






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_ :						Optional[Any]      =      logging.get_logger(__name__)

@dataclass
class     _SCREAMING_SNAKE_CASE       :
	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=_a		,   metadata={"""help""": """Overwrite the cached training and evaluation sets"""}      )





	def 						_A (		self				:					Dict	):
		UpperCamelCase     :List[Any]          =	self.task_name.lower()

class     _SCREAMING_SNAKE_CASE       (			_a      ):
	snake_case__							:     Optional[int]     	=  """train"""
	snake_case__							:     Union[str, Any]     	=  """dev"""
	snake_case__							:     Union[str, Any]     	=  """test"""

class     _SCREAMING_SNAKE_CASE       (			_a      ):
	snake_case__							:     GlueDataTrainingArguments
	snake_case__							:     str
	snake_case__							:     List[InputFeatures]





	def __init__(		self				:					List[Any] ,  __lowerCamelCase				:					GlueDataTrainingArguments ,  __lowerCamelCase				:					PreTrainedTokenizerBase ,  __lowerCamelCase				:					Optional[int] = None ,  __lowerCamelCase				:					Union[str, Split] = Split.train ,  __lowerCamelCase				:					Optional[str] = None ,  ):
		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 ,  )
		UpperCamelCase     :Tuple          =	args
		UpperCamelCase     :Dict          =	glue_processors[args.task_name]()
		UpperCamelCase     :Any          =	glue_output_modes[args.task_name]
		if isinstance(__lowerCamelCase ,  __lowerCamelCase	):
			try:
				UpperCamelCase     :Any          =	Split[mode]
			except KeyError:
				raise KeyError("""mode is not a valid split name"""	)
        # Load data features from cache or dataset file
		UpperCamelCase     :Optional[int]          =	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}""" ,  )
		UpperCamelCase     :Union[str, Any]          =	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)
			UpperCamelCase     ,      UpperCamelCase     :Dict          =	label_list[2], label_list[1]
		UpperCamelCase     :Tuple          =	label_list

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





	def __getitem__(		self				:					Optional[int] ,  __lowerCamelCase				:					Any	):
		return self.features[i]





	def 						_A (		self				:					int	):
		return self.label_list



"""simple docstring"""





import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 DetaImageProcessor







class 				SCREAMING_SNAKE_CASE					(		unittest.TestCase       ):






					"""simple docstring"""



					def __init__(       self			:				List[str]   ,lowercase_			:				Dict   ,lowercase_			:				Dict=7   ,lowercase_			:				Optional[int]=3   ,lowercase_			:				int=3_0   ,lowercase_			:				Optional[Any]=4_0_0   ,lowercase_			:				Any=True   ,lowercase_			:				List[str]=None   ,lowercase_			:				str=True   ,lowercase_			:				List[Any]=[0.5, 0.5, 0.5]   ,lowercase_			:				List[str]=[0.5, 0.5, 0.5]   ,lowercase_			:				Any=True   ,lowercase_			:				Union[str, Any]=1 / 2_5_5   ,lowercase_			:				str=True   ,):
						# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
						lowerCAmelCase__	:						str  				=	size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
						lowerCAmelCase__	:						Any  				=	parent
						lowerCAmelCase__	:						Tuple  				=	batch_size
						lowerCAmelCase__	:						List[str]  				=	num_channels
						lowerCAmelCase__	:						Optional[Any]  				=	min_resolution
						lowerCAmelCase__	:						Union[str, Any]  				=	max_resolution
						lowerCAmelCase__	:						Optional[int]  				=	do_resize
						lowerCAmelCase__	:						str  				=	size
						lowerCAmelCase__	:						Union[str, Any]  				=	do_normalize
						lowerCAmelCase__	:						List[str]  				=	image_mean
						lowerCAmelCase__	:						str  				=	image_std
						lowerCAmelCase__	:						Optional[Any]  				=	do_rescale
						lowerCAmelCase__	:						Union[str, Any]  				=	rescale_factor
						lowerCAmelCase__	:						Optional[Any]  				=	do_pad







					def        __lowerCAmelCase (       self			:				Optional[Any]		):
						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_normalize": self.do_normalize,
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_rescale": self.do_rescale,
						    "rescale_factor": self.rescale_factor,
						    "do_pad": self.do_pad,
						}







					def        __lowerCAmelCase (       self			:				List[str]   ,lowercase_			:				List[Any]   ,lowercase_			:				int=False		):
						if not batched:
							lowerCAmelCase__	:						Tuple  				=	image_inputs[0]
							if isinstance(lowercase_   ,Image.Image		):
								lowerCAmelCase__				,lowerCAmelCase__	:						List[str]  				=	image.size
							else:
								lowerCAmelCase__				,lowerCAmelCase__	:						Optional[int]  				=	image.shape[1], image.shape[2]
							if w < h:
								lowerCAmelCase__	:						Any  				=	int(self.size['''shortest_edge'''] * h / w		)
								lowerCAmelCase__	:						str  				=	self.size['''shortest_edge''']
							elif w > h:
								lowerCAmelCase__	:						Union[str, Any]  				=	self.size['''shortest_edge''']
								lowerCAmelCase__	:						Dict  				=	int(self.size['''shortest_edge'''] * w / h		)
							else:
								lowerCAmelCase__	:						List[str]  				=	self.size['''shortest_edge''']
								lowerCAmelCase__	:						str  				=	self.size['''shortest_edge''']

						else:
							lowerCAmelCase__	:						Optional[Any]  				=	[]
							for image in image_inputs:
								lowerCAmelCase__				,lowerCAmelCase__	:						Any  				=	self.get_expected_values([image]		)
								expected_values.append((expected_height, expected_width)		)
							lowerCAmelCase__	:						List[str]  				=	max(lowercase_   ,key=lambda lowercase_		: item[0]		)[0]
							lowerCAmelCase__	:						Any  				=	max(lowercase_   ,key=lambda lowercase_		: item[1]		)[1]

						return expected_height, expected_width







@require_torch
@require_vision
class 				SCREAMING_SNAKE_CASE					(		a_    ,							unittest.TestCase       ):






					"""simple docstring"""



					lowercase__				 =       DetaImageProcessor if is_vision_available() else None







					def        __lowerCAmelCase (       self			:				Any		):
						lowerCAmelCase__	:						Optional[Any]  				=	DetaImageProcessingTester(self		)







					@property
					def        __lowerCAmelCase (       self			:				Any		):
						return self.image_processor_tester.prepare_image_processor_dict()







					def        __lowerCAmelCase (       self			:				List[str]		):
						lowerCAmelCase__	:						str  				=	self.image_processing_class(**self.image_processor_dict		)
						self.assertTrue(hasattr(lowercase_   ,'''image_mean'''		)		)
						self.assertTrue(hasattr(lowercase_   ,'''image_std'''		)		)
						self.assertTrue(hasattr(lowercase_   ,'''do_normalize'''		)		)
						self.assertTrue(hasattr(lowercase_   ,'''do_resize'''		)		)
						self.assertTrue(hasattr(lowercase_   ,'''do_rescale'''		)		)
						self.assertTrue(hasattr(lowercase_   ,'''do_pad'''		)		)
						self.assertTrue(hasattr(lowercase_   ,'''size'''		)		)







					def        __lowerCAmelCase (       self			:				Dict		):
						lowerCAmelCase__	:						Dict  				=	self.image_processing_class.from_dict(self.image_processor_dict		)
						self.assertEqual(image_processor.size   ,{'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}		)
						self.assertEqual(image_processor.do_pad   ,lowercase_		)







					def        __lowerCAmelCase (       self			:				List[str]		):
						pass







					def        __lowerCAmelCase (       self			:				Union[str, Any]		):
						# Initialize image_processing
						lowerCAmelCase__	:						str  				=	self.image_processing_class(**self.image_processor_dict		)
						# create random PIL images
						lowerCAmelCase__	:						Union[str, Any]  				=	prepare_image_inputs(self.image_processor_tester   ,equal_resolution=lowercase_		)
						for image in image_inputs:
							self.assertIsInstance(lowercase_   ,Image.Image		)

						# Test not batched input
						lowerCAmelCase__	:						List[Any]  				=	image_processing(image_inputs[0]   ,return_tensors='''pt'''		).pixel_values

						lowerCAmelCase__				,lowerCAmelCase__	:						Tuple  				=	self.image_processor_tester.get_expected_values(lowercase_		)

						self.assertEqual(
						    encoded_images.shape   ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,)

						# Test batched
						lowerCAmelCase__				,lowerCAmelCase__	:						Optional[int]  				=	self.image_processor_tester.get_expected_values(lowercase_   ,batched=lowercase_		)

						lowerCAmelCase__	:						Optional[int]  				=	image_processing(lowercase_   ,return_tensors='''pt'''		).pixel_values
						self.assertEqual(
						    encoded_images.shape   ,(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )   ,)







					def        __lowerCAmelCase (       self			:				Dict		):
						# Initialize image_processing
						lowerCAmelCase__	:						Optional[Any]  				=	self.image_processing_class(**self.image_processor_dict		)
						# create random numpy tensors
						lowerCAmelCase__	:						Tuple  				=	prepare_image_inputs(self.image_processor_tester   ,equal_resolution=lowercase_   ,numpify=lowercase_		)
						for image in image_inputs:
							self.assertIsInstance(lowercase_   ,np.ndarray		)

						# Test not batched input
						lowerCAmelCase__	:						List[str]  				=	image_processing(image_inputs[0]   ,return_tensors='''pt'''		).pixel_values

						lowerCAmelCase__				,lowerCAmelCase__	:						Any  				=	self.image_processor_tester.get_expected_values(lowercase_		)

						self.assertEqual(
						    encoded_images.shape   ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,)

						# Test batched
						lowerCAmelCase__	:						str  				=	image_processing(lowercase_   ,return_tensors='''pt'''		).pixel_values

						lowerCAmelCase__				,lowerCAmelCase__	:						Optional[Any]  				=	self.image_processor_tester.get_expected_values(lowercase_   ,batched=lowercase_		)

						self.assertEqual(
						    encoded_images.shape   ,(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )   ,)







					def        __lowerCAmelCase (       self			:				Union[str, Any]		):
						# Initialize image_processing
						lowerCAmelCase__	:						Tuple  				=	self.image_processing_class(**self.image_processor_dict		)
						# create random PyTorch tensors
						lowerCAmelCase__	:						Any  				=	prepare_image_inputs(self.image_processor_tester   ,equal_resolution=lowercase_   ,torchify=lowercase_		)
						for image in image_inputs:
							self.assertIsInstance(lowercase_   ,torch.Tensor		)

						# Test not batched input
						lowerCAmelCase__	:						List[Any]  				=	image_processing(image_inputs[0]   ,return_tensors='''pt'''		).pixel_values

						lowerCAmelCase__				,lowerCAmelCase__	:						Any  				=	self.image_processor_tester.get_expected_values(lowercase_		)

						self.assertEqual(
						    encoded_images.shape   ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,)

						# Test batched
						lowerCAmelCase__	:						str  				=	image_processing(lowercase_   ,return_tensors='''pt'''		).pixel_values

						lowerCAmelCase__				,lowerCAmelCase__	:						List[Any]  				=	self.image_processor_tester.get_expected_values(lowercase_   ,batched=lowercase_		)

						self.assertEqual(
						    encoded_images.shape   ,(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )   ,)







					@slow
					def        __lowerCAmelCase (       self			:				Tuple		):
						# prepare image and target
						lowerCAmelCase__	:						Tuple  				=	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
						with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''   ,'''r'''		) as f:
							lowerCAmelCase__	:						Union[str, Any]  				=	json.loads(f.read()		)

						lowerCAmelCase__	:						str  				=	{'''image_id''': 3_9_7_6_9, '''annotations''': target}

						# encode them
						lowerCAmelCase__	:						Optional[Any]  				=	DetaImageProcessor()
						lowerCAmelCase__	:						Optional[int]  				=	image_processing(images=lowercase_   ,annotations=lowercase_   ,return_tensors='''pt'''		)

						# verify pixel values
						lowerCAmelCase__	:						Dict  				=	torch.Size([1, 3, 8_0_0, 1_0_6_6]		)
						self.assertEqual(encoding['''pixel_values'''].shape   ,lowercase_		)

						lowerCAmelCase__	:						Any  				=	torch.tensor([0.2796, 0.3138, 0.3481]		)
						self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]   ,lowercase_   ,atol=1E-4		)		)

						# verify area
						lowerCAmelCase__	:						Tuple  				=	torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']   ,lowercase_		)		)
						# verify boxes
						lowerCAmelCase__	:						List[str]  				=	torch.Size([6, 4]		)
						self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape   ,lowercase_		)
						lowerCAmelCase__	:						Tuple  				=	torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]   ,lowercase_   ,atol=1E-3		)		)
						# verify image_id
						lowerCAmelCase__	:						Optional[int]  				=	torch.tensor([3_9_7_6_9]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']   ,lowercase_		)		)
						# verify is_crowd
						lowerCAmelCase__	:						List[str]  				=	torch.tensor([0, 0, 0, 0, 0, 0]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']   ,lowercase_		)		)
						# verify class_labels
						lowerCAmelCase__	:						Any  				=	torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']   ,lowercase_		)		)
						# verify orig_size
						lowerCAmelCase__	:						int  				=	torch.tensor([4_8_0, 6_4_0]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']   ,lowercase_		)		)
						# verify size
						lowerCAmelCase__	:						str  				=	torch.tensor([8_0_0, 1_0_6_6]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']   ,lowercase_		)		)







					@slow
					def        __lowerCAmelCase (       self			:				Any		):
						# prepare image, target and masks_path
						lowerCAmelCase__	:						Dict  				=	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
						with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''   ,'''r'''		) as f:
							lowerCAmelCase__	:						str  				=	json.loads(f.read()		)

						lowerCAmelCase__	:						Tuple  				=	{'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}

						lowerCAmelCase__	:						Optional[Any]  				=	pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''		)

						# encode them
						lowerCAmelCase__	:						str  				=	DetaImageProcessor(format='''coco_panoptic'''		)
						lowerCAmelCase__	:						Optional[int]  				=	image_processing(images=lowercase_   ,annotations=lowercase_   ,masks_path=lowercase_   ,return_tensors='''pt'''		)

						# verify pixel values
						lowerCAmelCase__	:						Any  				=	torch.Size([1, 3, 8_0_0, 1_0_6_6]		)
						self.assertEqual(encoding['''pixel_values'''].shape   ,lowercase_		)

						lowerCAmelCase__	:						int  				=	torch.tensor([0.2796, 0.3138, 0.3481]		)
						self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]   ,lowercase_   ,atol=1E-4		)		)

						# verify area
						lowerCAmelCase__	:						Tuple  				=	torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']   ,lowercase_		)		)
						# verify boxes
						lowerCAmelCase__	:						Optional[Any]  				=	torch.Size([6, 4]		)
						self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape   ,lowercase_		)
						lowerCAmelCase__	:						str  				=	torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]   ,lowercase_   ,atol=1E-3		)		)
						# verify image_id
						lowerCAmelCase__	:						Union[str, Any]  				=	torch.tensor([3_9_7_6_9]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']   ,lowercase_		)		)
						# verify is_crowd
						lowerCAmelCase__	:						Union[str, Any]  				=	torch.tensor([0, 0, 0, 0, 0, 0]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']   ,lowercase_		)		)
						# verify class_labels
						lowerCAmelCase__	:						List[str]  				=	torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']   ,lowercase_		)		)
						# verify masks
						lowerCAmelCase__	:						Optional[int]  				=	8_2_2_8_7_3
						self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()   ,lowercase_		)
						# verify orig_size
						lowerCAmelCase__	:						List[Any]  				=	torch.tensor([4_8_0, 6_4_0]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']   ,lowercase_		)		)
						# verify size
						lowerCAmelCase__	:						Optional[Any]  				=	torch.tensor([8_0_0, 1_0_6_6]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']   ,lowercase_		)		)




