






'''simple docstring'''
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

a_					= logging.get_logger(__name__)





def  _a(  UpperCamelCase__				:						List[Any],	UpperCamelCase__				:						Optional[int],	UpperCamelCase__				:						List[Any]							):




      '''simple docstring'''






      return [
          int(1_0_0_0 * (box[0] / width)							),
          int(1_0_0_0 * (box[1] / height)							),
          int(1_0_0_0 * (box[2] / width)							),
          int(1_0_0_0 * (box[3] / height)							),
      ]





def  _a(  UpperCamelCase__				:						np.ndarray,	UpperCamelCase__				:						Optional[str],	UpperCamelCase__				:						Optional[str] = None							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				str									=tesseract_config if tesseract_config is not None else ''''''

      # apply OCR
      SCREAMING_SNAKE_CASE__     :				Tuple									=to_pil_image(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				Any									=pil_image.size
      SCREAMING_SNAKE_CASE__     :				str									=pytesseract.image_to_data(UpperCamelCase__,	lang=UpperCamelCase__,	output_type='''dict''',	config=UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				List[Any]									=data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

      # filter empty words and corresponding coordinates
      SCREAMING_SNAKE_CASE__     :				Tuple									=[idx for idx, word in enumerate(UpperCamelCase__							) if not word.strip()]
      SCREAMING_SNAKE_CASE__     :				Any									=[word for idx, word in enumerate(UpperCamelCase__							) if idx not in irrelevant_indices]
      SCREAMING_SNAKE_CASE__     :				Optional[Any]									=[coord for idx, coord in enumerate(UpperCamelCase__							) if idx not in irrelevant_indices]
      SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=[coord for idx, coord in enumerate(UpperCamelCase__							) if idx not in irrelevant_indices]
      SCREAMING_SNAKE_CASE__     :				int									=[coord for idx, coord in enumerate(UpperCamelCase__							) if idx not in irrelevant_indices]
      SCREAMING_SNAKE_CASE__     :				str									=[coord for idx, coord in enumerate(UpperCamelCase__							) if idx not in irrelevant_indices]

      # turn coordinates into (left, top, left+width, top+height) format
      SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=[]
      for x, y, w, h in zip(UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__							):
            SCREAMING_SNAKE_CASE__     :				Dict									=[x, y, x + w, y + h]
            actual_boxes.append(UpperCamelCase__							)

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

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

      return words, normalized_boxes







class      __SCREAMING_SNAKE_CASE      (			lowerCamelCase				):
 snake_case_  							=							["""pixel_values"""]




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

       '''simple docstring'''





       super().__init__(**__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=size if size is not None else {'''height''': 2_24, '''width''': 2_24}
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=get_size_dict(__lowercase      )

       SCREAMING_SNAKE_CASE__     :				List[Any]									=do_resize
       SCREAMING_SNAKE_CASE__     :				Any									=size
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=resample
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=apply_ocr
       SCREAMING_SNAKE_CASE__     :				List[str]									=ocr_lang
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=tesseract_config




 def      __magic_name__	(      self	:							List[str]					,      __lowercase	:							np.ndarray					,      __lowercase	:							Dict[str, int]					,      __lowercase	:							PILImageResampling = PILImageResampling.BILINEAR					,      __lowercase	:							Optional[Union[str, ChannelDimension]] = None					,      **__lowercase	:							Tuple					,      )	->						np.ndarray:

       '''simple docstring'''





       SCREAMING_SNAKE_CASE__     :				List[Any]									=get_size_dict(__lowercase      )
       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()}"      )
       SCREAMING_SNAKE_CASE__     :				List[Any]									=(size['''height'''], size['''width'''])
       return resize(__lowercase					,      size=__lowercase					,      resample=__lowercase					,      data_format=__lowercase					,      **__lowercase      )




 def      __magic_name__	(      self	:							Tuple					,      __lowercase	:							ImageInput					,      __lowercase	:							bool = None					,      __lowercase	:							Dict[str, int] = None					,      __lowercase	:							PILImageResampling = None					,      __lowercase	:							bool = None					,      __lowercase	:							Optional[str] = None					,      __lowercase	:							Optional[str] = None					,      __lowercase	:							Optional[Union[str, TensorType]] = None					,      __lowercase	:							ChannelDimension = ChannelDimension.FIRST					,      **__lowercase	:							str					,      )	->						PIL.Image.Image:

       '''simple docstring'''





       SCREAMING_SNAKE_CASE__     :				Any									=do_resize if do_resize is not None else self.do_resize
       SCREAMING_SNAKE_CASE__     :				List[Any]									=size if size is not None else self.size
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=get_size_dict(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				int									=resample if resample is not None else self.resample
       SCREAMING_SNAKE_CASE__     :				List[str]									=apply_ocr if apply_ocr is not None else self.apply_ocr
       SCREAMING_SNAKE_CASE__     :				int									=ocr_lang if ocr_lang is not None else self.ocr_lang
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=tesseract_config if tesseract_config is not None else self.tesseract_config

       SCREAMING_SNAKE_CASE__     :				List[str]									=make_list_of_images(__lowercase      )

       if not valid_images(__lowercase      ):
             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.
       SCREAMING_SNAKE_CASE__     :				int									=[to_numpy_array(__lowercase      ) for image in images]

       if apply_ocr:
             requires_backends(self					,      '''pytesseract'''      )
             SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=[]
             SCREAMING_SNAKE_CASE__     :				Optional[int]									=[]
             for image in images:
                   SCREAMING_SNAKE_CASE__     :				List[Any]									=apply_tesseract(__lowercase					,      __lowercase					,      __lowercase      )
                   words_batch.append(__lowercase      )
                   boxes_batch.append(__lowercase      )

       if do_resize:
             SCREAMING_SNAKE_CASE__     :				int									=[self.resize(image=__lowercase					,      size=__lowercase					,      resample=__lowercase      ) for image in images]

       # flip color channels from RGB to BGR (as Detectron2 requires this)
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=[flip_channel_order(__lowercase      ) for image in images]
       SCREAMING_SNAKE_CASE__     :				Tuple									=[to_channel_dimension_format(__lowercase					,      __lowercase      ) for image in images]

       SCREAMING_SNAKE_CASE__     :				int									=BatchFeature(data={'''pixel_values''': images}					,      tensor_type=__lowercase      )

       if apply_ocr:
             SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=words_batch
             SCREAMING_SNAKE_CASE__     :				Optional[int]									=boxes_batch
       return data







'''simple docstring'''
import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference






class      __SCREAMING_SNAKE_CASE      (			lowerCamelCase	,				unittest.TestCase				):
 snake_case_  							=							ShapEImgaImgPipeline
 snake_case_  							=							["""image"""]
 snake_case_  							=							["""image"""]
 snake_case_  							=							[
     """num_images_per_prompt""",
     """num_inference_steps""",
     """generator""",
     """latents""",
     """guidance_scale""",
     """frame_size""",
     """output_type""",
     """return_dict""",
 ]
 snake_case_  							=							False




 @property
 def      __magic_name__	(      self	:							List[Any]      )	->						List[Any]:
       return 32




 @property
 def      __magic_name__	(      self	:							List[str]      )	->						Optional[int]:
       return 32




 @property
 def      __magic_name__	(      self	:							Optional[int]      )	->						Optional[Any]:
       return self.time_input_dim * 4




 @property
 def      __magic_name__	(      self	:							Dict      )	->						Union[str, Any]:
       return 8




 @property
 def      __magic_name__	(      self	:							Optional[int]      )	->						Union[str, Any]:
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__     :				Dict									=CLIPVisionConfig(
           hidden_size=self.text_embedder_hidden_size					,      image_size=64					,      projection_dim=self.text_embedder_hidden_size					,      intermediate_size=37					,      num_attention_heads=4					,      num_channels=3					,      num_hidden_layers=5					,      patch_size=1					,      )

       SCREAMING_SNAKE_CASE__     :				str									=CLIPVisionModel(__lowercase      )
       return model




 @property
 def      __magic_name__	(      self	:							Tuple      )	->						List[str]:
       SCREAMING_SNAKE_CASE__     :				int									=CLIPImageProcessor(
           crop_size=2_24					,      do_center_crop=__lowercase					,      do_normalize=__lowercase					,      do_resize=__lowercase					,      image_mean=[0.48145466, 0.4578275, 0.40821073]					,      image_std=[0.26862954, 0.26130258, 0.27577711]					,      resample=3					,      size=2_24					,      )

       return image_processor




 @property
 def      __magic_name__	(      self	:							List[str]      )	->						Dict:
       torch.manual_seed(0      )

       SCREAMING_SNAKE_CASE__     :				str									={
           '''num_attention_heads''': 2,
           '''attention_head_dim''': 16,
           '''embedding_dim''': self.time_input_dim,
           '''num_embeddings''': 32,
           '''embedding_proj_dim''': self.text_embedder_hidden_size,
           '''time_embed_dim''': self.time_embed_dim,
           '''num_layers''': 1,
           '''clip_embed_dim''': self.time_input_dim * 2,
           '''additional_embeddings''': 0,
           '''time_embed_act_fn''': '''gelu''',
           '''norm_in_type''': '''layer''',
           '''embedding_proj_norm_type''': '''layer''',
           '''encoder_hid_proj_type''': None,
           '''added_emb_type''': None,
       }

       SCREAMING_SNAKE_CASE__     :				str									=PriorTransformer(**__lowercase      )
       return model




 @property
 def      __magic_name__	(      self	:							Tuple      )	->						List[str]:
       torch.manual_seed(0      )

       SCREAMING_SNAKE_CASE__     :				Optional[Any]									={
           '''param_shapes''': (
               (self.renderer_dim, 93),
               (self.renderer_dim, 8),
               (self.renderer_dim, 8),
               (self.renderer_dim, 8),
           ),
           '''d_latent''': self.time_input_dim,
           '''d_hidden''': self.renderer_dim,
           '''n_output''': 12,
           '''background''': (
               0.1,
               0.1,
               0.1,
           ),
       }
       SCREAMING_SNAKE_CASE__     :				str									=ShapERenderer(**__lowercase      )
       return model




 def      __magic_name__	(      self	:							List[Any]      )	->						List[Any]:
       SCREAMING_SNAKE_CASE__     :				int									=self.dummy_prior
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=self.dummy_image_encoder
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=self.dummy_image_processor
       SCREAMING_SNAKE_CASE__     :				Tuple									=self.dummy_renderer

       SCREAMING_SNAKE_CASE__     :				int									=HeunDiscreteScheduler(
           beta_schedule='''exp'''					,      num_train_timesteps=10_24					,      prediction_type='''sample'''					,      use_karras_sigmas=__lowercase					,      clip_sample=__lowercase					,      clip_sample_range=1.0					,      )
       SCREAMING_SNAKE_CASE__     :				Any									={
           '''prior''': prior,
           '''image_encoder''': image_encoder,
           '''image_processor''': image_processor,
           '''renderer''': renderer,
           '''scheduler''': scheduler,
       }

       return components




 def      __magic_name__	(      self	:							Any					,      __lowercase	:							List[str]					,      __lowercase	:							Any=0      )	->						Any:
       SCREAMING_SNAKE_CASE__     :				int									=floats_tensor((1, 3, 64, 64)					,      rng=random.Random(__lowercase      )      ).to(__lowercase      )

       if str(__lowercase      ).startswith('''mps'''      ):
             SCREAMING_SNAKE_CASE__     :				List[str]									=torch.manual_seed(__lowercase      )
       else:
             SCREAMING_SNAKE_CASE__     :				Optional[Any]									=torch.Generator(device=__lowercase      ).manual_seed(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Any									={
           '''image''': input_image,
           '''generator''': generator,
           '''num_inference_steps''': 1,
           '''frame_size''': 32,
           '''output_type''': '''np''',
       }
       return inputs




 def      __magic_name__	(      self	:							List[str]      )	->						str:
       SCREAMING_SNAKE_CASE__     :				int									='''cpu'''

       SCREAMING_SNAKE_CASE__     :				Any									=self.get_dummy_components()

       SCREAMING_SNAKE_CASE__     :				str									=self.pipeline_class(**__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Any									=pipe.to(__lowercase      )

       pipe.set_progress_bar_config(disable=__lowercase      )

       SCREAMING_SNAKE_CASE__     :				Dict									=pipe(**self.get_dummy_inputs(__lowercase      )      )
       SCREAMING_SNAKE_CASE__     :				Tuple									=output.images[0]
       SCREAMING_SNAKE_CASE__     :				List[Any]									=image[0, -3:, -3:, -1]

       assert image.shape == (20, 32, 32, 3)

       SCREAMING_SNAKE_CASE__     :				List[Any]									=np.array(
           [
               0.00039216,
               0.00039216,
               0.00039216,
               0.00039216,
               0.00039216,
               0.00039216,
               0.00039216,
               0.00039216,
               0.00039216,
           ]      )

       assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2




 def      __magic_name__	(      self	:							List[Any]      )	->						List[str]:
       # NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
       self._test_inference_batch_consistent(batch_sizes=[1, 2]      )




 def      __magic_name__	(      self	:							Optional[int]      )	->						str:
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=torch_device == '''cpu'''
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=True
       self._test_inference_batch_single_identical(
           batch_size=2					,      test_max_difference=__lowercase					,      relax_max_difference=__lowercase					,      )




 def      __magic_name__	(      self	:							Dict      )	->						List[str]:
       SCREAMING_SNAKE_CASE__     :				Any									=self.get_dummy_components()
       SCREAMING_SNAKE_CASE__     :				Dict									=self.pipeline_class(**__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=pipe.to(__lowercase      )
       pipe.set_progress_bar_config(disable=__lowercase      )

       SCREAMING_SNAKE_CASE__     :				Optional[int]									=1
       SCREAMING_SNAKE_CASE__     :				List[str]									=2

       SCREAMING_SNAKE_CASE__     :				Dict									=self.get_dummy_inputs(__lowercase      )

       for key in inputs.keys():
             if key in self.batch_params:
                   SCREAMING_SNAKE_CASE__     :				Tuple									=batch_size * [inputs[key]]

       SCREAMING_SNAKE_CASE__     :				List[Any]									=pipe(**__lowercase					,      num_images_per_prompt=__lowercase      )[0]

       assert images.shape[0] == batch_size * num_images_per_prompt






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




 def      __magic_name__	(      self	:							Optional[Any]      )	->						int:
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




 def      __magic_name__	(      self	:							int      )	->						Dict:
       SCREAMING_SNAKE_CASE__     :				List[str]									=load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/shap_e/corgi.png'''      )
       SCREAMING_SNAKE_CASE__     :				Dict									=load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/shap_e/test_shap_e_img2img_out.npy'''      )
       SCREAMING_SNAKE_CASE__     :				List[Any]									=ShapEImgaImgPipeline.from_pretrained('''openai/shap-e-img2img'''      )
       SCREAMING_SNAKE_CASE__     :				Tuple									=pipe.to(__lowercase      )
       pipe.set_progress_bar_config(disable=__lowercase      )

       SCREAMING_SNAKE_CASE__     :				Tuple									=torch.Generator(device=__lowercase      ).manual_seed(0      )

       SCREAMING_SNAKE_CASE__     :				Tuple									=pipe(
           __lowercase					,      generator=__lowercase					,      guidance_scale=3.0					,      num_inference_steps=64					,      frame_size=64					,      output_type='''np'''					,      ).images[0]

       assert images.shape == (20, 64, 64, 3)

       assert_mean_pixel_difference(__lowercase					,      __lowercase      )