'''simple docstring'''

import random
import unittest

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

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

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class 				lowercase    (    __lowerCamelCase			,					unittest.TestCase			):
   lowerCamelCase_            =KandinskyVaaPriorEmbaEmbPipeline
   lowerCamelCase_            =['prompt', 'image']
   lowerCamelCase_            =['prompt', 'image']
   lowerCamelCase_            =[
       'num_images_per_prompt',
       'strength',
       'generator',
       'num_inference_steps',
       'latents',
       'negative_prompt',
       'guidance_scale',
       'output_type',
       'return_dict',
   ]
   lowerCamelCase_            =False


   @property
   def __UpperCAmelCase							( self					:						List[Any])   ->       Union[str, Any]:
         return 32


   @property
   def __UpperCAmelCase							( self					:						Optional[int])   ->       Dict:
         return 32


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


   @property
   def __UpperCAmelCase							( self					:						List[str])   ->       Dict:
         return self.time_input_dim * 4


   @property
   def __UpperCAmelCase							( self					:						Optional[Any])   ->       Dict:
         return 100


   @property
   def __UpperCAmelCase							( self					:						Any)   ->       List[str]:
         lowercase_     			=   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
         return tokenizer


   @property
   def __UpperCAmelCase							( self					:						Dict)   ->       Optional[int]:
         torch.manual_seed(0)
         lowercase_     			=   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=1000 ,		)
         return CLIPTextModelWithProjection(__lowerCAmelCase)


   @property
   def __UpperCAmelCase							( self					:						Union[str, Any])   ->       List[str]:
         torch.manual_seed(0)

         lowercase_     			=   {
             "num_attention_heads": 2,
             "attention_head_dim": 12,
             "embedding_dim": self.text_embedder_hidden_size,
             "num_layers": 1,
         }

         lowercase_     			=   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
         lowercase_     			=   nn.Parameter(torch.ones(model.clip_std.shape))
         return model


   @property
   def __UpperCAmelCase							( self					:						Tuple)   ->       Optional[Any]:
         torch.manual_seed(0)
         lowercase_     			=   CLIPVisionConfig(
             hidden_size=self.text_embedder_hidden_size ,		image_size=224 ,		projection_dim=self.text_embedder_hidden_size ,		intermediate_size=37 ,		num_attention_heads=4 ,		num_channels=3 ,		num_hidden_layers=5 ,		patch_size=14 ,		)

         lowercase_     			=   CLIPVisionModelWithProjection(__lowerCAmelCase)
         return model


   @property
   def __UpperCAmelCase							( self					:						Union[str, Any])   ->       Union[str, Any]:
         lowercase_     			=   CLIPImageProcessor(
             crop_size=224 ,		do_center_crop=__lowerCAmelCase ,		do_normalize=__lowerCAmelCase ,		do_resize=__lowerCAmelCase ,		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=224 ,		)

         return image_processor


   def __UpperCAmelCase							( self					:						Dict)   ->       Dict:
         lowercase_     			=   self.dummy_prior
         lowercase_     			=   self.dummy_image_encoder
         lowercase_     			=   self.dummy_text_encoder
         lowercase_     			=   self.dummy_tokenizer
         lowercase_     			=   self.dummy_image_processor

         lowercase_     			=   UnCLIPScheduler(
             variance_type="fixed_small_log" ,		prediction_type="sample" ,		num_train_timesteps=1000 ,		clip_sample=__lowerCAmelCase ,		clip_sample_range=10.0 ,		)

         lowercase_     			=   {
             "prior": prior,
             "image_encoder": image_encoder,
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
             "scheduler": scheduler,
             "image_processor": image_processor,
         }

         return components


   def __UpperCAmelCase							( self					:						List[Any] ,		__lowerCAmelCase					:						Optional[Any] ,		__lowerCAmelCase					:						Optional[Any]=0)   ->       int:
         if str(__lowerCAmelCase).startswith("mps"):
               lowercase_     			=   torch.manual_seed(__lowerCAmelCase)
         else:
               lowercase_     			=   torch.Generator(device=__lowerCAmelCase).manual_seed(__lowerCAmelCase)

         lowercase_     			=   floats_tensor((1, 3, 64, 64) ,		rng=random.Random(__lowerCAmelCase)).to(__lowerCAmelCase)
         lowercase_     			=   image.cpu().permute(0 ,		2 ,		3 ,		1)[0]
         lowercase_     			=   Image.fromarray(np.uinta(__lowerCAmelCase)).convert("RGB").resize((256, 256))

         lowercase_     			=   {
             "prompt": "horse",
             "image": init_image,
             "strength": 0.5,
             "generator": generator,
             "guidance_scale": 4.0,
             "num_inference_steps": 2,
             "output_type": "np",
         }
         return inputs


   def __UpperCAmelCase							( self					:						Union[str, Any])   ->       List[Any]:
         lowercase_     			=   "cpu"

         lowercase_     			=   self.get_dummy_components()

         lowercase_     			=   self.pipeline_class(**__lowerCAmelCase)
         lowercase_     			=   pipe.to(__lowerCAmelCase)

         pipe.set_progress_bar_config(disable=__lowerCAmelCase)

         lowercase_     			=   pipe(**self.get_dummy_inputs(__lowerCAmelCase))
         lowercase_     			=   output.image_embeds

         lowercase_     			=   pipe(
             **self.get_dummy_inputs(__lowerCAmelCase) ,		return_dict=__lowerCAmelCase ,		)[0]

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

         assert image.shape == (1, 32)

         lowercase_     			=   np.array(
             [
                 0.107_1284,
                 1.333_0271,
                 0.6126_0223,
                 -0.669_1065,
                 -0.384_6852,
                 -1.030_3661,
                 0.2271_6111,
                 0.0334_8901,
                 0.3004_0675,
                 -0.2480_5029,
             ])

         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					:						Dict)   ->       Optional[int]:
         lowercase_     			=   torch_device == "cpu"
         lowercase_     			=   True
         lowercase_     			=   False

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


   @skip_mps
   def __UpperCAmelCase							( self					:						str)   ->       str:
         lowercase_     			=   torch_device == "cpu"
         lowercase_     			=   False

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







'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCAmelCase_			:			Tuple    	=   {"configuration_vilt": ["VILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViltConfig"]}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase_			:			Optional[int]    	=   ["ViltFeatureExtractor"]
       lowerCAmelCase_			:			str    	=   ["ViltImageProcessor"]
       lowerCAmelCase_			:			Any    	=   ["ViltProcessor"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase_			:			Any    	=   [
           "VILT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "ViltForImageAndTextRetrieval",
           "ViltForImagesAndTextClassification",
           "ViltForTokenClassification",
           "ViltForMaskedLM",
           "ViltForQuestionAnswering",
           "ViltLayer",
           "ViltModel",
           "ViltPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_vilt import VILT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViltConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_vilt import ViltFeatureExtractor
              from .image_processing_vilt import ViltImageProcessor
              from .processing_vilt import ViltProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_vilt import (
                  VILT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ViltForImageAndTextRetrieval,
                  ViltForImagesAndTextClassification,
                  ViltForMaskedLM,
                  ViltForQuestionAnswering,
                  ViltForTokenClassification,
                  ViltLayer,
                  ViltModel,
                  ViltPreTrainedModel,
              )


else:
       import sys

       lowerCAmelCase_			:			str    	=   _LazyModule(__name__, globals()["__file__"], _import_structure)






