



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       __lowerCAmelCase				(  unittest.TestCase ):




				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					tempfile.mkdtemp()

									# fmt: off
									_lowerCAmelCase      			=					["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing""", """,""", """low""", """lowest"""]
									# fmt: on
									_lowerCAmelCase      			=					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]	)	)

									_lowerCAmelCase      			=					{
									    """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,
									}

									_lowerCAmelCase      			=					os.path.join(self.tmpdirname		,       _snake_case	)
									with open(self.image_processor_file		,       """w"""		,       encoding="""utf-8"""	) as fp:
														json.dump(_snake_case		,       _snake_case	)




				def 							snake_case     (							self		,       **_snake_case	):



									"""simple docstring"""





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




				def 							snake_case     (							self		,       **_snake_case	):



									"""simple docstring"""





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




				def 							snake_case     (							self		,       **_snake_case	):



									"""simple docstring"""





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




				def 							snake_case     (							self	):



									"""simple docstring"""





									shutil.rmtree(self.tmpdirname	)




				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					[np.random.randint(255		,       size=(3, 30, 400)		,       dtype=np.uinta	)]

									_lowerCAmelCase      			=					[Image.fromarray(np.moveaxis(_snake_case		,       0		,       -1	)	) for x in image_inputs]

									return image_inputs




				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					self.get_tokenizer()
									_lowerCAmelCase      			=					self.get_rust_tokenizer()
									_lowerCAmelCase      			=					self.get_image_processor()

									_lowerCAmelCase      			=					FlavaProcessor(tokenizer=_snake_case		,       image_processor=_snake_case	)
									processor_slow.save_pretrained(self.tmpdirname	)
									_lowerCAmelCase      			=					FlavaProcessor.from_pretrained(self.tmpdirname		,       use_fast=_snake_case	)

									_lowerCAmelCase      			=					FlavaProcessor(tokenizer=_snake_case		,       image_processor=_snake_case	)
									processor_fast.save_pretrained(self.tmpdirname	)
									_lowerCAmelCase      			=					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		,       _snake_case	)
									self.assertIsInstance(processor_fast.tokenizer		,       _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		,       _snake_case	)
									self.assertIsInstance(processor_fast.image_processor		,       _snake_case	)




				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					FlavaProcessor(tokenizer=self.get_tokenizer()		,       image_processor=self.get_image_processor()	)
									processor.save_pretrained(self.tmpdirname	)

									_lowerCAmelCase      			=					self.get_tokenizer(bos_token="""(BOS)"""		,       eos_token="""(EOS)"""	)
									_lowerCAmelCase      			=					self.get_image_processor(do_normalize=_snake_case		,       padding_value=1.0	)

									_lowerCAmelCase      			=					FlavaProcessor.from_pretrained(
									    self.tmpdirname		,       bos_token="""(BOS)"""		,       eos_token="""(EOS)"""		,       do_normalize=_snake_case		,       padding_value=1.0	)

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

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




				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					self.get_image_processor()
									_lowerCAmelCase      			=					self.get_tokenizer()

									_lowerCAmelCase      			=					FlavaProcessor(tokenizer=_snake_case		,       image_processor=_snake_case	)

									_lowerCAmelCase      			=					self.prepare_image_inputs()

									_lowerCAmelCase      			=					image_processor(_snake_case		,       return_tensors="""np"""	)
									_lowerCAmelCase      			=					processor(images=_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	)

									# With rest of the args
									random.seed(1234	)
									_lowerCAmelCase      			=					image_processor(
									    _snake_case		,       return_image_mask=_snake_case		,       return_codebook_pixels=_snake_case		,       return_tensors="""np"""	)
									random.seed(1234	)
									_lowerCAmelCase      			=					processor(
									    images=_snake_case		,       return_image_mask=_snake_case		,       return_codebook_pixels=_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 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					self.get_image_processor()
									_lowerCAmelCase      			=					self.get_tokenizer()

									_lowerCAmelCase      			=					FlavaProcessor(tokenizer=_snake_case		,       image_processor=_snake_case	)

									_lowerCAmelCase      			=					"""lower newer"""

									_lowerCAmelCase      			=					processor(text=_snake_case	)

									_lowerCAmelCase      			=					tokenizer(_snake_case	)

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




				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					self.get_image_processor()
									_lowerCAmelCase      			=					self.get_tokenizer()

									_lowerCAmelCase      			=					FlavaProcessor(tokenizer=_snake_case		,       image_processor=_snake_case	)

									_lowerCAmelCase      			=					"""lower newer"""
									_lowerCAmelCase      			=					self.prepare_image_inputs()

									_lowerCAmelCase      			=					processor(text=_snake_case		,       images=_snake_case	)

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

									# add extra args
									_lowerCAmelCase      			=					processor(text=_snake_case		,       images=_snake_case		,       return_codebook_pixels=_snake_case		,       return_image_mask=_snake_case	)

									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(_snake_case	):
														processor()




				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					self.get_image_processor()
									_lowerCAmelCase      			=					self.get_tokenizer()

									_lowerCAmelCase      			=					FlavaProcessor(tokenizer=_snake_case		,       image_processor=_snake_case	)

									_lowerCAmelCase      			=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

									_lowerCAmelCase      			=					processor.batch_decode(_snake_case	)
									_lowerCAmelCase      			=					tokenizer.batch_decode(_snake_case	)

									self.assertListEqual(_snake_case		,       _snake_case	)




				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					self.get_image_processor()
									_lowerCAmelCase      			=					self.get_tokenizer()

									_lowerCAmelCase      			=					FlavaProcessor(tokenizer=_snake_case		,       image_processor=_snake_case	)

									_lowerCAmelCase      			=					"""lower newer"""
									_lowerCAmelCase      			=					self.prepare_image_inputs()

									_lowerCAmelCase      			=					processor(text=_snake_case		,       images=_snake_case	)

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








from __future__ import annotations


def 	_UpperCAmelCase       (   snake_case    ):





					"""simple docstring"""

					_lowerCAmelCase      			=					sorted(snake_case    )
					_lowerCAmelCase      			=					len(snake_case    )
					_lowerCAmelCase      			=					length >> 1
					return (
					    (sorted_list[mid_index] + sorted_list[mid_index - 1]) / 2
					    if length % 2 == 0
					    else sorted_list[mid_index]
					)


def 	_UpperCAmelCase       (   ):





					"""simple docstring"""

					import doctest

					doctest.testmod()


if __name__ == "__main__":
	main()



