



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


_snake_case		=  logging.get_logger(__name__)



@dataclass
class   UpperCAmelCase_    :
							lowerCamelCase__   			=		field(metadata={'help': 'The name of the task to train on: ' + ', '.join(glue_processors.keys())})
							lowerCamelCase__   			=		field(
							    metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'})
							lowerCamelCase__   			=		field(
							    default=128   ,					metadata={
							        'help': (
							            'The maximum total input sequence length after tokenization. Sequences longer '
							            'than this will be truncated, sequences shorter will be padded.'
							        )
							    }   ,					)
							lowerCamelCase__   			=		field(
							    default=a   ,					metadata={'help': 'Overwrite the cached training and evaluation sets'})







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    self.task_name.lower()



class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		'train'
							lowerCamelCase__   			=		'dev'
							lowerCamelCase__   			=		'test'



class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		42
							lowerCamelCase__   			=		42
							lowerCamelCase__   			=		42







							def __init__(   self,			__a,			__a,			__a = None,			__a = Split.train,			__a = None,			):

														'''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",			__a,			)
														_lowerCAmelCase     :							List[Any]    =    args
														_lowerCAmelCase     :							List[str]    =    glue_processors[args.task_name]()
														_lowerCAmelCase     :							List[str]    =    glue_output_modes[args.task_name]
														if isinstance(__a,			__a):
																					try:
																												_lowerCAmelCase     :							List[Any]    =    Split[mode]
																					except KeyError:
																												raise KeyError("mode is not a valid split name")
        # Load data features from cache or dataset file
														_lowerCAmelCase     :							Dict    =    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}",			)
														_lowerCAmelCase     :							Optional[int]    =    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)
																					_lowerCAmelCase   ,    _lowerCAmelCase     :							Union[str, Any]    =    label_list[2], label_list[1]
														_lowerCAmelCase     :							Optional[int]    =    label_list

														# Make sure only the first process in distributed training processes the dataset,
														# and the others will use the cache.
														_lowerCAmelCase     :							Any    =    cached_features_file + ".lock"
														with FileLock(__a):
																					if os.path.exists(__a) and not args.overwrite_cache:
																												_lowerCAmelCase     :							int    =    time.time()
																												_lowerCAmelCase     :							int    =    torch.load(__a)
																												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:
																																			_lowerCAmelCase     :							str    =    self.processor.get_dev_examples(args.data_dir)
																												elif mode == Split.test:
																																			_lowerCAmelCase     :							Dict    =    self.processor.get_test_examples(args.data_dir)
																												else:
																																			_lowerCAmelCase     :							str    =    self.processor.get_train_examples(args.data_dir)
																												if limit_length is not None:
																																			_lowerCAmelCase     :							Optional[Any]    =    examples[:limit_length]
																												_lowerCAmelCase     :							Any    =    glue_convert_examples_to_features(
																												    __a,			__a,			max_length=args.max_seq_length,			label_list=__a,			output_mode=self.output_mode,			)
																												_lowerCAmelCase     :							Dict    =    time.time()
																												torch.save(self.features,			__a)
																												# ^ 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):

														'''simple docstring'''

														return len(self.features)







							def __getitem__(   self,			__a):

														'''simple docstring'''

														return self.features[i]







							def    snake_case__      (   self):

														'''simple docstring'''

														return self.label_list





import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / "utils"))

from test_module.custom_image_processing import CustomImageProcessor  # noqa E402


_snake_case		=  get_tests_dir("fixtures")



class   UpperCAmelCase_    (       unittest.TestCase):







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    mock.Mock()
														_lowerCAmelCase     :							int    =    500
														_lowerCAmelCase     :							Tuple    =    {}
														_lowerCAmelCase     :							str    =    HTTPError
														_lowerCAmelCase     :							Union[str, Any]    =    {}

														# Download this model to make sure it's in the cache.
														_lowerCAmelCase     :							Tuple    =    ViTImageProcessor.from_pretrained("hf-internal-testing/tiny-random-vit")
														# Under the mock environment we get a 500 error when trying to reach the model.
														with mock.patch("requests.Session.request",			return_value=__a) as mock_head:
																					_lowerCAmelCase     :							Optional[int]    =    ViTImageProcessor.from_pretrained("hf-internal-testing/tiny-random-vit")
																					# This check we did call the fake head request
																					mock_head.assert_called()







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Tuple    =    ViTImageProcessor.from_pretrained(
														    "https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json")







							def    snake_case__      (   self):

														'''simple docstring'''

														with self.assertRaises(__a):
																					# config is in subfolder, the following should not work without specifying the subfolder
																					_lowerCAmelCase     :							int    =    AutoImageProcessor.from_pretrained("hf-internal-testing/stable-diffusion-all-variants")

														_lowerCAmelCase     :							Optional[Any]    =    AutoImageProcessor.from_pretrained(
														    "hf-internal-testing/stable-diffusion-all-variants",			subfolder="feature_extractor")

														self.assertIsNotNone(__a)



@is_staging_test
class   UpperCAmelCase_    (       unittest.TestCase):







							@classmethod
							def    snake_case__      (   cls):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    TOKEN
														HfFolder.save_token(__a)







							@classmethod
							def    snake_case__      (   cls):

														'''simple docstring'''

														try:
																					delete_repo(token=cls._token,			repo_id="test-image-processor")
														except HTTPError:
																					pass

														try:
																					delete_repo(token=cls._token,			repo_id="valid_org/test-image-processor-org")
														except HTTPError:
																					pass

														try:
																					delete_repo(token=cls._token,			repo_id="test-dynamic-image-processor")
														except HTTPError:
																					pass







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[int]    =    ViTImageProcessor.from_pretrained(__a)
														image_processor.push_to_hub("test-image-processor",			use_auth_token=self._token)

														_lowerCAmelCase     :							str    =    ViTImageProcessor.from_pretrained(f"{USER}/test-image-processor")
														for k, v in image_processor.__dict__.items():
																					self.assertEqual(__a,			getattr(__a,			__a))

														# Reset repo
														delete_repo(token=self._token,			repo_id="test-image-processor")

														# Push to hub via save_pretrained
														with tempfile.TemporaryDirectory() as tmp_dir:
																					image_processor.save_pretrained(
																					    __a,			repo_id="test-image-processor",			push_to_hub=__a,			use_auth_token=self._token)

														_lowerCAmelCase     :							Optional[int]    =    ViTImageProcessor.from_pretrained(f"{USER}/test-image-processor")
														for k, v in image_processor.__dict__.items():
																					self.assertEqual(__a,			getattr(__a,			__a))







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Any    =    ViTImageProcessor.from_pretrained(__a)
														image_processor.push_to_hub("valid_org/test-image-processor",			use_auth_token=self._token)

														_lowerCAmelCase     :							Tuple    =    ViTImageProcessor.from_pretrained("valid_org/test-image-processor")
														for k, v in image_processor.__dict__.items():
																					self.assertEqual(__a,			getattr(__a,			__a))

														# Reset repo
														delete_repo(token=self._token,			repo_id="valid_org/test-image-processor")

														# Push to hub via save_pretrained
														with tempfile.TemporaryDirectory() as tmp_dir:
																					image_processor.save_pretrained(
																					    __a,			repo_id="valid_org/test-image-processor-org",			push_to_hub=__a,			use_auth_token=self._token)

														_lowerCAmelCase     :							Optional[int]    =    ViTImageProcessor.from_pretrained("valid_org/test-image-processor-org")
														for k, v in image_processor.__dict__.items():
																					self.assertEqual(__a,			getattr(__a,			__a))







							def    snake_case__      (   self):

														'''simple docstring'''

														CustomImageProcessor.register_for_auto_class()
														_lowerCAmelCase     :							List[str]    =    CustomImageProcessor.from_pretrained(__a)

														image_processor.push_to_hub("test-dynamic-image-processor",			use_auth_token=self._token)

														# This has added the proper auto_map field to the config
														self.assertDictEqual(
														    image_processor.auto_map,			{"AutoImageProcessor": "custom_image_processing.CustomImageProcessor"},			)

														_lowerCAmelCase     :							Tuple    =    AutoImageProcessor.from_pretrained(
														    f"{USER}/test-dynamic-image-processor",			trust_remote_code=__a)
														# Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
														self.assertEqual(new_image_processor.__class__.__name__,			"CustomImageProcessor")
