




'''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


A     =logging.get_logger(__name__)





def 						snake_case_							(_a	:							Union[str, Any]    ,   _a	:							int     ):
    try:
        with open(_a    ,   '''rb'''     ) as flax_state_f:
            UpperCAmelCase			       =		from_bytes(_a    ,   flax_state_f.read()     )
    except UnpicklingError as e:
        try:
            with open(_a     ) 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(_a    ,   _a     )







def 						snake_case_							(_a	:							List[str]    ,   _a	:							List[Any]     ):

    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
    UpperCAmelCase			       =		flatten_dict(jax.tree_util.tree_map(lambda _a     : x.dtype == jnp.bfloataa    ,   _a     )     ).values()
    if any(_a     ):
        # 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.'''     )
        UpperCAmelCase			       =		jax.tree_util.tree_map(
            lambda _a     : params.astype(np.floataa     ) if params.dtype == jnp.bfloataa else params    ,   _a     )

    UpperCAmelCase			       =		''''''

    UpperCAmelCase			       =		flatten_dict(_a    ,   sep='''.'''     )
    UpperCAmelCase			       =		pt_model.state_dict()

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

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

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

        if "time_embedding" not in flax_key_tuple_array:
            for i, flax_key_tuple_string in enumerate(_a     ):
                UpperCAmelCase			       =		(
                    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'''     )
                )

        UpperCAmelCase			       =		'''.'''.join(_a     )

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

    pt_model.load_state_dict(_a     )

    # re-transform missing_keys to list
    UpperCAmelCase			       =		list(_a     )

    if len(_a     ) > 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(_a     ) > 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 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


A     =logging.getLogger(__name__)





def 						snake_case_							(_a	:							Dict    ,   _a	:							Union[str, Any]     ):
    return (preds == labels).mean()


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






@dataclass
class 			_a  :
       __a				:     str   				=					field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(processors.keys() )} )
       __a				:     str   				=					field(metadata={"""help""": """Should contain the data files for the task."""} )
       __a				:     int   				=					field(
           default=128							,							metadata={
               """help""": (
                   """The maximum total input sequence length after tokenization. Sequences longer """
                   """than this will be truncated, sequences shorter will be padded."""
               )
           }							,							)
       __a				:     bool   				=					field(
           default=__a							,							metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )






def 						snake_case_							():
    # 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.

    UpperCAmelCase			       =		HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)     )
    UpperCAmelCase       ,     UpperCAmelCase       ,     UpperCAmelCase			       =		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'''    ,   _a     )

    # Set seed
    set_seed(training_args.seed     )

    try:
        UpperCAmelCase			       =		processors[data_args.task_name]()
        UpperCAmelCase			       =		processor.get_labels()
        UpperCAmelCase			       =		len(_a     )
    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.

    UpperCAmelCase			       =		AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path    ,   num_labels=_a    ,   finetuning_task=data_args.task_name    ,   cache_dir=model_args.cache_dir    ,   )
    UpperCAmelCase			       =		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    ,   )
    UpperCAmelCase			       =		AutoModelForMultipleChoice.from_pretrained(
        model_args.model_name_or_path    ,   from_tf=bool('''.ckpt''' in model_args.model_name_or_path     )    ,   config=_a    ,   cache_dir=model_args.cache_dir    ,   )

    # Get datasets
    UpperCAmelCase			       =		(
        MultipleChoiceDataset(
            data_dir=data_args.data_dir    ,   tokenizer=_a    ,   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
    )
    UpperCAmelCase			       =		(
        MultipleChoiceDataset(
            data_dir=data_args.data_dir    ,   tokenizer=_a    ,   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(_a	:							EvalPrediction     ) -> Dict:
        UpperCAmelCase			       =		np.argmax(p.predictions    ,   axis=1     )
        return {"acc": simple_accuracy(_a    ,   p.label_ids     )}

    # Data collator
    UpperCAmelCase			       =		DataCollatorWithPadding(_a    ,   pad_to_multiple_of=8     ) if training_args.fpaa else None

    # Initialize our Trainer
    UpperCAmelCase			       =		Trainer(
        model=_a    ,   args=_a    ,   train_dataset=_a    ,   eval_dataset=_a    ,   compute_metrics=_a    ,   data_collator=_a    ,   )

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

        UpperCAmelCase			       =		trainer.evaluate()

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

                results.update(_a     )

    return results





def 						snake_case_							(_a	:							Optional[int]     ):
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
   main()




