

'''simple docstring'''





UpperCamelCase_							=					{
    """A""": ["""B""", """C""", """E"""],
    """B""": ["""A""", """D""", """E"""],
    """C""": ["""A""", """F""", """G"""],
    """D""": ["""B"""],
    """E""": ["""A""", """B""", """D"""],
    """F""": ["""C"""],
    """G""": ["""C"""],
}






def 							_lowerCAmelCase (      __magic_name__	:    dict      ,   __magic_name__	:    str      ,   __magic_name__	:    Optional[int]	)							->     list[str]:
						lowercase   :       Any								=set()
						# keep track of all the paths to be checked
						lowercase   :       List[Any]								=[[start]]

						# return path if start is goal
						if start == goal:
												return [start]

						# keeps looping until all possible paths have been checked
						while queue:
												# pop the first path from the queue
												lowercase   :       List[str]								=queue.pop(0	)
												# get the last node from the path
												lowercase   :       str								=path[-1]
												if node not in explored:
																		lowercase   :       Optional[int]								=graph[node]
																		# go through all neighbour nodes, construct a new path and
																		# push it into the queue
																		for neighbour in neighbours:
																								lowercase   :       int								=list(__magic_name__	)
																								new_path.append(__magic_name__	)
																								queue.append(__magic_name__	)
																								# return path if neighbour is goal
																								if neighbour == goal:
																														return new_path

            # mark node as explored
																		explored.add(__magic_name__	)

    # in case there's no path between the 2 nodes
						return []






def 							_lowerCAmelCase (      __magic_name__	:    dict      ,   __magic_name__	:    Optional[int]      ,   __magic_name__	:    Any	)							->     int:
						if not graph or start not in graph or target not in graph:
												return -1
						if start == target:
												return 0
						lowercase   :       Optional[int]								=[start]
						lowercase   :       List[str]								=set(__magic_name__	)
						# Keep tab on distances from `start` node.
						lowercase   :       int								={start: 0, target: -1}
						while queue:
												lowercase   :       Tuple								=queue.pop(0	)
												if node == target:
																		lowercase   :       str								=(
																		    dist[node] if dist[target] == -1 else min(dist[target]      ,   dist[node]	)
																		)
												for adjacent in graph[node]:
																		if adjacent not in visited:
																								visited.add(__magic_name__	)
																								queue.append(__magic_name__	)
																								lowercase   :       Any								=dist[node] + 1
						return dist[target]


if __name__ == "__main__":
							print(bfs_shortest_path(demo_graph, """G""", """D"""))  # returns ['G', 'C', 'A', 'B', 'D']
							print(bfs_shortest_path_distance(demo_graph, """G""", """D"""))  # returns 4








'''simple docstring'''





import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
							UpperCamelCase_							=					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""")
							UpperCamelCase_							=					parser.parse_args()

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

							UpperCamelCase_							=					model.state_dict()
							UpperCamelCase_							=					{}

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

    # Transformer Blocks #
							UpperCamelCase_							=					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"]:
																																			UpperCamelCase_							=					state_dict[
																																			    f'''{prefix}.h.{teacher_idx}.{layer}.{w}'''
																																			]
																					UpperCamelCase_							=					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"]:
																																			UpperCamelCase_							=					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"]:
																					UpperCamelCase_							=					state_dict[f'''{layer}''']
														if args.vocab_transform:
																					for w in ["weight", "bias"]:
																												UpperCamelCase_							=					state_dict[f'''lm_head.dense.{w}''']
																												UpperCamelCase_							=					state_dict[f'''lm_head.layer_norm.{w}''']
							elif args.model_type == "gpt2":
														for w in ["weight", "bias"]:
																					UpperCamelCase_							=					state_dict[f'''{prefix}.ln_f.{w}''']
														UpperCamelCase_							=					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)





