





'''simple docstring'''



from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


_SCREAMING_SNAKE_CASE					=    logging.get_logger(__name__)




def    _lowerCAmelCase						(				lowerCamelCase_			:  int		,       lowerCamelCase_			:  Optional[Any]				):
			try:
						with open(lowerCamelCase_		,       '''rb'''				) as flax_state_f:
									__lowercase										=							from_bytes(lowerCamelCase_		,       flax_state_f.read()				)
			except UnpicklingError as e:
						try:
									with open(lowerCamelCase_				) as f:
												if f.read().startswith('''version'''				):
															raise OSError(
															    '''You seem to have cloned a repository without having git-lfs installed. Please'''
															    ''' install git-lfs and run `git lfs install` followed by `git lfs pull` in the'''
															    ''' folder you cloned.'''				)
												else:
															raise ValueError from e
						except (UnicodeDecodeError, ValueError):
									raise EnvironmentError(f"Unable to convert {model_file} to Flax deserializable object. "				)

			return load_flax_weights_in_pytorch_model(lowerCamelCase_		,       lowerCamelCase_				)




def    _lowerCAmelCase						(				lowerCamelCase_			:  List[str]		,       lowerCamelCase_			:  Dict				):

			try:
						import torch  # noqa: F401
			except ImportError:
						logger.error(
						    '''Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see'''
						    ''' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'''
						    ''' instructions.'''				)
						raise

			# check if we have bf16 weights
			__lowercase										=							flatten_dict(jax.tree_util.tree_map(lambda lowerCamelCase_				: x.dtype == jnp.bfloataa		,       lowerCamelCase_				)				).values()
			if any(lowerCamelCase_				):
						# convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

						# and bf16 is not fully supported in PT yet.
						logger.warning(
						    '''Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '''
						    '''before loading those in PyTorch model.'''				)
						__lowercase										=							jax.tree_util.tree_map(
						    lambda lowerCamelCase_				: params.astype(np.floataa				) if params.dtype == jnp.bfloataa else params		,       lowerCamelCase_				)

			__lowercase										=							''''''

			__lowercase										=							flatten_dict(lowerCamelCase_		,       sep='''.'''				)
			__lowercase										=							pt_model.state_dict()

			# keep track of unexpected & missing keys
			__lowercase										=							[]
			__lowercase										=							set(pt_model_dict.keys()				)

			for flax_key_tuple, flax_tensor in flax_state_dict.items():
						__lowercase										=							flax_key_tuple.split('''.'''				)

						if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
									__lowercase										=							flax_key_tuple_array[:-1] + ['''weight''']
									__lowercase										=							jnp.transpose(lowerCamelCase_		,       (3, 2, 0, 1)				)
						elif flax_key_tuple_array[-1] == "kernel":
									__lowercase										=							flax_key_tuple_array[:-1] + ['''weight''']
									__lowercase										=							flax_tensor.T
						elif flax_key_tuple_array[-1] == "scale":
									__lowercase										=							flax_key_tuple_array[:-1] + ['''weight''']

						if "time_embedding" not in flax_key_tuple_array:
									for i, flax_key_tuple_string in enumerate(lowerCamelCase_				):
												__lowercase										=							(
												    flax_key_tuple_string.replace('''_0'''		,       '''.0'''				)
												    .replace('''_1'''		,       '''.1'''				)
												    .replace('''_2'''		,       '''.2'''				)
												    .replace('''_3'''		,       '''.3'''				)
												    .replace('''_4'''		,       '''.4'''				)
												    .replace('''_5'''		,       '''.5'''				)
												    .replace('''_6'''		,       '''.6'''				)
												    .replace('''_7'''		,       '''.7'''				)
												    .replace('''_8'''		,       '''.8'''				)
												    .replace('''_9'''		,       '''.9'''				)
												)

						__lowercase										=							'''.'''.join(lowerCamelCase_				)

						if flax_key in pt_model_dict:
									if flax_tensor.shape != pt_model_dict[flax_key].shape:
												raise ValueError(
												    f"Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected "
												    f"to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}."				)
									else:
												# add weight to pytorch dict
												__lowercase										=							np.asarray(lowerCamelCase_				) if not isinstance(lowerCamelCase_		,       np.ndarray				) else flax_tensor
												__lowercase										=							torch.from_numpy(lowerCamelCase_				)
												# remove from missing keys
												missing_keys.remove(lowerCamelCase_				)
						else:
									# weight is not expected by PyTorch model
									unexpected_keys.append(lowerCamelCase_				)

			pt_model.load_state_dict(lowerCamelCase_				)

			# re-transform missing_keys to list
			__lowercase										=							list(lowerCamelCase_				)

			if len(lowerCamelCase_				) > 0:
						logger.warning(
						    '''Some weights of the Flax model were not used when initializing the PyTorch model'''
						    f" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"
						    f" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"
						    ''' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'''
						    f" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"
						    ''' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'''
						    ''' FlaxBertForSequenceClassification model).'''				)
			if len(lowerCamelCase_				) > 0:
						logger.warning(
						    f"Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"
						    f" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"
						    ''' use it for predictions and inference.'''				)

			return pt_model












'''simple docstring'''



import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional

from torch.utils.data import ConcatDataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_WITH_LM_HEAD_MAPPING,
    AutoConfig,
    AutoModelWithLMHead,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    DataCollatorForPermutationLanguageModeling,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    PreTrainedTokenizer,
    TextDataset,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


_SCREAMING_SNAKE_CASE					=    logging.getLogger(__name__)


_SCREAMING_SNAKE_CASE					=    list(MODEL_WITH_LM_HEAD_MAPPING.keys())
_SCREAMING_SNAKE_CASE					=    tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)






@dataclass
class 							__lowercase       :







	'''simple docstring'''

	a    :				Optional[str]											=       field(
	    default=lowerCAmelCase__ ,      metadata={
	        "help": (
	            "The model checkpoint for weights initialization. Leave None if you want to train a model from"
	            " scratch."
	        )
	    } ,      )
	a    :				Optional[str]											=       field(
	    default=lowerCAmelCase__ ,      metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(lowerCAmelCase__						)} ,      )
	a    :				Optional[str]											=       field(
	    default=lowerCAmelCase__ ,      metadata={"help": "Pretrained config name or path if not the same as model_name"}						)
	a    :				Optional[str]											=       field(
	    default=lowerCAmelCase__ ,      metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}						)
	a    :				Optional[str]											=       field(
	    default=lowerCAmelCase__ ,      metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} ,      )






@dataclass
class 							__lowercase       :







	'''simple docstring'''

	a    :				Optional[str]											=       field(
	    default=lowerCAmelCase__ ,      metadata={"help": "The input training data file (a text file)."}						)
	a    :				Optional[str]											=       field(
	    default=lowerCAmelCase__ ,      metadata={
	        "help": (
	            "The input training data files (multiple files in glob format). "
	            "Very often splitting large files to smaller files can prevent tokenizer going out of memory"
	        )
	    } ,      )
	a    :				Optional[str]											=       field(
	    default=lowerCAmelCase__ ,      metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."} ,      )
	a    :				Optional[str]											=       field(
	    default=lowerCAmelCase__ ,      metadata={"help": "An optional input train ref data file for whole word mask in Chinese."} ,      )
	a    :				Optional[str]											=       field(
	    default=lowerCAmelCase__ ,      metadata={"help": "An optional input eval ref data file for whole word mask in Chinese."} ,      )
	a    :				bool											=       field(
	    default=lowerCAmelCase__ ,      metadata={"help": "Whether distinct lines of text in the dataset are to be handled as distinct sequences."} ,      )

	a    :				bool											=       field(
	    default=lowerCAmelCase__ ,      metadata={"help": "Train with masked-language modeling loss instead of language modeling."}						)
	a    :				bool											=       field(default=lowerCAmelCase__ ,      metadata={"help": "Whether ot not to use whole word mask."}						)
	a    :				float											=       field(
	    default=0.15 ,      metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}						)
	a    :				float											=       field(
	    default=1 / 6 ,      metadata={
	        "help": (
	            "Ratio of length of a span of masked tokens to surrounding context length for permutation language"
	            " modeling."
	        )
	    } ,      )
	a    :				int											=       field(
	    default=5 ,      metadata={"help": "Maximum length of a span of masked tokens for permutation language modeling."}						)

	a    :				int											=       field(
	    default=-1 ,      metadata={
	        "help": (
	            "Optional input sequence length after tokenization."
	            "The training dataset will be truncated in block of this size for training."
	            "Default to the model max input length for single sentence inputs (take into account special tokens)."
	        )
	    } ,      )
	a    :				bool											=       field(
	    default=lowerCAmelCase__ ,      metadata={"help": "Overwrite the cached training and evaluation sets"}						)





def    _lowerCAmelCase						(				lowerCamelCase_			:  DataTrainingArguments		,       lowerCamelCase_			:  PreTrainedTokenizer		,       lowerCamelCase_			:  bool = False		,       lowerCamelCase_			:  Optional[str] = None		,       ):
			def _dataset(lowerCamelCase_			:  str		,       lowerCamelCase_			:  Union[str, Any]=None				):
						if args.line_by_line:
									if ref_path is not None:
												if not args.whole_word_mask or not args.mlm:
															raise ValueError('''You need to set world whole masking and mlm to True for Chinese Whole Word Mask'''				)
												return LineByLineWithRefDataset(
												    tokenizer=lowerCamelCase_		,       file_path=lowerCamelCase_		,       block_size=args.block_size		,       ref_path=lowerCamelCase_		,       )

									return LineByLineTextDataset(tokenizer=lowerCamelCase_		,       file_path=lowerCamelCase_		,       block_size=args.block_size				)
						else:
									return TextDataset(
									    tokenizer=lowerCamelCase_		,       file_path=lowerCamelCase_		,       block_size=args.block_size		,       overwrite_cache=args.overwrite_cache		,       cache_dir=lowerCamelCase_		,       )

			if evaluate:
						return _dataset(args.eval_data_file		,       args.eval_ref_file				)
			elif args.train_data_files:
						return ConcatDataset([_dataset(lowerCamelCase_				) for f in glob(args.train_data_files				)]				)
			else:
						return _dataset(args.train_data_file		,       args.train_ref_file				)




def    _lowerCAmelCase						(				):
			# See all possible arguments in src/transformers/training_args.py
			# or by passing the --help flag to this script.
			# We now keep distinct sets of args, for a cleaner separation of concerns.

			__lowercase										=							HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
			__lowercase						,		__lowercase						,		__lowercase										=							parser.parse_args_into_dataclasses()

			if data_args.eval_data_file is None and training_args.do_eval:
						raise ValueError(
						    '''Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file '''
						    '''or remove the --do_eval argument.'''				)
			if (
			    os.path.exists(training_args.output_dir				)
			    and os.listdir(training_args.output_dir				)
			    and training_args.do_train
			    and not training_args.overwrite_output_dir
			):
						raise ValueError(
						    f"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
						    ''' --overwrite_output_dir to overcome.'''				)

			# Setup logging
			logging.basicConfig(
			    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''		,       datefmt='''%m/%d/%Y %H:%M:%S'''		,       level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN		,       )
			logger.warning(
			    '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''		,       training_args.local_rank		,       training_args.device		,       training_args.n_gpu		,       bool(training_args.local_rank != -1				)		,       training_args.fpaa		,       )
			# Set the verbosity to info of the Transformers logger (on main process only):
			if is_main_process(training_args.local_rank				):
						transformers.utils.logging.set_verbosity_info()
						transformers.utils.logging.enable_default_handler()
						transformers.utils.logging.enable_explicit_format()
			logger.info('''Training/evaluation parameters %s'''		,       lowerCamelCase_				)

			# Set seed
			set_seed(training_args.seed				)

			# Load pretrained model and tokenizer
			#
			# Distributed training:
			# The .from_pretrained methods guarantee that only one local process can concurrently
			# download model & vocab.

			if model_args.config_name:
						__lowercase										=							AutoConfig.from_pretrained(model_args.config_name		,       cache_dir=model_args.cache_dir				)
			elif model_args.model_name_or_path:
						__lowercase										=							AutoConfig.from_pretrained(model_args.model_name_or_path		,       cache_dir=model_args.cache_dir				)
			else:
						__lowercase										=							CONFIG_MAPPING[model_args.model_type]()
						logger.warning('''You are instantiating a new config instance from scratch.'''				)

			if model_args.tokenizer_name:
						__lowercase										=							AutoTokenizer.from_pretrained(model_args.tokenizer_name		,       cache_dir=model_args.cache_dir				)
			elif model_args.model_name_or_path:
						__lowercase										=							AutoTokenizer.from_pretrained(model_args.model_name_or_path		,       cache_dir=model_args.cache_dir				)
			else:
						raise ValueError(
						    '''You are instantiating a new tokenizer from scratch. This is not supported, but you can do it from another'''
						    ''' script, save it,and load it from here, using --tokenizer_name'''				)

			if model_args.model_name_or_path:
						__lowercase										=							AutoModelWithLMHead.from_pretrained(
						    model_args.model_name_or_path		,       from_tf=bool('''.ckpt''' in model_args.model_name_or_path				)		,       config=lowerCamelCase_		,       cache_dir=model_args.cache_dir		,       )
			else:
						logger.info('''Training new model from scratch'''				)
						__lowercase										=							AutoModelWithLMHead.from_config(lowerCamelCase_				)

			model.resize_token_embeddings(len(lowerCamelCase_				)				)

			if config.model_type in ["bert", "roberta", "distilbert", "camembert"] and not data_args.mlm:
						raise ValueError(
						    '''BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the'''
						    '''--mlm flag (masked language modeling).'''				)

			if data_args.block_size <= 0:
						__lowercase										=							tokenizer.max_len
						# Our input block size will be the max possible for the model
			else:
						__lowercase										=							min(data_args.block_size		,       tokenizer.max_len				)

			# Get datasets

			__lowercase										=							(
			    get_dataset(lowerCamelCase_		,       tokenizer=lowerCamelCase_		,       cache_dir=model_args.cache_dir				) if training_args.do_train else None
			)
			__lowercase										=							(
			    get_dataset(lowerCamelCase_		,       tokenizer=lowerCamelCase_		,       evaluate=lowerCamelCase_		,       cache_dir=model_args.cache_dir				)
			    if training_args.do_eval
			    else None
			)
			if config.model_type == "xlnet":
						__lowercase										=							DataCollatorForPermutationLanguageModeling(
						    tokenizer=lowerCamelCase_		,       plm_probability=data_args.plm_probability		,       max_span_length=data_args.max_span_length		,       )
			else:
						if data_args.mlm and data_args.whole_word_mask:
									__lowercase										=							DataCollatorForWholeWordMask(
									    tokenizer=lowerCamelCase_		,       mlm_probability=data_args.mlm_probability				)
						else:
									__lowercase										=							DataCollatorForLanguageModeling(
									    tokenizer=lowerCamelCase_		,       mlm=data_args.mlm		,       mlm_probability=data_args.mlm_probability				)

    # Initialize our Trainer
			__lowercase										=							Trainer(
			    model=lowerCamelCase_		,       args=lowerCamelCase_		,       data_collator=lowerCamelCase_		,       train_dataset=lowerCamelCase_		,       eval_dataset=lowerCamelCase_		,       prediction_loss_only=lowerCamelCase_		,       )

			# Training
			if training_args.do_train:
						__lowercase										=							(
						    model_args.model_name_or_path
						    if model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path				)
						    else None
						)
						trainer.train(model_path=lowerCamelCase_				)
						trainer.save_model()
						# For convenience, we also re-save the tokenizer to the same directory,
						# so that you can share your model easily on huggingface.co/models =)
						if trainer.is_world_master():
									tokenizer.save_pretrained(training_args.output_dir				)

    # Evaluation
			__lowercase										=							{}
			if training_args.do_eval:
						logger.info('''*** Evaluate ***'''				)

						__lowercase										=							trainer.evaluate()

						__lowercase										=							math.exp(eval_output['''eval_loss''']				)
						__lowercase										=							{'''perplexity''': perplexity}

						__lowercase										=							os.path.join(training_args.output_dir		,       '''eval_results_lm.txt'''				)
						if trainer.is_world_master():
									with open(lowerCamelCase_		,       '''w'''				) as writer:
												logger.info('''***** Eval results *****'''				)
												for key in sorted(result.keys()				):
															logger.info('''  %s = %s'''		,       lowerCamelCase_		,       str(result[key]				)				)
															writer.write('''%s = %s\n''' % (key, str(result[key]				))				)

						results.update(lowerCamelCase_				)

			return results




def    _lowerCAmelCase						(				lowerCamelCase_			:  str				):
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
	main()





