



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 			__snake_case   ( SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__					:   List[Any]       =       """m2m_100"""
							UpperCamelCase__					:   Union[str, Any]       =       ["""past_key_values"""]
							UpperCamelCase__					:   Any       =       {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}


							def __init__(				self	,	a_=128_112	,	a_=1_024	,	a_=12	,	a_=4_096	,	a_=16	,	a_=12	,	a_=4_096	,	a_=16	,	a_=0.05	,	a_=0.05	,	a_=True	,	a_=True	,	a_="relu"	,	a_=1_024	,	a_=0.1	,	a_=0.1	,	a_=0.0	,	a_=0.02	,	a_=2	,	a_=True	,	a_=1	,	a_=0	,	a_=2	,	**a_	,	):
											a__					   =		vocab_size
											a__					   =		max_position_embeddings
											a__					   =		d_model
											a__					   =		encoder_ffn_dim
											a__					   =		encoder_layers
											a__					   =		encoder_attention_heads
											a__					   =		decoder_ffn_dim
											a__					   =		decoder_layers
											a__					   =		decoder_attention_heads
											a__					   =		dropout
											a__					   =		attention_dropout
											a__					   =		activation_dropout
											a__					   =		activation_function
											a__					   =		init_std
											a__					   =		encoder_layerdrop
											a__					   =		decoder_layerdrop
											a__					   =		use_cache
											a__					   =		encoder_layers
											a__					   =		scale_embedding  # scale factor will be sqrt(d_model) if True

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




class 			__snake_case   ( SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							@property
							def _a   (				self		):
											a__					   =		OrderedDict(
											    [
											        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
											        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
											    ]		)

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

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


							def _a   (				self	,	a_	,	a_ = -1	,	a_ = -1	,	a_ = False	,	a_ = 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
											a__					   =		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
											a__					   =		tokenizer.num_special_tokens_to_add(a_		)
											a__					   =		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
											a__					   =		[""" """.join([tokenizer.unk_token]		) * seq_length] * batch_size
											a__					   =		dict(tokenizer(a_	,	return_tensors=a_		)		)
											return common_inputs


							def _a   (				self	,	a_	,	a_ = -1	,	a_ = -1	,	a_ = False	,	a_ = None	,	):
											a__					   =		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    a_	,	a_	,	a_	,	a_	,	a_		)

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

															a__					   =		torch.cat(
															    [common_inputs["""decoder_attention_mask"""], torch.ones(a_	,	a_		)]	,	dim=1		)

															a__					   =		[]
															# If the number of encoder and decoder layers are present in the model configuration, both are considered
															a__   ,      a__					   =		self.num_layers
															a__					   =		min(a_	,	a_		)
															a__					   =		max(a_	,	a_		) - min_num_layers
															a__					   =		"""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.
															a__					   =		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







							UpperCamelCase__					:   Tuple       =       _generate_dummy_inputs_for_default_and_seqaseq_lm






"""simple docstring"""







def 	lowerCAmelCase__      (  lowerCamelCase__      )			->				list:
							for i, _ in enumerate(lowerCamelCase__      ):
														if (i % 2 == 1) == (nums[i - 1] > nums[i]):
																					A   ,	A    =       nums[i], nums[i - 1]

							return nums


if __name__ == "__main__":
							print('Enter the array elements:')
							A     =      list(map(int, input().split()))
							print('The unsorted array is:')
							print(array)
							print('Array after Wiggle sort:')
							print(wiggle_sort(array))



