






'''simple docstring'''
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *












'''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 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        _UpperCAmelCase						(		snake_case				,					unittest.TestCase			):
    __lowerCamelCase:   Dict		       =    KandinskyVaaPriorPipeline
    __lowerCamelCase:   Optional[int]		       =    ['prompt']
    __lowerCamelCase:   Any		       =    ['prompt', 'negative_prompt']
    __lowerCamelCase:   List[Any]		       =    [
        'num_images_per_prompt',
        'generator',
        'num_inference_steps',
        'latents',
        'negative_prompt',
        'guidance_scale',
        'output_type',
        'return_dict',
    ]
    __lowerCamelCase:   List[Any]		       =    False




    @property
    def 				lowerCAmelCase__				(			self  :       Optional[Any]			):






       '''simple docstring'''






       return 3_2




    @property
    def 				lowerCAmelCase__				(			self  :       Any			):






       '''simple docstring'''






       return 3_2




    @property
    def 				lowerCAmelCase__				(			self  :       Any			):






       '''simple docstring'''






       return self.time_input_dim




    @property
    def 				lowerCAmelCase__				(			self  :       str			):






       '''simple docstring'''






       return self.time_input_dim * 4




    @property
    def 				lowerCAmelCase__				(			self  :       Union[str, Any]			):






       '''simple docstring'''






       return 1_0_0




    @property
    def 				lowerCAmelCase__				(			self  :       List[Any]			):






       '''simple docstring'''






       lowercase_	:    Any           =     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"			)
       return tokenizer




    @property
    def 				lowerCAmelCase__				(			self  :       Optional[Any]			):






       '''simple docstring'''






       torch.manual_seed(0			)
       lowercase_	:    Tuple           =     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-05  ,     num_attention_heads=4  ,     num_hidden_layers=5  ,     pad_token_id=1  ,     vocab_size=1_0_0_0  ,     )
       return CLIPTextModelWithProjection(a			)




    @property
    def 				lowerCAmelCase__				(			self  :       Tuple			):






       '''simple docstring'''






       torch.manual_seed(0			)

       lowercase_	:    List[str]           =     {
           "num_attention_heads": 2,
           "attention_head_dim": 1_2,
           "embedding_dim": self.text_embedder_hidden_size,
           "num_layers": 1,
       }

       lowercase_	:    Union[str, Any]           =     PriorTransformer(**a			)
       # 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
       lowercase_	:    List[Any]           =     nn.Parameter(torch.ones(model.clip_std.shape			)			)
       return model




    @property
    def 				lowerCAmelCase__				(			self  :       Tuple			):






       '''simple docstring'''






       torch.manual_seed(0			)
       lowercase_	:    Dict           =     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  ,     )

       lowercase_	:    Optional[Any]           =     CLIPVisionModelWithProjection(a			)
       return model




    @property
    def 				lowerCAmelCase__				(			self  :       List[str]			):






       '''simple docstring'''






       lowercase_	:    List[str]           =     CLIPImageProcessor(
           crop_size=2_2_4  ,     do_center_crop=a  ,     do_normalize=a  ,     do_resize=a  ,     image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]  ,     image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]  ,     resample=3  ,     size=2_2_4  ,     )

       return image_processor




    def 				lowerCAmelCase__				(			self  :       List[str]			):






       '''simple docstring'''






       lowercase_	:    Any           =     self.dummy_prior
       lowercase_	:    Optional[Any]           =     self.dummy_image_encoder
       lowercase_	:    List[Any]           =     self.dummy_text_encoder
       lowercase_	:    Any           =     self.dummy_tokenizer
       lowercase_	:    Optional[Any]           =     self.dummy_image_processor

       lowercase_	:    List[str]           =     UnCLIPScheduler(
           variance_type="fixed_small_log"  ,     prediction_type="sample"  ,     num_train_timesteps=1_0_0_0  ,     clip_sample=a  ,     clip_sample_range=10.0  ,     )

       lowercase_	:    List[Any]           =     {
           "prior": prior,
           "image_encoder": image_encoder,
           "text_encoder": text_encoder,
           "tokenizer": tokenizer,
           "scheduler": scheduler,
           "image_processor": image_processor,
       }

       return components




    def 				lowerCAmelCase__				(			self  :       Any  ,     a  :       Dict  ,     a  :       Dict=0			):






       '''simple docstring'''






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




    def 				lowerCAmelCase__				(			self  :       Union[str, Any]			):






       '''simple docstring'''






       lowercase_	:    str           =     "cpu"

       lowercase_	:    Any           =     self.get_dummy_components()

       lowercase_	:    int           =     self.pipeline_class(**a			)
       lowercase_	:    Any           =     pipe.to(a			)

       pipe.set_progress_bar_config(disable=a			)

       lowercase_	:    Any           =     pipe(**self.get_dummy_inputs(a			)			)
       lowercase_	:    List[Any]           =     output.image_embeds

       lowercase_	:    str           =     pipe(
           **self.get_dummy_inputs(a			)  ,     return_dict=a  ,     )[0]

       lowercase_	:    Any           =     image[0, -1_0:]
       lowercase_	:    Dict           =     image_from_tuple[0, -1_0:]

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

       lowercase_	:    int           =     np.array(
           [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156]			)

       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  :       Optional[Any]			):






       '''simple docstring'''






       lowercase_	:    int           =     torch_device == "cpu"
       lowercase_	:    Tuple           =     True
       lowercase_	:    str           =     False

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





    @skip_mps
    def 				lowerCAmelCase__				(			self  :       Union[str, Any]			):






       '''simple docstring'''






       lowercase_	:    Any           =     torch_device == "cpu"
       lowercase_	:    int           =     False

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




