






import unittest

from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import BertGenerationDecoder, BertGenerationEncoder






class 						lowercase_   :






      '''simple docstring'''




      def __init__( self :		int					,   __UpperCAmelCase :		Tuple					,   __UpperCAmelCase :		Dict=13					,   __UpperCAmelCase :		List[str]=7					,   __UpperCAmelCase :		List[Any]=True					,   __UpperCAmelCase :		Union[str, Any]=True					,   __UpperCAmelCase :		int=99					,   __UpperCAmelCase :		Any=32					,   __UpperCAmelCase :		str=5					,   __UpperCAmelCase :		Tuple=4					,   __UpperCAmelCase :		List[str]=37					,   __UpperCAmelCase :		Dict="gelu"					,   __UpperCAmelCase :		Union[str, Any]=0.1					,   __UpperCAmelCase :		Optional[Any]=0.1					,   __UpperCAmelCase :		Any=50					,   __UpperCAmelCase :		List[str]=0.02					,   __UpperCAmelCase :		Any=True					,   __UpperCAmelCase :		Optional[int]=None					,   )       ->List[str]:




       """simple docstring"""


       a						    = parent
       a						    = batch_size
       a						    = seq_length
       a						    = is_training
       a						    = use_input_mask
       a						    = vocab_size
       a						    = hidden_size
       a						    = num_hidden_layers
       a						    = num_attention_heads
       a						    = intermediate_size
       a						    = hidden_act
       a						    = hidden_dropout_prob
       a						    = attention_probs_dropout_prob
       a						    = max_position_embeddings
       a						    = initializer_range
       a						    = use_labels
       a						    = scope







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




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

       a						    = self.get_config()

       return config, input_ids, input_mask, token_labels







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




       """simple docstring"""


       return BertGenerationConfig(
           vocab_size=self.vocab_size					,   hidden_size=self.hidden_size					,   num_hidden_layers=self.num_hidden_layers					,   num_attention_heads=self.num_attention_heads					,   intermediate_size=self.intermediate_size					,   hidden_act=self.hidden_act					,   hidden_dropout_prob=self.hidden_dropout_prob					,   attention_probs_dropout_prob=self.attention_probs_dropout_prob					,   max_position_embeddings=self.max_position_embeddings					,   is_decoder=__UpperCAmelCase					,   initializer_range=self.initializer_range					,   )







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




       """simple docstring"""


       (
           (
           a
       )    ,	(
           a
       )    ,	(
           a
       )    ,	(
           a
       )    ,	
       )						    = self.prepare_config_and_inputs()

       a						    = True
       a						    = floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
       a						    = ids_tensor([self.batch_size, self.seq_length]					,   vocab_size=2    )

       return (
           config,
           input_ids,
           input_mask,
           token_labels,
           encoder_hidden_states,
           encoder_attention_mask,
       )







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




       """simple docstring"""


       a						    = BertGenerationEncoder(config=__UpperCAmelCase    )
       model.to(__UpperCAmelCase    )
       model.eval()
       a						    = model(__UpperCAmelCase					,   attention_mask=__UpperCAmelCase    )
       a						    = model(__UpperCAmelCase    )
       self.parent.assertEqual(result.last_hidden_state.shape					,   (self.batch_size, self.seq_length, self.hidden_size)    )







      def  __lowerCAmelCase   ( self :		List[Any]					,   __UpperCAmelCase :		Optional[Any]					,   __UpperCAmelCase :		int					,   __UpperCAmelCase :		Tuple					,   __UpperCAmelCase :		List[str]					,   __UpperCAmelCase :		str					,   __UpperCAmelCase :		str					,   **__UpperCAmelCase :		List[str]					,   )       ->Union[str, Any]:




       """simple docstring"""


       a						    = True
       a						    = BertGenerationEncoder(config=__UpperCAmelCase    )
       model.to(__UpperCAmelCase    )
       model.eval()
       a						    = model(
           __UpperCAmelCase					,   attention_mask=__UpperCAmelCase					,   encoder_hidden_states=__UpperCAmelCase					,   encoder_attention_mask=__UpperCAmelCase					,   )
       a						    = model(
           __UpperCAmelCase					,   attention_mask=__UpperCAmelCase					,   encoder_hidden_states=__UpperCAmelCase					,   )
       self.parent.assertEqual(result.last_hidden_state.shape					,   (self.batch_size, self.seq_length, self.hidden_size)    )







      def  __lowerCAmelCase   ( self :		Optional[int]					,   __UpperCAmelCase :		Dict					,   __UpperCAmelCase :		Dict					,   __UpperCAmelCase :		Tuple					,   __UpperCAmelCase :		int					,   __UpperCAmelCase :		Dict					,   __UpperCAmelCase :		Optional[int]					,   **__UpperCAmelCase :		List[str]					,   )       ->int:




       """simple docstring"""


       a						    = True
       a						    = True
       a						    = BertGenerationDecoder(config=__UpperCAmelCase    ).to(__UpperCAmelCase    ).eval()

       # first forward pass
       a						    = model(
           __UpperCAmelCase					,   attention_mask=__UpperCAmelCase					,   encoder_hidden_states=__UpperCAmelCase					,   encoder_attention_mask=__UpperCAmelCase					,   use_cache=__UpperCAmelCase					,   )
       a						    = outputs.past_key_values

       # create hypothetical multiple next token and extent to next_input_ids
       a						    = ids_tensor((self.batch_size, 3)					,   config.vocab_size    )
       a						    = ids_tensor((self.batch_size, 3)					,   vocab_size=2    )

       # append to next input_ids and
       a						    = torch.cat([input_ids, next_tokens]					,   dim=-1    )
       a						    = torch.cat([input_mask, next_mask]					,   dim=-1    )

       a						    = model(
           __UpperCAmelCase					,   attention_mask=__UpperCAmelCase					,   encoder_hidden_states=__UpperCAmelCase					,   encoder_attention_mask=__UpperCAmelCase					,   output_hidden_states=__UpperCAmelCase					,   )['''hidden_states'''][0]
       a						    = model(
           __UpperCAmelCase					,   attention_mask=__UpperCAmelCase					,   encoder_hidden_states=__UpperCAmelCase					,   encoder_attention_mask=__UpperCAmelCase					,   past_key_values=__UpperCAmelCase					,   output_hidden_states=__UpperCAmelCase					,   )['''hidden_states'''][0]

       # select random slice
       a						    = ids_tensor((1,)					,   output_from_past.shape[-1]    ).item()
       a						    = output_from_no_past[:, -3:, random_slice_idx].detach()
       a						    = output_from_past[:, :, random_slice_idx].detach()

       self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]    )

       # test that outputs are equal for slice
       self.parent.assertTrue(torch.allclose(__UpperCAmelCase					,   __UpperCAmelCase					,   atol=1e-3    )    )







      def  __lowerCAmelCase   ( self :		Tuple					,   __UpperCAmelCase :		int					,   __UpperCAmelCase :		Union[str, Any]					,   __UpperCAmelCase :		int					,   __UpperCAmelCase :		int					,   *__UpperCAmelCase :		Optional[int]					,   )       ->Any:




       """simple docstring"""


       a						    = BertGenerationDecoder(__UpperCAmelCase    )
       model.to(__UpperCAmelCase    )
       model.eval()
       a						    = model(__UpperCAmelCase					,   attention_mask=__UpperCAmelCase					,   labels=__UpperCAmelCase    )
       self.parent.assertEqual(result.logits.shape					,   (self.batch_size, self.seq_length, self.vocab_size)    )







      def  __lowerCAmelCase   ( self :		Any    )       ->int:




       """simple docstring"""


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






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






      '''simple docstring'''




      __snake_case	    =    (BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
      __snake_case	    =    (BertGenerationDecoder,) if is_torch_available() else ()
      __snake_case	    =    (
          {'''feature-extraction''': BertGenerationEncoder, '''text-generation''': BertGenerationDecoder}
          if is_torch_available()
          else {}
      )







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




       """simple docstring"""


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







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




       """simple docstring"""


       self.config_tester.run_common_tests()







      def  __lowerCAmelCase   ( self :		Dict    )       ->int:




       """simple docstring"""


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







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




       """simple docstring"""


       a    ,	a    ,	a    ,	a						    = self.model_tester.prepare_config_and_inputs()
       a						    = '''bert'''
       self.model_tester.create_and_check_model(__UpperCAmelCase					,   __UpperCAmelCase					,   __UpperCAmelCase					,   __UpperCAmelCase    )







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




       """simple docstring"""


       a						    = self.model_tester.prepare_config_and_inputs_for_decoder()
       self.model_tester.create_and_check_model_as_decoder(*__UpperCAmelCase    )







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




       """simple docstring"""


       a						    = self.model_tester.prepare_config_and_inputs_for_decoder()
       self.model_tester.create_and_check_decoder_model_past_large_inputs(*__UpperCAmelCase    )







      def  __lowerCAmelCase   ( self :		Dict    )       ->Dict:




       """simple docstring"""


       (
           (
           a
       )    ,	(
           a
       )    ,	(
           a
       )    ,	(
           a
       )    ,	(
           a
       )    ,	(
           a
       )    ,	
       )						    = self.model_tester.prepare_config_and_inputs_for_decoder()

       a						    = None

       self.model_tester.create_and_check_model_as_decoder(
           __UpperCAmelCase					,   __UpperCAmelCase					,   __UpperCAmelCase					,   __UpperCAmelCase					,   __UpperCAmelCase					,   __UpperCAmelCase					,   )







      def  __lowerCAmelCase   ( self :		Tuple    )       ->str:




       """simple docstring"""


       a						    = self.model_tester.prepare_config_and_inputs_for_decoder()
       self.model_tester.create_and_check_for_causal_lm(*__UpperCAmelCase    )







      @slow
      def  __lowerCAmelCase   ( self :		Union[str, Any]    )       ->Dict:




       """simple docstring"""


       a						    = BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''    )
       self.assertIsNotNone(__UpperCAmelCase    )






@require_torch
class 						lowercase_   (      unittest.TestCase  ):






      '''simple docstring'''




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




       """simple docstring"""


       a						    = BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''    )
       a						    = torch.tensor([[101, 7_592, 1_010, 2_026, 3_899, 2_003, 10_140, 102]]    )
       with torch.no_grad():
        a						    = model(__UpperCAmelCase    )[0]
       a						    = torch.Size([1, 8, 1_024]    )
       self.assertEqual(output.shape					,   __UpperCAmelCase    )
       a						    = torch.tensor(
           [[[0.1775, 0.0083, -0.0321], [1.6002, 0.1287, 0.3912], [2.1473, 0.5791, 0.6066]]]    )
       self.assertTrue(torch.allclose(output[:, :3, :3]					,   __UpperCAmelCase					,   atol=1e-4    )    )






@require_torch
class 						lowercase_   (      unittest.TestCase  ):






      '''simple docstring'''




      @slow
      def  __lowerCAmelCase   ( self :		Dict    )       ->Dict:




       """simple docstring"""


       a						    = BertGenerationDecoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''    )
       a						    = torch.tensor([[101, 7_592, 1_010, 2_026, 3_899, 2_003, 10_140, 102]]    )
       with torch.no_grad():
        a						    = model(__UpperCAmelCase    )[0]
       a						    = torch.Size([1, 8, 50_358]    )
       self.assertEqual(output.shape					,   __UpperCAmelCase    )
       a						    = torch.tensor(
           [[[-0.5788, -2.5994, -3.7054], [0.0438, 4.7997, 1.8795], [1.5862, 6.6409, 4.4638]]]    )
       self.assertTrue(torch.allclose(output[:, :3, :3]					,   __UpperCAmelCase					,   atol=1e-4    )    )










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))


