



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     a(      A       :    str ,     A       :    str ,     A       :    str ,     A       :    PreTrainedTokenizer ,     A       :    int ,     A       :    Optional[int] = None ,     )		->       int:
					"""simple docstring"""





					a					     =   {}

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

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

					if len(A       ) == 1:
										for k in files.keys():
															a					     =   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():
															a					     =   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]:
															a					     =   {k: v for k, v in ex.items() if k in input_names}
															a					     =   labelaid[ex[label_name]]
															yield (d, label)

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

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

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

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

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

					return train_ds, val_ds, test_ds, labelaid


_lowercase: str      			=		logging.getLogger(__name__)



@dataclass
class 	_lowercase							:





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




@dataclass
class 	_lowercase							:





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






def     a(      )		->       Dict:
					"""simple docstring"""





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

					a					     =   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 ,     )

					a     ,				a     ,				a     ,				a					     =   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 ,     )

					a					     =   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():
										a					     =   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       :    EvalPrediction       ) -> Dict:
										a					     =   np.argmax(p.predictions ,     axis=1       )

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

					# Initialize our Trainer
					a					     =   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
					a					     =   {}
					if training_args.do_eval:
										logger.info("*** Evaluate ***"       )
										a					     =   trainer.evaluate()
										a					     =   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 typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
				from PIL import Image

				from ..image_utils import load_image

if is_tf_available():
				from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
				import torch

				from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

_lowercase: Union[str, Any]      			=		logging.get_logger(__name__)



@add_end_docstrings(lowerCAmelCase	)
class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	def __init__(self		,       *lowerCamelCase_		,       **lowerCamelCase_		):
						"""simple docstring"""

						super().__init__(*lowerCamelCase_		,       **lowerCamelCase_		)
						requires_backends(self		,       "vision"		)
						self.check_model_type(
						    TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == "tf" else MODEL_FOR_VISION_2_SEQ_MAPPING		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_=None		,       lowerCamelCase_=None		,       lowerCamelCase_=None		):
						"""simple docstring"""

						a					     =   {}
						a					     =   {}

						if prompt is not None:
											a					     =   prompt

						if generate_kwargs is not None:
											a					     =   generate_kwargs
						if max_new_tokens is not None:
											if "generate_kwargs" not in forward_kwargs:
																a					     =   {}
											if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
																raise ValueError(
																    "'max_new_tokens' is defined twice, once in 'generate_kwargs' and once as a direct parameter,"
																    " please use only one"		)
											a					     =   max_new_tokens
						return preprocess_params, forward_kwargs, {}




	def __call__(self		,       lowerCamelCase_		,       **lowerCamelCase_		):
						"""simple docstring"""

						return super().__call__(lowerCamelCase_		,       **lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=None		):
						"""simple docstring"""

						a					     =   load_image(lowerCamelCase_		)

						if prompt is not None:
											if not isinstance(lowerCamelCase_		,       lowerCamelCase_		):
																raise ValueError(
																    F'''Received an invalid text input, got - {type(lowerCamelCase_		)} - but expected a single string. '''
																    "Note also that one single text can be provided for conditional image to text generation."		)

											a					     =   self.model.config.model_type

											if model_type == "git":
																a					     =   self.image_processor(images=lowerCamelCase_		,       return_tensors=self.framework		)
																a					     =   self.tokenizer(text=lowerCamelCase_		,       add_special_tokens=lowerCamelCase_		).input_ids
																a					     =   [self.tokenizer.cls_token_id] + input_ids
																a					     =   torch.tensor(lowerCamelCase_		).unsqueeze(0		)
																model_inputs.update({"input_ids": input_ids}		)

											elif model_type == "pix2struct":
																a					     =   self.image_processor(images=lowerCamelCase_		,       header_text=lowerCamelCase_		,       return_tensors=self.framework		)

											elif model_type != "vision-encoder-decoder":
																# vision-encoder-decoder does not support conditional generation
																a					     =   self.image_processor(images=lowerCamelCase_		,       return_tensors=self.framework		)
																a					     =   self.tokenizer(lowerCamelCase_		,       return_tensors=self.framework		)
																model_inputs.update(lowerCamelCase_		)

											else:
																raise ValueError(F'''Model type {model_type} does not support conditional text generation'''		)

						else:
											a					     =   self.image_processor(images=lowerCamelCase_		,       return_tensors=self.framework		)

						if self.model.config.model_type == "git" and prompt is None:
											a					     =   None

						return model_inputs




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=None		):
						"""simple docstring"""

						if (
						    "input_ids" in model_inputs
						    and isinstance(model_inputs["input_ids"]		,       lowerCamelCase_		)
						    and all(x is None for x in model_inputs["input_ids"]		)
						):
											a					     =   None

						if generate_kwargs is None:
											a					     =   {}
						# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
						#  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
						#  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
						#  in the `_prepare_model_inputs` method.
						a					     =   model_inputs.pop(self.model.main_input_name		)
						a					     =   self.model.generate(lowerCamelCase_		,       **lowerCamelCase_		,       **lowerCamelCase_		)
						return model_outputs




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   []
						for output_ids in model_outputs:
											a					     =   {
											    "generated_text": self.tokenizer.decode(
											        lowerCamelCase_		,       skip_special_tokens=lowerCamelCase_		,       )
											}
											records.append(lowerCamelCase_		)
						return records
