"""simple docstring"""






from __future__ import annotations


def     UpperCAmelCase__ (       lowerCAmelCase__  :list      ,	lowerCAmelCase__  :int      ,	lowerCAmelCase__  :int      ,	lowerCAmelCase__  :int	)				->					list:
      '''simple docstring'''


      lowercase														=			[]
      lowercase							,							lowercase														=			input_list[low:mid], input_list[mid : high + 1]
      while left and right:
            result.append((left if left[0] <= right[0] else right).pop(0	)	)
      lowercase														=			result + left + right
      return input_list


def     UpperCAmelCase__ (       lowerCAmelCase__  :list	)				->					list:
      '''simple docstring'''


      if len(lowerCAmelCase__	) <= 1:
            return input_list
      lowercase														=			list(lowerCAmelCase__	)

      # iteration for two-way merging
      lowercase														=			2
      while p <= len(lowerCAmelCase__	):
            # getting low, high and middle value for merge-sort of single list
            for i in range(0      ,	len(lowerCAmelCase__	)      ,	lowerCAmelCase__	):
                  lowercase														=			i
                  lowercase														=			i + p - 1
                  lowercase														=			(low + high + 1) // 2
                  lowercase														=			merge(lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__	)
            # final merge of last two parts
            if p * 2 >= len(lowerCAmelCase__	):
                  lowercase														=			i
                  lowercase														=			merge(lowerCAmelCase__      ,	0      ,	lowerCAmelCase__      ,	len(lowerCAmelCase__	) - 1	)
                  break
            p *= 2

      return input_list


if __name__ == "__main__":
       __lowerCAmelCase					: Tuple        =input("""Enter numbers separated by a comma:\n""").strip()
       if user_input == "":
              __lowerCAmelCase					: List[Any]        =[]
       else:
              __lowerCAmelCase					: List[Any]        =[int(item.strip()) for item in user_input.split(""",""")]
       print(iter_merge_sort(unsorted))






"""simple docstring"""






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

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


__lowerCAmelCase					: Tuple        =logging.get_logger(__name__)

__lowerCAmelCase					: Tuple        ={
    """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json""",
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}





class 				_A		( lowerCAmelCase			):
      snake_case__    :     int													=			'blenderbot-small'
      snake_case__    :     Optional[Any]													=			['past_key_values']
      snake_case__    :     Optional[Any]													=			{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}

      def __init__(		self	, __lowerCAmelCase=5_0265	, __lowerCAmelCase=512	, __lowerCAmelCase=8	, __lowerCAmelCase=2048	, __lowerCAmelCase=16	, __lowerCAmelCase=8	, __lowerCAmelCase=2048	, __lowerCAmelCase=16	, __lowerCAmelCase=0.0	, __lowerCAmelCase=0.0	, __lowerCAmelCase=True	, __lowerCAmelCase=True	, __lowerCAmelCase="gelu"	, __lowerCAmelCase=512	, __lowerCAmelCase=0.1	, __lowerCAmelCase=0.0	, __lowerCAmelCase=0.0	, __lowerCAmelCase=0.0_2	, __lowerCAmelCase=1	, __lowerCAmelCase=False	, __lowerCAmelCase=0	, __lowerCAmelCase=1	, __lowerCAmelCase=2	, __lowerCAmelCase=2	, **__lowerCAmelCase	, ):







            """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=__lowerCAmelCase	, bos_token_id=__lowerCAmelCase	, eos_token_id=__lowerCAmelCase	, is_encoder_decoder=__lowerCAmelCase	, decoder_start_token_id=__lowerCAmelCase	, forced_eos_token_id=__lowerCAmelCase	, **__lowerCAmelCase	, )






class 				_A		( lowerCAmelCase			):

      @property
      def        A__ (		self		):







            """simple docstring"""



            if self.task in ["default", "seq2seq-lm"]:
                  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_(__lowerCAmelCase	, direction="""inputs"""		)
            elif self.task == "causal-lm":
                  # TODO: figure this case out.
                  lowercase														=			OrderedDict(
                      [
                          ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                          ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                      ]		)
                  if self.use_past:
                        lowercase							,							lowercase														=			self.num_layers
                        for i in range(__lowerCAmelCase		):
                              lowercase														=			{0: """batch""", 2: """past_sequence + sequence"""}
                              lowercase														=			{0: """batch""", 2: """past_sequence + sequence"""}
            else:
                  lowercase														=			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        A__ (		self		):







            """simple docstring"""



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

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase = -1	, __lowerCAmelCase = -1	, __lowerCAmelCase = False	, __lowerCAmelCase = None	, ):







            """simple docstring"""



            lowercase														=			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		)

            # Generate decoder inputs
            lowercase														=			seq_length if not self.use_past else 1
            lowercase														=			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		)
            lowercase														=			{f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
            lowercase														=			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
                  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(__lowerCAmelCase	, __lowerCAmelCase		)]	, 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(__lowerCAmelCase	, __lowerCAmelCase		)
                  lowercase														=			max(__lowerCAmelCase	, __lowerCAmelCase		) - min_num_layers
                  lowercase														=			"""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.
                  lowercase														=			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        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase = -1	, __lowerCAmelCase = -1	, __lowerCAmelCase = False	, __lowerCAmelCase = None	, ):







            """simple docstring"""



            lowercase														=			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
                  lowercase							,							lowercase														=			common_inputs["""input_ids"""].shape
                  # Not using the same length for past_key_values
                  lowercase														=			seqlen + 2
                  lowercase							,							lowercase														=			self.num_layers
                  lowercase							,							lowercase														=			self.num_attention_heads
                  lowercase														=			(
                      batch,
                      num_encoder_attention_heads,
                      past_key_values_length,
                      self._config.hidden_size // num_encoder_attention_heads,
                  )

                  lowercase														=			common_inputs["""attention_mask"""].dtype
                  lowercase														=			torch.cat(
                      [common_inputs["""attention_mask"""], torch.ones(__lowerCAmelCase	, __lowerCAmelCase	, dtype=__lowerCAmelCase		)]	, dim=1		)
                  lowercase														=			[
                      (torch.zeros(__lowerCAmelCase		), torch.zeros(__lowerCAmelCase		)) for _ in range(__lowerCAmelCase		)
                  ]
            return common_inputs

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase = -1	, __lowerCAmelCase = -1	, __lowerCAmelCase = False	, __lowerCAmelCase = None	, ):







            """simple docstring"""



            lowercase														=			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
            lowercase														=			tokenizer.num_special_tokens_to_add(__lowerCAmelCase		)
            lowercase														=			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
            lowercase														=			[""" """.join([tokenizer.unk_token]		) * seq_length] * batch_size
            lowercase														=			dict(tokenizer(__lowerCAmelCase	, return_tensors=__lowerCAmelCase		)		)
            return common_inputs

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase = -1	, __lowerCAmelCase = -1	, __lowerCAmelCase = False	, __lowerCAmelCase = None	, ):







            """simple docstring"""



            if self.task in ["default", "seq2seq-lm"]:
                  lowercase														=			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":
                  lowercase														=			self._generate_dummy_inputs_for_causal_lm(
                      __lowerCAmelCase	, batch_size=__lowerCAmelCase	, seq_length=__lowerCAmelCase	, is_pair=__lowerCAmelCase	, framework=__lowerCAmelCase		)
            else:
                  lowercase														=			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        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



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



