



"""simple docstring"""




import unittest

from transformers import SpeechaTextaConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

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


if is_torch_available():
     import torch

     from transformers.models.speech_to_text_a.modeling_speech_to_text_a import (
         SpeechaTextaDecoder,
         SpeechaTextaForCausalLM,
     )


@require_torch
class 	a__		:


   def __init__(				self   :				int,  lowerCAmelCase   :				str,  lowerCAmelCase   :				Any=99,  lowerCAmelCase   :				str=13,  lowerCAmelCase   :				List[str]=16,  lowerCAmelCase   :				Dict=7,  lowerCAmelCase   :				Tuple=True,  lowerCAmelCase   :				Union[str, Any]=True,  lowerCAmelCase   :				List[str]=True,  lowerCAmelCase   :				Tuple=False,  lowerCAmelCase   :				Any=True,  lowerCAmelCase   :				Any=2,  lowerCAmelCase   :				Optional[Any]=32,  lowerCAmelCase   :				int=4,  lowerCAmelCase   :				Optional[int]=4,  lowerCAmelCase   :				Tuple=30,  lowerCAmelCase   :				Union[str, Any]=0,  lowerCAmelCase   :				Any=1,  lowerCAmelCase   :				Any=2,  lowerCAmelCase   :				Tuple=None,  )     ->							List[Any]:
         lowercase	:   Optional[Any]											= parent
         lowercase	:   Dict											= batch_size
         lowercase	:   List[Any]											= decoder_seq_length
         # For common tests
         lowercase	:   Optional[int]											= self.decoder_seq_length
         lowercase	:   int											= is_training
         lowercase	:   Optional[int]											= use_attention_mask
         lowercase	:   Optional[int]											= use_labels

         lowercase	:   int											= vocab_size
         lowercase	:   int											= d_model
         lowercase	:   str											= d_model
         lowercase	:   int											= decoder_layers
         lowercase	:   Union[str, Any]											= decoder_layers
         lowercase	:   str											= decoder_ffn_dim
         lowercase	:   Any											= decoder_attention_heads
         lowercase	:   Optional[Any]											= decoder_attention_heads
         lowercase	:   Optional[int]											= eos_token_id
         lowercase	:   Any											= bos_token_id
         lowercase	:   int											= pad_token_id
         lowercase	:   Any											= decoder_start_token_id
         lowercase	:   Tuple											= use_cache
         lowercase	:   List[Any]											= max_position_embeddings

         lowercase	:   List[str]											= None
         lowercase	:   str											= decoder_seq_length
         lowercase	:   Optional[Any]											= 2
         lowercase	:   Tuple											= 1


   def 							lowercase					(				self   :				List[str]      )     ->							Optional[int]:
         lowercase	:   Union[str, Any]											= ids_tensor([self.batch_size, self.decoder_seq_length],  self.vocab_size      )

         lowercase	:   Optional[Any]											= None
         if self.use_attention_mask:
               lowercase	:   Any											= ids_tensor([self.batch_size, self.decoder_seq_length],  vocab_size=2      )

         lowercase	:   int											= None
         if self.use_labels:
               lowercase	:   Tuple											= ids_tensor([self.batch_size, self.decoder_seq_length],  self.vocab_size      )

         lowercase	:   Tuple											= SpeechaTextaConfig(
             vocab_size=self.vocab_size,  d_model=self.d_model,  decoder_layers=self.decoder_layers,  decoder_ffn_dim=self.decoder_ffn_dim,  decoder_attention_heads=self.decoder_attention_heads,  eos_token_id=self.eos_token_id,  bos_token_id=self.bos_token_id,  use_cache=self.use_cache,  pad_token_id=self.pad_token_id,  decoder_start_token_id=self.decoder_start_token_id,  max_position_embeddings=self.max_position_embeddings,  )

         return (
             config,
             input_ids,
             attention_mask,
             lm_labels,
         )


   def 							lowercase					(				self   :				Any,  lowerCAmelCase   :				Optional[int],  lowerCAmelCase   :				int,  lowerCAmelCase   :				Union[str, Any],  lowerCAmelCase   :				Dict,  )     ->							Tuple:
         lowercase	:   Tuple											= True
         lowercase	:   List[Any]											= SpeechaTextaDecoder(config=lowerCAmelCase      ).to(lowerCAmelCase      ).eval()
         lowercase	:   Optional[int]											= input_ids[:2]

         input_ids[input_ids == 0] += 1
         # first forward pass
         lowercase	:   Optional[int]											= model(lowerCAmelCase,  use_cache=lowerCAmelCase      )
         lowercase	:   Union[str, Any]											= model(lowerCAmelCase      )
         lowercase	:   int											= model(lowerCAmelCase,  use_cache=lowerCAmelCase      )

         self.parent.assertTrue(len(lowerCAmelCase      ) == len(lowerCAmelCase      )      )
         self.parent.assertTrue(len(lowerCAmelCase      ) == len(lowerCAmelCase      ) + 1      )

         lowercase	:   List[Any]											= outputs['past_key_values']

         # create hypothetical next token and extent to next_input_ids
         lowercase	:   List[str]											= ids_tensor((2, 1),  config.vocab_size - 1      ) + 1

         # append to next input_ids and
         lowercase	:   int											= torch.cat([input_ids, next_tokens],  dim=-1      )

         print(lowerCAmelCase      )

         lowercase	:   Any											= model(lowerCAmelCase      )['last_hidden_state']
         lowercase	:   List[str]											= model(lowerCAmelCase,  past_key_values=lowerCAmelCase      )['last_hidden_state']

         # select random slice
         lowercase	:   str											= ids_tensor((1,),  output_from_past.shape[-1]      ).item()
         lowercase	:   Union[str, Any]											= output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
         lowercase	:   List[str]											= output_from_past[:, 0, random_slice_idx].detach()

         # test that outputs are equal for slice
         assert torch.allclose(lowerCAmelCase,  lowerCAmelCase,  atol=1e-3      )






   def 							lowercase					(				self   :				List[Any]      )     ->							Tuple:
         lowercase	:   Dict											= self.prepare_config_and_inputs()
         (
             (
             lowercase
         )						,     (
             lowercase
         )						,     (
             lowercase
         )						,     (
             lowercase
         )						,     
         )	:   str											= config_and_inputs

         lowercase	:   str											= {
             'input_ids': input_ids,
             'attention_mask': attention_mask,
         }
         return config, inputs_dict




@require_torch
class 	a__		(    SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__,	unittest.TestCase  ):
   _lowerCamelCase       				=					(SpeechaTextaDecoder, SpeechaTextaForCausalLM) if is_torch_available() else ()
   _lowerCamelCase       				=					(SpeechaTextaForCausalLM,) if is_torch_available() else ()
   _lowerCamelCase       				=					{'text-generation': SpeechaTextaForCausalLM} if is_torch_available() else {}
   _lowerCamelCase       				=					True
   _lowerCamelCase       				=					False


   def 							lowercase					(				self   :				Optional[Any],  )     ->							Optional[Any]:
         lowercase	:   str											= SpeechaTextaStandaloneDecoderModelTester(self,  is_training=lowerCAmelCase      )
         lowercase	:   str											= ConfigTester(self,  config_class=lowerCAmelCase      )


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


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


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


   def 							lowercase					(				self   :				Any      )     ->							Any:
         self.config_tester.run_common_tests()


   def 							lowercase					(				self   :				Optional[Any]      )     ->							Union[str, Any]:
         lowercase	:   Any											= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_decoder_model_past(*lowerCAmelCase      )






   def 							lowercase					(				self   :				Any      )     ->							int:
         return





"""simple docstring"""




from __future__ import annotations

from collections import defaultdict
from dataclasses import dataclass


@dataclass
class 	a__		:
   _lowerCamelCase       				=					42
   _lowerCamelCase       				=					None
   _lowerCamelCase       				=					None




def 	lowercase__  (      )  ->      TreeNode:






      '''simple docstring'''


      return TreeNode(3						,   TreeNode(9					)						,   TreeNode(20						,   TreeNode(15					)						,   TreeNode(7					)					)					)


def 	lowercase__  (      _UpperCAmelCase					)  ->      list[int]:






      '''simple docstring'''


      def depth_first_search(
          _UpperCAmelCase						,   _UpperCAmelCase						,   _UpperCAmelCase					) -> None:
            if not root:
                  return

            if depth == len(_UpperCAmelCase					):
                  right_view.append(root.val					)

            depth_first_search(root.right						,   depth + 1						,   _UpperCAmelCase					)
            depth_first_search(root.left						,   depth + 1						,   _UpperCAmelCase					)

      lowercase	:   list											= []
      if not root:
            return right_view

      depth_first_search(_UpperCAmelCase						,   0						,   _UpperCAmelCase					)
      return right_view


def 	lowercase__  (      _UpperCAmelCase					)  ->      list[int]:






      '''simple docstring'''


      def depth_first_search(
          _UpperCAmelCase						,   _UpperCAmelCase						,   _UpperCAmelCase					) -> None:
            if not root:
                  return

            if depth == len(_UpperCAmelCase					):
                  left_view.append(root.val					)

            depth_first_search(root.left						,   depth + 1						,   _UpperCAmelCase					)
            depth_first_search(root.right						,   depth + 1						,   _UpperCAmelCase					)

      lowercase	:   list											= []
      if not root:
            return left_view

      depth_first_search(_UpperCAmelCase						,   0						,   _UpperCAmelCase					)
      return left_view


def 	lowercase__  (      _UpperCAmelCase					)  ->      list[int]:






      '''simple docstring'''


      def breadth_first_search(_UpperCAmelCase						,   _UpperCAmelCase					) -> None:
            lowercase	:   List[str]											= [(root, 0)]
            lowercase	:   List[Any]											= defaultdict(_UpperCAmelCase					)

            while queue:
                  lowercase	:   Union[str, Any]											= queue.pop(0					)
                  lowercase						,     lowercase	:   str											= first

                  lookup[hd].append(node.val					)

                  if node.left:
                        queue.append((node.left, hd - 1)					)
                  if node.right:
                        queue.append((node.right, hd + 1)					)

            for pair in sorted(lookup.items()						,   key=lambda _UpperCAmelCase					: each[0]					):
                  top_view.append(pair[1][0]					)

      lowercase	:   list											= []
      if not root:
            return top_view

      breadth_first_search(_UpperCAmelCase						,   _UpperCAmelCase					)
      return top_view


def 	lowercase__  (      _UpperCAmelCase					)  ->      list[int]:






      '''simple docstring'''


      from collections import defaultdict

      def breadth_first_search(_UpperCAmelCase						,   _UpperCAmelCase					) -> None:
            lowercase	:   Any											= [(root, 0)]
            lowercase	:   Dict											= defaultdict(_UpperCAmelCase					)

            while queue:
                  lowercase	:   str											= queue.pop(0					)
                  lowercase						,     lowercase	:   List[Any]											= first
                  lookup[hd].append(node.val					)

                  if node.left:
                        queue.append((node.left, hd - 1)					)
                  if node.right:
                        queue.append((node.right, hd + 1)					)

            for pair in sorted(lookup.items()						,   key=lambda _UpperCAmelCase					: each[0]					):
                  bottom_view.append(pair[1][-1]					)

      lowercase	:   list											= []
      if not root:
            return bottom_view

      breadth_first_search(_UpperCAmelCase						,   _UpperCAmelCase					)
      return bottom_view


if __name__ == "__main__":
     import doctest

     doctest.testmod()
