'''simple docstring'''

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


lowercase_     		= [
    # 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 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						for pegasus_name, hf_name in PATTERNS:
												_lowerCAmelCase										=k.replace(a__			,      a__      )
						return k



def 							UpperCamelCase__			(			a__			,      a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=DEFAULTS.copy()
						cfg_kwargs.update(a__      )
						_lowerCAmelCase										=PegasusConfig(**a__      )
						_lowerCAmelCase										=PegasusForConditionalGeneration(a__      )
						_lowerCAmelCase										=torch_model.model.state_dict()
						_lowerCAmelCase										={}
						for k, v in tf_weights.items():
												_lowerCAmelCase										=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:
																		_lowerCAmelCase										=v.T
												_lowerCAmelCase										=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
						_lowerCAmelCase										=torch.zeros_like(mapping['shared.weight'][cfg.pad_token_id + 1]      )
						_lowerCAmelCase										=mapping['shared.weight']
						_lowerCAmelCase										=mapping['shared.weight']
						_lowerCAmelCase										={k: torch.zeros_like(a__      ) for k, v in sd.items() if k.endswith('bias'      ) and k not in mapping}
						mapping.update(**a__      )
						_lowerCAmelCase						,				_lowerCAmelCase										=torch_model.model.load_state_dict(a__			,      strict=a__      )
						_lowerCAmelCase										=[
						    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 							UpperCamelCase__			(			a__="./ckpt/aeslc/model.ckpt-32000"      ):


						'''simple docstring'''






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



def 							UpperCamelCase__			(			a__			,      a__      ):


						'''simple docstring'''






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

						# convert model
						_lowerCAmelCase										=get_tf_weights_as_numpy(a__      )
						_lowerCAmelCase										=task_specific_params[F'''summarization_{dataset}''']
						if dataset == "large":
												_lowerCAmelCase										=task_specific_params
						_lowerCAmelCase										=convert_pegasus(a__			,      a__      )
						torch_model.save_pretrained(a__      )
						_lowerCAmelCase										=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__":
							lowercase_     		= 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.''')
							lowercase_     		= parser.parse_args()
							if args.save_dir is None:
														lowercase_     		= Path(args.tf_ckpt_path).parent.name
														lowercase_     		= os.path.join('''pegasus''', dataset)
							convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)






'''simple docstring'''

import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch



def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=os.path.join(args.tf_model_dir			,      'parameters.json'      )
						_lowerCAmelCase										=json.loads(open(a__      ).read()      )
						if not params:
												raise ValueError(
												    F'''It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file.'''      )
						if not args.output.endswith('.pt'      ):
												_lowerCAmelCase										=args.output + '.pt'
						_lowerCAmelCase										=OrderedDict()
						with tf.device('/CPU:0'      ):
												_lowerCAmelCase										=tf.train.load_checkpoint(args.tf_model_dir      )
												_lowerCAmelCase										=reader.get_variable_to_shape_map()
												for key_name in shapes.keys():
																		_lowerCAmelCase										=reader.get_tensor(a__      ).astype(np.floataa      )
																		if key_name.endswith('/adam_m'      ) or key_name.endswith('/adam_v'      ):
																								continue
																		if key_name.startswith('pasts/'      ):
																								if key_name.startswith('pasts/mlp'      ):
																														_lowerCAmelCase										=int(key_name[9]      )
																								elif key_name.startswith('pasts/out'      ):
																														_lowerCAmelCase										=8
																								_lowerCAmelCase										='model.sqout.%d.weight' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
																								_lowerCAmelCase										=vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																								_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name.startswith('model/moe'      ):
																								_lowerCAmelCase										=int(key_name[9:].split('/'      )[0]      )
																								if key_name.endswith('/switch_gating/kernel'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.mlp.router.classifier.weight' % player
																														_lowerCAmelCase										=vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/softmlp/kernel'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.soft_bypass_mlp.weight' % player
																														_lowerCAmelCase										=vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/wo/kernel'      ) or key_name.endswith('/wi/kernel'      ):
																														_lowerCAmelCase										=key_name[-9:-7]
																														for i in range(1_6      ):
																																				_lowerCAmelCase										='model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight' % (player, i, nlayer)
																																				_lowerCAmelCase										=(
																																				    vnp[i].transpose([1, 0]      ).copy()
																																				)  # In Mesh-Tensorflow, it is one array, so it is divided
																																				_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name.startswith('model/mlp'      ):
																								_lowerCAmelCase										=int(key_name[9:].split('/'      )[0]      )
																								if key_name.endswith('/p1/kernel'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.mlp.wi.weight' % player
																														_lowerCAmelCase										=vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/p1/bias'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.mlp.wi.bias' % player
																														_lowerCAmelCase										=vnp.copy()  # same because it is one dimensional
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/p2/kernel'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.mlp.wo.weight' % player
																														_lowerCAmelCase										=vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/p2/bias'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.mlp.wo.bias' % player
																														_lowerCAmelCase										=vnp.copy()  # same because it is one dimensional
																														_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name.startswith('model/ln'      ):
																								_lowerCAmelCase										=int(key_name[8:].split('/'      )[0]      )
																								if key_name.endswith('/b'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.norm.bias' % player
																														_lowerCAmelCase										=vnp.copy()  # same because it is one dimensional
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/g'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.norm.weight' % player
																														_lowerCAmelCase										=vnp.copy()  # same because it is one dimensional
																														_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name.startswith('model/att'      ):
																								_lowerCAmelCase										=int(key_name[9:].split('/'      )[0]      )
																								if key_name.endswith('/qkv/kernel'      ):
																														_lowerCAmelCase										=vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
																														_lowerCAmelCase										=state[:, 0, :, :]
																														_lowerCAmelCase										=state[:, 1, :, :]
																														_lowerCAmelCase										=state[:, 2, :, :]
																														_lowerCAmelCase										=(
																														    state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]      )
																														    .transpose([1, 0]      )
																														    .copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														_lowerCAmelCase										=(
																														    state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]      )
																														    .transpose([1, 0]      )
																														    .copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														_lowerCAmelCase										=(
																														    state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]      )
																														    .transpose([1, 0]      )
																														    .copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														_lowerCAmelCase										='model.blocks.%d.self_attn.self_attn.q_proj.weight' % player
																														_lowerCAmelCase										=torch.tensor(a__      )
																														_lowerCAmelCase										='model.blocks.%d.self_attn.self_attn.k_proj.weight' % player
																														_lowerCAmelCase										=torch.tensor(a__      )
																														_lowerCAmelCase										='model.blocks.%d.self_attn.self_attn.v_proj.weight' % player
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/o/kernel'      ):
																														_lowerCAmelCase										='model.blocks.%d.self_attn.self_attn.out_proj.weight' % player
																														_lowerCAmelCase										=(
																														    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]      ).transpose([1, 0]      ).copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name.startswith('model/an'      ):
																								_lowerCAmelCase										=int(key_name[8:].split('/'      )[0]      )
																								if key_name.endswith('/b'      ):
																														_lowerCAmelCase										='model.blocks.%d.self_attn.norm.bias' % player
																														_lowerCAmelCase										=vnp.copy()  # same because it is one dimensional
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/g'      ):
																														_lowerCAmelCase										='model.blocks.%d.self_attn.norm.weight' % player
																														_lowerCAmelCase										=vnp.copy()  # same because it is one dimensional
																														_lowerCAmelCase										=torch.tensor(a__      )
																		elif (
																		    key_name.startswith('model/wte'      )
																		    or key_name.startswith('model/wpe'      )
																		    or key_name.startswith('model/ete'      )
																		):
																								_lowerCAmelCase										={'wte': 'embed_tokens', 'wpe': 'position_embeddings', 'ete': 'extra_position_embeddings'}[
																								    key_name[-3:]
																								]
																								_lowerCAmelCase										='model.%s.weight' % nlayer
																								_lowerCAmelCase										=vnp.copy()  # same in embedded
																								_lowerCAmelCase										=torch.tensor(a__      )
																								if key_name.startswith('model/wte'      ):
																														_lowerCAmelCase										='lm_head.weight'
																														_lowerCAmelCase										=vnp.copy()  # same in embedded
																														_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name.startswith('model/wob'      ):
																								_lowerCAmelCase										='final_logits_bias'
																								_lowerCAmelCase										=vnp.copy()  # same in embedded
																								_lowerCAmelCase										=state.reshape((1, -1)      )
																								_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name == "model/dense/kernel":
																								_lowerCAmelCase										='model.last_project.weight'
																								_lowerCAmelCase										=vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																								_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name == "model/dense_1/bias":
																								_lowerCAmelCase										='model.last_project.bias'
																								_lowerCAmelCase										=vnp.copy()  # same because it is one dimensional
																								_lowerCAmelCase										=torch.tensor(a__      )
						torch.save(a__			,      args.output      )


if __name__ == "__main__":
							lowercase_     		= argparse.ArgumentParser(
							    description='''model converter.''', formatter_class=argparse.ArgumentDefaultsHelpFormatter
							)
							parser.add_argument('''--tf_model_dir''', metavar='''PATH''', type=str, required=True, help='''import model''')
							parser.add_argument('''--output''', metavar='''PATH''', type=str, required=True, help='''output model''')
							lowercase_     		= parser.parse_args()
							convert_tf_gptsan_to_pt(args)





