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






@dataclass
class 				A       :
	snake_case__ :str
	snake_case__ :str
	snake_case__ :Optional[str]				       =       None
	snake_case__ :Optional[str]				       =       None





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Tuple							):


				"""simple docstring"""

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






@dataclass(frozen=SCREAMING_SNAKE_CASE__      )
class 				A       :
	snake_case__ :List[int]
	snake_case__ :Optional[List[int]]				       =       None
	snake_case__ :Optional[List[int]]				       =       None
	snake_case__ :Optional[Union[int, float]]				       =       None





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Any							):


				"""simple docstring"""

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






class 				A       :





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Any ,							__magic_name__     :    Optional[Any]							):


				"""simple docstring"""

				raise NotImplementedError()





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Dict ,							__magic_name__     :    Union[str, Any]							):


				"""simple docstring"""

				raise NotImplementedError()





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Any ,							__magic_name__     :    Optional[int]							):


				"""simple docstring"""

				raise NotImplementedError()





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[int] ,							__magic_name__     :    Tuple							):


				"""simple docstring"""

				raise NotImplementedError()





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Any							):


				"""simple docstring"""

				raise NotImplementedError()





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Tuple ,							__magic_name__     :    Union[str, Any]							):


				"""simple docstring"""

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







	@classmethod
	def 							__SCREAMING_SNAKE_CASE					(   cls     :    int ,							__magic_name__     :    Dict ,							__magic_name__     :    str=None							):


				"""simple docstring"""

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






class 				A       (		SCREAMING_SNAKE_CASE__      ):





	def __init__(   self     :    Union[str, Any] ,							__magic_name__     :    Tuple=None ,							__magic_name__     :    List[Any]=None ,							__magic_name__     :    Dict="classification" ,							__magic_name__     :    Union[str, Any]=False							):


				"""simple docstring"""

				lowerCAmelCase__									=							[] if labels is None else labels
				lowerCAmelCase__									=							[] if examples is None else examples
				lowerCAmelCase__									=							mode
				lowerCAmelCase__									=							verbose





	def __len__(   self     :    Dict							):


				"""simple docstring"""

				return len(self.examples							)





	def __getitem__(   self     :    Any ,							__magic_name__     :    Dict							):


				"""simple docstring"""

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





	@classmethod
	def 							__SCREAMING_SNAKE_CASE					(   cls     :    Optional[int] ,							__magic_name__     :    Optional[int] ,							__magic_name__     :    Optional[Any]="" ,							__magic_name__     :    Optional[int]=0 ,							__magic_name__     :    List[Any]=1 ,							__magic_name__     :    List[str]=None ,							__magic_name__     :    str=False ,							**__magic_name__     :    List[str]							):


				"""simple docstring"""

				lowerCAmelCase__									=							cls(**__magic_name__							)
				processor.add_examples_from_csv(
				    __magic_name__ ,							split_name=__magic_name__ ,							column_label=__magic_name__ ,							column_text=__magic_name__ ,							column_id=__magic_name__ ,							skip_first_row=__magic_name__ ,							overwrite_labels=__magic_name__ ,							overwrite_examples=__magic_name__ ,							)
				return processor





	@classmethod
	def 							__SCREAMING_SNAKE_CASE					(   cls     :    List[Any] ,							__magic_name__     :    Union[str, Any] ,							__magic_name__     :    Optional[Any]=None ,							**__magic_name__     :    Optional[Any]							):


				"""simple docstring"""

				lowerCAmelCase__									=							cls(**__magic_name__							)
				processor.add_examples(__magic_name__ ,							labels=__magic_name__							)
				return processor





	def 							__SCREAMING_SNAKE_CASE					(   self     :    str ,							__magic_name__     :    Union[str, Any] ,							__magic_name__     :    str="" ,							__magic_name__     :    Union[str, Any]=0 ,							__magic_name__     :    Optional[int]=1 ,							__magic_name__     :    Dict=None ,							__magic_name__     :    Optional[Any]=False ,							__magic_name__     :    Union[str, Any]=False ,							__magic_name__     :    Optional[int]=False ,							):


				"""simple docstring"""

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

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





	def 							__SCREAMING_SNAKE_CASE					(   self     :    int ,							__magic_name__     :    Optional[Any] ,							__magic_name__     :    Optional[int]=None ,							__magic_name__     :    int=None ,							__magic_name__     :    Optional[Any]=False ,							__magic_name__     :    List[str]=False							):


				"""simple docstring"""

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

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

				# Update labels
				if overwrite_labels:
							lowerCAmelCase__									=							list(__magic_name__							)
				else:
							lowerCAmelCase__									=							list(set(self.labels							).union(__magic_name__							)							)

				return self.examples







	def 							__SCREAMING_SNAKE_CASE					(   self     :    int ,							__magic_name__     :    Optional[int] ,							__magic_name__     :    Union[str, Any]=None ,							__magic_name__     :    List[str]=False ,							__magic_name__     :    Optional[Any]=0 ,							__magic_name__     :    List[str]=True ,							__magic_name__     :    str=None ,							):


				"""simple docstring"""

				if max_length is None:
							lowerCAmelCase__									=							tokenizer.max_len

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

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

							lowerCAmelCase__									=							tokenizer.encode(
							    example.text_a ,							add_special_tokens=__magic_name__ ,							max_length=min(__magic_name__ ,							tokenizer.max_len							) ,							)
							all_input_ids.append(__magic_name__							)

				lowerCAmelCase__									=							max(len(__magic_name__							) for input_ids in all_input_ids							)

				lowerCAmelCase__									=							[]
				for ex_index, (input_ids, example) in enumerate(zip(__magic_name__ ,							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.
							lowerCAmelCase__									=							[1 if mask_padding_with_zero else 0] * len(__magic_name__							)

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

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

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

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

				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__									=							tf.data.Dataset.from_generator(
							    __magic_name__ ,							({"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__									=							torch.tensor([f.input_ids for f in features] ,							dtype=torch.long							)
							lowerCAmelCase__									=							torch.tensor([f.attention_mask for f in features] ,							dtype=torch.long							)
							if self.mode == "classification":
										lowerCAmelCase__									=							torch.tensor([f.label for f in features] ,							dtype=torch.long							)
							elif self.mode == "regression":
										lowerCAmelCase__									=							torch.tensor([f.label for f in features] ,							dtype=torch.float							)

							lowerCAmelCase__									=							TensorDataset(__magic_name__ ,							__magic_name__ ,							__magic_name__							)
							return dataset
				else:
							raise ValueError("return_tensors should be one of 'tf' or 'pt'"							)







'''simple docstring'''

import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize






class 				A       :





	def __init__(   self     :    Union[str, Any] ,							__magic_name__     :    Union[str, Any] ,							__magic_name__     :    Optional[Any]=sys.maxsize							):


				"""simple docstring"""

				lowerCAmelCase__									=							"bilinear"
				lowerCAmelCase__									=							max_size
				lowerCAmelCase__									=							short_edge_length







	def __call__(   self     :    List[str] ,							__magic_name__     :    int							):


				"""simple docstring"""

				lowerCAmelCase__									=							[]
				for img in imgs:
							lowerCAmelCase__		,lowerCAmelCase__									=							img.shape[:2]
							# later: provide list and randomly choose index for resize
							lowerCAmelCase__									=							np.random.randint(self.short_edge_length[0] ,							self.short_edge_length[1] + 1							)
							if size == 0:
										return img
							lowerCAmelCase__									=							size * 1.0 / min(__magic_name__ ,							__magic_name__							)
							if h < w:
										lowerCAmelCase__		,lowerCAmelCase__									=							size, scale * w
							else:
										lowerCAmelCase__		,lowerCAmelCase__									=							scale * h, size
							if max(__magic_name__ ,							__magic_name__							) > self.max_size:
										lowerCAmelCase__									=							self.max_size * 1.0 / max(__magic_name__ ,							__magic_name__							)
										lowerCAmelCase__									=							newh * scale
										lowerCAmelCase__									=							neww * scale
							lowerCAmelCase__									=							int(neww + 0.5							)
							lowerCAmelCase__									=							int(newh + 0.5							)

							if img.dtype == np.uinta:
										lowerCAmelCase__									=							Image.fromarray(__magic_name__							)
										lowerCAmelCase__									=							pil_image.resize((neww, newh) ,							PILImageResampling.BILINEAR							)
										lowerCAmelCase__									=							np.asarray(__magic_name__							)
							else:
										lowerCAmelCase__									=							img.permute(2 ,							0 ,							1							).unsqueeze(0							)  # 3, 0, 1)  # hw(c) -> nchw
										lowerCAmelCase__									=							nn.functional.interpolate(
										    __magic_name__ ,							(newh, neww) ,							mode=self.interp_method ,							align_corners=__magic_name__							).squeeze(0							)
							img_augs.append(__magic_name__							)

				return img_augs






class 				A       :





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


				"""simple docstring"""

				lowerCAmelCase__									=							ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST] ,							cfg.INPUT.MAX_SIZE_TEST							)
				lowerCAmelCase__									=							cfg.INPUT.FORMAT
				lowerCAmelCase__									=							cfg.SIZE_DIVISIBILITY
				lowerCAmelCase__									=							cfg.PAD_VALUE
				lowerCAmelCase__									=							cfg.INPUT.MAX_SIZE_TEST
				lowerCAmelCase__									=							cfg.MODEL.DEVICE
				lowerCAmelCase__									=							torch.tensor(cfg.MODEL.PIXEL_STD							).to(self.device							).view(len(cfg.MODEL.PIXEL_STD							) ,							1 ,							1							)
				lowerCAmelCase__									=							torch.tensor(cfg.MODEL.PIXEL_MEAN							).to(self.device							).view(len(cfg.MODEL.PIXEL_STD							) ,							1 ,							1							)
				lowerCAmelCase__									=							lambda __magic_name__							: (x - self.pixel_mean) / self.pixel_std





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[Any] ,							__magic_name__     :    Optional[int]							):


				"""simple docstring"""

				lowerCAmelCase__									=							tuple(max(__magic_name__							) for s in zip(*[img.shape for img in images]							)							)
				lowerCAmelCase__									=							[im.shape[-2:] for im in images]
				lowerCAmelCase__									=							[
				    nn.functional.pad(
				        __magic_name__ ,							[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]] ,							value=self.pad_value ,							)
				    for size, im in zip(__magic_name__ ,							__magic_name__							)
				]

				return torch.stack(__magic_name__							), torch.tensor(__magic_name__							)







	def __call__(   self     :    List[str] ,							__magic_name__     :    int ,							__magic_name__     :    Optional[int]=False							):


				"""simple docstring"""

				with torch.no_grad():
							if not isinstance(__magic_name__ ,							__magic_name__							):
										lowerCAmelCase__									=							[images]
							if single_image:
										assert len(__magic_name__							) == 1
							for i in range(len(__magic_name__							)							):
										if isinstance(images[i] ,							torch.Tensor							):
													images.insert(__magic_name__ ,							images.pop(__magic_name__							).to(self.device							).float()							)
										elif not isinstance(images[i] ,							torch.Tensor							):
													images.insert(
													    __magic_name__ ,							torch.as_tensor(img_tensorize(images.pop(__magic_name__							) ,							input_format=self.input_format							)							)
													    .to(self.device							)
													    .float() ,							)
            # resize smallest edge
							lowerCAmelCase__									=							torch.tensor([im.shape[:2] for im in images]							)
							lowerCAmelCase__									=							self.aug(__magic_name__							)
							# transpose images and convert to torch tensors
							# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
							# now normalize before pad to avoid useless arithmetic
							lowerCAmelCase__									=							[self.normalizer(__magic_name__							) for x in images]
							# now pad them to do the following operations
							lowerCAmelCase__		,lowerCAmelCase__									=							self.pad(__magic_name__							)
							# Normalize

							if self.size_divisibility > 0:
										raise NotImplementedError()
							# pad
							lowerCAmelCase__									=							torch.true_divide(__magic_name__ ,							__magic_name__							)
							if single_image:
										return images[0], sizes[0], scales_yx[0]
							else:
										return images, sizes, scales_yx







def 					A       ( UpperCamelCase_   :       int  ,		UpperCamelCase_   :       Optional[Any] )   ->    List[str]:







			'''simple docstring'''



			boxes[:, 0::2] *= scale_yx[:, 1]
			boxes[:, 1::2] *= scale_yx[:, 0]
			return boxes







def 					A       ( UpperCamelCase_   :       Optional[Any]  ,		UpperCamelCase_   :       Tuple[int, int] )   ->    Tuple:







			'''simple docstring'''



			assert torch.isfinite(UpperCamelCase_ ).all(), "Box tensor contains infinite or NaN!"
			lowerCAmelCase__		,lowerCAmelCase__									=							box_size
			tensor[:, 0].clamp_(min=0  ,		max=UpperCamelCase_ )
			tensor[:, 1].clamp_(min=0  ,		max=UpperCamelCase_ )
			tensor[:, 2].clamp_(min=0  ,		max=UpperCamelCase_ )
			tensor[:, 3].clamp_(min=0  ,		max=UpperCamelCase_ )





