






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

_a    :   str        =			logging.get_logger(__name__)


def 				snake_case__							(							UpperCAmelCase							:  Dict    ,			UpperCAmelCase							:  Any    ,			UpperCAmelCase							:  Optional[Any]       ):
    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 				snake_case__							(							UpperCAmelCase							:  np.ndarray    ,			UpperCAmelCase							:  Optional[str]    ,			UpperCAmelCase							:  Optional[str]       ):
    lowerCAmelCase__   :Tuple      							=				to_pil_image(lowercase_       )
    lowerCAmelCase__   :int      							=				pil_image.size
    lowerCAmelCase__   :Dict      							=				pytesseract.image_to_data(lowercase_    ,			lang=lowercase_    ,			output_type="dict"    ,			config=lowercase_       )
    lowerCAmelCase__   :Dict      							=				data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]

    # filter empty words and corresponding coordinates
    lowerCAmelCase__   :List[Any]      							=				[idx for idx, word in enumerate(lowercase_       ) if not word.strip()]
    lowerCAmelCase__   :Union[str, Any]      							=				[word for idx, word in enumerate(lowercase_       ) if idx not in irrelevant_indices]
    lowerCAmelCase__   :Union[str, Any]      							=				[coord for idx, coord in enumerate(lowercase_       ) if idx not in irrelevant_indices]
    lowerCAmelCase__   :Dict      							=				[coord for idx, coord in enumerate(lowercase_       ) if idx not in irrelevant_indices]
    lowerCAmelCase__   :Any      							=				[coord for idx, coord in enumerate(lowercase_       ) if idx not in irrelevant_indices]
    lowerCAmelCase__   :List[str]      							=				[coord for idx, coord in enumerate(lowercase_       ) if idx not in irrelevant_indices]

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

    # finally, normalize the bounding boxes
    lowerCAmelCase__   :Union[str, Any]      							=				[]
    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 	_UpperCAmelCase  (  _A		):




      """simple docstring"""



      A          =    ['''pixel_values''']
      def __init__(   self    ,	_lowerCAmelCase = True    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = PILImageResampling.BILINEAR    ,	_lowerCAmelCase = True    ,	_lowerCAmelCase = 1 / 255    ,	_lowerCAmelCase = True    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = True    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = ""    ,	**_lowerCAmelCase    ,	):



          '''simple docstring'''
          super().__init__(**A__		)
          lowerCAmelCase__   :Union[str, Any]      							=				size if size is not None else {"""height""": 224, """width""": 224}
          lowerCAmelCase__   :Tuple      							=				get_size_dict(A__		)

          lowerCAmelCase__   :int      							=				do_resize
          lowerCAmelCase__   :Union[str, Any]      							=				size
          lowerCAmelCase__   :List[str]      							=				resample
          lowerCAmelCase__   :List[str]      							=				do_rescale
          lowerCAmelCase__   :Optional[int]      							=				rescale_value
          lowerCAmelCase__   :Union[str, Any]      							=				do_normalize
          lowerCAmelCase__   :List[Any]      							=				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          lowerCAmelCase__   :Dict      							=				image_std if image_std is not None else IMAGENET_STANDARD_STD
          lowerCAmelCase__   :List[str]      							=				apply_ocr
          lowerCAmelCase__   :Optional[int]      							=				ocr_lang
          lowerCAmelCase__   :int      							=				tesseract_config
      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase = PILImageResampling.BILINEAR    ,	_lowerCAmelCase = None    ,	**_lowerCAmelCase    ,	):



          '''simple docstring'''
          lowerCAmelCase__   :Dict      							=				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()}'''		)
          lowerCAmelCase__   :Tuple      							=				(size["""height"""], size["""width"""])
          return resize(A__    ,	size=A__    ,	resample=A__    ,	data_format=A__    ,	**A__		)
      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase = None    ,	**_lowerCAmelCase    ,	):



          '''simple docstring'''
          return rescale(A__    ,	scale=A__    ,	data_format=A__    ,	**A__		)
      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase = None    ,	**_lowerCAmelCase    ,	):



          '''simple docstring'''
          return normalize(A__    ,	mean=A__    ,	std=A__    ,	data_format=A__    ,	**A__		)



      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase=None    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = ChannelDimension.FIRST    ,	**_lowerCAmelCase    ,	):



          '''simple docstring'''
          lowerCAmelCase__   :Tuple      							=				do_resize if do_resize is not None else self.do_resize
          lowerCAmelCase__   :Any      							=				size if size is not None else self.size
          lowerCAmelCase__   :Tuple      							=				get_size_dict(A__		)
          lowerCAmelCase__   :str      							=				resample if resample is not None else self.resample
          lowerCAmelCase__   :Union[str, Any]      							=				do_rescale if do_rescale is not None else self.do_rescale
          lowerCAmelCase__   :Optional[int]      							=				rescale_factor if rescale_factor is not None else self.rescale_factor
          lowerCAmelCase__   :int      							=				do_normalize if do_normalize is not None else self.do_normalize
          lowerCAmelCase__   :str      							=				image_mean if image_mean is not None else self.image_mean
          lowerCAmelCase__   :List[Any]      							=				image_std if image_std is not None else self.image_std
          lowerCAmelCase__   :Tuple      							=				apply_ocr if apply_ocr is not None else self.apply_ocr
          lowerCAmelCase__   :List[str]      							=				ocr_lang if ocr_lang is not None else self.ocr_lang
          lowerCAmelCase__   :Union[str, Any]      							=				tesseract_config if tesseract_config is not None else self.tesseract_config

          lowerCAmelCase__   :Optional[int]      							=				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.
          lowerCAmelCase__   :List[Any]      							=				[to_numpy_array(A__		) for image in images]

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

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

          if do_rescale:
              lowerCAmelCase__   :Dict      							=				[self.rescale(image=A__    ,	scale=A__		) for image in images]

          if do_normalize:
              lowerCAmelCase__   :List[Any]      							=				[self.normalize(image=A__    ,	mean=A__    ,	std=A__		) for image in images]

          lowerCAmelCase__   :str      							=				[to_channel_dimension_format(A__    ,	A__		) for image in images]

          lowerCAmelCase__   :Optional[int]      							=				BatchFeature(data={"pixel_values": images}    ,	tensor_type=A__		)

          if apply_ocr:
              lowerCAmelCase__   :str      							=				words_batch
              lowerCAmelCase__   :Any      							=				boxes_batch
          return data











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 	_UpperCAmelCase  :




      """simple docstring"""



      def 			snake_case_      (   self		):



          '''simple docstring'''
          torch.manual_seed(0		)
          lowerCAmelCase__   :List[str]      							=				TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"		)

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

          torch.manual_seed(0		)
          lowerCAmelCase__   :Tuple      							=				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		)
          lowerCAmelCase__   :Dict      							=				DDPMScheduler(
              num_train_timesteps=1_000    ,	beta_schedule="squaredcos_cap_v2"    ,	beta_start=0.0001    ,	beta_end=0.02    ,	thresholding=_lowerCAmelCase    ,	dynamic_thresholding_ratio=0.95    ,	sample_max_value=1.0    ,	prediction_type="epsilon"    ,	variance_type="learned_range"    ,	)

          torch.manual_seed(0		)
          lowerCAmelCase__   :List[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		):



          '''simple docstring'''
          torch.manual_seed(0		)
          lowerCAmelCase__   :int      							=				TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"		)

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

          torch.manual_seed(0		)
          lowerCAmelCase__   :Union[str, Any]      							=				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		)
          lowerCAmelCase__   :Tuple      							=				DDPMScheduler(
              num_train_timesteps=1_000    ,	beta_schedule="squaredcos_cap_v2"    ,	beta_start=0.0001    ,	beta_end=0.02    ,	thresholding=_lowerCAmelCase    ,	dynamic_thresholding_ratio=0.95    ,	sample_max_value=1.0    ,	prediction_type="epsilon"    ,	variance_type="learned_range"    ,	)

          torch.manual_seed(0		)
          lowerCAmelCase__   :str      							=				DDPMScheduler(
              num_train_timesteps=1_000    ,	beta_schedule="squaredcos_cap_v2"    ,	beta_start=0.0001    ,	beta_end=0.02    ,	)

          torch.manual_seed(0		)
          lowerCAmelCase__   :List[str]      							=				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		):



          '''simple docstring'''
          lowerCAmelCase__   :Optional[Any]      							=				self.get_dummy_components()
          lowerCAmelCase__   :Any      							=				self.pipeline_class(**_lowerCAmelCase		)
          pipe.to(_lowerCAmelCase		)
          pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

          lowerCAmelCase__   :int      							=				self.get_dummy_inputs(_lowerCAmelCase		)

          lowerCAmelCase__   :Optional[int]      							=				inputs["prompt"]
          lowerCAmelCase__   :Tuple      							=				inputs["generator"]
          lowerCAmelCase__   :List[Any]      							=				inputs["num_inference_steps"]
          lowerCAmelCase__   :Tuple      							=				inputs["output_type"]

          if "image" in inputs:
              lowerCAmelCase__   :Dict      							=				inputs["image"]
          else:
              lowerCAmelCase__   :Union[str, Any]      							=				None

          if "mask_image" in inputs:
              lowerCAmelCase__   :int      							=				inputs["mask_image"]
          else:
              lowerCAmelCase__   :str      							=				None

          if "original_image" in inputs:
              lowerCAmelCase__   :List[Any]      							=				inputs["original_image"]
          else:
              lowerCAmelCase__   :Dict      							=				None

          lowerCAmelCase__							,lowerCAmelCase__   :int      							=				pipe.encode_prompt(_lowerCAmelCase		)

          # inputs with prompt converted to embeddings
          lowerCAmelCase__   :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:
              lowerCAmelCase__   :List[Any]      							=				image

          if mask_image is not None:
              lowerCAmelCase__   :Any      							=				mask_image

          if original_image is not None:
              lowerCAmelCase__   :Tuple      							=				original_image

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

          lowerCAmelCase__   :Optional[int]      							=				pipe(**_lowerCAmelCase		)[0]

          with tempfile.TemporaryDirectory() as tmpdir:
              pipe.save_pretrained(_lowerCAmelCase		)
              lowerCAmelCase__   :str      							=				self.pipeline_class.from_pretrained(_lowerCAmelCase		)
              pipe_loaded.to(_lowerCAmelCase		)
              pipe_loaded.set_progress_bar_config(disable=_lowerCAmelCase		)

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

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

          lowerCAmelCase__   :Any      							=				self.get_dummy_inputs(_lowerCAmelCase		)

          lowerCAmelCase__   :Tuple      							=				inputs["generator"]
          lowerCAmelCase__   :Union[str, Any]      							=				inputs["num_inference_steps"]
          lowerCAmelCase__   :str      							=				inputs["output_type"]

          # inputs with prompt converted to embeddings
          lowerCAmelCase__   :Union[str, 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:
              lowerCAmelCase__   :Tuple      							=				image

          if mask_image is not None:
              lowerCAmelCase__   :str      							=				mask_image

          if original_image is not None:
              lowerCAmelCase__   :Union[str, Any]      							=				original_image

          lowerCAmelCase__   :List[Any]      							=				pipe_loaded(**_lowerCAmelCase		)[0]

          lowerCAmelCase__   :Dict      							=				np.abs(to_np(_lowerCAmelCase		) - to_np(_lowerCAmelCase		)		).max()
          self.assertLess(_lowerCAmelCase    ,	1e-4		)



      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Tuple      							=				self.get_dummy_components()
          lowerCAmelCase__   :Optional[int]      							=				self.pipeline_class(**_lowerCAmelCase		)
          pipe.to(_lowerCAmelCase		)
          pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

          lowerCAmelCase__   :Optional[int]      							=				self.get_dummy_inputs(_lowerCAmelCase		)
          lowerCAmelCase__   :List[str]      							=				pipe(**_lowerCAmelCase		)[0]

          with tempfile.TemporaryDirectory() as tmpdir:
              pipe.save_pretrained(_lowerCAmelCase		)
              lowerCAmelCase__   :Dict      							=				self.pipeline_class.from_pretrained(_lowerCAmelCase		)
              pipe_loaded.to(_lowerCAmelCase		)
              pipe_loaded.set_progress_bar_config(disable=_lowerCAmelCase		)

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

          lowerCAmelCase__   :Optional[int]      							=				self.get_dummy_inputs(_lowerCAmelCase		)
          lowerCAmelCase__   :List[Any]      							=				pipe_loaded(**_lowerCAmelCase		)[0]

          lowerCAmelCase__   :str      							=				np.abs(to_np(_lowerCAmelCase		) - to_np(_lowerCAmelCase		)		).max()
          self.assertLess(_lowerCAmelCase    ,	1e-4		)



