





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

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


_A					:				Union[str, Any]				=			logging.getLogger(__name__)


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Tuple:
    return (preds == labels).mean()



@dataclass
class 					__snake_case	:


    '''simple docstring'''







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





@dataclass
class 					__snake_case	:


    '''simple docstring'''







    lowerCamelCase__    :		str				=	field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(processors.keys()    )}    )
    lowerCamelCase__    :		str				=	field(metadata={"""help""": """Should contain the data files for the task."""}    )
    lowerCamelCase__    :		int				=	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."""
            )
        }			,     )
    lowerCamelCase__    :		bool				=	field(
        default=__SCREAMING_SNAKE_CASE			,     metadata={"""help""": """Overwrite the cached training and evaluation sets"""}    )





def 							__snake_case							(    )				->						Tuple:
    # 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.

    SCREAMING_SNAKE_CASE__											=			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)	)
    SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			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''' ,							lowerCAmelCase_	)

    # Set seed
    set_seed(training_args.seed	)

    try:
        SCREAMING_SNAKE_CASE__											=			processors[data_args.task_name]()
        SCREAMING_SNAKE_CASE__											=			processor.get_labels()
        SCREAMING_SNAKE_CASE__											=			len(lowerCAmelCase_	)
    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.

    SCREAMING_SNAKE_CASE__											=			AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path ,							num_labels=lowerCAmelCase_ ,							finetuning_task=data_args.task_name ,							cache_dir=model_args.cache_dir ,							)
    SCREAMING_SNAKE_CASE__											=			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 ,							)
    SCREAMING_SNAKE_CASE__											=			AutoModelForMultipleChoice.from_pretrained(
        model_args.model_name_or_path ,							from_tf=bool('''.ckpt''' in model_args.model_name_or_path	) ,							config=lowerCAmelCase_ ,							cache_dir=model_args.cache_dir ,							)

    # Get datasets
    SCREAMING_SNAKE_CASE__											=			(
        MultipleChoiceDataset(
            data_dir=data_args.data_dir ,							tokenizer=lowerCAmelCase_ ,							task=data_args.task_name ,							max_seq_length=data_args.max_seq_length ,							overwrite_cache=data_args.overwrite_cache ,							mode=Split.train ,							)
        if training_args.do_train
        else None
    )
    SCREAMING_SNAKE_CASE__											=			(
        MultipleChoiceDataset(
            data_dir=data_args.data_dir ,							tokenizer=lowerCAmelCase_ ,							task=data_args.task_name ,							max_seq_length=data_args.max_seq_length ,							overwrite_cache=data_args.overwrite_cache ,							mode=Split.dev ,							)
        if training_args.do_eval
        else None
    )

    def compute_metrics(lowerCAmelCase_	) -> Dict:
        SCREAMING_SNAKE_CASE__											=			np.argmax(p.predictions ,							axis=1	)
        return {"acc": simple_accuracy(lowerCAmelCase_ ,							p.label_ids	)}

    # Data collator
    SCREAMING_SNAKE_CASE__											=			DataCollatorWithPadding(lowerCAmelCase_ ,							pad_to_multiple_of=8	) if training_args.fpaa else None

    # Initialize our Trainer
    SCREAMING_SNAKE_CASE__											=			Trainer(
        model=lowerCAmelCase_ ,							args=lowerCAmelCase_ ,							train_dataset=lowerCAmelCase_ ,							eval_dataset=lowerCAmelCase_ ,							compute_metrics=lowerCAmelCase_ ,							data_collator=lowerCAmelCase_ ,							)

    # 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
    SCREAMING_SNAKE_CASE__											=			{}
    if training_args.do_eval:
        logger.info('''*** Evaluate ***'''	)

        SCREAMING_SNAKE_CASE__											=			trainer.evaluate()

        SCREAMING_SNAKE_CASE__											=			os.path.join(training_args.output_dir ,							'''eval_results.txt'''	)
        if trainer.is_world_master():
            with open(lowerCAmelCase_ ,							'''w'''	) as writer:
                logger.info('''***** Eval results *****'''	)
                for key, value in result.items():
                    logger.info('''  %s = %s''' ,							lowerCAmelCase_ ,							lowerCAmelCase_	)
                    writer.write('''%s = %s\n''' % (key, value)	)

                results.update(lowerCAmelCase_	)

    return results


def 							__snake_case							(    lowerCAmelCase_	)				->						Any:
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
  main()


'''simple docstring'''
from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin






@dataclass
class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						42







class 	snake_case__       (      __SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		@register_to_config
		def __init__(      self				:			List[str]  ,	UpperCamelCase__				:			int = 16  ,	UpperCamelCase__				:			int = 88  ,	UpperCamelCase__				:			Optional[int] = None  ,	UpperCamelCase__				:			Optional[int] = None  ,	UpperCamelCase__				:			int = 1  ,	UpperCamelCase__				:			float = 0.0  ,	UpperCamelCase__				:			int = 32  ,	UpperCamelCase__				:			Optional[int] = None  ,	UpperCamelCase__				:			bool = False  ,	UpperCamelCase__				:			Optional[int] = None  ,	UpperCamelCase__				:			str = "geglu"  ,	UpperCamelCase__				:			bool = True  ,	UpperCamelCase__				:			bool = True  ,	)						->			List[Any]:






				"""simple docstring"""







				super().__init__()
				snake_case  :       Optional[int]     			=						num_attention_heads
				snake_case  :       int     			=						attention_head_dim
				snake_case  :       Dict     			=						num_attention_heads * attention_head_dim

				snake_case  :       Tuple     			=						in_channels

				snake_case  :       Optional[int]     			=						torch.nn.GroupNorm(num_groups=UpperCamelCase__  ,	num_channels=UpperCamelCase__  ,	eps=1e-6  ,	affine=UpperCamelCase__      )
				snake_case  :       List[Any]     			=						nn.Linear(UpperCamelCase__  ,	UpperCamelCase__      )

				# 3. Define transformers blocks
				snake_case  :       Union[str, Any]     			=						nn.ModuleList(
				    [
				        BasicTransformerBlock(
				            UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	dropout=UpperCamelCase__  ,	cross_attention_dim=UpperCamelCase__  ,	activation_fn=UpperCamelCase__  ,	attention_bias=UpperCamelCase__  ,	double_self_attention=UpperCamelCase__  ,	norm_elementwise_affine=UpperCamelCase__  ,	)
				        for d in range(UpperCamelCase__      )
				    ]      )

				snake_case  :       Any     			=						nn.Linear(UpperCamelCase__  ,	UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			List[str]  ,	UpperCamelCase__				:			Tuple  ,	UpperCamelCase__				:			Any=None  ,	UpperCamelCase__				:			Dict=None  ,	UpperCamelCase__				:			Dict=None  ,	UpperCamelCase__				:			Union[str, Any]=1  ,	UpperCamelCase__				:			Optional[int]=None  ,	UpperCamelCase__				:			bool = True  ,	)						->			Any:






				"""simple docstring"""







				snake_case			,snake_case			,snake_case			,snake_case  :       Tuple     			=						hidden_states.shape
				snake_case  :       List[Any]     			=						batch_frames // num_frames

				snake_case  :       Union[str, Any]     			=						hidden_states

				snake_case  :       Optional[Any]     			=						hidden_states[None, :].reshape(UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__      )
				snake_case  :       int     			=						hidden_states.permute(0  ,	2  ,	1  ,	3  ,	4      )

				snake_case  :       Tuple     			=						self.norm(UpperCamelCase__      )
				snake_case  :       Union[str, Any]     			=						hidden_states.permute(0  ,	3  ,	4  ,	2  ,	1      ).reshape(batch_size * height * width  ,	UpperCamelCase__  ,	UpperCamelCase__      )

				snake_case  :       Optional[Any]     			=						self.proj_in(UpperCamelCase__      )

				# 2. Blocks
				for block in self.transformer_blocks:
						snake_case  :       Union[str, Any]     			=						block(
						    UpperCamelCase__  ,	encoder_hidden_states=UpperCamelCase__  ,	timestep=UpperCamelCase__  ,	cross_attention_kwargs=UpperCamelCase__  ,	class_labels=UpperCamelCase__  ,	)

				# 3. Output
				snake_case  :       Dict     			=						self.proj_out(UpperCamelCase__      )
				snake_case  :       Tuple     			=						(
				    hidden_states[None, None, :]
				    .reshape(UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__      )
				    .permute(0  ,	3  ,	4  ,	1  ,	2      )
				    .contiguous()
				)
				snake_case  :       List[Any]     			=						hidden_states.reshape(UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__      )

				snake_case  :       Any     			=						hidden_states + residual

				if not return_dict:
						return (output,)

				return TransformerTemporalModelOutput(sample=UpperCamelCase__      )


