




import unittest

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

from diffusers import KandinskyVaaPriorPipeline, 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					(       _lowercase     ,       unittest.TestCase      ):

					'''simple docstring'''





					lowerCAmelCase    :	List[Any]       		=  KandinskyVaaPriorPipeline
					lowerCAmelCase    :	str       		=  ['prompt']
					lowerCAmelCase    :	Optional[Any]       		=  ['prompt', 'negative_prompt']
					lowerCAmelCase    :	Any       		=  [
					    'num_images_per_prompt',
					    'generator',
					    'num_inference_steps',
					    'latents',
					    'negative_prompt',
					    'guidance_scale',
					    'output_type',
					    'return_dict',
					]
					lowerCAmelCase    :	List[str]       		=  False
					@property
					def   lowerCamelCase_       (     self       :      int    ):
									return 32
					@property
					def   lowerCamelCase_       (     self       :      str    ):
									return 32
					@property
					def   lowerCamelCase_       (     self       :      Union[str, Any]    ):
									return self.time_input_dim
					@property
					def   lowerCamelCase_       (     self       :      str    ):
									return self.time_input_dim * 4
					@property
					def   lowerCamelCase_       (     self       :      Tuple    ):
									return 1_00
					@property
					def   lowerCamelCase_       (     self       :      List[str]    ):
									UpperCAmelCase_										=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''    )
									return tokenizer
					@property
					def   lowerCamelCase_       (     self       :      List[str]    ):
									torch.manual_seed(0    )
									UpperCAmelCase_										=					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=37 ,   layer_norm_eps=1E-05 ,   num_attention_heads=4 ,   num_hidden_layers=5 ,   pad_token_id=1 ,   vocab_size=10_00 ,   )
									return CLIPTextModelWithProjection(__UpperCamelCase    )
					@property
					def   lowerCamelCase_       (     self       :      Union[str, Any]    ):
									torch.manual_seed(0    )

									UpperCAmelCase_										=					{
									    '''num_attention_heads''': 2,
									    '''attention_head_dim''': 12,
									    '''embedding_dim''': self.text_embedder_hidden_size,
									    '''num_layers''': 1,
									}

									UpperCAmelCase_										=					PriorTransformer(**__UpperCamelCase    )
									# 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_										=					nn.Parameter(torch.ones(model.clip_std.shape    )    )
									return model
					@property
					def   lowerCamelCase_       (     self       :      List[Any]    ):
									torch.manual_seed(0    )
									UpperCAmelCase_										=					CLIPVisionConfig(
									    hidden_size=self.text_embedder_hidden_size ,   image_size=2_24 ,   projection_dim=self.text_embedder_hidden_size ,   intermediate_size=37 ,   num_attention_heads=4 ,   num_channels=3 ,   num_hidden_layers=5 ,   patch_size=14 ,   )

									UpperCAmelCase_										=					CLIPVisionModelWithProjection(__UpperCamelCase    )
									return model
					@property
					def   lowerCamelCase_       (     self       :      int    ):
									UpperCAmelCase_										=					CLIPImageProcessor(
									    crop_size=2_24 ,   do_center_crop=__UpperCamelCase ,   do_normalize=__UpperCamelCase ,   do_resize=__UpperCamelCase ,   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_24 ,   )

									return image_processor
					def   lowerCamelCase_       (     self       :      Optional[int]    ):
									UpperCAmelCase_										=					self.dummy_prior
									UpperCAmelCase_										=					self.dummy_image_encoder
									UpperCAmelCase_										=					self.dummy_text_encoder
									UpperCAmelCase_										=					self.dummy_tokenizer
									UpperCAmelCase_										=					self.dummy_image_processor

									UpperCAmelCase_										=					UnCLIPScheduler(
									    variance_type='''fixed_small_log''' ,   prediction_type='''sample''' ,   num_train_timesteps=10_00 ,   clip_sample=__UpperCamelCase ,   clip_sample_range=10.0 ,   )

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

									return components
					def   lowerCamelCase_       (     self       :      Tuple ,   __snake_case       :      Tuple ,   __snake_case       :      Optional[int]=0    ):
									if str(__UpperCamelCase    ).startswith('''mps'''    ):
													UpperCAmelCase_										=					torch.manual_seed(__UpperCamelCase    )
									else:
													UpperCAmelCase_										=					torch.Generator(device=__UpperCamelCase    ).manual_seed(__UpperCamelCase    )
									UpperCAmelCase_										=					{
									    '''prompt''': '''horse''',
									    '''generator''': generator,
									    '''guidance_scale''': 4.0,
									    '''num_inference_steps''': 2,
									    '''output_type''': '''np''',
									}
									return inputs
					def   lowerCamelCase_       (     self       :      List[Any]    ):
									UpperCAmelCase_										=					'''cpu'''

									UpperCAmelCase_										=					self.get_dummy_components()

									UpperCAmelCase_										=					self.pipeline_class(**__UpperCamelCase    )
									UpperCAmelCase_										=					pipe.to(__UpperCamelCase    )

									pipe.set_progress_bar_config(disable=__UpperCamelCase    )

									UpperCAmelCase_										=					pipe(**self.get_dummy_inputs(__UpperCamelCase    )    )
									UpperCAmelCase_										=					output.image_embeds

									UpperCAmelCase_										=					pipe(
									    **self.get_dummy_inputs(__UpperCamelCase    ) ,   return_dict=__UpperCamelCase ,   )[0]

									UpperCAmelCase_										=					image[0, -10:]
									UpperCAmelCase_										=					image_from_tuple[0, -10:]

									assert image.shape == (1, 32)

									UpperCAmelCase_										=					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   lowerCamelCase_       (     self       :      int    ):
									UpperCAmelCase_										=					torch_device == '''cpu'''
									UpperCAmelCase_										=					True
									UpperCAmelCase_										=					False

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




					@skip_mps
					def   lowerCamelCase_       (     self       :      Any    ):
									UpperCAmelCase_										=					torch_device == '''cpu'''
									UpperCAmelCase_										=					False

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











from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCamelCase      			=						logging.get_logger(__name__)

_lowerCamelCase      			=						{
    'facebook/data2vec-text-base': 'https://huggingface.co/data2vec/resolve/main/config.json',
}



class       a					(       _A      ):

     '''simple docstring'''





     lowerCAmelCase    :	Optional[Any]       		=  'data2vec-text'
     def __init__(     self       :      Optional[Any] ,   __snake_case       :      Optional[int]=3_05_22 ,   __snake_case       :      List[str]=7_68 ,   __snake_case       :      Tuple=12 ,   __snake_case       :      int=12 ,   __snake_case       :      Union[str, Any]=30_72 ,   __snake_case       :      List[Any]="gelu" ,   __snake_case       :      Any=0.1 ,   __snake_case       :      Union[str, Any]=0.1 ,   __snake_case       :      Tuple=5_12 ,   __snake_case       :      str=2 ,   __snake_case       :      str=0.02 ,   __snake_case       :      List[Any]=1E-12 ,   __snake_case       :      Any=1 ,   __snake_case       :      List[Any]=0 ,   __snake_case       :      Dict=2 ,   __snake_case       :      Any="absolute" ,   __snake_case       :      Union[str, Any]=True ,   __snake_case       :      Any=None ,   **__snake_case       :      List[Any] ,   ):
         super().__init__(pad_token_id=__snake_case ,   bos_token_id=__snake_case ,   eos_token_id=__snake_case ,   **__snake_case    )

         UpperCAmelCase_										=					vocab_size
         UpperCAmelCase_										=					hidden_size
         UpperCAmelCase_										=					num_hidden_layers
         UpperCAmelCase_										=					num_attention_heads
         UpperCAmelCase_										=					hidden_act
         UpperCAmelCase_										=					intermediate_size
         UpperCAmelCase_										=					hidden_dropout_prob
         UpperCAmelCase_										=					attention_probs_dropout_prob
         UpperCAmelCase_										=					max_position_embeddings
         UpperCAmelCase_										=					type_vocab_size
         UpperCAmelCase_										=					initializer_range
         UpperCAmelCase_										=					layer_norm_eps
         UpperCAmelCase_										=					position_embedding_type
         UpperCAmelCase_										=					use_cache
         UpperCAmelCase_										=					classifier_dropout



class       a					(       _A      ):

     '''simple docstring'''





     @property
     def   lowerCamelCase_       (     self       :      str    ):
         if self.task == "multiple-choice":
             UpperCAmelCase_										=					{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
         else:
             UpperCAmelCase_										=					{0: '''batch''', 1: '''sequence'''}
         return OrderedDict(
             [
                 ('''input_ids''', dynamic_axis),
                 ('''attention_mask''', dynamic_axis),
             ]    )





