


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:  List[str]   =   logging.get_logger(__name__)


@dataclass
class 			__lowerCAmelCase				:


	'''simple docstring'''





	_A       	=							42
	_A       	=							42
	_A       	=							None
	_A       	=							None

	def       snake_case__(	self:	Optional[int] ):
			return json.dumps(dataclasses.asdict(self ),				indent=2 ) + "\n"


@dataclass(frozen=UpperCAmelCase_			)
class 			__lowerCAmelCase				:


	'''simple docstring'''





	_A       	=							42
	_A       	=							None
	_A       	=							None
	_A       	=							None

	def       snake_case__(	self:	List[Any] ):
			return json.dumps(dataclasses.asdict(self ) ) + "\n"


class 			__lowerCAmelCase				:


	'''simple docstring'''





	def       snake_case__(	self:	Optional[int],				lowerCamelCase_:	int ):
			raise NotImplementedError()

	def       snake_case__(	self:	Optional[Any],				lowerCamelCase_:	Tuple ):
			raise NotImplementedError()

	def       snake_case__(	self:	str,				lowerCamelCase_:	Tuple ):
			raise NotImplementedError()

	def       snake_case__(	self:	Optional[int],				lowerCamelCase_:	Dict ):
			raise NotImplementedError()

	def       snake_case__(	self:	Any ):
			raise NotImplementedError()

	def       snake_case__(	self:	List[Any],				lowerCamelCase_:	Optional[int] ):
			if len(self.get_labels() ) > 1:
					lowercase__				:			List[Any]            =		self.get_labels()[int(example.label )]
			return example





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


class 			__lowerCAmelCase				(					UpperCAmelCase_			):


	'''simple docstring'''





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

	def __len__(	self:	Tuple ):
			return len(self.examples )

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

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

	@classmethod
	def       snake_case__(	cls:	Dict,				lowerCamelCase_:	int,				lowerCamelCase_:	int=None,				**lowerCamelCase_:	Tuple ):
			lowercase__				:			Optional[int]            =		cls(**lowerCamelCase_ )
			processor.add_examples(lowerCamelCase_,				labels=lowerCamelCase_ )
			return processor

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

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

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

			# Update examples
			if overwrite_examples:
					lowercase__				:			Tuple            =		examples
			else:
					self.examples.extend(lowerCamelCase_ )

			# Update labels
			if overwrite_labels:
					lowercase__				:			List[Any]            =		list(lowerCamelCase_ )
			else:
					lowercase__				:			Dict            =		list(set(self.labels ).union(lowerCamelCase_ ) )

			return self.examples





	def       snake_case__(	self:	List[Any],				lowerCamelCase_:	Optional[int],				lowerCamelCase_:	int=None,				lowerCamelCase_:	Optional[int]=False,				lowerCamelCase_:	str=0,				lowerCamelCase_:	List[Any]=True,				lowerCamelCase_:	Tuple=None,				):
			if max_length is None:
					lowercase__				:			str            =		tokenizer.max_len

			lowercase__				:			Any            =		{label: i for i, label in enumerate(self.labels )}

			lowercase__				:			Union[str, Any]            =		[]
			for ex_index, example in enumerate(self.examples ):
					if ex_index % 10000 == 0:
							logger.info(F"""Tokenizing example {ex_index}""" )

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

			lowercase__				:			List[Any]            =		max(len(lowerCamelCase_ ) for input_ids in all_input_ids )

			lowercase__				:			str            =		[]
			for ex_index, (input_ids, example) in enumerate(zip(lowerCamelCase_,				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.
					lowercase__				:			Tuple            =		[1 if mask_padding_with_zero else 0] * len(lowerCamelCase_ )

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

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

					if self.mode == "classification":
							lowercase__				:			Union[str, Any]            =		label_map[example.label]
					elif self.mode == "regression":
							lowercase__				:			List[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(lowerCamelCase_ ) for x in input_ids] )}""" )
							logger.info(F"""attention_mask: {" ".join([str(lowerCamelCase_ ) for x in attention_mask] )}""" )
							logger.info(F"""label: {example.label} (id = {label})""" )

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

			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)

					lowercase__				:			str            =		tf.data.Dataset.from_generator(
					    lowerCamelCase_,				({'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

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

					lowercase__				:			str            =		TensorDataset(lowerCamelCase_,				lowerCamelCase_,				lowerCamelCase_ )
					return dataset
			else:
					raise ValueError('return_tensors should be one of \'tf\' or \'pt\'' )









from typing import List

import PIL
import torch
from PIL import Image

from ...configuration_utils import ConfigMixin
from ...models.modeling_utils import ModelMixin
from ...utils import PIL_INTERPOLATION


class 			__lowerCAmelCase				(					_UpperCamelCase      ,						_UpperCamelCase			):


	'''simple docstring'''





	def __init__(	self:	Union[str, Any] ):
			super().__init__()

			self.register_buffer('watermark_image',				torch.zeros((62, 62, 4) ) )
			lowercase__				:			int            =		None





	def       snake_case__(	self:	Optional[int],				lowerCamelCase_:	List[PIL.Image.Image],				lowerCamelCase_:	Dict=None ):
			# copied from https://github.com/deep-floyd/IF/blob/b77482e36ca2031cb94dbca1001fc1e6400bf4ab/deepfloyd_if/modules/base.py#L287

			lowercase__				:			str            =		images[0].height
			lowercase__				:			Optional[Any]            =		images[0].width

			lowercase__				:			List[Any]            =		sample_size or h

			lowercase__				:			Any            =		min(h / sample_size,				w / sample_size )
			lowercase__      ,							lowercase__				:			Optional[int]            =		(int(h / coef ), int(w / coef )) if coef < 1 else (h, w)

			lowercase__      ,							lowercase__				:			Any            =		1024**2, img_w * img_h
			lowercase__				:			Union[str, Any]            =		(Sa / Sa) ** 0.5
			lowercase__      ,							lowercase__      ,							lowercase__				:			Optional[int]            =		int(K * 62 ), img_w - int(14 * K ), img_h - int(14 * K )

			if self.watermark_image_as_pil is None:
					lowercase__				:			Tuple            =		self.watermark_image.to(torch.uinta ).cpu().numpy()
					lowercase__				:			str            =		Image.fromarray(lowerCamelCase_,				mode='RGBA' )
					lowercase__				:			Tuple            =		watermark_image

			lowercase__				:			Optional[int]            =		self.watermark_image_as_pil.resize(
			    (wm_size, wm_size),				PIL_INTERPOLATION['bicubic'],				reducing_gap=lowerCamelCase_ )

			for pil_img in images:
					pil_img.paste(lowerCamelCase_,				box=(wm_x - wm_size, wm_y - wm_size, wm_x, wm_y),				mask=wm_img.split()[-1] )

			return images


