




import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


UpperCAmelCase_		:				Optional[Any]									=     logging.get_logger(__name__)




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				__magic_name__	:	List[str]								=R"""\w+[.]\d+"""
				__magic_name__	:	Tuple								=re.findall(lowerCamelCase	,     lowerCamelCase   )
				for pat in pats:
								__magic_name__	:	Tuple								=key.replace(lowerCamelCase	,     """_""".join(pat.split("""."""   )   )   )
				return key




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   ):
				__magic_name__	:	Any								=pt_tuple_key[:-1] + ("""scale""",)
				if (
				    any("""norm""" in str_ for str_ in pt_tuple_key   )
				    and (pt_tuple_key[-1] == "bias")
				    and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
				    and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
				):
								__magic_name__	:	List[str]								=pt_tuple_key[:-1] + ("""scale""",)
								return renamed_pt_tuple_key, pt_tensor
				elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
								__magic_name__	:	int								=pt_tuple_key[:-1] + ("""scale""",)
								return renamed_pt_tuple_key, pt_tensor

				# embedding
				if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
								__magic_name__	:	Union[str, Any]								=pt_tuple_key[:-1] + ("""embedding""",)
								return renamed_pt_tuple_key, pt_tensor

				# conv layer
				__magic_name__	:	Any								=pt_tuple_key[:-1] + ("""kernel""",)
				if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
								__magic_name__	:	Optional[int]								=pt_tensor.transpose(2	,     3	,     1	,     0   )
								return renamed_pt_tuple_key, pt_tensor

				# linear layer
				__magic_name__	:	Dict								=pt_tuple_key[:-1] + ("""kernel""",)
				if pt_tuple_key[-1] == "weight":
								__magic_name__	:	int								=pt_tensor.T
								return renamed_pt_tuple_key, pt_tensor

				# old PyTorch layer norm weight
				__magic_name__	:	Tuple								=pt_tuple_key[:-1] + ("""weight""",)
				if pt_tuple_key[-1] == "gamma":
								return renamed_pt_tuple_key, pt_tensor

				# old PyTorch layer norm bias
				__magic_name__	:	Any								=pt_tuple_key[:-1] + ("""bias""",)
				if pt_tuple_key[-1] == "beta":
								return renamed_pt_tuple_key, pt_tensor

				return pt_tuple_key, pt_tensor




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase=42   ):
				# Step 1: Convert pytorch tensor to numpy
				__magic_name__	:	Any								={k: v.numpy() for k, v in pt_state_dict.items()}

				# Step 2: Since the model is stateless, get random Flax params
				__magic_name__	:	Any								=flax_model.init_weights(PRNGKey(lowerCamelCase   )   )

				__magic_name__	:	Dict								=flatten_dict(lowerCamelCase   )
				__magic_name__	:	Dict								={}

				# Need to change some parameters name to match Flax names
				for pt_key, pt_tensor in pt_state_dict.items():
								__magic_name__	:	str								=rename_key(lowerCamelCase   )
								__magic_name__	:	Union[str, Any]								=tuple(renamed_pt_key.split("""."""   )   )

								# Correctly rename weight parameters
								__magic_name__	:	int								=rename_key_and_reshape_tensor(lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   )

								if flax_key in random_flax_state_dict:
												if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
																raise ValueError(
																    F"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
																    F"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."   )

        # also add unexpected weight so that warning is thrown
								__magic_name__	:	Optional[int]								=jnp.asarray(lowerCamelCase   )

				return unflatten_dict(lowerCamelCase   )










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

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase = None	,     ):
				__magic_name__	:	Optional[int]								={}

				if train_file is not None:
								__magic_name__	:	Optional[int]								=[train_file]
				if eval_file is not None:
								__magic_name__	:	Any								=[eval_file]
				if test_file is not None:
								__magic_name__	:	int								=[test_file]

				__magic_name__	:	Any								=datasets.load_dataset("""csv"""	,     data_files=lowerCamelCase   )
				__magic_name__	:	Optional[Any]								=list(ds[list(files.keys()   )[0]].features.keys()   )
				__magic_name__	:	Optional[Any]								=features_name.pop(lowerCamelCase   )
				__magic_name__	:	str								=list(set(ds[list(files.keys()   )[0]][label_name]   )   )
				__magic_name__	:	Union[str, Any]								={label: i for i, label in enumerate(lowerCamelCase   )}
				__magic_name__	:	Dict								=tokenizer.model_input_names
				__magic_name__	:	Any								={}

				if len(lowerCamelCase   ) == 1:
								for k in files.keys():
												__magic_name__	:	Dict								=ds[k].map(
												    lambda lowerCamelCase   : tokenizer.batch_encode_plus(
												        example[features_name[0]]	,     truncation=lowerCamelCase	,     max_length=lowerCamelCase	,     padding="""max_length"""   )	,     batched=lowerCamelCase	,     )
				elif len(lowerCamelCase   ) == 2:
								for k in files.keys():
												__magic_name__	:	Optional[Any]								=ds[k].map(
												    lambda lowerCamelCase   : tokenizer.batch_encode_plus(
												        (example[features_name[0]], example[features_name[1]])	,     truncation=lowerCamelCase	,     max_length=lowerCamelCase	,     padding="""max_length"""	,     )	,     batched=lowerCamelCase	,     )

				def gen_train():
								for ex in transformed_ds[datasets.Split.TRAIN]:
												__magic_name__	:	Any								={k: v for k, v in ex.items() if k in input_names}
												__magic_name__	:	Any								=labelaid[ex[label_name]]
												yield (d, label)

				def gen_val():
								for ex in transformed_ds[datasets.Split.VALIDATION]:
												__magic_name__	:	Dict								={k: v for k, v in ex.items() if k in input_names}
												__magic_name__	:	str								=labelaid[ex[label_name]]
												yield (d, label)

				def gen_test():
								for ex in transformed_ds[datasets.Split.TEST]:
												__magic_name__	:	Union[str, Any]								={k: v for k, v in ex.items() if k in input_names}
												__magic_name__	:	Optional[int]								=labelaid[ex[label_name]]
												yield (d, label)

				__magic_name__	:	Union[str, Any]								=(
				    tf.data.Dataset.from_generator(
				        lowerCamelCase	,     ({k: tf.intaa for k in input_names}, tf.intaa)	,     ({k: tf.TensorShape([None]   ) for k in input_names}, tf.TensorShape([]   ))	,     )
				    if datasets.Split.TRAIN in transformed_ds
				    else None
				)

				if train_ds is not None:
								__magic_name__	:	Optional[Any]								=train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]   )   )   )

				__magic_name__	:	Optional[Any]								=(
				    tf.data.Dataset.from_generator(
				        lowerCamelCase	,     ({k: tf.intaa for k in input_names}, tf.intaa)	,     ({k: tf.TensorShape([None]   ) for k in input_names}, tf.TensorShape([]   ))	,     )
				    if datasets.Split.VALIDATION in transformed_ds
				    else None
				)

				if val_ds is not None:
								__magic_name__	:	Any								=val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]   )   )   )

				__magic_name__	:	Any								=(
				    tf.data.Dataset.from_generator(
				        lowerCamelCase	,     ({k: tf.intaa for k in input_names}, tf.intaa)	,     ({k: tf.TensorShape([None]   ) for k in input_names}, tf.TensorShape([]   ))	,     )
				    if datasets.Split.TEST in transformed_ds
				    else None
				)

				if test_ds is not None:
								__magic_name__	:	Optional[int]								=test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]   )   )   )

				return train_ds, val_ds, test_ds, labelaid


UpperCAmelCase_		:				int									=     logging.getLogger(__name__)




@dataclass
class 		__A      :
						UpperCamelCase     						=					field(metadata={"""help""": """Which column contains the label"""}			)
						UpperCamelCase     						=					field(default=UpperCamelCase__					,						metadata={"""help""": """The path of the training file"""}			)
						UpperCamelCase     						=					field(default=UpperCamelCase__					,						metadata={"""help""": """The path of the development file"""}			)
						UpperCamelCase     						=					field(default=UpperCamelCase__					,						metadata={"""help""": """The path of the test file"""}			)
						UpperCamelCase     						=					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."""
						        )
						    }					,						)
						UpperCamelCase     						=					field(
						    default=UpperCamelCase__					,						metadata={"""help""": """Overwrite the cached training and evaluation sets"""}			)




@dataclass
class 		__A      :
						UpperCamelCase     						=					field(
						    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}			)
						UpperCamelCase     						=					field(
						    default=UpperCamelCase__					,						metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}			)
						UpperCamelCase     						=					field(
						    default=UpperCamelCase__					,						metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}			)
						UpperCamelCase     						=					field(default=UpperCamelCase__					,						metadata={"""help""": """Set this flag to use fast tokenization."""}			)
						# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
						# or just modify its tokenizer_config.json.
						UpperCamelCase     						=					field(
						    default=UpperCamelCase__					,						metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}					,						)




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.
				__magic_name__	:	List[Any]								=HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)   )
				__magic_name__				, __magic_name__				, __magic_name__	:	Union[str, 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	,     )
				logger.info(
				    F"n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1   )}, "
				    F"16-bits training: {training_args.fpaa}"   )
				logger.info(F"Training/evaluation parameters {training_args}"   )

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

				__magic_name__	:	Dict								=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	,     )

				__magic_name__				, __magic_name__				, __magic_name__				, __magic_name__	:	Optional[Any]								=get_tfds(
				    train_file=data_args.train_file	,     eval_file=data_args.dev_file	,     test_file=data_args.test_file	,     tokenizer=lowerCamelCase	,     label_column_id=data_args.label_column_id	,     max_seq_length=data_args.max_seq_length	,     )

				__magic_name__	:	Any								=AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path	,     num_labels=len(lowerCamelCase   )	,     labelaid=lowerCamelCase	,     idalabel={id: label for label, id in labelaid.items()}	,     finetuning_task="""text-classification"""	,     cache_dir=model_args.cache_dir	,     )

				with training_args.strategy.scope():
								__magic_name__	:	Any								=TFAutoModelForSequenceClassification.from_pretrained(
								    model_args.model_name_or_path	,     from_pt=bool(""".bin""" in model_args.model_name_or_path   )	,     config=lowerCamelCase	,     cache_dir=model_args.cache_dir	,     )

				def compute_metrics(lowerCamelCase   ) -> Dict:
								__magic_name__	:	Tuple								=np.argmax(p.predictions	,     axis=1   )

								return {"acc": (preds == p.label_ids).mean()}

				# Initialize our Trainer
				__magic_name__	:	int								=TFTrainer(
				    model=lowerCamelCase	,     args=lowerCamelCase	,     train_dataset=lowerCamelCase	,     eval_dataset=lowerCamelCase	,     compute_metrics=lowerCamelCase	,     )

				# Training
				if training_args.do_train:
								trainer.train()
								trainer.save_model()
								tokenizer.save_pretrained(training_args.output_dir   )

				# Evaluation
				__magic_name__	:	List[str]								={}
				if training_args.do_eval:
								logger.info("""*** Evaluate ***"""   )
								__magic_name__	:	List[str]								=trainer.evaluate()
								__magic_name__	:	Optional[Any]								=os.path.join(training_args.output_dir	,     """eval_results.txt"""   )

								with open(lowerCamelCase	,     """w"""   ) as writer:
												logger.info("""***** Eval results *****"""   )

												for key, value in result.items():
																logger.info(F"  {key} = {value}"   )
																writer.write(F"{key} = {value}\n"   )

												results.update(lowerCamelCase   )

				return results


if __name__ == "__main__":
							main()




