

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowerCAmelCase  :      List[str]			 ={
    """configuration_chinese_clip""": [
        """CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """ChineseCLIPConfig""",
        """ChineseCLIPOnnxConfig""",
        """ChineseCLIPTextConfig""",
        """ChineseCLIPVisionConfig""",
    ],
    """processing_chinese_clip""": ["""ChineseCLIPProcessor"""],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowerCAmelCase  :      List[Any]			 =["""ChineseCLIPFeatureExtractor"""]
					_lowerCAmelCase  :      Dict			 =["""ChineseCLIPImageProcessor"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowerCAmelCase  :      Optional[int]			 =[
					    """CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """ChineseCLIPModel""",
					    """ChineseCLIPPreTrainedModel""",
					    """ChineseCLIPTextModel""",
					    """ChineseCLIPVisionModel""",
					]

if TYPE_CHECKING:
					from .configuration_chinese_clip import (
					    CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    ChineseCLIPConfig,
					    ChineseCLIPOnnxConfig,
					    ChineseCLIPTextConfig,
					    ChineseCLIPVisionConfig,
					)
					from .processing_chinese_clip import ChineseCLIPProcessor

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_chinese_clip import ChineseCLIPFeatureExtractor, ChineseCLIPImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_chinese_clip import (
										    CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ChineseCLIPModel,
										    ChineseCLIPPreTrainedModel,
										    ChineseCLIPTextModel,
										    ChineseCLIPVisionModel,
										)

else:
					import sys

					_lowerCAmelCase  :      List[Any]			 =_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  :      Optional[Any]			 =logging.getLogger(__name__)

@dataclass
class __UpperCamelCase     :






					'''simple docstring'''



					__magic_name__        =   field(
					    default=1_2_8					,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 preprocessed datasets or not."}		)
					__magic_name__        =   field(
					    default=_a					,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."
					        )
					    }					,)
					__magic_name__        =   field(
					    default=_a					,metadata={
					        "help": (
					            "For debugging purposes or quicker training, truncate the number of training examples to this "
					            "value if set."
					        )
					    }					,)
					__magic_name__        =   field(
					    default=_a					,metadata={
					        "help": (
					            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
					            "value if set."
					        )
					    }					,)
					__magic_name__        =   field(
					    default=_a					,metadata={
					        "help": (
					            "For debugging purposes or quicker training, truncate the number of prediction examples to this "
					            "value if set."
					        )
					    }					,)

@dataclass
class __UpperCamelCase     :






					'''simple docstring'''



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



def 			_A	(      ):
	# 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__:	Optional[int]									= HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )
	UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__:	List[Any]									= 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"	,SCREAMING_SNAKE_CASE      )

	# 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()

	UpperCAmelCase__:	int									= training_args.get_process_log_level()
	logger.setLevel(SCREAMING_SNAKE_CASE      )
	datasets.utils.logging.set_verbosity(SCREAMING_SNAKE_CASE      )
	transformers.utils.logging.set_verbosity(SCREAMING_SNAKE_CASE      )
	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.
	UpperCAmelCase__:	Tuple									= None
	if os.path.isdir(training_args.output_dir      ) and training_args.do_train and not training_args.overwrite_output_dir:
		UpperCAmelCase__:	Any									= 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:
			UpperCAmelCase__:	Optional[int]									= 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:
			UpperCAmelCase__:	Optional[Any]									= 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	,)
		UpperCAmelCase__:	List[Any]									= train_dataset.features["label"].names

	if training_args.do_eval:
		UpperCAmelCase__:	List[Any]									= 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	,)
		UpperCAmelCase__:	Tuple									= eval_dataset.features["label"].names

	if training_args.do_predict:
		UpperCAmelCase__:	Optional[Any]									= 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	,)
		UpperCAmelCase__:	List[Any]									= predict_dataset.features["label"].names

	# Labels
	UpperCAmelCase__:	Union[str, Any]									= len(SCREAMING_SNAKE_CASE      )

	# Load pretrained model and tokenizer
	# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
	# download model & vocab.
	UpperCAmelCase__:	Optional[int]									= AutoConfig.from_pretrained(
	    model_args.config_name if model_args.config_name else model_args.model_name_or_path	,num_labels=SCREAMING_SNAKE_CASE	,idalabel={str(SCREAMING_SNAKE_CASE      ): label for i, label in enumerate(SCREAMING_SNAKE_CASE      )}	,labelaid={label: i for i, label in enumerate(SCREAMING_SNAKE_CASE      )}	,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	,)
	UpperCAmelCase__:	Dict									= 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	,)
	UpperCAmelCase__:	Optional[Any]									= AutoModelForSequenceClassification.from_pretrained(
	    model_args.model_name_or_path	,from_tf=bool(".ckpt" in model_args.model_name_or_path      )	,config=SCREAMING_SNAKE_CASE	,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:
		UpperCAmelCase__:	Union[str, Any]									= "max_length"
	else:
		# We will pad later, dynamically at batch creation, to the max sequence length in each batch
		UpperCAmelCase__:	Union[str, Any]									= False

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

	if training_args.do_train:
		if data_args.max_train_samples is not None:
			UpperCAmelCase__:	Optional[Any]									= min(len(SCREAMING_SNAKE_CASE      )	,data_args.max_train_samples      )
			UpperCAmelCase__:	Optional[Any]									= train_dataset.select(range(SCREAMING_SNAKE_CASE      )      )
		with training_args.main_process_first(desc="train dataset map pre-processing"      ):
			UpperCAmelCase__:	Dict									= train_dataset.map(
			    SCREAMING_SNAKE_CASE	,batched=SCREAMING_SNAKE_CASE	,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(SCREAMING_SNAKE_CASE      )      )	,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:
			UpperCAmelCase__:	Optional[int]									= min(len(SCREAMING_SNAKE_CASE      )	,data_args.max_eval_samples      )
			UpperCAmelCase__:	List[Any]									= eval_dataset.select(range(SCREAMING_SNAKE_CASE      )      )
		with training_args.main_process_first(desc="validation dataset map pre-processing"      ):
			UpperCAmelCase__:	Optional[Any]									= eval_dataset.map(
			    SCREAMING_SNAKE_CASE	,batched=SCREAMING_SNAKE_CASE	,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:
			UpperCAmelCase__:	Dict									= min(len(SCREAMING_SNAKE_CASE      )	,data_args.max_predict_samples      )
			UpperCAmelCase__:	Optional[Any]									= predict_dataset.select(range(SCREAMING_SNAKE_CASE      )      )
		with training_args.main_process_first(desc="prediction dataset map pre-processing"      ):
			UpperCAmelCase__:	int									= predict_dataset.map(
			    SCREAMING_SNAKE_CASE	,batched=SCREAMING_SNAKE_CASE	,load_from_cache_file=not data_args.overwrite_cache	,desc="Running tokenizer on prediction dataset"	,)

    # Get the metric function
	UpperCAmelCase__:	str									= 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(SCREAMING_SNAKE_CASE      ):
		UpperCAmelCase__:	str									= p.predictions[0] if isinstance(p.predictions	,SCREAMING_SNAKE_CASE      ) else p.predictions
		UpperCAmelCase__:	Union[str, Any]									= np.argmax(SCREAMING_SNAKE_CASE	,axis=1      )
		return metric.compute(predictions=SCREAMING_SNAKE_CASE	,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:
		UpperCAmelCase__:	Any									= default_data_collator
	elif training_args.fpaa:
		UpperCAmelCase__:	Any									= DataCollatorWithPadding(SCREAMING_SNAKE_CASE	,pad_to_multiple_of=8      )
	else:
		UpperCAmelCase__:	Tuple									= None

	# Initialize our Trainer
	UpperCAmelCase__:	Dict									= Trainer(
	    model=SCREAMING_SNAKE_CASE	,args=SCREAMING_SNAKE_CASE	,train_dataset=train_dataset if training_args.do_train else None	,eval_dataset=eval_dataset if training_args.do_eval else None	,compute_metrics=SCREAMING_SNAKE_CASE	,tokenizer=SCREAMING_SNAKE_CASE	,data_collator=SCREAMING_SNAKE_CASE	,)

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

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

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

	# Evaluation
	if training_args.do_eval:
		logger.info("*** Evaluate ***"      )
		UpperCAmelCase__:	Any									= trainer.evaluate(eval_dataset=SCREAMING_SNAKE_CASE      )

		UpperCAmelCase__:	Optional[int]									= data_args.max_eval_samples if data_args.max_eval_samples is not None else len(SCREAMING_SNAKE_CASE      )
		UpperCAmelCase__:	int									= min(SCREAMING_SNAKE_CASE	,len(SCREAMING_SNAKE_CASE      )      )

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

	# Prediction
	if training_args.do_predict:
		logger.info("*** Predict ***"      )
		UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__:	Union[str, Any]									= trainer.predict(SCREAMING_SNAKE_CASE	,metric_key_prefix="predict"      )

		UpperCAmelCase__:	Optional[Any]									= (
		    data_args.max_predict_samples if data_args.max_predict_samples is not None else len(SCREAMING_SNAKE_CASE      )
		)
		UpperCAmelCase__:	Union[str, Any]									= min(SCREAMING_SNAKE_CASE	,len(SCREAMING_SNAKE_CASE      )      )

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

		UpperCAmelCase__:	Any									= np.argmax(SCREAMING_SNAKE_CASE	,axis=1      )
		UpperCAmelCase__:	Optional[int]									= os.path.join(training_args.output_dir	,"predictions.txt"      )
		if trainer.is_world_process_zero():
			with open(SCREAMING_SNAKE_CASE	,"w"      ) as writer:
				writer.write("index\tprediction\n"      )
				for index, item in enumerate(SCREAMING_SNAKE_CASE      ):
					UpperCAmelCase__:	int									= label_list[item]
					writer.write(f"{index}\t{item}\n"      )


if __name__ == "__main__":
					main()