
"""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


SCREAMING_SNAKE_CASE						:						List[str]					   =       logging.getLogger(__name__)





@dataclass
class       snake_case_       :



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							str
	SCREAMING_SNAKE_CASE_:							List[str]
	SCREAMING_SNAKE_CASE_:							Optional[List[str]]





@dataclass
class       snake_case_       :



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							List[int]
	SCREAMING_SNAKE_CASE_:							List[int]
	SCREAMING_SNAKE_CASE_:							Optional[List[int]]   			=					None
	SCREAMING_SNAKE_CASE_:							Optional[List[int]]   			=					None





class       snake_case_       (    _a       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							Optional[Any]   			=					"""train"""
	SCREAMING_SNAKE_CASE_:							List[Any]   			=					"""dev"""
	SCREAMING_SNAKE_CASE_:							Optional[Any]   			=					"""test"""





class       snake_case_       :



	"""simple docstring"""

	@staticmethod
	def       _UpperCAmelCase						(      __a  ,					__a						):


					"""simple docstring"""

					raise NotImplementedError


	@staticmethod
	def       _UpperCAmelCase						(      __a						):


					"""simple docstring"""

					raise NotImplementedError






	@staticmethod
	def       _UpperCAmelCase						(      __a  ,					__a  ,					__a  ,					__a  ,					__a=False  ,					__a="[CLS]"  ,					__a=1  ,					__a="[SEP]"  ,					__a=False  ,					__a=False  ,					__a=0  ,					__a=0  ,					__a=-100  ,					__a=0  ,					__a=True  ,					):


					"""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_0000 == 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       ):



						"""simple docstring"""

						SCREAMING_SNAKE_CASE_:							List[InputFeatures]
						SCREAMING_SNAKE_CASE_:							int   			=					nn.CrossEntropyLoss().ignore_index


						def __init__(      self  ,					__a  ,					__a  ,					__a  ,					__a  ,					__a  ,					__a = None  ,					__a=False  ,					__a = Split.train  ,					):


										"""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						):


										"""simple docstring"""

										return len(self.features						)






						def __getitem__(      self  ,					__a						):


										"""simple docstring"""

										return self.features[i]


if is_tf_available():
					import tensorflow as tf





					class       snake_case_       :



						"""simple docstring"""

						SCREAMING_SNAKE_CASE_:							List[InputFeatures]
						SCREAMING_SNAKE_CASE_:							int   			=					-1_00


						def __init__(      self  ,					__a  ,					__a  ,					__a  ,					__a  ,					__a  ,					__a = None  ,					__a=False  ,					__a = Split.train  ,					):


										"""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       _UpperCAmelCase						(      self						):


										"""simple docstring"""

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

										return self.dataset


						def __len__(      self						):


										"""simple docstring"""

										return len(self.features						)






						def __getitem__(      self  ,					__a						):


										"""simple docstring"""

										return self.features[i]










"""simple docstring"""






from typing import Union

import fire
import torch
from tqdm import tqdm



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__ = "cpu"       ,lowerCAmelCase__ = None				):
				A__							 =       torch.load(lowerCAmelCase__       ,map_location=lowerCAmelCase__				)
				for k, v in tqdm(state_dict.items()				):
								if not isinstance(lowerCAmelCase__       ,torch.Tensor				):
												raise TypeError('FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin'				)
								A__							 =       v.half()
				if save_path is None:  # overwrite src_path
								A__							 =       src_path
				torch.save(lowerCAmelCase__       ,lowerCAmelCase__				)


if __name__ == "__main__":
					fire.Fire(convert)


