

import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


SCREAMING_SNAKE_CASE_						=					logging.get_logger(__name__)

@dataclass
class  a     :
						_lowercase			  =	42
						_lowercase			  =	42
						_lowercase			  =	None
						_lowercase			  =	None


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										return json.dumps(dataclasses.asdict(self		)  ,    indent=2		) + "\n"







@dataclass(frozen=UpperCAmelCase )
class  a     :
						_lowercase			  =	42
						_lowercase			  =	None
						_lowercase			  =	None
						_lowercase			  =	None


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										return json.dumps(dataclasses.asdict(self		)		) + "\n"







class  a     :


						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


										raise NotImplementedError()


						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


										raise NotImplementedError()


						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


										raise NotImplementedError()


						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


										raise NotImplementedError()


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										raise NotImplementedError()


						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


										if len(self.get_labels()		) > 1:
														_UpperCAmelCase				:						Optional[int]       						=			self.get_labels()[int(example.label		)]
										return example





						@classmethod
						def 				_UpperCAmelCase (				cls  ,    A_  ,    A_=None		):







										'''simple docstring'''


										with open(A_  ,    "r"  ,    encoding="utf-8-sig"		) as f:
														return list(csv.reader(A_  ,    delimiter="\t"  ,    quotechar=A_		)		)







class  a     (		UpperCAmelCase ):


						def __init__(				self  ,    A_=None  ,    A_=None  ,    A_="classification"  ,    A_=False		):







										'''simple docstring'''


										_UpperCAmelCase				:						Dict       						=			[] if labels is None else labels
										_UpperCAmelCase				:						List[Any]       						=			[] if examples is None else examples
										_UpperCAmelCase				:						Optional[int]       						=			mode
										_UpperCAmelCase				:						Optional[int]       						=			verbose


						def __len__(				self		):







										'''simple docstring'''


										return len(self.examples		)


						def __getitem__(				self  ,    A_		):







										'''simple docstring'''


										if isinstance(A_  ,    A_		):
														return SingleSentenceClassificationProcessor(labels=self.labels  ,    examples=self.examples[idx]		)
										return self.examples[idx]


						@classmethod
						def 				_UpperCAmelCase (				cls  ,    A_  ,    A_=""  ,    A_=0  ,    A_=1  ,    A_=None  ,    A_=False  ,    **A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						Dict       						=			cls(**A_		)
										processor.add_examples_from_csv(
										    A_  ,    split_name=A_  ,    column_label=A_  ,    column_text=A_  ,    column_id=A_  ,    skip_first_row=A_  ,    overwrite_labels=A_  ,    overwrite_examples=A_  ,    )
										return processor


						@classmethod
						def 				_UpperCAmelCase (				cls  ,    A_  ,    A_=None  ,    **A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						Optional[int]       						=			cls(**A_		)
										processor.add_examples(A_  ,    labels=A_		)
										return processor


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_=""  ,    A_=0  ,    A_=1  ,    A_=None  ,    A_=False  ,    A_=False  ,    A_=False  ,    ):







										'''simple docstring'''


										_UpperCAmelCase				:						Any       						=			self._read_tsv(A_		)
										if skip_first_row:
														_UpperCAmelCase				:						Tuple       						=			lines[1:]
										_UpperCAmelCase				:						Any       						=			[]
										_UpperCAmelCase				:						Union[str, Any]       						=			[]
										_UpperCAmelCase				:						Union[str, Any]       						=			[]
										for i, line in enumerate(A_		):
														texts.append(line[column_text]		)
														labels.append(line[column_label]		)
														if column_id is not None:
																		ids.append(line[column_id]		)
														else:
																		_UpperCAmelCase				:						Tuple       						=			f'{split_name}-{i}' if split_name else str(A_		)
																		ids.append(A_		)

										return self.add_examples(
										    A_  ,    A_  ,    A_  ,    overwrite_labels=A_  ,    overwrite_examples=A_		)


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_=None  ,    A_=None  ,    A_=False  ,    A_=False		):







										'''simple docstring'''


										if labels is not None and len(A_		) != len(A_		):
														raise ValueError(
														    f'Text and labels have mismatched lengths {len(A_		)} and {len(A_		)}'		)
										if ids is not None and len(A_		) != len(A_		):
														raise ValueError(f'Text and ids have mismatched lengths {len(A_		)} and {len(A_		)}'		)
										if ids is None:
														_UpperCAmelCase				:						Any       						=			[None] * len(A_		)
										if labels is None:
														_UpperCAmelCase				:						str       						=			[None] * len(A_		)
										_UpperCAmelCase				:						Any       						=			[]
										_UpperCAmelCase				:						Union[str, Any]       						=			set()
										for text_or_text_and_label, label, guid in zip(A_  ,    A_  ,    A_		):
														if isinstance(A_  ,    (tuple, list)		) and label is None:
																		_UpperCAmelCase						,							_UpperCAmelCase				:						str       						=			text_or_text_and_label
														else:
																		_UpperCAmelCase				:						Union[str, Any]       						=			text_or_text_and_label
														added_labels.add(A_		)
														examples.append(InputExample(guid=A_  ,    text_a=A_  ,    text_b=A_  ,    label=A_		)		)

										# Update examples
										if overwrite_examples:
														_UpperCAmelCase				:						int       						=			examples
										else:
														self.examples.extend(A_		)

										# Update labels
										if overwrite_labels:
														_UpperCAmelCase				:						List[Any]       						=			list(A_		)
										else:
														_UpperCAmelCase				:						int       						=			list(set(self.labels		).union(A_		)		)

										return self.examples





						def 				_UpperCAmelCase (				self  ,    A_  ,    A_=None  ,    A_=False  ,    A_=0  ,    A_=True  ,    A_=None  ,    ):







										'''simple docstring'''


										if max_length is None:
														_UpperCAmelCase				:						Optional[int]       						=			tokenizer.max_len

										_UpperCAmelCase				:						Union[str, Any]       						=			{label: i for i, label in enumerate(self.labels		)}

										_UpperCAmelCase				:						Optional[int]       						=			[]
										for ex_index, example in enumerate(self.examples		):
														if ex_index % 10000 == 0:
																		logger.info(f'Tokenizing example {ex_index}'		)

														_UpperCAmelCase				:						Optional[Any]       						=			tokenizer.encode(
														    example.text_a  ,    add_special_tokens=A_  ,    max_length=min(A_  ,    tokenizer.max_len		)  ,    )
														all_input_ids.append(A_		)

										_UpperCAmelCase				:						List[str]       						=			max(len(A_		) for input_ids in all_input_ids		)

										_UpperCAmelCase				:						Dict       						=			[]
										for ex_index, (input_ids, example) in enumerate(zip(A_  ,    self.examples		)		):
														if ex_index % 10000 == 0:
																		logger.info(f'Writing example {ex_index}/{len(self.examples		)}'		)
														# The mask has 1 for real tokens and 0 for padding tokens. Only real
														# tokens are attended to.
														_UpperCAmelCase				:						List[str]       						=			[1 if mask_padding_with_zero else 0] * len(A_		)

														# Zero-pad up to the sequence length.
														_UpperCAmelCase				:						List[Any]       						=			batch_length - len(A_		)
														if pad_on_left:
																		_UpperCAmelCase				:						Union[str, Any]       						=			([pad_token] * padding_length) + input_ids
																		_UpperCAmelCase				:						Optional[Any]       						=			([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
														else:
																		_UpperCAmelCase				:						Tuple       						=			input_ids + ([pad_token] * padding_length)
																		_UpperCAmelCase				:						List[str]       						=			attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

														if len(A_		) != batch_length:
																		raise ValueError(f'Error with input length {len(A_		)} vs {batch_length}'		)
														if len(A_		) != batch_length:
																		raise ValueError(f'Error with input length {len(A_		)} vs {batch_length}'		)

														if self.mode == "classification":
																		_UpperCAmelCase				:						List[Any]       						=			label_map[example.label]
														elif self.mode == "regression":
																		_UpperCAmelCase				:						int       						=			float(example.label		)
														else:
																		raise ValueError(self.mode		)

														if ex_index < 5 and self.verbose:
																		logger.info("*** Example ***"		)
																		logger.info(f'guid: {example.guid}'		)
																		logger.info(f'input_ids: {" ".join([str(A_		) for x in input_ids]		)}'		)
																		logger.info(f'attention_mask: {" ".join([str(A_		) for x in attention_mask]		)}'		)
																		logger.info(f'label: {example.label} (id = {label})'		)

														features.append(InputFeatures(input_ids=A_  ,    attention_mask=A_  ,    label=A_		)		)

										if return_tensors is None:
														return features
										elif return_tensors == "tf":
														if not is_tf_available():
																		raise RuntimeError("return_tensors set to 'tf' but TensorFlow 2.0 can't be imported"		)
														import tensorflow as tf

														def gen():
																		for ex in features:
																						yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

														_UpperCAmelCase				:						Optional[Any]       						=			tf.data.Dataset.from_generator(
														    A_  ,    ({"input_ids": tf.intaa, "attention_mask": tf.intaa}, tf.intaa)  ,    ({"input_ids": tf.TensorShape([None]		), "attention_mask": tf.TensorShape([None]		)}, tf.TensorShape([]		))  ,    )
														return dataset
										elif return_tensors == "pt":
														if not is_torch_available():
																		raise RuntimeError("return_tensors set to 'pt' but PyTorch can't be imported"		)
														import torch
														from torch.utils.data import TensorDataset

														_UpperCAmelCase				:						Tuple       						=			torch.tensor([f.input_ids for f in features]  ,    dtype=torch.long		)
														_UpperCAmelCase				:						str       						=			torch.tensor([f.attention_mask for f in features]  ,    dtype=torch.long		)
														if self.mode == "classification":
																		_UpperCAmelCase				:						int       						=			torch.tensor([f.label for f in features]  ,    dtype=torch.long		)
														elif self.mode == "regression":
																		_UpperCAmelCase				:						int       						=			torch.tensor([f.label for f in features]  ,    dtype=torch.float		)

														_UpperCAmelCase				:						Optional[int]       						=			TensorDataset(A_  ,    A_  ,    A_		)
														return dataset
										else:
														raise ValueError("return_tensors should be one of 'tf' or 'pt'"		)





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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


SCREAMING_SNAKE_CASE_						=					logging.get_logger(__name__)


if is_vision_available():
					import PIL

class  a     (		UpperCAmelCase ):
						_lowercase			  =	["pixel_values"]


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







										'''simple docstring'''


										super().__init__(**A_		)

										_UpperCAmelCase				:						str       						=			size if size is not None else {"height": 2560, "width": 1920}
										if isinstance(A_  ,    (tuple, list)		):
														# The previous feature extractor size parameter was in (width, height) format
														_UpperCAmelCase				:						List[Any]       						=			size[::-1]
										_UpperCAmelCase				:						Dict       						=			get_size_dict(A_		)

										_UpperCAmelCase				:						int       						=			do_resize
										_UpperCAmelCase				:						Dict       						=			size
										_UpperCAmelCase				:						List[Any]       						=			resample
										_UpperCAmelCase				:						Union[str, Any]       						=			do_thumbnail
										_UpperCAmelCase				:						Optional[int]       						=			do_align_long_axis
										_UpperCAmelCase				:						Optional[Any]       						=			do_pad
										_UpperCAmelCase				:						int       						=			do_rescale
										_UpperCAmelCase				:						List[Any]       						=			rescale_factor
										_UpperCAmelCase				:						Union[str, Any]       						=			do_normalize
										_UpperCAmelCase				:						Any       						=			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										_UpperCAmelCase				:						Dict       						=			image_std if image_std is not None else IMAGENET_STANDARD_STD


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_  ,    A_ = None		):







										'''simple docstring'''


										_UpperCAmelCase						,							_UpperCAmelCase				:						List[str]       						=			get_image_size(A_		)
										_UpperCAmelCase						,							_UpperCAmelCase				:						Any       						=			size["height"], size["width"]

										if (output_width < output_height and input_width > input_height) or (
										    output_width > output_height and input_width < input_height
										):
														_UpperCAmelCase				:						Optional[int]       						=			np.rotaa(A_  ,    3		)

										if data_format is not None:
														_UpperCAmelCase				:						List[str]       						=			to_channel_dimension_format(A_  ,    A_		)

										return image


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_  ,    A_ = False  ,    A_ = None  ,    ):







										'''simple docstring'''


										_UpperCAmelCase						,							_UpperCAmelCase				:						Optional[Any]       						=			size["height"], size["width"]
										_UpperCAmelCase						,							_UpperCAmelCase				:						List[Any]       						=			get_image_size(A_		)

										_UpperCAmelCase				:						Optional[Any]       						=			output_width - input_width
										_UpperCAmelCase				:						Any       						=			output_height - input_height

										if random_padding:
														_UpperCAmelCase				:						List[str]       						=			np.random.randint(low=0  ,    high=delta_height + 1		)
														_UpperCAmelCase				:						Optional[int]       						=			np.random.randint(low=0  ,    high=delta_width + 1		)
										else:
														_UpperCAmelCase				:						Union[str, Any]       						=			delta_height // 2
														_UpperCAmelCase				:						str       						=			delta_width // 2

										_UpperCAmelCase				:						Optional[Any]       						=			delta_height - pad_top
										_UpperCAmelCase				:						List[Any]       						=			delta_width - pad_left

										_UpperCAmelCase				:						Optional[Any]       						=			((pad_top, pad_bottom), (pad_left, pad_right))
										return pad(A_  ,    A_  ,    data_format=A_		)


						def 				_UpperCAmelCase (				self  ,    *A_  ,    **A_		):







										'''simple docstring'''


										logger.info("pad is deprecated and will be removed in version 4.27. Please use pad_image instead."		)
										return self.pad_image(*A_  ,    **A_		)


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_  ,    A_ = PILImageResampling.BICUBIC  ,    A_ = None  ,    **A_  ,    ):







										'''simple docstring'''


										_UpperCAmelCase						,							_UpperCAmelCase				:						Union[str, Any]       						=			get_image_size(A_		)
										_UpperCAmelCase						,							_UpperCAmelCase				:						Union[str, Any]       						=			size["height"], size["width"]

										# We always resize to the smallest of either the input or output size.
										_UpperCAmelCase				:						Dict       						=			min(A_  ,    A_		)
										_UpperCAmelCase				:						Tuple       						=			min(A_  ,    A_		)

										if height == input_height and width == input_width:
														return image

										if input_height > input_width:
														_UpperCAmelCase				:						Any       						=			int(input_width * height / input_height		)
										elif input_width > input_height:
														_UpperCAmelCase				:						Any       						=			int(input_height * width / input_width		)

										return resize(
										    A_  ,    size=(height, width)  ,    resample=A_  ,    reducing_gap=2.0  ,    data_format=A_  ,    **A_		)


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_  ,    A_ = PILImageResampling.BICUBIC  ,    A_ = None  ,    **A_  ,    ):







										'''simple docstring'''


										_UpperCAmelCase				:						Any       						=			get_size_dict(A_		)
										_UpperCAmelCase				:						List[str]       						=			min(size["height"]  ,    size["width"]		)
										_UpperCAmelCase				:						Optional[Any]       						=			get_resize_output_image_size(A_  ,    size=A_  ,    default_to_square=A_		)
										_UpperCAmelCase				:						str       						=			resize(A_  ,    size=A_  ,    resample=A_  ,    data_format=A_  ,    **A_		)
										return resized_image


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_  ,    A_ = None  ,    **A_  ,    ):







										'''simple docstring'''


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


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_  ,    A_  ,    A_ = None  ,    **A_  ,    ):







										'''simple docstring'''


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





						def 				_UpperCAmelCase (				self  ,    A_  ,    A_ = None  ,    A_ = None  ,    A_ = None  ,    A_ = None  ,    A_ = None  ,    A_ = None  ,    A_ = False  ,    A_ = None  ,    A_ = None  ,    A_ = None  ,    A_ = None  ,    A_ = None  ,    A_ = None  ,    A_ = ChannelDimension.FIRST  ,    **A_  ,    ):







										'''simple docstring'''


										_UpperCAmelCase				:						str       						=			do_resize if do_resize is not None else self.do_resize
										_UpperCAmelCase				:						str       						=			size if size is not None else self.size
										if isinstance(A_  ,    (tuple, list)		):
														# Previous feature extractor had size in (width, height) format
														_UpperCAmelCase				:						List[Any]       						=			size[::-1]
										_UpperCAmelCase				:						Union[str, Any]       						=			get_size_dict(A_		)
										_UpperCAmelCase				:						Dict       						=			resample if resample is not None else self.resample
										_UpperCAmelCase				:						str       						=			do_thumbnail if do_thumbnail is not None else self.do_thumbnail
										_UpperCAmelCase				:						Optional[Any]       						=			do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
										_UpperCAmelCase				:						List[str]       						=			do_pad if do_pad is not None else self.do_pad
										_UpperCAmelCase				:						Optional[Any]       						=			do_rescale if do_rescale is not None else self.do_rescale
										_UpperCAmelCase				:						int       						=			rescale_factor if rescale_factor is not None else self.rescale_factor
										_UpperCAmelCase				:						List[str]       						=			do_normalize if do_normalize is not None else self.do_normalize
										_UpperCAmelCase				:						Any       						=			image_mean if image_mean is not None else self.image_mean
										_UpperCAmelCase				:						List[Any]       						=			image_std if image_std is not None else self.image_std

										_UpperCAmelCase				:						str       						=			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."		)

										if do_rescale and rescale_factor is None:
														raise ValueError("Rescale factor must be specified if do_rescale is True."		)

										if do_pad and size is None:
														raise ValueError("Size must be specified if do_pad 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."		)

										# All transformations expect numpy arrays.
										_UpperCAmelCase				:						Union[str, Any]       						=			[to_numpy_array(A_		) for image in images]

										if do_align_long_axis:
														_UpperCAmelCase				:						Tuple       						=			[self.align_long_axis(A_  ,    size=A_		) for image in images]

										if do_resize:
														_UpperCAmelCase				:						Optional[int]       						=			[self.resize(image=A_  ,    size=A_  ,    resample=A_		) for image in images]

										if do_thumbnail:
														_UpperCAmelCase				:						Optional[Any]       						=			[self.thumbnail(image=A_  ,    size=A_		) for image in images]

										if do_pad:
														_UpperCAmelCase				:						List[Any]       						=			[self.pad_image(image=A_  ,    size=A_  ,    random_padding=A_		) for image in images]

										if do_rescale:
														_UpperCAmelCase				:						Tuple       						=			[self.rescale(image=A_  ,    scale=A_		) for image in images]

										if do_normalize:
														_UpperCAmelCase				:						int       						=			[self.normalize(image=A_  ,    mean=A_  ,    std=A_		) for image in images]

										_UpperCAmelCase				:						str       						=			[to_channel_dimension_format(A_  ,    A_		) for image in images]

										_UpperCAmelCase				:						Dict       						=			{"pixel_values": images}
										return BatchFeature(data=A_  ,    tensor_type=A_		)


