






"""simple docstring"""
def 				lowercase__		(					lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase       ):
				# Return True if there is node that has not iterated.
				_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    [False] * len(lowerCamelCase       )
				_SCREAMING_SNAKE_CASE   :		List[Any]       		=    []
				queue.append(lowerCamelCase       )
				_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    True

				while queue:
								_SCREAMING_SNAKE_CASE   :		Tuple       		=    queue.pop(0       )
								for ind in range(len(graph[u]       )       ):
												if visited[ind] is False and graph[u][ind] > 0:
																queue.append(lowerCamelCase       )
																_SCREAMING_SNAKE_CASE   :		List[Any]       		=    True
																_SCREAMING_SNAKE_CASE   :		List[str]       		=    u

				return visited[t]





def 				lowercase__		(					lowerCamelCase,						lowerCamelCase,						lowerCamelCase       ):
				# This array is filled by BFS and to store path
				_SCREAMING_SNAKE_CASE   :		List[str]       		=    [-1] * (len(lowerCamelCase       ))
				_SCREAMING_SNAKE_CASE   :		Any       		=    0
				while bfs(lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase       ):
								_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    float('Inf'       )
								_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    sink

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

								max_flow += path_flow
								_SCREAMING_SNAKE_CASE   :		List[Any]       		=    sink

								while v != source:
												_SCREAMING_SNAKE_CASE   :		List[str]       		=    parent[v]
												graph[u][v] -= path_flow
												graph[v][u] += path_flow
												_SCREAMING_SNAKE_CASE   :		Tuple       		=    parent[v]
				return max_flow


lowerCAmelCase__    						=			[
    [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],
]

lowerCAmelCase__						, lowerCAmelCase__    						=			0, 5






print(ford_fulkerson(graph, source, sink))









"""simple docstring"""
import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
			lowerCAmelCase__    						=			argparse.ArgumentParser(
			    description=(
			        'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned'
			        ' Distillation'
			    )
			)
			parser.add_argument('--model_type', default='roberta', choices=['roberta', 'gpt2'])
			parser.add_argument('--model_name', default='roberta-large', type=str)
			parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_roberta_048131723.pth', type=str)
			parser.add_argument('--vocab_transform', action='store_true')
			lowerCAmelCase__    						=			parser.parse_args()

			if args.model_type == "roberta":
						lowerCAmelCase__    						=			RobertaForMaskedLM.from_pretrained(args.model_name)
						lowerCAmelCase__    						=			'roberta'
			elif args.model_type == "gpt2":
						lowerCAmelCase__    						=			GPTaLMHeadModel.from_pretrained(args.model_name)
						lowerCAmelCase__    						=			'transformer'

			lowerCAmelCase__    						=			model.state_dict()
			lowerCAmelCase__    						=			{}

			# Embeddings #
			if args.model_type == "gpt2":
						for param_name in ["wte.weight", "wpe.weight"]:
									lowerCAmelCase__    						=			state_dict[F'{prefix}.{param_name}']
			else:
						for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
									lowerCAmelCase__    						=			F'{prefix}.embeddings.{w}.weight'
									lowerCAmelCase__    						=			state_dict[param_name]
						for w in ["weight", "bias"]:
									lowerCAmelCase__    						=			F'{prefix}.embeddings.LayerNorm.{w}'
									lowerCAmelCase__    						=			state_dict[param_name]

    # Transformer Blocks #
			lowerCAmelCase__    						=			0
			for teacher_idx in [0, 2, 4, 7, 9, 11]:
						if args.model_type == "gpt2":
									for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
												for w in ["weight", "bias"]:
															lowerCAmelCase__    						=			state_dict[
															    F'{prefix}.h.{teacher_idx}.{layer}.{w}'
															]
									lowerCAmelCase__    						=			state_dict[F'{prefix}.h.{teacher_idx}.attn.bias']
						else:
									for layer in [
									    "attention.self.query",
									    "attention.self.key",
									    "attention.self.value",
									    "attention.output.dense",
									    "attention.output.LayerNorm",
									    "intermediate.dense",
									    "output.dense",
									    "output.LayerNorm",
									]:
												for w in ["weight", "bias"]:
															lowerCAmelCase__    						=			state_dict[
															    F'{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}'
															]
						std_idx += 1

			# Language Modeling Head ###s
			if args.model_type == "roberta":
						for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
									lowerCAmelCase__    						=			state_dict[F'{layer}']
						if args.vocab_transform:
									for w in ["weight", "bias"]:
												lowerCAmelCase__    						=			state_dict[F'lm_head.dense.{w}']
												lowerCAmelCase__    						=			state_dict[F'lm_head.layer_norm.{w}']
			elif args.model_type == "gpt2":
						for w in ["weight", "bias"]:
									lowerCAmelCase__    						=			state_dict[F'{prefix}.ln_f.{w}']
						lowerCAmelCase__    						=			state_dict['lm_head.weight']

			print(F'N layers selected for distillation: {std_idx}')
			print(F'Number of params transferred for distillation: {len(compressed_sd.keys())}')

			print(F'Save transferred checkpoint to {args.dump_checkpoint}.')
			torch.save(compressed_sd, args.dump_checkpoint)

