






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 GLPNImageProcessor


class        __UpperCamelCase    (     unittest.TestCase   ):







	def __init__(  self      :							str       ,				_lowerCAmelCase      :							Dict       ,				_lowerCAmelCase      :							str=7       ,				_lowerCAmelCase      :							str=3       ,				_lowerCAmelCase      :							List[str]=18       ,				_lowerCAmelCase      :							Tuple=30       ,				_lowerCAmelCase      :							Optional[Any]=400       ,				_lowerCAmelCase      :							str=True       ,				_lowerCAmelCase      :							Union[str, Any]=32       ,				_lowerCAmelCase      :							List[Any]=True       ,				)		-> Dict:
		"""simple docstring"""







		__lowercase			       =  parent
		__lowercase			       =  batch_size
		__lowercase			       =  num_channels
		__lowercase			       =  image_size
		__lowercase			       =  min_resolution
		__lowercase			       =  max_resolution
		__lowercase			       =  do_resize
		__lowercase			       =  size_divisor
		__lowercase			       =  do_rescale







	def  _a   (  self      :							Optional[int]			)		-> Tuple:
		"""simple docstring"""







		return {
		    "do_resize": self.do_resize,
		    "size_divisor": self.size_divisor,
		    "do_rescale": self.do_rescale,
		}


@require_torch
@require_vision
class        __UpperCamelCase    (     __UpperCAmelCase   ,			unittest.TestCase   ):
	__snake_case	:Dict   =       GLPNImageProcessor if is_vision_available() else None







	def  _a   (  self      :							List[Any]			)		-> str:
		"""simple docstring"""







		__lowercase			       =  GLPNImageProcessingTester(self			)







	@property
	def  _a   (  self      :							List[str]			)		-> int:
		"""simple docstring"""







		return self.image_processor_tester.prepare_image_processor_dict()







	def  _a   (  self      :							Tuple			)		-> str:
		"""simple docstring"""







		__lowercase			       =  self.image_processing_class(**self.image_processor_dict			)
		self.assertTrue(hasattr(UpperCAmelCase_       ,				"""do_resize"""			)			)
		self.assertTrue(hasattr(UpperCAmelCase_       ,				"""size_divisor"""			)			)
		self.assertTrue(hasattr(UpperCAmelCase_       ,				"""resample"""			)			)
		self.assertTrue(hasattr(UpperCAmelCase_       ,				"""do_rescale"""			)			)







	def  _a   (  self      :							List[Any]			)		-> int:
		"""simple docstring"""







		pass







	def  _a   (  self      :							List[str]			)		-> Optional[int]:
		"""simple docstring"""







		__lowercase			       =  self.image_processing_class(**self.image_processor_dict			)
		# create random PIL images
		__lowercase			       =  prepare_image_inputs(self.image_processor_tester       ,				equal_resolution=UpperCAmelCase_			)
		for image in image_inputs:
			self.assertIsInstance(UpperCAmelCase_       ,				Image.Image			)

		# Test not batched input (GLPNImageProcessor doesn't support batching)
		__lowercase			       =  image_processing(image_inputs[0]       ,				return_tensors="""pt"""			).pixel_values
		self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0			)
		self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0			)







	def  _a   (  self      :							str			)		-> str:
		"""simple docstring"""







		__lowercase			       =  self.image_processing_class(**self.image_processor_dict			)
		# create random numpy tensors
		__lowercase			       =  prepare_image_inputs(self.image_processor_tester       ,				equal_resolution=UpperCAmelCase_       ,				numpify=UpperCAmelCase_			)
		for image in image_inputs:
			self.assertIsInstance(UpperCAmelCase_       ,				np.ndarray			)

		# Test not batched input (GLPNImageProcessor doesn't support batching)
		__lowercase			       =  image_processing(image_inputs[0]       ,				return_tensors="""pt"""			).pixel_values
		self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0			)
		self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0			)







	def  _a   (  self      :							List[str]			)		-> Optional[Any]:
		"""simple docstring"""







		__lowercase			       =  self.image_processing_class(**self.image_processor_dict			)
		# create random PyTorch tensors
		__lowercase			       =  prepare_image_inputs(self.image_processor_tester       ,				equal_resolution=UpperCAmelCase_       ,				torchify=UpperCAmelCase_			)
		for image in image_inputs:
			self.assertIsInstance(UpperCAmelCase_       ,				torch.Tensor			)

		# Test not batched input (GLPNImageProcessor doesn't support batching)
		__lowercase			       =  image_processing(image_inputs[0]       ,				return_tensors="""pt"""			).pixel_values
		self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0			)
		self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0			)









import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


__UpperCamelCase      :      Any     						=						logging.get_logger(__name__)


@dataclass
class        __UpperCamelCase    :
	__snake_case	:str   =       field(metadata={'help': 'The name of the task to train on: ' + ', '.join(glue_processors.keys()   )}   )
	__snake_case	:str   =       field(
	    metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}   )
	__snake_case	:int   =       field(
	    default=1_2_8   ,			metadata={
	        'help': (
	            'The maximum total input sequence length after tokenization. Sequences longer '
	            'than this will be truncated, sequences shorter will be padded.'
	        )
	    }   ,			)
	__snake_case	:bool   =       field(
	    default=_lowerCAmelCase   ,			metadata={'help': 'Overwrite the cached training and evaluation sets'}   )







	def  _a   (  self      :							Dict			)		-> List[Any]:
		"""simple docstring"""







		__lowercase             =  self.task_name.lower()


class        __UpperCamelCase    (     _lowerCAmelCase   ):
	__snake_case	:Optional[int]   =       'train'
	__snake_case	:int   =       'dev'
	__snake_case	:Any   =       'test'


class        __UpperCamelCase    (     _lowerCAmelCase   ):
	__snake_case	:GlueDataTrainingArguments
	__snake_case	:str
	__snake_case	:List[InputFeatures]







	def __init__(  self      :							Dict       ,				_lowerCAmelCase      :							GlueDataTrainingArguments       ,				_lowerCAmelCase      :							PreTrainedTokenizerBase       ,				_lowerCAmelCase      :							Optional[int] = None       ,				_lowerCAmelCase      :							Union[str, Split] = Split.train       ,				_lowerCAmelCase      :							Optional[str] = None       ,				)		-> List[Any]:
		"""simple docstring"""







		warnings.warn(
		    """This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets """
		    """library. You can have a look at this example script for pointers: """
		    """https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"""       ,				_lowerCAmelCase       ,				)
		__lowercase             =  args
		__lowercase             =  glue_processors[args.task_name]()
		__lowercase             =  glue_output_modes[args.task_name]
		if isinstance(_lowerCAmelCase       ,				_lowerCAmelCase			):
			try:
				__lowercase             =  Split[mode]
			except KeyError:
				raise KeyError("""mode is not a valid split name"""			)
        # Load data features from cache or dataset file
		__lowercase             =  os.path.join(
		    cache_dir if cache_dir is not None else args.data_dir       ,				F'cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}'       ,				)
		__lowercase             =  self.processor.get_labels()
		if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
		    "RobertaTokenizer",
		    "RobertaTokenizerFast",
		    "XLMRobertaTokenizer",
		    "BartTokenizer",
		    "BartTokenizerFast",
		):
			# HACK(label indices are swapped in RoBERTa pretrained model)
			__lowercase       ,						__lowercase             =  label_list[2], label_list[1]
		__lowercase             =  label_list

		# Make sure only the first process in distributed training processes the dataset,
		# and the others will use the cache.
		__lowercase             =  cached_features_file + """.lock"""
		with FileLock(_lowerCAmelCase			):
			if os.path.exists(_lowerCAmelCase			) and not args.overwrite_cache:
				__lowercase             =  time.time()
				__lowercase             =  torch.load(_lowerCAmelCase			)
				logger.info(
				    F'Loading features from cached file {cached_features_file} [took %.3f s]'       ,				time.time() - start			)
			else:
				logger.info(F'Creating features from dataset file at {args.data_dir}'			)

				if mode == Split.dev:
					__lowercase             =  self.processor.get_dev_examples(args.data_dir			)
				elif mode == Split.test:
					__lowercase             =  self.processor.get_test_examples(args.data_dir			)
				else:
					__lowercase             =  self.processor.get_train_examples(args.data_dir			)
				if limit_length is not None:
					__lowercase             =  examples[:limit_length]
				__lowercase             =  glue_convert_examples_to_features(
				    _lowerCAmelCase       ,				_lowerCAmelCase       ,				max_length=args.max_seq_length       ,				label_list=_lowerCAmelCase       ,				output_mode=self.output_mode       ,				)
				__lowercase             =  time.time()
				torch.save(self.features       ,				_lowerCAmelCase			)
				# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
				logger.info(
				    F'Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]'			)







	def __len__(  self      :							Dict			)		-> Optional[int]:
		"""simple docstring"""







		return len(self.features			)







	def __getitem__(  self      :							Tuple       ,				_lowerCAmelCase      :							Optional[int]			)		-> InputFeatures:
		"""simple docstring"""







		return self.features[i]







	def  _a   (  self      :							str			)		-> int:
		"""simple docstring"""







		return self.label_list

