


"""simple docstring"""


import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


a_       							=    logging.getLogger(__name__)
@dataclass
class snake_case      :
		__UpperCamelCase           =			42
		__UpperCamelCase           =			42
		__UpperCamelCase           =			42





@dataclass
class snake_case      :
		__UpperCamelCase           =			42
		__UpperCamelCase           =			42
		__UpperCamelCase           =			None
		__UpperCamelCase           =			None





class snake_case      (  _a):
		__UpperCamelCase           =			"""train"""
		__UpperCamelCase           =			"""dev"""
		__UpperCamelCase           =			"""test"""





class snake_case      :







		@staticmethod
		def  a_ (					a__      :       Optional[int]    ,   a__      :       Any		)       ->						List[InputExample]:





						'''simple docstring'''







						raise NotImplementedError







		@staticmethod
		def  a_ (					a__      :       str		)       ->						List[str]:





						'''simple docstring'''







						raise NotImplementedError







		@staticmethod
		def  a_ (					a__      :       Optional[Any]    ,   a__      :       Optional[Any]    ,   a__      :       Tuple    ,   a__      :       str    ,   a__      :       str=False    ,   a__      :       Union[str, Any]="[CLS]"    ,   a__      :       Optional[Any]=1    ,   a__      :       List[Any]="[SEP]"    ,   a__      :       str=False    ,   a__      :       Union[str, Any]=False    ,   a__      :       Dict=0    ,   a__      :       Optional[Any]=0    ,   a__      :       List[Any]=-1_00    ,   a__      :       Union[str, Any]=0    ,   a__      :       List[str]=True    ,   )       ->						List[InputFeatures]:





						'''simple docstring'''







						_A					  =			{label: i for i, label in enumerate(snake_case_		)}

						_A					  =			[]
						for ex_index, example in enumerate(snake_case_		):
										if ex_index % 1_00_00 == 0:
														logger.info("Writing example %d of %d"    ,   snake_case_    ,   len(snake_case_		)		)

										_A					  =			[]
										_A					  =			[]
										for word, label in zip(example.words    ,   example.labels		):
														_A					  =			tokenizer.tokenize(snake_case_		)

														# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
														if len(snake_case_		) > 0:
																		tokens.extend(snake_case_		)
																		# Use the real label id for the first token of the word, and padding ids for the remaining tokens
																		label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(snake_case_		) - 1)		)

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
										_A					  =			tokenizer.num_special_tokens_to_add()
										if len(snake_case_		) > max_seq_length - special_tokens_count:
														_A					  =			tokens[: (max_seq_length - special_tokens_count)]
														_A					  =			label_ids[: (max_seq_length - special_tokens_count)]

										# The convention in BERT is:
										# (a) For sequence pairs:
										#  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
										#  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
										# (b) For single sequences:
										#  tokens:   [CLS] the dog is hairy . [SEP]
										#  type_ids:   0   0   0   0  0     0   0
										#
										# Where "type_ids" are used to indicate whether this is the first
										# sequence or the second sequence. The embedding vectors for `type=0` and
										# `type=1` were learned during pre-training and are added to the wordpiece
										# embedding vector (and position vector). This is not *strictly* necessary
										# since the [SEP] token unambiguously separates the sequences, but it makes
										# it easier for the model to learn the concept of sequences.
										#
										# For classification tasks, the first vector (corresponding to [CLS]) is
										# used as the "sentence vector". Note that this only makes sense because
										# the entire model is fine-tuned.
										tokens += [sep_token]
										label_ids += [pad_token_label_id]
										if sep_token_extra:
														# roberta uses an extra separator b/w pairs of sentences
														tokens += [sep_token]
														label_ids += [pad_token_label_id]
										_A					  =			[sequence_a_segment_id] * len(snake_case_		)

										if cls_token_at_end:
														tokens += [cls_token]
														label_ids += [pad_token_label_id]
														segment_ids += [cls_token_segment_id]
										else:
														_A					  =			[cls_token] + tokens
														_A					  =			[pad_token_label_id] + label_ids
														_A					  =			[cls_token_segment_id] + segment_ids

										_A					  =			tokenizer.convert_tokens_to_ids(snake_case_		)

										# The mask has 1 for real tokens and 0 for padding tokens. Only real
										# tokens are attended to.
										_A					  =			[1 if mask_padding_with_zero else 0] * len(snake_case_		)

										# Zero-pad up to the sequence length.
										_A					  =			max_seq_length - len(snake_case_		)
										if pad_on_left:
														_A					  =			([pad_token] * padding_length) + input_ids
														_A					  =			([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
														_A					  =			([pad_token_segment_id] * padding_length) + segment_ids
														_A					  =			([pad_token_label_id] * padding_length) + label_ids
										else:
														input_ids += [pad_token] * padding_length
														input_mask += [0 if mask_padding_with_zero else 1] * padding_length
														segment_ids += [pad_token_segment_id] * padding_length
														label_ids += [pad_token_label_id] * padding_length

										assert len(snake_case_		) == max_seq_length
										assert len(snake_case_		) == max_seq_length
										assert len(snake_case_		) == max_seq_length
										assert len(snake_case_		) == max_seq_length

										if ex_index < 5:
														logger.info("*** Example ***"		)
														logger.info("guid: %s"    ,   example.guid		)
														logger.info("tokens: %s"    ,   " ".join([str(snake_case_		) for x in tokens]		)		)
														logger.info("input_ids: %s"    ,   " ".join([str(snake_case_		) for x in input_ids]		)		)
														logger.info("input_mask: %s"    ,   " ".join([str(snake_case_		) for x in input_mask]		)		)
														logger.info("segment_ids: %s"    ,   " ".join([str(snake_case_		) for x in segment_ids]		)		)
														logger.info("label_ids: %s"    ,   " ".join([str(snake_case_		) for x in label_ids]		)		)

										if "token_type_ids" not in tokenizer.model_input_names:
														_A					  =			None

										features.append(
										    InputFeatures(
										        input_ids=snake_case_    ,   attention_mask=snake_case_    ,   token_type_ids=snake_case_    ,   label_ids=snake_case_		)		)
						return features


if is_torch_available():
		import torch
		from torch import nn
		from torch.utils.data import Dataset
		class snake_case      (  _a):
				__UpperCamelCase           =			42
				__UpperCamelCase           =			nn.CrossEntropyLoss().ignore_index







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





								'''simple docstring'''







								_A					  =			os.path.join(
								    snake_case_    ,   "cached_{}_{}_{}".format(mode.value    ,   tokenizer.__class__.__name__    ,   str(snake_case_		)		)    ,   )

								# 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(snake_case_		):
												if os.path.exists(snake_case_		) and not overwrite_cache:
																logger.info(F"""Loading features from cached file {cached_features_file}"""		)
																_A					  =			torch.load(snake_case_		)
												else:
																logger.info(F"""Creating features from dataset file at {data_dir}"""		)
																_A					  =			token_classification_task.read_examples_from_file(snake_case_    ,   snake_case_		)
																# TODO clean up all this to leverage built-in features of tokenizers
																_A					  =			token_classification_task.convert_examples_to_features(
																    snake_case_    ,   snake_case_    ,   snake_case_    ,   snake_case_    ,   cls_token_at_end=bool(model_type in ["xlnet"]		)    ,   cls_token=tokenizer.cls_token    ,   cls_token_segment_id=2 if model_type in ["xlnet"] else 0    ,   sep_token=tokenizer.sep_token    ,   sep_token_extra=snake_case_    ,   pad_on_left=bool(tokenizer.padding_side == "left"		)    ,   pad_token=tokenizer.pad_token_id    ,   pad_token_segment_id=tokenizer.pad_token_type_id    ,   pad_token_label_id=self.pad_token_label_id    ,   )
																logger.info(F"""Saving features into cached file {cached_features_file}"""		)
																torch.save(self.features    ,   snake_case_		)







				def __len__(					self      :       Optional[int]		)       ->						int:





								'''simple docstring'''







								return len(self.features		)







				def __getitem__(					self      :       Any    ,   a__      :       str		)       ->						InputFeatures:





								'''simple docstring'''







								return self.features[i]


if is_tf_available():
		import tensorflow as tf
		class snake_case      :
				__UpperCamelCase           =			42
				__UpperCamelCase           =			-100







				def __init__(					self      :       Optional[Any]    ,   a__      :       str    ,   a__      :       Dict    ,   a__      :       Any    ,   a__      :       str    ,   a__      :       Optional[int]    ,   a__      :       List[str] = None    ,   a__      :       List[str]=False    ,   a__      :       int = Split.train    ,   )       ->						List[Any]:





								'''simple docstring'''







								_A					  =			token_classification_task.read_examples_from_file(snake_case_    ,   snake_case_		)
								# TODO clean up all this to leverage built-in features of tokenizers
								_A					  =			token_classification_task.convert_examples_to_features(
								    snake_case_    ,   snake_case_    ,   snake_case_    ,   snake_case_    ,   cls_token_at_end=bool(model_type in ["xlnet"]		)    ,   cls_token=tokenizer.cls_token    ,   cls_token_segment_id=2 if model_type in ["xlnet"] else 0    ,   sep_token=tokenizer.sep_token    ,   sep_token_extra=snake_case_    ,   pad_on_left=bool(tokenizer.padding_side == "left"		)    ,   pad_token=tokenizer.pad_token_id    ,   pad_token_segment_id=tokenizer.pad_token_type_id    ,   pad_token_label_id=self.pad_token_label_id    ,   )

								def gen():
												for ex in self.features:
																if ex.token_type_ids is None:
																				yield (
																				    {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
																				    ex.label_ids,
																				)
																else:
																				yield (
																				    {
																				        "input_ids": ex.input_ids,
																				        "attention_mask": ex.attention_mask,
																				        "token_type_ids": ex.token_type_ids,
																				    },
																				    ex.label_ids,
																				)

								if "token_type_ids" not in tokenizer.model_input_names:
												_A					  =			tf.data.Dataset.from_generator(
												    snake_case_    ,   ({"input_ids": tf.intaa, "attention_mask": tf.intaa}, tf.intaa)    ,   (
												        {"input_ids": tf.TensorShape([None]		), "attention_mask": tf.TensorShape([None]		)},
												        tf.TensorShape([None]		),
												    )    ,   )
								else:
												_A					  =			tf.data.Dataset.from_generator(
												    snake_case_    ,   ({"input_ids": tf.intaa, "attention_mask": tf.intaa, "token_type_ids": tf.intaa}, tf.intaa)    ,   (
												        {
												            "input_ids": tf.TensorShape([None]		),
												            "attention_mask": tf.TensorShape([None]		),
												            "token_type_ids": tf.TensorShape([None]		),
												        },
												        tf.TensorShape([None]		),
												    )    ,   )







				def  a_ (					self      :       int		)       ->						Union[str, Any]:





								'''simple docstring'''







								_A					  =			self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features		)		)		)

								return self.dataset







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





								'''simple docstring'''







								return len(self.features		)







				def __getitem__(					self      :       Optional[Any]    ,   a__      :       Any		)       ->						InputFeatures:





								'''simple docstring'''







								return self.features[i]



"""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)