


"""simple docstring"""


import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


a_       							=    logging.get_logger(__name__)
@dataclass
class snake_case      :
		__UpperCamelCase           =			field(metadata={'help': 'The name of the task to train on: ' + ', '.join(glue_processors.keys())})
		__UpperCamelCase           =			field(
		    metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'})
		__UpperCamelCase           =			field(
		    default=128     ,				metadata={
		        'help': (
		            'The maximum total input sequence length after tokenization. Sequences longer '
		            'than this will be truncated, sequences shorter will be padded.'
		        )
		    }     ,				)
		__UpperCamelCase           =			field(
		    default=_UpperCamelCase     ,				metadata={'help': 'Overwrite the cached training and evaluation sets'})







		def  a_ (					self      :       Optional[Any]		)       ->						Tuple:





						'''simple docstring'''







						_A					  =			self.task_name.lower()





class snake_case      (  _UpperCamelCase):
		__UpperCamelCase           =			'train'
		__UpperCamelCase           =			'dev'
		__UpperCamelCase           =			'test'





class snake_case      (  _UpperCamelCase):
		__UpperCamelCase           =			42
		__UpperCamelCase           =			42
		__UpperCamelCase           =			42







		def __init__(					self      :       Optional[int]    ,   a__      :       GlueDataTrainingArguments    ,   a__      :       PreTrainedTokenizerBase    ,   a__      :       Optional[int] = None    ,   a__      :       Union[str, Split] = Split.train    ,   a__      :       Optional[str] = None    ,   )       ->						Tuple:





						'''simple docstring'''







						warnings.warn(
						    "This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets "
						    "library. You can have a look at this example script for pointers: "
						    "https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"    ,   a__    ,   )
						_A					  =			args
						_A					  =			glue_processors[args.task_name]()
						_A					  =			glue_output_modes[args.task_name]
						if isinstance(a__    ,   a__		):
										try:
														_A					  =			Split[mode]
										except KeyError:
														raise KeyError("mode is not a valid split name"		)
        # Load data features from cache or dataset file
						_A					  =			os.path.join(
						    cache_dir if cache_dir is not None else args.data_dir    ,   F"""cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}"""    ,   )
						_A					  =			self.processor.get_labels()
						if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
						    "RobertaTokenizer",
						    "RobertaTokenizerFast",
						    "XLMRobertaTokenizer",
						    "BartTokenizer",
						    "BartTokenizerFast",
						):
										# HACK(label indices are swapped in RoBERTa pretrained model)
										_A  ,		_A					  =			label_list[2], label_list[1]
						_A					  =			label_list

						# Make sure only the first process in distributed training processes the dataset,
						# and the others will use the cache.
						_A					  =			cached_features_file + ".lock"
						with FileLock(a__		):
										if os.path.exists(a__		) and not args.overwrite_cache:
														_A					  =			time.time()
														_A					  =			torch.load(a__		)
														logger.info(
														    F"""Loading features from cached file {cached_features_file} [took %.3f s]"""    ,   time.time() - start		)
										else:
														logger.info(F"""Creating features from dataset file at {args.data_dir}"""		)

														if mode == Split.dev:
																		_A					  =			self.processor.get_dev_examples(args.data_dir		)
														elif mode == Split.test:
																		_A					  =			self.processor.get_test_examples(args.data_dir		)
														else:
																		_A					  =			self.processor.get_train_examples(args.data_dir		)
														if limit_length is not None:
																		_A					  =			examples[:limit_length]
														_A					  =			glue_convert_examples_to_features(
														    a__    ,   a__    ,   max_length=args.max_seq_length    ,   label_list=a__    ,   output_mode=self.output_mode    ,   )
														_A					  =			time.time()
														torch.save(self.features    ,   a__		)
														# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
														logger.info(
														    F"""Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"""		)







		def __len__(					self      :       List[Any]		)       ->						Any:





						'''simple docstring'''







						return len(self.features		)







		def __getitem__(					self      :       Tuple    ,   a__      :       Union[str, Any]		)       ->						InputFeatures:





						'''simple docstring'''







						return self.features[i]







		def  a_ (					self      :       Optional[int]		)       ->						List[Any]:





						'''simple docstring'''







						return self.label_list



"""simple docstring"""


import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch






def      a__ (				__lowercase   )	->	List[Any]:
				_A					  =			os.path.join(args.tf_model_dir     ,			"parameters.json"   )
				_A					  =			json.loads(open(__lowercase   ).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"   ):
								_A					  =			args.output + ".pt"
				_A					  =			OrderedDict()
				with tf.device("/CPU:0"   ):
								_A					  =			tf.train.load_checkpoint(args.tf_model_dir   )
								_A					  =			reader.get_variable_to_shape_map()
								for key_name in shapes.keys():
												_A					  =			reader.get_tensor(__lowercase   ).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"   ):
																				_A					  =			int(key_name[9]   )
																elif key_name.startswith("pasts/out"   ):
																				_A					  =			8
																_A					  =			"model.sqout.%d.weight" % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
																_A					  =			vnp.transpose([1, 0]   ).copy()  # Mesh-Tensorflow is a diagonal matrix
																_A					  =			torch.tensor(__lowercase   )
												elif key_name.startswith("model/moe"   ):
																_A					  =			int(key_name[9:].split("/"   )[0]   )
																if key_name.endswith("/switch_gating/kernel"   ):
																				_A					  =			"model.blocks.%d.feed_forward.mlp.router.classifier.weight" % player
																				_A					  =			vnp.transpose([1, 0]   ).copy()  # Mesh-Tensorflow is a diagonal matrix
																				_A					  =			torch.tensor(__lowercase   )
																elif key_name.endswith("/softmlp/kernel"   ):
																				_A					  =			"model.blocks.%d.feed_forward.soft_bypass_mlp.weight" % player
																				_A					  =			vnp.transpose([1, 0]   ).copy()  # Mesh-Tensorflow is a diagonal matrix
																				_A					  =			torch.tensor(__lowercase   )
																elif key_name.endswith("/wo/kernel"   ) or key_name.endswith("/wi/kernel"   ):
																				_A					  =			key_name[-9:-7]
																				for i in range(16   ):
																								_A					  =			"model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight" % (player, i, nlayer)
																								_A					  =			(
																								    vnp[i].transpose([1, 0]   ).copy()
																								)  # In Mesh-Tensorflow, it is one array, so it is divided
																								_A					  =			torch.tensor(__lowercase   )
												elif key_name.startswith("model/mlp"   ):
																_A					  =			int(key_name[9:].split("/"   )[0]   )
																if key_name.endswith("/p1/kernel"   ):
																				_A					  =			"model.blocks.%d.feed_forward.mlp.wi.weight" % player
																				_A					  =			vnp.transpose([1, 0]   ).copy()  # Mesh-Tensorflow is a diagonal matrix
																				_A					  =			torch.tensor(__lowercase   )
																elif key_name.endswith("/p1/bias"   ):
																				_A					  =			"model.blocks.%d.feed_forward.mlp.wi.bias" % player
																				_A					  =			vnp.copy()  # same because it is one dimensional
																				_A					  =			torch.tensor(__lowercase   )
																elif key_name.endswith("/p2/kernel"   ):
																				_A					  =			"model.blocks.%d.feed_forward.mlp.wo.weight" % player
																				_A					  =			vnp.transpose([1, 0]   ).copy()  # Mesh-Tensorflow is a diagonal matrix
																				_A					  =			torch.tensor(__lowercase   )
																elif key_name.endswith("/p2/bias"   ):
																				_A					  =			"model.blocks.%d.feed_forward.mlp.wo.bias" % player
																				_A					  =			vnp.copy()  # same because it is one dimensional
																				_A					  =			torch.tensor(__lowercase   )
												elif key_name.startswith("model/ln"   ):
																_A					  =			int(key_name[8:].split("/"   )[0]   )
																if key_name.endswith("/b"   ):
																				_A					  =			"model.blocks.%d.feed_forward.norm.bias" % player
																				_A					  =			vnp.copy()  # same because it is one dimensional
																				_A					  =			torch.tensor(__lowercase   )
																elif key_name.endswith("/g"   ):
																				_A					  =			"model.blocks.%d.feed_forward.norm.weight" % player
																				_A					  =			vnp.copy()  # same because it is one dimensional
																				_A					  =			torch.tensor(__lowercase   )
												elif key_name.startswith("model/att"   ):
																_A					  =			int(key_name[9:].split("/"   )[0]   )
																if key_name.endswith("/qkv/kernel"   ):
																				_A					  =			vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
																				_A					  =			state[:, 0, :, :]
																				_A					  =			state[:, 1, :, :]
																				_A					  =			state[:, 2, :, :]
																				_A					  =			(
																				    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
																				_A					  =			(
																				    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
																				_A					  =			(
																				    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
																				_A					  =			"model.blocks.%d.self_attn.self_attn.q_proj.weight" % player
																				_A					  =			torch.tensor(__lowercase   )
																				_A					  =			"model.blocks.%d.self_attn.self_attn.k_proj.weight" % player
																				_A					  =			torch.tensor(__lowercase   )
																				_A					  =			"model.blocks.%d.self_attn.self_attn.v_proj.weight" % player
																				_A					  =			torch.tensor(__lowercase   )
																elif key_name.endswith("/o/kernel"   ):
																				_A					  =			"model.blocks.%d.self_attn.self_attn.out_proj.weight" % player
																				_A					  =			(
																				    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]   ).transpose([1, 0]   ).copy()
																				)  # Mesh-Tensorflow is a diagonal matrix
																				_A					  =			torch.tensor(__lowercase   )
												elif key_name.startswith("model/an"   ):
																_A					  =			int(key_name[8:].split("/"   )[0]   )
																if key_name.endswith("/b"   ):
																				_A					  =			"model.blocks.%d.self_attn.norm.bias" % player
																				_A					  =			vnp.copy()  # same because it is one dimensional
																				_A					  =			torch.tensor(__lowercase   )
																elif key_name.endswith("/g"   ):
																				_A					  =			"model.blocks.%d.self_attn.norm.weight" % player
																				_A					  =			vnp.copy()  # same because it is one dimensional
																				_A					  =			torch.tensor(__lowercase   )
												elif (
												    key_name.startswith("model/wte"   )
												    or key_name.startswith("model/wpe"   )
												    or key_name.startswith("model/ete"   )
												):
																_A					  =			{"wte": "embed_tokens", "wpe": "position_embeddings", "ete": "extra_position_embeddings"}[
																    key_name[-3:]
																]
																_A					  =			"model.%s.weight" % nlayer
																_A					  =			vnp.copy()  # same in embedded
																_A					  =			torch.tensor(__lowercase   )
																if key_name.startswith("model/wte"   ):
																				_A					  =			"lm_head.weight"
																				_A					  =			vnp.copy()  # same in embedded
																				_A					  =			torch.tensor(__lowercase   )
												elif key_name.startswith("model/wob"   ):
																_A					  =			"final_logits_bias"
																_A					  =			vnp.copy()  # same in embedded
																_A					  =			state.reshape((1, -1)   )
																_A					  =			torch.tensor(__lowercase   )
												elif key_name == "model/dense/kernel":
																_A					  =			"model.last_project.weight"
																_A					  =			vnp.transpose([1, 0]   ).copy()  # Mesh-Tensorflow is a diagonal matrix
																_A					  =			torch.tensor(__lowercase   )
												elif key_name == "model/dense_1/bias":
																_A					  =			"model.last_project.bias"
																_A					  =			vnp.copy()  # same because it is one dimensional
																_A					  =			torch.tensor(__lowercase   )
				torch.save(__lowercase     ,			args.output   )


if __name__ == "__main__":
		a_       							=    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")
		a_       							=    parser.parse_args()
		convert_tf_gptsan_to_pt(args)