
"""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


lowerCamelCase__        =		logging.get_logger(__name__)

lowerCamelCase__        =		{
    """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 			A__	(     _lowerCamelCase):
		A_						:	List[Any]           =     'marian'
		A_						:	Optional[int]           =     ['past_key_values']
		A_						:	List[str]           =     {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}

		def __init__(				self	,		_SCREAMING_SNAKE_CASE=5_81_01	,		_SCREAMING_SNAKE_CASE=None	,		_SCREAMING_SNAKE_CASE=10_24	,		_SCREAMING_SNAKE_CASE=12	,		_SCREAMING_SNAKE_CASE=40_96	,		_SCREAMING_SNAKE_CASE=16	,		_SCREAMING_SNAKE_CASE=12	,		_SCREAMING_SNAKE_CASE=40_96	,		_SCREAMING_SNAKE_CASE=16	,		_SCREAMING_SNAKE_CASE=0.0	,		_SCREAMING_SNAKE_CASE=0.0	,		_SCREAMING_SNAKE_CASE=True	,		_SCREAMING_SNAKE_CASE=True	,		_SCREAMING_SNAKE_CASE="gelu"	,		_SCREAMING_SNAKE_CASE=10_24	,		_SCREAMING_SNAKE_CASE=0.1	,		_SCREAMING_SNAKE_CASE=0.0	,		_SCREAMING_SNAKE_CASE=0.0	,		_SCREAMING_SNAKE_CASE=0.02	,		_SCREAMING_SNAKE_CASE=5_81_00	,		_SCREAMING_SNAKE_CASE=False	,		_SCREAMING_SNAKE_CASE=5_81_00	,		_SCREAMING_SNAKE_CASE=0	,		_SCREAMING_SNAKE_CASE=0	,		_SCREAMING_SNAKE_CASE=True	,		**_SCREAMING_SNAKE_CASE	,		):
			__lowerCAmelCase      :						Tuple						      =				vocab_size
			__lowerCAmelCase      :						Optional[Any]						      =				decoder_vocab_size or vocab_size
			__lowerCAmelCase      :						Optional[int]						      =				max_position_embeddings
			__lowerCAmelCase      :						Union[str, Any]						      =				d_model
			__lowerCAmelCase      :						str						      =				encoder_ffn_dim
			__lowerCAmelCase      :						Optional[int]						      =				encoder_layers
			__lowerCAmelCase      :						Optional[Any]						      =				encoder_attention_heads
			__lowerCAmelCase      :						Dict						      =				decoder_ffn_dim
			__lowerCAmelCase      :						Optional[Any]						      =				decoder_layers
			__lowerCAmelCase      :						Any						      =				decoder_attention_heads
			__lowerCAmelCase      :						Optional[Any]						      =				dropout
			__lowerCAmelCase      :						List[Any]						      =				attention_dropout
			__lowerCAmelCase      :						Optional[int]						      =				activation_dropout
			__lowerCAmelCase      :						List[Any]						      =				activation_function
			__lowerCAmelCase      :						List[str]						      =				init_std
			__lowerCAmelCase      :						Optional[int]						      =				encoder_layerdrop
			__lowerCAmelCase      :						List[Any]						      =				decoder_layerdrop
			__lowerCAmelCase      :						Tuple						      =				use_cache
			__lowerCAmelCase      :						Optional[Any]						      =				encoder_layers
			__lowerCAmelCase      :						str						      =				scale_embedding  # scale factor will be sqrt(d_model) if True
			__lowerCAmelCase      :						Union[str, Any]						      =				share_encoder_decoder_embeddings
			super().__init__(
			    pad_token_id=_SCREAMING_SNAKE_CASE	,		eos_token_id=_SCREAMING_SNAKE_CASE	,		is_encoder_decoder=_SCREAMING_SNAKE_CASE	,		decoder_start_token_id=_SCREAMING_SNAKE_CASE	,		forced_eos_token_id=_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE	,		)




class 			A__	(     _lowerCamelCase):

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

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

				if self.use_past:
					self.fill_with_past_key_values_(_SCREAMING_SNAKE_CASE	,		direction='inputs'			)
			elif self.task == "causal-lm":
				# TODO: figure this case out.
				__lowerCAmelCase      :						int						      =				OrderedDict(
				    [
				        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
				        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
				    ]			)
				if self.use_past:
					__lowerCAmelCase      ,       __lowerCAmelCase      :						Optional[Any]						      =				self.num_layers
					for i in range(_SCREAMING_SNAKE_CASE			):
						__lowerCAmelCase      :						List[Any]						      =				{0: 'batch', 2: 'past_sequence + sequence'}
						__lowerCAmelCase      :						List[str]						      =				{0: 'batch', 2: 'past_sequence + sequence'}
			else:
				__lowerCAmelCase      :						int						      =				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 							__lowerCamelCase							(				self			):
			if self.task in ["default", "seq2seq-lm"]:
				__lowerCAmelCase      :						List[str]						      =				super().outputs
			else:
				__lowerCAmelCase      :						Dict						      =				super(_SCREAMING_SNAKE_CASE	,		self			).outputs
				if self.use_past:
					__lowerCAmelCase      ,       __lowerCAmelCase      :						str						      =				self.num_layers
					for i in range(_SCREAMING_SNAKE_CASE			):
						__lowerCAmelCase      :						int						      =				{0: 'batch', 2: 'past_sequence + sequence'}
						__lowerCAmelCase      :						Optional[int]						      =				{0: 'batch', 2: 'past_sequence + sequence'}
			return common_outputs

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = -1	,		_SCREAMING_SNAKE_CASE = -1	,		_SCREAMING_SNAKE_CASE = False	,		_SCREAMING_SNAKE_CASE = None	,		):
			__lowerCAmelCase      :						Union[str, Any]						      =				self._generate_dummy_inputs_for_encoder_and_decoder(
			    _SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)

			# Generate decoder inputs
			__lowerCAmelCase      :						str						      =				seq_length if not self.use_past else 1
			__lowerCAmelCase      :						str						      =				self._generate_dummy_inputs_for_encoder_and_decoder(
			    _SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						int						      =				{f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
			__lowerCAmelCase      :						Dict						      =				dict(**_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE			)

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

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

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

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

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = -1	,		_SCREAMING_SNAKE_CASE = -1	,		_SCREAMING_SNAKE_CASE = False	,		_SCREAMING_SNAKE_CASE = None	,		):
			__lowerCAmelCase      :						int						      =				self._generate_dummy_inputs_for_encoder_and_decoder(
			    _SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)

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

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

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = -1	,		_SCREAMING_SNAKE_CASE = -1	,		_SCREAMING_SNAKE_CASE = False	,		_SCREAMING_SNAKE_CASE = None	,		):
			# 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
			__lowerCAmelCase      :						int						      =				compute_effective_axis_dimension(
			    _SCREAMING_SNAKE_CASE	,		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      :						Union[str, Any]						      =				tokenizer.num_special_tokens_to_add(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						List[Any]						      =				compute_effective_axis_dimension(
			    _SCREAMING_SNAKE_CASE	,		fixed_dimension=OnnxConfig.default_fixed_sequence	,		num_token_to_add=_SCREAMING_SNAKE_CASE			)

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

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = -1	,		_SCREAMING_SNAKE_CASE = -1	,		_SCREAMING_SNAKE_CASE = False	,		_SCREAMING_SNAKE_CASE = None	,		):
			if self.task in ["default", "seq2seq-lm"]:
				__lowerCAmelCase      :						int						      =				self._generate_dummy_inputs_for_default_and_seqaseq_lm(
				    _SCREAMING_SNAKE_CASE	,		batch_size=_SCREAMING_SNAKE_CASE	,		seq_length=_SCREAMING_SNAKE_CASE	,		is_pair=_SCREAMING_SNAKE_CASE	,		framework=_SCREAMING_SNAKE_CASE			)

			else:
				__lowerCAmelCase      :						Optional[Any]						      =				self._generate_dummy_inputs_for_causal_lm(
				    _SCREAMING_SNAKE_CASE	,		batch_size=_SCREAMING_SNAKE_CASE	,		seq_length=_SCREAMING_SNAKE_CASE	,		is_pair=_SCREAMING_SNAKE_CASE	,		framework=_SCREAMING_SNAKE_CASE			)

			return common_inputs

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
			if self.task in ["default", "seq2seq-lm"]:
				__lowerCAmelCase      :						Optional[int]						      =				super()._flatten_past_key_values_(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)
			else:
				__lowerCAmelCase      :						Optional[int]						      =				super(_SCREAMING_SNAKE_CASE	,		self			)._flatten_past_key_values_(
				    _SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)







		@property
		def 							__lowerCamelCase							(				self			):
			return 1E-4

"""simple docstring"""







def    __lowerCAmelCase    (_UpperCamelCase		):
	if len(_UpperCamelCase		) <= 1:
		return lst

	__lowerCAmelCase      :						str						      =				1

	while i < len(_UpperCamelCase		):
		if lst[i - 1] <= lst[i]:
			i += 1
		else:
			__lowerCAmelCase      ,       __lowerCAmelCase      :						List[Any]						      =				lst[i], lst[i - 1]
			i -= 1
			if i == 0:
				__lowerCAmelCase      :						int						      =				1

	return lst


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