
'''simple docstring'''
import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask


if is_torch_available():
  import torch

  from transformers import BlipTextModel
  from transformers.models.blip.modeling_blip import BLIP_PRETRAINED_MODEL_ARCHIVE_LIST






class  UpperCAmelCase_					:





 def __init__(     self			,							lowercase_			,							lowercase_=12			,							lowercase_=7			,							lowercase_=True			,							lowercase_=True			,							lowercase_=True			,							lowercase_=99			,							lowercase_=32			,							lowercase_=32			,							lowercase_=5			,							lowercase_=4			,							lowercase_=37			,							lowercase_=0.1			,							lowercase_=0.1			,							lowercase_=5_12			,							lowercase_=0.02			,							lowercase_=0			,							lowercase_=None			,							):
     snake_case_			:     int        =    parent
     snake_case_			:     Optional[int]        =    batch_size
     snake_case_			:     int        =    seq_length
     snake_case_			:     str        =    is_training
     snake_case_			:     Optional[Any]        =    use_input_mask
     snake_case_			:     Any        =    use_labels
     snake_case_			:     Tuple        =    vocab_size
     snake_case_			:     List[str]        =    hidden_size
     snake_case_			:     Optional[int]        =    projection_dim
     snake_case_			:     Dict        =    num_hidden_layers
     snake_case_			:     List[Any]        =    num_attention_heads
     snake_case_			:     List[str]        =    intermediate_size
     snake_case_			:     str        =    dropout
     snake_case_			:     Optional[Any]        =    attention_dropout
     snake_case_			:     Optional[int]        =    max_position_embeddings
     snake_case_			:     Optional[Any]        =    initializer_range
     snake_case_			:     str        =    scope
     snake_case_			:     int        =    bos_token_id





 def 							snake_case__		(     self):
     snake_case_			:     str        =    ids_tensor([self.batch_size, self.seq_length]			,							self.vocab_size)

     snake_case_			:     str        =    None
     if self.use_input_mask:
         snake_case_			:     Union[str, Any]        =    random_attention_mask([self.batch_size, self.seq_length])

     if input_mask is not None:
         snake_case_      ,			snake_case_			:     Optional[int]        =    input_mask.shape
         snake_case_			:     Optional[Any]        =    np.random.randint(1			,							seq_length - 1			,							size=(batch_size,))
         for batch_idx, start_index in enumerate(lowercase_):
             snake_case_			:     Optional[int]        =    1
             snake_case_			:     Any        =    0

     snake_case_			:     Optional[Any]        =    self.get_config()

     return config, input_ids, input_mask





 def 							snake_case__		(     self):
     return BlipTextConfig(
         vocab_size=self.vocab_size			,							hidden_size=self.hidden_size			,							projection_dim=self.projection_dim			,							num_hidden_layers=self.num_hidden_layers			,							num_attention_heads=self.num_attention_heads			,							intermediate_size=self.intermediate_size			,							dropout=self.dropout			,							attention_dropout=self.attention_dropout			,							max_position_embeddings=self.max_position_embeddings			,							initializer_range=self.initializer_range			,							bos_token_id=self.bos_token_id			,							)





 def 							snake_case__		(     self			,							lowercase_			,							lowercase_			,							lowercase_):
     snake_case_			:     Any        =    BlipTextModel(config=lowercase_)
     model.to(lowercase_)
     model.eval()
     with torch.no_grad():
         snake_case_			:     int        =    model(lowercase_			,							attention_mask=lowercase_)
         snake_case_			:     Dict        =    model(lowercase_)
     self.parent.assertEqual(result.last_hidden_state.shape			,							(self.batch_size, self.seq_length, self.hidden_size))
     self.parent.assertEqual(result.pooler_output.shape			,							(self.batch_size, self.hidden_size))





 def 							snake_case__		(     self):
     snake_case_			:     str        =    self.prepare_config_and_inputs()
     snake_case_      ,			snake_case_      ,			snake_case_			:     List[Any]        =    config_and_inputs
     snake_case_			:     List[Any]        =    {"input_ids": input_ids, "attention_mask": input_mask}
     return config, inputs_dict






@require_torch
class  UpperCAmelCase_					(						snake_case__     ,    unittest.TestCase       ):
 UpperCAmelCase_		     =    (BlipTextModel,) if is_torch_available() else ()
 UpperCAmelCase_		     =    False
 UpperCAmelCase_		     =    False
 UpperCAmelCase_		     =    False





 def 							snake_case__		(     self):
     snake_case_			:     str        =    BlipTextModelTester(self)
     snake_case_			:     Optional[Any]        =    ConfigTester(self			,							config_class=lowercase_			,							hidden_size=37)





 def 							snake_case__		(     self):
     self.config_tester.run_common_tests()





 def 							snake_case__		(     self):
     snake_case_			:     Union[str, Any]        =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowercase_)





 def 							snake_case__		(     self):
     pass





 def 							snake_case__		(     self):
     pass





 @unittest.skip(reason="Blip does not use inputs_embeds")
 def 							snake_case__		(     self):
     pass





 @unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING")
 def 							snake_case__		(     self):
     pass





 @unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING")
 def 							snake_case__		(     self):
     pass





 @slow
 def 							snake_case__		(     self):
     for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         snake_case_			:     List[str]        =    BlipTextModel.from_pretrained(lowercase_)
         self.assertIsNotNone(lowercase_)





 def 							snake_case__		(     self):
     super().test_pt_tf_model_equivalence(allow_missing_keys=lowercase_)






'''simple docstring'''
def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    if right <= left:
        return
    snake_case_			:     Dict        =    left
    snake_case_			:     Tuple        =    right
    snake_case_			:     List[str]        =    sorting[left]
    while i <= b:
        if sorting[i] < pivot:
            snake_case_      ,			snake_case_			:     Union[str, Any]        =    sorting[i], sorting[a]
            a += 1
            i += 1
        elif sorting[i] > pivot:
            snake_case_      ,			snake_case_			:     List[str]        =    sorting[i], sorting[b]
            b -= 1
        else:
            i += 1
    quick_sort_apartition(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		a - 1							)
    quick_sort_apartition(__SCREAMING_SNAKE_CASE,		b + 1,		__SCREAMING_SNAKE_CASE							)

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    if left < right:
        snake_case_			:     Dict        =    lomuto_partition(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							)
        quick_sort_lomuto_partition(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		pivot_index - 1							)
        quick_sort_lomuto_partition(__SCREAMING_SNAKE_CASE,		pivot_index + 1,		__SCREAMING_SNAKE_CASE							)

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    snake_case_			:     List[Any]        =    sorting[right]
    snake_case_			:     Optional[int]        =    left
    for i in range(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
        if sorting[i] < pivot:
            snake_case_      ,			snake_case_			:     Dict        =    sorting[i], sorting[store_index]
            store_index += 1
    snake_case_      ,			snake_case_			:     Optional[Any]        =    sorting[store_index], sorting[right]
    return store_index

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    if len(__SCREAMING_SNAKE_CASE							) <= 1:
        return sorting
    return (
        three_way_radix_quicksort([i for i in sorting if i < sorting[0]]							)
        + [i for i in sorting if i == sorting[0]]
        + three_way_radix_quicksort([i for i in sorting if i > sorting[0]]							)
    )


if __name__ == "__main__":
  import doctest

  doctest.testmod(verbose=True)

  a_           =    input("Enter numbers separated by a comma:\n").strip()
  a_           =    [int(item) for item in user_input.split(",")]
  quick_sort_apartition(unsorted, 0, len(unsorted) - 1)
  print(unsorted)




