




import pytest

from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs



@pytest.mark.parametrize(
    "kwargs, expected"       ,    [
        ({"num_shards": 0, "max_num_jobs": 1}, []),
        ({"num_shards": 10, "max_num_jobs": 1}, [range(10      )]),
        ({"num_shards": 10, "max_num_jobs": 10}, [range(lowerCAmelCase_       ,    i + 1      ) for i in range(10      )]),
        ({"num_shards": 1, "max_num_jobs": 10}, [range(1      )]),
        ({"num_shards": 10, "max_num_jobs": 3}, [range(0       ,    4      ), range(4       ,    7      ), range(7       ,    10      )]),
        ({"num_shards": 3, "max_num_jobs": 10}, [range(0       ,    1      ), range(1       ,    2      ), range(2       ,    3      )]),
    ]       ,    )
def 							_A       (						lowerCAmelCase_					: List[str]       ,    lowerCAmelCase_					: Optional[Any]      ):
     """simple docstring"""

     lowerCAmelCase__          =			_distribute_shards(**lowerCAmelCase_      )
     assert out == expected



@pytest.mark.parametrize(
    "gen_kwargs, max_num_jobs, expected"       ,    [
        ({"foo": 0}, 10, [{"foo": 0}]),
        ({"shards": [0, 1, 2, 3]}, 1, [{"shards": [0, 1, 2, 3]}]),
        ({"shards": [0, 1, 2, 3]}, 4, [{"shards": [0]}, {"shards": [1]}, {"shards": [2]}, {"shards": [3]}]),
        ({"shards": [0, 1]}, 4, [{"shards": [0]}, {"shards": [1]}]),
        ({"shards": [0, 1, 2, 3]}, 2, [{"shards": [0, 1]}, {"shards": [2, 3]}]),
    ]       ,    )
def 							_A       (						lowerCAmelCase_					: str       ,    lowerCAmelCase_					: Optional[Any]       ,    lowerCAmelCase_					: Tuple      ):
     """simple docstring"""

     lowerCAmelCase__          =			_split_gen_kwargs(lowerCAmelCase_       ,    lowerCAmelCase_      )
     assert out == expected






@pytest.mark.parametrize(
    "gen_kwargs, expected"       ,    [
        ({"foo": 0}, 1),
        ({"shards": [0]}, 1),
        ({"shards": [0, 1, 2, 3]}, 4),
        ({"shards": [0, 1, 2, 3], "foo": 0}, 4),
        ({"shards": [0, 1, 2, 3], "other": (0, 1)}, 4),
        ({"shards": [0, 1, 2, 3], "shards2": [0, 1]}, RuntimeError),
    ]       ,    )
def 							_A       (						lowerCAmelCase_					: Dict       ,    lowerCAmelCase_					: Tuple      ):
     """simple docstring"""

     if expected is RuntimeError:
          with pytest.raises(lowerCAmelCase_      ):
               _number_of_shards_in_gen_kwargs(lowerCAmelCase_      )
     else:
          lowerCAmelCase__          =			_number_of_shards_in_gen_kwargs(lowerCAmelCase_      )
          assert out == expected






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, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast





@require_vision
class 			__lowerCamelCase	(   unittest.TestCase   ):




    """simple docstring"""

    def        a				(	self					:					Dict       )				->	Optional[int]:
         lowerCAmelCase__          =			tempfile.mkdtemp()

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

         lowerCAmelCase__          =			BlipaProcessor(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )

         processor.save_pretrained(self.tmpdirname       )


    def        a				(	self					:					int       ,     **SCREAMING_SNAKE_CASE__					:					Optional[Any]       )				->	Optional[Any]:
         return AutoProcessor.from_pretrained(self.tmpdirname       ,     **SCREAMING_SNAKE_CASE__       ).tokenizer


    def        a				(	self					:					Tuple       ,     **SCREAMING_SNAKE_CASE__					:					Tuple       )				->	Optional[Any]:
         return AutoProcessor.from_pretrained(self.tmpdirname       ,     **SCREAMING_SNAKE_CASE__       ).image_processor


    def        a				(	self					:					str       )				->	int:
         shutil.rmtree(self.tmpdirname       )


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

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

         return image_inputs


    def        a				(	self					:					str       )				->	Dict:
         lowerCAmelCase__          =			BlipaProcessor(tokenizer=self.get_tokenizer()       ,     image_processor=self.get_image_processor()       )
         processor.save_pretrained(self.tmpdirname       )

         lowerCAmelCase__          =			self.get_tokenizer(bos_token="(BOS)"       ,     eos_token="(EOS)"       )
         lowerCAmelCase__          =			self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE__       ,     padding_value=1.0       )

         lowerCAmelCase__          =			BlipaProcessor.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        a				(	self					:					int       )				->	str:
         lowerCAmelCase__          =			self.get_image_processor()
         lowerCAmelCase__          =			self.get_tokenizer()

         lowerCAmelCase__          =			BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__       ,     image_processor=SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			self.prepare_image_inputs()

         lowerCAmelCase__          =			image_processor(SCREAMING_SNAKE_CASE__       ,     return_tensors="np"       )
         lowerCAmelCase__          =			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        a				(	self					:					Tuple       )				->	int:
         lowerCAmelCase__          =			self.get_image_processor()
         lowerCAmelCase__          =			self.get_tokenizer()

         lowerCAmelCase__          =			BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__       ,     image_processor=SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			"lower newer"

         lowerCAmelCase__          =			processor(text=SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			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        a				(	self					:					Dict       )				->	str:
         lowerCAmelCase__          =			self.get_image_processor()
         lowerCAmelCase__          =			self.get_tokenizer()

         lowerCAmelCase__          =			BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__       ,     image_processor=SCREAMING_SNAKE_CASE__       )

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

         lowerCAmelCase__          =			processor(text=SCREAMING_SNAKE_CASE__       ,     images=SCREAMING_SNAKE_CASE__       )

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

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


    def        a				(	self					:					str       )				->	List[str]:
         lowerCAmelCase__          =			self.get_image_processor()
         lowerCAmelCase__          =			self.get_tokenizer()

         lowerCAmelCase__          =			BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__       ,     image_processor=SCREAMING_SNAKE_CASE__       )

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

         lowerCAmelCase__          =			processor.batch_decode(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			tokenizer.batch_decode(SCREAMING_SNAKE_CASE__       )

         self.assertListEqual(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					List[str]       )				->	Any:
         lowerCAmelCase__          =			self.get_image_processor()
         lowerCAmelCase__          =			self.get_tokenizer()

         lowerCAmelCase__          =			BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__       ,     image_processor=SCREAMING_SNAKE_CASE__       )

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

         lowerCAmelCase__          =			processor(text=SCREAMING_SNAKE_CASE__       ,     images=SCREAMING_SNAKE_CASE__       )

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