'''simple docstring'''
import argparse
import glob
import logging
import os
import time
from argparse import Namespace

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset

from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors


a=  logging.getLogger(__name__)






class 			__lowercase		(						lowercase_						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						'''sequence-classification'''







     def __init__(		self     ,			_lowerCamelCase    ):



         if type(lowerCamelCase_    ) == dict:
             __UpperCamelCase	:	List[Any]		    =  Namespace(**lowerCamelCase_    )
         __UpperCamelCase	:	List[str]		    =  glue_output_modes[hparams.task]
         __UpperCamelCase	:	Any		    =  glue_tasks_num_labels[hparams.task]

         super().__init__(lowerCamelCase_     ,			lowerCamelCase_     ,			self.mode    )







     def lowerCAmelCase     (		self     ,			**_lowerCamelCase    ):
         return self.model(**lowerCamelCase_    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	Dict		    =  {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}

         if self.config.model_type not in ["distilbert", "bart"]:
             __UpperCamelCase	:	List[str]		    =  batch[2] if self.config.model_type in ["""bert""", """xlnet""", """albert"""] else None

         __UpperCamelCase	:	List[Any]		    =  self(**lowerCamelCase_    )
         __UpperCamelCase	:	Optional[Any]		    =  outputs[0]

         __UpperCamelCase	:	Optional[Any]		    =  self.trainer.lr_schedulers[0]["""scheduler"""]
         __UpperCamelCase	:	Optional[Any]		    =  {"""loss""": loss, """rate""": lr_scheduler.get_last_lr()[-1]}
         return {"loss": loss, "log": tensorboard_logs}







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Dict		    =  self.hparams
         __UpperCamelCase	:	Tuple		    =  processors[args.task]()
         __UpperCamelCase	:	Any		    =  processor.get_labels()

         for mode in ["train", "dev"]:
             __UpperCamelCase	:	str		    =  self._feature_file(lowerCamelCase_    )
             if os.path.exists(lowerCamelCase_    ) and not args.overwrite_cache:
                 logger.info('Loading features from cached file %s'     ,			lowerCamelCase_    )
             else:
                 logger.info('Creating features from dataset file at %s'     ,			args.data_dir    )
                 __UpperCamelCase	:	int		    =  (
                     processor.get_dev_examples(args.data_dir    )
                     if mode == """dev"""
                     else processor.get_train_examples(args.data_dir    )
                 )
                 __UpperCamelCase	:	List[Any]		    =  convert_examples_to_features(
                     lowerCamelCase_     ,			self.tokenizer     ,			max_length=args.max_seq_length     ,			label_list=self.labels     ,			output_mode=args.glue_output_mode     ,			)
                 logger.info('Saving features into cached file %s'     ,			lowerCamelCase_    )
                 torch.save(lowerCamelCase_     ,			lowerCamelCase_    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase = False    ):
         __UpperCamelCase	:	Optional[Any]		    =  """dev""" if mode == """test""" else mode

         __UpperCamelCase	:	Optional[Any]		    =  self._feature_file(lowerCamelCase_    )
         logger.info('Loading features from cached file %s'     ,			lowerCamelCase_    )
         __UpperCamelCase	:	str		    =  torch.load(lowerCamelCase_    )
         __UpperCamelCase	:	Any		    =  torch.tensor([f.input_ids for f in features]     ,			dtype=torch.long    )
         __UpperCamelCase	:	List[str]		    =  torch.tensor([f.attention_mask for f in features]     ,			dtype=torch.long    )
         __UpperCamelCase	:	List[str]		    =  torch.tensor([f.token_type_ids for f in features]     ,			dtype=torch.long    )
         if self.hparams.glue_output_mode == "classification":
             __UpperCamelCase	:	str		    =  torch.tensor([f.label for f in features]     ,			dtype=torch.long    )
         elif self.hparams.glue_output_mode == "regression":
             __UpperCamelCase	:	Optional[Any]		    =  torch.tensor([f.label for f in features]     ,			dtype=torch.float    )

         return DataLoader(
             TensorDataset(lowerCamelCase_     ,			lowerCamelCase_     ,			lowerCamelCase_     ,			lowerCamelCase_    )     ,			batch_size=lowerCamelCase_     ,			shuffle=lowerCamelCase_     ,			)







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	List[Any]		    =  {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}

         if self.config.model_type not in ["distilbert", "bart"]:
             __UpperCamelCase	:	Tuple		    =  batch[2] if self.config.model_type in ["""bert""", """xlnet""", """albert"""] else None

         __UpperCamelCase	:	int		    =  self(**lowerCamelCase_    )
         __UpperCamelCase	:	Any		    =  outputs[:2]
         __UpperCamelCase	:	Tuple		    =  logits.detach().cpu().numpy()
         __UpperCamelCase	:	Any		    =  inputs["""labels"""].detach().cpu().numpy()

         return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	Union[str, Any]		    =  torch.stack([x['val_loss'] for x in outputs]    ).mean().detach().cpu().item()
         __UpperCamelCase	:	Union[str, Any]		    =  np.concatenate([x['pred'] for x in outputs]     ,			axis=0    )

         if self.hparams.glue_output_mode == "classification":
             __UpperCamelCase	:	List[str]		    =  np.argmax(lowerCamelCase_     ,			axis=1    )
         elif self.hparams.glue_output_mode == "regression":
             __UpperCamelCase	:	Union[str, Any]		    =  np.squeeze(lowerCamelCase_    )

         __UpperCamelCase	:	Optional[Any]		    =  np.concatenate([x['target'] for x in outputs]     ,			axis=0    )
         __UpperCamelCase	:	str		    =  [[] for _ in range(out_label_ids.shape[0]    )]
         __UpperCamelCase	:	str		    =  [[] for _ in range(out_label_ids.shape[0]    )]

         __UpperCamelCase	:	Dict		    =  {**{"""val_loss""": val_loss_mean}, **compute_metrics(self.hparams.task     ,			lowerCamelCase_     ,			lowerCamelCase_    )}

         __UpperCamelCase	:	List[str]		    =  dict(results.items()    )
         __UpperCamelCase	:	str		    =  results
         return ret, preds_list, out_label_list







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	List[Any]		    =  self._eval_end(lowerCamelCase_    )
         __UpperCamelCase	:	List[Any]		    =  ret["""log"""]
         return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	int		    =  self._eval_end(lowerCamelCase_    )
         __UpperCamelCase	:	Optional[Any]		    =  ret["""log"""]
         # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
         return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}







     @staticmethod
     def lowerCAmelCase     (		_lowerCamelCase     ,			_lowerCamelCase    ):
         BaseTransformer.add_model_specific_args(lowerCamelCase_     ,			lowerCamelCase_    )
         parser.add_argument(
             '--max_seq_length'     ,			default=1_2_8     ,			type=lowerCamelCase_     ,			help=(
                 'The maximum total input sequence length after tokenization. Sequences longer '
                 'than this will be truncated, sequences shorter will be padded.'
             )     ,			)

         parser.add_argument(
             '--task'     ,			default=''     ,			type=lowerCamelCase_     ,			required=lowerCamelCase_     ,			help='The GLUE task to run'     ,			)
         parser.add_argument(
             '--gpus'     ,			default=0     ,			type=lowerCamelCase_     ,			help='The number of GPUs allocated for this, it is by default 0 meaning none'     ,			)

         parser.add_argument(
             '--overwrite_cache'     ,			action='store_true'     ,			help='Overwrite the cached training and evaluation sets'    )

         return parser







def  _UpperCamelCase (     ):




    """simple docstring"""



    __UpperCamelCase	:	str		    =  argparse.ArgumentParser()
    add_generic_args(lowerCamelCase_				,     os.getcwd()			)
    __UpperCamelCase	:	Tuple		    =  GLUETransformer.add_model_specific_args(lowerCamelCase_				,     os.getcwd()			)
    __UpperCamelCase	:	Any		    =  parser.parse_args()

    # If output_dir not provided, a folder will be generated in pwd
    if args.output_dir is None:
        __UpperCamelCase	:	Optional[int]		    =  os.path.join(
            './results'				,     f"""{args.task}_{time.strftime('%Y%m%d_%H%M%S'			)}"""				,     )
        os.makedirs(args.output_dir			)

    __UpperCamelCase	:	Union[str, Any]		    =  GLUETransformer(lowerCamelCase_			)
    __UpperCamelCase	:	Any		    =  generic_train(lowerCamelCase_				,     lowerCamelCase_			)

    # Optionally, predict on dev set and write to output_dir
    if args.do_predict:
        __UpperCamelCase	:	Tuple		    =  sorted(glob.glob(os.path.join(args.output_dir				,     'checkpoint-epoch=*.ckpt'			)				,     recursive=lowerCamelCase_			)			)
        __UpperCamelCase	:	int		    =  model.load_from_checkpoint(checkpoints[-1]			)
        return trainer.test(lowerCamelCase_			)


if __name__ == "__main__":
       main()




'''simple docstring'''
import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin






class 			__lowercase		(						_lowerCamelCase			,							_lowerCamelCase						):







     """simple docstring"""






     @register_to_config
     def __init__(		self     ,			*,
         _lowerCamelCase = 4     ,			_lowerCamelCase = 7_6_8     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			):
         super().__init__()

         __UpperCamelCase	:	Optional[int]		    =  nn.Parameter(torch.zeros(_lowerCamelCase    )    )

         # parameters for additional clip time embeddings
         __UpperCamelCase	:	Dict		    =  nn.Linear(_lowerCamelCase     ,			_lowerCamelCase    )
         __UpperCamelCase	:	int		    =  nn.Linear(_lowerCamelCase     ,			_lowerCamelCase    )

         # parameters for encoder hidden states
         __UpperCamelCase	:	Optional[Any]		    =  clip_extra_context_tokens
         __UpperCamelCase	:	Dict		    =  nn.Linear(
             _lowerCamelCase     ,			self.clip_extra_context_tokens * cross_attention_dim    )
         __UpperCamelCase	:	str		    =  nn.Linear(_lowerCamelCase     ,			_lowerCamelCase    )
         __UpperCamelCase	:	List[str]		    =  nn.LayerNorm(_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			*, _lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         if do_classifier_free_guidance:
             # Add the classifier free guidance embeddings to the image embeddings
             __UpperCamelCase	:	int		    =  image_embeddings.shape[0]
             __UpperCamelCase	:	Optional[Any]		    =  self.learned_classifier_free_guidance_embeddings.unsqueeze(0    )
             __UpperCamelCase	:	List[Any]		    =  classifier_free_guidance_embeddings.expand(
                 _lowerCamelCase     ,			-1    )
             __UpperCamelCase	:	Optional[Any]		    =  torch.cat([classifier_free_guidance_embeddings, image_embeddings]     ,			dim=0    )

         # The image embeddings batch size and the text embeddings batch size are equal
         assert image_embeddings.shape[0] == prompt_embeds.shape[0]

         __UpperCamelCase	:	Optional[Any]		    =  prompt_embeds.shape[0]

         # "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
         # adding CLIP embeddings to the existing timestep embedding, ...
         __UpperCamelCase	:	Optional[int]		    =  self.embedding_proj(_lowerCamelCase    )
         __UpperCamelCase	:	List[str]		    =  self.clip_image_embeddings_project_to_time_embeddings(_lowerCamelCase    )
         __UpperCamelCase	:	Optional[int]		    =  time_projected_image_embeddings + time_projected_prompt_embeds

         # ... and by projecting CLIP embeddings into four
         # extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
         __UpperCamelCase	:	Optional[int]		    =  self.clip_extra_context_tokens_proj(_lowerCamelCase    )
         __UpperCamelCase	:	Tuple		    =  clip_extra_context_tokens.reshape(_lowerCamelCase     ,			-1     ,			self.clip_extra_context_tokens    )
         __UpperCamelCase	:	Dict		    =  clip_extra_context_tokens.permute(0     ,			2     ,			1    )

         __UpperCamelCase	:	Union[str, Any]		    =  self.encoder_hidden_states_proj(_lowerCamelCase    )
         __UpperCamelCase	:	Optional[Any]		    =  self.text_encoder_hidden_states_norm(_lowerCamelCase    )
         __UpperCamelCase	:	Tuple		    =  torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]     ,			dim=1    )

         return text_encoder_hidden_states, additive_clip_time_embeddings



