


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 lowerCAmelCase_		(					unittest.TestCase			):

 '''simple docstring'''

 def 		UpperCamelCase__	(      self   ):
       snake_case_			= tempfile.mkdtemp()

       snake_case_			= CLIPImageProcessor()
       snake_case_			= BertTokenizer.from_pretrained(
           '''hf-internal-testing/tiny-random-BertModel'''						, model_input_names=['''input_ids''', '''attention_mask''']   )

       snake_case_			= GitProcessor(_SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE   )

       processor.save_pretrained(self.tmpdirname   )



 def 		UpperCamelCase__	(      self						, **_UpperCAmelCase   ):
       return AutoProcessor.from_pretrained(self.tmpdirname						, **_SCREAMING_SNAKE_CASE   ).tokenizer



 def 		UpperCamelCase__	(      self						, **_UpperCAmelCase   ):
       return AutoProcessor.from_pretrained(self.tmpdirname						, **_SCREAMING_SNAKE_CASE   ).image_processor



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



 def 		UpperCamelCase__	(      self   ):
       snake_case_			= [np.random.randint(2_55						, size=(3, 30, 4_00)						, dtype=np.uinta   )]

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

       return image_inputs



 def 		UpperCamelCase__	(      self   ):
       snake_case_			= GitProcessor(tokenizer=self.get_tokenizer()						, image_processor=self.get_image_processor()   )
       processor.save_pretrained(self.tmpdirname   )

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

       snake_case_			= GitProcessor.from_pretrained(
           self.tmpdirname						, bos_token='''(BOS)'''						, eos_token='''(EOS)'''						, do_normalize=_SCREAMING_SNAKE_CASE						, padding_value=1.0   )

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

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



 def 		UpperCamelCase__	(      self   ):
       snake_case_			= self.get_image_processor()
       snake_case_			= self.get_tokenizer()

       snake_case_			= GitProcessor(tokenizer=_SCREAMING_SNAKE_CASE						, image_processor=_SCREAMING_SNAKE_CASE   )

       snake_case_			= self.prepare_image_inputs()

       snake_case_			= image_processor(_SCREAMING_SNAKE_CASE						, return_tensors='''np'''   )
       snake_case_			= processor(images=_SCREAMING_SNAKE_CASE						, 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   ):
       snake_case_			= self.get_image_processor()
       snake_case_			= self.get_tokenizer()

       snake_case_			= GitProcessor(tokenizer=_SCREAMING_SNAKE_CASE						, image_processor=_SCREAMING_SNAKE_CASE   )

       snake_case_			= '''lower newer'''

       snake_case_			= processor(text=_SCREAMING_SNAKE_CASE   )

       snake_case_			= tokenizer(_SCREAMING_SNAKE_CASE						, return_token_type_ids=_SCREAMING_SNAKE_CASE   )

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



 def 		UpperCamelCase__	(      self   ):
       snake_case_			= self.get_image_processor()
       snake_case_			= self.get_tokenizer()

       snake_case_			= GitProcessor(tokenizer=_SCREAMING_SNAKE_CASE						, image_processor=_SCREAMING_SNAKE_CASE   )

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

       snake_case_			= processor(text=_SCREAMING_SNAKE_CASE						, images=_SCREAMING_SNAKE_CASE   )

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

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



 def 		UpperCamelCase__	(      self   ):
       snake_case_			= self.get_image_processor()
       snake_case_			= self.get_tokenizer()

       snake_case_			= GitProcessor(tokenizer=_SCREAMING_SNAKE_CASE						, image_processor=_SCREAMING_SNAKE_CASE   )

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

       snake_case_			= processor.batch_decode(_SCREAMING_SNAKE_CASE   )
       snake_case_			= tokenizer.batch_decode(_SCREAMING_SNAKE_CASE   )

       self.assertListEqual(_SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE   )





 def 		UpperCamelCase__	(      self   ):
       snake_case_			= self.get_image_processor()
       snake_case_			= self.get_tokenizer()

       snake_case_			= GitProcessor(tokenizer=_SCREAMING_SNAKE_CASE						, image_processor=_SCREAMING_SNAKE_CASE   )

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

       snake_case_			= processor(text=_SCREAMING_SNAKE_CASE						, images=_SCREAMING_SNAKE_CASE   )

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




import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


__snake_case  : Dict		       =    500_000

__snake_case       ,       __snake_case  : List[str]		       =    os.path.split(__file__)
__snake_case  : Optional[Any]		       =    os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))





@get_duration
def 	_UpperCAmelCase       (				_UpperCamelCase       :      datasets.Dataset  )      -> Union[str, Any]:
    A_      =  dataset.select(range(0,  len(_UpperCamelCase  ),  2  )  )





@get_duration
def 	_UpperCAmelCase       (				_UpperCamelCase       :      datasets.Dataset  )      -> str:
    A_      =  dataset.sort('''numbers'''  )





@get_duration
def 	_UpperCAmelCase       (				_UpperCamelCase       :      datasets.Dataset  )      -> str:
    A_      =  dataset.shuffle()





@get_duration
def 	_UpperCAmelCase       (				_UpperCamelCase       :      datasets.Dataset  )      -> Optional[Any]:
    A_      =  dataset.train_test_split(0.1  )





@get_duration
def 	_UpperCAmelCase       (				_UpperCamelCase       :      datasets.Dataset,  _UpperCamelCase       :      Optional[int]=10  )      -> Dict:
    for shard_id in range(_UpperCamelCase  ):
        A_      =  dataset.shard(_UpperCamelCase,  _UpperCamelCase  )





def 	_UpperCAmelCase       (				)      -> Any:
    A_      =  {'''num examples''': SPEED_TEST_N_EXAMPLES}
    A_      =  (select, sort, shuffle, train_test_split, shard)
    with tempfile.TemporaryDirectory() as tmp_dir:
        print('''generating dataset'''  )
        A_      =  datasets.Features({'''text''': datasets.Value('''string'''  ), '''numbers''': datasets.Value('''float32'''  )}  )
        A_      =  generate_example_dataset(
            os.path.join(_UpperCamelCase,  '''dataset.arrow'''  ),  _UpperCamelCase,  num_examples=_UpperCamelCase  )
        print('''Functions'''  )
        for func in functions:
            print(func.__name__  )
            A_      =  func(_UpperCamelCase  )

    with open(_UpperCamelCase,  '''wb'''  ) as f:
        f.write(json.dumps(_UpperCamelCase  ).encode('''utf-8'''  )  )


if __name__ == "__main__":  # useful to run the profiler
  benchmark_indices_mapping()






