





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 		UpperCamelCase__			:



     '''simple docstring'''

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




           '''simple docstring'''


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






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




           '''simple docstring'''


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

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

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

           SCREAMING_SNAKE_CASE		=						self.get_config()

           return config, input_ids, input_mask, token_labels






     def     SCREAMING_SNAKE_CASE__  (							self						:   Tuple	)					->		Dict:




           '''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=lowerCamelCase__						,initializer_range=self.initializer_range						,)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[Any]	)					->		Tuple:




           '''simple docstring'''


           (
               SCREAMING_SNAKE_CASE			
           )		=						self.prepare_config_and_inputs()

           SCREAMING_SNAKE_CASE		=						True
           SCREAMING_SNAKE_CASE		=						floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
           SCREAMING_SNAKE_CASE		=						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     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]						,lowerCamelCase__						:   Optional[int]						,lowerCamelCase__						:   Tuple						,lowerCamelCase__						:   Optional[Any]						,lowerCamelCase__						:   Dict						,**lowerCamelCase__						:   Optional[Any]						,)					->		Optional[int]:




           '''simple docstring'''


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






     def     SCREAMING_SNAKE_CASE__  (							self						:   int						,lowerCamelCase__						:   str						,lowerCamelCase__						:   Union[str, Any]						,lowerCamelCase__						:   Optional[Any]						,lowerCamelCase__						:   List[str]						,lowerCamelCase__						:   Dict						,lowerCamelCase__						:   List[Any]						,**lowerCamelCase__						:   Optional[int]						,)					->		Union[str, Any]:




           '''simple docstring'''


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






     def     SCREAMING_SNAKE_CASE__  (							self						:   Any						,lowerCamelCase__						:   Tuple						,lowerCamelCase__						:   Optional[Any]						,lowerCamelCase__						:   Dict						,lowerCamelCase__						:   List[Any]						,lowerCamelCase__						:   int						,lowerCamelCase__						:   Optional[Any]						,**lowerCamelCase__						:   Dict						,)					->		int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						True
           SCREAMING_SNAKE_CASE		=						True
           SCREAMING_SNAKE_CASE		=						BertGenerationDecoder(config=lowerCamelCase__	).to(lowerCamelCase__	).eval()

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

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

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

           SCREAMING_SNAKE_CASE		=						model(
               lowerCamelCase__						,attention_mask=lowerCamelCase__						,encoder_hidden_states=lowerCamelCase__						,encoder_attention_mask=lowerCamelCase__						,output_hidden_states=lowerCamelCase__						,)['''hidden_states'''][0]
           SCREAMING_SNAKE_CASE		=						model(
               lowerCamelCase__						,attention_mask=lowerCamelCase__						,encoder_hidden_states=lowerCamelCase__						,encoder_attention_mask=lowerCamelCase__						,past_key_values=lowerCamelCase__						,output_hidden_states=lowerCamelCase__						,)['''hidden_states'''][0]

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






     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[Any]						,lowerCamelCase__						:   Any						,lowerCamelCase__						:   Optional[int]						,lowerCamelCase__						:   List[str]						,lowerCamelCase__						:   List[str]						,*lowerCamelCase__						:   Union[str, Any]						,)					->		List[Any]:




           '''simple docstring'''


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






     def     SCREAMING_SNAKE_CASE__  (							self						:   Any	)					->		str:




           '''simple docstring'''


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

@require_torch
class 		UpperCamelCase__			(				__lowerCAmelCase							,				__lowerCAmelCase							,				__lowerCAmelCase							,				unittest.TestCase       ):



     '''simple docstring'''

     __snake_case   :     Dict		     =      (BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
     __snake_case   :     Optional[Any]		     =      (BertGenerationDecoder,) if is_torch_available() else ()
     __snake_case   :     Any		     =      (
         {"feature-extraction": BertGenerationEncoder, "text-generation": BertGenerationDecoder}
         if is_torch_available()
         else {}
     )






     def     SCREAMING_SNAKE_CASE__  (							self						:   Tuple	)					->		Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						BertGenerationEncoderTester(self	)
           SCREAMING_SNAKE_CASE		=						ConfigTester(self						,config_class=lowerCamelCase__						,hidden_size=37	)






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




           '''simple docstring'''


           self.config_tester.run_common_tests()






     def     SCREAMING_SNAKE_CASE__  (							self						:   str	)					->		List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Tuple	)					->		int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs()
           SCREAMING_SNAKE_CASE		=						'''bert'''
           self.model_tester.create_and_check_model(lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[str]	)					->		str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs_for_decoder()
           self.model_tester.create_and_check_model_as_decoder(*lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Dict	)					->		Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs_for_decoder()
           self.model_tester.create_and_check_decoder_model_past_large_inputs(*lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   int	)					->		Dict:




           '''simple docstring'''


           (
               SCREAMING_SNAKE_CASE			
           )		=						self.model_tester.prepare_config_and_inputs_for_decoder()

           SCREAMING_SNAKE_CASE		=						None

           self.model_tester.create_and_check_model_as_decoder(
               lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__						,)






     def     SCREAMING_SNAKE_CASE__  (							self						:   str	)					->		Dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs_for_decoder()
           self.model_tester.create_and_check_for_causal_lm(*lowerCamelCase__	)






     @slow
     def     SCREAMING_SNAKE_CASE__  (							self						:   Any	)					->		Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						BertGenerationEncoder.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder"""	)
           self.assertIsNotNone(lowerCamelCase__	)

@require_torch
class 		UpperCamelCase__			(				unittest.TestCase       ):



     '''simple docstring'''

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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						BertGenerationEncoder.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder"""	)
           SCREAMING_SNAKE_CASE		=						torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 10140, 102]]	)
           with torch.no_grad():
                 SCREAMING_SNAKE_CASE		=						model(lowerCamelCase__	)[0]
           SCREAMING_SNAKE_CASE		=						torch.Size([1, 8, 1024]	)
           self.assertEqual(output.shape						,lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						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]						,lowerCamelCase__						,atol=1e-4	)	)

@require_torch
class 		UpperCamelCase__			(				unittest.TestCase       ):



     '''simple docstring'''

     @slow
     def     SCREAMING_SNAKE_CASE__  (							self						:   Dict	)					->		int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						BertGenerationDecoder.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder"""	)
           SCREAMING_SNAKE_CASE		=						torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 10140, 102]]	)
           with torch.no_grad():
                 SCREAMING_SNAKE_CASE		=						model(lowerCamelCase__	)[0]
           SCREAMING_SNAKE_CASE		=						torch.Size([1, 8, 50358]	)
           self.assertEqual(output.shape						,lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						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]						,lowerCamelCase__						,atol=1e-4	)	)









class 		UpperCamelCase__			:



     '''simple docstring'''

     def __init__(							self						:   int	)					->		str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						{}






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[Any]	)					->		None:




           '''simple docstring'''


           print(self.vertex	)
           for i in self.vertex:
                 print(lowerCamelCase__						,""" -> """						,""" -> """.join([str(lowerCamelCase__	) for j in self.vertex[i]]	)	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[Any]						,lowerCamelCase__						:   int						,lowerCamelCase__						:   int	)					->		None:




           '''simple docstring'''


           if from_vertex in self.vertex:
                 self.vertex[from_vertex].append(lowerCamelCase__	)
           else:
                 # else make a new vertex
                 SCREAMING_SNAKE_CASE		=						[to_vertex]






     def     SCREAMING_SNAKE_CASE__  (							self						:   str	)					->		None:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						[False] * len(self.vertex	)

           # call the recursive helper function
           for i in range(len(self.vertex	)	):
                 if not visited[i]:
                       self.dfs_recursive(lowerCamelCase__						,lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Any						,lowerCamelCase__						:   int						,lowerCamelCase__						:   list	)					->		None:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						True

           print(lowerCamelCase__						,end=""" """	)

           # Recur for all the vertices that are adjacent to this node
           for i in self.vertex:
                 if not visited[i]:
                       self.dfs_recursive(lowerCamelCase__						,lowerCamelCase__	)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE_						       =	Graph()
      g.add_edge(0, 1)
      g.add_edge(0, 2)
      g.add_edge(1, 2)
      g.add_edge(2, 0)
      g.add_edge(2, 3)
      g.add_edge(3, 3)

      g.print_graph()
      print("""DFS:""")
      g.dfs()

      # OUTPUT:
      # 0  ->  1 -> 2
      # 1  ->  2
      # 2  ->  0 -> 3
      # 3  ->  3
      # DFS:
      #  0 1 2 3


