



"""simple docstring"""






import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


lowerCAmelCase__      =    logging.get_logger(__name__)

lowerCAmelCase__      =    {
    '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/config.json''',
    # See all BART models at https://huggingface.co/models?filter=bart
}






class 	__snake_case	(     _lowercase):
  snake_case__    :       Optional[int]        =						"bart"
  snake_case__    :       List[str]        =						["past_key_values"]
  snake_case__    :       str        =						{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




  def __init__(			self					:						Union[str, Any]						, __lowerCAmelCase					:						List[Any]=5_0_2_6_5						, __lowerCAmelCase					:						Dict=1_0_2_4						, __lowerCAmelCase					:						List[str]=1_2						, __lowerCAmelCase					:						Any=4_0_9_6						, __lowerCAmelCase					:						List[str]=1_6						, __lowerCAmelCase					:						List[str]=1_2						, __lowerCAmelCase					:						List[Any]=4_0_9_6						, __lowerCAmelCase					:						List[str]=1_6						, __lowerCAmelCase					:						Dict=0.0						, __lowerCAmelCase					:						Optional[int]=0.0						, __lowerCAmelCase					:						Optional[Any]="gelu"						, __lowerCAmelCase					:						List[str]=1_0_2_4						, __lowerCAmelCase					:						Tuple=0.1						, __lowerCAmelCase					:						Dict=0.0						, __lowerCAmelCase					:						Tuple=0.0						, __lowerCAmelCase					:						Tuple=0.02						, __lowerCAmelCase					:						Union[str, Any]=0.0						, __lowerCAmelCase					:						str=False						, __lowerCAmelCase					:						Union[str, Any]=True						, __lowerCAmelCase					:						Union[str, Any]=3						, __lowerCAmelCase					:						Dict=1						, __lowerCAmelCase					:						List[Any]=0						, __lowerCAmelCase					:						int=2						, __lowerCAmelCase					:						int=True						, __lowerCAmelCase					:						List[Any]=2						, __lowerCAmelCase					:						List[Any]=2						, **__lowerCAmelCase					:						Optional[Any]						, ):






         """simple docstring"""






         _lowerCamelCase						:    Dict         =			vocab_size
         _lowerCamelCase						:    Optional[Any]         =			max_position_embeddings
         _lowerCamelCase						:    Dict         =			d_model
         _lowerCamelCase						:    Optional[Any]         =			encoder_ffn_dim
         _lowerCamelCase						:    str         =			encoder_layers
         _lowerCamelCase						:    Any         =			encoder_attention_heads
         _lowerCamelCase						:    str         =			decoder_ffn_dim
         _lowerCamelCase						:    Optional[int]         =			decoder_layers
         _lowerCamelCase						:    int         =			decoder_attention_heads
         _lowerCamelCase						:    int         =			dropout
         _lowerCamelCase						:    Optional[int]         =			attention_dropout
         _lowerCamelCase						:    str         =			activation_dropout
         _lowerCamelCase						:    int         =			activation_function
         _lowerCamelCase						:    Tuple         =			init_std
         _lowerCamelCase						:    Any         =			encoder_layerdrop
         _lowerCamelCase						:    Union[str, Any]         =			decoder_layerdrop
         _lowerCamelCase						:    List[Any]         =			classifier_dropout
         _lowerCamelCase						:    Union[str, Any]         =			use_cache
         _lowerCamelCase						:    int         =			encoder_layers
         _lowerCamelCase						:    Dict         =			scale_embedding  # scale factor will be sqrt(d_model) if True

         super().__init__(
             num_labels=__lowerCAmelCase						, pad_token_id=__lowerCAmelCase						, bos_token_id=__lowerCAmelCase						, eos_token_id=__lowerCAmelCase						, is_encoder_decoder=__lowerCAmelCase						, decoder_start_token_id=__lowerCAmelCase						, forced_eos_token_id=__lowerCAmelCase						, **__lowerCAmelCase						, )

         # ensure backward compatibility for BART CNN models
         if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated'''						, __lowerCAmelCase		):
                _lowerCamelCase						:    int         =			self.bos_token_id
                warnings.warn(
                    f'''Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '''
                    '''The config can simply be saved and uploaded again to be fixed.'''		)






class 	__snake_case	(     _lowercase):




  @property
  def 						SCREAMING_SNAKE_CASE    (			self					:						List[Any]		):






         """simple docstring"""






         if self.task in ["default", "seq2seq-lm"]:
                _lowerCamelCase						:    Union[str, Any]         =			OrderedDict(
                    [
                        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                    ]		)

                if self.use_past:
                       _lowerCamelCase						:    str         =			{0: 'batch'}
                       _lowerCamelCase						:    List[str]         =			{0: 'batch', 1: 'past_decoder_sequence + sequence'}
                else:
                       _lowerCamelCase						:    Optional[Any]         =			{0: 'batch', 1: 'decoder_sequence'}
                       _lowerCamelCase						:    Any         =			{0: 'batch', 1: 'decoder_sequence'}

                if self.use_past:
                       self.fill_with_past_key_values_(__lowerCAmelCase						, direction='''inputs'''		)
         elif self.task == "causal-lm":
                # TODO: figure this case out.
                _lowerCamelCase						:    List[Any]         =			OrderedDict(
                    [
                        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                    ]		)
                if self.use_past:
                       _lowerCamelCase						:    Tuple         =			self.num_layers
                       for i in range(__lowerCAmelCase		):
                              _lowerCamelCase						:    Optional[Any]         =			{0: 'batch', 2: 'past_sequence + sequence'}
                              _lowerCamelCase						:    int         =			{0: 'batch', 2: 'past_sequence + sequence'}
         else:
                _lowerCamelCase						:    Tuple         =			OrderedDict(
                    [
                        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                        ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
                        ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
                    ]		)

         return common_inputs




  @property
  def 						SCREAMING_SNAKE_CASE    (			self					:						int		):






         """simple docstring"""






         if self.task in ["default", "seq2seq-lm"]:
                _lowerCamelCase						:    Any         =			super().outputs
         else:
                _lowerCamelCase						:    Tuple         =			super(__lowerCAmelCase						, self		).outputs
                if self.use_past:
                       _lowerCamelCase						:    Tuple         =			self.num_layers
                       for i in range(__lowerCAmelCase		):
                              _lowerCamelCase						:    Tuple         =			{0: 'batch', 2: 'past_sequence + sequence'}
                              _lowerCamelCase						:    Union[str, Any]         =			{0: 'batch', 2: 'past_sequence + sequence'}
         return common_outputs




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[Any]						, __lowerCAmelCase					:						Union[str, Any]						, __lowerCAmelCase					:						Any = -1						, __lowerCAmelCase					:						Tuple = -1						, __lowerCAmelCase					:						List[Any] = False						, __lowerCAmelCase					:						Optional[int] = None						, ):






         """simple docstring"""






         _lowerCamelCase						:    Tuple         =			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             __lowerCAmelCase						, __lowerCAmelCase						, __lowerCAmelCase						, __lowerCAmelCase						, __lowerCAmelCase		)

         # Generate decoder inputs
         _lowerCamelCase						:    Dict         =			seq_length if not self.use_past else 1
         _lowerCamelCase						:    List[Any]         =			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             __lowerCAmelCase						, __lowerCAmelCase						, __lowerCAmelCase						, __lowerCAmelCase						, __lowerCAmelCase		)
         _lowerCamelCase						:    Optional[int]         =			{f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
         _lowerCamelCase						:    List[str]         =			dict(**__lowerCAmelCase						, **__lowerCAmelCase		)

         if self.use_past:
                if not is_torch_available():
                       raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''		)
                else:
                       import torch
                _lowerCamelCase						:    Optional[int]         =			common_inputs['input_ids'].shape
                _lowerCamelCase						:    Dict         =			common_inputs['decoder_input_ids'].shape[1]
                _lowerCamelCase						:    List[str]         =			self.num_attention_heads
                _lowerCamelCase						:    Optional[Any]         =			(
                    batch,
                    num_encoder_attention_heads,
                    encoder_seq_length,
                    self._config.hidden_size // num_encoder_attention_heads,
                )
                _lowerCamelCase						:    Union[str, Any]         =			decoder_seq_length + 3
                _lowerCamelCase						:    Optional[Any]         =			(
                    batch,
                    num_decoder_attention_heads,
                    decoder_past_length,
                    self._config.hidden_size // num_decoder_attention_heads,
                )

                _lowerCamelCase						:    Dict         =			torch.cat(
                    [common_inputs['''decoder_attention_mask'''], torch.ones(__lowerCAmelCase						, __lowerCAmelCase		)]						, dim=1		)

                _lowerCamelCase						:    Union[str, Any]         =			[]
                # If the number of encoder and decoder layers are present in the model configuration, both are considered
                _lowerCamelCase						:    str         =			self.num_layers
                _lowerCamelCase						:    Tuple         =			min(__lowerCAmelCase						, __lowerCAmelCase		)
                _lowerCamelCase						:    int         =			max(__lowerCAmelCase						, __lowerCAmelCase		) - min_num_layers
                _lowerCamelCase						:    Any         =			'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

                for _ in range(__lowerCAmelCase		):
                       common_inputs["past_key_values"].append(
                           (
                               torch.zeros(__lowerCAmelCase		),
                               torch.zeros(__lowerCAmelCase		),
                               torch.zeros(__lowerCAmelCase		),
                               torch.zeros(__lowerCAmelCase		),
                           )		)
                # TODO: test this.
                _lowerCamelCase						:    Optional[Any]         =			encoder_shape if remaining_side_name == 'encoder' else decoder_shape
                for _ in range(__lowerCAmelCase						, __lowerCAmelCase		):
                       common_inputs["past_key_values"].append((torch.zeros(__lowerCAmelCase		), torch.zeros(__lowerCAmelCase		))		)
         return common_inputs




  def 						SCREAMING_SNAKE_CASE    (			self					:						str						, __lowerCAmelCase					:						str						, __lowerCAmelCase					:						Optional[int] = -1						, __lowerCAmelCase					:						Tuple = -1						, __lowerCAmelCase					:						List[str] = False						, __lowerCAmelCase					:						Any = None						, ):






         """simple docstring"""






         _lowerCamelCase						:    str         =			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             __lowerCAmelCase						, __lowerCAmelCase						, __lowerCAmelCase						, __lowerCAmelCase						, __lowerCAmelCase		)

         if self.use_past:
                if not is_torch_available():
                       raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''		)
                else:
                       import torch
                _lowerCamelCase						:    Any         =			common_inputs['input_ids'].shape
                # Not using the same length for past_key_values
                _lowerCamelCase						:    List[str]         =			seqlen + 2
                _lowerCamelCase						:    List[Any]         =			self.num_layers
                _lowerCamelCase						:    List[str]         =			self.num_attention_heads
                _lowerCamelCase						:    str         =			(
                    batch,
                    num_encoder_attention_heads,
                    past_key_values_length,
                    self._config.hidden_size // num_encoder_attention_heads,
                )

                _lowerCamelCase						:    str         =			common_inputs['attention_mask'].dtype
                _lowerCamelCase						:    List[str]         =			torch.cat(
                    [common_inputs['''attention_mask'''], torch.ones(__lowerCAmelCase						, __lowerCAmelCase						, dtype=__lowerCAmelCase		)]						, dim=1		)
                _lowerCamelCase						:    Union[str, Any]         =			[
                    (torch.zeros(__lowerCAmelCase		), torch.zeros(__lowerCAmelCase		)) for _ in range(__lowerCAmelCase		)
                ]
         return common_inputs




  def 						SCREAMING_SNAKE_CASE    (			self					:						Dict						, __lowerCAmelCase					:						int						, __lowerCAmelCase					:						int = -1						, __lowerCAmelCase					:						Any = -1						, __lowerCAmelCase					:						Optional[Any] = False						, __lowerCAmelCase					:						Union[str, Any] = None						, ):






         """simple docstring"""






         _lowerCamelCase						:    List[Any]         =			compute_effective_axis_dimension(
             __lowerCAmelCase						, fixed_dimension=OnnxConfig.default_fixed_batch						, num_token_to_add=0		)

         # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
         _lowerCamelCase						:    List[str]         =			tokenizer.num_special_tokens_to_add(__lowerCAmelCase		)
         _lowerCamelCase						:    int         =			compute_effective_axis_dimension(
             __lowerCAmelCase						, fixed_dimension=OnnxConfig.default_fixed_sequence						, num_token_to_add=__lowerCAmelCase		)

         # Generate dummy inputs according to compute batch and sequence
         _lowerCamelCase						:    str         =			[' '.join([tokenizer.unk_token]		) * seq_length] * batch_size
         _lowerCamelCase						:    List[str]         =			dict(tokenizer(__lowerCAmelCase						, return_tensors=__lowerCAmelCase		)		)
         return common_inputs




  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[int]						, __lowerCAmelCase					:						List[str]						, __lowerCAmelCase					:						Any = -1						, __lowerCAmelCase					:						Any = -1						, __lowerCAmelCase					:						Optional[Any] = False						, __lowerCAmelCase					:						str = None						, ):






         """simple docstring"""






         if self.task in ["default", "seq2seq-lm"]:
                _lowerCamelCase						:    Optional[int]         =			self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                    __lowerCAmelCase						, batch_size=__lowerCAmelCase						, seq_length=__lowerCAmelCase						, is_pair=__lowerCAmelCase						, framework=__lowerCAmelCase		)

         elif self.task == "causal-lm":
                _lowerCamelCase						:    Tuple         =			self._generate_dummy_inputs_for_causal_lm(
                    __lowerCAmelCase						, batch_size=__lowerCAmelCase						, seq_length=__lowerCAmelCase						, is_pair=__lowerCAmelCase						, framework=__lowerCAmelCase		)
         else:
                _lowerCamelCase						:    Tuple         =			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                    __lowerCAmelCase						, batch_size=__lowerCAmelCase						, seq_length=__lowerCAmelCase						, is_pair=__lowerCAmelCase						, framework=__lowerCAmelCase		)

         return common_inputs




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[Any]						, __lowerCAmelCase					:						Any						, __lowerCAmelCase					:						Tuple						, __lowerCAmelCase					:						Any						, __lowerCAmelCase					:						Tuple		):






         """simple docstring"""






         if self.task in ["default", "seq2seq-lm"]:
                _lowerCamelCase						:    int         =			super()._flatten_past_key_values_(__lowerCAmelCase						, __lowerCAmelCase						, __lowerCAmelCase						, __lowerCAmelCase		)
         else:
                _lowerCamelCase						:    List[Any]         =			super(__lowerCAmelCase						, self		)._flatten_past_key_values_(
                    __lowerCAmelCase						, __lowerCAmelCase						, __lowerCAmelCase						, __lowerCAmelCase		)







"""simple docstring"""






def 	snake_case_  (				A_ :		list						):


       '''simple docstring'''





       _lowerCamelCase						:    Union[str, Any]         =			len(A_						)
       for i in range(1,    A_						):
              _lowerCamelCase						:    Tuple         =			collection[i]
              _lowerCamelCase						:    Dict         =			0
              _lowerCamelCase						:    Any         =			i - 1

              while low <= high:
                     _lowerCamelCase						:    Optional[int]         =			(low + high) // 2
                     if val < collection[mid]:
                            _lowerCamelCase						:    List[str]         =			mid - 1
                     else:
                            _lowerCamelCase						:    Dict         =			mid + 1
              for j in range(A_,    A_,    -1						):
                     _lowerCamelCase						:    Optional[int]         =			collection[j - 1]
              _lowerCamelCase						:    Tuple         =			val
       return collection


if __name__ == "__main__":
   lowerCAmelCase__      =    input('''Enter numbers separated by a comma:\n''').strip()
   lowerCAmelCase__      =    [int(item) for item in user_input.split(''',''')]
   print(binary_insertion_sort(unsorted))


