

'''simple docstring'''


import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


lowerCAmelCase__										= [
    # tf -> hf
    ("/", "."),
    ("layer_", "layers."),
    ("kernel", "weight"),
    ("beta", "bias"),
    ("gamma", "weight"),
    ("pegasus", "model"),
]
lowerCAmelCase__										= [
    (".output.dense", ".fc2"),
    ("intermediate.LayerNorm", "final_layer_norm"),
    ("intermediate.dense", "fc1"),
]

lowerCAmelCase__										= (
    INIT_COMMON
    + [
        ("attention.self.LayerNorm", "self_attn_layer_norm"),
        ("attention.output.dense", "self_attn.out_proj"),
        ("attention.self", "self_attn"),
        ("attention.encdec.LayerNorm", "encoder_attn_layer_norm"),
        ("attention.encdec_output.dense", "encoder_attn.out_proj"),
        ("attention.encdec", "encoder_attn"),
        ("key", "k_proj"),
        ("value", "v_proj"),
        ("query", "q_proj"),
        ("decoder.LayerNorm", "decoder.layernorm_embedding"),
    ]
    + END_COMMON
)

lowerCAmelCase__										= (
    INIT_COMMON
    + [
        ("embeddings.word_embeddings", "shared.weight"),
        ("embeddings.position_embeddings", "embed_positions.weight"),
        ("attention.self.LayerNorm", "self_attn_layer_norm"),
        ("attention.output.dense", "self_attn.output"),
        ("attention.self", "self_attn.self"),
        ("encoder.LayerNorm", "encoder.layernorm_embedding"),
    ]
    + END_COMMON
)

lowerCAmelCase__										= [
    "encdec/key/bias",
    "encdec/query/bias",
    "encdec/value/bias",
    "self/key/bias",
    "self/query/bias",
    "self/value/bias",
    "encdec_output/dense/bias",
    "attention/output/dense/bias",
]







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase  )    ->    Union[str, Any]:
	for tf_name, hf_name in patterns:
		UpperCAmelCase_		:      List[Any]       	=					k.replace(__A   ,__A  )
	return k







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase  )    ->    Tuple:
	UpperCAmelCase_		:      int       	=					BigBirdPegasusConfig(**__A  )
	UpperCAmelCase_		:      str       	=					BigBirdPegasusForConditionalGeneration(__A  )
	UpperCAmelCase_		:      Optional[Any]       	=					torch_model.state_dict()
	UpperCAmelCase_		:      Any       	=					{}

	# separating decoder weights
	UpperCAmelCase_		:      Union[str, Any]       	=					{k: tf_weights[k] for k in tf_weights if k.startswith('pegasus/decoder'  )}
	UpperCAmelCase_		:      int       	=					{k: tf_weights[k] for k in tf_weights if not k.startswith('pegasus/decoder'  )}

	for k, v in tqdm(decoder_weights.items()   ,'tf -> hf conversion'  ):
		UpperCAmelCase_		:      List[str]       	=					[k.endswith(__A  ) for ending in KEYS_TO_IGNORE]
		if any(__A  ):
			continue
		UpperCAmelCase_		:      Any       	=					DECODER_PATTERNS
		UpperCAmelCase_		:      str       	=					rename_state_dict_key(__A   ,__A  )
		if new_k not in state_dict:
			raise ValueError(f"""could not find new key {new_k} in state dict. (converted from {k})"""  )
		if any(True if i in k else False for i in ['dense', 'query', 'key', 'value']  ):
			UpperCAmelCase_		:      Optional[Any]       	=					v.T
		UpperCAmelCase_		:      Optional[int]       	=					torch.from_numpy(__A  )
		assert v.shape == state_dict[new_k].shape, f"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""

	for k, v in tqdm(remaining_weights.items()   ,'tf -> hf conversion'  ):
		UpperCAmelCase_		:      int       	=					[k.endswith(__A  ) for ending in KEYS_TO_IGNORE]
		if any(__A  ):
			continue
		UpperCAmelCase_		:      int       	=					REMAINING_PATTERNS
		UpperCAmelCase_		:      Dict       	=					rename_state_dict_key(__A   ,__A  )
		if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
			raise ValueError(f"""could not find new key {new_k} in state dict. (converted from {k})"""  )
		if any(True if i in k else False for i in ['dense', 'query', 'key', 'value']  ):
			UpperCAmelCase_		:      str       	=					v.T
		UpperCAmelCase_		:      int       	=					torch.from_numpy(__A  )
		if k != "pegasus/embeddings/position_embeddings":
			assert v.shape == state_dict[new_k].shape, f"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""

	UpperCAmelCase_		:      List[str]       	=					mapping["""model.embed_positions.weight"""]
	UpperCAmelCase_		:      int       	=					mapping.pop('model.embed_positions.weight'  )
	UpperCAmelCase_		:      Optional[Any]       	=					torch_model.load_state_dict(__A   ,strict=__A  )
	UpperCAmelCase_		:      Any       	=					[
	    k
	    for k in missing
	    if k
	    not in [
	        """final_logits_bias""",
	        """model.encoder.embed_tokens.weight""",
	        """model.decoder.embed_tokens.weight""",
	        """lm_head.weight""",
	    ]
	]
	assert unexpected_missing == [], f"""no matches found for the following torch keys {unexpected_missing}"""
	assert extra == [], f"""no matches found for the following tf keys {extra}"""
	return torch_model







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    Optional[Any]:
	UpperCAmelCase_		:      List[Any]       	=					tf.train.list_variables(__A  )
	UpperCAmelCase_		:      Dict       	=					{}
	UpperCAmelCase_		:      Any       	=					["""global_step"""]
	for name, shape in tqdm(__A   ,desc='converting tf checkpoint to dict'  ):
		UpperCAmelCase_		:      Any       	=					any(pat in name for pat in ignore_name  )
		if skip_key:
			continue
		UpperCAmelCase_		:      Optional[Any]       	=					tf.train.load_variable(__A   ,__A  )
		UpperCAmelCase_		:      Union[str, Any]       	=					array
	return tf_weights







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )    ->    List[Any]:
	UpperCAmelCase_		:      str       	=					get_tf_weights_as_numpy(__A  )
	UpperCAmelCase_		:      int       	=					convert_bigbird_pegasus(__A   ,__A  )
	torch_model.save_pretrained(__A  )


if __name__ == "__main__":
						lowerCAmelCase__										= argparse.ArgumentParser()
						parser.add_argument("--tf_ckpt_path", type=str, help="passed to tf.train.list_variables")
						parser.add_argument("--save_dir", default=None, type=str, help="Path to the output PyTorch model.")
						lowerCAmelCase__										= parser.parse_args()
						lowerCAmelCase__										= {}
						convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)









'''simple docstring'''


import math



class 					lowercase						:


							def __init__(			self    ,			_snake_case=0)							->				Union[str, Any]:  # a graph with Node 0,1,...,N-1
								UpperCAmelCase_		:      Tuple       	=					n
								UpperCAmelCase_		:      Optional[Any]       	=					[
								    [math.inf for j in range(0    ,			_snake_case)] for i in range(0    ,			_snake_case)
								]  # adjacency matrix for weight
								UpperCAmelCase_		:      Tuple       	=					[
								    [math.inf for j in range(0    ,			_snake_case)] for i in range(0    ,			_snake_case)
								]  # dp[i][j] stores minimum distance from i to j


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case)							->				List[Any]:
								UpperCAmelCase_		:      Union[str, Any]       	=					w


							def 						_snake_case							(			self)							->				str:
								for k in range(0    ,			self.n):
									for i in range(0    ,			self.n):
										for j in range(0    ,			self.n):
											UpperCAmelCase_		:      Optional[int]       	=					min(self.dp[i][j]    ,			self.dp[i][k] + self.dp[k][j])





							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case)							->				str:
								return self.dp[u][v]


if __name__ == "__main__":
						lowerCAmelCase__										= Graph(5)
						graph.add_edge(0, 2, 9)
						graph.add_edge(0, 4, 10)
						graph.add_edge(1, 3, 5)
						graph.add_edge(2, 3, 7)
						graph.add_edge(3, 0, 10)
						graph.add_edge(3, 1, 2)
						graph.add_edge(3, 2, 1)
						graph.add_edge(3, 4, 6)
						graph.add_edge(4, 1, 3)
						graph.add_edge(4, 2, 4)
						graph.add_edge(4, 3, 9)
						graph.floyd_warshall()
						graph.show_min(1, 4)
						graph.show_min(0, 3)






