
"""simple docstring"""







import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
							from PIL import Image

							from transformers import CLIPSegProcessor, ViTImageProcessor




@require_vision
class 			A__	(     unittest.TestCase):

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Dict						      =				tempfile.mkdtemp()

			# fmt: off
			__lowerCAmelCase      :						int						      =				['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
			# fmt: on
			__lowerCAmelCase      :						str						      =				dict(zip(_SCREAMING_SNAKE_CASE	,		range(len(_SCREAMING_SNAKE_CASE			)			)			)			)
			__lowerCAmelCase      :						Optional[int]						      =				['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
			__lowerCAmelCase      :						Any						      =				{'unk_token': '<unk>'}

			__lowerCAmelCase      :						Union[str, Any]						      =				os.path.join(self.tmpdirname	,		VOCAB_FILES_NAMES['vocab_file']			)
			__lowerCAmelCase      :						str						      =				os.path.join(self.tmpdirname	,		VOCAB_FILES_NAMES['merges_file']			)
			with open(self.vocab_file	,		'w'	,		encoding='utf-8'			) as fp:
				fp.write(json.dumps(_SCREAMING_SNAKE_CASE			) + '\n'			)
			with open(self.merges_file	,		'w'	,		encoding='utf-8'			) as fp:
				fp.write('\n'.join(_SCREAMING_SNAKE_CASE			)			)

			__lowerCAmelCase      :						int						      =				{
			    'do_resize': True,
			    'size': 20,
			    'do_center_crop': True,
			    'crop_size': 18,
			    'do_normalize': True,
			    'image_mean': [0.4814_5466, 0.457_8275, 0.4082_1073],
			    'image_std': [0.2686_2954, 0.2613_0258, 0.2757_7711],
			}
			__lowerCAmelCase      :						List[str]						      =				os.path.join(self.tmpdirname	,		_SCREAMING_SNAKE_CASE			)
			with open(self.image_processor_file	,		'w'	,		encoding='utf-8'			) as fp:
				json.dump(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self	,		**_SCREAMING_SNAKE_CASE			):
			return CLIPTokenizer.from_pretrained(self.tmpdirname	,		**_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self	,		**_SCREAMING_SNAKE_CASE			):
			return CLIPTokenizerFast.from_pretrained(self.tmpdirname	,		**_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self	,		**_SCREAMING_SNAKE_CASE			):
			return ViTImageProcessor.from_pretrained(self.tmpdirname	,		**_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self			):
			shutil.rmtree(self.tmpdirname			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Tuple						      =				[np.random.randint(2_55	,		size=(3, 30, 4_00)	,		dtype=np.uinta			)]

			__lowerCAmelCase      :						Optional[int]						      =				[Image.fromarray(np.moveaxis(_SCREAMING_SNAKE_CASE	,		0	,		-1			)			) for x in image_inputs]

			return image_inputs

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						List[str]						      =				self.get_tokenizer()
			__lowerCAmelCase      :						int						      =				self.get_rust_tokenizer()
			__lowerCAmelCase      :						int						      =				self.get_image_processor()

			__lowerCAmelCase      :						int						      =				CLIPSegProcessor(tokenizer=_SCREAMING_SNAKE_CASE	,		image_processor=_SCREAMING_SNAKE_CASE			)
			processor_slow.save_pretrained(self.tmpdirname			)
			__lowerCAmelCase      :						Any						      =				CLIPSegProcessor.from_pretrained(self.tmpdirname	,		use_fast=_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						str						      =				CLIPSegProcessor(tokenizer=_SCREAMING_SNAKE_CASE	,		image_processor=_SCREAMING_SNAKE_CASE			)
			processor_fast.save_pretrained(self.tmpdirname			)
			__lowerCAmelCase      :						List[str]						      =				CLIPSegProcessor.from_pretrained(self.tmpdirname			)

			self.assertEqual(processor_slow.tokenizer.get_vocab()	,		tokenizer_slow.get_vocab()			)
			self.assertEqual(processor_fast.tokenizer.get_vocab()	,		tokenizer_fast.get_vocab()			)
			self.assertEqual(tokenizer_slow.get_vocab()	,		tokenizer_fast.get_vocab()			)
			self.assertIsInstance(processor_slow.tokenizer	,		_SCREAMING_SNAKE_CASE			)
			self.assertIsInstance(processor_fast.tokenizer	,		_SCREAMING_SNAKE_CASE			)

			self.assertEqual(processor_slow.image_processor.to_json_string()	,		image_processor.to_json_string()			)
			self.assertEqual(processor_fast.image_processor.to_json_string()	,		image_processor.to_json_string()			)
			self.assertIsInstance(processor_slow.image_processor	,		_SCREAMING_SNAKE_CASE			)
			self.assertIsInstance(processor_fast.image_processor	,		_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						List[Any]						      =				CLIPSegProcessor(tokenizer=self.get_tokenizer()	,		image_processor=self.get_image_processor()			)
			processor.save_pretrained(self.tmpdirname			)

			__lowerCAmelCase      :						Optional[Any]						      =				self.get_tokenizer(bos_token='(BOS)'	,		eos_token='(EOS)'			)
			__lowerCAmelCase      :						Optional[int]						      =				self.get_image_processor(do_normalize=_SCREAMING_SNAKE_CASE	,		padding_value=1.0			)

			__lowerCAmelCase      :						Union[str, Any]						      =				CLIPSegProcessor.from_pretrained(
			    self.tmpdirname	,		bos_token='(BOS)'	,		eos_token='(EOS)'	,		do_normalize=_SCREAMING_SNAKE_CASE	,		padding_value=1.0			)

			self.assertEqual(processor.tokenizer.get_vocab()	,		tokenizer_add_kwargs.get_vocab()			)
			self.assertIsInstance(processor.tokenizer	,		_SCREAMING_SNAKE_CASE			)

			self.assertEqual(processor.image_processor.to_json_string()	,		image_processor_add_kwargs.to_json_string()			)
			self.assertIsInstance(processor.image_processor	,		_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						List[Any]						      =				self.get_image_processor()
			__lowerCAmelCase      :						str						      =				self.get_tokenizer()

			__lowerCAmelCase      :						Union[str, Any]						      =				CLIPSegProcessor(tokenizer=_SCREAMING_SNAKE_CASE	,		image_processor=_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						Dict						      =				self.prepare_image_inputs()

			__lowerCAmelCase      :						int						      =				image_processor(_SCREAMING_SNAKE_CASE	,		return_tensors='np'			)
			__lowerCAmelCase      :						Union[str, Any]						      =				processor(images=_SCREAMING_SNAKE_CASE	,		return_tensors='np'			)

			for key in input_feat_extract.keys():
				self.assertAlmostEqual(input_feat_extract[key].sum()	,		input_processor[key].sum()	,		delta=1E-2			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Optional[Any]						      =				self.get_image_processor()
			__lowerCAmelCase      :						List[str]						      =				self.get_tokenizer()

			__lowerCAmelCase      :						Tuple						      =				CLIPSegProcessor(tokenizer=_SCREAMING_SNAKE_CASE	,		image_processor=_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						Any						      =				'lower newer'

			__lowerCAmelCase      :						Dict						      =				processor(text=_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						Tuple						      =				tokenizer(_SCREAMING_SNAKE_CASE			)

			for key in encoded_tok.keys():
				self.assertListEqual(encoded_tok[key]	,		encoded_processor[key]			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Optional[int]						      =				self.get_image_processor()
			__lowerCAmelCase      :						Dict						      =				self.get_tokenizer()

			__lowerCAmelCase      :						Tuple						      =				CLIPSegProcessor(tokenizer=_SCREAMING_SNAKE_CASE	,		image_processor=_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						Any						      =				'lower newer'
			__lowerCAmelCase      :						List[str]						      =				self.prepare_image_inputs()

			__lowerCAmelCase      :						List[Any]						      =				processor(text=_SCREAMING_SNAKE_CASE	,		images=_SCREAMING_SNAKE_CASE			)

			self.assertListEqual(list(inputs.keys()			)	,		['input_ids', 'attention_mask', 'pixel_values']			)

			# test if it raises when no input is passed
			with pytest.raises(_SCREAMING_SNAKE_CASE			):
				processor()

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Any						      =				self.get_image_processor()
			__lowerCAmelCase      :						List[str]						      =				self.get_tokenizer()

			__lowerCAmelCase      :						Any						      =				CLIPSegProcessor(tokenizer=_SCREAMING_SNAKE_CASE	,		image_processor=_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						List[str]						      =				self.prepare_image_inputs()
			__lowerCAmelCase      :						Union[str, Any]						      =				self.prepare_image_inputs()

			__lowerCAmelCase      :						Dict						      =				processor(images=_SCREAMING_SNAKE_CASE	,		visual_prompt=_SCREAMING_SNAKE_CASE			)

			self.assertListEqual(list(inputs.keys()			)	,		['pixel_values', 'conditional_pixel_values']			)

			# test if it raises when no input is passed
			with pytest.raises(_SCREAMING_SNAKE_CASE			):
				processor()







		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Dict						      =				self.get_image_processor()
			__lowerCAmelCase      :						str						      =				self.get_tokenizer()

			__lowerCAmelCase      :						Any						      =				CLIPSegProcessor(tokenizer=_SCREAMING_SNAKE_CASE	,		image_processor=_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						Optional[Any]						      =				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

			__lowerCAmelCase      :						List[Any]						      =				processor.batch_decode(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Any						      =				tokenizer.batch_decode(_SCREAMING_SNAKE_CASE			)

			self.assertListEqual(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)



"""simple docstring"""







import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Value
from .base import TaskTemplate




@dataclass(frozen=_lowerCamelCase)
class 			A__	(     _lowerCamelCase):
		# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
		A_						:	str           =     field(default='text-classification'	,					metadata={'include_in_asdict_even_if_is_default': True})
		A_						:	ClassVar[Features]           =     Features({'text': Value('string')})
		A_						:	ClassVar[Features]           =     Features({'labels': ClassLabel})
		A_						:	str           =     "text"
		A_						:	str           =     "labels"

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE			):
			if self.label_column not in features:
				raise ValueError(f"Column {self.label_column} is not present in features."			)
			if not isinstance(features[self.label_column]	,		_SCREAMING_SNAKE_CASE			):
				raise ValueError(f"Column {self.label_column} is not a ClassLabel."			)
			__lowerCAmelCase      :						Any						      =				copy.deepcopy(self			)
			__lowerCAmelCase      :						Dict						      =				self.label_schema.copy()
			__lowerCAmelCase      :						List[Any]						      =				features[self.label_column]
			__lowerCAmelCase      :						Dict						      =				label_schema
			return task_template







		@property
		def 							__lowerCamelCase							(				self			):
			return {
			    self.text_column: "text",
			    self.label_column: "labels",
			}