'''simple docstring'''

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						:


   def __init__(   self				:	List[Any]   ,		__lowerCAmelCase				:	List[str]   ,		__lowerCAmelCase				:	Any=13   ,		__lowerCAmelCase				:	str=7   ,		__lowerCAmelCase				:	Dict=True   ,		__lowerCAmelCase				:	List[str]=True   ,		__lowerCAmelCase				:	Tuple=99   ,		__lowerCAmelCase				:	Any=32   ,		__lowerCAmelCase				:	Union[str, Any]=5   ,		__lowerCAmelCase				:	List[str]=4   ,		__lowerCAmelCase				:	Union[str, Any]=37   ,		__lowerCAmelCase				:	Optional[int]="gelu"   ,		__lowerCAmelCase				:	str=0.1   ,		__lowerCAmelCase				:	Any=0.1   ,		__lowerCAmelCase				:	Union[str, Any]=50   ,		__lowerCAmelCase				:	Union[str, Any]=0.02   ,		__lowerCAmelCase				:	Optional[int]=True   ,		__lowerCAmelCase				:	List[str]=None   ,		)      ->					Optional[int]:
         lowercase_     =      parent
         lowercase_     =      batch_size
         lowercase_     =      seq_length
         lowercase_     =      is_training
         lowercase_     =      use_input_mask
         lowercase_     =      vocab_size
         lowercase_     =      hidden_size
         lowercase_     =      num_hidden_layers
         lowercase_     =      num_attention_heads
         lowercase_     =      intermediate_size
         lowercase_     =      hidden_act
         lowercase_     =      hidden_dropout_prob
         lowercase_     =      attention_probs_dropout_prob
         lowercase_     =      max_position_embeddings
         lowercase_     =      initializer_range
         lowercase_     =      use_labels
         lowercase_     =      scope


   def __UpperCAmelCase       (   self				:	List[Any])      ->					Dict:
         lowercase_     =      ids_tensor([self.batch_size, self.seq_length]   ,		self.vocab_size)

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

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

         lowercase_     =      self.get_config()

         return config, input_ids, input_mask, token_labels


   def __UpperCAmelCase       (   self				:	Any)      ->					List[Any]:
         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=A__   ,		initializer_range=self.initializer_range   ,		)


   def __UpperCAmelCase       (   self				:	Any)      ->					int:
         (
             lowercase_     
         )     =      self.prepare_config_and_inputs()

         lowercase_     =      True
         lowercase_     =      floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
         lowercase_     =      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 __UpperCAmelCase       (   self				:	Optional[Any]   ,		__lowerCAmelCase				:	int   ,		__lowerCAmelCase				:	int   ,		__lowerCAmelCase				:	Optional[Any]   ,		__lowerCAmelCase				:	Any   ,		**__lowerCAmelCase				:	List[Any]   ,		)      ->					Dict:
         lowercase_     =      BertGenerationEncoder(config=A__)
         model.to(A__)
         model.eval()
         lowercase_     =      model(A__   ,		attention_mask=A__)
         lowercase_     =      model(A__)
         self.parent.assertEqual(result.last_hidden_state.shape   ,		(self.batch_size, self.seq_length, self.hidden_size))


   def __UpperCAmelCase       (   self				:	List[str]   ,		__lowerCAmelCase				:	Optional[int]   ,		__lowerCAmelCase				:	Any   ,		__lowerCAmelCase				:	int   ,		__lowerCAmelCase				:	Optional[Any]   ,		__lowerCAmelCase				:	Any   ,		__lowerCAmelCase				:	Optional[int]   ,		**__lowerCAmelCase				:	Tuple   ,		)      ->					Union[str, Any]:
         lowercase_     =      True
         lowercase_     =      BertGenerationEncoder(config=A__)
         model.to(A__)
         model.eval()
         lowercase_     =      model(
             A__   ,		attention_mask=A__   ,		encoder_hidden_states=A__   ,		encoder_attention_mask=A__   ,		)
         lowercase_     =      model(
             A__   ,		attention_mask=A__   ,		encoder_hidden_states=A__   ,		)
         self.parent.assertEqual(result.last_hidden_state.shape   ,		(self.batch_size, self.seq_length, self.hidden_size))


   def __UpperCAmelCase       (   self				:	Union[str, Any]   ,		__lowerCAmelCase				:	List[Any]   ,		__lowerCAmelCase				:	Union[str, Any]   ,		__lowerCAmelCase				:	List[str]   ,		__lowerCAmelCase				:	Optional[Any]   ,		__lowerCAmelCase				:	str   ,		__lowerCAmelCase				:	Union[str, Any]   ,		**__lowerCAmelCase				:	Union[str, Any]   ,		)      ->					Any:
         lowercase_     =      True
         lowercase_     =      True
         lowercase_     =      BertGenerationDecoder(config=A__).to(A__).eval()

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

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

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

         lowercase_     =      model(
             A__   ,		attention_mask=A__   ,		encoder_hidden_states=A__   ,		encoder_attention_mask=A__   ,		output_hidden_states=A__   ,		)["""hidden_states"""][0]
         lowercase_     =      model(
             A__   ,		attention_mask=A__   ,		encoder_hidden_states=A__   ,		encoder_attention_mask=A__   ,		past_key_values=A__   ,		output_hidden_states=A__   ,		)["""hidden_states"""][0]

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


   def __UpperCAmelCase       (   self				:	str   ,		__lowerCAmelCase				:	Optional[Any]   ,		__lowerCAmelCase				:	str   ,		__lowerCAmelCase				:	int   ,		__lowerCAmelCase				:	Tuple   ,		*__lowerCAmelCase				:	int   ,		)      ->					List[str]:
         lowercase_     =      BertGenerationDecoder(A__)
         model.to(A__)
         model.eval()
         lowercase_     =      model(A__   ,		attention_mask=A__   ,		labels=A__)
         self.parent.assertEqual(result.logits.shape   ,		(self.batch_size, self.seq_length, self.vocab_size))


   def __UpperCAmelCase       (   self				:	int)      ->					Dict:
         lowercase_     =      self.prepare_config_and_inputs()
         lowercase_     =      {"""input_ids""": input_ids, """attention_mask""": input_mask}
         return config, inputs_dict





@require_torch
class 							lowercase						(						__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase ,				unittest.TestCase ):
   lowerCamelCase_    					=(BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
   lowerCamelCase_    					=(BertGenerationDecoder,) if is_torch_available() else ()
   lowerCamelCase_    					=(
       {'''feature-extraction''': BertGenerationEncoder, '''text-generation''': BertGenerationDecoder}
       if is_torch_available()
       else {}
   )


   def __UpperCAmelCase       (   self				:	Optional[int])      ->					Optional[int]:
         lowercase_     =      BertGenerationEncoderTester(self)
         lowercase_     =      ConfigTester(self   ,		config_class=A__   ,		hidden_size=37)


   def __UpperCAmelCase       (   self				:	List[str])      ->					List[str]:
         self.config_tester.run_common_tests()


   def __UpperCAmelCase       (   self				:	Union[str, Any])      ->					Optional[Any]:
         lowercase_     =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*A__)


   def __UpperCAmelCase       (   self				:	int)      ->					str:
         lowercase_     =      self.model_tester.prepare_config_and_inputs()
         lowercase_     =      """bert"""
         self.model_tester.create_and_check_model(A__   ,		A__   ,		A__   ,		A__)


   def __UpperCAmelCase       (   self				:	Optional[Any])      ->					Optional[int]:
         lowercase_     =      self.model_tester.prepare_config_and_inputs_for_decoder()
         self.model_tester.create_and_check_model_as_decoder(*A__)


   def __UpperCAmelCase       (   self				:	List[Any])      ->					Dict:
         lowercase_     =      self.model_tester.prepare_config_and_inputs_for_decoder()
         self.model_tester.create_and_check_decoder_model_past_large_inputs(*A__)


   def __UpperCAmelCase       (   self				:	List[Any])      ->					Optional[int]:
         # This regression test was failing with PyTorch < 1.3
         (
             lowercase_     
         )     =      self.model_tester.prepare_config_and_inputs_for_decoder()

         lowercase_     =      None

         self.model_tester.create_and_check_model_as_decoder(
             A__   ,		A__   ,		A__   ,		A__   ,		A__   ,		A__   ,		)


   def __UpperCAmelCase       (   self				:	str)      ->					int:
         lowercase_     =      self.model_tester.prepare_config_and_inputs_for_decoder()
         self.model_tester.create_and_check_for_causal_lm(*A__)


   @slow
   def __UpperCAmelCase       (   self				:	List[str])      ->					Dict:
         lowercase_     =      BertGenerationEncoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder")
         self.assertIsNotNone(A__)





@require_torch
class 							lowercase						(						unittest.TestCase ):


   @slow
   def __UpperCAmelCase       (   self				:	List[Any])      ->					int:
         lowercase_     =      BertGenerationEncoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder")
         lowercase_     =      torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 1_0140, 102]])
         with torch.no_grad():
               lowercase_     =      model(A__)[0]
         lowercase_     =      torch.Size([1, 8, 1024])
         self.assertEqual(output.shape   ,		A__)
         lowercase_     =      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]   ,		A__   ,		atol=1e-4))





@require_torch
class 							lowercase						(						unittest.TestCase ):


   @slow
   def __UpperCAmelCase       (   self				:	Tuple)      ->					str:
         lowercase_     =      BertGenerationDecoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder")
         lowercase_     =      torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 1_0140, 102]])
         with torch.no_grad():
               lowercase_     =      model(A__)[0]
         lowercase_     =      torch.Size([1, 8, 5_0358])
         self.assertEqual(output.shape   ,		A__)
         lowercase_     =      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]   ,		A__   ,		atol=1e-4))







'''simple docstring'''

# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

lowerCAmelCase_ : Dict						  =      TypeVar("T")





class 							lowercase						(						Generic[T] ):


   def __init__(   self				:	int   ,		__lowerCAmelCase				:	bool = True)      ->					None:
         lowercase_     =      {}  # dictionary of lists
         lowercase_     =      directed


   def __UpperCAmelCase       (   self				:	int   ,		__lowerCAmelCase				:	T   ,		__lowerCAmelCase				:	T)      ->					GraphAdjacencyList[T]:

         if not self.directed:  # For undirected graphs
               # if both source vertex and destination vertex are both present in the
               # adjacency list, add destination vertex to source vertex list of adjacent
               # vertices and add source vertex to destination vertex list of adjacent
               # vertices.
               if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                     self.adj_list[source_vertex].append(__lowerCAmelCase)
                     self.adj_list[destination_vertex].append(__lowerCAmelCase)
               # if only source vertex is present in adjacency list, add destination vertex
               # to source vertex list of adjacent vertices, then create a new vertex with
               # destination vertex as key and assign a list containing the source vertex
               # as it's first adjacent vertex.
               elif source_vertex in self.adj_list:
                     self.adj_list[source_vertex].append(__lowerCAmelCase)
                     lowercase_     =      [source_vertex]
               # if only destination vertex is present in adjacency list, add source vertex
               # to destination vertex list of adjacent vertices, then create a new vertex
               # with source vertex as key and assign a list containing the source vertex
               # as it's first adjacent vertex.
               elif destination_vertex in self.adj_list:
                     self.adj_list[destination_vertex].append(__lowerCAmelCase)
                     lowercase_     =      [destination_vertex]
               # if both source vertex and destination vertex are not present in adjacency
               # list, create a new vertex with source vertex as key and assign a list
               # containing the destination vertex as it's first adjacent vertex also
               # create a new vertex with destination vertex as key and assign a list
               # containing the source vertex as it's first adjacent vertex.
               else:
                     lowercase_     =      [destination_vertex]
                     lowercase_     =      [source_vertex]
         else:  # For directed graphs
               # if both source vertex and destination vertex are present in adjacency
               # list, add destination vertex to source vertex list of adjacent vertices.
               if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                     self.adj_list[source_vertex].append(__lowerCAmelCase)
               # if only source vertex is present in adjacency list, add destination
               # vertex to source vertex list of adjacent vertices and create a new vertex
               # with destination vertex as key, which has no adjacent vertex
               elif source_vertex in self.adj_list:
                     self.adj_list[source_vertex].append(__lowerCAmelCase)
                     lowercase_     =      []
               # if only destination vertex is present in adjacency list, create a new
               # vertex with source vertex as key and assign a list containing destination
               # vertex as first adjacent vertex
               elif destination_vertex in self.adj_list:
                     lowercase_     =      [destination_vertex]
               # if both source vertex and destination vertex are not present in adjacency
               # list, create a new vertex with source vertex as key and a list containing
               # destination vertex as it's first adjacent vertex. Then create a new vertex
               # with destination vertex as key, which has no adjacent vertex
               else:
                     lowercase_     =      [destination_vertex]
                     lowercase_     =      []

         return self


   def __repr__(   self				:	str)      ->					str:
         return pformat(self.adj_list)






