

import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser


def        __lowercase    (  UpperCAmelCase__=None		,					UpperCAmelCase__=None      ):

					"""simple docstring"""






					return field(default_factory=lambda: default		,					metadata=UpperCAmelCase__      )





@dataclass
class     snake_case_			:


							"""simple docstring"""





							__UpperCAmelCase       =field(
							    metadata={"""help""": """The csv file to plot."""}			, )
							__UpperCAmelCase       =field(
							    default=_a			, metadata={"""help""": """Whether to plot along batch size or sequence length. Defaults to sequence length."""}			, )
							__UpperCAmelCase       =field(
							    default=_a			, metadata={"""help""": """Whether the csv file has time results or memory results. Defaults to memory results."""}			, )
							__UpperCAmelCase       =field(
							    default=_a			, metadata={"""help""": """Disable logarithmic scale when plotting"""}			, )
							__UpperCAmelCase       =field(
							    default=_a			, metadata={
							        """help""": """Whether the csv file has training results or inference results. Defaults to inference results."""
							    }			, )
							__UpperCAmelCase       =field(
							    default=_a			, metadata={"""help""": """Filename under which the plot will be saved. If unused no plot is saved."""}			, )
							__UpperCAmelCase       =list_field(
							    default=_a			, metadata={"""help""": """List of model names that are used instead of the ones in the csv file."""}							)


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					try:
										int(UpperCAmelCase__      )
										return True
					except ValueError:
										return False


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					try:
										float(UpperCAmelCase__      )
										return True
					except ValueError:
										return False





class     snake_case_			:


							"""simple docstring"""





							def __init__( self					,						_A							):
												__lowerCAmelCase					=							args
												__lowerCAmelCase					=							defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}							)

												with open(self.args.csv_file					,						newline=''							) as csv_file:
																	__lowerCAmelCase					=							csv.DictReader(_A							)
																	for row in reader:
																						__lowerCAmelCase					=							row['model']
																						self.result_dict[model_name]["bsz"].append(int(row['batch_size']							)							)
																						self.result_dict[model_name]["seq_len"].append(int(row['sequence_length']							)							)
																						if can_convert_to_int(row['result']							):
																											# value is not None
																											__lowerCAmelCase					=							int(row['result']							)
																						elif can_convert_to_float(row['result']							):
																											# value is not None
																											__lowerCAmelCase					=							float(row['result']							)




							def        A__  ( self							):
												__lowerCAmelCase,	__lowerCAmelCase					=							plt.subplots()
												__lowerCAmelCase					=							'Time usage' if self.args.is_time else 'Memory usage'
												__lowerCAmelCase					=							title_str + ' for training' if self.args.is_train else title_str + ' for inference'

												if not self.args.no_log_scale:
																	# set logarithm scales
																	ax.set_xscale('log'							)
																	ax.set_yscale('log'							)

												for axis in [ax.xaxis, ax.yaxis]:
																	axis.set_major_formatter(ScalarFormatter()							)

												for model_name_idx, model_name in enumerate(self.result_dict.keys()							):
																	__lowerCAmelCase					=							sorted(set(self.result_dict[model_name]['bsz']							)							)
																	__lowerCAmelCase					=							sorted(set(self.result_dict[model_name]['seq_len']							)							)
																	__lowerCAmelCase					=							self.result_dict[model_name]['result']

																	((__lowerCAmelCase),	(__lowerCAmelCase))					=							(
																	    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
																	)

																	__lowerCAmelCase					=							(
																	    model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
																	)

																	for inner_loop_value in inner_loop_array:
																						if self.args.plot_along_batch:
																											__lowerCAmelCase					=							np.asarray(
																											    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]					,						dtype=_A					,						)
																						else:
																											__lowerCAmelCase					=							np.asarray(
																											    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]					,						dtype=np.floataa					,						)

																						((__lowerCAmelCase),	(__lowerCAmelCase))					=							(
																						    ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
																						)

																						__lowerCAmelCase					=							np.asarray(_A					,						_A							)[: len(_A							)]
																						plt.scatter(
																						    _A					,						_A					,						label=F"""{label_model_name} - {inner_loop_label}: {inner_loop_value}"""							)
																						plt.plot(_A					,						_A					,						'--'							)

																	title_str += F""" {label_model_name} vs."""

												__lowerCAmelCase					=							title_str[:-4]
												__lowerCAmelCase					=							'Time in s' if self.args.is_time else 'Memory in MB'

												# plot
												plt.title(_A							)
												plt.xlabel(_A							)
												plt.ylabel(_A							)
												plt.legend()

												if self.args.figure_png_file is not None:
																	plt.savefig(self.args.figure_png_file							)
												else:
																	plt.show()


def        __lowercase    (  ):

					"""simple docstring"""






					__lowerCAmelCase					=							HfArgumentParser(UpperCAmelCase__      )
					__lowerCAmelCase					=							parser.parse_args_into_dataclasses()[0]
					__lowerCAmelCase					=							Plot(args=UpperCAmelCase__      )
					plot.plot()


if __name__ == "__main__":
		main()









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        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__ = None		,					):

					"""simple docstring"""






					__lowerCAmelCase					=							{}

					if train_file is not None:
										__lowerCAmelCase					=							[train_file]
					if eval_file is not None:
										__lowerCAmelCase					=							[eval_file]
					if test_file is not None:
										__lowerCAmelCase					=							[test_file]

					__lowerCAmelCase					=							datasets.load_dataset('csv'		,					data_files=UpperCAmelCase__      )
					__lowerCAmelCase					=							list(ds[list(files.keys()      )[0]].features.keys()      )
					__lowerCAmelCase					=							features_name.pop(UpperCAmelCase__      )
					__lowerCAmelCase					=							list(set(ds[list(files.keys()      )[0]][label_name]      )      )
					__lowerCAmelCase					=							{label: i for i, label in enumerate(UpperCAmelCase__      )}
					__lowerCAmelCase					=							tokenizer.model_input_names
					__lowerCAmelCase					=							{}

					if len(UpperCAmelCase__      ) == 1:
										for k in files.keys():
															__lowerCAmelCase					=							ds[k].map(
															    lambda UpperCAmelCase__      : tokenizer.batch_encode_plus(
															        example[features_name[0]]		,					truncation=UpperCAmelCase__		,					max_length=UpperCAmelCase__		,					padding='max_length'      )		,					batched=UpperCAmelCase__		,					)
					elif len(UpperCAmelCase__      ) == 2:
										for k in files.keys():
															__lowerCAmelCase					=							ds[k].map(
															    lambda UpperCAmelCase__      : tokenizer.batch_encode_plus(
															        (example[features_name[0]], example[features_name[1]])		,					truncation=UpperCAmelCase__		,					max_length=UpperCAmelCase__		,					padding='max_length'		,					)		,					batched=UpperCAmelCase__		,					)

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

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

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

					__lowerCAmelCase					=							(
					    tf.data.Dataset.from_generator(
					        UpperCAmelCase__		,					({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:
										__lowerCAmelCase					=							train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]      )      )      )

					__lowerCAmelCase					=							(
					    tf.data.Dataset.from_generator(
					        UpperCAmelCase__		,					({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:
										__lowerCAmelCase					=							val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]      )      )      )

					__lowerCAmelCase					=							(
					    tf.data.Dataset.from_generator(
					        UpperCAmelCase__		,					({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:
										__lowerCAmelCase					=							test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]      )      )      )

					return train_ds, val_ds, test_ds, labelaid


lowerCamelCase      				=     logging.getLogger(__name__)





@dataclass
class     snake_case_			:


							"""simple docstring"""





							__UpperCAmelCase       =field(metadata={"""help""": """Which column contains the label"""}							)
							__UpperCAmelCase       =field(default=_a			, metadata={"""help""": """The path of the training file"""}							)
							__UpperCAmelCase       =field(default=_a			, metadata={"""help""": """The path of the development file"""}							)
							__UpperCAmelCase       =field(default=_a			, 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=_a			, metadata={"""help""": """Overwrite the cached training and evaluation sets"""}							)





@dataclass
class     snake_case_			:


							"""simple docstring"""





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


def        __lowercase    (  ):

					"""simple docstring"""






					__lowerCAmelCase					=							HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)      )
					__lowerCAmelCase,	__lowerCAmelCase,	__lowerCAmelCase					=							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.

					__lowerCAmelCase					=							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,	__lowerCAmelCase,	__lowerCAmelCase,	__lowerCAmelCase					=							get_tfds(
					    train_file=data_args.train_file		,					eval_file=data_args.dev_file		,					test_file=data_args.test_file		,					tokenizer=UpperCAmelCase__		,					label_column_id=data_args.label_column_id		,					max_seq_length=data_args.max_seq_length		,					)

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

					with training_args.strategy.scope():
										__lowerCAmelCase					=							TFAutoModelForSequenceClassification.from_pretrained(
										    model_args.model_name_or_path		,					from_pt=bool('.bin' in model_args.model_name_or_path      )		,					config=UpperCAmelCase__		,					cache_dir=model_args.cache_dir		,					)

					def compute_metrics(UpperCAmelCase__      ) -> Dict:
										__lowerCAmelCase					=							np.argmax(p.predictions		,					axis=1      )

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

					# Initialize our Trainer
					__lowerCAmelCase					=							TFTrainer(
					    model=UpperCAmelCase__		,					args=UpperCAmelCase__		,					train_dataset=UpperCAmelCase__		,					eval_dataset=UpperCAmelCase__		,					compute_metrics=UpperCAmelCase__		,					)

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

					# Evaluation
					__lowerCAmelCase					=							{}
					if training_args.do_eval:
										logger.info('*** Evaluate ***'      )
										__lowerCAmelCase					=							trainer.evaluate()
										__lowerCAmelCase					=							os.path.join(training_args.output_dir		,					'eval_results.txt'      )

										with open(UpperCAmelCase__		,					'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(UpperCAmelCase__      )

					return results


if __name__ == "__main__":
		main()






