




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

lowerCAmelCase_						=			logging.get_logger(__name__)






def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__   )			->							Optional[Any]:





     '''simple docstring'''


     return [
         int(10_00 * (box[0] / width)   ),
         int(10_00 * (box[1] / height)   ),
         int(10_00 * (box[2] / width)   ),
         int(10_00 * (box[3] / height)   ),
     ]






def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__ = None   )			->							Optional[int]:





     '''simple docstring'''


     lowercase :       int       =			tesseract_config if tesseract_config is not None else ''''''

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

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

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

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

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

     return words, normalized_boxes



class 						_A (  _lowerCamelCase				):
  _UpperCamelCase       :      Optional[Any]			=      ['''pixel_values''']






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






       """simple docstring"""







       super().__init__(**_A				)
       lowercase :       Any       =			size if size is not None else {'''height''': 224, '''width''': 224}
       lowercase :       List[str]       =			get_size_dict(_A				)

       lowercase :       Optional[int]       =			do_resize
       lowercase :       List[Any]       =			size
       lowercase :       Tuple       =			resample
       lowercase :       Any       =			apply_ocr
       lowercase :       Optional[Any]       =			ocr_lang
       lowercase :       Optional[int]       =			tesseract_config






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






       """simple docstring"""







       lowercase :       Optional[int]       =			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()}"""				)
       lowercase :       Union[str, Any]       =			(size['''height'''], size['''width'''])
       return resize(_A				,						size=_A				,						resample=_A				,						data_format=_A				,						**_A				)






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






       """simple docstring"""







       lowercase :       Any       =			do_resize if do_resize is not None else self.do_resize
       lowercase :       int       =			size if size is not None else self.size
       lowercase :       Union[str, Any]       =			get_size_dict(_A				)
       lowercase :       str       =			resample if resample is not None else self.resample
       lowercase :       Tuple       =			apply_ocr if apply_ocr is not None else self.apply_ocr
       lowercase :       Tuple       =			ocr_lang if ocr_lang is not None else self.ocr_lang
       lowercase :       Optional[int]       =			tesseract_config if tesseract_config is not None else self.tesseract_config

       lowercase :       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.'''				)

       # All transformations expect numpy arrays.
       lowercase :       List[Any]       =			[to_numpy_array(_A				) for image in images]

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

       if do_resize:
            lowercase :       List[str]       =			[self.resize(image=_A				,						size=_A				,						resample=_A				) for image in images]

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

       lowercase :       Union[str, Any]       =			BatchFeature(data={'''pixel_values''': images}				,						tensor_type=_A				)

       if apply_ocr:
            lowercase :       str       =			words_batch
            lowercase :       int       =			boxes_batch
       return data





import os
import sys
import unittest


lowerCAmelCase_						=			os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
lowerCAmelCase_						=			os.path.join(git_repo_path, 'src', 'diffusers')
class 						_A (  unittest.TestCase				):






  def  __a      (  self		:     Any				)		->   str:






       """simple docstring"""







       lowercase :       List[str]       =			find_backend('''    if not is_torch_available():'''				)
       self.assertEqual(_A				,						'''torch'''				)

       # backend_with_underscore = find_backend("    if not is_tensorflow_text_available():")
       # self.assertEqual(backend_with_underscore, "tensorflow_text")

       lowercase :       str       =			find_backend('''    if not (is_torch_available() and is_transformers_available()):'''				)
       self.assertEqual(_A				,						'''torch_and_transformers'''				)

       # double_backend_with_underscore = find_backend(
       #    "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
       # )
       # self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")

       lowercase :       str       =			find_backend(
           '''    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):'''				)
       self.assertEqual(_A				,						'''torch_and_transformers_and_onnx'''				)






  def  __a      (  self		:     Optional[int]				)		->   Any:






       """simple docstring"""







       lowercase :       Dict       =			read_init()
       # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
       self.assertIn('''torch'''				,						_A				)
       self.assertIn('''torch_and_transformers'''				,						_A				)
       self.assertIn('''flax_and_transformers'''				,						_A				)
       self.assertIn('''torch_and_transformers_and_onnx'''				,						_A				)

       # Likewise, we can't assert on the exact content of a key
       self.assertIn('''UNet2DModel'''				,						objects['''torch''']				)
       self.assertIn('''FlaxUNet2DConditionModel'''				,						objects['''flax''']				)
       self.assertIn('''StableDiffusionPipeline'''				,						objects['''torch_and_transformers''']				)
       self.assertIn('''FlaxStableDiffusionPipeline'''				,						objects['''flax_and_transformers''']				)
       self.assertIn('''LMSDiscreteScheduler'''				,						objects['''torch_and_scipy''']				)
       self.assertIn('''OnnxStableDiffusionPipeline'''				,						objects['''torch_and_transformers_and_onnx''']				)






  def  __a      (  self		:     Union[str, Any]				)		->   str:






       """simple docstring"""







       lowercase :       List[str]       =			create_dummy_object('''CONSTANT'''				,						'''\'torch\''''				)
       self.assertEqual(_A				,						'''\nCONSTANT = None\n'''				)

       lowercase :       List[Any]       =			create_dummy_object('''function'''				,						'''\'torch\''''				)
       self.assertEqual(
           _A				,						'''\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'''				)

       lowercase :       Tuple       =			'''
class FakeClass(metaclass=DummyObject):
    _backends = \'torch\'

    def __init__(self, *args, **kwargs):
        requires_backends(self, \'torch\')

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, \'torch\')

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, \'torch\')
'''
       lowercase :       List[Any]       =			create_dummy_object('''FakeClass'''				,						'''\'torch\''''				)
       self.assertEqual(_A				,						_A				)






  def  __a      (  self		:     Union[str, Any]				)		->   str:






       """simple docstring"""







       lowercase :       Optional[int]       =			'''# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, ["torch"])


class FakeClass(metaclass=DummyObject):
    _backends = ["torch"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
'''
       lowercase :       int       =			create_dummy_files({'''torch''': ['''CONSTANT''', '''function''', '''FakeClass''']}				)
       self.assertEqual(dummy_files['''torch''']				,						_A				)