





"""simple docstring"""



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


a_				=    logging.get_logger(__name__)

a_				=    {
    """google/bigbird-pegasus-large-arxiv""": (
        """https://huggingface.co/google/bigbird-pegasus-large-arxiv/resolve/main/config.json"""
    ),
    """google/bigbird-pegasus-large-pubmed""": (
        """https://huggingface.co/google/bigbird-pegasus-large-pubmed/resolve/main/config.json"""
    ),
    """google/bigbird-pegasus-large-bigpatent""": (
        """https://huggingface.co/google/bigbird-pegasus-large-bigpatent/resolve/main/config.json"""
    ),
    # See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus
}






class 	__snake_case      (	SCREAMING_SNAKE_CASE__  ):





 """simple docstring"""


 _lowerCamelCase				 =  """bigbird_pegasus"""
 _lowerCamelCase				 =  ["""past_key_values"""]
 _lowerCamelCase				 =  {
     """num_attention_heads""": """encoder_attention_heads""",
     """hidden_size""": """d_model""",
     """attention_probs_dropout_prob""": """attention_dropout""",
 }

 def __init__(					self      ,		__lowerCamelCase=9_6103      ,		__lowerCamelCase=4096      ,		__lowerCamelCase=16      ,		__lowerCamelCase=4096      ,		__lowerCamelCase=16      ,		__lowerCamelCase=16      ,		__lowerCamelCase=4096      ,		__lowerCamelCase=16      ,		__lowerCamelCase=0.0      ,		__lowerCamelCase=0.0      ,		__lowerCamelCase=True      ,		__lowerCamelCase=True      ,		__lowerCamelCase="gelu_new"      ,		__lowerCamelCase=1024      ,		__lowerCamelCase=0.1      ,		__lowerCamelCase=0.0      ,		__lowerCamelCase=0.0      ,		__lowerCamelCase=0.0_2      ,		__lowerCamelCase=2      ,		__lowerCamelCase=0.0      ,		__lowerCamelCase=True      ,		__lowerCamelCase=0      ,		__lowerCamelCase=2      ,		__lowerCamelCase=1      ,		__lowerCamelCase="block_sparse"      ,		__lowerCamelCase=64      ,		__lowerCamelCase=3      ,		__lowerCamelCase=False      ,		**__lowerCamelCase      ,		):



     '''simple docstring'''




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

     # extra config
     __A					:		Optional[int]						=  attention_type
     __A					:		List[Any]						=  block_size
     __A					:		Optional[Any]						=  num_random_blocks
     __A					:		int						=  use_bias

     super().__init__(
         pad_token_id=__lowerCamelCase      ,		bos_token_id=__lowerCamelCase      ,		eos_token_id=__lowerCamelCase      ,		is_encoder_decoder=__lowerCamelCase      ,		decoder_start_token_id=__lowerCamelCase      ,		**__lowerCamelCase      ,		)






class 	__snake_case      (	SCREAMING_SNAKE_CASE__  ):





 """simple docstring"""


 @property
 def       UpperCamelCase__(					self						):



     '''simple docstring'''




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

         if self.use_past:
             __A					:		List[str]						=  {0: '''batch'''}
             __A					:		Optional[int]						=  {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
         else:
             __A					:		List[Any]						=  {0: '''batch''', 1: '''decoder_sequence'''}
             __A					:		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.
         __A					:		List[Any]						=  OrderedDict(
             [
                 ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                 ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
             ]						)
         if self.use_past:
             __A				, __A					:		List[str]						=  self.num_layers
             for i in range(__lowerCamelCase						):
                 __A					:		Tuple						=  {0: '''batch''', 2: '''past_sequence + sequence'''}
                 __A					:		Tuple						=  {0: '''batch''', 2: '''past_sequence + sequence'''}
     else:
         __A					:		Dict						=  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       UpperCamelCase__(					self						):



     '''simple docstring'''




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

 def       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase = -1      ,		__lowerCamelCase = -1      ,		__lowerCamelCase = False      ,		__lowerCamelCase = None      ,		):



     '''simple docstring'''




     __A					:		Tuple						=  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         __lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase						)

     # Generate decoder inputs
     __A					:		Any						=  seq_length if not self.use_past else 1
     __A					:		int						=  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         __lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase						)
     __A					:		str						=  {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
     __A					:		int						=  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
         __A				, __A					:		Union[str, Any]						=  common_inputs['''input_ids'''].shape
         __A					:		Optional[int]						=  common_inputs['''decoder_input_ids'''].shape[1]
         __A				, __A					:		Optional[int]						=  self.num_attention_heads
         __A					:		Tuple						=  (
             batch,
             num_encoder_attention_heads,
             encoder_seq_length,
             self._config.hidden_size // num_encoder_attention_heads,
         )
         __A					:		Tuple						=  decoder_seq_length + 3
         __A					:		str						=  (
             batch,
             num_decoder_attention_heads,
             decoder_past_length,
             self._config.hidden_size // num_decoder_attention_heads,
         )

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

         __A					:		str						=  []
         # If the number of encoder and decoder layers are present in the model configuration, both are considered
         __A				, __A					:		List[str]						=  self.num_layers
         __A					:		Any						=  min(__lowerCamelCase      ,		__lowerCamelCase						)
         __A					:		Any						=  max(__lowerCamelCase      ,		__lowerCamelCase						) - min_num_layers
         __A					:		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.
         __A					:		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       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase = -1      ,		__lowerCamelCase = -1      ,		__lowerCamelCase = False      ,		__lowerCamelCase = None      ,		):



     '''simple docstring'''




     __A					:		Optional[Any]						=  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
         __A				, __A					:		Tuple						=  common_inputs['''input_ids'''].shape
         # Not using the same length for past_key_values
         __A					:		Dict						=  seqlen + 2
         __A				, __A					:		Any						=  self.num_layers
         __A				, __A					:		List[str]						=  self.num_attention_heads
         __A					:		Tuple						=  (
             batch,
             num_encoder_attention_heads,
             past_key_values_length,
             self._config.hidden_size // num_encoder_attention_heads,
         )

         __A					:		str						=  common_inputs['''attention_mask'''].dtype
         __A					:		str						=  torch.cat(
             [common_inputs['''attention_mask'''], torch.ones(__lowerCamelCase      ,		__lowerCamelCase      ,		dtype=__lowerCamelCase						)]      ,		dim=1						)
         __A					:		Optional[Any]						=  [
             (torch.zeros(__lowerCamelCase						), torch.zeros(__lowerCamelCase						)) for _ in range(__lowerCamelCase						)
         ]
     return common_inputs

 def       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase = -1      ,		__lowerCamelCase = -1      ,		__lowerCamelCase = False      ,		__lowerCamelCase = None      ,		):



     '''simple docstring'''




     __A					:		Tuple						=  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
     __A					:		int						=  tokenizer.num_special_tokens_to_add(__lowerCamelCase						)
     __A					:		List[str]						=  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
     __A					:		Tuple						=  [''' '''.join([tokenizer.unk_token]						) * seq_length] * batch_size
     __A					:		Optional[Any]						=  dict(tokenizer(__lowerCamelCase      ,		return_tensors=__lowerCamelCase						)						)
     return common_inputs

 def       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase = -1      ,		__lowerCamelCase = -1      ,		__lowerCamelCase = False      ,		__lowerCamelCase = None      ,		):



     '''simple docstring'''




     if self.task in ["default", "seq2seq-lm"]:
         __A					:		Tuple						=  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":
         __A					:		str						=  self._generate_dummy_inputs_for_causal_lm(
             __lowerCamelCase      ,		batch_size=__lowerCamelCase      ,		seq_length=__lowerCamelCase      ,		is_pair=__lowerCamelCase      ,		framework=__lowerCamelCase						)
     else:
         __A					:		Optional[Any]						=  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       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase						):



     '''simple docstring'''




     if self.task in ["default", "seq2seq-lm"]:
         __A					:		Optional[int]						=  super()._flatten_past_key_values_(__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase						)
     else:
         __A					:		Union[str, Any]						=  super(__lowerCamelCase      ,		self						)._flatten_past_key_values_(
             __lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase						)











"""simple docstring"""



def 	__lowercase  (     snake_case_						:						Union[str, Any]		)     ->Optional[int]:



    '''simple docstring'''







    __A					:		Dict						=  [701, 301, 132, 57, 23, 10, 4, 1]
    for gap in gaps:
        for i in range(snake_case_       ,len(snake_case_		)		):
            __A					:		Tuple						=  collection[i]
            __A					:		Union[str, Any]						=  i
            while j >= gap and collection[j - gap] > insert_value:
                __A					:		Optional[int]						=  collection[j - gap]
                j -= gap
            if j != i:
                __A					:		Optional[int]						=  insert_value
    return collection


if __name__ == "__main__":
       from doctest import testmod

       testmod()
       a_				=    input("""Enter numbers separated by a comma:\n""").strip()
       a_				=    [int(item) for item in user_input.split(""",""")]
       print(shell_sort(unsorted))




