




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				):







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



          '''simple docstring'''



          a     =				tempfile.mkdtemp()

          a     =				BlipImageProcessor()
          a     =				GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model"			)
          a     =				BertTokenizerFast.from_pretrained("hf-internal-testing/tiny-random-bert"			)

          a     =				InstructBlipProcessor(A  ,		A  ,		A			)

          processor.save_pretrained(self.tmpdirname			)







     def       lowerCAmelCase_    (     self  ,		**A			)  ->   str:



          '''simple docstring'''



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







     def       lowerCAmelCase_    (     self  ,		**A			)  ->   List[Any]:



          '''simple docstring'''



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







     def       lowerCAmelCase_    (     self  ,		**A			)  ->   Any:



          '''simple docstring'''



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







     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          shutil.rmtree(self.tmpdirname			)







     def       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''simple docstring'''



          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       lowerCAmelCase_    (     self			)  ->   str:



          '''simple docstring'''



          a     =				InstructBlipProcessor(
              tokenizer=self.get_tokenizer()  ,		image_processor=self.get_image_processor()  ,		qformer_tokenizer=self.get_qformer_tokenizer()  ,		)
          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     =				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       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          a     =				self.get_image_processor()
          a     =				self.get_tokenizer()
          a     =				self.get_qformer_tokenizer()

          a     =				InstructBlipProcessor(
              tokenizer=A  ,		image_processor=A  ,		qformer_tokenizer=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			)







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



          '''simple docstring'''



          a     =				self.get_image_processor()
          a     =				self.get_tokenizer()
          a     =				self.get_qformer_tokenizer()

          a     =				InstructBlipProcessor(
              tokenizer=A  ,		image_processor=A  ,		qformer_tokenizer=A			)

          a     =				"lower newer"

          a     =				processor(text=A			)

          a     =				tokenizer(A  ,		return_token_type_ids=A			)
          a     =				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       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



          a     =				self.get_image_processor()
          a     =				self.get_tokenizer()
          a     =				self.get_qformer_tokenizer()

          a     =				InstructBlipProcessor(
              tokenizer=A  ,		image_processor=A  ,		qformer_tokenizer=A			)

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

          a     =				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       lowerCAmelCase_    (     self			)  ->   Union[str, Any]:



          '''simple docstring'''



          a     =				self.get_image_processor()
          a     =				self.get_tokenizer()
          a     =				self.get_qformer_tokenizer()

          a     =				InstructBlipProcessor(
              tokenizer=A  ,		image_processor=A  ,		qformer_tokenizer=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       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



          a     =				self.get_image_processor()
          a     =				self.get_tokenizer()
          a     =				self.get_qformer_tokenizer()

          a     =				InstructBlipProcessor(
              tokenizer=A  ,		image_processor=A  ,		qformer_tokenizer=A			)

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

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

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







import unittest

from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin




@require_rjieba
@require_tokenizers
class       a__	( UpperCamelCase__       , unittest.TestCase				):
     a		:      List[Any]		 =     RoFormerTokenizer
     a		:      Tuple		 =     RoFormerTokenizerFast
     a		:      Dict		 =     True
     a		:      Optional[Any]		 =     True







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



          '''simple docstring'''



          super().setUp()







     def       lowerCAmelCase_    (     self  ,		**A			)  ->   Tuple:



          '''simple docstring'''



          return self.tokenizer_class.from_pretrained("junnyu/roformer_chinese_base"  ,		**A			)







     def       lowerCAmelCase_    (     self  ,		**A			)  ->   Union[str, Any]:



          '''simple docstring'''



          return self.rust_tokenizer_class.from_pretrained("junnyu/roformer_chinese_base"  ,		**A			)







     def       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''simple docstring'''



          a     =				"永和服装饰品有限公司,今天天气非常好"
          a     =				"永和 服装 饰品 有限公司 , 今 天 天 气 非常 好"
          return input_text, output_text







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



          '''simple docstring'''



          a     =				self.get_tokenizer()
          a  ,				a     =				self.get_chinese_input_output_texts()
          a     =				tokenizer.tokenize(A			)

          self.assertListEqual(A  ,		output_text.split()			)

          a     =				tokens + [tokenizer.unk_token]
          a     =				[22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(A			)  ,		A			)







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



          '''simple docstring'''



          a     =				self.get_rust_tokenizer()
          a  ,				a     =				self.get_chinese_input_output_texts()
          a     =				tokenizer.tokenize(A			)
          self.assertListEqual(A  ,		output_text.split()			)
          a     =				tokens + [tokenizer.unk_token]
          a     =				[22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(A			)  ,		A			)







     def       lowerCAmelCase_    (     self			)  ->   Any:



          '''simple docstring'''



          pass







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



          '''simple docstring'''



          pass







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



          '''simple docstring'''



          pass

