



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

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


UpperCamelCase__			=    logging.get_logger(__name__)

UpperCamelCase__			=    {
    'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json',
    # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
}
class       _lowerCAmelCase (			_UpperCAmelCase      ):






			"""simple docstring"""





			lowercase__   :	Union[str, Any]  			=      '''m2m_100'''
			lowercase__   :	Optional[Any]  			=      ['''past_key_values''']
			lowercase__   :	str  			=      {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}





			def __init__(					self						:							Optional[int]      ,  lowercase						:							List[str]=128_112      ,  lowercase						:							Tuple=1_024      ,  lowercase						:							List[str]=12      ,  lowercase						:							int=4_096      ,  lowercase						:							Dict=16      ,  lowercase						:							Any=12      ,  lowercase						:							List[str]=4_096      ,  lowercase						:							Union[str, Any]=16      ,  lowercase						:							List[Any]=0.05      ,  lowercase						:							Tuple=0.05      ,  lowercase						:							Optional[Any]=True      ,  lowercase						:							Dict=True      ,  lowercase						:							List[str]="relu"      ,  lowercase						:							Any=1_024      ,  lowercase						:							List[str]=0.1      ,  lowercase						:							str=0.1      ,  lowercase						:							List[Any]=0.0      ,  lowercase						:							Dict=0.02      ,  lowercase						:							List[Any]=2      ,  lowercase						:							List[Any]=True      ,  lowercase						:							str=1      ,  lowercase						:							Dict=0      ,  lowercase						:							Optional[Any]=2      ,  **lowercase						:							Union[str, Any]      ,  )    ->       List[Any]:
									"""simple docstring"""







									__lowercase											=					vocab_size
									__lowercase											=					max_position_embeddings
									__lowercase											=					d_model
									__lowercase											=					encoder_ffn_dim
									__lowercase											=					encoder_layers
									__lowercase											=					encoder_attention_heads
									__lowercase											=					decoder_ffn_dim
									__lowercase											=					decoder_layers
									__lowercase											=					decoder_attention_heads
									__lowercase											=					dropout
									__lowercase											=					attention_dropout
									__lowercase											=					activation_dropout
									__lowercase											=					activation_function
									__lowercase											=					init_std
									__lowercase											=					encoder_layerdrop
									__lowercase											=					decoder_layerdrop
									__lowercase											=					use_cache
									__lowercase											=					encoder_layers
									__lowercase											=					scale_embedding  # scale factor will be sqrt(d_model) if True

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





class       _lowerCAmelCase (			_UpperCAmelCase      ):






			"""simple docstring"""





			@property
			def 		snake_case__      (					self						:							Any )    ->       Mapping[str, Mapping[int, str]]:
									"""simple docstring"""







									__lowercase											=					OrderedDict(
									    [
									        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
									        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
									    ] )

									if self.use_past:
															__lowercase											=					{0: """batch"""}
															__lowercase											=					{0: """batch""", 1: """past_decoder_sequence + sequence"""}
									else:
															__lowercase											=					{0: """batch""", 1: """decoder_sequence"""}
															__lowercase											=					{0: """batch""", 1: """decoder_sequence"""}

									if self.use_past:
															self.fill_with_past_key_values_(_UpperCAmelCase      ,  direction="""inputs""" )
									return common_inputs





			def 		snake_case__      (					self						:							Tuple      ,  lowercase						:							List[str]      ,  lowercase						:							Union[str, Any] = -1      ,  lowercase						:							List[Any] = -1      ,  lowercase						:							int = False      ,  lowercase						:							Tuple = None      ,  )    ->       Mapping[str, Any]:
									"""simple docstring"""







									__lowercase											=					compute_effective_axis_dimension(
									    _UpperCAmelCase      ,  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
									__lowercase											=					tokenizer.num_special_tokens_to_add(_UpperCAmelCase )
									__lowercase											=					compute_effective_axis_dimension(
									    _UpperCAmelCase      ,  fixed_dimension=OnnxConfig.default_fixed_sequence      ,  num_token_to_add=_UpperCAmelCase )

									# Generate dummy inputs according to compute batch and sequence
									__lowercase											=					[""" """.join([tokenizer.unk_token] ) * seq_length] * batch_size
									__lowercase											=					dict(tokenizer(_UpperCAmelCase      ,  return_tensors=_UpperCAmelCase ) )
									return common_inputs





			def 		snake_case__      (					self						:							List[Any]      ,  lowercase						:							Any      ,  lowercase						:							Dict = -1      ,  lowercase						:							List[Any] = -1      ,  lowercase						:							Union[str, Any] = False      ,  lowercase						:							str = None      ,  )    ->       Mapping[str, Any]:
									"""simple docstring"""







									__lowercase											=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    _UpperCAmelCase      ,  _UpperCAmelCase      ,  _UpperCAmelCase      ,  _UpperCAmelCase      ,  _UpperCAmelCase )

									# Generate decoder inputs
									__lowercase											=					seq_length if not self.use_past else 1
									__lowercase											=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    _UpperCAmelCase      ,  _UpperCAmelCase      ,  _UpperCAmelCase      ,  _UpperCAmelCase      ,  _UpperCAmelCase )
									__lowercase											=					{F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
									__lowercase											=					dict(**_UpperCAmelCase      ,  **_UpperCAmelCase )

									if self.use_past:
															if not is_torch_available():
																					raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
															else:
																					import torch
															__lowercase						,      __lowercase											=					common_inputs["""input_ids"""].shape
															__lowercase											=					common_inputs["""decoder_input_ids"""].shape[1]
															__lowercase						,      __lowercase											=					self.num_attention_heads
															__lowercase											=					(
															    batch,
															    num_encoder_attention_heads,
															    encoder_seq_length,
															    self._config.hidden_size // num_encoder_attention_heads,
															)
															__lowercase											=					decoder_seq_length + 3
															__lowercase											=					(
															    batch,
															    num_decoder_attention_heads,
															    decoder_past_length,
															    self._config.hidden_size // num_decoder_attention_heads,
															)

															__lowercase											=					torch.cat(
															    [common_inputs["""decoder_attention_mask"""], torch.ones(_UpperCAmelCase      ,  _UpperCAmelCase )]      ,  dim=1 )

															__lowercase											=					[]
															# If the number of encoder and decoder layers are present in the model configuration, both are considered
															__lowercase						,      __lowercase											=					self.num_layers
															__lowercase											=					min(_UpperCAmelCase      ,  _UpperCAmelCase )
															__lowercase											=					max(_UpperCAmelCase      ,  _UpperCAmelCase ) - min_num_layers
															__lowercase											=					"""encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

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







			lowercase__   :	List[str]  			=      _generate_dummy_inputs_for_default_and_seqaseq_lm













def  UpperCAmelCase__						(    lowerCamelCase	:			list    ):
   lowerCamelCase									=1.3
   lowerCamelCase									=len(lowerCamelCase    )
   lowerCamelCase									=False

   while not completed:
      # Update the gap value for a next comb
      lowerCamelCase									=int(gap / shrink_factor    )
      if gap <= 1:
         lowerCamelCase									=True

      lowerCamelCase									=0
      while index + gap < len(lowerCamelCase    ):
         if data[index] > data[index + gap]:
            # Swap values
            lowerCamelCase				,       lowerCamelCase									=data[index + gap], data[index]
            lowerCamelCase									=False
         index += 1

   return data


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   _UpperCAmelCase					:						List[Any]         =     input('Enter numbers separated by a comma:\n').strip()
   _UpperCAmelCase					:						Optional[Any]         =     [int(item) for item in user_input.split(',')]
   print(comb_sort(unsorted))