



'''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


SCREAMING_SNAKE_CASE__     : Optional[Any]				=   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     : Any				=   {
    "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 				__lowerCAmelCase(							SCREAMING_SNAKE_CASE__      ):
			__snake_case  :		Tuple		=      'm2m_100'
			__snake_case  :		List[str]		=      ['past_key_values']
			__snake_case  :		Any		=      {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}






			def __init__(   self  :							str      ,       SCREAMING_SNAKE_CASE  :							Tuple=128_112      ,       SCREAMING_SNAKE_CASE  :							Optional[Any]=1_024      ,       SCREAMING_SNAKE_CASE  :							int=12      ,       SCREAMING_SNAKE_CASE  :							Any=4_096      ,       SCREAMING_SNAKE_CASE  :							Any=16      ,       SCREAMING_SNAKE_CASE  :							Optional[int]=12      ,       SCREAMING_SNAKE_CASE  :							Tuple=4_096      ,       SCREAMING_SNAKE_CASE  :							Dict=16      ,       SCREAMING_SNAKE_CASE  :							Tuple=0.05      ,       SCREAMING_SNAKE_CASE  :							Union[str, Any]=0.05      ,       SCREAMING_SNAKE_CASE  :							Dict=True      ,       SCREAMING_SNAKE_CASE  :							Union[str, Any]=True      ,       SCREAMING_SNAKE_CASE  :							Optional[int]="relu"      ,       SCREAMING_SNAKE_CASE  :							List[str]=1_024      ,       SCREAMING_SNAKE_CASE  :							str=0.1      ,       SCREAMING_SNAKE_CASE  :							Optional[Any]=0.1      ,       SCREAMING_SNAKE_CASE  :							Optional[Any]=0.0      ,       SCREAMING_SNAKE_CASE  :							List[str]=0.02      ,       SCREAMING_SNAKE_CASE  :							int=2      ,       SCREAMING_SNAKE_CASE  :							Optional[int]=True      ,       SCREAMING_SNAKE_CASE  :							Any=1      ,       SCREAMING_SNAKE_CASE  :							Optional[Any]=0      ,       SCREAMING_SNAKE_CASE  :							str=2      ,       **SCREAMING_SNAKE_CASE  :							Union[str, Any]      ,       ):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      vocab_size
					SCREAMING_SNAKE_CASE_						:List[Any]										=      max_position_embeddings
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      d_model
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      encoder_ffn_dim
					SCREAMING_SNAKE_CASE_						:int										=      encoder_layers
					SCREAMING_SNAKE_CASE_						:List[str]										=      encoder_attention_heads
					SCREAMING_SNAKE_CASE_						:Tuple										=      decoder_ffn_dim
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      decoder_layers
					SCREAMING_SNAKE_CASE_						:int										=      decoder_attention_heads
					SCREAMING_SNAKE_CASE_						:int										=      dropout
					SCREAMING_SNAKE_CASE_						:List[str]										=      attention_dropout
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      activation_dropout
					SCREAMING_SNAKE_CASE_						:str										=      activation_function
					SCREAMING_SNAKE_CASE_						:int										=      init_std
					SCREAMING_SNAKE_CASE_						:List[Any]										=      encoder_layerdrop
					SCREAMING_SNAKE_CASE_						:List[str]										=      decoder_layerdrop
					SCREAMING_SNAKE_CASE_						:Any										=      use_cache
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      encoder_layers
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      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 				__lowerCAmelCase(							SCREAMING_SNAKE_CASE__      ):






			@property
			def 				_lowercase	(   self  :							List[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_						:int										=      {0: 'batch'}
							SCREAMING_SNAKE_CASE_						:List[str]										=      {0: 'batch', 1: 'past_decoder_sequence + sequence'}
					else:
							SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      {0: 'batch', 1: 'decoder_sequence'}
							SCREAMING_SNAKE_CASE_						:Any										=      {0: 'batch', 1: 'decoder_sequence'}

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






			def 				_lowercase	(   self  :							str      ,       SCREAMING_SNAKE_CASE  :							PreTrainedTokenizer      ,       SCREAMING_SNAKE_CASE  :							int = -1      ,       SCREAMING_SNAKE_CASE  :							int = -1      ,       SCREAMING_SNAKE_CASE  :							bool = False      ,       SCREAMING_SNAKE_CASE  :							Optional[TensorType] = None      ,       ):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[Any]										=      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
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      tokenizer.num_special_tokens_to_add(a_	)
					SCREAMING_SNAKE_CASE_						:Dict										=      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
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      [' '.join([tokenizer.unk_token]	) * seq_length] * batch_size
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      dict(tokenizer(a_      ,       return_tensors=a_	)	)
					return common_inputs






			def 				_lowercase	(   self  :							int      ,       SCREAMING_SNAKE_CASE  :							PreTrainedTokenizer      ,       SCREAMING_SNAKE_CASE  :							int = -1      ,       SCREAMING_SNAKE_CASE  :							int = -1      ,       SCREAMING_SNAKE_CASE  :							bool = False      ,       SCREAMING_SNAKE_CASE  :							Optional[TensorType] = None      ,       ):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Dict										=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
					    a_      ,       a_      ,       a_      ,       a_      ,       a_	)

					# Generate decoder inputs
					SCREAMING_SNAKE_CASE_						:Dict										=      seq_length if not self.use_past else 1
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
					    a_      ,       a_      ,       a_      ,       a_      ,       a_	)
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      {f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      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
							SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:List[str]										=      common_inputs['input_ids'].shape
							SCREAMING_SNAKE_CASE_						:str										=      common_inputs['decoder_input_ids'].shape[1]
							SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:Optional[int]										=      self.num_attention_heads
							SCREAMING_SNAKE_CASE_						:Any										=      (
							    batch,
							    num_encoder_attention_heads,
							    encoder_seq_length,
							    self._config.hidden_size // num_encoder_attention_heads,
							)
							SCREAMING_SNAKE_CASE_						:Optional[Any]										=      decoder_seq_length + 3
							SCREAMING_SNAKE_CASE_						:Tuple										=      (
							    batch,
							    num_decoder_attention_heads,
							    decoder_past_length,
							    self._config.hidden_size // num_decoder_attention_heads,
							)

							SCREAMING_SNAKE_CASE_						:Any										=      torch.cat(
							    [common_inputs['decoder_attention_mask'], torch.ones(a_      ,       a_	)]      ,       dim=1	)

							SCREAMING_SNAKE_CASE_						:Optional[int]										=      []
							# If the number of encoder and decoder layers are present in the model configuration, both are considered
							SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:List[str]										=      self.num_layers
							SCREAMING_SNAKE_CASE_						:Optional[int]										=      min(a_      ,       a_	)
							SCREAMING_SNAKE_CASE_						:str										=      max(a_      ,       a_	) - min_num_layers
							SCREAMING_SNAKE_CASE_						:List[Any]										=      '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.
							SCREAMING_SNAKE_CASE_						:Optional[Any]										=      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

			__snake_case  :		Dict		=      _generate_dummy_inputs_for_default_and_seqaseq_lm




'''simple docstring'''

from __future__ import annotations



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					list[list[int]]     ,		_UpperCamelCase   :					list[list[int]] )		->       list[int]:





   '''simple docstring'''







   assert len(_UpperCamelCase ) == len(_UpperCamelCase )

   UpperCamelCase__			    =    len(_UpperCamelCase )
   UpperCamelCase__			    =    list(range(_UpperCamelCase ) )
   UpperCamelCase__			    =    [-1] * n  # who the donor has donated to
   UpperCamelCase__			    =    [-1] * n  # who the recipient has received from
   UpperCamelCase__			    =    [0] * n

   while unmatched_donors:
      UpperCamelCase__			    =    unmatched_donors[0]
      UpperCamelCase__			    =    donor_pref[donor]
      UpperCamelCase__			    =    donor_preference[num_donations[donor]]
      num_donations[donor] += 1
      UpperCamelCase__			    =    recipient_pref[recipient]
      UpperCamelCase__			    =    rec_record[recipient]

      if prev_donor != -1:
         if rec_preference.index(_UpperCamelCase ) > rec_preference.index(_UpperCamelCase ):
            UpperCamelCase__			    =    donor
            UpperCamelCase__			    =    recipient
            unmatched_donors.append(_UpperCamelCase )
            unmatched_donors.remove(_UpperCamelCase )
      else:
         UpperCamelCase__			    =    donor
         UpperCamelCase__			    =    recipient
         unmatched_donors.remove(_UpperCamelCase )
   return donor_record