
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, List, Optional

import numpy as np
import torch
from utils_hans import HansDataset, InputFeatures, hans_processors, hans_tasks_num_labels

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import is_main_process


SCREAMING_SNAKE_CASE : List[Any]          =    logging.getLogger(__name__)





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





@dataclass
class 					snake_case__  :
	UpperCAmelCase   :							str         = field(
	    metadata={"""help""": """The name of the task to train selected in the list: """ + """, """.join(hans_processors.keys()       )}       )
	UpperCAmelCase   :							str         = field(
	    metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}       )
	UpperCAmelCase   :							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."""
	        )
	    }      ,    )
	UpperCAmelCase   :							bool         = field(
	    default=__A      ,    metadata={"""help""": """Overwrite the cached training and evaluation sets"""}       )





def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: List[InputFeatures]      ):






							"""simple docstring"""



							a_       :   Optional[int]			=	default_data_collator(SCREAMING_SNAKE_CASE_      )
							a_       :   Optional[int]			=	batch.pop("""pairID"""	,	SCREAMING_SNAKE_CASE_      )
							return batch





def 						_lowerCamelCase		(							):






							"""simple docstring"""



							a_       :   List[str]			=	HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )
							a_       :   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 if training_args.local_rank in [-1, 0] else logging.WARN	,	)
							logger.warning(
							    """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"""	,	training_args.local_rank	,	training_args.device	,	training_args.n_gpu	,	bool(training_args.local_rank != -1      )	,	training_args.fpaa	,	)
							# Set the verbosity to info of the Transformers logger (on main process only):
							if is_main_process(training_args.local_rank      ):
														transformers.utils.logging.set_verbosity_info()
														transformers.utils.logging.enable_default_handler()
														transformers.utils.logging.enable_explicit_format()
							logger.info("""Training/evaluation parameters %s"""	,	SCREAMING_SNAKE_CASE_      )

							# Set seed
							set_seed(training_args.seed      )

							try:
														a_       :   Dict			=	hans_tasks_num_labels[data_args.task_name]
							except KeyError:
														raise ValueError("""Task not found: %s""" % (data_args.task_name)      )

							# Load pretrained model and tokenizer
							#
							# Distributed training:
							# The .from_pretrained methods guarantee that only one local process can concurrently
							# download model & vocab.

							a_       :   Optional[Any]			=	AutoConfig.from_pretrained(
							    model_args.config_name if model_args.config_name else model_args.model_name_or_path	,	num_labels=SCREAMING_SNAKE_CASE_	,	finetuning_task=data_args.task_name	,	cache_dir=model_args.cache_dir	,	)
							a_       :   int			=	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_       :   Tuple			=	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	,	)

							# Get datasets
							a_       :   Optional[int]			=	(
							    HansDataset(
							        data_dir=data_args.data_dir	,	tokenizer=SCREAMING_SNAKE_CASE_	,	task=data_args.task_name	,	max_seq_length=data_args.max_seq_length	,	overwrite_cache=data_args.overwrite_cache	,	)
							    if training_args.do_train
							    else None
							)
							a_       :   Dict			=	(
							    HansDataset(
							        data_dir=data_args.data_dir	,	tokenizer=SCREAMING_SNAKE_CASE_	,	task=data_args.task_name	,	max_seq_length=data_args.max_seq_length	,	overwrite_cache=data_args.overwrite_cache	,	evaluate=SCREAMING_SNAKE_CASE_	,	)
							    if training_args.do_eval
							    else None
							)

							# Initialize our Trainer
							a_       :   Any			=	Trainer(
							    model=SCREAMING_SNAKE_CASE_	,	args=SCREAMING_SNAKE_CASE_	,	train_dataset=SCREAMING_SNAKE_CASE_	,	eval_dataset=SCREAMING_SNAKE_CASE_	,	data_collator=SCREAMING_SNAKE_CASE_	,	)

							# Training
							if training_args.do_train:
														trainer.train(
														    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path      ) else None      )
														trainer.save_model()
														# For convenience, we also re-save the tokenizer to the same directory,
														# so that you can share your model easily on huggingface.co/models =)
														if trainer.is_world_master():
																					tokenizer.save_pretrained(training_args.output_dir      )

    # Evaluation
							if training_args.do_eval:
														logger.info("""*** Evaluate ***"""      )

														a_       :   Any			=	trainer.predict(SCREAMING_SNAKE_CASE_      )
														a_       :   str			=	output.predictions
														a_       :   str			=	np.argmax(SCREAMING_SNAKE_CASE_	,	axis=1      )

														a_       :   Optional[int]			=	[ex.pairID for ex in eval_dataset]
														a_       :   int			=	os.path.join(training_args.output_dir	,	"""hans_predictions.txt"""      )
														a_       :   Any			=	eval_dataset.get_labels()
														if trainer.is_world_master():
																					with open(SCREAMING_SNAKE_CASE_	,	"""w"""      ) as writer:
																												writer.write("""pairID,gold_label\n"""      )
																												for pid, pred in zip(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_      ):
																																			writer.write("""ex""" + str(SCREAMING_SNAKE_CASE_      ) + """,""" + label_list[int(SCREAMING_SNAKE_CASE_      )] + """\n"""      )

														trainer._log(output.metrics      )





def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: Dict      ):






							"""simple docstring"""



							main()


if __name__ == "__main__":
	main()






from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE : Dict          =    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE : str          =    {
    "apple/mobilevit-small": "https://huggingface.co/apple/mobilevit-small/resolve/main/config.json",
    "apple/mobilevit-x-small": "https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json",
    "apple/mobilevit-xx-small": "https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json",
    "apple/deeplabv3-mobilevit-small": (
        "https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json"
    ),
    "apple/deeplabv3-mobilevit-x-small": (
        "https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json"
    ),
    "apple/deeplabv3-mobilevit-xx-small": (
        "https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json"
    ),
    # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
}





class 					snake_case__  ( __A       ):
	UpperCAmelCase   :							List[str]         = """mobilevit"""





	def __init__(  self				,    UpperCamelCase_=3				,    UpperCamelCase_=256				,    UpperCamelCase_=2				,    UpperCamelCase_=[144, 192, 240]				,    UpperCamelCase_=[16, 32, 64, 96, 128, 160, 640]				,    UpperCamelCase_=4				,    UpperCamelCase_=2.0				,    UpperCamelCase_=4.0				,    UpperCamelCase_="silu"				,    UpperCamelCase_=3				,    UpperCamelCase_=32				,    UpperCamelCase_=0.1				,    UpperCamelCase_=0.0				,    UpperCamelCase_=0.1				,    UpperCamelCase_=0.02				,    UpperCamelCase_=1e-5				,    UpperCamelCase_=True				,    UpperCamelCase_=256				,    UpperCamelCase_=[6, 12, 18]				,    UpperCamelCase_=0.1				,    UpperCamelCase_=255				,    **UpperCamelCase_				,    )		->			str:
								"""simple docstring"""






								super().__init__(**UpperCamelCase_							)

								a_       :   Optional[int]			=	num_channels
								a_       :   Optional[Any]			=	image_size
								a_       :   int			=	patch_size
								a_       :   List[str]			=	hidden_sizes
								a_       :   Optional[int]			=	neck_hidden_sizes
								a_       :   List[Any]			=	num_attention_heads
								a_       :   Union[str, Any]			=	mlp_ratio
								a_       :   List[str]			=	expand_ratio
								a_       :   str			=	hidden_act
								a_       :   Union[str, Any]			=	conv_kernel_size
								a_       :   Optional[Any]			=	output_stride
								a_       :   Union[str, Any]			=	hidden_dropout_prob
								a_       :   List[Any]			=	attention_probs_dropout_prob
								a_       :   List[Any]			=	classifier_dropout_prob
								a_       :   Union[str, Any]			=	initializer_range
								a_       :   List[Any]			=	layer_norm_eps
								a_       :   str			=	qkv_bias

								# decode head attributes for semantic segmentation
								a_       :   Optional[Any]			=	aspp_out_channels
								a_       :   Any			=	atrous_rates
								a_       :   Union[str, Any]			=	aspp_dropout_prob
								a_       :   Any			=	semantic_loss_ignore_index





class 					snake_case__  ( __A       ):
	UpperCAmelCase   :							Optional[int]         = version.parse("""1.11"""       )





	@property
	def     A				(  self							)		->			Mapping[str, Mapping[int, str]]:
								"""simple docstring"""






								return OrderedDict([("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""})]							)





	@property
	def     A				(  self							)		->			Mapping[str, Mapping[int, str]]:
								"""simple docstring"""






								if self.task == "image-classification":
															return OrderedDict([("""logits""", {0: """batch"""})]							)
								else:
															return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})]							)





	@property
	def     A				(  self							)		->			float:
								"""simple docstring"""






								return 1e-4


