



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


a			:     Union[str, Any]     = logging.get_logger(__name__)







@dataclass
class 							_a							:
			A					=  42
			A					=  42
			A					=  None
			A					=  None

			def     __snake_case					(self				)					->      List[str]:
						return json.dumps(dataclasses.asdict(self				),      indent=2				) + "\n"







@dataclass(frozen=_lowerCAmelCase				)
class 							_a							:
			A					=  42
			A					=  None
			A					=  None
			A					=  None

			def     __snake_case					(self				)					->      Any:
						return json.dumps(dataclasses.asdict(self				)				) + "\n"







class 							_a							:

			def     __snake_case					(self,      SCREAMING_SNAKE_CASE_				)					->      int:
						raise NotImplementedError()

			def     __snake_case					(self,      SCREAMING_SNAKE_CASE_				)					->      Optional[int]:
						raise NotImplementedError()

			def     __snake_case					(self,      SCREAMING_SNAKE_CASE_				)					->      int:
						raise NotImplementedError()

			def     __snake_case					(self,      SCREAMING_SNAKE_CASE_				)					->      Any:
						raise NotImplementedError()

			def     __snake_case					(self				)					->      Optional[Any]:
						raise NotImplementedError()

			def     __snake_case					(self,      SCREAMING_SNAKE_CASE_				)					->      Optional[Any]:
						if len(self.get_labels()				) > 1:
									UpperCAmelCase_:		Any       			=    self.get_labels()[int(example.label				)]
						return example

			@classmethod
			def     __snake_case					(cls,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_=None				)					->      Any:
						with open(SCREAMING_SNAKE_CASE_,      """r""",      encoding="""utf-8-sig"""				) as f:
									return list(csv.reader(SCREAMING_SNAKE_CASE_,      delimiter="""\t""",      quotechar=SCREAMING_SNAKE_CASE_				)				)







class 							_a							(      _lowerCAmelCase				):

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

			def __len__(self				)					->      Optional[int]:
						return len(self.examples				)

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

			@classmethod
			def     __snake_case					(cls,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_="",      SCREAMING_SNAKE_CASE_=0,      SCREAMING_SNAKE_CASE_=1,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=False,      **SCREAMING_SNAKE_CASE_				)					->      Optional[Any]:
						UpperCAmelCase_:		List[Any]       			=    cls(**SCREAMING_SNAKE_CASE_				)
						processor.add_examples_from_csv(
						    SCREAMING_SNAKE_CASE_,      split_name=SCREAMING_SNAKE_CASE_,      column_label=SCREAMING_SNAKE_CASE_,      column_text=SCREAMING_SNAKE_CASE_,      column_id=SCREAMING_SNAKE_CASE_,      skip_first_row=SCREAMING_SNAKE_CASE_,      overwrite_labels=SCREAMING_SNAKE_CASE_,      overwrite_examples=SCREAMING_SNAKE_CASE_,      )
						return processor

			@classmethod
			def     __snake_case					(cls,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_=None,      **SCREAMING_SNAKE_CASE_				)					->      int:
						UpperCAmelCase_:		int       			=    cls(**SCREAMING_SNAKE_CASE_				)
						processor.add_examples(SCREAMING_SNAKE_CASE_,      labels=SCREAMING_SNAKE_CASE_				)
						return processor

			def     __snake_case					(self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_="",      SCREAMING_SNAKE_CASE_=0,      SCREAMING_SNAKE_CASE_=1,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=False,      SCREAMING_SNAKE_CASE_=False,      SCREAMING_SNAKE_CASE_=False,      )					->      Tuple:
						UpperCAmelCase_:		Tuple       			=    self._read_tsv(SCREAMING_SNAKE_CASE_				)
						if skip_first_row:
									UpperCAmelCase_:		List[str]       			=    lines[1:]
						UpperCAmelCase_:		Union[str, Any]       			=    []
						UpperCAmelCase_:		Union[str, Any]       			=    []
						UpperCAmelCase_:		Tuple       			=    []
						for i, line in enumerate(SCREAMING_SNAKE_CASE_				):
									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(SCREAMING_SNAKE_CASE_				)
												ids.append(SCREAMING_SNAKE_CASE_				)

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

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

						# Update examples
						if overwrite_examples:
									UpperCAmelCase_:		Optional[Any]       			=    examples
						else:
									self.examples.extend(SCREAMING_SNAKE_CASE_				)

						# Update labels
						if overwrite_labels:
									UpperCAmelCase_:		Optional[Any]       			=    list(SCREAMING_SNAKE_CASE_				)
						else:
									UpperCAmelCase_:		List[str]       			=    list(set(self.labels				).union(SCREAMING_SNAKE_CASE_				)				)

						return self.examples

			def     __snake_case					(self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=False,      SCREAMING_SNAKE_CASE_=0,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=None,      )					->      Optional[Any]:
						if max_length is None:
									UpperCAmelCase_:		List[Any]       			=    tokenizer.max_len

						UpperCAmelCase_:		Optional[Any]       			=    {label: i for i, label in enumerate(self.labels				)}

						UpperCAmelCase_:		List[Any]       			=    []
						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=SCREAMING_SNAKE_CASE_,      max_length=min(SCREAMING_SNAKE_CASE_,      tokenizer.max_len				),      )
									all_input_ids.append(SCREAMING_SNAKE_CASE_				)

						UpperCAmelCase_:		int       			=    max(len(SCREAMING_SNAKE_CASE_				) for input_ids in all_input_ids				)

						UpperCAmelCase_:		Optional[Any]       			=    []
						for ex_index, (input_ids, example) in enumerate(zip(SCREAMING_SNAKE_CASE_,      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_:		Union[str, Any]       			=    [1 if mask_padding_with_zero else 0] * len(SCREAMING_SNAKE_CASE_				)

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

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

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

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

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

									UpperCAmelCase_:		Union[str, Any]       			=    TensorDataset(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				)
									return dataset
						else:
									raise ValueError("""return_tensors should be one of 'tf' or 'pt'"""				)











import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import ViTImageProcessor







class 							_a							(      unittest.TestCase				):

			def __init__(self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_=7,      SCREAMING_SNAKE_CASE_=3,      SCREAMING_SNAKE_CASE_=18,      SCREAMING_SNAKE_CASE_=30,      SCREAMING_SNAKE_CASE_=400,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5],      SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5],      )					->      Union[str, Any]:
						UpperCAmelCase_:		Any       			=    size if size is not None else {"""height""": 18, """width""": 18}
						UpperCAmelCase_:		str       			=    parent
						UpperCAmelCase_:		int       			=    batch_size
						UpperCAmelCase_:		Tuple       			=    num_channels
						UpperCAmelCase_:		Optional[int]       			=    image_size
						UpperCAmelCase_:		Optional[int]       			=    min_resolution
						UpperCAmelCase_:		Optional[int]       			=    max_resolution
						UpperCAmelCase_:		Optional[Any]       			=    do_resize
						UpperCAmelCase_:		Optional[int]       			=    size
						UpperCAmelCase_:		int       			=    do_normalize
						UpperCAmelCase_:		List[str]       			=    image_mean
						UpperCAmelCase_:		Tuple       			=    image_std

			def     __snake_case					(self				)					->      Tuple:
						return {
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_normalize": self.do_normalize,
						    "do_resize": self.do_resize,
						    "size": self.size,
						}







@require_torch
@require_vision
class 							_a							(      _lowerCAmelCase   ,		unittest.TestCase				):
			A					=  ViTImageProcessor if is_vision_available() else None

			def     __snake_case					(self				)					->      Optional[int]:
						UpperCAmelCase_:		str       			=    ViTImageProcessingTester(self				)

			@property
			def     __snake_case					(self				)					->      List[str]:
						return self.image_processor_tester.prepare_image_processor_dict()

			def     __snake_case					(self				)					->      int:
						UpperCAmelCase_:		Any       			=    self.image_processing_class(**self.image_processor_dict				)
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      """image_mean"""				)				)
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      """image_std"""				)				)
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      """do_normalize"""				)				)
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      """do_resize"""				)				)
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      """size"""				)				)

			def     __snake_case					(self				)					->      Union[str, Any]:
						UpperCAmelCase_:		List[str]       			=    self.image_processing_class.from_dict(self.image_processor_dict				)
						self.assertEqual(image_processor.size,      {"""height""": 18, """width""": 18}				)

						UpperCAmelCase_:		Union[str, Any]       			=    self.image_processing_class.from_dict(self.image_processor_dict,      size=42				)
						self.assertEqual(image_processor.size,      {"""height""": 42, """width""": 42}				)

			def     __snake_case					(self				)					->      Optional[Any]:
						pass

			def     __snake_case					(self				)					->      List[str]:
						# Initialize image_processing
						UpperCAmelCase_:		Any       			=    self.image_processing_class(**self.image_processor_dict				)
						# create random PIL images
						UpperCAmelCase_:		List[Any]       			=    prepare_image_inputs(self.image_processor_tester,      equal_resolution=SCREAMING_SNAKE_CASE_				)
						for image in image_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE_,      Image.Image				)

						# Test not batched input
						UpperCAmelCase_:		List[str]       			=    image_processing(image_inputs[0],      return_tensors="""pt"""				).pixel_values
						self.assertEqual(
						    encoded_images.shape,      (
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.size["""height"""],
						        self.image_processor_tester.size["""width"""],
						    ),      )

						# Test batched
						UpperCAmelCase_:		str       			=    image_processing(SCREAMING_SNAKE_CASE_,      return_tensors="""pt"""				).pixel_values
						self.assertEqual(
						    encoded_images.shape,      (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.size["""height"""],
						        self.image_processor_tester.size["""width"""],
						    ),      )

			def     __snake_case					(self				)					->      Dict:
						# Initialize image_processing
						UpperCAmelCase_:		List[Any]       			=    self.image_processing_class(**self.image_processor_dict				)
						# create random numpy tensors
						UpperCAmelCase_:		int       			=    prepare_image_inputs(self.image_processor_tester,      equal_resolution=SCREAMING_SNAKE_CASE_,      numpify=SCREAMING_SNAKE_CASE_				)
						for image in image_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE_,      np.ndarray				)

						# Test not batched input
						UpperCAmelCase_:		Dict       			=    image_processing(image_inputs[0],      return_tensors="""pt"""				).pixel_values
						self.assertEqual(
						    encoded_images.shape,      (
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.size["""height"""],
						        self.image_processor_tester.size["""width"""],
						    ),      )

						# Test batched
						UpperCAmelCase_:		Optional[Any]       			=    image_processing(SCREAMING_SNAKE_CASE_,      return_tensors="""pt"""				).pixel_values
						self.assertEqual(
						    encoded_images.shape,      (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.size["""height"""],
						        self.image_processor_tester.size["""width"""],
						    ),      )

			def     __snake_case					(self				)					->      Tuple:
						# Initialize image_processing
						UpperCAmelCase_:		List[Any]       			=    self.image_processing_class(**self.image_processor_dict				)
						# create random PyTorch tensors
						UpperCAmelCase_:		Any       			=    prepare_image_inputs(self.image_processor_tester,      equal_resolution=SCREAMING_SNAKE_CASE_,      torchify=SCREAMING_SNAKE_CASE_				)
						for image in image_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE_,      torch.Tensor				)

						# Test not batched input
						UpperCAmelCase_:		Dict       			=    image_processing(image_inputs[0],      return_tensors="""pt"""				).pixel_values
						self.assertEqual(
						    encoded_images.shape,      (
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.size["""height"""],
						        self.image_processor_tester.size["""width"""],
						    ),      )

						# Test batched
						UpperCAmelCase_:		str       			=    image_processing(SCREAMING_SNAKE_CASE_,      return_tensors="""pt"""				).pixel_values
						self.assertEqual(
						    encoded_images.shape,      (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.size["""height"""],
						        self.image_processor_tester.size["""width"""],
						    ),      )






