


'''simple docstring'''






import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
__A	:	Tuple     =     logging.get_logger(__name__)

__A	:	Union[str, Any]     =     [
    ['attention', 'attn'],
    ['encoder_attention', 'encoder_attn'],
    ['q_lin', 'q_proj'],
    ['k_lin', 'k_proj'],
    ['v_lin', 'v_proj'],
    ['out_lin', 'out_proj'],
    ['norm_embeddings', 'layernorm_embedding'],
    ['position_embeddings', 'embed_positions'],
    ['embeddings', 'embed_tokens'],
    ['ffn.lin', 'fc'],
]





def      UpperCAmelCase      (		lowerCamelCase_  :Any ):







		'''simple docstring'''
		if k == "embeddings.weight":
				return "shared.weight"

		for parlai_name, hf_name in PATTERNS:
				snake_case_		:							Optional[int]			       =						k.replace(lowerCamelCase_				,      lowerCamelCase_ )

		if k.startswith("""encoder""" ):
				snake_case_		:							str			       =						k.replace(""".attn"""				,      """.self_attn""" )
				snake_case_		:							str			       =						k.replace("""norm1"""				,      """self_attn_layer_norm""" )
				snake_case_		:							List[str]			       =						k.replace("""norm2"""				,      """final_layer_norm""" )
		elif k.startswith("""decoder""" ):
				snake_case_		:							Dict			       =						k.replace("""norm1"""				,      """self_attn_layer_norm""" )
				snake_case_		:							Optional[int]			       =						k.replace("""norm2"""				,      """encoder_attn_layer_norm""" )
				snake_case_		:							Tuple			       =						k.replace("""norm3"""				,      """final_layer_norm""" )
		return k





def      UpperCAmelCase      (		lowerCamelCase_  :List[Any] ):







		'''simple docstring'''
		snake_case_		:							Dict			       =						[
		    """model.encoder.layernorm_embedding.weight""",
		    """model.encoder.layernorm_embedding.bias""",
		    """model.decoder.layernorm_embedding.weight""",
		    """model.decoder.layernorm_embedding.bias""",
		]
		for k in keys:
				snake_case_		:							str			       =						sd.pop(lowerCamelCase_ )
				snake_case_		:							Dict			       =						k.replace("""layernorm_embedding"""				,      """layer_norm""" )
				assert new_k not in sd
				snake_case_		:							Union[str, Any]			       =						v


__A	:	Dict     =     ['START']





@torch.no_grad()
def      UpperCAmelCase      (		lowerCamelCase_  :Tuple				,      lowerCamelCase_  :Optional[Any]				,      lowerCamelCase_  :Any ):







		'''simple docstring'''
		snake_case_		:							Tuple			       =						torch.load(lowerCamelCase_				,      map_location="""cpu""" )
		snake_case_		:							str			       =						model["""model"""]
		snake_case_		:							Dict			       =						BlenderbotConfig.from_json_file(lowerCamelCase_ )
		snake_case_		:							str			       =						BlenderbotForConditionalGeneration(lowerCamelCase_ )
		snake_case_		:							Tuple			       =						m.model.state_dict().keys()
		snake_case_		:							List[Any]			       =						[]
		snake_case_		:							str			       =						{}
		for k, v in sd.items():
				if k in IGNORE_KEYS:
						continue

				snake_case_		:							Any			       =						rename_state_dict_key(lowerCamelCase_ )
				if new_k not in valid_keys:
						failures.append([k, new_k] )
				else:
						snake_case_		:							int			       =						v
		if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
				rename_layernorm_keys(lowerCamelCase_ )
		m.model.load_state_dict(lowerCamelCase_				,      strict=lowerCamelCase_ )
		m.half()
		m.save_pretrained(lowerCamelCase_ )


if __name__ == "__main__":
							__A	:	Dict     =     argparse.ArgumentParser()
							# Required parameters
							parser.add_argument('--src_path', type=str, help='like blenderbot-model.bin')
							parser.add_argument('--save_dir', default='hf_blenderbot', type=str, help='Where to save converted model.')
							parser.add_argument(
							    '--hf_config_json', default='blenderbot-3b-config.json', type=str, help='Path to config to use'
							)
							__A	:	Union[str, Any]     =     parser.parse_args()
							convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)



'''simple docstring'''






def      UpperCAmelCase      (		lowerCamelCase_  :Tuple				,      lowerCamelCase_  :Optional[Any]				,      lowerCamelCase_  :str				,      lowerCamelCase_  :str ):







		'''simple docstring'''
		# Return True if there is node that has not iterated.
		snake_case_		:							Optional[int]			       =						[False] * len(lowerCamelCase_ )
		snake_case_		:							Dict			       =						[]
		queue.append(lowerCamelCase_ )
		snake_case_		:							Tuple			       =						True

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

		return visited[t]





def      UpperCAmelCase      (		lowerCamelCase_  :Optional[Any]				,      lowerCamelCase_  :Dict				,      lowerCamelCase_  :Union[str, Any] ):







		'''simple docstring'''
		# This array is filled by BFS and to store path
		snake_case_		:							Optional[int]			       =						[-1] * (len(lowerCamelCase_ ))
		snake_case_		:							Tuple			       =						0
		while bfs(lowerCamelCase_				,      lowerCamelCase_				,      lowerCamelCase_				,      lowerCamelCase_ ):
				snake_case_		:							int			       =						float("""Inf""" )
				snake_case_		:							Any			       =						sink

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

				max_flow += path_flow
				snake_case_		:							Dict			       =						sink

				while v != source:
						snake_case_		:							Optional[int]			       =						parent[v]
						graph[u][v] -= path_flow
						graph[v][u] += path_flow
						snake_case_		:							Any			       =						parent[v]
		return max_flow


__A	:	Union[str, Any]     =     [
    [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],
]

__A,   __A	:	Union[str, Any]     =     0, 5






print(ford_fulkerson(graph, source, sink))