:




										'''simple docstring'''



										_UpperCamelCase     :   Tuple      =       torch_device == '''cpu'''
										_UpperCamelCase     :   List[Any]      =       True
										_UpperCamelCase     :   Optional[Any]      =       False

										self._test_inference_batch_single_identical(
										    test_max_difference=lowerCAmelCase__,					relax_max_difference=lowerCAmelCase__,					test_mean_pixel_difference=lowerCAmelCase__,					)






				@skip_mps
				def 							snake_case      (					self						:       List[Any]				)      -> Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase     :   List[str]      =       torch_device == '''cpu'''
										_UpperCamelCase     :   Dict      =       False

										self._test_attention_slicing_forward_pass(
										    test_max_difference=lowerCAmelCase__,					test_mean_pixel_difference=lowerCAmelCase__,					)











"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a							:						Union[str, Any]   							=			logging.get_logger(__name__)

__a							:						Optional[Any]   							=			{
    'tau/splinter-base': 'https://huggingface.co/tau/splinter-base/resolve/main/config.json',
    'tau/splinter-base-qass': 'https://huggingface.co/tau/splinter-base-qass/resolve/main/config.json',
    'tau/splinter-large': 'https://huggingface.co/tau/splinter-large/resolve/main/config.json',
    'tau/splinter-large-qass': 'https://huggingface.co/tau/splinter-large-qass/resolve/main/config.json',
    # See all Splinter models at https://huggingface.co/models?filter=splinter
}







class 							_SCREAMING_SNAKE_CASE    (	__snake_case				):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE     ='splinter'




							def __init__( self:  Dict					,		__A:  List[Any]=30522					,		__A:  str=768					,		__A:  int=12					,		__A:  str=12					,		__A:  Tuple=3072					,		__A:  Dict="gelu"					,		__A:  Optional[int]=0.1					,		__A:  List[str]=0.1					,		__A:  str=512					,		__A:  List[Any]=2					,		__A:  Any=0.0_2					,		__A:  Any=1e-12					,		__A:  Tuple=True					,		__A:  List[Any]=0					,		__A:  List[str]=104					,		**__A:  Union[str, Any]					,		):

												'''simple docstring'''

												super().__init__(pad_token_id=__A					,		**__A     )

												a__ 							=	vocab_size
												a__ 							=	max_position_embeddings
												a__ 							=	hidden_size
												a__ 							=	num_hidden_layers
												a__ 							=	num_attention_heads
												a__ 							=	intermediate_size
												a__ 							=	hidden_act
												a__ 							=	hidden_dropout_prob
												a__ 							=	attention_probs_dropout_prob
												a__ 							=	initializer_range
												a__ 							=	type_vocab_size
												a__ 							=	layer_norm_eps
												a__ 							=	use_cache
												a__ 							=	question_token_id


