import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


_lowerCAmelCase        =       '\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.\n\nIn March 2021, Hugging Face raised $40 million in a Series B funding round.[3]\n\nOn April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]\n'






class     __UpperCAmelCase(    unittest.TestCase      ,	A__				):






 """simple docstring"""




 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       str	      =				load_tool('''summarization'''      )
        self.tool.setup()
        A_							:       Optional[int]	      =				load_tool('''summarization''' ,       remote=__magic_name__      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Optional[int]	      =				self.tool(__magic_name__      )
        self.assertEqual(
            __magic_name__ ,       '''Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.''' ,       )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Optional[int]	      =				self.remote_tool(__magic_name__      )
        self.assertEqual(
            __magic_name__ ,       '''Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.''' ,       )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Union[str, Any]	      =				self.tool(text=__magic_name__      )
        self.assertEqual(
            __magic_name__ ,       '''Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.''' ,       )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       List[str]	      =				self.remote_tool(text=__magic_name__      )
        self.assertEqual(
            __magic_name__ ,       '''Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.''' ,       )



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, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast






@require_vision
class     __UpperCAmelCase(    unittest.TestCase				):






 """simple docstring"""




 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       int	      =				tempfile.mkdtemp()

        A_							:       str	      =				CLIPImageProcessor()
        A_							:       List[Any]	      =				BertTokenizer.from_pretrained(
            '''hf-internal-testing/tiny-random-BertModel''' ,       model_input_names=['''input_ids''', '''attention_mask''']      )

        A_							:       Optional[int]	      =				GitProcessor(__magic_name__ ,       __magic_name__      )

        processor.save_pretrained(self.tmpdirname      )
 def  UpperCAmelCase   (    self ,       **__magic_name__      ):

        """simple docstring"""

        return AutoProcessor.from_pretrained(self.tmpdirname ,       **__magic_name__      ).tokenizer
 def  UpperCAmelCase   (    self ,       **__magic_name__      ):

        """simple docstring"""

        return AutoProcessor.from_pretrained(self.tmpdirname ,       **__magic_name__      ).image_processor
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        shutil.rmtree(self.tmpdirname      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Union[str, Any]	      =				[np.random.randint(255 ,       size=(3, 30, 400) ,       dtype=np.uinta      )]

        A_							:       Union[str, Any]	      =				[Image.fromarray(np.moveaxis(__magic_name__ ,       0 ,       -1      )      ) for x in image_inputs]

        return image_inputs
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Any	      =				GitProcessor(tokenizer=self.get_tokenizer() ,       image_processor=self.get_image_processor()      )
        processor.save_pretrained(self.tmpdirname      )

        A_							:       int	      =				self.get_tokenizer(bos_token='''(BOS)''' ,       eos_token='''(EOS)'''      )
        A_							:       str	      =				self.get_image_processor(do_normalize=__magic_name__ ,       padding_value=1.0      )

        A_							:       int	      =				GitProcessor.from_pretrained(
            self.tmpdirname ,       bos_token='''(BOS)''' ,       eos_token='''(EOS)''' ,       do_normalize=__magic_name__ ,       padding_value=1.0      )

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

        self.assertEqual(processor.image_processor.to_json_string() ,       image_processor_add_kwargs.to_json_string()      )
        self.assertIsInstance(processor.image_processor ,       __magic_name__      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       List[str]	      =				self.get_image_processor()
        A_							:       Tuple	      =				self.get_tokenizer()

        A_							:       Optional[int]	      =				GitProcessor(tokenizer=__magic_name__ ,       image_processor=__magic_name__      )

        A_							:       str	      =				self.prepare_image_inputs()

        A_							:       List[str]	      =				image_processor(__magic_name__ ,       return_tensors='''np'''      )
        A_							:       int	      =				processor(images=__magic_name__ ,       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  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       List[str]	      =				self.get_image_processor()
        A_							:       str	      =				self.get_tokenizer()

        A_							:       List[str]	      =				GitProcessor(tokenizer=__magic_name__ ,       image_processor=__magic_name__      )

        A_							:       Tuple	      =				'''lower newer'''

        A_							:       Tuple	      =				processor(text=__magic_name__      )

        A_							:       List[Any]	      =				tokenizer(__magic_name__ ,       return_token_type_ids=__magic_name__      )

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

        """simple docstring"""

        A_							:       List[str]	      =				self.get_image_processor()
        A_							:       Tuple	      =				self.get_tokenizer()

        A_							:       List[str]	      =				GitProcessor(tokenizer=__magic_name__ ,       image_processor=__magic_name__      )

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

        A_							:       Union[str, Any]	      =				processor(text=__magic_name__ ,       images=__magic_name__      )

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

        # test if it raises when no input is passed
        with pytest.raises(__magic_name__      ):
               processor()
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Optional[Any]	      =				self.get_image_processor()
        A_							:       Optional[int]	      =				self.get_tokenizer()

        A_							:       Optional[int]	      =				GitProcessor(tokenizer=__magic_name__ ,       image_processor=__magic_name__      )

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

        A_							:       List[Any]	      =				processor.batch_decode(__magic_name__      )
        A_							:       Dict	      =				tokenizer.batch_decode(__magic_name__      )

        self.assertListEqual(__magic_name__ ,       __magic_name__      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       int	      =				self.get_image_processor()
        A_							:       Optional[int]	      =				self.get_tokenizer()

        A_							:       str	      =				GitProcessor(tokenizer=__magic_name__ ,       image_processor=__magic_name__      )

        A_							:       Tuple	      =				'''lower newer'''
        A_							:       List[str]	      =				self.prepare_image_inputs()

        A_							:       Optional[Any]	      =				processor(text=__magic_name__ ,       images=__magic_name__      )

        # For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
        self.assertListEqual(list(inputs.keys()      ) ,       ['''input_ids''', '''attention_mask''', '''pixel_values''']      )


