import json
import os
import random
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert 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 FlavaImageProcessor, FlavaProcessor
				from transformers.models.flava.image_processing_flava import (
				    FLAVA_CODEBOOK_MEAN,
				    FLAVA_CODEBOOK_STD,
				    FLAVA_IMAGE_MEAN,
				    FLAVA_IMAGE_STD,
				)





@require_vision
class 					__snake_case    (     unittest.TestCase      ):




					def      __a     (  self						)	->    List[str]:



									'''simple docstring'''







									snake_case__   :      str   				= tempfile.mkdtemp()

									# fmt: off
									snake_case__   :      str   				= ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
									# fmt: on
									snake_case__   :      Any   				= os.path.join(self.tmpdirname       ,			VOCAB_FILES_NAMES['vocab_file']						)

									with open(self.vocab_file       ,			'w'       ,			encoding='utf-8'						) as fp:
													fp.write(''.join([x + '\n' for x in vocab_tokens]						)						)

									snake_case__   :      int   				= {
									    'image_mean': FLAVA_IMAGE_MEAN,
									    'image_std': FLAVA_IMAGE_STD,
									    'do_normalize': True,
									    'do_resize': True,
									    'size': 224,
									    'do_center_crop': True,
									    'crop_size': 224,
									    'input_size_patches': 14,
									    'total_mask_patches': 75,
									    'mask_group_max_patches': None,
									    'mask_group_min_patches': 16,
									    'mask_group_min_aspect_ratio': 0.3,
									    'mask_group_max_aspect_ratio': None,
									    'codebook_do_resize': True,
									    'codebook_size': 112,
									    'codebook_do_center_crop': True,
									    'codebook_crop_size': 112,
									    'codebook_do_map_pixels': True,
									    'codebook_do_normalize': True,
									    'codebook_image_mean': FLAVA_CODEBOOK_MEAN,
									    'codebook_image_std': FLAVA_CODEBOOK_STD,
									}

									snake_case__   :      Any   				= os.path.join(self.tmpdirname       ,			_lowercase						)
									with open(self.image_processor_file       ,			'w'       ,			encoding='utf-8'						) as fp:
													json.dump(_lowercase       ,			_lowercase						)




					def      __a     (  self       ,			**__UpperCamelCase						)	->    Any:



									'''simple docstring'''







									return BertTokenizer.from_pretrained(self.tmpdirname       ,			**_lowercase						)




					def      __a     (  self       ,			**__UpperCamelCase						)	->    Tuple:



									'''simple docstring'''







									return BertTokenizerFast.from_pretrained(self.tmpdirname       ,			**_lowercase						)




					def      __a     (  self       ,			**__UpperCamelCase						)	->    Dict:



									'''simple docstring'''







									return FlavaImageProcessor.from_pretrained(self.tmpdirname       ,			**_lowercase						)




					def      __a     (  self						)	->    Union[str, Any]:



									'''simple docstring'''







									shutil.rmtree(self.tmpdirname						)




					def      __a     (  self						)	->    int:



									'''simple docstring'''







									snake_case__   :      Union[str, Any]   				= [np.random.randint(255       ,			size=(3, 30, 400)       ,			dtype=np.uinta						)]

									snake_case__   :      Tuple   				= [Image.fromarray(np.moveaxis(_lowercase       ,			0       ,			-1						)						) for x in image_inputs]

									return image_inputs




					def      __a     (  self						)	->    Any:



									'''simple docstring'''







									snake_case__   :      Dict   				= self.get_tokenizer()
									snake_case__   :      Optional[Any]   				= self.get_rust_tokenizer()
									snake_case__   :      Any   				= self.get_image_processor()

									snake_case__   :      Dict   				= FlavaProcessor(tokenizer=_lowercase       ,			image_processor=_lowercase						)
									processor_slow.save_pretrained(self.tmpdirname						)
									snake_case__   :      List[Any]   				= FlavaProcessor.from_pretrained(self.tmpdirname       ,			use_fast=_lowercase						)

									snake_case__   :      Tuple   				= FlavaProcessor(tokenizer=_lowercase       ,			image_processor=_lowercase						)
									processor_fast.save_pretrained(self.tmpdirname						)
									snake_case__   :      int   				= FlavaProcessor.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       ,			_lowercase						)
									self.assertIsInstance(processor_fast.tokenizer       ,			_lowercase						)

									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       ,			_lowercase						)
									self.assertIsInstance(processor_fast.image_processor       ,			_lowercase						)




					def      __a     (  self						)	->    Optional[int]:



									'''simple docstring'''







									snake_case__   :      Any   				= FlavaProcessor(tokenizer=self.get_tokenizer()       ,			image_processor=self.get_image_processor()						)
									processor.save_pretrained(self.tmpdirname						)

									snake_case__   :      Any   				= self.get_tokenizer(bos_token='(BOS)'       ,			eos_token='(EOS)'						)
									snake_case__   :      Dict   				= self.get_image_processor(do_normalize=_lowercase       ,			padding_value=1.0						)

									snake_case__   :      int   				= FlavaProcessor.from_pretrained(
									    self.tmpdirname       ,			bos_token='(BOS)'       ,			eos_token='(EOS)'       ,			do_normalize=_lowercase       ,			padding_value=1.0						)

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

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




					def      __a     (  self						)	->    Dict:



									'''simple docstring'''







									snake_case__   :      Union[str, Any]   				= self.get_image_processor()
									snake_case__   :      Any   				= self.get_tokenizer()

									snake_case__   :      int   				= FlavaProcessor(tokenizer=_lowercase       ,			image_processor=_lowercase						)

									snake_case__   :      Dict   				= self.prepare_image_inputs()

									snake_case__   :      List[Any]   				= image_processor(_lowercase       ,			return_tensors='np'						)
									snake_case__   :      Optional[int]   				= processor(images=_lowercase       ,			return_tensors='np'						)

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

									# With rest of the args
									random.seed(1234						)
									snake_case__   :      List[str]   				= image_processor(
									    _lowercase       ,			return_image_mask=_lowercase       ,			return_codebook_pixels=_lowercase       ,			return_tensors='np'						)
									random.seed(1234						)
									snake_case__   :      Any   				= processor(
									    images=_lowercase       ,			return_image_mask=_lowercase       ,			return_codebook_pixels=_lowercase       ,			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      __a     (  self						)	->    int:



									'''simple docstring'''







									snake_case__   :      Optional[Any]   				= self.get_image_processor()
									snake_case__   :      Any   				= self.get_tokenizer()

									snake_case__   :      str   				= FlavaProcessor(tokenizer=_lowercase       ,			image_processor=_lowercase						)

									snake_case__   :      int   				= 'lower newer'

									snake_case__   :      Union[str, Any]   				= processor(text=_lowercase						)

									snake_case__   :      Any   				= tokenizer(_lowercase						)

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




					def      __a     (  self						)	->    Optional[int]:



									'''simple docstring'''







									snake_case__   :      List[Any]   				= self.get_image_processor()
									snake_case__   :      Optional[Any]   				= self.get_tokenizer()

									snake_case__   :      int   				= FlavaProcessor(tokenizer=_lowercase       ,			image_processor=_lowercase						)

									snake_case__   :      Optional[int]   				= 'lower newer'
									snake_case__   :      Dict   				= self.prepare_image_inputs()

									snake_case__   :      Union[str, Any]   				= processor(text=_lowercase       ,			images=_lowercase						)

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

									# add extra args
									snake_case__   :      Optional[int]   				= processor(text=_lowercase       ,			images=_lowercase       ,			return_codebook_pixels=_lowercase       ,			return_image_mask=_lowercase						)

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

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




					def      __a     (  self						)	->    List[Any]:



									'''simple docstring'''







									snake_case__   :      Any   				= self.get_image_processor()
									snake_case__   :      List[Any]   				= self.get_tokenizer()

									snake_case__   :      str   				= FlavaProcessor(tokenizer=_lowercase       ,			image_processor=_lowercase						)

									snake_case__   :      int   				= [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

									snake_case__   :      List[str]   				= processor.batch_decode(_lowercase						)
									snake_case__   :      int   				= tokenizer.batch_decode(_lowercase						)

									self.assertListEqual(_lowercase       ,			_lowercase						)




					def      __a     (  self						)	->    List[Any]:



									'''simple docstring'''







									snake_case__   :      int   				= self.get_image_processor()
									snake_case__   :      List[str]   				= self.get_tokenizer()

									snake_case__   :      str   				= FlavaProcessor(tokenizer=_lowercase       ,			image_processor=_lowercase						)

									snake_case__   :      Optional[int]   				= 'lower newer'
									snake_case__   :      Dict   				= self.prepare_image_inputs()

									snake_case__   :      Dict   				= processor(text=_lowercase       ,			images=_lowercase						)

									self.assertListEqual(list(inputs.keys()						)       ,			processor.model_input_names						)



"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_convnext import ConvNextImageProcessor


__UpperCamelCase       :    str         =		logging.get_logger(__name__)
class   UpperCAmelCase_	(				lowercase__   ):
    def __init__(	self			:       List[Any]   ,						*_lowercase			:       Optional[Any]   ,						**_lowercase			:       str		)						->    None:
       warnings.warn(
           "The class ConvNextFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
           " Please use ConvNextImageProcessor instead."   ,						_lowercase   ,						)
       super().__init__(*_lowercase   ,						**_lowercase		)