
"""simple docstring"""


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   _SCREAMING_SNAKE_CASE (	self       : Any )       ->				Dict:
       A  				=				tempfile.mkdtemp()

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

       A  				=				{
           '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,
       }

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


      def   _SCREAMING_SNAKE_CASE (	self       : Dict					,**A_       : int )       ->				Optional[int]:
       return BertTokenizer.from_pretrained(self.tmpdirname					,**A_ )


      def   _SCREAMING_SNAKE_CASE (	self       : Tuple					,**A_       : List[Any] )       ->				Optional[int]:
       return BertTokenizerFast.from_pretrained(self.tmpdirname					,**A_ )


      def   _SCREAMING_SNAKE_CASE (	self       : List[Any]					,**A_       : str )       ->				List[Any]:
       return FlavaImageProcessor.from_pretrained(self.tmpdirname					,**A_ )


      def   _SCREAMING_SNAKE_CASE (	self       : str )       ->				Union[str, Any]:
       shutil.rmtree(self.tmpdirname )


      def   _SCREAMING_SNAKE_CASE (	self       : List[Any] )       ->				Union[str, Any]:
       A  				=				[np.random.randint(255					,size=(3, 30, 400)					,dtype=np.uinta )]

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

       return image_inputs


      def   _SCREAMING_SNAKE_CASE (	self       : str )       ->				List[Any]:
       A  				=				self.get_tokenizer()
       A  				=				self.get_rust_tokenizer()
       A  				=				self.get_image_processor()

       A  				=				FlavaProcessor(tokenizer=A_					,image_processor=A_ )
       processor_slow.save_pretrained(self.tmpdirname )
       A  				=				FlavaProcessor.from_pretrained(self.tmpdirname					,use_fast=A_ )

       A  				=				FlavaProcessor(tokenizer=A_					,image_processor=A_ )
       processor_fast.save_pretrained(self.tmpdirname )
       A  				=				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					,A_ )
       self.assertIsInstance(processor_fast.tokenizer					,A_ )

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


      def   _SCREAMING_SNAKE_CASE (	self       : Optional[int] )       ->				Tuple:
       A  				=				FlavaProcessor(tokenizer=self.get_tokenizer()					,image_processor=self.get_image_processor() )
       processor.save_pretrained(self.tmpdirname )

       A  				=				self.get_tokenizer(bos_token='(BOS)'					,eos_token='(EOS)' )
       A  				=				self.get_image_processor(do_normalize=A_					,padding_value=1.0 )

       A  				=				FlavaProcessor.from_pretrained(
           self.tmpdirname					,bos_token='(BOS)'					,eos_token='(EOS)'					,do_normalize=A_					,padding_value=1.0 )

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

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


      def   _SCREAMING_SNAKE_CASE (	self       : Tuple )       ->				Any:
       A  				=				self.get_image_processor()
       A  				=				self.get_tokenizer()

       A  				=				FlavaProcessor(tokenizer=A_					,image_processor=A_ )

       A  				=				self.prepare_image_inputs()

       A  				=				image_processor(A_					,return_tensors='np' )
       A  				=				processor(images=A_					,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 )
       A  				=				image_processor(
           A_					,return_image_mask=A_					,return_codebook_pixels=A_					,return_tensors='np' )
       random.seed(1234 )
       A  				=				processor(
           images=A_					,return_image_mask=A_					,return_codebook_pixels=A_					,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   _SCREAMING_SNAKE_CASE (	self       : Union[str, Any] )       ->				str:
       A  				=				self.get_image_processor()
       A  				=				self.get_tokenizer()

       A  				=				FlavaProcessor(tokenizer=A_					,image_processor=A_ )

       A  				=				'lower newer'

       A  				=				processor(text=A_ )

       A  				=				tokenizer(A_ )

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


      def   _SCREAMING_SNAKE_CASE (	self       : Optional[Any] )       ->				Optional[int]:
       A  				=				self.get_image_processor()
       A  				=				self.get_tokenizer()

       A  				=				FlavaProcessor(tokenizer=A_					,image_processor=A_ )

       A  				=				'lower newer'
       A  				=				self.prepare_image_inputs()

       A  				=				processor(text=A_					,images=A_ )

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

       # add extra args
       A  				=				processor(text=A_					,images=A_					,return_codebook_pixels=A_					,return_image_mask=A_ )

       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(A_ ):
        processor()


      def   _SCREAMING_SNAKE_CASE (	self       : Tuple )       ->				Optional[int]:
       A  				=				self.get_image_processor()
       A  				=				self.get_tokenizer()

       A  				=				FlavaProcessor(tokenizer=A_					,image_processor=A_ )

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

       A  				=				processor.batch_decode(A_ )
       A  				=				tokenizer.batch_decode(A_ )

       self.assertListEqual(A_					,A_ )




      def   _SCREAMING_SNAKE_CASE (	self       : int )       ->				List[Any]:
       A  				=				self.get_image_processor()
       A  				=				self.get_tokenizer()

       A  				=				FlavaProcessor(tokenizer=A_					,image_processor=A_ )

       A  				=				'lower newer'
       A  				=				self.prepare_image_inputs()

       A  				=				processor(text=A_					,images=A_ )

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

"""simple docstring"""


import json
import os
import unittest

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class 			lowerCAmelCase_			( _lowercase						,   unittest.TestCase      ):



						'''simple docstring'''


						_lowerCamelCase:      List[str] =      HerbertTokenizer
						_lowerCamelCase:      Any =      HerbertTokenizerFast
						_lowerCamelCase:      Optional[int] =      True


						def   _SCREAMING_SNAKE_CASE (	self       : Dict )       ->				Tuple:
							super().setUp()

							# Use a simpler test file without japanese/chinese characters
							with open(F'{get_tests_dir()}/fixtures/sample_text_no_unicode.txt'					,encoding='utf-8' ) as f_data:
								A  				=				f_data.read().replace('\n\n'					,'\n' ).strip()

							A  				=				[
							    '<s>',
							    '</s>',
							    'l',
							    'o',
							    'w',
							    'e',
							    'r',
							    's',
							    't',
							    'i',
							    'd',
							    'n',
							    'w</w>',
							    'r</w>',
							    't</w>',
							    'lo',
							    'low',
							    'er</w>',
							    'low</w>',
							    'lowest</w>',
							    'newer</w>',
							    'wider</w>',
							    ',</w>',
							    '<unk>',
							]
							A  				=				dict(zip(A_					,range(len(A_ ) ) ) )
							A  				=				['l o 123', 'lo w 1456', 'e r</w> 1789', '']

							A  				=				os.path.join(self.tmpdirname					,VOCAB_FILES_NAMES['vocab_file'] )
							A  				=				os.path.join(self.tmpdirname					,VOCAB_FILES_NAMES['merges_file'] )
							with open(self.vocab_file					,'w' ) as fp:
								fp.write(json.dumps(A_ ) )
							with open(self.merges_file					,'w' ) as fp:
								fp.write('\n'.join(A_ ) )


						def   _SCREAMING_SNAKE_CASE (	self       : Tuple					,A_       : List[str] )       ->				int:
							A  				=				'lower newer'
							A  				=				'lower newer'
							return input_text, output_text


						def   _SCREAMING_SNAKE_CASE (	self       : List[Any] )       ->				int:
							A  				=				self.tokenizer_class(vocab_file=self.vocab_file					,merges_file=self.merges_file )

							A  				=				'lower'
							A  				=				['low', 'er</w>']
							A  				=				tokenizer.tokenize(A_ )
							self.assertListEqual(A_					,A_ )

							A  				=				tokens + ['<unk>']
							A  				=				[16, 17, 23]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(A_ )					,A_ )


						def   _SCREAMING_SNAKE_CASE (	self       : Dict )       ->				Optional[int]:
							if not self.test_rust_tokenizer:
								return

							A  				=				self.get_tokenizer()
							A  				=				self.get_rust_tokenizer()

							A  				=				'lower,newer'

							A  				=				tokenizer.tokenize(A_ )
							A  				=				rust_tokenizer.tokenize(A_ )
							self.assertListEqual(A_					,A_ )

							A  				=				tokenizer.encode(A_					,add_special_tokens=A_ )
							A  				=				rust_tokenizer.encode(A_					,add_special_tokens=A_ )
							self.assertListEqual(A_					,A_ )

							A  				=				self.get_rust_tokenizer()
							A  				=				tokenizer.encode(A_ )
							A  				=				rust_tokenizer.encode(A_ )
							self.assertListEqual(A_					,A_ )




						@slow
						def   _SCREAMING_SNAKE_CASE (	self       : str )       ->				List[Any]:
							A  				=				self.tokenizer_class.from_pretrained('allegro/herbert-base-cased' )

							A  				=				tokenizer.encode('konstruowanie sekwencji'					,add_special_tokens=A_ )
							A  				=				tokenizer.encode('konstruowanie wielu sekwencji'					,add_special_tokens=A_ )

							A  				=				tokenizer.build_inputs_with_special_tokens(A_ )
							A  				=				tokenizer.build_inputs_with_special_tokens(A_					,A_ )

							assert encoded_sentence == [0] + text + [2]
							assert encoded_pair == [0] + text + [2] + text_a + [2]