"""simple docstring"""






from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
       import PIL

__lowerCAmelCase    :       str					     =logging.get_logger(__name__)


def 			UpperCAmelCase__       (				lowerCAmelCase__     :str		) ->    List[List[ImageInput]]:
      '''simple docstring'''


      if isinstance(lowerCAmelCase__					,		(list, tuple)		) and isinstance(videos[0]					,		(list, tuple)		) and is_valid_image(videos[0][0]		):
            return videos

      elif isinstance(lowerCAmelCase__					,		(list, tuple)		) and is_valid_image(videos[0]		):
            return [videos]

      elif is_valid_image(lowerCAmelCase__		):
            return [[videos]]

      raise ValueError(f'Could not make batched video from {videos}'		)






class  _A						(       lowerCAmelCase	):
      snake_case__			:   Optional[Any]							  =	['pixel_values']

      def __init__(						self		,       __lowerCAmelCase = True		,       __lowerCAmelCase = None		,       __lowerCAmelCase = PILImageResampling.BILINEAR		,       __lowerCAmelCase = True		,       __lowerCAmelCase = None		,       __lowerCAmelCase = True		,       __lowerCAmelCase = 1 / 255		,       __lowerCAmelCase = True		,       __lowerCAmelCase = True		,       __lowerCAmelCase = None		,       __lowerCAmelCase = None		,       **__lowerCAmelCase		,       ):







            """simple docstring"""



            super().__init__(**__lowerCAmelCase						)
            lowercase														=	size if size is not None else {"""shortest_edge""": 256}
            lowercase														=	get_size_dict(__lowerCAmelCase		,       default_to_square=__lowerCAmelCase						)
            lowercase														=	crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
            lowercase														=	get_size_dict(__lowerCAmelCase		,       param_name="""crop_size"""						)

            lowercase														=	do_resize
            lowercase														=	size
            lowercase														=	do_center_crop
            lowercase														=	crop_size
            lowercase														=	resample
            lowercase														=	do_rescale
            lowercase														=	rescale_factor
            lowercase														=	offset
            lowercase														=	do_normalize
            lowercase														=	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
            lowercase														=	image_std if image_std is not None else IMAGENET_STANDARD_STD

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase = PILImageResampling.BILINEAR		,       __lowerCAmelCase = None		,       **__lowerCAmelCase		,       ):







            """simple docstring"""



            lowercase														=	get_size_dict(__lowerCAmelCase		,       default_to_square=__lowerCAmelCase						)
            if "shortest_edge" in size:
                  lowercase														=	get_resize_output_image_size(__lowerCAmelCase		,       size["""shortest_edge"""]		,       default_to_square=__lowerCAmelCase						)
            elif "height" in size and "width" in size:
                  lowercase														=	(size["""height"""], size["""width"""])
            else:
                  raise ValueError(f'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'						)
            return resize(__lowerCAmelCase		,       size=__lowerCAmelCase		,       resample=__lowerCAmelCase		,       data_format=__lowerCAmelCase		,       **__lowerCAmelCase						)

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase = None		,       **__lowerCAmelCase		,       ):







            """simple docstring"""



            lowercase														=	get_size_dict(__lowerCAmelCase						)
            if "height" not in size or "width" not in size:
                  raise ValueError(f'Size must have \'height\' and \'width\' as keys. Got {size.keys()}'						)
            return center_crop(__lowerCAmelCase		,       size=(size["""height"""], size["""width"""])		,       data_format=__lowerCAmelCase		,       **__lowerCAmelCase						)

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase = True		,       __lowerCAmelCase = None		,       **__lowerCAmelCase		,       ):







            """simple docstring"""



            lowercase														=	image.astype(np.floataa						)
            if offset:
                  lowercase														=	image - (scale / 2)
            return rescale(__lowerCAmelCase		,       scale=__lowerCAmelCase		,       data_format=__lowerCAmelCase		,       **__lowerCAmelCase						)

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase = None		,       **__lowerCAmelCase		,       ):







            """simple docstring"""



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

      def 				A__       (						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 = ChannelDimension.FIRST		,       ):







            """simple docstring"""



            if do_resize and size is None or resample is None:
                  raise ValueError("""Size and resample must be specified if do_resize is True."""						)

            if do_center_crop and crop_size is None:
                  raise ValueError("""Crop size must be specified if do_center_crop 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("""Image mean and std must be specified if do_normalize is True."""						)

            if offset and not do_rescale:
                  raise ValueError("""For offset, do_rescale must also be set to True."""						)

            # All transformations expect numpy arrays.
            lowercase														=	to_numpy_array(__lowerCAmelCase						)

            if do_resize:
                  lowercase														=	self.resize(image=__lowerCAmelCase		,       size=__lowerCAmelCase		,       resample=__lowerCAmelCase						)

            if do_center_crop:
                  lowercase														=	self.center_crop(__lowerCAmelCase		,       size=__lowerCAmelCase						)

            if do_rescale:
                  lowercase														=	self.rescale(image=__lowerCAmelCase		,       scale=__lowerCAmelCase		,       offset=__lowerCAmelCase						)

            if do_normalize:
                  lowercase														=	self.normalize(image=__lowerCAmelCase		,       mean=__lowerCAmelCase		,       std=__lowerCAmelCase						)

            lowercase														=	to_channel_dimension_format(__lowerCAmelCase		,       __lowerCAmelCase						)
            return image







      def 				A__       (						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"""



            lowercase														=	do_resize if do_resize is not None else self.do_resize
            lowercase														=	resample if resample is not None else self.resample
            lowercase														=	do_center_crop if do_center_crop is not None else self.do_center_crop
            lowercase														=	do_rescale if do_rescale is not None else self.do_rescale
            lowercase														=	rescale_factor if rescale_factor is not None else self.rescale_factor
            lowercase														=	offset if offset is not None else self.offset
            lowercase														=	do_normalize if do_normalize is not None else self.do_normalize
            lowercase														=	image_mean if image_mean is not None else self.image_mean
            lowercase														=	image_std if image_std is not None else self.image_std

            lowercase														=	size if size is not None else self.size
            lowercase														=	get_size_dict(__lowerCAmelCase		,       default_to_square=__lowerCAmelCase						)
            lowercase														=	crop_size if crop_size is not None else self.crop_size
            lowercase														=	get_size_dict(__lowerCAmelCase		,       param_name="""crop_size"""						)

            if not valid_images(__lowerCAmelCase						):
                  raise ValueError(
                      """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                      """torch.Tensor, tf.Tensor or jax.ndarray."""						)

            lowercase														=	make_batched(__lowerCAmelCase						)

            lowercase														=	[
                [
                    self._preprocess_image(
                        image=__lowerCAmelCase		,       do_resize=__lowerCAmelCase		,       size=__lowerCAmelCase		,       resample=__lowerCAmelCase		,       do_center_crop=__lowerCAmelCase		,       crop_size=__lowerCAmelCase		,       do_rescale=__lowerCAmelCase		,       rescale_factor=__lowerCAmelCase		,       offset=__lowerCAmelCase		,       do_normalize=__lowerCAmelCase		,       image_mean=__lowerCAmelCase		,       image_std=__lowerCAmelCase		,       data_format=__lowerCAmelCase		,       )
                    for img in video
                ]
                for video in videos
            ]

            lowercase														=	{"""pixel_values""": videos}
            return BatchFeature(data=__lowerCAmelCase		,       tensor_type=__lowerCAmelCase						)




"""simple docstring"""






import logging
import random

import ray

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


__lowerCAmelCase    :       int					     =logging.getLogger(__name__)





class  _A						:

      def __init__(						self						):







            """simple docstring"""



            lowercase														=	False

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase						):







            """simple docstring"""



            if not self.initialized:
                  lowercase														=	RagRetriever(
                      __lowerCAmelCase		,       question_encoder_tokenizer=__lowerCAmelCase		,       generator_tokenizer=__lowerCAmelCase		,       index=__lowerCAmelCase		,       init_retrieval=__lowerCAmelCase		,       )
                  lowercase														=	True

      def 				A__       (						self						):







            """simple docstring"""



            self.retriever.index.init_index()







      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase						):







            """simple docstring"""



            lowercase							,							lowercase														=	self.retriever._main_retrieve(__lowerCAmelCase		,       __lowerCAmelCase						)
            return doc_ids, retrieved_doc_embeds






class  _A						(       lowerCAmelCase	):

      def __init__(						self		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase=None						):







            """simple docstring"""



            if index is not None and index.is_initialized() and len(__lowerCAmelCase						) > 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__(
                __lowerCAmelCase		,       question_encoder_tokenizer=__lowerCAmelCase		,       generator_tokenizer=__lowerCAmelCase		,       index=__lowerCAmelCase		,       init_retrieval=__lowerCAmelCase		,       )
            lowercase														=	retrieval_workers
            if len(self.retrieval_workers						) > 0:
                  ray.get(
                      [
                          worker.create_rag_retriever.remote(__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase						)
                          for worker in self.retrieval_workers
                      ]						)

      def 				A__       (						self						):







            """simple docstring"""



            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 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase						):







            """simple docstring"""



            if len(self.retrieval_workers						) > 0:
                  # Select a random retrieval actor.
                  lowercase														=	self.retrieval_workers[random.randint(0		,       len(self.retrieval_workers						) - 1						)]
                  lowercase							,							lowercase														=	ray.get(random_worker.retrieve.remote(__lowerCAmelCase		,       __lowerCAmelCase						)						)
            else:
                  lowercase							,							lowercase														=	self._main_retrieve(__lowerCAmelCase		,       __lowerCAmelCase						)
            return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__lowerCAmelCase						)

      @classmethod
      def 				A__       (						cls		,       __lowerCAmelCase		,       __lowerCAmelCase=None		,       **__lowerCAmelCase						):







            """simple docstring"""



            return super(__lowerCAmelCase		,       cls						).get_tokenizers(__lowerCAmelCase		,       __lowerCAmelCase		,       **__lowerCAmelCase						)







      @classmethod
      def 				A__       (						cls		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase=None		,       **__lowerCAmelCase						):







            """simple docstring"""



            lowercase														=	kwargs.pop("""config"""		,       __lowerCAmelCase						) or RagConfig.from_pretrained(__lowerCAmelCase		,       **__lowerCAmelCase						)
            lowercase														=	RagTokenizer.from_pretrained(__lowerCAmelCase		,       config=__lowerCAmelCase						)
            lowercase														=	rag_tokenizer.question_encoder
            lowercase														=	rag_tokenizer.generator
            if indexed_dataset is not None:
                  lowercase														=	"""custom"""
                  lowercase														=	CustomHFIndex(config.retrieval_vector_size		,       __lowerCAmelCase						)
            else:
                  lowercase														=	cls._build_index(__lowerCAmelCase						)
            return cls(
                __lowerCAmelCase		,       question_encoder_tokenizer=__lowerCAmelCase		,       generator_tokenizer=__lowerCAmelCase		,       retrieval_workers=__lowerCAmelCase		,       index=__lowerCAmelCase		,       )



