





"""simple docstring"""





import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


_UpperCamelCase							=      logging.getLogger(__name__)



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Tuple    ,						SCREAMING_SNAKE_CASE       :							str   ):


							'''simple docstring'''







							return (preds == labels).mean()
@dataclass
class 		SCREAMING_SNAKE_CASE_							:
				"""simple docstring"""
				__snake_case   :       str	 =		field(
				    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}   )
				__snake_case   :       Optional[str]	 =		field(
				    default=snake_case__       ,  metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}   )
				__snake_case   :       Optional[str]	 =		field(
				    default=snake_case__       ,  metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}   )
				__snake_case   :       Optional[str]	 =		field(
				    default=snake_case__       ,  metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}       ,  )

@dataclass
class 		SCREAMING_SNAKE_CASE_							:
				"""simple docstring"""
				__snake_case   :       str	 =		field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(processors.keys()   )}   )
				__snake_case   :       str	 =		field(metadata={"""help""": """Should contain the data files for the task."""}   )
				__snake_case   :       int	 =		field(
				    default=1_28       ,  metadata={
				        """help""": (
				            """The maximum total input sequence length after tokenization. Sequences longer """
				            """than this will be truncated, sequences shorter will be padded."""
				        )
				    }       ,  )
				__snake_case   :       bool	 =		field(
				    default=snake_case__       ,  metadata={"""help""": """Overwrite the cached training and evaluation sets"""}   )



def 				lowerCAmelCase_			(      ):


							'''simple docstring'''







							__lowerCamelCase      :      Dict         =HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)   )
							__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase      :      Optional[Any]         =parser.parse_args_into_dataclasses()

							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'''    ,						SCREAMING_SNAKE_CASE   )

							# Set seed
							set_seed(training_args.seed   )

							try:
														__lowerCamelCase      :      str         =processors[data_args.task_name]()
														__lowerCamelCase      :      Optional[Any]         =processor.get_labels()
														__lowerCamelCase      :      List[Any]         =len(SCREAMING_SNAKE_CASE   )
							except KeyError:
														raise ValueError('''Task not found: %s''' % (data_args.task_name)   )

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

							__lowerCamelCase      :      Tuple         =AutoConfig.from_pretrained(
							    model_args.config_name if model_args.config_name else model_args.model_name_or_path    ,						num_labels=SCREAMING_SNAKE_CASE    ,						finetuning_task=data_args.task_name    ,						cache_dir=model_args.cache_dir    ,						)
							__lowerCamelCase      :      int         =AutoTokenizer.from_pretrained(
							    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path    ,						cache_dir=model_args.cache_dir    ,						)
							__lowerCamelCase      :      Union[str, Any]         =AutoModelForMultipleChoice.from_pretrained(
							    model_args.model_name_or_path    ,						from_tf=bool('''.ckpt''' in model_args.model_name_or_path   )    ,						config=SCREAMING_SNAKE_CASE    ,						cache_dir=model_args.cache_dir    ,						)

							# Get datasets
							__lowerCamelCase      :      Union[str, Any]         =(
							    MultipleChoiceDataset(
							        data_dir=data_args.data_dir    ,						tokenizer=SCREAMING_SNAKE_CASE    ,						task=data_args.task_name    ,						max_seq_length=data_args.max_seq_length    ,						overwrite_cache=data_args.overwrite_cache    ,						mode=Split.train    ,						)
							    if training_args.do_train
							    else None
							)
							__lowerCamelCase      :      Optional[int]         =(
							    MultipleChoiceDataset(
							        data_dir=data_args.data_dir    ,						tokenizer=SCREAMING_SNAKE_CASE    ,						task=data_args.task_name    ,						max_seq_length=data_args.max_seq_length    ,						overwrite_cache=data_args.overwrite_cache    ,						mode=Split.dev    ,						)
							    if training_args.do_eval
							    else None
							)

							def compute_metrics(SCREAMING_SNAKE_CASE       :							EvalPrediction   ) -> Dict:
														__lowerCamelCase      :      Dict         =np.argmax(p.predictions    ,						axis=1   )
														return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE    ,						p.label_ids   )}

							# Data collator
							__lowerCamelCase      :      Optional[int]         =DataCollatorWithPadding(SCREAMING_SNAKE_CASE    ,						pad_to_multiple_of=8   ) if training_args.fpaa else None

							# Initialize our Trainer
							__lowerCamelCase      :      Union[str, Any]         =Trainer(
							    model=SCREAMING_SNAKE_CASE    ,						args=SCREAMING_SNAKE_CASE    ,						train_dataset=SCREAMING_SNAKE_CASE    ,						eval_dataset=SCREAMING_SNAKE_CASE    ,						compute_metrics=SCREAMING_SNAKE_CASE    ,						data_collator=SCREAMING_SNAKE_CASE    ,						)

							# Training
							if training_args.do_train:
														trainer.train(
														    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path   ) else None   )
														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
							__lowerCamelCase      :      str         ={}
							if training_args.do_eval:
														logger.info('''*** Evaluate ***'''   )

														__lowerCamelCase      :      int         =trainer.evaluate()

														__lowerCamelCase      :      Any         =os.path.join(training_args.output_dir    ,						'''eval_results.txt'''   )
														if trainer.is_world_master():
																					with open(SCREAMING_SNAKE_CASE    ,						'''w'''   ) as writer:
																												logger.info('''***** Eval results *****'''   )
																												for key, value in result.items():
																																			logger.info('''  %s = %s'''    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )
																																			writer.write('''%s = %s\n''' % (key, value)   )

																												results.update(SCREAMING_SNAKE_CASE   )

							return results



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Dict   ):


							'''simple docstring'''







							main()


if __name__ == "__main__":
							main()











"""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


_UpperCamelCase							=      logging.get_logger(__name__)



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							str    ,						SCREAMING_SNAKE_CASE       :							str   ):


							'''simple docstring'''







							try:
														with open(SCREAMING_SNAKE_CASE    ,						'''rb'''   ) as flax_state_f:
																					__lowerCamelCase      :      int         =from_bytes(SCREAMING_SNAKE_CASE    ,						flax_state_f.read()   )
							except UnpicklingError as e:
														try:
																					with open(SCREAMING_SNAKE_CASE   ) 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(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							Union[str, Any]   ):


							'''simple docstring'''







							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
							__lowerCamelCase      :      str         =flatten_dict(jax.tree_util.tree_map(lambda SCREAMING_SNAKE_CASE   : x.dtype == jnp.bfloataa    ,						SCREAMING_SNAKE_CASE   )   ).values()
							if any(SCREAMING_SNAKE_CASE   ):
														# 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.'''   )
														__lowerCamelCase      :      List[str]         =jax.tree_util.tree_map(
														    lambda SCREAMING_SNAKE_CASE   : params.astype(np.floataa   ) if params.dtype == jnp.bfloataa else params    ,						SCREAMING_SNAKE_CASE   )

							__lowerCamelCase      :      int         =''''''

							__lowerCamelCase      :      List[str]         =flatten_dict(SCREAMING_SNAKE_CASE    ,						sep='''.'''   )
							__lowerCamelCase      :      List[str]         =pt_model.state_dict()

							# keep track of unexpected & missing keys
							__lowerCamelCase      :      Union[str, Any]         =[]
							__lowerCamelCase      :      int         =set(pt_model_dict.keys()   )

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

														if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
																					__lowerCamelCase      :      Optional[int]         =flax_key_tuple_array[:-1] + ['''weight''']
																					__lowerCamelCase      :      Union[str, Any]         =jnp.transpose(SCREAMING_SNAKE_CASE    ,						(3, 2, 0, 1)   )
														elif flax_key_tuple_array[-1] == "kernel":
																					__lowerCamelCase      :      Optional[Any]         =flax_key_tuple_array[:-1] + ['''weight''']
																					__lowerCamelCase      :      Dict         =flax_tensor.T
														elif flax_key_tuple_array[-1] == "scale":
																					__lowerCamelCase      :      str         =flax_key_tuple_array[:-1] + ['''weight''']

														if "time_embedding" not in flax_key_tuple_array:
																					for i, flax_key_tuple_string in enumerate(SCREAMING_SNAKE_CASE   ):
																												__lowerCamelCase      :      Union[str, Any]         =(
																												    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'''   )
																												)

														__lowerCamelCase      :      Optional[int]         ='''.'''.join(SCREAMING_SNAKE_CASE   )

														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
																												__lowerCamelCase      :      Tuple         =np.asarray(SCREAMING_SNAKE_CASE   ) if not isinstance(SCREAMING_SNAKE_CASE    ,						np.ndarray   ) else flax_tensor
																												__lowerCamelCase      :      Any         =torch.from_numpy(SCREAMING_SNAKE_CASE   )
																												# remove from missing keys
																												missing_keys.remove(SCREAMING_SNAKE_CASE   )
														else:
																					# weight is not expected by PyTorch model
																					unexpected_keys.append(SCREAMING_SNAKE_CASE   )

							pt_model.load_state_dict(SCREAMING_SNAKE_CASE   )

							# re-transform missing_keys to list
							__lowerCamelCase      :      Optional[Any]         =list(SCREAMING_SNAKE_CASE   )

							if len(SCREAMING_SNAKE_CASE   ) > 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(SCREAMING_SNAKE_CASE   ) > 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




