




'''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


UpperCAmelCase_							:	str	     =		logging.get_logger(__name__)

UpperCAmelCase_							:	str	     =		{
    "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      lowerCAmelCase (			lowerCAmelCase__):
       __lowercase	:   Dict										=      """marian"""
       __lowercase	:   List[str]										=      ["""past_key_values"""]
       __lowercase	:   Union[str, Any]										=      {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}


       def __init__( self     ,	__SCREAMING_SNAKE_CASE=5_8101     ,	__SCREAMING_SNAKE_CASE=None     ,	__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=0.02     ,	__SCREAMING_SNAKE_CASE=5_8100     ,	__SCREAMING_SNAKE_CASE=False     ,	__SCREAMING_SNAKE_CASE=5_8100     ,	__SCREAMING_SNAKE_CASE=0     ,	__SCREAMING_SNAKE_CASE=0     ,	__SCREAMING_SNAKE_CASE=True     ,	**__SCREAMING_SNAKE_CASE     ,	)   ->	List[str]:



            '''simple docstring'''




            __snake_case            =	vocab_size
            __snake_case            =	decoder_vocab_size or vocab_size
            __snake_case            =	max_position_embeddings
            __snake_case            =	d_model
            __snake_case            =	encoder_ffn_dim
            __snake_case            =	encoder_layers
            __snake_case            =	encoder_attention_heads
            __snake_case            =	decoder_ffn_dim
            __snake_case            =	decoder_layers
            __snake_case            =	decoder_attention_heads
            __snake_case            =	dropout
            __snake_case            =	attention_dropout
            __snake_case            =	activation_dropout
            __snake_case            =	activation_function
            __snake_case            =	init_std
            __snake_case            =	encoder_layerdrop
            __snake_case            =	decoder_layerdrop
            __snake_case            =	use_cache
            __snake_case            =	encoder_layers
            __snake_case            =	scale_embedding  # scale factor will be sqrt(d_model) if True
            __snake_case            =	share_encoder_decoder_embeddings
            super().__init__(
                pad_token_id=__a     ,	eos_token_id=__a     ,	is_encoder_decoder=__a     ,	decoder_start_token_id=__a     ,	forced_eos_token_id=__a     ,	**__a     ,	)





class      lowerCAmelCase (			lowerCAmelCase__):


       @property
       # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            if self.task in ["default", "seq2seq-lm"]:
                 __snake_case            =	OrderedDict(
                     [
                         ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                         ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                     ]	)

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

                 if self.use_past:
                      self.fill_with_past_key_values_(__a     ,	direction='''inputs'''	)
            elif self.task == "causal-lm":
                 # TODO: figure this case out.
                 __snake_case            =	OrderedDict(
                     [
                         ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                         ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                     ]	)
                 if self.use_past:
                      __snake_case       ,     __snake_case            =	self.num_layers
                      for i in range(__a	):
                           __snake_case            =	{0: '''batch''', 2: '''past_sequence + sequence'''}
                           __snake_case            =	{0: '''batch''', 2: '''past_sequence + sequence'''}
            else:
                 __snake_case            =	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	)   ->	List[str]:



            '''simple docstring'''




            if self.task in ["default", "seq2seq-lm"]:
                 __snake_case            =	super().outputs
            else:
                 __snake_case            =	super(__a     ,	self	).outputs
                 if self.use_past:
                      __snake_case       ,     __snake_case            =	self.num_layers
                      for i in range(__a	):
                           __snake_case            =	{0: '''batch''', 2: '''past_sequence + sequence'''}
                           __snake_case            =	{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     ,	)   ->	List[Any]:



            '''simple docstring'''




            __snake_case            =	self._generate_dummy_inputs_for_encoder_and_decoder(
                __a     ,	__a     ,	__a     ,	__a     ,	__a	)

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

                 __snake_case            =	torch.cat(
                     [common_inputs['''decoder_attention_mask'''], torch.ones(__a     ,	__a	)]     ,	dim=1	)

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


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = -1     ,	__SCREAMING_SNAKE_CASE = -1     ,	__SCREAMING_SNAKE_CASE = False     ,	__SCREAMING_SNAKE_CASE = None     ,	)   ->	List[Any]:



            '''simple docstring'''




            __snake_case            =	self._generate_dummy_inputs_for_encoder_and_decoder(
                __a     ,	__a     ,	__a     ,	__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
                 __snake_case       ,     __snake_case            =	common_inputs['''input_ids'''].shape
                 # Not using the same length for past_key_values
                 __snake_case            =	seqlen + 2
                 __snake_case       ,     __snake_case            =	self.num_layers
                 __snake_case       ,     __snake_case            =	self.num_attention_heads
                 __snake_case            =	(
                     batch,
                     num_encoder_attention_heads,
                     past_key_values_length,
                     self._config.hidden_size // num_encoder_attention_heads,
                 )

                 __snake_case            =	common_inputs['''attention_mask'''].dtype
                 __snake_case            =	torch.cat(
                     [common_inputs['''attention_mask'''], torch.ones(__a     ,	__a     ,	dtype=__a	)]     ,	dim=1	)
                 __snake_case            =	[
                     (torch.zeros(__a	), torch.zeros(__a	)) for _ in range(__a	)
                 ]
            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     ,	)   ->	Dict:



            '''simple docstring'''




            __snake_case            =	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
            __snake_case            =	tokenizer.num_special_tokens_to_add(__a	)
            __snake_case            =	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
            __snake_case            =	[''' '''.join([tokenizer.unk_token]	) * seq_length] * batch_size
            __snake_case            =	dict(tokenizer(__a     ,	return_tensors=__a	)	)
            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     ,	)   ->	List[str]:



            '''simple docstring'''




            if self.task in ["default", "seq2seq-lm"]:
                 __snake_case            =	self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                     __a     ,	batch_size=__a     ,	seq_length=__a     ,	is_pair=__a     ,	framework=__a	)

            else:
                 __snake_case            =	self._generate_dummy_inputs_for_causal_lm(
                     __a     ,	batch_size=__a     ,	seq_length=__a     ,	is_pair=__a     ,	framework=__a	)

            return common_inputs


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)   ->	Optional[Any]:



            '''simple docstring'''




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




       @property
       def    lowerCAmelCase   ( self	)   ->	Optional[int]:



            '''simple docstring'''




            return 1E-4










"""simple docstring"""


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			__lowerCAmelCase							       =     [False] * len(_UpperCamelCase )
			__lowerCAmelCase							       =     []
			queue.append(_UpperCamelCase )
			__lowerCAmelCase							       =     True

			while queue:
						__lowerCAmelCase							       =     queue.pop(0 )
						for ind in range(len(graph[u] ) ):
									if visited[ind] is False and graph[u][ind] > 0:
												queue.append(_UpperCamelCase )
												__lowerCAmelCase							       =     True
												__lowerCAmelCase							       =     u

			return visited[t]


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			__lowerCAmelCase							       =     [-1] * (len(_UpperCamelCase ))
			__lowerCAmelCase							       =     0
			while bfs(_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase ):
						__lowerCAmelCase							       =     float("Inf" )
						__lowerCAmelCase							       =     sink

						while s != source:
									# Find the minimum value in select path
									__lowerCAmelCase							       =     min(_UpperCamelCase       ,					graph[parent[s]][s] )
									__lowerCAmelCase							       =     parent[s]

						max_flow += path_flow
						__lowerCAmelCase							       =     sink

						while v != source:
									__lowerCAmelCase							       =     parent[v]
									graph[u][v] -= path_flow
									graph[v][u] += path_flow
									__lowerCAmelCase							       =     parent[v]
			return max_flow


A	:  Union[str, Any]            =     [
    [0, 1_6, 1_3, 0, 0, 0],
    [0, 0, 1_0, 1_2, 0, 0],
    [0, 4, 0, 0, 1_4, 0],
    [0, 0, 9, 0, 0, 2_0],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

A       ,       A	:  Any            =     0, 5


print(ford_fulkerson(graph, source, sink))




