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  ):



 '''simple docstring'''

 def    __snake_case   (     self    :						List[str]					)	->							Any:
        _UpperCamelCase       :    List[Any]					  =					tempfile.mkdtemp()

        # fmt: off
        _UpperCamelCase       :    Tuple					  =					['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
        # fmt: on
        _UpperCamelCase       :    Optional[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]					)					)

        _UpperCamelCase       :    str					  =					{
            'image_mean': FLAVA_IMAGE_MEAN,
            'image_std': FLAVA_IMAGE_STD,
            'do_normalize': True,
            'do_resize': True,
            'size': 2_2_4,
            'do_center_crop': True,
            'crop_size': 2_2_4,
            'input_size_patches': 1_4,
            'total_mask_patches': 7_5,
            'mask_group_max_patches': None,
            'mask_group_min_patches': 1_6,
            'mask_group_min_aspect_ratio': 0.3,
            'mask_group_max_aspect_ratio': None,
            'codebook_do_resize': True,
            'codebook_size': 1_1_2,
            'codebook_do_center_crop': True,
            'codebook_crop_size': 1_1_2,
            'codebook_do_map_pixels': True,
            'codebook_do_normalize': True,
            'codebook_image_mean': FLAVA_CODEBOOK_MEAN,
            'codebook_image_std': FLAVA_CODEBOOK_STD,
        }

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

 def    __snake_case   (     self    :						Tuple    ,     **lowerCamelCase    :						List[Any]					)	->							List[Any]:
        return BertTokenizer.from_pretrained(self.tmpdirname    ,     **lowerCamelCase					)

 def    __snake_case   (     self    :						Tuple    ,     **lowerCamelCase    :						List[Any]					)	->							List[Any]:
        return BertTokenizerFast.from_pretrained(self.tmpdirname    ,     **lowerCamelCase					)

 def    __snake_case   (     self    :						Dict    ,     **lowerCamelCase    :						Optional[Any]					)	->							Any:
        return FlavaImageProcessor.from_pretrained(self.tmpdirname    ,     **lowerCamelCase					)

 def    __snake_case   (     self    :						List[Any]					)	->							List[Any]:
        shutil.rmtree(self.tmpdirname					)

 def    __snake_case   (     self    :						List[str]					)	->							List[str]:
        _UpperCamelCase       :    Union[str, Any]					  =					[np.random.randint(2_5_5    ,     size=(3, 3_0, 4_0_0)    ,     dtype=np.uinta					)]

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

        return image_inputs

 def    __snake_case   (     self    :						Union[str, Any]					)	->							Tuple:
        _UpperCamelCase       :    List[str]					  =					self.get_tokenizer()
        _UpperCamelCase       :    List[str]					  =					self.get_rust_tokenizer()
        _UpperCamelCase       :    Tuple					  =					self.get_image_processor()

        _UpperCamelCase       :    List[Any]					  =					FlavaProcessor(tokenizer=lowerCamelCase    ,     image_processor=lowerCamelCase					)
        processor_slow.save_pretrained(self.tmpdirname					)
        _UpperCamelCase       :    int					  =					FlavaProcessor.from_pretrained(self.tmpdirname    ,     use_fast=lowerCamelCase					)

        _UpperCamelCase       :    Optional[Any]					  =					FlavaProcessor(tokenizer=lowerCamelCase    ,     image_processor=lowerCamelCase					)
        processor_fast.save_pretrained(self.tmpdirname					)
        _UpperCamelCase       :    Optional[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    ,     lowerCamelCase					)
        self.assertIsInstance(processor_fast.tokenizer    ,     lowerCamelCase					)

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

 def    __snake_case   (     self    :						Any					)	->							Union[str, Any]:
        _UpperCamelCase       :    Optional[Any]					  =					FlavaProcessor(tokenizer=self.get_tokenizer()    ,     image_processor=self.get_image_processor()					)
        processor.save_pretrained(self.tmpdirname					)

        _UpperCamelCase       :    Optional[Any]					  =					self.get_tokenizer(bos_token='(BOS)'    ,     eos_token='(EOS)'					)
        _UpperCamelCase       :    Dict					  =					self.get_image_processor(do_normalize=lowerCamelCase    ,     padding_value=1.0					)

        _UpperCamelCase       :    Dict					  =					FlavaProcessor.from_pretrained(
            self.tmpdirname    ,     bos_token='(BOS)'    ,     eos_token='(EOS)'    ,     do_normalize=lowerCamelCase    ,     padding_value=1.0					)

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

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

 def    __snake_case   (     self    :						Optional[Any]					)	->							int:
        _UpperCamelCase       :    Optional[int]					  =					self.get_image_processor()
        _UpperCamelCase       :    Union[str, Any]					  =					self.get_tokenizer()

        _UpperCamelCase       :    Optional[int]					  =					FlavaProcessor(tokenizer=lowerCamelCase    ,     image_processor=lowerCamelCase					)

        _UpperCamelCase       :    List[Any]					  =					self.prepare_image_inputs()

        _UpperCamelCase       :    Any					  =					image_processor(lowerCamelCase    ,     return_tensors='np'					)
        _UpperCamelCase       :    str					  =					processor(images=lowerCamelCase    ,     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(1_2_3_4					)
        _UpperCamelCase       :    Optional[Any]					  =					image_processor(
            lowerCamelCase    ,     return_image_mask=lowerCamelCase    ,     return_codebook_pixels=lowerCamelCase    ,     return_tensors='np'					)
        random.seed(1_2_3_4					)
        _UpperCamelCase       :    str					  =					processor(
            images=lowerCamelCase    ,     return_image_mask=lowerCamelCase    ,     return_codebook_pixels=lowerCamelCase    ,     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    :						List[Any]					)	->							Union[str, Any]:
        _UpperCamelCase       :    Optional[int]					  =					self.get_image_processor()
        _UpperCamelCase       :    Tuple					  =					self.get_tokenizer()

        _UpperCamelCase       :    Dict					  =					FlavaProcessor(tokenizer=lowerCamelCase    ,     image_processor=lowerCamelCase					)

        _UpperCamelCase       :    Any					  =					'lower newer'

        _UpperCamelCase       :    Union[str, Any]					  =					processor(text=lowerCamelCase					)

        _UpperCamelCase       :    Tuple					  =					tokenizer(lowerCamelCase					)

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

 def    __snake_case   (     self    :						Dict					)	->							Tuple:
        _UpperCamelCase       :    Dict					  =					self.get_image_processor()
        _UpperCamelCase       :    Union[str, Any]					  =					self.get_tokenizer()

        _UpperCamelCase       :    List[str]					  =					FlavaProcessor(tokenizer=lowerCamelCase    ,     image_processor=lowerCamelCase					)

        _UpperCamelCase       :    List[Any]					  =					'lower newer'
        _UpperCamelCase       :    Dict					  =					self.prepare_image_inputs()

        _UpperCamelCase       :    str					  =					processor(text=lowerCamelCase    ,     images=lowerCamelCase					)

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

        # add extra args
        _UpperCamelCase       :    Dict					  =					processor(text=lowerCamelCase    ,     images=lowerCamelCase    ,     return_codebook_pixels=lowerCamelCase    ,     return_image_mask=lowerCamelCase					)

        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(lowerCamelCase					):
               processor()

 def    __snake_case   (     self    :						Dict					)	->							Any:
        _UpperCamelCase       :    Optional[int]					  =					self.get_image_processor()
        _UpperCamelCase       :    Any					  =					self.get_tokenizer()

        _UpperCamelCase       :    str					  =					FlavaProcessor(tokenizer=lowerCamelCase    ,     image_processor=lowerCamelCase					)

        _UpperCamelCase       :    Union[str, Any]					  =					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        _UpperCamelCase       :    Union[str, Any]					  =					processor.batch_decode(lowerCamelCase					)
        _UpperCamelCase       :    List[str]					  =					tokenizer.batch_decode(lowerCamelCase					)

        self.assertListEqual(lowerCamelCase    ,     lowerCamelCase					)

 def    __snake_case   (     self    :						Union[str, Any]					)	->							str:
        _UpperCamelCase       :    Dict					  =					self.get_image_processor()
        _UpperCamelCase       :    Dict					  =					self.get_tokenizer()

        _UpperCamelCase       :    str					  =					FlavaProcessor(tokenizer=lowerCamelCase    ,     image_processor=lowerCamelCase					)

        _UpperCamelCase       :    List[str]					  =					'lower newer'
        _UpperCamelCase       :    int					  =					self.prepare_image_inputs()

        _UpperCamelCase       :    Union[str, Any]					  =					processor(text=lowerCamelCase    ,     images=lowerCamelCase					)

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




def SCREAMING_SNAKE_CASE			(       lowercase_:		list[int]			):

       # validate:
       if not isinstance(lowercase_						,  lowercase_			) or not all(
           isinstance(lowercase_						,  lowercase_			) and item >= 0 for item in citations			):
              raise ValueError('The citations should be a list of non negative integers.'			)

       citations.sort()
       _UpperCamelCase       :    List[str]					  =					len(lowercase_			)

       for i in range(lowercase_			):
              if citations[len_citations - 1 - i] <= i:
                     return i

       return len_citations


if __name__ == "__main__":
 import doctest

 doctest.testmod()



