
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


lowercase__				:				Tuple						=      logging.get_logger(__name__)

lowercase__				:				Dict						=      {
    "facebook/mbart-large-cc25": "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json",
    # See all MBART models at https://huggingface.co/models?filter=mbart
}






class 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_     ):

							"""simple docstring"""






							_snake_case  		=	'mbart'
							_snake_case  		=	['past_key_values']
							_snake_case  		=	{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}



							def __init__(	self  ,						SCREAMING_SNAKE_CASE_=50265  ,						SCREAMING_SNAKE_CASE_=1024  ,						SCREAMING_SNAKE_CASE_=12  ,						SCREAMING_SNAKE_CASE_=4096  ,						SCREAMING_SNAKE_CASE_=16  ,						SCREAMING_SNAKE_CASE_=12  ,						SCREAMING_SNAKE_CASE_=4096  ,						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_=1024  ,						SCREAMING_SNAKE_CASE_=0.1  ,						SCREAMING_SNAKE_CASE_=0.0  ,						SCREAMING_SNAKE_CASE_=0.0  ,						SCREAMING_SNAKE_CASE_=0.0_2  ,						SCREAMING_SNAKE_CASE_=0.0  ,						SCREAMING_SNAKE_CASE_=False  ,						SCREAMING_SNAKE_CASE_=1  ,						SCREAMING_SNAKE_CASE_=0  ,						SCREAMING_SNAKE_CASE_=2  ,						SCREAMING_SNAKE_CASE_=2  ,						**SCREAMING_SNAKE_CASE_  ,						)-> List[str]:





													'''simple docstring'''






													__UpperCamelCase  	=      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  	=      classifier_dropout
													__UpperCamelCase  	=      use_cache
													__UpperCamelCase  	=      encoder_layers
													__UpperCamelCase  	=      scale_embedding  # scale factor will be sqrt(d_model) if True
													super().__init__(
													    pad_token_id=SCREAMING_SNAKE_CASE_  ,						bos_token_id=SCREAMING_SNAKE_CASE_  ,						eos_token_id=SCREAMING_SNAKE_CASE_  ,						is_encoder_decoder=SCREAMING_SNAKE_CASE_  ,						forced_eos_token_id=SCREAMING_SNAKE_CASE_  ,						**SCREAMING_SNAKE_CASE_  ,						)






class 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_     ):

							"""simple docstring"""






							@property
							def      A__		(	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_(SCREAMING_SNAKE_CASE_  ,						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(SCREAMING_SNAKE_CASE_      ):
																															__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
							def      A__		(	self      )-> Mapping[str, Mapping[int, str]]:





													'''simple docstring'''






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



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





													'''simple docstring'''






													__UpperCamelCase  	=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
													    SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )

													# Generate decoder inputs
													__UpperCamelCase  	=      seq_length if not self.use_past else 1
													__UpperCamelCase  	=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
													    SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      {F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
													__UpperCamelCase  	=      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
																			__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(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )]  ,						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(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )
																			__UpperCamelCase  	=      max(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      ) - min_num_layers
																			__UpperCamelCase  	=      '''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.
																			__UpperCamelCase  	=      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      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_ = -1  ,						SCREAMING_SNAKE_CASE_ = -1  ,						SCREAMING_SNAKE_CASE_ = False  ,						SCREAMING_SNAKE_CASE_ = None  ,						)-> Mapping[str, Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
													    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
																			__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(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						dtype=SCREAMING_SNAKE_CASE_      )]  ,						dim=1      )
																			__UpperCamelCase  	=      [
																			    (torch.zeros(SCREAMING_SNAKE_CASE_      ), torch.zeros(SCREAMING_SNAKE_CASE_      )) for _ in range(SCREAMING_SNAKE_CASE_      )
																			]
													return common_inputs



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





													'''simple docstring'''






													__UpperCamelCase  	=      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
													__UpperCamelCase  	=      tokenizer.num_special_tokens_to_add(SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      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
													__UpperCamelCase  	=      [''' '''.join([tokenizer.unk_token]      ) * seq_length] * batch_size
													__UpperCamelCase  	=      dict(tokenizer(SCREAMING_SNAKE_CASE_  ,						return_tensors=SCREAMING_SNAKE_CASE_      )      )
													return common_inputs



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





													'''simple docstring'''






													if self.task in ["default", "seq2seq-lm"]:
																			__UpperCamelCase  	=      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_      )

													elif self.task == "causal-lm":
																			__UpperCamelCase  	=      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_      )
													else:
																			__UpperCamelCase  	=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
																			    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      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )-> Dict:





													'''simple docstring'''






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










'''simple docstring'''






def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->int:






       '''simple docstring'''




       if len(lowerCamelCase   ) < len(lowerCamelCase   ):
              return levenshtein_distance(lowerCamelCase ,					lowerCamelCase   )

       if len(lowerCamelCase   ) == 0:
              return len(lowerCamelCase   )

       lowercase	:		List[Any]    =   list(range(len(lowerCamelCase   ) + 1   )   )

       for i, ca in enumerate(lowerCamelCase   ):
              lowercase	:		Any    =   [i + 1]

              for j, ca in enumerate(lowerCamelCase   ):
                     # Calculate insertions, deletions and substitutions
                     lowercase	:		List[Any]    =   previous_row[j + 1] + 1
                     lowercase	:		str    =   current_row[j] + 1
                     lowercase	:		List[Any]    =   previous_row[j] + (ca != ca)

                     # Get the minimum to append to the current row
                     current_row.append(min(lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase   )   )

              # Store the previous row
              lowercase	:		List[str]    =   current_row

       # Returns the last element (distance)
       return previous_row[-1]


if __name__ == "__main__":
      lowercase						:   Dict      						=							input('''Enter the first word:\n''').strip()
      lowercase						:   Tuple      						=							input('''Enter the second word:\n''').strip()

      lowercase						:   Tuple      						=							levenshtein_distance(first_word, second_word)
      print(f'Levenshtein distance between {first_word} and {second_word} is {result}')




