

import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput

class 						__UpperCamelCase    (					__lowerCamelCase						):




	def __init__(		self	:						List[str]		,	lowerCAmelCase	:						List[str]		,	lowerCAmelCase	:						Union[str, Any]		,	lowerCAmelCase	:						Any							):


		'''simple docstring'''







		UpperCAmelCase_       				=			dataset
		UpperCAmelCase_       				=			process
		UpperCAmelCase_       				=			params




	def __len__(		self	:						int							):


		'''simple docstring'''







		return len(self.dataset							)




	def __getitem__(		self	:						Optional[int]		,	lowerCAmelCase	:						List[str]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.dataset[i]
		UpperCAmelCase_       				=			self.process(a_		,	**self.params							)
		return processed


class 						__UpperCamelCase    (					__lowerCamelCase						):




	def __init__(		self	:						List[Any]		,	lowerCAmelCase	:						Tuple		,	lowerCAmelCase	:						Tuple		,	lowerCAmelCase	:						List[str]		,	lowerCAmelCase	:						List[str]=None							):


		'''simple docstring'''







		UpperCAmelCase_       				=			loader
		UpperCAmelCase_       				=			infer
		UpperCAmelCase_       				=			params
		if loader_batch_size == 1:
			# Let's spare some time by deactivating altogether
			UpperCAmelCase_       				=			None
		UpperCAmelCase_       				=			loader_batch_size

		# Internal bookkeeping
		UpperCAmelCase_       				=			None
		UpperCAmelCase_       				=			None




	def __len__(		self	:						Optional[int]							):


		'''simple docstring'''







		return len(self.loader							)




	def __iter__(		self	:						Tuple							):


		'''simple docstring'''







		UpperCAmelCase_       				=			iter(self.loader							)
		return self




	def       __A					(		self	:						Optional[Any]							):


		'''simple docstring'''







		if isinstance(self._loader_batch_data		,	torch.Tensor							):
			# Batch data is simple tensor, just fetch the slice
			UpperCAmelCase_       				=			self._loader_batch_data[self._loader_batch_index]
		else:
			# Batch data is assumed to be BaseModelOutput (or dict)
			UpperCAmelCase_       				=			{}
			for k, element in self._loader_batch_data.items():
				if isinstance(a_		,	a_							):
					# Convert ModelOutput to tuple first
					UpperCAmelCase_       				=			element.to_tuple()
					if isinstance(element[0]		,	torch.Tensor							):
						UpperCAmelCase_       				=			tuple(el[self._loader_batch_index].unsqueeze(0							) for el in element							)
					elif isinstance(element[0]		,	np.ndarray							):
						UpperCAmelCase_       				=			tuple(np.expand_dims(el[self._loader_batch_index]		,	0							) for el in element							)
					continue
				if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(a_		,	a_							):
					# Those are stored as lists of tensors so need specific unbatching.
					if isinstance(element[0]		,	torch.Tensor							):
						UpperCAmelCase_       				=			tuple(el[self._loader_batch_index].unsqueeze(0							) for el in element							)
					elif isinstance(element[0]		,	np.ndarray							):
						UpperCAmelCase_       				=			tuple(np.expand_dims(el[self._loader_batch_index]		,	0							) for el in element							)
					continue
				if element is None:
					# This can happen for optional data that get passed around
					UpperCAmelCase_       				=			None
				elif isinstance(element[self._loader_batch_index]		,	torch.Tensor							):
					# Take correct batch data, but make it looked like batch_size=1
					# For compatibility with other methods within transformers

					UpperCAmelCase_       				=			element[self._loader_batch_index].unsqueeze(0							)
				elif isinstance(element[self._loader_batch_index]		,	np.ndarray							):
					# Take correct batch data, but make it looked like batch_size=1
					# For compatibility with other methods within transformers
					UpperCAmelCase_       				=			np.expand_dims(element[self._loader_batch_index]		,	0							)
				else:
					# This is typically a list, so no need to `unsqueeze`.
					UpperCAmelCase_       				=			element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
			UpperCAmelCase_       				=			self._loader_batch_data.__class__(a_							)
		self._loader_batch_index += 1
		return result




	def       __A					(		self	:						Dict							):


		'''simple docstring'''







		if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
			# We are currently unrolling a batch so we just need to return
			# the current item within a batch
			return self.loader_batch_item()

		# We're out of items within a batch
		UpperCAmelCase_       				=			next(self.iterator							)
		UpperCAmelCase_       				=			self.infer(a_		,	**self.params							)
		# We now have a batch of "inferred things".
		if self.loader_batch_size is not None:
			# Try to infer the size of the batch
			if isinstance(a_		,	torch.Tensor							):
				UpperCAmelCase_       				=			processed
			else:
				UpperCAmelCase_       				=			list(processed.keys()							)[0]
				UpperCAmelCase_       				=			processed[key]
			if isinstance(a_		,	a_							):
				UpperCAmelCase_       				=			len(a_							)
			else:
				UpperCAmelCase_       				=			first_tensor.shape[0]
			if 0 < observed_batch_size < self.loader_batch_size:
				# could be last batch so we can't unroll as many
				# elements.
				UpperCAmelCase_       				=			observed_batch_size
			# Setting internal index to unwrap the batch
			UpperCAmelCase_       				=			processed
			UpperCAmelCase_       				=			0
			return self.loader_batch_item()
		else:
			# We're not unrolling batches
			return processed


class 						__UpperCamelCase    (					__lowerCamelCase						):




	def __init__(		self	:						Tuple		,	lowerCAmelCase	:						Dict		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						int		,	lowerCAmelCase	:						List[str]=None							):


		'''simple docstring'''







		super().__init__(a_		,	a_		,	a_							)




	def __iter__(		self	:						Tuple							):


		'''simple docstring'''







		UpperCAmelCase_       				=			iter(self.loader							)
		UpperCAmelCase_       				=			None
		return self




	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







		if self.subiterator is None:
			UpperCAmelCase_       				=			self.infer(next(self.iterator							)		,	**self.params							)
		try:
			# Try to return next item
			UpperCAmelCase_       				=			next(self.subiterator							)
		except StopIteration:
			# When a preprocess iterator ends, we can start lookig at the next item
			# ChunkIterator will keep feeding until ALL elements of iterator
			# all have created their subiterator and have been iterating against.
			#
			# Another way to look at it, is we're basically flattening lists of lists
			# into a single list, but with generators
			UpperCAmelCase_       				=			self.infer(next(self.iterator							)		,	**self.params							)
			UpperCAmelCase_       				=			next(self.subiterator							)
		return processed


class 						__UpperCamelCase    (					__lowerCamelCase						):




	def __iter__(		self	:						Optional[int]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			iter(self.loader							)
		return self




	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			False
		UpperCAmelCase_       				=			[]
		if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
			while self._loader_batch_index < self.loader_batch_size:
				UpperCAmelCase_       				=			self.loader_batch_item()
				UpperCAmelCase_       				=			item.pop("is_last"							)
				accumulator.append(a_							)
				if is_last:
					return accumulator

		while not is_last:
			UpperCAmelCase_       				=			self.infer(next(self.iterator							)		,	**self.params							)
			if self.loader_batch_size is not None:
				if isinstance(a_		,	torch.Tensor							):
					UpperCAmelCase_       				=			processed
				else:
					UpperCAmelCase_       				=			list(processed.keys()							)[0]
					UpperCAmelCase_       				=			processed[key]
				if isinstance(a_		,	a_							):
					UpperCAmelCase_       				=			len(a_							)
				else:
					UpperCAmelCase_       				=			first_tensor.shape[0]
				if 0 < observed_batch_size < self.loader_batch_size:
					# could be last batch so we can't unroll as many
					# elements.
					UpperCAmelCase_       				=			observed_batch_size
				UpperCAmelCase_       				=			processed
				UpperCAmelCase_       				=			0
				while self._loader_batch_index < self.loader_batch_size:
					UpperCAmelCase_       				=			self.loader_batch_item()
					UpperCAmelCase_       				=			item.pop("is_last"							)
					accumulator.append(a_							)
					if is_last:
						return accumulator
			else:
				UpperCAmelCase_       				=			processed
				UpperCAmelCase_       				=			item.pop("is_last"							)
				accumulator.append(a_							)
		return accumulator


class 						__UpperCamelCase    (					__lowerCamelCase						):




	def __init__(		self	:						int		,	lowerCAmelCase	:						Optional[int]		,	lowerCAmelCase	:						List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			dataset
		UpperCAmelCase_       				=			key




	def __len__(		self	:						str							):


		'''simple docstring'''







		return len(self.dataset							)




	def __getitem__(		self	:						List[Any]		,	lowerCAmelCase	:						Optional[int]							):


		'''simple docstring'''







		return self.dataset[i][self.key]


class 						__UpperCamelCase    (					__lowerCamelCase						):




	def __init__(		self	:						Union[str, Any]		,	lowerCAmelCase	:						Union[str, Any]		,	lowerCAmelCase	:						List[str]		,	lowerCAmelCase	:						int							):


		'''simple docstring'''







		UpperCAmelCase_       				=			dataset
		UpperCAmelCase_       				=			keya
		UpperCAmelCase_       				=			keya




	def __len__(		self	:						int							):


		'''simple docstring'''







		return len(self.dataset							)




	def __getitem__(		self	:						int		,	lowerCAmelCase	:						Dict							):


		'''simple docstring'''







		return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}









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

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



def       __lowerCAmelCase      (						A    ):
	if isinstance(A       ,     (list, tuple)    ) and isinstance(videos[0]       ,     (list, tuple)    ) and is_valid_image(videos[0][0]    ):
		return videos

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

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

	raise ValueError(F"Could not make batched video from {videos}"    )




class 						__UpperCamelCase    (					lowercase						):
	SCREAMING_SNAKE_CASE__    				=     ['pixel_values']




	def __init__(		self	:						int		,	lowerCAmelCase	:						bool = True		,	lowerCAmelCase	:						Dict[str, int] = None		,	lowerCAmelCase	:						PILImageResampling = PILImageResampling.BILINEAR		,	lowerCAmelCase	:						bool = True		,	lowerCAmelCase	:						Dict[str, int] = None		,	lowerCAmelCase	:						bool = True		,	lowerCAmelCase	:						Union[int, float] = 1 / 255		,	lowerCAmelCase	:						bool = True		,	lowerCAmelCase	:						bool = True		,	lowerCAmelCase	:						Optional[Union[float, List[float]]] = None		,	lowerCAmelCase	:						Optional[Union[float, List[float]]] = None		,	**lowerCAmelCase	:						List[Any]		,	):


		'''simple docstring'''







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

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




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


		'''simple docstring'''







		UpperCAmelCase_       				=			get_size_dict(lowerCAmelCase		,	default_to_square=lowerCAmelCase							)
		if "shortest_edge" in size:
			UpperCAmelCase_       				=			get_resize_output_image_size(lowerCAmelCase		,	size["shortest_edge"]		,	default_to_square=lowerCAmelCase							)
		elif "height" in size and "width" in size:
			UpperCAmelCase_       				=			(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	:						List[str]		,	lowerCAmelCase	:						np.ndarray		,	lowerCAmelCase	:						Dict[str, int]		,	lowerCAmelCase	:						Optional[Union[str, ChannelDimension]] = None		,	**lowerCAmelCase	:						Dict		,	):


		'''simple docstring'''







		UpperCAmelCase_       				=			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	:						List[Any]		,	lowerCAmelCase	:						np.ndarray		,	lowerCAmelCase	:						Union[int, float]		,	lowerCAmelCase	:						bool = True		,	lowerCAmelCase	:						Optional[Union[str, ChannelDimension]] = None		,	**lowerCAmelCase	:						List[str]		,	):


		'''simple docstring'''







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




	def       __A					(		self	:						Tuple		,	lowerCAmelCase	:						np.ndarray		,	lowerCAmelCase	:						Union[float, List[float]]		,	lowerCAmelCase	:						Union[float, List[float]]		,	lowerCAmelCase	:						Optional[Union[str, ChannelDimension]] = None		,	**lowerCAmelCase	:						int		,	):


		'''simple docstring'''







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




	def       __A					(		self	:						Union[str, Any]		,	lowerCAmelCase	:						ImageInput		,	lowerCAmelCase	:						bool = None		,	lowerCAmelCase	:						Dict[str, int] = None		,	lowerCAmelCase	:						PILImageResampling = None		,	lowerCAmelCase	:						bool = None		,	lowerCAmelCase	:						Dict[str, int] = None		,	lowerCAmelCase	:						bool = None		,	lowerCAmelCase	:						float = None		,	lowerCAmelCase	:						bool = None		,	lowerCAmelCase	:						bool = None		,	lowerCAmelCase	:						Optional[Union[float, List[float]]] = None		,	lowerCAmelCase	:						Optional[Union[float, List[float]]] = None		,	lowerCAmelCase	:						Optional[ChannelDimension] = 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.
		UpperCAmelCase_       				=			to_numpy_array(lowerCAmelCase							)

		if do_resize:
			UpperCAmelCase_       				=			self.resize(image=lowerCAmelCase		,	size=lowerCAmelCase		,	resample=lowerCAmelCase							)

		if do_center_crop:
			UpperCAmelCase_       				=			self.center_crop(lowerCAmelCase		,	size=lowerCAmelCase							)

		if do_rescale:
			UpperCAmelCase_       				=			self.rescale(image=lowerCAmelCase		,	scale=lowerCAmelCase		,	offset=lowerCAmelCase							)

		if do_normalize:
			UpperCAmelCase_       				=			self.normalize(image=lowerCAmelCase		,	mean=lowerCAmelCase		,	std=lowerCAmelCase							)

		UpperCAmelCase_       				=			to_channel_dimension_format(lowerCAmelCase		,	lowerCAmelCase							)
		return image




	def       __A					(		self	:						Optional[int]		,	lowerCAmelCase	:						ImageInput		,	lowerCAmelCase	:						bool = None		,	lowerCAmelCase	:						Dict[str, int] = None		,	lowerCAmelCase	:						PILImageResampling = None		,	lowerCAmelCase	:						bool = None		,	lowerCAmelCase	:						Dict[str, int] = None		,	lowerCAmelCase	:						bool = None		,	lowerCAmelCase	:						float = None		,	lowerCAmelCase	:						bool = None		,	lowerCAmelCase	:						bool = None		,	lowerCAmelCase	:						Optional[Union[float, List[float]]] = None		,	lowerCAmelCase	:						Optional[Union[float, List[float]]] = None		,	lowerCAmelCase	:						Optional[Union[str, TensorType]] = None		,	lowerCAmelCase	:						ChannelDimension = ChannelDimension.FIRST		,	**lowerCAmelCase	:						Dict		,	):


		'''simple docstring'''







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

		UpperCAmelCase_       				=			size if size is not None else self.size
		UpperCAmelCase_       				=			get_size_dict(lowerCAmelCase		,	default_to_square=lowerCAmelCase							)
		UpperCAmelCase_       				=			crop_size if crop_size is not None else self.crop_size
		UpperCAmelCase_       				=			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."							)

		UpperCAmelCase_       				=			make_batched(lowerCAmelCase							)

		UpperCAmelCase_       				=			[
		    [
		        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
		]

		UpperCAmelCase_       				=			{"pixel_values": videos}
		return BatchFeature(data=lowerCAmelCase		,	tensor_type=lowerCAmelCase							)