
"""simple docstring"""

import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available

class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):
     lowercase		     =		["image_processor", "tokenizer"]
     lowercase		     =		"OwlViTImageProcessor"
     lowercase		     =		("CLIPTokenizer", "CLIPTokenizerFast")





     def __init__(			self			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  **__UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= None
      if "feature_extractor" in kwargs:
       warnings.warn(
           'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
           ' instead.'			,  __UpperCAmelCase			,  )
       __UpperCamelCase       						= kwargs.pop('feature_extractor'   )

      __UpperCamelCase       						= image_processor if image_processor is not None else feature_extractor
      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`.'   )

      super().__init__(__UpperCAmelCase			,  __UpperCAmelCase   )





     def __call__(			self			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  __UpperCAmelCase="max_length"			,  __UpperCAmelCase="np"			,  **__UpperCAmelCase   ):

      '''simple docstring'''




      if text is None and query_images is None and images is None:
       raise ValueError(
           'You have to specify at least one text or query image or image. All three cannot be none.'   )

      if text is not None:
       if isinstance(__UpperCAmelCase			,  __UpperCAmelCase   ) or (isinstance(__UpperCAmelCase			,  __UpperCAmelCase   ) and not isinstance(text[0]			,  __UpperCAmelCase   )):
        __UpperCamelCase       						= [self.tokenizer(__UpperCAmelCase			,  padding=__UpperCAmelCase			,  return_tensors=__UpperCAmelCase			,  **__UpperCAmelCase   )]

       elif isinstance(__UpperCAmelCase			,  __UpperCAmelCase   ) and isinstance(text[0]			,  __UpperCAmelCase   ):
        __UpperCamelCase       						= []

        # Maximum number of queries across batch
        __UpperCamelCase       						= max([len(__UpperCAmelCase   ) for t in text]   )

        # Pad all batch samples to max number of text queries
        for t in text:
         if len(__UpperCAmelCase   ) != max_num_queries:
          __UpperCamelCase       						= t + [' '] * (max_num_queries - len(__UpperCAmelCase   ))

         __UpperCamelCase       						= self.tokenizer(__UpperCAmelCase			,  padding=__UpperCAmelCase			,  return_tensors=__UpperCAmelCase			,  **__UpperCAmelCase   )
         encodings.append(__UpperCAmelCase   )
       else:
        raise TypeError('Input text should be a string, a list of strings or a nested list of strings'   )

       if return_tensors == "np":
        __UpperCamelCase       						= np.concatenate([encoding['input_ids'] for encoding in encodings]			,  axis=0   )
        __UpperCamelCase       						= np.concatenate([encoding['attention_mask'] for encoding in encodings]			,  axis=0   )

       elif return_tensors == "jax" and is_flax_available():
        import jax.numpy as jnp

        __UpperCamelCase       						= jnp.concatenate([encoding['input_ids'] for encoding in encodings]			,  axis=0   )
        __UpperCamelCase       						= jnp.concatenate([encoding['attention_mask'] for encoding in encodings]			,  axis=0   )

       elif return_tensors == "pt" and is_torch_available():
        import torch

        __UpperCamelCase       						= torch.cat([encoding['input_ids'] for encoding in encodings]			,  dim=0   )
        __UpperCamelCase       						= torch.cat([encoding['attention_mask'] for encoding in encodings]			,  dim=0   )

       elif return_tensors == "tf" and is_tf_available():
        import tensorflow as tf

        __UpperCamelCase       						= tf.stack([encoding['input_ids'] for encoding in encodings]			,  axis=0   )
        __UpperCamelCase       						= tf.stack([encoding['attention_mask'] for encoding in encodings]			,  axis=0   )

       else:
        raise ValueError('Target return tensor type could not be returned'   )

       __UpperCamelCase       						= BatchEncoding()
       __UpperCamelCase       						= input_ids
       __UpperCamelCase       						= attention_mask

      if query_images is not None:
       __UpperCamelCase       						= BatchEncoding()
       __UpperCamelCase       						= self.image_processor(
           __UpperCAmelCase			,  return_tensors=__UpperCAmelCase			,  **__UpperCAmelCase   ).pixel_values
       __UpperCamelCase       						= query_pixel_values

      if images is not None:
       __UpperCamelCase       						= self.image_processor(__UpperCAmelCase			,  return_tensors=__UpperCAmelCase			,  **__UpperCAmelCase   )

      if text is not None and images is not None:
       __UpperCamelCase       						= image_features.pixel_values
       return encoding
      elif query_images is not None and images is not None:
       __UpperCamelCase       						= image_features.pixel_values
       return encoding
      elif text is not None or query_images is not None:
       return encoding
      else:
       return BatchEncoding(data=dict(**__UpperCAmelCase   )			,  tensor_type=__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self			,  *__UpperCAmelCase			,  **__UpperCAmelCase   ):

      '''simple docstring'''




      return self.image_processor.post_process(*__UpperCAmelCase			,  **__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self			,  *__UpperCAmelCase			,  **__UpperCAmelCase   ):

      '''simple docstring'''




      return self.image_processor.post_process_object_detection(*__UpperCAmelCase			,  **__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self			,  *__UpperCAmelCase			,  **__UpperCAmelCase   ):

      '''simple docstring'''




      return self.image_processor.post_process_image_guided_detection(*__UpperCAmelCase			,  **__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self			,  *__UpperCAmelCase			,  **__UpperCAmelCase   ):

      '''simple docstring'''




      return self.tokenizer.batch_decode(*__UpperCAmelCase			,  **__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self			,  *__UpperCAmelCase			,  **__UpperCAmelCase   ):

      '''simple docstring'''




      return self.tokenizer.decode(*__UpperCAmelCase			,  **__UpperCAmelCase   )





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      warnings.warn(
          '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'			,  __UpperCAmelCase			,  )
      return self.image_processor_class







     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      warnings.warn(
          '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'			,  __UpperCAmelCase			,  )
      return self.image_processor





"""simple docstring"""

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ,						unittest.TestCase    ):
     lowercase		     =		KandinskyInpaintPipeline
     lowercase		     =		["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
     lowercase		     =		[
         "prompt",
         "negative_prompt",
         "image_embeds",
         "negative_image_embeds",
         "image",
         "mask_image",
     ]
     lowercase		     =		[
         "generator",
         "height",
         "width",
         "latents",
         "guidance_scale",
         "negative_prompt",
         "num_inference_steps",
         "return_dict",
         "guidance_scale",
         "num_images_per_prompt",
         "output_type",
         "return_dict",
     ]
     lowercase		     =		False





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return 32





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return 32





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return self.time_input_dim





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return self.time_input_dim * 4





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return 100





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'   )
      return tokenizer





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      torch.manual_seed(0   )
      __UpperCamelCase       						= MCLIPConfig(
          numDims=self.cross_attention_dim			,  transformerDimensions=self.text_embedder_hidden_size			,  hidden_size=self.text_embedder_hidden_size			,  intermediate_size=37			,  num_attention_heads=4			,  num_hidden_layers=5			,  vocab_size=1005			,  )

      __UpperCamelCase       						= MultilingualCLIP(__UpperCAmelCase   )
      __UpperCamelCase       						= text_encoder.eval()

      return text_encoder





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      torch.manual_seed(0   )

      __UpperCamelCase       						= {
          'in_channels': 9,
          # Out channels is double in channels because predicts mean and variance
          'out_channels': 8,
          'addition_embed_type': 'text_image',
          'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
          'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
          'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
          'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
          'layers_per_block': 1,
          'encoder_hid_dim': self.text_embedder_hidden_size,
          'encoder_hid_dim_type': 'text_image_proj',
          'cross_attention_dim': self.cross_attention_dim,
          'attention_head_dim': 4,
          'resnet_time_scale_shift': 'scale_shift',
          'class_embed_type': None,
      }

      __UpperCamelCase       						= UNetaDConditionModel(**__UpperCAmelCase   )
      return model





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return {
          "block_out_channels": [32, 64],
          "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
          "in_channels": 3,
          "latent_channels": 4,
          "layers_per_block": 1,
          "norm_num_groups": 8,
          "norm_type": "spatial",
          "num_vq_embeddings": 12,
          "out_channels": 3,
          "up_block_types": [
              "AttnUpDecoderBlock2D",
              "UpDecoderBlock2D",
          ],
          "vq_embed_dim": 4,
      }





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      torch.manual_seed(0   )
      __UpperCamelCase       						= VQModel(**self.dummy_movq_kwargs   )
      return model





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.dummy_text_encoder
      __UpperCamelCase       						= self.dummy_tokenizer
      __UpperCamelCase       						= self.dummy_unet
      __UpperCamelCase       						= self.dummy_movq

      __UpperCamelCase       						= DDIMScheduler(
          num_train_timesteps=1000			,  beta_schedule='linear'			,  beta_start=0.0_0_0_8_5			,  beta_end=0.0_1_2			,  clip_sample=__UpperCAmelCase			,  set_alpha_to_one=__UpperCAmelCase			,  steps_offset=1			,  prediction_type='epsilon'			,  thresholding=__UpperCAmelCase			,  )

      __UpperCamelCase       						= {
          'text_encoder': text_encoder,
          'tokenizer': tokenizer,
          'unet': unet,
          'scheduler': scheduler,
          'movq': movq,
      }

      return components





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase=0   ):

      '''simple docstring'''




      __UpperCamelCase       						= floats_tensor((1, self.cross_attention_dim)			,  rng=random.Random(__UpperCAmelCase   )   ).to(__UpperCAmelCase   )
      __UpperCamelCase       						= floats_tensor((1, self.cross_attention_dim)			,  rng=random.Random(seed + 1   )   ).to(__UpperCAmelCase   )
      # create init_image
      __UpperCamelCase       						= floats_tensor((1, 3, 64, 64)			,  rng=random.Random(__UpperCAmelCase   )   ).to(__UpperCAmelCase   )
      __UpperCamelCase       						= image.cpu().permute(0			,  2			,  3			,  1   )[0]
      __UpperCamelCase       						= Image.fromarray(np.uinta(__UpperCAmelCase   )   ).convert('RGB'   ).resize((256, 256)   )
      # create mask
      __UpperCamelCase       						= np.ones((64, 64)			,  dtype=np.floataa   )
      __UpperCamelCase       						= 0

      if str(__UpperCAmelCase   ).startswith('mps'   ):
       __UpperCamelCase       						= torch.manual_seed(__UpperCAmelCase   )
      else:
       __UpperCamelCase       						= torch.Generator(device=__UpperCAmelCase   ).manual_seed(__UpperCAmelCase   )
      __UpperCamelCase       						= {
          'prompt': 'horse',
          'image': init_image,
          'mask_image': mask,
          'image_embeds': image_embeds,
          'negative_image_embeds': negative_image_embeds,
          'generator': generator,
          'height': 64,
          'width': 64,
          'num_inference_steps': 2,
          'guidance_scale': 4.0,
          'output_type': 'np',
      }
      return inputs





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __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.images

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

      __UpperCamelCase       						= image[0, -3:, -3:, -1]
      __UpperCamelCase       						= image_from_tuple[0, -3:, -3:, -1]

      print(F'image.shape {image.shape}'   )

      assert image.shape == (1, 64, 64, 3)

      __UpperCamelCase       						= np.array(
          [0.8_3_2_6_9_1_9, 0.7_3_7_9_0_4_6_7, 0.2_0_9_1_8_5_8_1, 0.9_3_0_9_6_1_2, 0.5_5_1_1_7_9_1, 0.4_3_7_1_3_3_2_8, 0.5_5_1_3_3_2_1, 0.4_9_9_2_2_9_3_4, 0.5_9_4_9_7_7_8_6]   )

      assert (
          np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
      ), F' expected_slice {expected_slice}, but got {image_slice.flatten()}'
      assert (
          np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2
      ), F' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'







     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      super().test_inference_batch_single_identical(expected_max_diff=3E-3   )


@slow
@require_torch_gpu
class __lowerCAmelCase	(   unittest.TestCase    ):





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()







     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'   )

      __UpperCamelCase       						= load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'   )
      __UpperCamelCase       						= np.ones((768, 768)			,  dtype=np.floataa   )
      __UpperCamelCase       						= 0

      __UpperCamelCase       						= 'a hat'

      __UpperCamelCase       						= KandinskyPriorPipeline.from_pretrained(
          'kandinsky-community/kandinsky-2-1-prior'			,  torch_dtype=torch.floataa   )
      pipe_prior.to(__UpperCAmelCase   )

      __UpperCamelCase       						= KandinskyInpaintPipeline.from_pretrained(
          'kandinsky-community/kandinsky-2-1-inpaint'			,  torch_dtype=torch.floataa   )
      __UpperCamelCase       						= pipeline.to(__UpperCAmelCase   )
      pipeline.set_progress_bar_config(disable=__UpperCAmelCase   )

      __UpperCamelCase       						= torch.Generator(device='cpu'   ).manual_seed(0   )
      __UpperCamelCase						,			__UpperCamelCase       						= pipe_prior(
          __UpperCAmelCase			,  generator=__UpperCAmelCase			,  num_inference_steps=5			,  negative_prompt=''			,  ).to_tuple()

      __UpperCamelCase       						= pipeline(
          __UpperCAmelCase			,  image=__UpperCAmelCase			,  mask_image=__UpperCAmelCase			,  image_embeds=__UpperCAmelCase			,  negative_image_embeds=__UpperCAmelCase			,  generator=__UpperCAmelCase			,  num_inference_steps=100			,  height=768			,  width=768			,  output_type='np'			,  )

      __UpperCamelCase       						= output.images[0]

      assert image.shape == (768, 768, 3)

      assert_mean_pixel_difference(__UpperCAmelCase			,  __UpperCAmelCase   )



