




'''simple docstring'''







import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np







class 					_a :





     '''simple docstring'''



     def 			snake_case_      (				self							)      ->				Optional[int]:
          torch.manual_seed(0							)
          snake_case			:      Tuple        =			TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5"""							)

          torch.manual_seed(0							)
          snake_case			:      Any        =			AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5"""							)

          torch.manual_seed(0							)
          snake_case			:      List[str]        =			UNetaDConditionModel(
              sample_size=32							,layers_per_block=1							,block_out_channels=[32, 64]							,down_block_types=[
                  """ResnetDownsampleBlock2D""",
                  """SimpleCrossAttnDownBlock2D""",
              ]							,mid_block_type="""UNetMidBlock2DSimpleCrossAttn"""							,up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""]							,in_channels=3							,out_channels=6							,cross_attention_dim=32							,encoder_hid_dim=32							,attention_head_dim=8							,addition_embed_type="""text"""							,addition_embed_type_num_heads=2							,cross_attention_norm="""group_norm"""							,resnet_time_scale_shift="""scale_shift"""							,act_fn="""gelu"""							,)
          unet.set_attn_processor(AttnAddedKVProcessor()							)  # For reproducibility tests

          torch.manual_seed(0							)
          snake_case			:      Optional[int]        =			DDPMScheduler(
              num_train_timesteps=1_000							,beta_schedule="""squaredcos_cap_v2"""							,beta_start=0.0_001							,beta_end=0.02							,thresholding=__a							,dynamic_thresholding_ratio=0.95							,sample_max_value=1.0							,prediction_type="""epsilon"""							,variance_type="""learned_range"""							,)

          torch.manual_seed(0							)
          snake_case			:      str        =			IFWatermarker()

          return {
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
              "unet": unet,
              "scheduler": scheduler,
              "watermarker": watermarker,
              "safety_checker": None,
              "feature_extractor": None,
          }







     def 			snake_case_      (				self							)      ->				Tuple:
          torch.manual_seed(0							)
          snake_case			:      Any        =			TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5"""							)

          torch.manual_seed(0							)
          snake_case			:      Optional[int]        =			AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5"""							)

          torch.manual_seed(0							)
          snake_case			:      Tuple        =			UNetaDConditionModel(
              sample_size=32							,layers_per_block=[1, 2]							,block_out_channels=[32, 64]							,down_block_types=[
                  """ResnetDownsampleBlock2D""",
                  """SimpleCrossAttnDownBlock2D""",
              ]							,mid_block_type="""UNetMidBlock2DSimpleCrossAttn"""							,up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""]							,in_channels=6							,out_channels=6							,cross_attention_dim=32							,encoder_hid_dim=32							,attention_head_dim=8							,addition_embed_type="""text"""							,addition_embed_type_num_heads=2							,cross_attention_norm="""group_norm"""							,resnet_time_scale_shift="""scale_shift"""							,act_fn="""gelu"""							,class_embed_type="""timestep"""							,mid_block_scale_factor=1.414							,time_embedding_act_fn="""gelu"""							,time_embedding_dim=32							,)
          unet.set_attn_processor(AttnAddedKVProcessor()							)  # For reproducibility tests

          torch.manual_seed(0							)
          snake_case			:      Tuple        =			DDPMScheduler(
              num_train_timesteps=1_000							,beta_schedule="""squaredcos_cap_v2"""							,beta_start=0.0_001							,beta_end=0.02							,thresholding=__a							,dynamic_thresholding_ratio=0.95							,sample_max_value=1.0							,prediction_type="""epsilon"""							,variance_type="""learned_range"""							,)

          torch.manual_seed(0							)
          snake_case			:      Tuple        =			DDPMScheduler(
              num_train_timesteps=1_000							,beta_schedule="""squaredcos_cap_v2"""							,beta_start=0.0_001							,beta_end=0.02							,)

          torch.manual_seed(0							)
          snake_case			:      Optional[Any]        =			IFWatermarker()

          return {
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
              "unet": unet,
              "scheduler": scheduler,
              "image_noising_scheduler": image_noising_scheduler,
              "watermarker": watermarker,
              "safety_checker": None,
              "feature_extractor": None,
          }







     def 			snake_case_      (				self							)      ->				int:
          snake_case			:      Union[str, Any]        =			self.get_dummy_components()
          snake_case			:      Any        =			self.pipeline_class(**__a							)
          pipe.to(__a							)
          pipe.set_progress_bar_config(disable=__a							)

          snake_case			:      Dict        =			self.get_dummy_inputs(__a							)

          snake_case			:      Union[str, Any]        =			inputs["""prompt"""]
          snake_case			:      Tuple        =			inputs["""generator"""]
          snake_case			:      List[str]        =			inputs["""num_inference_steps"""]
          snake_case			:      str        =			inputs["""output_type"""]

          if "image" in inputs:
               snake_case			:      Any        =			inputs["""image"""]
          else:
               snake_case			:      str        =			None

          if "mask_image" in inputs:
               snake_case			:      Any        =			inputs["""mask_image"""]
          else:
               snake_case			:      Optional[Any]        =			None

          if "original_image" in inputs:
               snake_case			:      List[str]        =			inputs["""original_image"""]
          else:
               snake_case			:      Union[str, Any]        =			None

          snake_case ,		snake_case			:      Optional[int]        =			pipe.encode_prompt(__a							)

          # inputs with prompt converted to embeddings
          snake_case			:      Optional[int]        =			{
              """prompt_embeds""": prompt_embeds,
              """negative_prompt_embeds""": negative_prompt_embeds,
              """generator""": generator,
              """num_inference_steps""": num_inference_steps,
              """output_type""": output_type,
          }

          if image is not None:
               snake_case			:      Tuple        =			image

          if mask_image is not None:
               snake_case			:      Any        =			mask_image

          if original_image is not None:
               snake_case			:      int        =			original_image

          # set all optional components to None
          for optional_component in pipe._optional_components:
               setattr(__a							,__a							,__a							)

          snake_case			:      List[Any]        =			pipe(**__a							)[0]

          with tempfile.TemporaryDirectory() as tmpdir:
               pipe.save_pretrained(__a							)
               snake_case			:      int        =			self.pipeline_class.from_pretrained(__a							)
               pipe_loaded.to(__a							)
               pipe_loaded.set_progress_bar_config(disable=__a							)

          pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()							)  # For reproducibility tests

          for optional_component in pipe._optional_components:
               self.assertTrue(
                   getattr(__a							,__a							) is None							,F'''`{optional_component}` did not stay set to None after loading.'''							,)

          snake_case			:      Tuple        =			self.get_dummy_inputs(__a							)

          snake_case			:      Optional[Any]        =			inputs["""generator"""]
          snake_case			:      Optional[int]        =			inputs["""num_inference_steps"""]
          snake_case			:      Optional[int]        =			inputs["""output_type"""]

          # inputs with prompt converted to embeddings
          snake_case			:      Optional[Any]        =			{
              """prompt_embeds""": prompt_embeds,
              """negative_prompt_embeds""": negative_prompt_embeds,
              """generator""": generator,
              """num_inference_steps""": num_inference_steps,
              """output_type""": output_type,
          }

          if image is not None:
               snake_case			:      str        =			image

          if mask_image is not None:
               snake_case			:      List[str]        =			mask_image

          if original_image is not None:
               snake_case			:      List[str]        =			original_image

          snake_case			:      Optional[Any]        =			pipe_loaded(**__a							)[0]

          snake_case			:      Optional[Any]        =			np.abs(to_np(__a							) - to_np(__a							)							).max()
          self.assertLess(__a							,1E-4							)







     def 			snake_case_      (				self							)      ->				Tuple:
          snake_case			:      Union[str, Any]        =			self.get_dummy_components()
          snake_case			:      Any        =			self.pipeline_class(**__a							)
          pipe.to(__a							)
          pipe.set_progress_bar_config(disable=__a							)

          snake_case			:      Tuple        =			self.get_dummy_inputs(__a							)
          snake_case			:      Union[str, Any]        =			pipe(**__a							)[0]

          with tempfile.TemporaryDirectory() as tmpdir:
               pipe.save_pretrained(__a							)
               snake_case			:      List[Any]        =			self.pipeline_class.from_pretrained(__a							)
               pipe_loaded.to(__a							)
               pipe_loaded.set_progress_bar_config(disable=__a							)

          pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()							)  # For reproducibility tests

          snake_case			:      Union[str, Any]        =			self.get_dummy_inputs(__a							)
          snake_case			:      Any        =			pipe_loaded(**__a							)[0]

          snake_case			:      List[Any]        =			np.abs(to_np(__a							) - to_np(__a							)							).max()
          self.assertLess(__a							,1E-4							)









'''simple docstring'''







from typing import Dict, Iterable, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    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

lowercase      :   Optional[Any]      =       logging.get_logger(__name__)



def        lowerCamelCase__				(      __lowercase     ,   __lowercase     ,   __lowercase				):
     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        lowerCamelCase__				(      __lowercase     ,   __lowercase     ,   __lowercase				):
     snake_case			:      List[Any]        =			to_pil_image(__lowercase				)
     snake_case ,		snake_case			:      Union[str, Any]        =			pil_image.size
     snake_case			:      Optional[Any]        =			pytesseract.image_to_data(__lowercase     ,   lang=__lowercase     ,   output_type="""dict"""     ,   config=__lowercase				)
     snake_case ,		snake_case ,		snake_case ,		snake_case ,		snake_case			:      Dict        =			data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]

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

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

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

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

     return words, normalized_boxes







class 					_a (a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     Dict          =					["""pixel_values"""]







     def __init__(				self							,__a = True							,__a = None							,__a = PILImageResampling.BILINEAR							,__a = True							,__a = 1 / 255							,__a = True							,__a = None							,__a = None							,__a = True							,__a = None							,__a = ""							,**__a							,)      ->				None:
          super().__init__(**__a							)
          snake_case			:      int        =			size if size is not None else {"""height""": 224, """width""": 224}
          snake_case			:      Union[str, Any]        =			get_size_dict(__a							)

          snake_case			:      List[str]        =			do_resize
          snake_case			:      Dict        =			size
          snake_case			:      Dict        =			resample
          snake_case			:      Union[str, Any]        =			do_rescale
          snake_case			:      Union[str, Any]        =			rescale_value
          snake_case			:      List[str]        =			do_normalize
          snake_case			:      int        =			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          snake_case			:      int        =			image_std if image_std is not None else IMAGENET_STANDARD_STD
          snake_case			:      List[Any]        =			apply_ocr
          snake_case			:      Any        =			ocr_lang
          snake_case			:      Optional[int]        =			tesseract_config







     def 			snake_case_      (				self							,__a							,__a							,__a = PILImageResampling.BILINEAR							,__a = None							,**__a							,)      ->				np.ndarray:
          snake_case			:      List[str]        =			get_size_dict(__a							)
          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()}'''							)
          snake_case			:      Tuple        =			(size["""height"""], size["""width"""])
          return resize(__a							,size=__a							,resample=__a							,data_format=__a							,**__a							)







     def 			snake_case_      (				self							,__a							,__a							,__a = None							,**__a							,)      ->				np.ndarray:
          return rescale(__a							,scale=__a							,data_format=__a							,**__a							)







     def 			snake_case_      (				self							,__a							,__a							,__a							,__a = None							,**__a							,)      ->				np.ndarray:
          return normalize(__a							,mean=__a							,std=__a							,data_format=__a							,**__a							)







     def 			snake_case_      (				self							,__a							,__a = None							,__a = None							,__a=None							,__a = None							,__a = None							,__a = None							,__a = None							,__a = None							,__a = None							,__a = None							,__a = None							,__a = None							,__a = ChannelDimension.FIRST							,**__a							,)      ->				PIL.Image.Image:
          snake_case			:      int        =			do_resize if do_resize is not None else self.do_resize
          snake_case			:      List[Any]        =			size if size is not None else self.size
          snake_case			:      Any        =			get_size_dict(__a							)
          snake_case			:      Any        =			resample if resample is not None else self.resample
          snake_case			:      str        =			do_rescale if do_rescale is not None else self.do_rescale
          snake_case			:      List[str]        =			rescale_factor if rescale_factor is not None else self.rescale_factor
          snake_case			:      Dict        =			do_normalize if do_normalize is not None else self.do_normalize
          snake_case			:      Optional[int]        =			image_mean if image_mean is not None else self.image_mean
          snake_case			:      Tuple        =			image_std if image_std is not None else self.image_std
          snake_case			:      List[str]        =			apply_ocr if apply_ocr is not None else self.apply_ocr
          snake_case			:      str        =			ocr_lang if ocr_lang is not None else self.ocr_lang
          snake_case			:      Dict        =			tesseract_config if tesseract_config is not None else self.tesseract_config

          snake_case			:      Any        =			make_list_of_images(__a							)

          if not valid_images(__a							):
               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."""							)

          if do_rescale and rescale_factor is None:
               raise ValueError("""Rescale factor must be specified if do_rescale is True."""							)

          if do_normalize and (image_mean is None or image_std is None):
               raise ValueError("""If do_normalize is True, image_mean and image_std must be specified."""							)

          # All transformations expect numpy arrays.
          snake_case			:      Union[str, Any]        =			[to_numpy_array(__a							) for image in images]

          # Tesseract OCR to get words + normalized bounding boxes
          if apply_ocr:
               requires_backends(self							,"""pytesseract"""							)
               snake_case			:      int        =			[]
               snake_case			:      Optional[Any]        =			[]
               for image in images:
                    snake_case ,		snake_case			:      Tuple        =			apply_tesseract(__a							,__a							,__a							)
                    words_batch.append(__a							)
                    boxes_batch.append(__a							)

          if do_resize:
               snake_case			:      Any        =			[self.resize(image=__a							,size=__a							,resample=__a							) for image in images]

          if do_rescale:
               snake_case			:      Tuple        =			[self.rescale(image=__a							,scale=__a							) for image in images]

          if do_normalize:
               snake_case			:      Optional[Any]        =			[self.normalize(image=__a							,mean=__a							,std=__a							) for image in images]

          snake_case			:      Dict        =			[to_channel_dimension_format(__a							,__a							) for image in images]

          snake_case			:      List[Any]        =			BatchFeature(data={"""pixel_values""": images}							,tensor_type=__a							)

          if apply_ocr:
               snake_case			:      Optional[Any]        =			words_batch
               snake_case			:      Optional[int]        =			boxes_batch
          return data



