"""simple docstring"""





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


_UpperCamelCase       :						int     =   logging.get_logger(__name__)

_UpperCamelCase       :						Tuple     =   {
    "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 					UpperCAmelCase_     (   _a):
      lowerCamelCase__		:   Union[str, Any]			  =  "splinter"


      def __init__(       self		,      a=3_0_5_2_2		,      a=7_6_8		,      a=1_2		,      a=1_2		,      a=3_0_7_2		,      a="gelu"		,      a=0.1		,      a=0.1		,      a=5_1_2		,      a=2		,      a=0.02		,      a=1e-12		,      a=True		,      a=0		,      a=1_0_4		,      **a		,      )	-> Tuple:
             super().__init__(pad_token_id=a		,      **a							)

             lowercase__      :		Tuple          =     vocab_size
             lowercase__      :		Dict          =     max_position_embeddings
             lowercase__      :		List[str]          =     hidden_size
             lowercase__      :		Optional[Any]          =     num_hidden_layers
             lowercase__      :		Tuple          =     num_attention_heads
             lowercase__      :		List[Any]          =     intermediate_size
             lowercase__      :		Any          =     hidden_act
             lowercase__      :		Optional[Any]          =     hidden_dropout_prob
             lowercase__      :		Optional[Any]          =     attention_probs_dropout_prob
             lowercase__      :		Tuple          =     initializer_range
             lowercase__      :		Optional[int]          =     type_vocab_size
             lowercase__      :		int          =     layer_norm_eps
             lowercase__      :		Any          =     use_cache
             lowercase__      :		List[str]          =     question_token_id


"""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 					UpperCAmelCase_     (   _a			,							unittest.TestCase):
      lowerCamelCase__		:   Tuple			  =  KandinskyPriorPipeline
      lowerCamelCase__		:   Tuple			  =  ["prompt"]
      lowerCamelCase__		:   List[str]			  =  ["prompt", "negative_prompt"]
      lowerCamelCase__		:   List[Any]			  =  [
          "num_images_per_prompt",
          "generator",
          "num_inference_steps",
          "latents",
          "negative_prompt",
          "guidance_scale",
          "output_type",
          "return_dict",
      ]
      lowerCamelCase__		:   Optional[Any]			  =  False


      @property
      def 		_UpperCAmelCase	(       self							)	-> Union[str, Any]:
             return 3_2


      @property
      def 		_UpperCAmelCase	(       self							)	-> str:
             return 3_2


      @property
      def 		_UpperCAmelCase	(       self							)	-> Optional[Any]:
             return self.time_input_dim


      @property
      def 		_UpperCAmelCase	(       self							)	-> Any:
             return self.time_input_dim * 4


      @property
      def 		_UpperCAmelCase	(       self							)	-> Optional[int]:
             return 1_0_0


      @property
      def 		_UpperCAmelCase	(       self							)	-> List[Any]:
             lowercase__      :		Tuple          =     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'							)
             return tokenizer


      @property
      def 		_UpperCAmelCase	(       self							)	-> Any:
             torch.manual_seed(0							)
             lowercase__      :		Optional[int]          =     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 		_UpperCAmelCase	(       self							)	-> Union[str, Any]:
             torch.manual_seed(0							)

             lowercase__      :		int          =     {
                 'num_attention_heads': 2,
                 'attention_head_dim': 1_2,
                 'embedding_dim': self.text_embedder_hidden_size,
                 'num_layers': 1,
             }

             lowercase__      :		Tuple          =     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__      :		int          =     nn.Parameter(torch.ones(model.clip_std.shape							)							)
             return model


      @property
      def 		_UpperCAmelCase	(       self							)	-> Optional[int]:
             torch.manual_seed(0							)
             lowercase__      :		Optional[int]          =     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 		_UpperCAmelCase	(       self							)	-> Tuple:
             lowercase__      :		Dict          =     CLIPImageProcessor(
                 crop_size=2_2_4		,      do_center_crop=a		,      do_normalize=a		,      do_resize=a		,      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 		_UpperCAmelCase	(       self							)	-> Dict:
             lowercase__      :		List[str]          =     self.dummy_prior
             lowercase__      :		Tuple          =     self.dummy_image_encoder
             lowercase__      :		Optional[Any]          =     self.dummy_text_encoder
             lowercase__      :		List[str]          =     self.dummy_tokenizer
             lowercase__      :		Optional[int]          =     self.dummy_image_processor

             lowercase__      :		List[Any]          =     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[str]          =     {
                 'prior': prior,
                 'image_encoder': image_encoder,
                 'text_encoder': text_encoder,
                 'tokenizer': tokenizer,
                 'scheduler': scheduler,
                 'image_processor': image_processor,
             }

             return components


      def 		_UpperCAmelCase	(       self		,      a		,      a=0							)	-> Union[str, Any]:
             if str(a							).startswith('mps'							):
                    lowercase__      :		Optional[Any]          =     torch.manual_seed(a							)
             else:
                    lowercase__      :		Any          =     torch.Generator(device=a							).manual_seed(a							)
             lowercase__      :		str          =     {
                 'prompt': 'horse',
                 'generator': generator,
                 'guidance_scale': 4.0,
                 'num_inference_steps': 2,
                 'output_type': 'np',
             }
             return inputs


      def 		_UpperCAmelCase	(       self							)	-> Dict:
             lowercase__      :		Optional[int]          =     'cpu'

             lowercase__      :		Optional[Any]          =     self.get_dummy_components()

             lowercase__      :		Optional[Any]          =     self.pipeline_class(**a							)
             lowercase__      :		Optional[int]          =     pipe.to(a							)

             pipe.set_progress_bar_config(disable=a							)

             lowercase__      :		Optional[int]          =     pipe(**self.get_dummy_inputs(a							)							)
             lowercase__      :		Optional[Any]          =     output.image_embeds

             lowercase__      :		Dict          =     pipe(
                 **self.get_dummy_inputs(a							)		,      return_dict=a		,      )[0]

             lowercase__      :		List[str]          =     image[0, -1_0:]
             lowercase__      :		Optional[Any]          =     image_from_tuple[0, -1_0:]

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

             lowercase__      :		Union[str, 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 		_UpperCAmelCase	(       self							)	-> List[Any]:
             lowercase__      :		Tuple          =     torch_device == 'cpu'
             lowercase__      :		Union[str, Any]          =     True
             lowercase__      :		int          =     False

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


      @skip_mps
      def 		_UpperCAmelCase	(       self							)	-> int:
             lowercase__      :		Tuple          =     torch_device == 'cpu'
             lowercase__      :		Any          =     False

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

