




import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


__lowerCAmelCase	:       Optional[Any]     =	[
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ['memory_attention', 'encoder_attn'],
    ['attention', 'attn'],
    ['/', '.'],
    ['.LayerNorm.gamma', '_layer_norm.weight'],
    ['.LayerNorm.beta', '_layer_norm.bias'],
    ['r.layer_', 'r.layers.'],
    ['output_proj', 'out_proj'],
    ['ffn.dense_1.', 'fc2.'],
    ['ffn.dense.', 'fc1.'],
    ['ffn_layer_norm', 'final_layer_norm'],
    ['kernel', 'weight'],
    ['encoder_layer_norm.', 'encoder.layer_norm.'],
    ['decoder_layer_norm.', 'decoder.layer_norm.'],
    ['embeddings.weights', 'shared.weight'],
]




def     __magic_name__       (				A	:      int     ):






			'''simple docstring'''







			for pegasus_name, hf_name in PATTERNS:
						a					=    k.replace(A,						A     )
			return k




def     __magic_name__       (				A	:      dict,						A	:      dict     ):






			'''simple docstring'''







			a					=    DEFAULTS.copy()
			cfg_kwargs.update(A     )
			a					=    PegasusConfig(**A     )
			a					=    PegasusForConditionalGeneration(A     )
			a					=    torch_model.model.state_dict()
			a					=    {}
			for k, v in tf_weights.items():
						a					=    rename_state_dict_key(A     )
						if new_k not in sd:
									raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})"""     )

						if "dense" in k or "proj" in new_k:
									a					=    v.T
						a					=    torch.tensor(A,						dtype=sd[new_k].dtype     )
						assert v.shape == sd[new_k].shape, F"""{new_k}, {k}, {v.shape}, {sd[new_k].shape}"""
			# make sure embedding.padding_idx is respected
			a					=    torch.zeros_like(mapping["shared.weight"][cfg.pad_token_id + 1]     )
			a					=    mapping["shared.weight"]
			a					=    mapping["shared.weight"]
			a					=    {k: torch.zeros_like(A     ) for k, v in sd.items() if k.endswith("bias"     ) and k not in mapping}
			mapping.update(**A     )
			a					,	a					=    torch_model.model.load_state_dict(A,						strict=A     )
			a					=    [
			    k for k in missing if k not in ["encoder.embed_positions.weight", "decoder.embed_positions.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     __magic_name__       (				A	:      Union[str, Any]="./ckpt/aeslc/model.ckpt-32000"     ):






			'''simple docstring'''







			a					=    tf.train.list_variables(A     )
			a					=    {}
			a					=    ["Adafactor", "global_step"]
			for name, shape in tqdm(A,						desc="converting tf checkpoint to dict"     ):
						a					=    any(pat in name for pat in ignore_name     )
						if skip_key:
									continue
						a					=    tf.train.load_variable(A,						A     )
						a					=    array
			return tf_weights




def     __magic_name__       (				A	:      str,						A	:      str     ):






			'''simple docstring'''







			a					=    Path(A     ).parent.name
			a					=    task_specific_params[F"""summarization_{dataset}"""]["max_position_embeddings"]
			a					=    PegasusTokenizer.from_pretrained("sshleifer/pegasus",						model_max_length=A     )
			assert tok.model_max_length == desired_max_model_length
			tok.save_pretrained(A     )

			# convert model
			a					=    get_tf_weights_as_numpy(A     )
			a					=    task_specific_params[F"""summarization_{dataset}"""]
			if dataset == "large":
						a					=    task_specific_params
			a					=    convert_pegasus(A,						A     )
			torch_model.save_pretrained(A     )
			a					=    torch_model.state_dict()
			sd.pop("model.decoder.embed_positions.weight"     )
			sd.pop("model.encoder.embed_positions.weight"     )
			torch.save(A,						Path(A     ) / "pytorch_model.bin"     )


if __name__ == "__main__":
		__lowerCAmelCase	:       Any     =	argparse.ArgumentParser()
		# Required parameters
		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	:       List[str]     =	parser.parse_args()
		if args.save_dir is None:
				__lowerCAmelCase	:       str     =	Path(args.tf_ckpt_path).parent.name
				__lowerCAmelCase	:       List[Any]     =	os.path.join('pegasus', dataset)
		convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)












from __future__ import annotations

import time

import numpy as np

__lowerCAmelCase	:       List[str]     =	[8, 5, 9, 7]
__lowerCAmelCase	:       str     =	[
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
__lowerCAmelCase	:       Optional[Any]     =	[
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]




class 					snake_case__							:
				"""simple docstring"""



				def __init__(   self       :  Any      ,   __lowerCamelCase       :  list[int]      ,   __lowerCamelCase       :  list[list[int]]      ,   __lowerCamelCase       :  list[list[int]]      ,   )      ->  None:
							a					=    claim_vector
							a					=    allocated_resources_table
							a					=    maximum_claim_table





				def 							__UpperCAmelCase							(   self       :  List[str]							)      ->  list[int]:
							return [
							    sum(p_item[i] for p_item in self.__allocated_resources_table							)
							    for i in range(len(self.__allocated_resources_table[0]							)							)
							]





				def 							__UpperCAmelCase							(   self       :  str							)      ->  list[int]:
							return np.array(self.__claim_vector							) - np.array(
							    self.__processes_resource_summation()							)





				def 							__UpperCAmelCase							(   self       :  Dict							)      ->  list[list[int]]:
							return [
							    list(np.array(self.__maximum_claim_table[i]							) - np.array(__lowerCamelCase							)							)
							    for i, allocated_resource in enumerate(self.__allocated_resources_table							)
							]





				def 							__UpperCAmelCase							(   self       :  Dict							)      ->  dict[int, list[int]]:
							return {self.__need().index(__lowerCamelCase							): i for i in self.__need()}





				def 							__UpperCAmelCase							(   self       :  Optional[Any]      ,   **__lowerCamelCase       :  Any							)      ->  None:
							a					=    self.__need()
							a					=    self.__allocated_resources_table
							a					=    self.__available_resources()
							a					=    self.__need_index_manager()
							for kw, val in kwargs.items():
										if kw and val is True:
													self.__pretty_data()
							print("_" * 50 + "\n"							)
							while need_list:
										a					=    False
										for each_need in need_list:
													a					=    True
													for index, need in enumerate(__lowerCamelCase							):
																if need > available_resources[index]:
																			a					=    False
																			break
													if execution:
																a					=    True
																# get the original index of the process from ind_ctrl db
																for original_need_index, need_clone in need_index_manager.items():
																			if each_need == need_clone:
																						a					=    original_need_index
																print(f"""Process {process_number + 1} is executing."""							)
																# remove the process run from stack
																need_list.remove(__lowerCamelCase							)
																# update available/freed resources stack
																a					=    np.array(__lowerCamelCase							) + np.array(
																    alloc_resources_table[process_number]							)
																print(
																    "Updated available resource stack for processes: "
																    + " ".join([str(__lowerCamelCase							) for x in available_resources]							)							)
																break
										if safe:
													print("The process is in a safe state.\n"							)
										else:
													print("System in unsafe state. Aborting...\n"							)
													break





				def 							__UpperCAmelCase							(   self       :  Any							)      ->  str:
							print(" " * 9 + "Allocated Resource Table"							)
							for item in self.__allocated_resources_table:
										print(
										    f"""P{self.__allocated_resources_table.index(__lowerCamelCase							) + 1}"""
										    + " ".join(f"""{it:>8}""" for it in item							)
										    + "\n"							)
							print(" " * 9 + "System Resource Table"							)
							for item in self.__maximum_claim_table:
										print(
										    f"""P{self.__maximum_claim_table.index(__lowerCamelCase							) + 1}"""
										    + " ".join(f"""{it:>8}""" for it in item							)
										    + "\n"							)
							print(
							    "Current Usage by Active Processes: "
							    + " ".join(str(__lowerCamelCase							) for x in self.__claim_vector							)							)
							print(
							    "Initial Available Resources:       "
							    + " ".join(str(__lowerCamelCase							) for x in self.__available_resources()							)							)
							time.sleep(1							)


if __name__ == "__main__":
		import doctest

		doctest.testmod()






