
def       A       (					lowercase )   ->  Dict:
		'''simple docstring'''

		UpperCamelCase		=     len(lowercase )
		while cur > 1:
				# Find the maximum number in arr
				UpperCamelCase		=     arr.index(max(arr[0:cur] ) )
				# Reverse from 0 to mi
				UpperCamelCase		=     arr[mi::-1] + arr[mi + 1 : len(lowercase )]
				# Reverse whole list
				UpperCamelCase		=     arr[cur - 1 :: -1] + arr[cur : len(lowercase )]
				cur -= 1
		return arr


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


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


_UpperCAmelCase					: List[Any]      =       logging.get_logger(__name__)

_UpperCAmelCase					: Optional[Any]      =       {
    "Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json",
    # See all Marian models at https://huggingface.co/models?filter=marian
}


class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):
		__lowercase       :       int	     =				"marian"
		__lowercase       :       List[str]	     =				["past_key_values"]
		__lowercase       :       Any	     =				{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}



		def __init__( self				,      A_=58_101				,      A_=None				,      A_=1_024				,      A_=12				,      A_=4_096				,      A_=16				,      A_=12				,      A_=4_096				,      A_=16				,      A_=0.0				,      A_=0.0				,      A_=True				,      A_=True				,      A_="gelu"				,      A_=1_024				,      A_=0.1				,      A_=0.0				,      A_=0.0				,      A_=0.02				,      A_=58_100				,      A_=False				,      A_=58_100				,      A_=0				,      A_=0				,      A_=True				,      **A_				,      )  ->					Optional[int]:
				"""simple docstring"""
				UpperCamelCase		=     vocab_size
				UpperCamelCase		=     decoder_vocab_size or vocab_size
				UpperCamelCase		=     max_position_embeddings
				UpperCamelCase		=     d_model
				UpperCamelCase		=     encoder_ffn_dim
				UpperCamelCase		=     encoder_layers
				UpperCamelCase		=     encoder_attention_heads
				UpperCamelCase		=     decoder_ffn_dim
				UpperCamelCase		=     decoder_layers
				UpperCamelCase		=     decoder_attention_heads
				UpperCamelCase		=     dropout
				UpperCamelCase		=     attention_dropout
				UpperCamelCase		=     activation_dropout
				UpperCamelCase		=     activation_function
				UpperCamelCase		=     init_std
				UpperCamelCase		=     encoder_layerdrop
				UpperCamelCase		=     decoder_layerdrop
				UpperCamelCase		=     use_cache
				UpperCamelCase		=     encoder_layers
				UpperCamelCase		=     scale_embedding  # scale factor will be sqrt(d_model) if True
				UpperCamelCase		=     share_encoder_decoder_embeddings
				super().__init__(
				    pad_token_id=A_				,      eos_token_id=A_				,      is_encoder_decoder=A_				,      decoder_start_token_id=A_				,      forced_eos_token_id=A_				,      **A_				,      )






class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):



		@property
		# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
		def __UpperCamelCase ( self       )  ->					Mapping[str, Mapping[int, str]]:
				"""simple docstring"""
				if self.task in ["default", "seq2seq-lm"]:
						UpperCamelCase		=     OrderedDict(
						    [
						        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
						        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
						    ]       )

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

						if self.use_past:
								self.fill_with_past_key_values_(A_				,      direction='inputs'       )
				elif self.task == "causal-lm":
						# TODO: figure this case out.
						UpperCamelCase		=     OrderedDict(
						    [
						        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
						        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
						    ]       )
						if self.use_past:
								UpperCamelCase	,  UpperCamelCase		=     self.num_layers
								for i in range(A_       ):
										UpperCamelCase		=     {0: 'batch', 2: 'past_sequence + sequence'}
										UpperCamelCase		=     {0: 'batch', 2: 'past_sequence + sequence'}
				else:
						UpperCamelCase		=     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
		# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
		def __UpperCamelCase ( self       )  ->					Mapping[str, Mapping[int, str]]:
				"""simple docstring"""
				if self.task in ["default", "seq2seq-lm"]:
						UpperCamelCase		=     super().outputs
				else:
						UpperCamelCase		=     super(A_				,      self       ).outputs
						if self.use_past:
								UpperCamelCase	,  UpperCamelCase		=     self.num_layers
								for i in range(A_       ):
										UpperCamelCase		=     {0: 'batch', 2: 'past_sequence + sequence'}
										UpperCamelCase		=     {0: 'batch', 2: 'past_sequence + sequence'}
				return common_outputs



		def __UpperCamelCase ( self				,      A_				,      A_ = -1				,      A_ = -1				,      A_ = False				,      A_ = None				,      )  ->					Mapping[str, Any]:
				"""simple docstring"""
				UpperCamelCase		=     self._generate_dummy_inputs_for_encoder_and_decoder(
				    A_				,      A_				,      A_				,      A_				,      A_       )

				# Generate decoder inputs
				UpperCamelCase		=     seq_length if not self.use_past else 1
				UpperCamelCase		=     self._generate_dummy_inputs_for_encoder_and_decoder(
				    A_				,      A_				,      A_				,      A_				,      A_       )
				UpperCamelCase		=     {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
				UpperCamelCase		=     dict(**A_				,      **A_       )

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

						UpperCamelCase		=     torch.cat(
						    [common_inputs['decoder_attention_mask'], torch.ones(A_				,      A_       )]				,      dim=1       )

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

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



		def __UpperCamelCase ( self				,      A_				,      A_ = -1				,      A_ = -1				,      A_ = False				,      A_ = None				,      )  ->					Mapping[str, Any]:
				"""simple docstring"""
				UpperCamelCase		=     self._generate_dummy_inputs_for_encoder_and_decoder(
				    A_				,      A_				,      A_				,      A_				,      A_       )

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

						UpperCamelCase		=     common_inputs['attention_mask'].dtype
						UpperCamelCase		=     torch.cat(
						    [common_inputs['attention_mask'], torch.ones(A_				,      A_				,      dtype=A_       )]				,      dim=1       )
						UpperCamelCase		=     [
						    (torch.zeros(A_       ), torch.zeros(A_       )) for _ in range(A_       )
						]
				return common_inputs



		def __UpperCamelCase ( self				,      A_				,      A_ = -1				,      A_ = -1				,      A_ = False				,      A_ = None				,      )  ->					Mapping[str, Any]:
				"""simple docstring"""
				# Copied from OnnxConfig.generate_dummy_inputs
				# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
				# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
				UpperCamelCase		=     compute_effective_axis_dimension(
				    A_				,      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
				UpperCamelCase		=     tokenizer.num_special_tokens_to_add(A_       )
				UpperCamelCase		=     compute_effective_axis_dimension(
				    A_				,      fixed_dimension=OnnxConfig.default_fixed_sequence				,      num_token_to_add=A_       )

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



		def __UpperCamelCase ( self				,      A_				,      A_ = -1				,      A_ = -1				,      A_ = False				,      A_ = None				,      )  ->					Mapping[str, Any]:
				"""simple docstring"""
				if self.task in ["default", "seq2seq-lm"]:
						UpperCamelCase		=     self._generate_dummy_inputs_for_default_and_seqaseq_lm(
						    A_				,      batch_size=A_				,      seq_length=A_				,      is_pair=A_				,      framework=A_       )

				else:
						UpperCamelCase		=     self._generate_dummy_inputs_for_causal_lm(
						    A_				,      batch_size=A_				,      seq_length=A_				,      is_pair=A_				,      framework=A_       )

				return common_inputs



		def __UpperCamelCase ( self				,      A_				,      A_				,      A_				,      A_       )  ->					int:
				"""simple docstring"""
				if self.task in ["default", "seq2seq-lm"]:
						UpperCamelCase		=     super()._flatten_past_key_values_(A_				,      A_				,      A_				,      A_       )
				else:
						UpperCamelCase		=     super(A_				,      self       )._flatten_past_key_values_(
						    A_				,      A_				,      A_				,      A_       )



		@property
		def __UpperCamelCase ( self       )  ->					float:
				"""simple docstring"""
				return 1e-4
