






from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
     import PIL

# soft dependency
if is_pytesseract_available():
     import pytesseract

UpperCAmelCase__				   =		logging.get_logger(__name__)
def 						_a    (      a :str	,							a :Dict	,							a :Dict )							->			int:
 return [
     int(1_000 * (box[0] / width) ),
     int(1_000 * (box[1] / height) ),
     int(1_000 * (box[2] / width) ),
     int(1_000 * (box[3] / height) ),
 ]
def 						_a    (      a :np.ndarray	,							a :Optional[str]	,							a :Optional[str] = None )							->			Any:
 a		  =      tesseract_config if tesseract_config is not None else ''''''

 # apply OCR
 a		  =      to_pil_image(a )
 a  ,     a		  =      pil_image.size
 a		  =      pytesseract.image_to_data(a	,							lang=a	,							output_type='''dict'''	,							config=a )
 a  ,     a  ,     a  ,     a  ,     a		  =      data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

 # filter empty words and corresponding coordinates
 a		  =      [idx for idx, word in enumerate(a ) if not word.strip()]
 a		  =      [word for idx, word in enumerate(a ) if idx not in irrelevant_indices]
 a		  =      [coord for idx, coord in enumerate(a ) if idx not in irrelevant_indices]
 a		  =      [coord for idx, coord in enumerate(a ) if idx not in irrelevant_indices]
 a		  =      [coord for idx, coord in enumerate(a ) if idx not in irrelevant_indices]
 a		  =      [coord for idx, coord in enumerate(a ) if idx not in irrelevant_indices]

 # turn coordinates into (left, top, left+width, top+height) format
 a		  =      []
 for x, y, w, h in zip(a	,							a	,							a	,							a ):
  a		  =      [x, y, x + w, y + h]
  actual_boxes.append(a )

 # finally, normalize the bounding boxes
 a		  =      []
 for box in actual_boxes:
  normalized_boxes.append(normalize_box(a	,							a	,							a ) )

 assert len(a ) == len(a ), "Not as many words as there are bounding boxes"

 return words, normalized_boxes






class 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      __snake_case       =  ['''pixel_values''']







      def __init__(      self      :						int ,					__UpperCAmelCase      :						bool = True ,					__UpperCAmelCase      :						Dict[str, int] = None ,					__UpperCAmelCase      :						PILImageResampling = PILImageResampling.BILINEAR ,					__UpperCAmelCase      :						bool = True ,					__UpperCAmelCase      :						Optional[str] = None ,					__UpperCAmelCase      :						Optional[str] = "" ,					**__UpperCAmelCase      :						Any ,					)    ->None:




       """simple docstring"""


       super().__init__(**__UpperCAmelCase  )
       a		  =      size if size is not None else {'''height''': 224, '''width''': 224}
       a		  =      get_size_dict(__UpperCAmelCase  )

       a		  =      do_resize
       a		  =      size
       a		  =      resample
       a		  =      apply_ocr
       a		  =      ocr_lang
       a		  =      tesseract_config







      def       __lowerCAmelCase					(      self      :						Any ,					__UpperCAmelCase      :						np.ndarray ,					__UpperCAmelCase      :						Dict[str, int] ,					__UpperCAmelCase      :						PILImageResampling = PILImageResampling.BILINEAR ,					__UpperCAmelCase      :						Optional[Union[str, ChannelDimension]] = None ,					**__UpperCAmelCase      :						Dict ,					)    ->np.ndarray:




       """simple docstring"""


       a		  =      get_size_dict(__UpperCAmelCase  )
       if "height" not in size or "width" not in size:
        raise ValueError(F"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""  )
       a		  =      (size['''height'''], size['''width'''])
       return resize(__UpperCAmelCase ,					size=__UpperCAmelCase ,					resample=__UpperCAmelCase ,					data_format=__UpperCAmelCase ,					**__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						List[Any] ,					__UpperCAmelCase      :						ImageInput ,					__UpperCAmelCase      :						bool = None ,					__UpperCAmelCase      :						Dict[str, int] = None ,					__UpperCAmelCase      :						PILImageResampling = None ,					__UpperCAmelCase      :						bool = None ,					__UpperCAmelCase      :						Optional[str] = None ,					__UpperCAmelCase      :						Optional[str] = None ,					__UpperCAmelCase      :						Optional[Union[str, TensorType]] = None ,					__UpperCAmelCase      :						ChannelDimension = ChannelDimension.FIRST ,					**__UpperCAmelCase      :						Optional[int] ,					)    ->PIL.Image.Image:




       """simple docstring"""


       a		  =      do_resize if do_resize is not None else self.do_resize
       a		  =      size if size is not None else self.size
       a		  =      get_size_dict(__UpperCAmelCase  )
       a		  =      resample if resample is not None else self.resample
       a		  =      apply_ocr if apply_ocr is not None else self.apply_ocr
       a		  =      ocr_lang if ocr_lang is not None else self.ocr_lang
       a		  =      tesseract_config if tesseract_config is not None else self.tesseract_config

       a		  =      make_list_of_images(__UpperCAmelCase  )

       if not valid_images(__UpperCAmelCase  ):
        raise ValueError(
            '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
            '''torch.Tensor, tf.Tensor or jax.ndarray.'''  )

       if do_resize and size is None:
        raise ValueError('''Size must be specified if do_resize is True.'''  )

       # All transformations expect numpy arrays.
       a		  =      [to_numpy_array(__UpperCAmelCase  ) for image in images]

       if apply_ocr:
        requires_backends(self ,					'''pytesseract'''  )
        a		  =      []
        a		  =      []
        for image in images:
         a  ,     a		  =      apply_tesseract(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase  )
         words_batch.append(__UpperCAmelCase  )
         boxes_batch.append(__UpperCAmelCase  )

       if do_resize:
        a		  =      [self.resize(image=__UpperCAmelCase ,					size=__UpperCAmelCase ,					resample=__UpperCAmelCase  ) for image in images]

       # flip color channels from RGB to BGR (as Detectron2 requires this)
       a		  =      [flip_channel_order(__UpperCAmelCase  ) for image in images]
       a		  =      [to_channel_dimension_format(__UpperCAmelCase ,					__UpperCAmelCase  ) for image in images]

       a		  =      BatchFeature(data={'''pixel_values''': images} ,					tensor_type=__UpperCAmelCase  )

       if apply_ocr:
        a		  =      words_batch
        a		  =      boxes_batch
       return data










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 		lowercase_					(	lowercase ,  unittest.TestCase   ):






      '''simple docstring'''




      __snake_case       =  KandinskyVaaPriorPipeline
      __snake_case       =  ['''prompt''']
      __snake_case       =  ['''prompt''', '''negative_prompt''']
      __snake_case       =  [
          '''num_images_per_prompt''',
          '''generator''',
          '''num_inference_steps''',
          '''latents''',
          '''negative_prompt''',
          '''guidance_scale''',
          '''output_type''',
          '''return_dict''',
      ]
      __snake_case       =  False







      @property
      def       __lowerCAmelCase					(      self      :						Optional[Any]  )    ->Union[str, Any]:




       """simple docstring"""


       return 32







      @property
      def       __lowerCAmelCase					(      self      :						Dict  )    ->Any:




       """simple docstring"""


       return 32







      @property
      def       __lowerCAmelCase					(      self      :						int  )    ->List[str]:




       """simple docstring"""


       return self.time_input_dim







      @property
      def       __lowerCAmelCase					(      self      :						Tuple  )    ->Any:




       """simple docstring"""


       return self.time_input_dim * 4







      @property
      def       __lowerCAmelCase					(      self      :						Any  )    ->List[Any]:




       """simple docstring"""


       return 100







      @property
      def       __lowerCAmelCase					(      self      :						List[Any]  )    ->str:




       """simple docstring"""


       a		  =      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''  )
       return tokenizer







      @property
      def       __lowerCAmelCase					(      self      :						Tuple  )    ->str:




       """simple docstring"""


       torch.manual_seed(0  )
       a		  =      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-0_5 ,					num_attention_heads=4 ,					num_hidden_layers=5 ,					pad_token_id=1 ,					vocab_size=1_000 ,					)
       return CLIPTextModelWithProjection(__UpperCAmelCase  )







      @property
      def       __lowerCAmelCase					(      self      :						List[Any]  )    ->Optional[Any]:




       """simple docstring"""


       torch.manual_seed(0  )

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

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







      @property
      def       __lowerCAmelCase					(      self      :						Optional[int]  )    ->List[Any]:




       """simple docstring"""


       torch.manual_seed(0  )
       a		  =      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 ,					)

       a		  =      CLIPVisionModelWithProjection(__UpperCAmelCase  )
       return model







      @property
      def       __lowerCAmelCase					(      self      :						Tuple  )    ->int:




       """simple docstring"""


       a		  =      CLIPImageProcessor(
           crop_size=224 ,					do_center_crop=__UpperCAmelCase ,					do_normalize=__UpperCAmelCase ,					do_resize=__UpperCAmelCase ,					image_mean=[0.48145466, 0.4578275, 0.40821073] ,					image_std=[0.26862954, 0.26130258, 0.27577711] ,					resample=3 ,					size=224 ,					)

       return image_processor







      def       __lowerCAmelCase					(      self      :						List[Any]  )    ->Optional[Any]:




       """simple docstring"""


       a		  =      self.dummy_prior
       a		  =      self.dummy_image_encoder
       a		  =      self.dummy_text_encoder
       a		  =      self.dummy_tokenizer
       a		  =      self.dummy_image_processor

       a		  =      UnCLIPScheduler(
           variance_type='''fixed_small_log''' ,					prediction_type='''sample''' ,					num_train_timesteps=1_000 ,					clip_sample=__UpperCAmelCase ,					clip_sample_range=10.0 ,					)

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

       return components







      def       __lowerCAmelCase					(      self      :						Optional[Any] ,					__UpperCAmelCase      :						List[Any] ,					__UpperCAmelCase      :						str=0  )    ->int:




       """simple docstring"""


       if str(__UpperCAmelCase  ).startswith('''mps'''  ):
        a		  =      torch.manual_seed(__UpperCAmelCase  )
       else:
        a		  =      torch.Generator(device=__UpperCAmelCase  ).manual_seed(__UpperCAmelCase  )
       a		  =      {
           '''prompt''': '''horse''',
           '''generator''': generator,
           '''guidance_scale''': 4.0,
           '''num_inference_steps''': 2,
           '''output_type''': '''np''',
       }
       return inputs







      def       __lowerCAmelCase					(      self      :						str  )    ->Tuple:




       """simple docstring"""


       a		  =      '''cpu'''

       a		  =      self.get_dummy_components()

       a		  =      self.pipeline_class(**__UpperCAmelCase  )
       a		  =      pipe.to(__UpperCAmelCase  )

       pipe.set_progress_bar_config(disable=__UpperCAmelCase  )

       a		  =      pipe(**self.get_dummy_inputs(__UpperCAmelCase  )  )
       a		  =      output.image_embeds

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

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

       assert image.shape == (1, 32)

       a		  =      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      :						List[Any]  )    ->Optional[Any]:




       """simple docstring"""


       a		  =      torch_device == '''cpu'''
       a		  =      True
       a		  =      False

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







      @skip_mps
      def       __lowerCAmelCase					(      self      :						List[str]  )    ->Union[str, Any]:




       """simple docstring"""


       a		  =      torch_device == '''cpu'''
       a		  =      False

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


