





'''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

__UpperCamelCase					    =	logging.get_logger(__name__)







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase ) ->				Dict:



			"""simple docstring"""


			return [
			    int(1000 * (box[0] / width) ),
			    int(1000 * (box[1] / height) ),
			    int(1000 * (box[2] / width) ),
			    int(1000 * (box[3] / height) ),
			]







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase ) ->				Any:



			"""simple docstring"""


			__snake_case					:      List[str]							      =     to_pil_image(_lowerCamelCase )
			__snake_case      ,     __snake_case					:      Union[str, Any]							      =     pil_image.size
			__snake_case					:      Tuple							      =     pytesseract.image_to_data(_lowerCamelCase    ,	lang=_lowerCamelCase    ,	output_type="""dict"""    ,	config=_lowerCamelCase )
			__snake_case      ,     __snake_case      ,     __snake_case      ,     __snake_case      ,     __snake_case					:      List[str]							      =     data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]

			# filter empty words and corresponding coordinates
			__snake_case					:      Dict							      =     [idx for idx, word in enumerate(_lowerCamelCase ) if not word.strip()]
			__snake_case					:      int							      =     [word for idx, word in enumerate(_lowerCamelCase ) if idx not in irrelevant_indices]
			__snake_case					:      Optional[int]							      =     [coord for idx, coord in enumerate(_lowerCamelCase ) if idx not in irrelevant_indices]
			__snake_case					:      Dict							      =     [coord for idx, coord in enumerate(_lowerCamelCase ) if idx not in irrelevant_indices]
			__snake_case					:      Optional[int]							      =     [coord for idx, coord in enumerate(_lowerCamelCase ) if idx not in irrelevant_indices]
			__snake_case					:      List[str]							      =     [coord for idx, coord in enumerate(_lowerCamelCase ) if idx not in irrelevant_indices]

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

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

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

			return words, normalized_boxes




class  _A (					__lowercase		):
		lowercase__:   Optional[int]				     =   ['''pixel_values''']

		def __init__(   self					:   List[str]  ,      __magic_name__					:   bool = True  ,      __magic_name__					:   Dict[str, int] = None  ,      __magic_name__					:   PILImageResampling = PILImageResampling.BILINEAR  ,      __magic_name__					:   bool = True  ,      __magic_name__					:   float = 1 / 2_55  ,      __magic_name__					:   bool = True  ,      __magic_name__					:   Union[float, Iterable[float]] = None  ,      __magic_name__					:   Union[float, Iterable[float]] = None  ,      __magic_name__					:   bool = True  ,      __magic_name__					:   Optional[str] = None  ,      __magic_name__					:   Optional[str] = ""  ,      **__magic_name__					:   str  ,      )					->    None:


					"""simple docstring"""

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

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

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


					"""simple docstring"""

					__snake_case					:      Tuple							      =     get_size_dict(__magic_name__			)
					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					:      Any							      =     (size["""height"""], size["""width"""])
					return resize(__magic_name__  ,      size=__magic_name__  ,      resample=__magic_name__  ,      data_format=__magic_name__  ,      **__magic_name__			)

		def 	lowercase__     (   self					:   Union[str, Any]  ,      __magic_name__					:   np.ndarray  ,      __magic_name__					:   Union[int, float]  ,      __magic_name__					:   Optional[Union[str, ChannelDimension]] = None  ,      **__magic_name__					:   Dict  ,      )					->    np.ndarray:


					"""simple docstring"""

					return rescale(__magic_name__  ,      scale=__magic_name__  ,      data_format=__magic_name__  ,      **__magic_name__			)

		def 	lowercase__     (   self					:   List[str]  ,      __magic_name__					:   np.ndarray  ,      __magic_name__					:   Union[float, Iterable[float]]  ,      __magic_name__					:   Union[float, Iterable[float]]  ,      __magic_name__					:   Optional[Union[str, ChannelDimension]] = None  ,      **__magic_name__					:   Optional[Any]  ,      )					->    np.ndarray:


					"""simple docstring"""

					return normalize(__magic_name__  ,      mean=__magic_name__  ,      std=__magic_name__  ,      data_format=__magic_name__  ,      **__magic_name__			)

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


					"""simple docstring"""

					__snake_case					:      Dict							      =     do_resize if do_resize is not None else self.do_resize
					__snake_case					:      List[str]							      =     size if size is not None else self.size
					__snake_case					:      Dict							      =     get_size_dict(__magic_name__			)
					__snake_case					:      List[Any]							      =     resample if resample is not None else self.resample
					__snake_case					:      Union[str, Any]							      =     do_rescale if do_rescale is not None else self.do_rescale
					__snake_case					:      int							      =     rescale_factor if rescale_factor is not None else self.rescale_factor
					__snake_case					:      Optional[int]							      =     do_normalize if do_normalize is not None else self.do_normalize
					__snake_case					:      int							      =     image_mean if image_mean is not None else self.image_mean
					__snake_case					:      Dict							      =     image_std if image_std is not None else self.image_std
					__snake_case					:      Dict							      =     apply_ocr if apply_ocr is not None else self.apply_ocr
					__snake_case					:      Optional[Any]							      =     ocr_lang if ocr_lang is not None else self.ocr_lang
					__snake_case					:      List[str]							      =     tesseract_config if tesseract_config is not None else self.tesseract_config

					__snake_case					:      str							      =     make_list_of_images(__magic_name__			)

					if not valid_images(__magic_name__			):
								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					:      Optional[Any]							      =     [to_numpy_array(__magic_name__			) for image in images]

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

					if do_resize:
								__snake_case					:      Union[str, Any]							      =     [self.resize(image=__magic_name__  ,      size=__magic_name__  ,      resample=__magic_name__			) for image in images]

					if do_rescale:
								__snake_case					:      str							      =     [self.rescale(image=__magic_name__  ,      scale=__magic_name__			) for image in images]

					if do_normalize:
								__snake_case					:      Optional[int]							      =     [self.normalize(image=__magic_name__  ,      mean=__magic_name__  ,      std=__magic_name__			) for image in images]

					__snake_case					:      int							      =     [to_channel_dimension_format(__magic_name__  ,      __magic_name__			) for image in images]

					__snake_case					:      Union[str, Any]							      =     BatchFeature(data={"""pixel_values""": images}  ,      tensor_type=__magic_name__			)

					if apply_ocr:
								__snake_case					:      str							      =     words_batch
								__snake_case					:      List[Any]							      =     boxes_batch
					return data




"""simple docstring"""



import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


UpperCamelCase__          =	logging.getLogger(__name__)





class 				a			:



      def __init__(   self      ):
          UpperCAmelCase__	:      Union[str, Any]     							=					False



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          if not self.initialized:
              UpperCAmelCase__	:      Optional[Any]     							=					RagRetriever(
                  UpperCamelCase_     ,	question_encoder_tokenizer=UpperCamelCase_     ,	generator_tokenizer=UpperCamelCase_     ,	index=UpperCamelCase_     ,	init_retrieval=UpperCamelCase_     ,	)
              UpperCAmelCase__	:      Union[str, Any]     							=					True



      def  __snake_case							(   self      ):
          self.retriever.index.init_index()



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          UpperCAmelCase__							,							UpperCAmelCase__	:      Union[str, Any]     							=					self.retriever._main_retrieve(UpperCamelCase_     ,	UpperCamelCase_      )
          return doc_ids, retrieved_doc_embeds





class 				a			(					lowercase      ):



      def __init__(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_=None      ):
          if index is not None and index.is_initialized() and len(UpperCamelCase_      ) > 0:
              raise ValueError(
                  'When using Ray for distributed fine-tuning, '
                  'you\'ll need to provide the paths instead, '
                  'as the dataset and the index are loaded '
                  'separately. More info in examples/rag/use_own_knowledge_dataset.py '      )
          super().__init__(
              UpperCamelCase_     ,	question_encoder_tokenizer=UpperCamelCase_     ,	generator_tokenizer=UpperCamelCase_     ,	index=UpperCamelCase_     ,	init_retrieval=UpperCamelCase_     ,	)
          UpperCAmelCase__	:      Union[str, Any]     							=					retrieval_workers
          if len(self.retrieval_workers      ) > 0:
              ray.get(
                  [
                      worker.create_rag_retriever.remote(UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_      )
                      for worker in self.retrieval_workers
                  ]      )



      def  __snake_case							(   self      ):
          logger.info('initializing retrieval'      )

          if len(self.retrieval_workers      ) > 0:
              ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]      )
          else:
              # Non-distributed training. Load index into this same process.
              self.index.init_index()



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          if len(self.retrieval_workers      ) > 0:
              # Select a random retrieval actor.
              UpperCAmelCase__	:      Optional[Any]     							=					self.retrieval_workers[random.randint(0     ,	len(self.retrieval_workers      ) - 1      )]
              UpperCAmelCase__							,							UpperCAmelCase__	:      List[Any]     							=					ray.get(random_worker.retrieve.remote(UpperCamelCase_     ,	UpperCamelCase_      )      )
          else:
              UpperCAmelCase__							,							UpperCAmelCase__	:      Dict     							=					self._main_retrieve(UpperCamelCase_     ,	UpperCamelCase_      )
          return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(UpperCamelCase_      )



      @classmethod
      def  __snake_case							(   cls     ,	UpperCamelCase_     ,	UpperCamelCase_=None     ,	**UpperCamelCase_      ):
          return super(UpperCamelCase_     ,	cls      ).get_tokenizers(UpperCamelCase_     ,	UpperCamelCase_     ,	**UpperCamelCase_      )



      @classmethod
      def  __snake_case							(   cls     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_=None     ,	**UpperCamelCase_      ):
          UpperCAmelCase__	:      Union[str, Any]     							=					kwargs.pop('config'     ,	UpperCamelCase_      ) or RagConfig.from_pretrained(UpperCamelCase_     ,	**UpperCamelCase_      )
          UpperCAmelCase__	:      Union[str, Any]     							=					RagTokenizer.from_pretrained(UpperCamelCase_     ,	config=UpperCamelCase_      )
          UpperCAmelCase__	:      List[Any]     							=					rag_tokenizer.question_encoder
          UpperCAmelCase__	:      Optional[Any]     							=					rag_tokenizer.generator
          if indexed_dataset is not None:
              UpperCAmelCase__	:      List[str]     							=					'custom'
              UpperCAmelCase__	:      Tuple     							=					CustomHFIndex(config.retrieval_vector_size     ,	UpperCamelCase_      )
          else:
              UpperCAmelCase__	:      Optional[Any]     							=					cls._build_index(UpperCamelCase_      )
          return cls(
              UpperCamelCase_     ,	question_encoder_tokenizer=UpperCamelCase_     ,	generator_tokenizer=UpperCamelCase_     ,	retrieval_workers=UpperCamelCase_     ,	index=UpperCamelCase_     ,	)

