



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase		       =      {
    """configuration_instructblip""": [
        """INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """InstructBlipConfig""",
        """InstructBlipQFormerConfig""",
        """InstructBlipVisionConfig""",
    ],
    """processing_instructblip""": ["""InstructBlipProcessor"""],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase		       =      [
						    """INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """InstructBlipQFormerModel""",
						    """InstructBlipPreTrainedModel""",
						    """InstructBlipForConditionalGeneration""",
						    """InstructBlipVisionModel""",
						]

if TYPE_CHECKING:
						from .configuration_instructblip import (
						    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    InstructBlipConfig,
						    InstructBlipQFormerConfig,
						    InstructBlipVisionConfig,
						)
						from .processing_instructblip import InstructBlipProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_instructblip import (
												    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
												    InstructBlipForConditionalGeneration,
												    InstructBlipPreTrainedModel,
												    InstructBlipQFormerModel,
												    InstructBlipVisionModel,
												)

else:
						import sys

						lowerCamelCase		       =      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)







import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")

require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/text-classification/requirements.txt""")

lowerCamelCase		       =      logging.getLogger(__name__)


@dataclass
class 				_a						:





							'''simple docstring'''

							A				:Optional[int] =       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."
							        )
							    }       ,  )
							A				:bool =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={"help": "Overwrite the cached preprocessed datasets or not."}			)
							A				:bool =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={
							        "help": (
							            "Whether to pad all samples to `max_seq_length`. "
							            "If False, will pad the samples dynamically when batching to the maximum length in the batch."
							        )
							    }       ,  )
							A				:Optional[int] =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={
							        "help": (
							            "For debugging purposes or quicker training, truncate the number of training examples to this "
							            "value if set."
							        )
							    }       ,  )
							A				:Optional[int] =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={
							        "help": (
							            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
							            "value if set."
							        )
							    }       ,  )
							A				:Optional[int] =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={
							        "help": (
							            "For debugging purposes or quicker training, truncate the number of prediction examples to this "
							            "value if set."
							        )
							    }       ,  )





@dataclass
class 				_a						:





							'''simple docstring'''

							A				:str =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}			)
							A				:str =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={"help": "Evaluation language. Also train language if `train_language` is set to None."}			)
							A				:Optional[str] =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={"help": "Train language if it is different from the evaluation language."}			)
							A				:Optional[str] =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={"help": "Pretrained config name or path if not the same as model_name"}			)
							A				:Optional[str] =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}			)
							A				:Optional[str] =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}       ,  )
							A				:Optional[bool] =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={"help": "arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"}       ,  )
							A				:bool =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}       ,  )
							A				:str =       field(
							    default="main"       ,  metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}       ,  )
							A				:bool =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={
							        "help": (
							            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
							            "with private models)."
							        )
							    }       ,  )
							A				:bool =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."}       ,  )






def     SCREAMING_SNAKE_CASE(      )	->						int:
				# 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.

				a__	:							List[str]      						= HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
				a__						,   a__						,   a__	:							Optional[int]      						= parser.parse_args_into_dataclasses()

				# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
				# information sent is the one passed as arguments along with your Python/PyTorch versions.
				send_example_telemetry("run_xnli"	,				__UpperCamelCase )

				# Setup logging
				logging.basicConfig(
				    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"	,				datefmt="%m/%d/%Y %H:%M:%S"	,				handlers=[logging.StreamHandler(sys.stdout )]	,				)

				if training_args.should_log:
								# The default of training_args.log_level is passive, so we set log level at info here to have that default.
								transformers.utils.logging.set_verbosity_info()

				a__	:							int      						= training_args.get_process_log_level()
				logger.setLevel(__UpperCamelCase )
				datasets.utils.logging.set_verbosity(__UpperCamelCase )
				transformers.utils.logging.set_verbosity(__UpperCamelCase )
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()

				# Log on each process the small summary:
				logger.warning(
				    F'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
				    + F'distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}' )
				logger.info(F'Training/evaluation parameters {training_args}' )

				# Detecting last checkpoint.
				a__	:							Union[str, Any]      						= None
				if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
								a__	:							Dict      						= get_last_checkpoint(training_args.output_dir )
								if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
												raise ValueError(
												    F'Output directory ({training_args.output_dir}) already exists and is not empty. '
												    "Use --overwrite_output_dir to overcome." )
								elif last_checkpoint is not None:
												logger.info(
												    F'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
												    "the `--output_dir` or add `--overwrite_output_dir` to train from scratch." )

    # Set seed before initializing model.
				set_seed(training_args.seed )

				# In distributed training, the load_dataset function guarantees that only one local process can concurrently
				# download the dataset.
				# Downloading and loading xnli dataset from the hub.
				if training_args.do_train:
								if model_args.train_language is None:
												a__	:							Any      						= load_dataset(
												    "xnli"	,				model_args.language	,				split="train"	,				cache_dir=model_args.cache_dir	,				use_auth_token=True if model_args.use_auth_token else None	,				)
								else:
												a__	:							List[str]      						= load_dataset(
												    "xnli"	,				model_args.train_language	,				split="train"	,				cache_dir=model_args.cache_dir	,				use_auth_token=True if model_args.use_auth_token else None	,				)
								a__	:							List[Any]      						= train_dataset.features["label"].names

				if training_args.do_eval:
								a__	:							Tuple      						= load_dataset(
								    "xnli"	,				model_args.language	,				split="validation"	,				cache_dir=model_args.cache_dir	,				use_auth_token=True if model_args.use_auth_token else None	,				)
								a__	:							int      						= eval_dataset.features["label"].names

				if training_args.do_predict:
								a__	:							Dict      						= load_dataset(
								    "xnli"	,				model_args.language	,				split="test"	,				cache_dir=model_args.cache_dir	,				use_auth_token=True if model_args.use_auth_token else None	,				)
								a__	:							Optional[Any]      						= predict_dataset.features["label"].names

				# Labels
				a__	:							List[Any]      						= len(__UpperCamelCase )

				# Load pretrained model and tokenizer
				# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.
				a__	:							List[Any]      						= AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path	,				num_labels=__UpperCamelCase	,				idalabel={str(__UpperCamelCase ): label for i, label in enumerate(__UpperCamelCase )}	,				labelaid={label: i for i, label in enumerate(__UpperCamelCase )}	,				finetuning_task="xnli"	,				cache_dir=model_args.cache_dir	,				revision=model_args.model_revision	,				use_auth_token=True if model_args.use_auth_token else None	,				)
				a__	:							Union[str, Any]      						= AutoTokenizer.from_pretrained(
				    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path	,				do_lower_case=model_args.do_lower_case	,				cache_dir=model_args.cache_dir	,				use_fast=model_args.use_fast_tokenizer	,				revision=model_args.model_revision	,				use_auth_token=True if model_args.use_auth_token else None	,				)
				a__	:							Optional[int]      						= AutoModelForSequenceClassification.from_pretrained(
				    model_args.model_name_or_path	,				from_tf=bool(".ckpt" in model_args.model_name_or_path )	,				config=__UpperCamelCase	,				cache_dir=model_args.cache_dir	,				revision=model_args.model_revision	,				use_auth_token=True if model_args.use_auth_token else None	,				ignore_mismatched_sizes=model_args.ignore_mismatched_sizes	,				)

				# Preprocessing the datasets
				# Padding strategy
				if data_args.pad_to_max_length:
								a__	:							Dict      						= "max_length"
				else:
								# We will pad later, dynamically at batch creation, to the max sequence length in each batch
								a__	:							Optional[Any]      						= False

				def preprocess_function(__UpperCamelCase ):
								# Tokenize the texts
								return tokenizer(
								    examples["premise"]	,				examples["hypothesis"]	,				padding=__UpperCamelCase	,				max_length=data_args.max_seq_length	,				truncation=__UpperCamelCase	,				)

				if training_args.do_train:
								if data_args.max_train_samples is not None:
												a__	:							int      						= min(len(__UpperCamelCase )	,				data_args.max_train_samples )
												a__	:							Tuple      						= train_dataset.select(range(__UpperCamelCase ) )
								with training_args.main_process_first(desc="train dataset map pre-processing" ):
												a__	:							Optional[int]      						= train_dataset.map(
												    __UpperCamelCase	,				batched=__UpperCamelCase	,				load_from_cache_file=not data_args.overwrite_cache	,				desc="Running tokenizer on train dataset"	,				)
								# Log a few random samples from the training set:
								for index in random.sample(range(len(__UpperCamelCase ) )	,				3 ):
												logger.info(F'Sample {index} of the training set: {train_dataset[index]}.' )

				if training_args.do_eval:
								if data_args.max_eval_samples is not None:
												a__	:							Optional[int]      						= min(len(__UpperCamelCase )	,				data_args.max_eval_samples )
												a__	:							Optional[Any]      						= eval_dataset.select(range(__UpperCamelCase ) )
								with training_args.main_process_first(desc="validation dataset map pre-processing" ):
												a__	:							List[Any]      						= eval_dataset.map(
												    __UpperCamelCase	,				batched=__UpperCamelCase	,				load_from_cache_file=not data_args.overwrite_cache	,				desc="Running tokenizer on validation dataset"	,				)

				if training_args.do_predict:
								if data_args.max_predict_samples is not None:
												a__	:							int      						= min(len(__UpperCamelCase )	,				data_args.max_predict_samples )
												a__	:							List[str]      						= predict_dataset.select(range(__UpperCamelCase ) )
								with training_args.main_process_first(desc="prediction dataset map pre-processing" ):
												a__	:							Union[str, Any]      						= predict_dataset.map(
												    __UpperCamelCase	,				batched=__UpperCamelCase	,				load_from_cache_file=not data_args.overwrite_cache	,				desc="Running tokenizer on prediction dataset"	,				)

    # Get the metric function
				a__	:							List[Any]      						= evaluate.load("xnli" )

				# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
				# predictions and label_ids field) and has to return a dictionary string to float.
				def compute_metrics(__UpperCamelCase ):
								a__	:							Dict      						= p.predictions[0] if isinstance(p.predictions	,				__UpperCamelCase ) else p.predictions
								a__	:							Any      						= np.argmax(__UpperCamelCase	,				axis=1 )
								return metric.compute(predictions=__UpperCamelCase	,				references=p.label_ids )

				# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
				if data_args.pad_to_max_length:
								a__	:							List[Any]      						= default_data_collator
				elif training_args.fpaa:
								a__	:							List[Any]      						= DataCollatorWithPadding(__UpperCamelCase	,				pad_to_multiple_of=8 )
				else:
								a__	:							Tuple      						= None

				# Initialize our Trainer
				a__	:							List[str]      						= Trainer(
				    model=__UpperCamelCase	,				args=__UpperCamelCase	,				train_dataset=train_dataset if training_args.do_train else None	,				eval_dataset=eval_dataset if training_args.do_eval else None	,				compute_metrics=__UpperCamelCase	,				tokenizer=__UpperCamelCase	,				data_collator=__UpperCamelCase	,				)

				# Training
				if training_args.do_train:
								a__	:							str      						= None
								if training_args.resume_from_checkpoint is not None:
												a__	:							int      						= training_args.resume_from_checkpoint
								elif last_checkpoint is not None:
												a__	:							Optional[int]      						= last_checkpoint
								a__	:							Any      						= trainer.train(resume_from_checkpoint=__UpperCamelCase )
								a__	:							Tuple      						= train_result.metrics
								a__	:							Union[str, Any]      						= (
								    data_args.max_train_samples if data_args.max_train_samples is not None else len(__UpperCamelCase )
								)
								a__	:							Optional[int]      						= min(__UpperCamelCase	,				len(__UpperCamelCase ) )

								trainer.save_model()  # Saves the tokenizer too for easy upload

								trainer.log_metrics("train"	,				__UpperCamelCase )
								trainer.save_metrics("train"	,				__UpperCamelCase )
								trainer.save_state()

				# Evaluation
				if training_args.do_eval:
								logger.info("*** Evaluate ***" )
								a__	:							Optional[int]      						= trainer.evaluate(eval_dataset=__UpperCamelCase )

								a__	:							Tuple      						= data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__UpperCamelCase )
								a__	:							List[Any]      						= min(__UpperCamelCase	,				len(__UpperCamelCase ) )

								trainer.log_metrics("eval"	,				__UpperCamelCase )
								trainer.save_metrics("eval"	,				__UpperCamelCase )

				# Prediction
				if training_args.do_predict:
								logger.info("*** Predict ***" )
								a__						,   a__						,   a__	:							int      						= trainer.predict(__UpperCamelCase	,				metric_key_prefix="predict" )

								a__	:							int      						= (
								    data_args.max_predict_samples if data_args.max_predict_samples is not None else len(__UpperCamelCase )
								)
								a__	:							Union[str, Any]      						= min(__UpperCamelCase	,				len(__UpperCamelCase ) )

								trainer.log_metrics("predict"	,				__UpperCamelCase )
								trainer.save_metrics("predict"	,				__UpperCamelCase )

								a__	:							Dict      						= np.argmax(__UpperCamelCase	,				axis=1 )
								a__	:							str      						= os.path.join(training_args.output_dir	,				"predictions.txt" )
								if trainer.is_world_process_zero():
												with open(__UpperCamelCase	,				"w" ) as writer:
																writer.write("index\tprediction\n" )
																for index, item in enumerate(__UpperCamelCase ):
																				a__	:							int      						= label_list[item]
																				writer.write(F'{index}\t{item}\n' )


if __name__ == "__main__":
						main()


