





import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class       __A							(   lowerCamelCase__			,unittest.TestCase	):




      """simple docstring"""







      UpperCAmelCase__							=   OpenAIGPTTokenizer
      UpperCAmelCase__							=   OpenAIGPTTokenizerFast
      UpperCAmelCase__							=   True
      UpperCAmelCase__							=   False


      def 					__snake_case      (		self):



             """simple docstring"""





             super().setUp()

             # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
             _lowerCamelCase				:  Tuple						=		[
                 '''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>''',
                 '''<unk>''',
             ]
             _lowerCamelCase				:  Any						=		dict(zip(a__   ,				range(len(a__))))
             _lowerCamelCase				:  List[str]						=		['''#version: 0.2''', '''l o''', '''lo w''', '''e r</w>''', '''''']

             _lowerCamelCase				:  str						=		os.path.join(self.tmpdirname   ,				VOCAB_FILES_NAMES['''vocab_file'''])
             _lowerCamelCase				:  int						=		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 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             return "lower newer", "lower newer"


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Optional[Any]						=		OpenAIGPTTokenizer(self.vocab_file   ,				self.merges_file)

             _lowerCamelCase				:  Dict						=		'''lower'''
             _lowerCamelCase				:  Optional[Any]						=		['''low''', '''er</w>''']
             _lowerCamelCase				:  List[Any]						=		tokenizer.tokenize(a__)
             self.assertListEqual(a__   ,				a__)

             _lowerCamelCase				:  Any						=		tokens + ['''<unk>''']
             _lowerCamelCase				:  Optional[int]						=		[14, 15, 20]
             self.assertListEqual(tokenizer.convert_tokens_to_ids(a__)   ,				a__)


      def 					__snake_case      (		self   ,				a__=15):



             """simple docstring"""





             for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                    with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
                           _lowerCamelCase				:  Dict						=		self.rust_tokenizer_class.from_pretrained(a__   ,				**a__)

                           # Simple input
                           _lowerCamelCase				:  Optional[int]						=		'''This is a simple input'''
                           _lowerCamelCase				:  Optional[Any]						=		['''This is a simple input 1''', '''This is a simple input 2''']
                           _lowerCamelCase				:  Optional[int]						=		('''This is a simple input''', '''This is a pair''')
                           _lowerCamelCase				:  List[Any]						=		[
                               ('''This is a simple input 1''', '''This is a simple input 2'''),
                               ('''This is a simple pair 1''', '''This is a simple pair 2'''),
                           ]

                           # Simple input tests
                           self.assertRaises(a__   ,				tokenizer_r.encode   ,				a__   ,				max_length=a__   ,				padding='''max_length''')

                           # Simple input
                           self.assertRaises(a__   ,				tokenizer_r.encode_plus   ,				a__   ,				max_length=a__   ,				padding='''max_length''')

                           # Simple input
                           self.assertRaises(
                               a__   ,				tokenizer_r.batch_encode_plus   ,				a__   ,				max_length=a__   ,				padding='''max_length'''   ,				)

                           # Pair input
                           self.assertRaises(a__   ,				tokenizer_r.encode   ,				a__   ,				max_length=a__   ,				padding='''max_length''')

                           # Pair input
                           self.assertRaises(a__   ,				tokenizer_r.encode_plus   ,				a__   ,				max_length=a__   ,				padding='''max_length''')

                           # Pair input
                           self.assertRaises(
                               a__   ,				tokenizer_r.batch_encode_plus   ,				a__   ,				max_length=a__   ,				padding='''max_length'''   ,				)



      def 					__snake_case      (		self):



             """simple docstring"""





             pass







@require_ftfy
@require_spacy
@require_tokenizers
class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      pass








import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
       from PIL import Image

       from transformers import (
           AutoProcessor,
           BertTokenizerFast,
           BlipImageProcessor,
           GPTaTokenizer,
           InstructBlipProcessor,
           PreTrainedTokenizerFast,
       )







@require_vision
class       __A							(   unittest.TestCase	):




      """simple docstring"""







      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[Any]						=		tempfile.mkdtemp()

             _lowerCamelCase				:  List[str]						=		BlipImageProcessor()
             _lowerCamelCase				:  Union[str, Any]						=		GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model''')
             _lowerCamelCase				:  Any						=		BertTokenizerFast.from_pretrained('''hf-internal-testing/tiny-random-bert''')

             _lowerCamelCase				:  Any						=		InstructBlipProcessor(a__   ,				a__   ,				a__)

             processor.save_pretrained(self.tmpdirname)


      def 					__snake_case      (		self   ,				**a__):



             """simple docstring"""





             return AutoProcessor.from_pretrained(self.tmpdirname   ,				**a__).tokenizer


      def 					__snake_case      (		self   ,				**a__):



             """simple docstring"""





             return AutoProcessor.from_pretrained(self.tmpdirname   ,				**a__).image_processor


      def 					__snake_case      (		self   ,				**a__):



             """simple docstring"""





             return AutoProcessor.from_pretrained(self.tmpdirname   ,				**a__).qformer_tokenizer


      def 					__snake_case      (		self):



             """simple docstring"""





             shutil.rmtree(self.tmpdirname)


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[Any]						=		[np.random.randint(255   ,				size=(3, 30, 400)   ,				dtype=np.uinta)]

             _lowerCamelCase				:  List[str]						=		[Image.fromarray(np.moveaxis(a__   ,				0   ,				-1)) for x in image_inputs]

             return image_inputs


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Optional[Any]						=		InstructBlipProcessor(
                 tokenizer=self.get_tokenizer()   ,				image_processor=self.get_image_processor()   ,				qformer_tokenizer=self.get_qformer_tokenizer()   ,				)
             processor.save_pretrained(self.tmpdirname)

             _lowerCamelCase				:  Optional[int]						=		self.get_tokenizer(bos_token='''(BOS)'''   ,				eos_token='''(EOS)''')
             _lowerCamelCase				:  str						=		self.get_image_processor(do_normalize=a__   ,				padding_value=1.0)

             _lowerCamelCase				:  Tuple						=		InstructBlipProcessor.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__)
             self.assertIsInstance(processor.qformer_tokenizer   ,				a__)


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[str]						=		self.get_image_processor()
             _lowerCamelCase				:  Optional[int]						=		self.get_tokenizer()
             _lowerCamelCase				:  Any						=		self.get_qformer_tokenizer()

             _lowerCamelCase				:  Dict						=		InstructBlipProcessor(
                 tokenizer=a__   ,				image_processor=a__   ,				qformer_tokenizer=a__)

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

             _lowerCamelCase				:  List[str]						=		image_processor(a__   ,				return_tensors='''np''')
             _lowerCamelCase				:  List[Any]						=		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)


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Any						=		self.get_image_processor()
             _lowerCamelCase				:  Dict						=		self.get_tokenizer()
             _lowerCamelCase				:  int						=		self.get_qformer_tokenizer()

             _lowerCamelCase				:  Tuple						=		InstructBlipProcessor(
                 tokenizer=a__   ,				image_processor=a__   ,				qformer_tokenizer=a__)

             _lowerCamelCase				:  List[Any]						=		'''lower newer'''

             _lowerCamelCase				:  Any						=		processor(text=a__)

             _lowerCamelCase				:  Optional[int]						=		tokenizer(a__   ,				return_token_type_ids=a__)
             _lowerCamelCase				:  Optional[Any]						=		qformer_tokenizer(a__   ,				return_token_type_ids=a__)

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

             for key in encoded_tokens_qformer.keys():
                    self.assertListEqual(encoded_tokens_qformer[key]   ,				encoded_processor['''qformer_''' + key])


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[Any]						=		self.get_image_processor()
             _lowerCamelCase				:  Optional[int]						=		self.get_tokenizer()
             _lowerCamelCase				:  Optional[Any]						=		self.get_qformer_tokenizer()

             _lowerCamelCase				:  List[Any]						=		InstructBlipProcessor(
                 tokenizer=a__   ,				image_processor=a__   ,				qformer_tokenizer=a__)

             _lowerCamelCase				:  List[Any]						=		'''lower newer'''
             _lowerCamelCase				:  Tuple						=		self.prepare_image_inputs()

             _lowerCamelCase				:  Tuple						=		processor(text=a__   ,				images=a__)

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

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


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  int						=		self.get_image_processor()
             _lowerCamelCase				:  List[Any]						=		self.get_tokenizer()
             _lowerCamelCase				:  List[Any]						=		self.get_qformer_tokenizer()

             _lowerCamelCase				:  Optional[Any]						=		InstructBlipProcessor(
                 tokenizer=a__   ,				image_processor=a__   ,				qformer_tokenizer=a__)

             _lowerCamelCase				:  List[Any]						=		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

             _lowerCamelCase				:  Tuple						=		processor.batch_decode(a__)
             _lowerCamelCase				:  str						=		tokenizer.batch_decode(a__)

             self.assertListEqual(a__   ,				a__)



      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[str]						=		self.get_image_processor()
             _lowerCamelCase				:  Dict						=		self.get_tokenizer()
             _lowerCamelCase				:  str						=		self.get_qformer_tokenizer()

             _lowerCamelCase				:  str						=		InstructBlipProcessor(
                 tokenizer=a__   ,				image_processor=a__   ,				qformer_tokenizer=a__)

             _lowerCamelCase				:  str						=		'''lower newer'''
             _lowerCamelCase				:  str						=		self.prepare_image_inputs()

             _lowerCamelCase				:  List[Any]						=		processor(text=a__   ,				images=a__)

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

