





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:						int		,  _lowerCAmelCase:						str		,  _lowerCAmelCase:						Union[str, Any]=13		,  _lowerCAmelCase:						List[Any]=7		,  _lowerCAmelCase:						Dict=True		,  _lowerCAmelCase:						int=True		,  _lowerCAmelCase:						Tuple=99		,  _lowerCAmelCase:						Optional[Any]=32		,  _lowerCAmelCase:						Any=5		,  _lowerCAmelCase:						List[str]=4		,  _lowerCAmelCase:						Dict=37		,  _lowerCAmelCase:						List[Any]="gelu"		,  _lowerCAmelCase:						Dict=0.1		,  _lowerCAmelCase:						Any=0.1		,  _lowerCAmelCase:						Optional[Any]=50		,  _lowerCAmelCase:						Optional[Any]=0.02		,  _lowerCAmelCase:						List[str]=True		,  _lowerCAmelCase:						List[Any]=None		,  )						->    int:
         '''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 	lowerCAmelCase__    (	self:						str       )						->    Any:
         '''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 	lowerCAmelCase__    (	self:						Any       )						->    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=snake_case_		,  initializer_range=self.initializer_range		,  )



      def 	lowerCAmelCase__    (	self:						List[str]       )						->    List[str]:
         '''simple docstring'''

         (
             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 	lowerCAmelCase__    (	self:						Any		,  _lowerCAmelCase:						Any		,  _lowerCAmelCase:						Dict		,  _lowerCAmelCase:						str		,  _lowerCAmelCase:						Any		,  **_lowerCAmelCase:						Dict		,  )						->    Dict:
         '''simple docstring'''

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



      def 	lowerCAmelCase__    (	self:						str		,  _lowerCAmelCase:						Tuple		,  _lowerCAmelCase:						Union[str, Any]		,  _lowerCAmelCase:						Optional[Any]		,  _lowerCAmelCase:						Dict		,  _lowerCAmelCase:						Tuple		,  _lowerCAmelCase:						Tuple		,  **_lowerCAmelCase:						Dict		,  )						->    Optional[int]:
         '''simple docstring'''

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



      def 	lowerCAmelCase__    (	self:						Dict		,  _lowerCAmelCase:						int		,  _lowerCAmelCase:						Union[str, Any]		,  _lowerCAmelCase:						Tuple		,  _lowerCAmelCase:						List[str]		,  _lowerCAmelCase:						Tuple		,  _lowerCAmelCase:						Optional[int]		,  **_lowerCAmelCase:						Union[str, Any]		,  )						->    Optional[int]:
         '''simple docstring'''

         UpperCAmelCase_											=True
         UpperCAmelCase_											=True
         UpperCAmelCase_											=BertGenerationDecoder(config=snake_case_       ).to(snake_case_       ).eval()

         # first forward pass
         UpperCAmelCase_											=model(
             snake_case_		,  attention_mask=snake_case_		,  encoder_hidden_states=snake_case_		,  encoder_attention_mask=snake_case_		,  use_cache=snake_case_		,  )
         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(
             snake_case_		,  attention_mask=snake_case_		,  encoder_hidden_states=snake_case_		,  encoder_attention_mask=snake_case_		,  output_hidden_states=snake_case_		,  )["hidden_states"][0]
         UpperCAmelCase_											=model(
             snake_case_		,  attention_mask=snake_case_		,  encoder_hidden_states=snake_case_		,  encoder_attention_mask=snake_case_		,  past_key_values=snake_case_		,  output_hidden_states=snake_case_		,  )["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(snake_case_		,  snake_case_		,  atol=1e-3       )       )



      def 	lowerCAmelCase__    (	self:						int		,  _lowerCAmelCase:						Union[str, Any]		,  _lowerCAmelCase:						Union[str, Any]		,  _lowerCAmelCase:						Union[str, Any]		,  _lowerCAmelCase:						Dict		,  *_lowerCAmelCase:						Optional[int]		,  )						->    Union[str, Any]:
         '''simple docstring'''

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






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

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


@require_torch
class      A       (	_snake_case		,      _snake_case		,      _snake_case		,      unittest.TestCase						):
      _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:						List[str]       )						->    Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_											=BertGenerationEncoderTester(self       )
         UpperCAmelCase_											=ConfigTester(self		,  config_class=snake_case_		,  hidden_size=37       )



      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    Union[str, Any]:
         '''simple docstring'''

         self.config_tester.run_common_tests()



      def 	lowerCAmelCase__    (	self:						Tuple       )						->    int:
         '''simple docstring'''

         UpperCAmelCase_											=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*snake_case_       )



      def 	lowerCAmelCase__    (	self:						List[Any]       )						->    Dict:
         '''simple docstring'''

         UpperCAmelCase_											=self.model_tester.prepare_config_and_inputs()
         UpperCAmelCase_											="bert"
         self.model_tester.create_and_check_model(snake_case_		,  snake_case_		,  snake_case_		,  snake_case_       )



      def 	lowerCAmelCase__    (	self:						int       )						->    Any:
         '''simple docstring'''

         UpperCAmelCase_											=self.model_tester.prepare_config_and_inputs_for_decoder()
         self.model_tester.create_and_check_model_as_decoder(*snake_case_       )



      def 	lowerCAmelCase__    (	self:						Any       )						->    Optional[Any]:
         '''simple docstring'''

         UpperCAmelCase_											=self.model_tester.prepare_config_and_inputs_for_decoder()
         self.model_tester.create_and_check_decoder_model_past_large_inputs(*snake_case_       )



      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    Dict:
         '''simple docstring'''

         (
             UpperCAmelCase_					
         )											=self.model_tester.prepare_config_and_inputs_for_decoder()

         UpperCAmelCase_											=None

         self.model_tester.create_and_check_model_as_decoder(
             snake_case_		,  snake_case_		,  snake_case_		,  snake_case_		,  snake_case_		,  snake_case_		,  )



      def 	lowerCAmelCase__    (	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(*snake_case_       )






      @slow
      def 	lowerCAmelCase__    (	self:						Tuple       )						->    Dict:
         '''simple docstring'''

         UpperCAmelCase_											=BertGenerationEncoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder"       )
         self.assertIsNotNone(snake_case_       )


@require_torch
class      A       (	unittest.TestCase						):



      @slow
      def 	lowerCAmelCase__    (	self:						List[Any]       )						->    Dict:
         '''simple docstring'''

         UpperCAmelCase_											=BertGenerationEncoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder"       )
         UpperCAmelCase_											=torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 1_0140, 102]]       )
         with torch.no_grad():
            UpperCAmelCase_											=model(snake_case_       )[0]
         UpperCAmelCase_											=torch.Size([1, 8, 1024]       )
         self.assertEqual(output.shape		,  snake_case_       )
         UpperCAmelCase_											=torch.tensor(
             [[[0.17_75, 0.00_83, -0.03_21], [1.60_02, 0.12_87, 0.39_12], [2.14_73, 0.57_91, 0.60_66]]]       )
         self.assertTrue(torch.allclose(output[:, :3, :3]		,  snake_case_		,  atol=1e-4       )       )


@require_torch
class      A       (	unittest.TestCase						):



      @slow
      def 	lowerCAmelCase__    (	self:						str       )						->    Any:
         '''simple docstring'''

         UpperCAmelCase_											=BertGenerationDecoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder"       )
         UpperCAmelCase_											=torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 1_0140, 102]]       )
         with torch.no_grad():
            UpperCAmelCase_											=model(snake_case_       )[0]
         UpperCAmelCase_											=torch.Size([1, 8, 5_0358]       )
         self.assertEqual(output.shape		,  snake_case_       )
         UpperCAmelCase_											=torch.tensor(
             [[[-0.57_88, -2.59_94, -3.70_54], [0.04_38, 4.79_97, 1.87_95], [1.58_62, 6.64_09, 4.46_38]]]       )
         self.assertTrue(torch.allclose(output[:, :3, :3]		,  snake_case_		,  atol=1e-4       )       )











from __future__ import annotations




def     a__ (					lowercase__  ,    lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_						,       UpperCAmelCase_											=set(lowercase__  ), [start]

   while stack:
      UpperCAmelCase_											=stack.pop()
      explored.add(lowercase__  )
      # Differences from BFS:
      # 1) pop last element instead of first one
      # 2) add adjacent elements to stack without exploring them
      for adj in reversed(graph[v]  ):
         if adj not in explored:
            stack.append(lowercase__  )
   return explored


__lowercase  :   Dict						      ={
    """A""": ["""B""", """C""", """D"""],
    """B""": ["""A""", """D""", """E"""],
    """C""": ["""A""", """F"""],
    """D""": ["""B""", """D"""],
    """E""": ["""B""", """F"""],
    """F""": ["""C""", """E""", """G"""],
    """G""": ["""F"""],
}

if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(depth_first_search(G, """A"""))




