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 					A							:


   def __init__(self					:						List[Any]	,  __UpperCAmelCase					:						Dict	,  __UpperCAmelCase					:						int=1_3	,  __UpperCAmelCase					:						List[str]=7	,  __UpperCAmelCase					:						Dict=True	,  __UpperCAmelCase					:						int=True	,  __UpperCAmelCase					:						Union[str, Any]=9_9	,  __UpperCAmelCase					:						Dict=3_2	,  __UpperCAmelCase					:						Dict=5	,  __UpperCAmelCase					:						Optional[Any]=4	,  __UpperCAmelCase					:						Tuple=3_7	,  __UpperCAmelCase					:						Optional[Any]="gelu"	,  __UpperCAmelCase					:						List[Any]=0.1	,  __UpperCAmelCase					:						Optional[Any]=0.1	,  __UpperCAmelCase					:						Union[str, Any]=5_0	,  __UpperCAmelCase					:						Union[str, Any]=0.02	,  __UpperCAmelCase					:						Optional[Any]=True	,  __UpperCAmelCase					:						str=None	,  )						->    Dict:
         """simple docstring"""



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


   def       lowercase_				(self					:						str		)						->    Optional[int]:
         """simple docstring"""



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

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

         if self.use_labels:
               UpperCAmelCase__								=  ids_tensor([self.batch_size, self.seq_length]	,  self.vocab_size		)

         UpperCAmelCase__								=  self.get_config()

         return config, input_ids, input_mask, token_labels


   def       lowercase_				(self					:						Any		)						->    str:
         """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       lowercase_				(self					:						Dict		)						->    Tuple:
         """simple docstring"""



         (
             (
             UpperCAmelCase__
         )		,				(
             UpperCAmelCase__
         )		,				(
             UpperCAmelCase__
         )		,				(
             UpperCAmelCase__
         )		,				
         )								=  self.prepare_config_and_inputs()

         UpperCAmelCase__								=  True
         UpperCAmelCase__								=  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
         UpperCAmelCase__								=  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       lowercase_				(self					:						int	,  __UpperCAmelCase					:						Dict	,  __UpperCAmelCase					:						str	,  __UpperCAmelCase					:						List[str]	,  __UpperCAmelCase					:						Union[str, Any]	,  **__UpperCAmelCase					:						List[str]	,  )						->    Optional[int]:
         """simple docstring"""



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


   def       lowercase_				(self					:						str	,  __UpperCAmelCase					:						Tuple	,  __UpperCAmelCase					:						Dict	,  __UpperCAmelCase					:						Dict	,  __UpperCAmelCase					:						int	,  __UpperCAmelCase					:						List[Any]	,  __UpperCAmelCase					:						List[Any]	,  **__UpperCAmelCase					:						List[Any]	,  )						->    int:
         """simple docstring"""



         UpperCAmelCase__								=  True
         UpperCAmelCase__								=  BertGenerationEncoder(config=__UpperCAmelCase		)
         model.to(__UpperCAmelCase		)
         model.eval()
         UpperCAmelCase__								=  model(
             __UpperCAmelCase	,  attention_mask=__UpperCAmelCase	,  encoder_hidden_states=__UpperCAmelCase	,  encoder_attention_mask=__UpperCAmelCase	,  )
         UpperCAmelCase__								=  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       lowercase_				(self					:						int	,  __UpperCAmelCase					:						str	,  __UpperCAmelCase					:						Optional[int]	,  __UpperCAmelCase					:						str	,  __UpperCAmelCase					:						Any	,  __UpperCAmelCase					:						Optional[Any]	,  __UpperCAmelCase					:						Optional[Any]	,  **__UpperCAmelCase					:						Union[str, Any]	,  )						->    List[str]:
         """simple docstring"""



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

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

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

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

         UpperCAmelCase__								=  model(
             __UpperCAmelCase	,  attention_mask=__UpperCAmelCase	,  encoder_hidden_states=__UpperCAmelCase	,  encoder_attention_mask=__UpperCAmelCase	,  output_hidden_states=__UpperCAmelCase	,  )["hidden_states"][0]
         UpperCAmelCase__								=  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
         UpperCAmelCase__								=  ids_tensor((1,)	,  output_from_past.shape[-1]		).item()
         UpperCAmelCase__								=  output_from_no_past[:, -3:, random_slice_idx].detach()
         UpperCAmelCase__								=  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       lowercase_				(self					:						Dict	,  __UpperCAmelCase					:						int	,  __UpperCAmelCase					:						int	,  __UpperCAmelCase					:						List[str]	,  __UpperCAmelCase					:						List[str]	,  *__UpperCAmelCase					:						Union[str, Any]	,  )						->    Optional[Any]:
         """simple docstring"""



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





   def       lowercase_				(self					:						List[Any]		)						->    Optional[int]:
         """simple docstring"""



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





@require_torch
class 					A							(    UpperCAmelCase_		,    UpperCAmelCase_		,    UpperCAmelCase_		,    unittest.TestCase					):
   __UpperCAmelCase			:			str              =      (BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
   __UpperCAmelCase			:			List[str]              =      (BertGenerationDecoder,) if is_torch_available() else ()
   __UpperCAmelCase			:			Optional[Any]              =      (
       {'feature-extraction': BertGenerationEncoder, 'text-generation': BertGenerationDecoder}
       if is_torch_available()
       else {}
   )


   def       lowercase_				(self					:						str		)						->    List[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  BertGenerationEncoderTester(self		)
         UpperCAmelCase__								=  ConfigTester(self	,  config_class=__UpperCAmelCase	,  hidden_size=3_7		)


   def       lowercase_				(self					:						List[Any]		)						->    Union[str, Any]:
         """simple docstring"""



         self.config_tester.run_common_tests()


   def       lowercase_				(self					:						Any		)						->    Optional[int]:
         """simple docstring"""



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


   def       lowercase_				(self					:						int		)						->    Any:
         """simple docstring"""



         UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs()
         UpperCAmelCase__								=  "bert"
         self.model_tester.create_and_check_model(__UpperCAmelCase	,  __UpperCAmelCase	,  __UpperCAmelCase	,  __UpperCAmelCase		)


   def       lowercase_				(self					:						Dict		)						->    Dict:
         """simple docstring"""



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


   def       lowercase_				(self					:						Dict		)						->    Union[str, Any]:
         """simple docstring"""



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


   def       lowercase_				(self					:						List[str]		)						->    int:
         """simple docstring"""



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

         UpperCAmelCase__								=  None

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


   def       lowercase_				(self					:						Any		)						->    Optional[Any]:
         """simple docstring"""



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





   @slow
   def       lowercase_				(self					:						Optional[Any]		)						->    str:
         """simple docstring"""



         UpperCAmelCase__								=  BertGenerationEncoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder"		)
         self.assertIsNotNone(__UpperCAmelCase		)





@require_torch
class 					A							(    unittest.TestCase					):


   @slow
   def       lowercase_				(self					:						List[str]		)						->    Any:
         """simple docstring"""



         UpperCAmelCase__								=  BertGenerationEncoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder"		)
         UpperCAmelCase__								=  torch.tensor([[1_0_1, 7_5_9_2, 1_0_1_0, 2_0_2_6, 3_8_9_9, 2_0_0_3, 1_0_1_4_0, 1_0_2]]		)
         with torch.no_grad():
               UpperCAmelCase__								=  model(__UpperCAmelCase		)[0]
         UpperCAmelCase__								=  torch.Size([1, 8, 1_0_2_4]		)
         self.assertEqual(output.shape	,  __UpperCAmelCase		)
         UpperCAmelCase__								=  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 					A							(    unittest.TestCase					):


   @slow
   def       lowercase_				(self					:						Dict		)						->    Any:
         """simple docstring"""



         UpperCAmelCase__								=  BertGenerationDecoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder"		)
         UpperCAmelCase__								=  torch.tensor([[1_0_1, 7_5_9_2, 1_0_1_0, 2_0_2_6, 3_8_9_9, 2_0_0_3, 1_0_1_4_0, 1_0_2]]		)
         with torch.no_grad():
               UpperCAmelCase__								=  model(__UpperCAmelCase		)[0]
         UpperCAmelCase__								=  torch.Size([1, 8, 5_0_3_5_8]		)
         self.assertEqual(output.shape	,  __UpperCAmelCase		)
         UpperCAmelCase__								=  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		)		)




from __future__ import annotations




def        lowerCAmelCase_			(					__A							)					->    list[int]:







      '''simple docstring'''


      if len(__A							) == 0:
            return array

      UpperCAmelCase__		,				UpperCAmelCase__								=  min(__A							), max(__A							)

      # Compute the variables
      UpperCAmelCase__								=  _max - _min + 1
      UpperCAmelCase__		,				UpperCAmelCase__								=  [0] * holes_range, [0] * holes_range

      # Make the sorting.
      for i in array:
            UpperCAmelCase__								=  i - _min
            UpperCAmelCase__								=  i
            holes_repeat[index] += 1

      # Makes the array back by replacing the numbers.
      UpperCAmelCase__								=  0
      for i in range(__A							):
            while holes_repeat[i] > 0:
                  UpperCAmelCase__								=  holes[i]
                  index += 1
                  holes_repeat[i] -= 1

    # Returns the sorted array.
      return array


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      UpperCamelCase__    							= input('Enter numbers separated by comma:\n')
      UpperCamelCase__    							= [int(x) for x in user_input.split(',')]
      print(pigeon_sort(unsorted))



