


'''simple docstring'''






import random


def A_					(				__SCREAMING_SNAKE_CASE :					Optional[Any]       )     ->  Dict:

					def is_sorted(__SCREAMING_SNAKE_CASE :					str       ):
										for i in range(len(__SCREAMING_SNAKE_CASE       ) - 1       ):
															if collection[i] > collection[i + 1]:
																				return False
										return True

					while not is_sorted(__SCREAMING_SNAKE_CASE       ):
										random.shuffle(__SCREAMING_SNAKE_CASE       )
					return collection


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






'''simple docstring'''






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


_A      =     logging.get_logger(__name__)

_A      =     {
    """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      SCREAMING_SNAKE_CASE_	( snake_case		):
			__a					:      Any					=  '''m2m_100'''
			__a					:      List[str]					=  ['''past_key_values''']
			__a					:      str					=  {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}




			def __init__(      self					,      lowercase=1_2_8_1_1_2					,      lowercase=1_0_2_4					,      lowercase=1_2					,      lowercase=4_0_9_6					,      lowercase=1_6					,      lowercase=1_2					,      lowercase=4_0_9_6					,      lowercase=1_6					,      lowercase=0.0_5					,      lowercase=0.0_5					,      lowercase=True					,      lowercase=True					,      lowercase="relu"					,      lowercase=1_0_2_4					,      lowercase=0.1					,      lowercase=0.1					,      lowercase=0.0					,      lowercase=0.0_2					,      lowercase=2					,      lowercase=True					,      lowercase=1					,      lowercase=0					,      lowercase=2					,      **lowercase					,      )			->						Optional[int]:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							int			   = vocab_size
								__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = max_position_embeddings
								__SCREAMING_SNAKE_CASE					:							int			   = d_model
								__SCREAMING_SNAKE_CASE					:							List[Any]			   = encoder_ffn_dim
								__SCREAMING_SNAKE_CASE					:							int			   = encoder_layers
								__SCREAMING_SNAKE_CASE					:							Any			   = encoder_attention_heads
								__SCREAMING_SNAKE_CASE					:							List[str]			   = decoder_ffn_dim
								__SCREAMING_SNAKE_CASE					:							Any			   = decoder_layers
								__SCREAMING_SNAKE_CASE					:							str			   = decoder_attention_heads
								__SCREAMING_SNAKE_CASE					:							List[Any]			   = dropout
								__SCREAMING_SNAKE_CASE					:							Tuple			   = attention_dropout
								__SCREAMING_SNAKE_CASE					:							int			   = activation_dropout
								__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = activation_function
								__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = init_std
								__SCREAMING_SNAKE_CASE					:							int			   = encoder_layerdrop
								__SCREAMING_SNAKE_CASE					:							Dict			   = decoder_layerdrop
								__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = use_cache
								__SCREAMING_SNAKE_CASE					:							Optional[int]			   = encoder_layers
								__SCREAMING_SNAKE_CASE					:							List[str]			   = scale_embedding  # scale factor will be sqrt(d_model) if True

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






class      SCREAMING_SNAKE_CASE_	( snake_case		):




			@property
			def 			_snake_case   (      self       )			->						Mapping[str, Mapping[int, str]]:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							Any			   = OrderedDict(
								    [
								        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
								        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
								    ]       )

								if self.use_past:
													__SCREAMING_SNAKE_CASE					:							str			   = {0: '''batch'''}
													__SCREAMING_SNAKE_CASE					:							Any			   = {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
								else:
													__SCREAMING_SNAKE_CASE					:							Tuple			   = {0: '''batch''', 1: '''decoder_sequence'''}
													__SCREAMING_SNAKE_CASE					:							str			   = {0: '''batch''', 1: '''decoder_sequence'''}

								if self.use_past:
													self.fill_with_past_key_values_(lowercase					,      direction='''inputs'''       )
								return common_inputs




			def 			_snake_case   (      self					,      lowercase					,      lowercase = -1					,      lowercase = -1					,      lowercase = False					,      lowercase = None					,      )			->						Mapping[str, Any]:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							Dict			   = compute_effective_axis_dimension(
								    lowercase					,      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
								__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = tokenizer.num_special_tokens_to_add(lowercase       )
								__SCREAMING_SNAKE_CASE					:							Optional[int]			   = compute_effective_axis_dimension(
								    lowercase					,      fixed_dimension=OnnxConfig.default_fixed_sequence					,      num_token_to_add=lowercase       )

								# Generate dummy inputs according to compute batch and sequence
								__SCREAMING_SNAKE_CASE					:							List[Any]			   = [''' '''.join([tokenizer.unk_token]       ) * seq_length] * batch_size
								__SCREAMING_SNAKE_CASE					:							Dict			   = dict(tokenizer(lowercase					,      return_tensors=lowercase       )       )
								return common_inputs




			def 			_snake_case   (      self					,      lowercase					,      lowercase = -1					,      lowercase = -1					,      lowercase = False					,      lowercase = None					,      )			->						Mapping[str, Any]:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							Dict			   = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    lowercase					,      lowercase					,      lowercase					,      lowercase					,      lowercase       )

								# Generate decoder inputs
								__SCREAMING_SNAKE_CASE					:							str			   = seq_length if not self.use_past else 1
								__SCREAMING_SNAKE_CASE					:							List[str]			   = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    lowercase					,      lowercase					,      lowercase					,      lowercase					,      lowercase       )
								__SCREAMING_SNAKE_CASE					:							Any			   = {f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
								__SCREAMING_SNAKE_CASE					:							List[str]			   = dict(**lowercase					,      **lowercase       )

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

													__SCREAMING_SNAKE_CASE					:							List[Any]			   = torch.cat(
													    [common_inputs['''decoder_attention_mask'''], torch.ones(lowercase					,      lowercase       )]					,      dim=1       )

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

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

			__a					:      Optional[int]					=  _generate_dummy_inputs_for_default_and_seqaseq_lm


