




'''simple docstring'''





from typing import List

from ...processing_utils import ProcessorMixin
from ...utils import is_torch_available


if is_torch_available():
  import torch




class      UpperCamelCase       (	snake_case__       ):
  a     :Tuple          =					['image_processor', 'tokenizer']
  a     :int          =					'OneFormerImageProcessor'
  a     :int          =					('CLIPTokenizer', 'CLIPTokenizerFast')



  def __init__(			self		,     __lowerCAmelCase=None		,     __lowerCAmelCase=None		,     __lowerCAmelCase = 77		,     __lowerCAmelCase = 77		,     **__lowerCAmelCase ):







      '''simple docstring'''


      if image_processor is None:
          raise ValueError("""You need to specify an `image_processor`.""" )
      if tokenizer is None:
          raise ValueError("""You need to specify a `tokenizer`.""" )

      _a				:							int							       =      max_seq_length
      _a				:							Optional[Any]							       =      task_seq_length

      super().__init__(__lowerCAmelCase		,     __lowerCAmelCase )



  def     a_							(			self		,     __lowerCAmelCase=None		,     __lowerCAmelCase=77 ):







      '''simple docstring'''


      if text_list is None:
          raise ValueError("""tokens cannot be None.""" )

      _a				:							Any							       =      self.tokenizer(__lowerCAmelCase		,     padding="""max_length"""		,     max_length=__lowerCAmelCase		,     truncation=__lowerCAmelCase )

      _a       , _a				:							Optional[Any]							       =      tokens["""attention_mask"""], tokens["""input_ids"""]

      _a				:							str							       =      []
      for attn_mask, input_id in zip(__lowerCAmelCase		,     __lowerCAmelCase ):
          _a				:							Tuple							       =      torch.tensor(__lowerCAmelCase ) * torch.tensor(__lowerCAmelCase )
          token_inputs.append(token.unsqueeze(0 ) )

      _a				:							List[str]							       =      torch.cat(__lowerCAmelCase		,     dim=0 )
      return token_inputs



  def __call__(			self		,     __lowerCAmelCase=None		,     __lowerCAmelCase=None		,     __lowerCAmelCase=None		,     **__lowerCAmelCase ):







      '''simple docstring'''


      if task_inputs is None:
          raise ValueError("""You have to specify the task_input. Found None.""" )
      elif images is None:
          raise ValueError("""You have to specify the image. Found None.""" )

      if not all(task in ["""semantic""", """instance""", """panoptic"""] for task in task_inputs ):
          raise ValueError("""task_inputs must be semantic, instance, or panoptic.""" )

      _a				:							int							       =      self.image_processor(__lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     **__lowerCAmelCase )

      if isinstance(__lowerCAmelCase		,     __lowerCAmelCase ):
          _a				:							List[str]							       =      [task_inputs]

      if isinstance(__lowerCAmelCase		,     __lowerCAmelCase ) and all(isinstance(__lowerCAmelCase		,     __lowerCAmelCase ) for task_input in task_inputs ):
          _a				:							List[str]							       =      []
          for task in task_inputs:
              _a				:							Dict							       =      f'''the task is {task}'''
              task_token_inputs.append(__lowerCAmelCase )
          _a				:							str							       =      self._preprocess_text(__lowerCAmelCase		,     max_length=self.task_seq_length )
      else:
          raise TypeError("""Task Inputs should be a string or a list of strings.""" )

      if hasattr(__lowerCAmelCase		,     """text_inputs""" ):
          _a				:							int							       =      encoded_inputs.text_inputs

          _a				:							Tuple							       =      []
          for texts in texts_list:
              _a				:							int							       =      self._preprocess_text(__lowerCAmelCase		,     max_length=self.max_seq_length )
              text_inputs.append(text_input_list.unsqueeze(0 ) )

          _a				:							Any							       =      torch.cat(__lowerCAmelCase		,     dim=0 )

      return encoded_inputs



  def     a_							(			self		,     __lowerCAmelCase=None		,     __lowerCAmelCase=None		,     __lowerCAmelCase=None		,     **__lowerCAmelCase ):







      '''simple docstring'''


      if task_inputs is None:
          raise ValueError("""You have to specify the task_input. Found None.""" )
      elif images is None:
          raise ValueError("""You have to specify the image. Found None.""" )

      if not all(task in ["""semantic""", """instance""", """panoptic"""] for task in task_inputs ):
          raise ValueError("""task_inputs must be semantic, instance, or panoptic.""" )

      _a				:							Dict							       =      self.image_processor.encode_inputs(__lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     **__lowerCAmelCase )

      if isinstance(__lowerCAmelCase		,     __lowerCAmelCase ):
          _a				:							Optional[Any]							       =      [task_inputs]

      if isinstance(__lowerCAmelCase		,     __lowerCAmelCase ) and all(isinstance(__lowerCAmelCase		,     __lowerCAmelCase ) for task_input in task_inputs ):
          _a				:							Dict							       =      []
          for task in task_inputs:
              _a				:							str							       =      f'''the task is {task}'''
              task_token_inputs.append(__lowerCAmelCase )
          _a				:							List[Any]							       =      self._preprocess_text(__lowerCAmelCase		,     max_length=self.task_seq_length )
      else:
          raise TypeError("""Task Inputs should be a string or a list of strings.""" )

      if hasattr(__lowerCAmelCase		,     """text_inputs""" ):
          _a				:							Union[str, Any]							       =      encoded_inputs.text_inputs

          _a				:							int							       =      []
          for texts in texts_list:
              _a				:							Optional[Any]							       =      self._preprocess_text(__lowerCAmelCase		,     max_length=self.max_seq_length )
              text_inputs.append(text_input_list.unsqueeze(0 ) )

          _a				:							Dict							       =      torch.cat(__lowerCAmelCase		,     dim=0 )

      return encoded_inputs



  def     a_							(			self		,     *__lowerCAmelCase		,     **__lowerCAmelCase ):







      '''simple docstring'''


      return self.image_processor.post_process_semantic_segmentation(*__lowerCAmelCase		,     **__lowerCAmelCase )



  def     a_							(			self		,     *__lowerCAmelCase		,     **__lowerCAmelCase ):







      '''simple docstring'''


      return self.image_processor.post_process_instance_segmentation(*__lowerCAmelCase		,     **__lowerCAmelCase )



  def     a_							(			self		,     *__lowerCAmelCase		,     **__lowerCAmelCase ):







      '''simple docstring'''


      return self.image_processor.post_process_panoptic_segmentation(*__lowerCAmelCase		,     **__lowerCAmelCase )






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 lowerCAmelCase_		(					lowerCamelCase__   ,		unittest.TestCase			):

 '''simple docstring'''

 __snake_case				=    KandinskyVaaPriorEmbaEmbPipeline
 __snake_case				=    ["prompt", "image"]
 __snake_case				=    ["prompt", "image"]
 __snake_case				=    [
     "num_images_per_prompt",
     "strength",
     "generator",
     "num_inference_steps",
     "latents",
     "negative_prompt",
     "guidance_scale",
     "output_type",
     "return_dict",
 ]
 __snake_case				=    False



 @property
 def 		UpperCamelCase__	(      self   ):
       return 32



 @property
 def 		UpperCamelCase__	(      self   ):
       return 32



 @property
 def 		UpperCamelCase__	(      self   ):
       return self.time_input_dim



 @property
 def 		UpperCamelCase__	(      self   ):
       return self.time_input_dim * 4



 @property
 def 		UpperCamelCase__	(      self   ):
       return 1_00



 @property
 def 		UpperCamelCase__	(      self   ):
       snake_case_			= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )
       return tokenizer



 @property
 def 		UpperCamelCase__	(      self   ):
       torch.manual_seed(0   )
       snake_case_			= 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 		UpperCamelCase__	(      self   ):
       torch.manual_seed(0   )

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

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



 @property
 def 		UpperCamelCase__	(      self   ):
       torch.manual_seed(0   )
       snake_case_			= 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						, )

       snake_case_			= CLIPVisionModelWithProjection(_UpperCAmelCase   )
       return model



 @property
 def 		UpperCamelCase__	(      self   ):
       snake_case_			= 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 		UpperCamelCase__	(      self   ):
       snake_case_			= self.dummy_prior
       snake_case_			= self.dummy_image_encoder
       snake_case_			= self.dummy_text_encoder
       snake_case_			= self.dummy_tokenizer
       snake_case_			= self.dummy_image_processor

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

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

       return components



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase=0   ):
       if str(_UpperCAmelCase   ).startswith('''mps'''   ):
             snake_case_			= torch.manual_seed(_UpperCAmelCase   )
       else:
             snake_case_			= torch.Generator(device=_UpperCAmelCase   ).manual_seed(_UpperCAmelCase   )

       snake_case_			= floats_tensor((1, 3, 64, 64)						, rng=random.Random(_UpperCAmelCase   )   ).to(_UpperCAmelCase   )
       snake_case_			= image.cpu().permute(0						, 2						, 3						, 1   )[0]
       snake_case_			= Image.fromarray(np.uinta(_UpperCAmelCase   )   ).convert('''RGB'''   ).resize((2_56, 2_56)   )

       snake_case_			= {
           '''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   ):
       snake_case_			= '''cpu'''

       snake_case_			= self.get_dummy_components()

       snake_case_			= self.pipeline_class(**_UpperCAmelCase   )
       snake_case_			= pipe.to(_UpperCAmelCase   )

       pipe.set_progress_bar_config(disable=_UpperCAmelCase   )

       snake_case_			= pipe(**self.get_dummy_inputs(_UpperCAmelCase   )   )
       snake_case_			= output.image_embeds

       snake_case_			= pipe(
           **self.get_dummy_inputs(_UpperCAmelCase   )						, return_dict=_UpperCAmelCase						, )[0]

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

       assert image.shape == (1, 32)

       snake_case_			= np.array(
           [
               0.1_071_284,
               1.3_330_271,
               0.61_260_223,
               -0.6_691_065,
               -0.3_846_852,
               -1.0_303_661,
               0.22_716_111,
               0.03_348_901,
               0.30_040_675,
               -0.24_805_029,
           ]   )

       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   ):
       snake_case_			= torch_device == '''cpu'''
       snake_case_			= True
       snake_case_			= False

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





 @skip_mps
 def 		UpperCamelCase__	(      self   ):
       snake_case_			= torch_device == '''cpu'''
       snake_case_			= False

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