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		(__lowercase   ,     __lowercase   ,     __lowercase   ,     __lowercase   ,     __lowercase   ,     __lowercase = None   ,     )			->			Dict:

						'''simple docstring'''



						_snake_case		:	int							=       {}

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

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

						if len(A__		) == 1:
												for k in files.keys():
																		_snake_case		:	Any							=       ds[k].map(
																		    lambda __lowercase		: 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():
																		_snake_case		:	Dict							=       ds[k].map(
																		    lambda __lowercase		: 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]:
																		_snake_case		:	Union[str, Any]							=       {k: v for k, v in ex.items() if k in input_names}
																		_snake_case		:	Any							=       labelaid[ex[label_name]]
																		yield (d, label)

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

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

						_snake_case		:	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:
												_snake_case		:	Optional[Any]							=       train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]		)		)		)

						_snake_case		:	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:
												_snake_case		:	Optional[Any]							=       val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]		)		)		)

						_snake_case		:	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:
												_snake_case		:	List[Any]							=       test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]		)		)		)

						return train_ds, val_ds, test_ds, labelaid


__SCREAMING_SNAKE_CASE		:       int      =					logging.getLogger(__name__)



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




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




def    snake_case		()			->			Union[str, Any]:

						'''simple docstring'''



						_snake_case		:	Dict							=       HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)		)
						_snake_case		:	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.

						_snake_case		:	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   ,     )

						_snake_case		:	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   ,     )

						_snake_case		:	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():
												_snake_case		:	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(__lowercase		) -> Dict:
												_snake_case		:	Any							=       np.argmax(p.predictions   ,     axis=1		)

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

						# Initialize our Trainer
						_snake_case		:	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
						_snake_case		:	List[str]							=       {}
						if training_args.do_eval:
												logger.info("*** Evaluate ***"		)
												_snake_case		:	str							=       trainer.evaluate()
												_snake_case		:	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()







"""simple docstring"""
import enum
import warnings

from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
			import tensorflow as tf







class      UpperCamelCase_			(enum.Enum	):
	__magic_name__       =							0
	__magic_name__       =							1
	__magic_name__       =							2







@add_end_docstrings(__A	)
class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''
    In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria) are discovered. The
    voice of Nicholas\'s young son, Tsarevich Alexei Nikolaevich, narrates the remainder of the story. 1883 Western
    Siberia, a young Grigori Rasputin is asked by his father and a group of men to perform magic. Rasputin has a vision
    and denounces one of the men as a horse thief. Although his father initially slaps him for making such an
    accusation, Rasputin watches as the man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
    the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, with people, even a bishop,
    begging for his blessing. <eod> </s> <eos>
    '''





	def __init__( self   :					List[Any]      ,      *lowerCAmelCase_   :					List[str]      ,      **lowerCAmelCase_   :					List[Any]		)		->     Optional[int]:
				super().__init__(*lowerCAmelCase_      ,      **lowerCAmelCase_		)
				self.check_model_type(
				    TF_MODEL_FOR_CAUSAL_LM_MAPPING if self.framework == "tf" else MODEL_FOR_CAUSAL_LM_MAPPING		)
				if "prefix" not in self._preprocess_params:
							# This is very specific. The logic is quite complex and needs to be done
							# as a "default".
							# It also defines both some preprocess_kwargs and generate_kwargs
							# which is why we cannot put them in their respective methods.
							UpperCAmelCase_	:					Any     	=   None
							if self.model.config.prefix is not None:
										UpperCAmelCase_	:					Any     	=   self.model.config.prefix
							if prefix is None and self.model.__class__.__name__ in [
							    "XLNetLMHeadModel",
							    "TransfoXLLMHeadModel",
							    "TFXLNetLMHeadModel",
							    "TFTransfoXLLMHeadModel",
							]:
										# For XLNet and TransformerXL we add an article to the prompt to give more state to the model.
										UpperCAmelCase_	:					Optional[int]     	=   self.XL_PREFIX
							if prefix is not None:
										# Recalculate some generate_kwargs linked to prefix.
										UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	:					List[str]     	=   self._sanitize_parameters(prefix=lowerCAmelCase_      ,      **self._forward_params		)
										UpperCAmelCase_	:					List[Any]     	=   {**self._preprocess_params, **preprocess_params}
										UpperCAmelCase_	:					Optional[int]     	=   {**self._forward_params, **forward_params}





	def 	_SCREAMING_SNAKE_CASE ( self   :					str      ,      lowerCAmelCase_   :					int=None      ,      lowerCAmelCase_   :					Optional[Any]=None      ,      lowerCAmelCase_   :					int=None      ,      lowerCAmelCase_   :					Optional[int]=None      ,      lowerCAmelCase_   :					List[Any]=None      ,      lowerCAmelCase_   :					Dict=None      ,      lowerCAmelCase_   :					Tuple=None      ,      lowerCAmelCase_   :					List[str]=None      ,      **lowerCAmelCase_   :					Optional[Any]      ,      )		->     int:
				UpperCAmelCase_	:					Union[str, Any]     	=   {}
				if prefix is not None:
							UpperCAmelCase_	:					Tuple     	=   prefix
				if prefix:
							UpperCAmelCase_	:					Optional[Any]     	=   self.tokenizer(
							    lowerCAmelCase_      ,      padding=lowerCAmelCase_      ,      add_special_tokens=lowerCAmelCase_      ,      return_tensors=self.framework		)
							UpperCAmelCase_	:					List[str]     	=   prefix_inputs["input_ids"].shape[-1]

				if handle_long_generation is not None:
							if handle_long_generation not in {"hole"}:
										raise ValueError(
										    f"""{handle_long_generation} is not a valid value for `handle_long_generation` parameter expected"""
										    " [None, 'hole']"		)
							UpperCAmelCase_	:					Dict     	=   handle_long_generation

				preprocess_params.update(lowerCAmelCase_		)
				UpperCAmelCase_	:					Dict     	=   generate_kwargs

				UpperCAmelCase_	:					Dict     	=   {}
				if return_full_text is not None and return_type is None:
							if return_text is not None:
										raise ValueError("`return_text` is mutually exclusive with `return_full_text`"		)
							if return_tensors is not None:
										raise ValueError("`return_full_text` is mutually exclusive with `return_tensors`"		)
							UpperCAmelCase_	:					Tuple     	=   ReturnType.FULL_TEXT if return_full_text else ReturnType.NEW_TEXT
				if return_tensors is not None and return_type is None:
							if return_text is not None:
										raise ValueError("`return_text` is mutually exclusive with `return_tensors`"		)
							UpperCAmelCase_	:					int     	=   ReturnType.TENSORS
				if return_type is not None:
							UpperCAmelCase_	:					int     	=   return_type
				if clean_up_tokenization_spaces is not None:
							UpperCAmelCase_	:					Tuple     	=   clean_up_tokenization_spaces

				if stop_sequence is not None:
							UpperCAmelCase_	:					Union[str, Any]     	=   self.tokenizer.encode(lowerCAmelCase_      ,      add_special_tokens=lowerCAmelCase_		)
							if len(lowerCAmelCase_		) > 1:
										warnings.warn(
										    "Stopping on a multiple token sequence is not yet supported on transformers. The first token of"
										    " the stop sequence will be used as the stop sequence string in the interim."		)
							UpperCAmelCase_	:					Optional[int]     	=   stop_sequence_ids[0]

				return preprocess_params, forward_params, postprocess_params





	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]      ,      *lowerCAmelCase_   :					Dict      ,      **lowerCAmelCase_   :					Dict		)		->     Union[str, Any]:
				# Parse arguments
				if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
							kwargs.update({"add_space_before_punct_symbol": True}		)

				return super()._parse_and_tokenize(*lowerCAmelCase_      ,      **lowerCAmelCase_		)





	def __call__( self   :					List[Any]      ,      lowerCAmelCase_   :					Any      ,      **lowerCAmelCase_   :					Union[str, Any]		)		->     Dict:
				return super().__call__(lowerCAmelCase_      ,      **lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]      ,      lowerCAmelCase_   :					Dict      ,      lowerCAmelCase_   :					List[str]=""      ,      lowerCAmelCase_   :					Any=None      ,      **lowerCAmelCase_   :					Optional[Any]		)		->     Dict:
				UpperCAmelCase_	:					Tuple     	=   self.tokenizer(
				    prefix + prompt_text      ,      padding=lowerCAmelCase_      ,      add_special_tokens=lowerCAmelCase_      ,      return_tensors=self.framework		)
				UpperCAmelCase_	:					Any     	=   prompt_text

				if handle_long_generation == "hole":
							UpperCAmelCase_	:					Optional[Any]     	=   inputs["input_ids"].shape[-1]
							if "max_new_tokens" in generate_kwargs:
										UpperCAmelCase_	:					Dict     	=   generate_kwargs["max_new_tokens"]
							else:
										UpperCAmelCase_	:					List[str]     	=   generate_kwargs.get("max_length"      ,      self.model.config.max_length		) - cur_len
										if new_tokens < 0:
													raise ValueError("We cannot infer how many new tokens are expected"		)
							if cur_len + new_tokens > self.tokenizer.model_max_length:
										UpperCAmelCase_	:					Tuple     	=   self.tokenizer.model_max_length - new_tokens
										if keep_length <= 0:
													raise ValueError(
													    "We cannot use `hole` to handle this generation the number of desired tokens exceeds the"
													    " models max length"		)

										UpperCAmelCase_	:					Dict     	=   inputs["input_ids"][:, -keep_length:]
										if "attention_mask" in inputs:
													UpperCAmelCase_	:					Union[str, Any]     	=   inputs["attention_mask"][:, -keep_length:]

				return inputs





	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]      ,      lowerCAmelCase_   :					Union[str, Any]      ,      **lowerCAmelCase_   :					str		)		->     Dict:
				UpperCAmelCase_	:					Optional[Any]     	=   model_inputs["input_ids"]
				UpperCAmelCase_	:					str     	=   model_inputs.get("attention_mask"      ,      lowerCAmelCase_		)
				# Allow empty prompts
				if input_ids.shape[1] == 0:
							UpperCAmelCase_	:					List[str]     	=   None
							UpperCAmelCase_	:					Union[str, Any]     	=   None
							UpperCAmelCase_	:					str     	=   1
				else:
							UpperCAmelCase_	:					Union[str, Any]     	=   input_ids.shape[0]
				UpperCAmelCase_	:					Any     	=   model_inputs.pop("prompt_text"		)

				# If there is a prefix, we may need to adjust the generation length. Do so without permanently modifying
				# generate_kwargs, as some of the parameterization may come from the initialization of the pipeline.
				UpperCAmelCase_	:					Any     	=   generate_kwargs.pop("prefix_length"      ,      0		)
				if prefix_length > 0:
							UpperCAmelCase_	:					str     	=   "max_new_tokens" in generate_kwargs or (
							    "generation_config" in generate_kwargs
							    and generate_kwargs["generation_config"].max_new_tokens is not None
							)
							if not has_max_new_tokens:
										UpperCAmelCase_	:					Tuple     	=   generate_kwargs.get("max_length"		) or self.model.config.max_length
										generate_kwargs["max_length"] += prefix_length
							UpperCAmelCase_	:					Optional[int]     	=   "min_new_tokens" in generate_kwargs or (
							    "generation_config" in generate_kwargs
							    and generate_kwargs["generation_config"].min_new_tokens is not None
							)
							if not has_min_new_tokens and "min_length" in generate_kwargs:
										generate_kwargs["min_length"] += prefix_length

        # BS x SL
				UpperCAmelCase_	:					int     	=   self.model.generate(input_ids=lowerCAmelCase_      ,      attention_mask=lowerCAmelCase_      ,      **lowerCAmelCase_		)
				UpperCAmelCase_	:					Dict     	=   generated_sequence.shape[0]
				if self.framework == "pt":
							UpperCAmelCase_	:					Optional[int]     	=   generated_sequence.reshape(lowerCAmelCase_      ,      out_b // in_b      ,      *generated_sequence.shape[1:]		)
				elif self.framework == "tf":
							UpperCAmelCase_	:					List[Any]     	=   tf.reshape(lowerCAmelCase_      ,      (in_b, out_b // in_b, *generated_sequence.shape[1:])		)
				return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]      ,      lowerCAmelCase_   :					Dict      ,      lowerCAmelCase_   :					List[str]=ReturnType.FULL_TEXT      ,      lowerCAmelCase_   :					Dict=True		)		->     List[str]:
				UpperCAmelCase_	:					List[Any]     	=   model_outputs["generated_sequence"][0]
				UpperCAmelCase_	:					int     	=   model_outputs["input_ids"]
				UpperCAmelCase_	:					List[str]     	=   model_outputs["prompt_text"]
				UpperCAmelCase_	:					Union[str, Any]     	=   generated_sequence.numpy().tolist()
				UpperCAmelCase_	:					int     	=   []
				for sequence in generated_sequence:
							if return_type == ReturnType.TENSORS:
										UpperCAmelCase_	:					Optional[Any]     	=   {"generated_token_ids": sequence}
							elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
										# Decode text
										UpperCAmelCase_	:					str     	=   self.tokenizer.decode(
										    lowerCAmelCase_      ,      skip_special_tokens=lowerCAmelCase_      ,      clean_up_tokenization_spaces=lowerCAmelCase_      ,      )

										# Remove PADDING prompt of the sequence if XLNet or Transfo-XL model is used
										if input_ids is None:
													UpperCAmelCase_	:					List[Any]     	=   0
										else:
													UpperCAmelCase_	:					str     	=   len(
													    self.tokenizer.decode(
													        input_ids[0]      ,      skip_special_tokens=lowerCAmelCase_      ,      clean_up_tokenization_spaces=lowerCAmelCase_      ,      )		)

										if return_type == ReturnType.FULL_TEXT:
													UpperCAmelCase_	:					Union[str, Any]     	=   prompt_text + text[prompt_length:]
										else:
													UpperCAmelCase_	:					Dict     	=   text[prompt_length:]

										UpperCAmelCase_	:					int     	=   {"generated_text": all_text}
							records.append(lowerCAmelCase_		)

				return records



