






"""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   _lowercase			:


      def __init__(     self					:						Union[str, Any]				, a					:						Any				, a					:						Any=9_9				, a					:						int=1_3				, a					:						List[Any]=1_6				, a					:						List[str]=7				, a					:						Union[str, Any]=True				, a					:						List[str]=True				, a					:						List[Any]=True				, a					:						Any=False				, a					:						Optional[int]=True				, a					:						str=2				, a					:						Optional[int]=3_2				, a					:						Dict=4				, a					:						List[str]=4				, a					:						Union[str, Any]=3_0				, a					:						int=0				, a					:						Tuple=1				, a					:						Any=2				, a					:						int=None				, ):





            """simple docstring"""







            __snake_case   :     Any	      =parent
            __snake_case   :     str	      =batch_size
            __snake_case   :     str	      =decoder_seq_length
            # For common tests
            __snake_case   :     Optional[Any]	      =self.decoder_seq_length
            __snake_case   :     List[Any]	      =is_training
            __snake_case   :     List[Any]	      =use_attention_mask
            __snake_case   :     Optional[Any]	      =use_labels

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

            __snake_case   :     Any	      =None
            __snake_case   :     Union[str, Any]	      =decoder_seq_length
            __snake_case   :     Tuple	      =2
            __snake_case   :     List[str]	      =1


      def _UpperCamelCase    (     self					:						List[str]    ):





            """simple docstring"""







            __snake_case   :     int	      =ids_tensor([self.batch_size, self.decoder_seq_length]				, self.vocab_size    )

            __snake_case   :     List[Any]	      =None
            if self.use_attention_mask:
                  __snake_case   :     Union[str, Any]	      =ids_tensor([self.batch_size, self.decoder_seq_length]				, vocab_size=2    )

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

            __snake_case   :     Optional[Any]	      =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 _UpperCamelCase    (     self					:						List[Any]				, a					:						Any				, a					:						List[str]				, a					:						List[Any]				, a					:						Optional[int]				, ):





            """simple docstring"""







            __snake_case   :     Any	      =True
            __snake_case   :     List[str]	      =SpeechaTextaDecoder(config=a    ).to(a    ).eval()
            __snake_case   :     Dict	      =input_ids[:2]

            input_ids[input_ids == 0] += 1
            # first forward pass
            __snake_case   :     List[Any]	      =model(a				, use_cache=a    )
            __snake_case   :     Dict	      =model(a    )
            __snake_case   :     Tuple	      =model(a				, use_cache=a    )

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

            __snake_case   :     Optional[int]	      =outputs['''past_key_values''']

            # create hypothetical next token and extent to next_input_ids
            __snake_case   :     int	      =ids_tensor((2, 1)				, config.vocab_size - 1    ) + 1

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

            print(a    )

            __snake_case   :     Dict	      =model(a    )['''last_hidden_state''']
            __snake_case   :     Any	      =model(a				, past_key_values=a    )['''last_hidden_state''']

            # select random slice
            __snake_case   :     Tuple	      =ids_tensor((1,)				, output_from_past.shape[-1]    ).item()
            __snake_case   :     Any	      =output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
            __snake_case   :     Optional[Any]	      =output_from_past[:, 0, random_slice_idx].detach()

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







      def _UpperCamelCase    (     self					:						Union[str, Any]    ):





            """simple docstring"""







            __snake_case   :     Optional[int]	      =self.prepare_config_and_inputs()
            (
                (
                __snake_case
            )      ,					(
                __snake_case
            )      ,					(
                __snake_case
            )      ,					(
                __snake_case
            )      ,					
            )   :     Any	      =config_and_inputs

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



@require_torch
class   _lowercase			(  lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase		,	unittest.TestCase    ):
      _a :  Optional[int]										= (SpeechaTextaDecoder, SpeechaTextaForCausalLM) if is_torch_available() else ()
      _a :  Union[str, Any]										= (SpeechaTextaForCausalLM,) if is_torch_available() else ()
      _a :  List[str]										= {'''text-generation''': SpeechaTextaForCausalLM} if is_torch_available() else {}
      _a :  List[str]										= True
      _a :  str										= False


      def _UpperCamelCase    (     self					:						Optional[int]				, ):





            """simple docstring"""







            __snake_case   :     Optional[int]	      =SpeechaTextaStandaloneDecoderModelTester(self				, is_training=a    )
            __snake_case   :     Dict	      =ConfigTester(self				, config_class=a    )


      def _UpperCamelCase    (     self					:						Tuple    ):





            """simple docstring"""







            pass


      def _UpperCamelCase    (     self					:						List[str]    ):





            """simple docstring"""







            pass


      def _UpperCamelCase    (     self					:						Optional[Any]    ):





            """simple docstring"""







            pass


      def _UpperCamelCase    (     self					:						str    ):





            """simple docstring"""







            self.config_tester.run_common_tests()


      def _UpperCamelCase    (     self					:						Optional[int]    ):





            """simple docstring"""







            __snake_case   :     int	      =self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_decoder_model_past(*a    )







      def _UpperCamelCase    (     self					:						int    ):





            """simple docstring"""







            return










"""simple docstring"""





from __future__ import annotations


class   _lowercase			:


      def __init__(     self					:						int				, a					:						int = 0    ):





            """simple docstring"""







            __snake_case   :     Optional[Any]	      =value
            __snake_case   :     Node | None	      =None
            __snake_case   :     Node | None	      =None



def      __lowercase       (     a			:					Node | None							,		a			:					Node | None			)       ->       Node | None:
      if treea is None:
            return treea
      if treea is None:
            return treea

      __snake_case   :     Dict	      =treea.value + treea.value
      __snake_case   :     int	      =merge_two_binary_trees(treea.left							,		treea.left			)
      __snake_case   :     List[str]	      =merge_two_binary_trees(treea.right							,		treea.right			)
      return treea



def      __lowercase       (     a			:					Node | None			)       ->       None:
      if root:
            print(root.value			)
            print_preorder(root.left			)
            print_preorder(root.right			)


if __name__ == "__main__":
    UpperCamelCase_						:					List[str]      				=			Node(1)
    UpperCamelCase_						:					List[Any]      				=			Node(2)
    UpperCamelCase_						:					Optional[Any]      				=			Node(3)
    UpperCamelCase_						:					int      				=			Node(4)

    UpperCamelCase_						:					int      				=			Node(2)
    UpperCamelCase_						:					List[str]      				=			Node(4)
    UpperCamelCase_						:					Any      				=			Node(6)
    UpperCamelCase_						:					Union[str, Any]      				=			Node(9)
    UpperCamelCase_						:					Dict      				=			Node(5)

    print("""Tree1 is: """)
    print_preorder(treea)
    print("""Tree2 is: """)
    print_preorder(treea)
    UpperCamelCase_						:					Optional[Any]      				=			merge_two_binary_trees(treea, treea)
    print("""Merged Tree is: """)
    print_preorder(merged_tree)


