

"""simple docstring"""




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

from utils import logger



class   __lowercase    (			SCREAMING_SNAKE_CASE__     ):




			"""simple docstring"""







			def __init__(			self  :					List[str]       ,						lowerCAmelCase__  :					Any       ,						lowerCAmelCase__  :					Dict):
									SCREAMING_SNAKE_CASE_:			Any  		=	params

									SCREAMING_SNAKE_CASE_:			Dict  		=	np.array(_SCREAMING_SNAKE_CASE)
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	np.array([len(_SCREAMING_SNAKE_CASE) for t in data])

									self.check()
									self.remove_long_sequences()
									self.remove_empty_sequences()
									self.remove_unknown_sequences()
									self.check()
									self.print_statistics()



			def __getitem__(			self  :					Optional[Any]       ,						lowerCAmelCase__  :					Optional[Any]):
									return (self.token_ids[index], self.lengths[index])



			def __len__(			self  :					List[Any]):
									return len(self.lengths)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]):
									assert len(self.token_ids) == len(self.lengths)
									assert all(self.lengths[i] == len(self.token_ids[i]) for i in range(len(self.lengths)))



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]):
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	self.params.max_model_input_size
									SCREAMING_SNAKE_CASE_:			str  		=	self.lengths > max_len
									logger.info(F"Splitting {sum(_SCREAMING_SNAKE_CASE)} too long sequences.")

									def divide_chunks(lowerCAmelCase__  :					Optional[Any]       ,						lowerCAmelCase__  :					Tuple):
															return [l[i : i + n] for i in range(0       ,						len(_SCREAMING_SNAKE_CASE)       ,						_SCREAMING_SNAKE_CASE)]

									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	[]
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	[]
									if self.params.mlm:
															SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	self.params.special_tok_ids["cls_token"], self.params.special_tok_ids["sep_token"]
									else:
															SCREAMING_SNAKE_CASE_:			Any  		=	self.params.special_tok_ids["bos_token"], self.params.special_tok_ids["eos_token"]

									for seq_, len_ in zip(self.token_ids       ,						self.lengths):
															assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
															if len_ <= max_len:
																					new_tok_ids.append(seq_)
																					new_lengths.append(len_)
															else:
																					SCREAMING_SNAKE_CASE_:			List[Any]  		=	[]
																					for sub_s in divide_chunks(seq_       ,						max_len - 2):
																											if sub_s[0] != cls_id:
																																	SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	np.insert(_SCREAMING_SNAKE_CASE       ,						0       ,						_SCREAMING_SNAKE_CASE)
																											if sub_s[-1] != sep_id:
																																	SCREAMING_SNAKE_CASE_:			Optional[int]  		=	np.insert(_SCREAMING_SNAKE_CASE       ,						len(_SCREAMING_SNAKE_CASE)       ,						_SCREAMING_SNAKE_CASE)
																											assert len(_SCREAMING_SNAKE_CASE) <= max_len
																											assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
																											sub_seqs.append(_SCREAMING_SNAKE_CASE)

																					new_tok_ids.extend(_SCREAMING_SNAKE_CASE)
																					new_lengths.extend([len(_SCREAMING_SNAKE_CASE) for l in sub_seqs])

									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	np.array(_SCREAMING_SNAKE_CASE)
									SCREAMING_SNAKE_CASE_:			Any  		=	np.array(_SCREAMING_SNAKE_CASE)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					int):
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	len(self)
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	self.lengths > 11
									SCREAMING_SNAKE_CASE_:			Dict  		=	self.token_ids[indices]
									SCREAMING_SNAKE_CASE_:			Any  		=	self.lengths[indices]
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	len(self)
									logger.info(F"Remove {init_size - new_size} too short (<=11 tokens) sequences.")



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]):
									if "unk_token" not in self.params.special_tok_ids:
															return
									else:
															SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	self.params.special_tok_ids["unk_token"]
									SCREAMING_SNAKE_CASE_:			Tuple  		=	len(self)
									SCREAMING_SNAKE_CASE_:			int  		=	np.array([np.count_nonzero(a == unk_token_id) for a in self.token_ids])
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	(unk_occs / self.lengths) < 0.5
									SCREAMING_SNAKE_CASE_:			List[str]  		=	self.token_ids[indices]
									SCREAMING_SNAKE_CASE_:			Dict  		=	self.lengths[indices]
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	len(self)
									logger.info(F"Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).")



			def 	_SCREAMING_SNAKE_CASE      (			self  :					int):
									if not self.params.is_master:
															return
									logger.info(F"{len(self)} sequences")
									# data_len = sum(self.lengths)
									# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
									# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

									# unk_idx = self.params.special_tok_ids['unk_token']
									# nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
									# logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Any       ,						lowerCAmelCase__  :					Optional[int]):
									SCREAMING_SNAKE_CASE_:			int  		=	[t[0] for t in batch]
									SCREAMING_SNAKE_CASE_:			Tuple  		=	[t[1] for t in batch]
									assert len(_SCREAMING_SNAKE_CASE) == len(_SCREAMING_SNAKE_CASE)

									# Max for paddings
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	max(_SCREAMING_SNAKE_CASE)

									# Pad token ids
									if self.params.mlm:
															SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	self.params.special_tok_ids["pad_token"]
									else:
															SCREAMING_SNAKE_CASE_:			List[Any]  		=	self.params.special_tok_ids["unk_token"]
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	[list(t.astype(_SCREAMING_SNAKE_CASE)) + [pad_idx] * (max_seq_len_ - len(_SCREAMING_SNAKE_CASE)) for t in token_ids]
									assert len(tk_) == len(_SCREAMING_SNAKE_CASE)
									assert all(len(_SCREAMING_SNAKE_CASE) == max_seq_len_ for t in tk_)

									SCREAMING_SNAKE_CASE_:			Tuple  		=	torch.tensor(tk_)  # (bs, max_seq_len_)
									SCREAMING_SNAKE_CASE_:			Any  		=	torch.tensor(_SCREAMING_SNAKE_CASE)  # (bs)
									return tk_t, lg_t









from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


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



class   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			_UpperCAmelCase       :							Any      							=					['''pixel_values''']



			def __init__(			self  :					List[Any]       ,						lowerCAmelCase__  :					bool = True       ,						lowerCAmelCase__  :					Dict[str, int] = None       ,						lowerCAmelCase__  :					PILImageResampling = PILImageResampling.BICUBIC       ,						lowerCAmelCase__  :					bool = True       ,						lowerCAmelCase__  :					Union[int, float] = 1 / 255       ,						lowerCAmelCase__  :					bool = True       ,						lowerCAmelCase__  :					Optional[Union[float, List[float]]] = None       ,						lowerCAmelCase__  :					Optional[Union[float, List[float]]] = None       ,						lowerCAmelCase__  :					bool = True       ,						**lowerCAmelCase__  :					Tuple       ,						):
									super().__init__(**lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[str]  		=	size if size is not None else {"height": 384, "width": 384}
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	get_size_dict(lowerCAmelCase__       ,						default_to_square=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			int  		=	do_resize
									SCREAMING_SNAKE_CASE_:			Dict  		=	size
									SCREAMING_SNAKE_CASE_:			int  		=	resample
									SCREAMING_SNAKE_CASE_:			str  		=	do_rescale
									SCREAMING_SNAKE_CASE_:			str  		=	rescale_factor
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	do_normalize
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
									SCREAMING_SNAKE_CASE_:			List[str]  		=	image_std if image_std is not None else OPENAI_CLIP_STD
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	do_convert_rgb



			def 	_SCREAMING_SNAKE_CASE      (			self  :					str       ,						lowerCAmelCase__  :					np.ndarray       ,						lowerCAmelCase__  :					Dict[str, int]       ,						lowerCAmelCase__  :					PILImageResampling = PILImageResampling.BICUBIC       ,						lowerCAmelCase__  :					Optional[Union[str, ChannelDimension]] = None       ,						**lowerCAmelCase__  :					Tuple       ,						):
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	get_size_dict(lowerCAmelCase__       ,						default_to_square=lowerCAmelCase__)
									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()}")
									SCREAMING_SNAKE_CASE_:			int  		=	(size["height"], size["width"])
									return resize(lowerCAmelCase__       ,						size=lowerCAmelCase__       ,						resample=lowerCAmelCase__       ,						data_format=lowerCAmelCase__       ,						**lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple       ,						lowerCAmelCase__  :					np.ndarray       ,						lowerCAmelCase__  :					Union[int, float]       ,						lowerCAmelCase__  :					Optional[Union[str, ChannelDimension]] = None       ,						**lowerCAmelCase__  :					List[Any]       ,						):
									return rescale(lowerCAmelCase__       ,						scale=lowerCAmelCase__       ,						data_format=lowerCAmelCase__       ,						**lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]       ,						lowerCAmelCase__  :					np.ndarray       ,						lowerCAmelCase__  :					Union[float, List[float]]       ,						lowerCAmelCase__  :					Union[float, List[float]]       ,						lowerCAmelCase__  :					Optional[Union[str, ChannelDimension]] = None       ,						**lowerCAmelCase__  :					List[Any]       ,						):
									return normalize(lowerCAmelCase__       ,						mean=lowerCAmelCase__       ,						std=lowerCAmelCase__       ,						data_format=lowerCAmelCase__       ,						**lowerCAmelCase__)




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]       ,						lowerCAmelCase__  :					ImageInput       ,						lowerCAmelCase__  :					Optional[bool] = None       ,						lowerCAmelCase__  :					Optional[Dict[str, int]] = None       ,						lowerCAmelCase__  :					PILImageResampling = None       ,						lowerCAmelCase__  :					Optional[bool] = None       ,						lowerCAmelCase__  :					Optional[float] = None       ,						lowerCAmelCase__  :					Optional[bool] = None       ,						lowerCAmelCase__  :					Optional[Union[float, List[float]]] = None       ,						lowerCAmelCase__  :					Optional[Union[float, List[float]]] = None       ,						lowerCAmelCase__  :					Optional[Union[str, TensorType]] = None       ,						lowerCAmelCase__  :					bool = None       ,						lowerCAmelCase__  :					ChannelDimension = ChannelDimension.FIRST       ,						**lowerCAmelCase__  :					Dict       ,						):
									SCREAMING_SNAKE_CASE_:			Any  		=	do_resize if do_resize is not None else self.do_resize
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	resample if resample is not None else self.resample
									SCREAMING_SNAKE_CASE_:			Dict  		=	do_rescale if do_rescale is not None else self.do_rescale
									SCREAMING_SNAKE_CASE_:			Any  		=	rescale_factor if rescale_factor is not None else self.rescale_factor
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	do_normalize if do_normalize is not None else self.do_normalize
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	image_mean if image_mean is not None else self.image_mean
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	image_std if image_std is not None else self.image_std
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

									SCREAMING_SNAKE_CASE_:			str  		=	size if size is not None else self.size
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	get_size_dict(lowerCAmelCase__       ,						default_to_square=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			List[Any]  		=	make_list_of_images(lowerCAmelCase__)

									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.")

									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_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.")

									# PIL RGBA images are converted to RGB
									if do_convert_rgb:
															SCREAMING_SNAKE_CASE_:			List[Any]  		=	[convert_to_rgb(lowerCAmelCase__) for image in images]

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

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

									if do_rescale:
															SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	[self.rescale(image=lowerCAmelCase__       ,						scale=lowerCAmelCase__) for image in images]

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

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

									SCREAMING_SNAKE_CASE_:			List[str]  		=	BatchFeature(data={"pixel_values": images}       ,						tensor_type=lowerCAmelCase__)

									return encoded_outputs






