

"""simple docstring"""





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


__UpperCamelCase						:   int							   =	logging.get_logger(__name__)







@dataclass
class        SCREAMING_SNAKE_CASE      :






					"""simple docstring"""



					lowercase__       				= 42
					lowercase__       				= 42
					lowercase__       				= None
					lowercase__       				= None







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







@dataclass(frozen=a_ )
class        SCREAMING_SNAKE_CASE      :






					"""simple docstring"""



					lowercase__       				= 42
					lowercase__       				= None
					lowercase__       				= None
					lowercase__       				= None







					def  __lowerCAmelCase				( self    :       Optional[int]	):
						return json.dumps(dataclasses.asdict(self	)	) + "\n"







class        SCREAMING_SNAKE_CASE      :






					"""simple docstring"""



					def  __lowerCAmelCase				( self    :       str     ,lowercase_    :       Tuple	):
						raise NotImplementedError()







					def  __lowerCAmelCase				( self    :       Optional[Any]     ,lowercase_    :       int	):
						raise NotImplementedError()







					def  __lowerCAmelCase				( self    :       List[Any]     ,lowercase_    :       Optional[Any]	):
						raise NotImplementedError()







					def  __lowerCAmelCase				( self    :       Dict     ,lowercase_    :       List[str]	):
						raise NotImplementedError()







					def  __lowerCAmelCase				( self    :       List[str]	):
						raise NotImplementedError()







					def  __lowerCAmelCase				( self    :       Tuple     ,lowercase_    :       str	):
						if len(self.get_labels()	) > 1:
							lowerCAmelCase__			:		Optional[int]						       =			self.get_labels()[int(example.label	)]
						return example







					@classmethod
					def  __lowerCAmelCase				( cls    :       List[Any]     ,lowercase_    :       List[str]     ,lowercase_    :       str=None	):
						with open(lowercase_     ,'''r'''     ,encoding='''utf-8-sig'''	) as f:
							return list(csv.reader(lowercase_     ,delimiter='''\t'''     ,quotechar=lowercase_	)	)







class        SCREAMING_SNAKE_CASE      (	a_ ):






					"""simple docstring"""



					def __init__( self    :       Any     ,lowercase_    :       List[Any]=None     ,lowercase_    :       str=None     ,lowercase_    :       Optional[Any]="classification"     ,lowercase_    :       Optional[int]=False	):
						lowerCAmelCase__			:		Optional[int]						       =			[] if labels is None else labels
						lowerCAmelCase__			:		Any						       =			[] if examples is None else examples
						lowerCAmelCase__			:		Tuple						       =			mode
						lowerCAmelCase__			:		int						       =			verbose







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







					def __getitem__( self    :       Union[str, Any]     ,lowercase_    :       Optional[int]	):
						if isinstance(lowercase_     ,lowercase_	):
							return SingleSentenceClassificationProcessor(labels=self.labels     ,examples=self.examples[idx]	)
						return self.examples[idx]







					@classmethod
					def  __lowerCAmelCase				( cls    :       str     ,lowercase_    :       int     ,lowercase_    :       Optional[Any]=""     ,lowercase_    :       int=0     ,lowercase_    :       Union[str, Any]=1     ,lowercase_    :       List[str]=None     ,lowercase_    :       int=False     ,**lowercase_    :       Any	):
						lowerCAmelCase__			:		Tuple						       =			cls(**lowercase_	)
						processor.add_examples_from_csv(
						    lowercase_     ,split_name=lowercase_     ,column_label=lowercase_     ,column_text=lowercase_     ,column_id=lowercase_     ,skip_first_row=lowercase_     ,overwrite_labels=lowercase_     ,overwrite_examples=lowercase_     ,)
						return processor







					@classmethod
					def  __lowerCAmelCase				( cls    :       Optional[Any]     ,lowercase_    :       Tuple     ,lowercase_    :       Any=None     ,**lowercase_    :       Any	):
						lowerCAmelCase__			:		Optional[Any]						       =			cls(**lowercase_	)
						processor.add_examples(lowercase_     ,labels=lowercase_	)
						return processor







					def  __lowerCAmelCase				( self    :       Dict     ,lowercase_    :       List[Any]     ,lowercase_    :       List[str]=""     ,lowercase_    :       Optional[Any]=0     ,lowercase_    :       int=1     ,lowercase_    :       Optional[Any]=None     ,lowercase_    :       str=False     ,lowercase_    :       Optional[Any]=False     ,lowercase_    :       Tuple=False     ,):
						lowerCAmelCase__			:		Union[str, Any]						       =			self._read_tsv(lowercase_	)
						if skip_first_row:
							lowerCAmelCase__			:		Dict						       =			lines[1:]
						lowerCAmelCase__			:		str						       =			[]
						lowerCAmelCase__			:		Any						       =			[]
						lowerCAmelCase__			:		Dict						       =			[]
						for i, line in enumerate(lowercase_	):
							texts.append(line[column_text]	)
							labels.append(line[column_label]	)
							if column_id is not None:
								ids.append(line[column_id]	)
							else:
								lowerCAmelCase__			:		List[str]						       =			F'{split_name}-{i}' if split_name else str(lowercase_	)
								ids.append(lowercase_	)

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







					def  __lowerCAmelCase				( self    :       Tuple     ,lowercase_    :       Union[str, Any]     ,lowercase_    :       Optional[Any]=None     ,lowercase_    :       Optional[int]=None     ,lowercase_    :       Union[str, Any]=False     ,lowercase_    :       List[Any]=False	):
						if labels is not None and len(lowercase_	) != len(lowercase_	):
							raise ValueError(
							    F'Text and labels have mismatched lengths {len(lowercase_	)} and {len(lowercase_	)}'	)
						if ids is not None and len(lowercase_	) != len(lowercase_	):
							raise ValueError(F'Text and ids have mismatched lengths {len(lowercase_	)} and {len(lowercase_	)}'	)
						if ids is None:
							lowerCAmelCase__			:		Union[str, Any]						       =			[None] * len(lowercase_	)
						if labels is None:
							lowerCAmelCase__			:		Optional[int]						       =			[None] * len(lowercase_	)
						lowerCAmelCase__			:		Optional[Any]						       =			[]
						lowerCAmelCase__			:		Optional[Any]						       =			set()
						for text_or_text_and_label, label, guid in zip(lowercase_     ,lowercase_     ,lowercase_	):
							if isinstance(lowercase_     ,(tuple, list)	) and label is None:
								lowerCAmelCase__			:		Union[str, Any]						       =			text_or_text_and_label
							else:
								lowerCAmelCase__			:		Union[str, Any]						       =			text_or_text_and_label
							added_labels.add(lowercase_	)
							examples.append(InputExample(guid=lowercase_     ,text_a=lowercase_     ,text_b=lowercase_     ,label=lowercase_	)	)

						# Update examples
						if overwrite_examples:
							lowerCAmelCase__			:		Tuple						       =			examples
						else:
							self.examples.extend(lowercase_	)

						# Update labels
						if overwrite_labels:
							lowerCAmelCase__			:		Dict						       =			list(lowercase_	)
						else:
							lowerCAmelCase__			:		List[str]						       =			list(set(self.labels	).union(lowercase_	)	)

						return self.examples







					def  __lowerCAmelCase				( self    :       int     ,lowercase_    :       Union[str, Any]     ,lowercase_    :       Dict=None     ,lowercase_    :       List[Any]=False     ,lowercase_    :       Dict=0     ,lowercase_    :       Optional[Any]=True     ,lowercase_    :       Any=None     ,):
						if max_length is None:
							lowerCAmelCase__			:		Union[str, Any]						       =			tokenizer.max_len

						lowerCAmelCase__			:		Tuple						       =			{label: i for i, label in enumerate(self.labels	)}

						lowerCAmelCase__			:		List[str]						       =			[]
						for ex_index, example in enumerate(self.examples	):
							if ex_index % 1_0_0_0_0 == 0:
								logger.info(F'Tokenizing example {ex_index}'	)

							lowerCAmelCase__			:		List[Any]						       =			tokenizer.encode(
							    example.text_a     ,add_special_tokens=lowercase_     ,max_length=min(lowercase_     ,tokenizer.max_len	)     ,)
							all_input_ids.append(lowercase_	)

						lowerCAmelCase__			:		Optional[Any]						       =			max(len(lowercase_	) for input_ids in all_input_ids	)

						lowerCAmelCase__			:		Union[str, Any]						       =			[]
						for ex_index, (input_ids, example) in enumerate(zip(lowercase_     ,self.examples	)	):
							if ex_index % 1_0_0_0_0 == 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.
							lowerCAmelCase__			:		Tuple						       =			[1 if mask_padding_with_zero else 0] * len(lowercase_	)

							# Zero-pad up to the sequence length.
							lowerCAmelCase__			:		str						       =			batch_length - len(lowercase_	)
							if pad_on_left:
								lowerCAmelCase__			:		str						       =			([pad_token] * padding_length) + input_ids
								lowerCAmelCase__			:		int						       =			([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
							else:
								lowerCAmelCase__			:		int						       =			input_ids + ([pad_token] * padding_length)
								lowerCAmelCase__			:		List[str]						       =			attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

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

							if self.mode == "classification":
								lowerCAmelCase__			:		Tuple						       =			label_map[example.label]
							elif self.mode == "regression":
								lowerCAmelCase__			:		Any						       =			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(lowercase_	) for x in input_ids]	)}'	)
								logger.info(F'attention_mask: {" ".join([str(lowercase_	) for x in attention_mask]	)}'	)
								logger.info(F'label: {example.label} (id = {label})'	)

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

						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)

							lowerCAmelCase__			:		Union[str, Any]						       =			tf.data.Dataset.from_generator(
							    lowercase_     ,({'''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

							lowerCAmelCase__			:		List[str]						       =			torch.tensor([f.input_ids for f in features]     ,dtype=torch.long	)
							lowerCAmelCase__			:		Dict						       =			torch.tensor([f.attention_mask for f in features]     ,dtype=torch.long	)
							if self.mode == "classification":
								lowerCAmelCase__			:		Union[str, Any]						       =			torch.tensor([f.label for f in features]     ,dtype=torch.long	)
							elif self.mode == "regression":
								lowerCAmelCase__			:		str						       =			torch.tensor([f.label for f in features]     ,dtype=torch.float	)

							lowerCAmelCase__			:		List[str]						       =			TensorDataset(lowercase_     ,lowercase_     ,lowercase_	)
							return dataset
						else:
							raise ValueError('''return_tensors should be one of \'tf\' or \'pt\''''	)







"""simple docstring"""





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 normalize, rescale, resize, to_channel_dimension_format
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, logging


__UpperCamelCase						:   List[Any]							   =	logging.get_logger(__name__)







class        SCREAMING_SNAKE_CASE      (	a_ ):






					"""simple docstring"""



					lowercase__       				= ["pixel_values"]







					def __init__( self    :       Tuple     ,lowercase_    :       bool = True     ,lowercase_    :       Optional[Dict[str, int]] = None     ,lowercase_    :       PILImageResampling = PILImageResampling.BILINEAR     ,lowercase_    :       bool = True     ,lowercase_    :       Union[int, float] = 1 / 2_5_5     ,lowercase_    :       bool = True     ,lowercase_    :       Optional[Union[float, List[float]]] = None     ,lowercase_    :       Optional[Union[float, List[float]]] = None     ,**lowercase_    :       Tuple     ,):
						super().__init__(**lowercase_	)
						lowerCAmelCase__			:		Any						       =			size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
						lowerCAmelCase__			:		Tuple						       =			get_size_dict(lowercase_	)
						lowerCAmelCase__			:		Tuple						       =			do_resize
						lowerCAmelCase__			:		Tuple						       =			do_rescale
						lowerCAmelCase__			:		Union[str, Any]						       =			do_normalize
						lowerCAmelCase__			:		Optional[int]						       =			size
						lowerCAmelCase__			:		List[Any]						       =			resample
						lowerCAmelCase__			:		int						       =			rescale_factor
						lowerCAmelCase__			:		int						       =			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						lowerCAmelCase__			:		Any						       =			image_std if image_std is not None else IMAGENET_STANDARD_STD







					def  __lowerCAmelCase				( self    :       List[Any]     ,lowercase_    :       np.ndarray     ,lowercase_    :       Dict[str, int]     ,lowercase_    :       PILImageResampling = PILImageResampling.BILINEAR     ,lowercase_    :       Optional[Union[str, ChannelDimension]] = None     ,**lowercase_    :       str     ,):
						lowerCAmelCase__			:		Union[str, Any]						       =			get_size_dict(lowercase_	)
						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()}'	)
						return resize(
						    lowercase_     ,size=(size['''height'''], size['''width'''])     ,resample=lowercase_     ,data_format=lowercase_     ,**lowercase_	)







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







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







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

						lowerCAmelCase__			:		Any						       =			size if size is not None else self.size
						lowerCAmelCase__			:		Dict						       =			get_size_dict(lowercase_	)

						lowerCAmelCase__			:		Union[str, Any]						       =			make_list_of_images(lowercase_	)

						if not valid_images(lowercase_	):
							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.'''	)

						# All transformations expect numpy arrays.
						lowerCAmelCase__			:		Dict						       =			[to_numpy_array(lowercase_	) for image in images]

						if do_resize:
							lowerCAmelCase__			:		Optional[Any]						       =			[self.resize(image=lowercase_     ,size=lowercase_     ,resample=lowercase_	) for image in images]

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

						if do_normalize:
							lowerCAmelCase__			:		Dict						       =			[self.normalize(image=lowercase_     ,mean=lowercase_     ,std=lowercase_	) for image in images]

						lowerCAmelCase__			:		Tuple						       =			[to_channel_dimension_format(lowercase_     ,lowercase_	) for image in images]

						lowerCAmelCase__			:		int						       =			{'''pixel_values''': images}
						return BatchFeature(data=lowercase_     ,tensor_type=lowercase_	)




