

"""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


__UpperCAmelCase			=					logging.getLogger(__name__)







class   __lowercase      (     __lowerCamelCase       ):
  snake_case_												=							"""sequence-classification"""

  def __init__(	self		:      str       ,A		:      int     ):






   '''simple docstring'''
   if type(A     ) == dict:
    UpperCAmelCase__       :	Tuple        =       Namespace(**A     )
   UpperCAmelCase__       :	List[str]        =       glue_output_modes[hparams.task]
   UpperCAmelCase__       :	Optional[int]        =       glue_tasks_num_labels[hparams.task]

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

  def  __lowercase							(	self		:      int       ,**A		:      Dict     ):






   '''simple docstring'''
   return self.model(**A     )

  def  __lowercase							(	self		:      Dict       ,A		:      Any       ,A		:      Optional[int]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Any        =       {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}

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

   UpperCAmelCase__       :	Union[str, Any]        =       self(**A     )
   UpperCAmelCase__       :	Dict        =       outputs[0]

   UpperCAmelCase__       :	Union[str, 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  __lowercase							(	self		:      str     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Dict        =       self.hparams
   UpperCAmelCase__       :	str        =       processors[args.task]()
   UpperCAmelCase__       :	List[Any]        =       processor.get_labels()

   for mode in ["train", "dev"]:
    UpperCAmelCase__       :	Union[str, Any]        =       self._feature_file(A     )
    if os.path.exists(A     ) and not args.overwrite_cache:
     logger.info("""Loading features from cached file %s"""       ,A     )
    else:
     logger.info("""Creating features from dataset file at %s"""       ,args.data_dir     )
     UpperCAmelCase__       :	Optional[int]        =       (
         processor.get_dev_examples(args.data_dir     )
         if mode == """dev"""
         else processor.get_train_examples(args.data_dir     )
     )
     UpperCAmelCase__       :	Tuple        =       convert_examples_to_features(
         A       ,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"""       ,A     )
     torch.save(A       ,A     )

  def  __lowercase							(	self		:      List[Any]       ,A		:      str       ,A		:      int       ,A		:      bool = False     ):






   '''simple docstring'''
   UpperCAmelCase__       :	str        =       """dev""" if mode == """test""" else mode

   UpperCAmelCase__       :	Dict        =       self._feature_file(A     )
   logger.info("""Loading features from cached file %s"""       ,A     )
   UpperCAmelCase__       :	Any        =       torch.load(A     )
   UpperCAmelCase__       :	Dict        =       torch.tensor([f.input_ids for f in features]       ,dtype=torch.long     )
   UpperCAmelCase__       :	Dict        =       torch.tensor([f.attention_mask for f in features]       ,dtype=torch.long     )
   UpperCAmelCase__       :	Union[str, Any]        =       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[int]        =       torch.tensor([f.label for f in features]       ,dtype=torch.float     )

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

  def  __lowercase							(	self		:      List[str]       ,A		:      Union[str, Any]       ,A		:      str     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Dict        =       {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}

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

   UpperCAmelCase__       :	Dict        =       self(**A     )
   UpperCAmelCase__ ,   UpperCAmelCase__       :	Optional[Any]        =       outputs[:2]
   UpperCAmelCase__       :	Optional[Any]        =       logits.detach().cpu().numpy()
   UpperCAmelCase__       :	Union[str, Any]        =       inputs["""labels"""].detach().cpu().numpy()

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

  def  __lowercase							(	self		:      Dict       ,A		:      Any     ):






   '''simple docstring'''
   UpperCAmelCase__       :	str        =       torch.stack([x["""val_loss"""] for x in outputs]     ).mean().detach().cpu().item()
   UpperCAmelCase__       :	Optional[int]        =       np.concatenate([x["""pred"""] for x in outputs]       ,axis=0     )

   if self.hparams.glue_output_mode == "classification":
    UpperCAmelCase__       :	str        =       np.argmax(A       ,axis=1     )
   elif self.hparams.glue_output_mode == "regression":
    UpperCAmelCase__       :	Dict        =       np.squeeze(A     )

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

   UpperCAmelCase__       :	List[Any]        =       {**{"""val_loss""": val_loss_mean}, **compute_metrics(self.hparams.task       ,A       ,A     )}

   UpperCAmelCase__       :	Any        =       dict(results.items()     )
   UpperCAmelCase__       :	Optional[int]        =       results
   return ret, preds_list, out_label_list

  def  __lowercase							(	self		:      int       ,A		:      list     ):






   '''simple docstring'''
   UpperCAmelCase__ ,   UpperCAmelCase__ ,   UpperCAmelCase__       :	str        =       self._eval_end(A     )
   UpperCAmelCase__       :	str        =       ret["""log"""]
   return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}

  def  __lowercase							(	self		:      Any       ,A		:      int     ):






   '''simple docstring'''
   UpperCAmelCase__ ,   UpperCAmelCase__ ,   UpperCAmelCase__       :	Tuple        =       self._eval_end(A     )
   UpperCAmelCase__       :	Union[str, 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  __lowercase							(	A		:      List[str]       ,A		:      Union[str, Any]     ):






   '''simple docstring'''
   BaseTransformer.add_model_specific_args(A       ,A     )
   parser.add_argument(
       """--max_seq_length"""       ,default=128       ,type=A       ,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=A       ,required=A       ,help="""The GLUE task to run"""       ,)
   parser.add_argument(
       """--gpus"""       ,default=0       ,type=A       ,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 	lowerCAmelCase    (       ):
 '''simple docstring'''





 UpperCAmelCase__       :	List[str]        =       argparse.ArgumentParser()
 add_generic_args(__UpperCamelCase				,     os.getcwd()   )
 UpperCAmelCase__       :	Tuple        =       GLUETransformer.add_model_specific_args(__UpperCamelCase				,     os.getcwd()   )
 UpperCAmelCase__       :	Optional[int]        =       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__       :	str        =       GLUETransformer(__UpperCamelCase   )
 UpperCAmelCase__       :	Tuple        =       generic_train(__UpperCamelCase				,     __UpperCamelCase   )

 # 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=__UpperCamelCase   )   )
  UpperCAmelCase__       :	Optional[int]        =       model.load_from_checkpoint(checkpoints[-1]   )
  return trainer.test(__UpperCamelCase   )


if __name__ == "__main__":
      main()











import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np
class 						UpperCAmelCase  :







						'''simple docstring'''





						def 			snake_case__		(		self				:	List[Any]		):






							"""simple docstring"""






							torch.manual_seed(0		)
							snake_case_						       =	TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"		)

							torch.manual_seed(0		)
							snake_case_						       =	AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"		)

							torch.manual_seed(0		)
							snake_case_						       =	UNetaDConditionModel(
							    sample_size=32			,      layers_per_block=1			,      block_out_channels=[32, 64]			,      down_block_types=[
							        "ResnetDownsampleBlock2D",
							        "SimpleCrossAttnDownBlock2D",
							    ]			,      mid_block_type="UNetMidBlock2DSimpleCrossAttn"			,      up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]			,      in_channels=3			,      out_channels=6			,      cross_attention_dim=32			,      encoder_hid_dim=32			,      attention_head_dim=8			,      addition_embed_type="text"			,      addition_embed_type_num_heads=2			,      cross_attention_norm="group_norm"			,      resnet_time_scale_shift="scale_shift"			,      act_fn="gelu"			,      )
							unet.set_attn_processor(AttnAddedKVProcessor()		)  # For reproducibility tests

							torch.manual_seed(0		)
							snake_case_						       =	DDPMScheduler(
							    num_train_timesteps=10_00			,      beta_schedule="squaredcos_cap_v2"			,      beta_start=0.0001			,      beta_end=0.02			,      thresholding=__lowercase			,      dynamic_thresholding_ratio=0.95			,      sample_max_value=1.0			,      prediction_type="epsilon"			,      variance_type="learned_range"			,      )

							torch.manual_seed(0		)
							snake_case_						       =	IFWatermarker()

							return {
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							    "unet": unet,
							    "scheduler": scheduler,
							    "watermarker": watermarker,
							    "safety_checker": None,
							    "feature_extractor": None,
							}






						def 			snake_case__		(		self				:	List[str]		):






							"""simple docstring"""






							torch.manual_seed(0		)
							snake_case_						       =	TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"		)

							torch.manual_seed(0		)
							snake_case_						       =	AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"		)

							torch.manual_seed(0		)
							snake_case_						       =	UNetaDConditionModel(
							    sample_size=32			,      layers_per_block=[1, 2]			,      block_out_channels=[32, 64]			,      down_block_types=[
							        "ResnetDownsampleBlock2D",
							        "SimpleCrossAttnDownBlock2D",
							    ]			,      mid_block_type="UNetMidBlock2DSimpleCrossAttn"			,      up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]			,      in_channels=6			,      out_channels=6			,      cross_attention_dim=32			,      encoder_hid_dim=32			,      attention_head_dim=8			,      addition_embed_type="text"			,      addition_embed_type_num_heads=2			,      cross_attention_norm="group_norm"			,      resnet_time_scale_shift="scale_shift"			,      act_fn="gelu"			,      class_embed_type="timestep"			,      mid_block_scale_factor=1.414			,      time_embedding_act_fn="gelu"			,      time_embedding_dim=32			,      )
							unet.set_attn_processor(AttnAddedKVProcessor()		)  # For reproducibility tests

							torch.manual_seed(0		)
							snake_case_						       =	DDPMScheduler(
							    num_train_timesteps=10_00			,      beta_schedule="squaredcos_cap_v2"			,      beta_start=0.0001			,      beta_end=0.02			,      thresholding=__lowercase			,      dynamic_thresholding_ratio=0.95			,      sample_max_value=1.0			,      prediction_type="epsilon"			,      variance_type="learned_range"			,      )

							torch.manual_seed(0		)
							snake_case_						       =	DDPMScheduler(
							    num_train_timesteps=10_00			,      beta_schedule="squaredcos_cap_v2"			,      beta_start=0.0001			,      beta_end=0.02			,      )

							torch.manual_seed(0		)
							snake_case_						       =	IFWatermarker()

							return {
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							    "unet": unet,
							    "scheduler": scheduler,
							    "image_noising_scheduler": image_noising_scheduler,
							    "watermarker": watermarker,
							    "safety_checker": None,
							    "feature_extractor": None,
							}






						def 			snake_case__		(		self				:	List[str]		):






							"""simple docstring"""






							snake_case_						       =	self.get_dummy_components()
							snake_case_						       =	self.pipeline_class(**__lowercase		)
							pipe.to(__lowercase		)
							pipe.set_progress_bar_config(disable=__lowercase		)

							snake_case_						       =	self.get_dummy_inputs(__lowercase		)

							snake_case_						       =	inputs["prompt"]
							snake_case_						       =	inputs["generator"]
							snake_case_						       =	inputs["num_inference_steps"]
							snake_case_						       =	inputs["output_type"]

							if "image" in inputs:
								snake_case_						       =	inputs["image"]
							else:
								snake_case_						       =	None

							if "mask_image" in inputs:
								snake_case_						       =	inputs["mask_image"]
							else:
								snake_case_						       =	None

							if "original_image" in inputs:
								snake_case_						       =	inputs["original_image"]
							else:
								snake_case_						       =	None

							snake_case_       , snake_case_						       =	pipe.encode_prompt(__lowercase		)

							# inputs with prompt converted to embeddings
							snake_case_						       =	{
							    "prompt_embeds": prompt_embeds,
							    "negative_prompt_embeds": negative_prompt_embeds,
							    "generator": generator,
							    "num_inference_steps": num_inference_steps,
							    "output_type": output_type,
							}

							if image is not None:
								snake_case_						       =	image

							if mask_image is not None:
								snake_case_						       =	mask_image

							if original_image is not None:
								snake_case_						       =	original_image

							# set all optional components to None
							for optional_component in pipe._optional_components:
								setattr(__lowercase			,      __lowercase			,      __lowercase		)

							snake_case_						       =	pipe(**__lowercase		)[0]

							with tempfile.TemporaryDirectory() as tmpdir:
								pipe.save_pretrained(__lowercase		)
								snake_case_						       =	self.pipeline_class.from_pretrained(__lowercase		)
								pipe_loaded.to(__lowercase		)
								pipe_loaded.set_progress_bar_config(disable=__lowercase		)

							pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()		)  # For reproducibility tests

							for optional_component in pipe._optional_components:
								self.assertTrue(
								    getattr(__lowercase			,      __lowercase		) is None			,      f"`{optional_component}` did not stay set to None after loading."			,      )

							snake_case_						       =	self.get_dummy_inputs(__lowercase		)

							snake_case_						       =	inputs["generator"]
							snake_case_						       =	inputs["num_inference_steps"]
							snake_case_						       =	inputs["output_type"]

							# inputs with prompt converted to embeddings
							snake_case_						       =	{
							    "prompt_embeds": prompt_embeds,
							    "negative_prompt_embeds": negative_prompt_embeds,
							    "generator": generator,
							    "num_inference_steps": num_inference_steps,
							    "output_type": output_type,
							}

							if image is not None:
								snake_case_						       =	image

							if mask_image is not None:
								snake_case_						       =	mask_image

							if original_image is not None:
								snake_case_						       =	original_image

							snake_case_						       =	pipe_loaded(**__lowercase		)[0]

							snake_case_						       =	np.abs(to_np(__lowercase		) - to_np(__lowercase		)		).max()
							self.assertLess(__lowercase			,      1E-4		)






						def 			snake_case__		(		self				:	Union[str, Any]		):






							"""simple docstring"""






							snake_case_						       =	self.get_dummy_components()
							snake_case_						       =	self.pipeline_class(**__lowercase		)
							pipe.to(__lowercase		)
							pipe.set_progress_bar_config(disable=__lowercase		)

							snake_case_						       =	self.get_dummy_inputs(__lowercase		)
							snake_case_						       =	pipe(**__lowercase		)[0]

							with tempfile.TemporaryDirectory() as tmpdir:
								pipe.save_pretrained(__lowercase		)
								snake_case_						       =	self.pipeline_class.from_pretrained(__lowercase		)
								pipe_loaded.to(__lowercase		)
								pipe_loaded.set_progress_bar_config(disable=__lowercase		)

							pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()		)  # For reproducibility tests

							snake_case_						       =	self.get_dummy_inputs(__lowercase		)
							snake_case_						       =	pipe_loaded(**__lowercase		)[0]

							snake_case_						       =	np.abs(to_np(__lowercase		) - to_np(__lowercase		)		).max()
							self.assertLess(__lowercase			,      1E-4		)
