

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


lowerCAmelCase__	   = logging.getLogger(__name__)

@dataclass
class 					a__						:
			"""simple docstring"""







			__lowerCamelCase        =       42
			__lowerCamelCase        =       42
			__lowerCamelCase        =       42

@dataclass
class 					a__						:
			"""simple docstring"""







			__lowerCamelCase        =       42
			__lowerCamelCase        =       42
			__lowerCamelCase        =       None
			__lowerCamelCase        =       None

class 					a__						(					snake_case			):
			"""simple docstring"""







			__lowerCamelCase        =       'train'
			__lowerCamelCase        =       'dev'
			__lowerCamelCase        =       'test'

class 					a__						:
			"""simple docstring"""







			@staticmethod
			def      UpperCamelCase		(    lowercase      ,      lowercase       )  ->						List[InputExample]:
							'''simple docstring'''






							raise NotImplementedError


			@staticmethod
			def      UpperCamelCase		(    lowercase       )  ->						List[str]:
							'''simple docstring'''






							raise NotImplementedError


			@staticmethod
			def      UpperCamelCase		(    lowercase      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase=False      ,      lowercase="[CLS]"      ,      lowercase=1      ,      lowercase="[SEP]"      ,      lowercase=False      ,      lowercase=False      ,      lowercase=0      ,      lowercase=0      ,      lowercase=-100      ,      lowercase=0      ,      lowercase=True      ,      )  ->						List[InputFeatures]:
							'''simple docstring'''






							A__        =			{label: i for i, label in enumerate(lowercase       )}

							A__        =			[]
							for ex_index, example in enumerate(lowercase       ):
											if ex_index % 10000 == 0:
															logger.info("Writing example %d of %d"      ,      lowercase      ,      len(lowercase       )       )

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

															# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
															if len(lowercase       ) > 0:
																			tokens.extend(lowercase       )
																			# 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(lowercase       ) - 1)       )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
											A__        =			tokenizer.num_special_tokens_to_add()
											if len(lowercase       ) > 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(lowercase       )

											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(lowercase       )

											# 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(lowercase       )

											# Zero-pad up to the sequence length.
											A__        =			max_seq_length - len(lowercase       )
											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(lowercase       ) == max_seq_length
											assert len(lowercase       ) == max_seq_length
											assert len(lowercase       ) == max_seq_length
											assert len(lowercase       ) == max_seq_length

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

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

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


if is_torch_available():
	import torch
	from torch import nn
	from torch.utils.data import Dataset

	class 					a__						(					snake_case			):
				"""simple docstring"""







				__lowerCamelCase        =       42
				__lowerCamelCase        =       nn.CrossEntropyLoss().ignore_index


				def __init__(    self      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase = None      ,      lowercase=False      ,      lowercase = Split.train      ,      )  ->						Optional[int]:
								'''simple docstring'''






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

								# 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(lowercase       ):
												if os.path.exists(lowercase       ) and not overwrite_cache:
																logger.info(F'Loading features from cached file {cached_features_file}'       )
																A__        =			torch.load(lowercase       )
												else:
																logger.info(F'Creating features from dataset file at {data_dir}'       )
																A__        =			token_classification_task.read_examples_from_file(lowercase      ,      lowercase       )
																# TODO clean up all this to leverage built-in features of tokenizers
																A__        =			token_classification_task.convert_examples_to_features(
																    lowercase      ,      lowercase      ,      lowercase      ,      lowercase      ,      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=lowercase      ,      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      ,      lowercase       )


				def __len__(    self       )  ->						Union[str, Any]:
								'''simple docstring'''






								return len(self.features       )


				def __getitem__(    self      ,      lowercase       )  ->						InputFeatures:
								'''simple docstring'''






								return self.features[i]


if is_tf_available():
	import tensorflow as tf

	class 					a__						:
				"""simple docstring"""







				__lowerCamelCase        =       42
				__lowerCamelCase        =       -100


				def __init__(    self      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase = None      ,      lowercase=False      ,      lowercase = Split.train      ,      )  ->						List[Any]:
								'''simple docstring'''






								A__        =			token_classification_task.read_examples_from_file(lowercase      ,      lowercase       )
								# TODO clean up all this to leverage built-in features of tokenizers
								A__        =			token_classification_task.convert_examples_to_features(
								    lowercase      ,      lowercase      ,      lowercase      ,      lowercase      ,      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=lowercase      ,      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(
												    lowercase      ,      ({"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(
												    lowercase      ,      ({"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       )  ->						List[Any]:
								'''simple docstring'''






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

								return self.dataset


				def __len__(    self       )  ->						Optional[Any]:
								'''simple docstring'''






								return len(self.features       )


				def __getitem__(    self      ,      lowercase       )  ->						InputFeatures:
								'''simple docstring'''






								return self.features[i]




from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser

@dataclass
class 					a__						:
			"""simple docstring"""







			__lowerCamelCase        =       field(
			    metadata={'help': 'The output directory where the model will be written.'}							,	)
			__lowerCamelCase        =       field(
			    metadata={
			        'help': (
			            'The encoder model checkpoint for weights initialization.'
			            'Don\'t set if you want to train an encoder model from scratch.'
			        )
			    }							,	)
			__lowerCamelCase        =       field(
			    metadata={
			        'help': (
			            'The decoder model checkpoint for weights initialization.'
			            'Don\'t set if you want to train a decoder model from scratch.'
			        )
			    }							,	)
			__lowerCamelCase        =       field(
			    default=snake_case							,	metadata={'help': 'Pretrained encoder config name or path if not the same as encoder_model_name'}			)
			__lowerCamelCase        =       field(
			    default=snake_case							,	metadata={'help': 'Pretrained decoder config name or path if not the same as decoder_model_name'}			)



def 					lowerCAmelCase__							(	)			->      List[Any]:




				'''simple docstring'''





				A__        =			HfArgumentParser((ModelArguments,)      )
				((A__)    ,				)        =			parser.parse_args_into_dataclasses()

				# Load pretrained model and tokenizer

				# Use explicit specified encoder config
				if model_args.encoder_config_name:
								A__        =			AutoConfig.from_pretrained(model_args.encoder_config_name      )
				# Use pretrained encoder model's config
				else:
								A__        =			AutoConfig.from_pretrained(model_args.encoder_model_name_or_path      )

				# Use explicit specified decoder config
				if model_args.decoder_config_name:
								A__        =			AutoConfig.from_pretrained(model_args.decoder_config_name      )
				# Use pretrained decoder model's config
				else:
								A__        =			AutoConfig.from_pretrained(model_args.decoder_model_name_or_path      )

				# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
				A__        =			True
				A__        =			True

				A__        =			FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
				    encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path	,       decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path	,       encoder_config=SCREAMING_SNAKE_CASE_	,       decoder_config=SCREAMING_SNAKE_CASE_	,       )

				# GPT2 only has bos/eos tokens but not decoder_start/pad tokens
				A__        =			decoder_config.decoder_start_token_id
				A__        =			decoder_config.pad_token_id
				if decoder_start_token_id is None:
								A__        =			decoder_config.bos_token_id
				if pad_token_id is None:
								A__        =			decoder_config.eos_token_id

				# This is necessary to make Flax's generate() work
				A__        =			decoder_config.eos_token_id
				A__        =			decoder_start_token_id
				A__        =			pad_token_id

				A__        =			AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path      )

				A__        =			AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path      )
				A__        =			tokenizer.convert_ids_to_tokens(model.config.pad_token_id      )

				model.save_pretrained(model_args.output_dir      )
				image_processor.save_pretrained(model_args.output_dir      )
				tokenizer.save_pretrained(model_args.output_dir      )


if __name__ == "__main__":
	main()

