






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


__lowerCamelCase   :     List[Any] 		= logging.get_logger(__name__)

__lowerCamelCase   :     Dict 		= {
    """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 				SCREAMING_SNAKE_CASE__		(							_UpperCamelCase		):





					"""simple docstring"""






					a_      =						"marian"
					a_      =						["past_key_values"]
					a_      =						{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}



					def __init__(   self	:    int				,						__A	:    List[Any]=5_8_1_0_1				,						__A	:    Optional[Any]=None				,						__A	:    int=1_0_2_4				,						__A	:    str=1_2				,						__A	:    List[Any]=4_0_9_6				,						__A	:    Tuple=1_6				,						__A	:    Tuple=1_2				,						__A	:    Tuple=4_0_9_6				,						__A	:    str=1_6				,						__A	:    Tuple=0.0				,						__A	:    Any=0.0				,						__A	:    str=True				,						__A	:    Dict=True				,						__A	:    List[Any]="gelu"				,						__A	:    List[str]=1_0_2_4				,						__A	:    Any=0.1				,						__A	:    Optional[Any]=0.0				,						__A	:    Any=0.0				,						__A	:    Union[str, Any]=0.0_2				,						__A	:    int=5_8_1_0_0				,						__A	:    int=False				,						__A	:    Dict=5_8_1_0_0				,						__A	:    Optional[Any]=0				,						__A	:    List[Any]=0				,						__A	:    Optional[Any]=True				,						**__A	:    Dict				,						):
						snake_case__					: Optional[int]  =    vocab_size
						snake_case__					: Optional[Any]  =    decoder_vocab_size or vocab_size
						snake_case__					: List[Any]  =    max_position_embeddings
						snake_case__					: Any  =    d_model
						snake_case__					: Optional[int]  =    encoder_ffn_dim
						snake_case__					: Optional[int]  =    encoder_layers
						snake_case__					: int  =    encoder_attention_heads
						snake_case__					: Any  =    decoder_ffn_dim
						snake_case__					: str  =    decoder_layers
						snake_case__					: List[Any]  =    decoder_attention_heads
						snake_case__					: str  =    dropout
						snake_case__					: Union[str, Any]  =    attention_dropout
						snake_case__					: str  =    activation_dropout
						snake_case__					: str  =    activation_function
						snake_case__					: str  =    init_std
						snake_case__					: Tuple  =    encoder_layerdrop
						snake_case__					: Tuple  =    decoder_layerdrop
						snake_case__					: Optional[Any]  =    use_cache
						snake_case__					: Dict  =    encoder_layers
						snake_case__					: Tuple  =    scale_embedding  # scale factor will be sqrt(d_model) if True
						snake_case__					: str  =    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 				SCREAMING_SNAKE_CASE__		(							_UpperCamelCase		):





					"""simple docstring"""






					@property
					# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
					def 			_lowercase	(   self	:    List[Any]			):



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

							if self.use_past:
								snake_case__					: Dict  =    {0: "batch"}
								snake_case__					: Tuple  =    {0: "batch", 1: "past_decoder_sequence + sequence"}
							else:
								snake_case__					: Union[str, Any]  =    {0: "batch", 1: "decoder_sequence"}
								snake_case__					: Union[str, Any]  =    {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__					: Dict  =    OrderedDict(
							    [
							        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
							        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
							    ]			)
							if self.use_past:
								snake_case__					: Optional[int]  =    self.num_layers
								for i in range(a_			):
									snake_case__					: List[str]  =    {0: "batch", 2: "past_sequence + sequence"}
									snake_case__					: List[Any]  =    {0: "batch", 2: "past_sequence + sequence"}
						else:
							snake_case__					: Optional[Any]  =    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 			_lowercase	(   self	:    str			):



						if self.task in ["default", "seq2seq-lm"]:
							snake_case__					: List[Any]  =    super().outputs
						else:
							snake_case__					: Union[str, Any]  =    super(a_				,						self			).outputs
							if self.use_past:
								snake_case__					: int  =    self.num_layers
								for i in range(a_			):
									snake_case__					: str  =    {0: "batch", 2: "past_sequence + sequence"}
									snake_case__					: Dict  =    {0: "batch", 2: "past_sequence + sequence"}
						return common_outputs



					def 			_lowercase	(   self	:    Any				,						__A	:    PreTrainedTokenizer				,						__A	:    int = -1				,						__A	:    int = -1				,						__A	:    bool = False				,						__A	:    Optional[TensorType] = None				,						):
						snake_case__					: List[str]  =    self._generate_dummy_inputs_for_encoder_and_decoder(
						    a_				,						a_				,						a_				,						a_				,						a_			)

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

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

							snake_case__					: Optional[int]  =    []
							# If the number of encoder and decoder layers are present in the model configuration, both are considered
							snake_case__					: Any  =    self.num_layers
							snake_case__					: Dict  =    min(a_				,						a_			)
							snake_case__					: str  =    max(a_				,						a_			) - min_num_layers
							snake_case__					: 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.
							snake_case__					: Tuple  =    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 			_lowercase	(   self	:    List[str]				,						__A	:    PreTrainedTokenizer				,						__A	:    int = -1				,						__A	:    int = -1				,						__A	:    bool = False				,						__A	:    Optional[TensorType] = None				,						):
						snake_case__					: List[Any]  =    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__					: Any  =    common_inputs["input_ids"].shape
							# Not using the same length for past_key_values
							snake_case__					: Optional[int]  =    seqlen + 2
							snake_case__					: Tuple  =    self.num_layers
							snake_case__					: Union[str, Any]  =    self.num_attention_heads
							snake_case__					: List[str]  =    (
							    batch,
							    num_encoder_attention_heads,
							    past_key_values_length,
							    self._config.hidden_size // num_encoder_attention_heads,
							)

							snake_case__					: Any  =    common_inputs["attention_mask"].dtype
							snake_case__					: Tuple  =    torch.cat(
							    [common_inputs["attention_mask"], torch.ones(a_				,						a_				,						dtype=a_			)]				,						dim=1			)
							snake_case__					: Union[str, Any]  =    [
							    (torch.zeros(a_			), torch.zeros(a_			)) for _ in range(a_			)
							]
						return common_inputs



					def 			_lowercase	(   self	:    Optional[int]				,						__A	:    PreTrainedTokenizer				,						__A	:    int = -1				,						__A	:    int = -1				,						__A	:    bool = False				,						__A	:    Optional[TensorType] = None				,						):
						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
						snake_case__					: List[str]  =    tokenizer.num_special_tokens_to_add(a_			)
						snake_case__					: int  =    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__					: Dict  =    [" ".join([tokenizer.unk_token]			) * seq_length] * batch_size
						snake_case__					: List[Any]  =    dict(tokenizer(a_				,						return_tensors=a_			)			)
						return common_inputs



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



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

						else:
							snake_case__					: Optional[Any]  =    self._generate_dummy_inputs_for_causal_lm(
							    a_				,						batch_size=a_				,						seq_length=a_				,						is_pair=a_				,						framework=a_			)

						return common_inputs



					def 			_lowercase	(   self	:    Any				,						__A	:    Any				,						__A	:    Any				,						__A	:    Optional[Any]				,						__A	:    Dict			):



						if self.task in ["default", "seq2seq-lm"]:
							snake_case__					: Tuple  =    super()._flatten_past_key_values_(a_				,						a_				,						a_				,						a_			)
						else:
							snake_case__					: Any  =    super(a_				,						self			)._flatten_past_key_values_(
							    a_				,						a_				,						a_				,						a_			)






					@property
					def 			_lowercase	(   self	:    Optional[Any]			):
						return 1e-4










"""simple docstring"""






__UpperCAmelCase						=			[
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]






def      lowercase__ (					lowerCAmelCase__   :			Dict							,			lowerCAmelCase__   :			Optional[int]							,			lowerCAmelCase__   :			List[str]							,			lowerCAmelCase__   :			Tuple			)    ->			Dict:



			'''simple docstring'''
			# Return True if there is node that has not iterated.
			a__  :	int		 =			[False] * len(lowerCAmelCase__			)
			a__  :	Optional[Any]		 =			[s]
			a__  :	Optional[int]		 =			True

			while queue:
						a__  :	Any		 =			queue.pop(0			)
						for ind in range(len(graph[u]			)			):
									if visited[ind] is False and graph[u][ind] > 0:
												queue.append(lowerCAmelCase__			)
												a__  :	Any		 =			True
												a__  :	Union[str, Any]		 =			u

			return visited[t]






def      lowercase__ (					lowerCAmelCase__   :			List[str]							,			lowerCAmelCase__   :			List[Any]							,			lowerCAmelCase__   :			Optional[Any]			)    ->			Any:



			'''simple docstring'''
			a__  :	Any		 =			[-1] * (len(lowerCAmelCase__			))
			a__  :	Any		 =			0
			a__  :	List[str]		 =			[]
			a__  :	Tuple		 =			[i[:] for i in graph]  # Record original cut, copy.
			while bfs(lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__			):
						a__  :	Any		 =			float("Inf"			)
						a__  :	Optional[int]		 =			sink

						while s != source:
									# Find the minimum value in select path
									a__  :	Union[str, Any]		 =			min(lowerCAmelCase__							,			graph[parent[s]][s]			)
									a__  :	List[str]		 =			parent[s]

						max_flow += path_flow
						a__  :	List[str]		 =			sink

						while v != source:
									a__  :	int		 =			parent[v]
									graph[u][v] -= path_flow
									graph[v][u] += path_flow
									a__  :	List[str]		 =			parent[v]

			for i in range(len(lowerCAmelCase__			)			):
						for j in range(len(graph[0]			)			):
									if graph[i][j] == 0 and temp[i][j] > 0:
												res.append((i, j)			)

			return res


if __name__ == "__main__":
		print(mincut(test_graph, source=0, sink=5))