






"""simple docstring"""
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 							snake_case (  A__      ,A__      ,A__      ,A__      ,A__      ,A__ = None      ,):
			UpperCAmelCase_	:					int     	=   {}

			if train_file is not None:
						UpperCAmelCase_	:					Tuple     	=   [train_file]
			if eval_file is not None:
						UpperCAmelCase_	:					Union[str, Any]     	=   [eval_file]
			if test_file is not None:
						UpperCAmelCase_	:					Tuple     	=   [test_file]

			UpperCAmelCase_	:					Any     	=   datasets.load_dataset("csv"      ,data_files=A__		)
			UpperCAmelCase_	:					Optional[int]     	=   list(ds[list(files.keys()		)[0]].features.keys()		)
			UpperCAmelCase_	:					List[str]     	=   features_name.pop(A__		)
			UpperCAmelCase_	:					Optional[Any]     	=   list(set(ds[list(files.keys()		)[0]][label_name]		)		)
			UpperCAmelCase_	:					int     	=   {label: i for i, label in enumerate(A__		)}
			UpperCAmelCase_	:					int     	=   tokenizer.model_input_names
			UpperCAmelCase_	:					Tuple     	=   {}

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

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

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

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

			UpperCAmelCase_	:					List[Any]     	=   (
			    tf.data.Dataset.from_generator(
			        A__      ,({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:
						UpperCAmelCase_	:					Optional[Any]     	=   train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]		)		)		)

			UpperCAmelCase_	:					Optional[Any]     	=   (
			    tf.data.Dataset.from_generator(
			        A__      ,({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:
						UpperCAmelCase_	:					Optional[Any]     	=   val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]		)		)		)

			UpperCAmelCase_	:					Dict     	=   (
			    tf.data.Dataset.from_generator(
			        A__      ,({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:
						UpperCAmelCase_	:					List[Any]     	=   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      UpperCamelCase_			:
	__magic_name__       =							field(metadata={'''help''': '''Which column contains the label'''}	)
	__magic_name__       =							field(default=__A							,		metadata={'''help''': '''The path of the training file'''}	)
	__magic_name__       =							field(default=__A							,		metadata={'''help''': '''The path of the development file'''}	)
	__magic_name__       =							field(default=__A							,		metadata={'''help''': '''The path of the test file'''}	)
	__magic_name__       =							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.'''
	        )
	    }							,		)
	__magic_name__       =							field(
	    default=__A							,		metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}	)







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





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_	:					Dict     	=   HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)		)
			UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	:					Optional[int]     	=   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.

			UpperCAmelCase_	:					Optional[Any]     	=   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_	,      UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	:					Tuple     	=   get_tfds(
			    train_file=data_args.train_file      ,eval_file=data_args.dev_file      ,test_file=data_args.test_file      ,tokenizer=A__      ,label_column_id=data_args.label_column_id      ,max_seq_length=data_args.max_seq_length      ,)

			UpperCAmelCase_	:					Optional[int]     	=   AutoConfig.from_pretrained(
			    model_args.config_name if model_args.config_name else model_args.model_name_or_path      ,num_labels=len(A__		)      ,labelaid=A__      ,idalabel={id: label for label, id in labelaid.items()}      ,finetuning_task="text-classification"      ,cache_dir=model_args.cache_dir      ,)

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

			def compute_metrics(A__		) -> Dict:
						UpperCAmelCase_	:					Any     	=   np.argmax(p.predictions      ,axis=1		)

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

			# Initialize our Trainer
			UpperCAmelCase_	:					Optional[int]     	=   TFTrainer(
			    model=A__      ,args=A__      ,train_dataset=A__      ,eval_dataset=A__      ,compute_metrics=A__      ,)

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

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

						with open(A__      ,"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(A__		)

			return results


if __name__ == "__main__":
			main()








from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


lowercase_													=      logging.get_logger(__name__)

lowercase_													=      {
    '''EleutherAI/gpt-neo-1.3B''': '''https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json''',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}






class     __a			(  SCREAMING_SNAKE_CASE					):
	SCREAMING_SNAKE_CASE  	=							"gpt_neo"
	SCREAMING_SNAKE_CASE  	=							["past_key_values"]
	SCREAMING_SNAKE_CASE  	=							{"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}



	def __init__(    self				:     Optional[int]					,						snake_case_				:     int=5_02_57					,						snake_case_				:     Tuple=20_48					,						snake_case_				:     Optional[int]=20_48					,						snake_case_				:     int=24					,						snake_case_				:     List[Any]=[[["global", "local"], 12]]					,						snake_case_				:     Tuple=16					,						snake_case_				:     Optional[int]=None					,						snake_case_				:     Dict=2_56					,						snake_case_				:     List[str]="gelu_new"					,						snake_case_				:     List[Any]=0.0					,						snake_case_				:     Tuple=0.0					,						snake_case_				:     Tuple=0.0					,						snake_case_				:     str=0.1					,						snake_case_				:     Union[str, Any]=1e-5					,						snake_case_				:     str=0.0_2					,						snake_case_				:     Union[str, Any]=True					,						snake_case_				:     Optional[int]=5_02_56					,						snake_case_				:     Tuple=5_02_56					,						**snake_case_				:     Optional[int]					,						)->				Tuple:
						__lowerCAmelCase						 =vocab_size
						__lowerCAmelCase						 =max_position_embeddings
						__lowerCAmelCase						 =hidden_size
						__lowerCAmelCase						 =num_layers
						__lowerCAmelCase						 =num_heads
						__lowerCAmelCase						 =intermediate_size
						__lowerCAmelCase						 =window_size
						__lowerCAmelCase						 =activation_function
						__lowerCAmelCase						 =resid_dropout
						__lowerCAmelCase						 =embed_dropout
						__lowerCAmelCase						 =attention_dropout
						__lowerCAmelCase						 =classifier_dropout
						__lowerCAmelCase						 =layer_norm_epsilon
						__lowerCAmelCase						 =initializer_range
						__lowerCAmelCase						 =use_cache

						__lowerCAmelCase						 =bos_token_id
						__lowerCAmelCase						 =eos_token_id

						__lowerCAmelCase						 =attention_types
						__lowerCAmelCase						 =self.expand_attention_types_params(snake_case_)

						if len(self.attention_layers) != self.num_layers:
											raise ValueError(
											    """Configuration for convolutional module is incorrect. """
											    """It is required that `len(config.attention_layers)` == `config.num_layers` """
											    F"""but is `len(config.attention_layers) = {len(self.attention_layers)}`, """
											    F"""`config.num_layers = {self.num_layers}`. """
											    """`config.attention_layers` is prepared using `config.attention_types`. """
											    """Please verify the value of `config.attention_types` argument.""")

						super().__init__(bos_token_id=snake_case_					,						eos_token_id=snake_case_					,						**snake_case_)



	@staticmethod
	def 		UpperCamelCase				(    snake_case_				:     Optional[int])->				Any:
						__lowerCAmelCase						 =[]
						for item in attention_types:
											for _ in range(item[1]):
																attentions.extend(item[0])
						return attentions






def        __lowerCAmelCase		(	__lowerCamelCase						:     Any					, __lowerCamelCase						:     List[str]					, __lowerCamelCase						:     Optional[Any]					, __lowerCamelCase						:     int						)	->    int:
					import torch

					__lowerCAmelCase						 =input.size()
					__lowerCAmelCase						 =len(__lowerCamelCase						)
					__lowerCAmelCase						 =shape[dimension]

					__lowerCAmelCase						 =torch.arange(0					, __lowerCamelCase					, __lowerCamelCase						)
					__lowerCAmelCase						 =torch.div(sizedim - size					, __lowerCamelCase					, rounding_mode="""floor"""						) + 1
					__lowerCAmelCase						 =torch.arange(__lowerCamelCase						) + low_indices[:min_length][:, None]

					__lowerCAmelCase						 =[slice(__lowerCamelCase						)] * rank
					__lowerCAmelCase						 =indices
					__lowerCAmelCase						 =input[s]

					__lowerCAmelCase						 =list(range(0					, rank + 1						)						)
					perm.append(perm.pop(dimension + 1						)						)

					return sliced.permute(__lowerCamelCase						)






def        __lowerCAmelCase		(	__lowerCamelCase						:     Optional[Any]					, __lowerCamelCase						:     Any						)	->    List[str]:
					import torch

					__lowerCAmelCase						 =torch.arange(1					, __lowerCamelCase						)
					__lowerCAmelCase						 =torch.remainder(__lowerCamelCase					, __lowerCamelCase						)
					__lowerCAmelCase						 =remainders == 0
					__lowerCAmelCase						 =candidates[divisor_indices]
					__lowerCAmelCase						 =torch.max(__lowerCamelCase						)
					return largest_divisor, torch.div(__lowerCamelCase					, __lowerCamelCase					, rounding_mode="""floor"""						)






class     __a			(  SCREAMING_SNAKE_CASE					):



	@property
	def 		UpperCamelCase				(    self				:     Union[str, Any])->				Mapping[str, Mapping[int, str]]:
						__lowerCAmelCase						 =OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}})
						if self.use_past:
											self.fill_with_past_key_values_(snake_case_					,						direction="""inputs""")
											__lowerCAmelCase						 ={0: """batch""", 1: """past_sequence + sequence"""}
						else:
											__lowerCAmelCase						 ={0: """batch""", 1: """sequence"""}

						return common_inputs



	@property
	def 		UpperCamelCase				(    self				:     Optional[int])->				int:
						return self._config.num_heads



	def 		UpperCamelCase				(    self				:     int					,						snake_case_				:     PreTrainedTokenizer					,						snake_case_				:     int = -1					,						snake_case_				:     int = -1					,						snake_case_				:     bool = False					,						snake_case_				:     Optional[TensorType] = None					,						)->				Mapping[str, Any]:
						__lowerCAmelCase						 =super(snake_case_					,						self).generate_dummy_inputs(
						    snake_case_					,						batch_size=snake_case_					,						seq_length=snake_case_					,						is_pair=snake_case_					,						framework=snake_case_)

						# We need to order the input in the way they appears in the forward()
						__lowerCAmelCase						 =OrderedDict({"""input_ids""": common_inputs["""input_ids"""]})

						# Need to add the past_keys
						if self.use_past:
											if not is_torch_available():
																raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""")
											else:
																import torch

																__lowerCAmelCase ,						__lowerCAmelCase						 =common_inputs["""input_ids"""].shape
																# Not using the same length for past_key_values
																__lowerCAmelCase						 =seqlen + 2
																__lowerCAmelCase						 =(
																    batch,
																    self.num_attention_heads,
																    past_key_values_length,
																    self._config.hidden_size // self.num_attention_heads,
																)
																__lowerCAmelCase						 =[
																    (torch.zeros(snake_case_), torch.zeros(snake_case_)) for _ in range(self.num_layers)
																]

						__lowerCAmelCase						 =common_inputs["""attention_mask"""]
						if self.use_past:
											__lowerCAmelCase						 =ordered_inputs["""attention_mask"""].dtype
											__lowerCAmelCase						 =torch.cat(
											    [ordered_inputs["""attention_mask"""], torch.ones(snake_case_					,						snake_case_					,						dtype=snake_case_)]					,						dim=1)

						return ordered_inputs



	@property
	def 		UpperCamelCase				(    self				:     Union[str, Any])->				int:
						return 13




