






from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask


if is_tf_available():
     import tensorflow as tf

     from transformers import TFBlipTextModel
     from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST






class 						lowercase_   :






      '''simple docstring'''




      def __init__( self :		Tuple					,   __UpperCAmelCase :		Dict					,   __UpperCAmelCase :		Dict=12					,   __UpperCAmelCase :		Tuple=7					,   __UpperCAmelCase :		str=True					,   __UpperCAmelCase :		List[str]=True					,   __UpperCAmelCase :		Tuple=True					,   __UpperCAmelCase :		List[Any]=99					,   __UpperCAmelCase :		Tuple=32					,   __UpperCAmelCase :		Any=32					,   __UpperCAmelCase :		str=2					,   __UpperCAmelCase :		Union[str, Any]=4					,   __UpperCAmelCase :		Tuple=37					,   __UpperCAmelCase :		str=0.1					,   __UpperCAmelCase :		Any=0.1					,   __UpperCAmelCase :		Any=512					,   __UpperCAmelCase :		Optional[Any]=0.02					,   __UpperCAmelCase :		Union[str, Any]=0					,   __UpperCAmelCase :		Union[str, Any]=None					,   )       ->Dict:




       """simple docstring"""


       a						    = parent
       a						    = batch_size
       a						    = seq_length
       a						    = is_training
       a						    = use_input_mask
       a						    = use_labels
       a						    = vocab_size
       a						    = hidden_size
       a						    = projection_dim
       a						    = num_hidden_layers
       a						    = num_attention_heads
       a						    = intermediate_size
       a						    = dropout
       a						    = attention_dropout
       a						    = max_position_embeddings
       a						    = initializer_range
       a						    = scope
       a						    = bos_token_id







      def  __lowerCAmelCase   ( self :		Optional[int]    )       ->Any:




       """simple docstring"""


       a						    = ids_tensor([self.batch_size, self.seq_length]					,   self.vocab_size    )

       a						    = None
       if self.use_input_mask:
        a						    = random_attention_mask([self.batch_size, self.seq_length]    )

       if input_mask is not None:
        a						    = input_mask.numpy()
        a    ,	a						    = input_mask.shape
        a						    = np.random.randint(1					,   seq_length - 1					,   size=(batch_size,)    )
        for batch_idx, start_index in enumerate(__UpperCAmelCase    ):
         a						    = 1
         a						    = 0

       a						    = self.get_config()

       return config, input_ids, tf.convert_to_tensor(__UpperCAmelCase    )







      def  __lowerCAmelCase   ( self :		Tuple    )       ->Dict:




       """simple docstring"""


       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  __lowerCAmelCase   ( self :		Tuple					,   __UpperCAmelCase :		Dict					,   __UpperCAmelCase :		List[str]					,   __UpperCAmelCase :		str    )       ->Union[str, Any]:




       """simple docstring"""


       a						    = TFBlipTextModel(config=__UpperCAmelCase    )
       a						    = model(__UpperCAmelCase					,   attention_mask=__UpperCAmelCase					,   training=__UpperCAmelCase    )
       a						    = model(__UpperCAmelCase					,   training=__UpperCAmelCase    )
       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  __lowerCAmelCase   ( self :		str    )       ->List[str]:




       """simple docstring"""


       a						    = self.prepare_config_and_inputs()
       a    ,	a    ,	a						    = config_and_inputs
       a						    = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
       return config, inputs_dict






@require_tf
class 						lowercase_   (      lowercase					,    unittest.TestCase  ):






      '''simple docstring'''




      __snake_case	    =    (TFBlipTextModel,) if is_tf_available() else ()
      __snake_case	    =    False
      __snake_case	    =    False
      __snake_case	    =    False







      def  __lowerCAmelCase   ( self :		Union[str, Any]    )       ->List[str]:




       """simple docstring"""


       a						    = BlipTextModelTester(self    )
       a						    = ConfigTester(self					,   config_class=__UpperCAmelCase					,   hidden_size=37    )







      def  __lowerCAmelCase   ( self :		Any    )       ->List[str]:




       """simple docstring"""


       self.config_tester.run_common_tests()







      def  __lowerCAmelCase   ( self :		Any    )       ->List[str]:




       """simple docstring"""


       a						    = self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__UpperCAmelCase    )







      def  __lowerCAmelCase   ( self :		Dict    )       ->List[Any]:




       """simple docstring"""


       pass







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




       """simple docstring"""


       pass







      @unittest.skip(reason='''Blip does not use inputs_embeds'''    )
      def  __lowerCAmelCase   ( self :		int    )       ->List[str]:




       """simple docstring"""


       pass







      @unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING'''    )
      def  __lowerCAmelCase   ( self :		Union[str, Any]    )       ->List[str]:




       """simple docstring"""


       pass







      @unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING'''    )
      def  __lowerCAmelCase   ( self :		str    )       ->Any:




       """simple docstring"""


       pass







      @slow
      def  __lowerCAmelCase   ( self :		List[Any]    )       ->str:




       """simple docstring"""


       for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        a						    = TFBlipTextModel.from_pretrained(__UpperCAmelCase    )
        self.assertIsNotNone(__UpperCAmelCase    )







      def  __lowerCAmelCase   ( self :		List[Any]					,   __UpperCAmelCase :		Any=True    )       ->str:




       """simple docstring"""


       super().test_pt_tf_model_equivalence(allow_missing_keys=__UpperCAmelCase    )










def 		_a       ( a					:list					)				->     list:

 if len(a					) < 2:
  return collection

 def circle_sort_util(a					:list      ,				a					:int      ,				a					:int					) -> bool:
  a						    = False

  if low == high:
   return swapped

  a						    = low
  a						    = high

  while left < right:
   if collection[left] > collection[right]:
    a    ,	a						    = (
        collection[right],
        collection[left],
    )
    a						    = True

   left += 1
   right -= 1

  if left == right and collection[left] > collection[right + 1]:
   a    ,	a						    = (
       collection[right + 1],
       collection[left],
   )

   a						    = True

  a						    = low + int((high - low) / 2					)
  a						    = circle_sort_util(a      ,				a      ,				a					)
  a						    = circle_sort_util(a      ,				mid + 1      ,				a					)

  return swapped or left_swap or right_swap

 a						    = True

 while is_not_sorted is True:
  a						    = circle_sort_util(a      ,				0      ,				len(a					) - 1					)

 return collection


if __name__ == "__main__":
     UpperCAmelCase__							  =						input("Enter numbers separated by a comma:\n").strip()
     UpperCAmelCase__							  =						[int(item) for item in user_input.split(",")]
     print(circle_sort(unsorted))


