


"""simple docstring"""


import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration


def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




			__lowerCAmelCase							       =     [
			    "encoder.version",
			    "decoder.version",
			    "model.encoder.version",
			    "model.decoder.version",
			    "decoder.output_projection.weight",
			    "_float_tensor",
			    "encoder.embed_positions._float_tensor",
			    "decoder.embed_positions._float_tensor",
			]
			for k in ignore_keys:
						state_dict.pop(_UpperCamelCase       ,					_UpperCamelCase )


def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




			__lowerCAmelCase							       =     list(s_dict.keys() )
			for key in keys:
						if "transformer_layers" in key:
									__lowerCAmelCase							       =     s_dict.pop(_UpperCamelCase )
						elif "subsample" in key:
									__lowerCAmelCase							       =     s_dict.pop(_UpperCamelCase )


def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




			__lowerCAmelCase       ,      __lowerCAmelCase							       =     emb.weight.shape
			__lowerCAmelCase							       =     nn.Linear(_UpperCamelCase       ,					_UpperCamelCase       ,					bias=_UpperCamelCase )
			__lowerCAmelCase							       =     emb.weight.data
			return lin_layer


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			__lowerCAmelCase							       =     torch.load(_UpperCamelCase       ,					map_location="cpu" )
			__lowerCAmelCase							       =     mam_aaa["args"]
			__lowerCAmelCase							       =     mam_aaa["model"]
			__lowerCAmelCase							       =     state_dict["decoder.output_projection.weight"]

			remove_ignore_keys_(_UpperCamelCase )
			rename_keys(_UpperCamelCase )

			__lowerCAmelCase							       =     state_dict["decoder.embed_tokens.weight"].shape[0]

			__lowerCAmelCase							       =     args.share_decoder_input_output_embed

			__lowerCAmelCase							       =     [int(_UpperCamelCase ) for i in args.conv_kernel_sizes.split("," )]
			__lowerCAmelCase							       =     SpeechaTextConfig(
			    vocab_size=_UpperCamelCase       ,					max_source_positions=args.max_source_positions       ,					max_target_positions=args.max_target_positions       ,					encoder_layers=args.encoder_layers       ,					decoder_layers=args.decoder_layers       ,					encoder_attention_heads=args.encoder_attention_heads       ,					decoder_attention_heads=args.decoder_attention_heads       ,					encoder_ffn_dim=args.encoder_ffn_embed_dim       ,					decoder_ffn_dim=args.decoder_ffn_embed_dim       ,					d_model=args.encoder_embed_dim       ,					dropout=args.dropout       ,					attention_dropout=args.attention_dropout       ,					activation_dropout=args.activation_dropout       ,					activation_function="relu"       ,					num_conv_layers=len(_UpperCamelCase )       ,					conv_channels=args.conv_channels       ,					conv_kernel_sizes=_UpperCamelCase       ,					input_feat_per_channel=args.input_feat_per_channel       ,					input_channels=args.input_channels       ,					tie_word_embeddings=_UpperCamelCase       ,					num_beams=5       ,					max_length=200       ,					use_cache=_UpperCamelCase       ,					decoder_start_token_id=2       ,					early_stopping=_UpperCamelCase       ,					)

			__lowerCAmelCase							       =     SpeechaTextForConditionalGeneration(_UpperCamelCase )
			__lowerCAmelCase       ,      __lowerCAmelCase							       =     model.model.load_state_dict(_UpperCamelCase       ,					strict=_UpperCamelCase )
			if len(_UpperCamelCase ) > 0 and not set(_UpperCamelCase ) <= {
			    "encoder.embed_positions.weights",
			    "decoder.embed_positions.weights",
			}:
						raise ValueError(
						    "Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,"
						    f" but all the following weights are missing {missing}" )

			if tie_embeds:
						__lowerCAmelCase							       =     make_linear_from_emb(model.model.decoder.embed_tokens )
			else:
						__lowerCAmelCase							       =     lm_head_weights

			model.save_pretrained(_UpperCamelCase )


if __name__ == "__main__":
	A	:  Optional[int]            =     argparse.ArgumentParser()
	# Required parameters
	parser.add_argument("--fairseq_path", type=str, help="Path to the fairseq model (.pt) file.")
	parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
	A	:  List[str]            =     parser.parse_args()
	convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)








"""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))




