'''simple docstring'''

import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class   SCREAMING_SNAKE_CASE			(						__lowercase    ,			unittest.TestCase):







		"""simple docstring"""
		lowercase   :      str       =							ShapEPipeline
		lowercase   :      int       =							['prompt']
		lowercase   :      str       =							['prompt']
		lowercase   :      Any       =							[
		    'num_images_per_prompt',
		    'num_inference_steps',
		    'generator',
		    'latents',
		    'guidance_scale',
		    'frame_size',
		    'output_type',
		    'return_dict',
		]
		lowercase   :      Optional[int]       =							False






		@property
		def 		UpperCamelCase__       (   self				) ->     str:
								return 32






		@property
		def 		UpperCamelCase__       (   self				) ->     List[Any]:
								return 32






		@property
		def 		UpperCamelCase__       (   self				) ->     Optional[Any]:
								return self.time_input_dim * 4






		@property
		def 		UpperCamelCase__       (   self				) ->     Optional[Any]:
								return 8






		@property
		def 		UpperCamelCase__       (   self				) ->     List[str]:
								_lowerCAmelCase										=CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'				)
								return tokenizer






		@property
		def 		UpperCamelCase__       (   self				) ->     Optional[Any]:
								torch.manual_seed(0				)
								_lowerCAmelCase										=CLIPTextConfig(
								    bos_token_id=0		,						eos_token_id=2		,						hidden_size=self.text_embedder_hidden_size		,						projection_dim=self.text_embedder_hidden_size		,						intermediate_size=37		,						layer_norm_eps=1E-05		,						num_attention_heads=4		,						num_hidden_layers=5		,						pad_token_id=1		,						vocab_size=1000		,						)
								return CLIPTextModelWithProjection(__A				)






		@property
		def 		UpperCamelCase__       (   self				) ->     int:
								torch.manual_seed(0				)

								_lowerCAmelCase										={
								    'num_attention_heads': 2,
								    'attention_head_dim': 16,
								    'embedding_dim': self.time_input_dim,
								    'num_embeddings': 32,
								    'embedding_proj_dim': self.text_embedder_hidden_size,
								    'time_embed_dim': self.time_embed_dim,
								    'num_layers': 1,
								    'clip_embed_dim': self.time_input_dim * 2,
								    'additional_embeddings': 0,
								    'time_embed_act_fn': 'gelu',
								    'norm_in_type': 'layer',
								    'encoder_hid_proj_type': None,
								    'added_emb_type': None,
								}

								_lowerCAmelCase										=PriorTransformer(**__A				)
								return model






		@property
		def 		UpperCamelCase__       (   self				) ->     int:
								torch.manual_seed(0				)

								_lowerCAmelCase										={
								    'param_shapes': (
								        (self.renderer_dim, 93),
								        (self.renderer_dim, 8),
								        (self.renderer_dim, 8),
								        (self.renderer_dim, 8),
								    ),
								    'd_latent': self.time_input_dim,
								    'd_hidden': self.renderer_dim,
								    'n_output': 12,
								    'background': (
								        0.1,
								        0.1,
								        0.1,
								    ),
								}
								_lowerCAmelCase										=ShapERenderer(**__A				)
								return model






		def 		UpperCamelCase__       (   self				) ->     int:
								_lowerCAmelCase										=self.dummy_prior
								_lowerCAmelCase										=self.dummy_text_encoder
								_lowerCAmelCase										=self.dummy_tokenizer
								_lowerCAmelCase										=self.dummy_renderer

								_lowerCAmelCase										=HeunDiscreteScheduler(
								    beta_schedule='exp'		,						num_train_timesteps=1024		,						prediction_type='sample'		,						use_karras_sigmas=__A		,						clip_sample=__A		,						clip_sample_range=1.0		,						)
								_lowerCAmelCase										={
								    'prior': prior,
								    'text_encoder': text_encoder,
								    'tokenizer': tokenizer,
								    'renderer': renderer,
								    'scheduler': scheduler,
								}

								return components






		def 		UpperCamelCase__       (   self		,						__A		,						__A=0				) ->     Dict:
								if str(__A				).startswith('mps'				):
														_lowerCAmelCase										=torch.manual_seed(__A				)
								else:
														_lowerCAmelCase										=torch.Generator(device=__A				).manual_seed(__A				)
								_lowerCAmelCase										={
								    'prompt': 'horse',
								    'generator': generator,
								    'num_inference_steps': 1,
								    'frame_size': 32,
								    'output_type': 'np',
								}
								return inputs






		def 		UpperCamelCase__       (   self				) ->     int:
								_lowerCAmelCase										='cpu'

								_lowerCAmelCase										=self.get_dummy_components()

								_lowerCAmelCase										=self.pipeline_class(**__A				)
								_lowerCAmelCase										=pipe.to(__A				)

								pipe.set_progress_bar_config(disable=__A				)

								_lowerCAmelCase										=pipe(**self.get_dummy_inputs(__A				)				)
								_lowerCAmelCase										=output.images[0]
								_lowerCAmelCase										=image[0, -3:, -3:, -1]

								assert image.shape == (20, 32, 32, 3)

								_lowerCAmelCase										=np.array(
								    [
								        0.00_039_216,
								        0.00_039_216,
								        0.00_039_216,
								        0.00_039_216,
								        0.00_039_216,
								        0.00_039_216,
								        0.00_039_216,
								        0.00_039_216,
								        0.00_039_216,
								    ]				)

								assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2






		def 		UpperCamelCase__       (   self				) ->     List[Any]:
								# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
								self._test_inference_batch_consistent(batch_sizes=[1, 2]				)






		def 		UpperCamelCase__       (   self				) ->     int:
								_lowerCAmelCase										=torch_device == 'cpu'
								_lowerCAmelCase										=True

								self._test_inference_batch_single_identical(
								    batch_size=2		,						test_max_difference=__A		,						relax_max_difference=__A		,						)






		def 		UpperCamelCase__       (   self				) ->     Dict:
								_lowerCAmelCase										=self.get_dummy_components()
								_lowerCAmelCase										=self.pipeline_class(**__A				)
								_lowerCAmelCase										=pipe.to(__A				)
								pipe.set_progress_bar_config(disable=__A				)

								_lowerCAmelCase										=1
								_lowerCAmelCase										=2

								_lowerCAmelCase										=self.get_dummy_inputs(__A				)

								for key in inputs.keys():
														if key in self.batch_params:
																				_lowerCAmelCase										=batch_size * [inputs[key]]

								_lowerCAmelCase										=pipe(**__A		,						num_images_per_prompt=__A				)[0]

								assert images.shape[0] == batch_size * num_images_per_prompt

@slow
@require_torch_gpu
class   SCREAMING_SNAKE_CASE			(						unittest.TestCase):







		"""simple docstring"""






		def 		UpperCamelCase__       (   self				) ->     Union[str, Any]:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()






		def 		UpperCamelCase__       (   self				) ->     Tuple:
								_lowerCAmelCase										=load_numpy(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/shap_e/test_shap_e_np_out.npy'				)
								_lowerCAmelCase										=ShapEPipeline.from_pretrained('openai/shap-e'				)
								_lowerCAmelCase										=pipe.to(__A				)
								pipe.set_progress_bar_config(disable=__A				)

								_lowerCAmelCase										=torch.Generator(device=__A				).manual_seed(0				)

								_lowerCAmelCase										=pipe(
								    'a shark'		,						generator=__A		,						guidance_scale=15.0		,						num_inference_steps=64		,						frame_size=64		,						output_type='np'		,						).images[0]

								assert images.shape == (20, 64, 64, 3)

								assert_mean_pixel_difference(__A		,						__A				)






'''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


lowercase_     		= logging.getLogger(__name__)
class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      int       =							'sequence-classification'






		def __init__(   self		,						__A				) ->     List[Any]:
								if type(__A				) == dict:
														_lowerCAmelCase										=Namespace(**__A				)
								_lowerCAmelCase										=glue_output_modes[hparams.task]
								_lowerCAmelCase										=glue_tasks_num_labels[hparams.task]

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






		def 		UpperCamelCase__       (   self		,						**__A				) ->     Any:
								return self.model(**__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A				) ->     Union[str, Any]:
								_lowerCAmelCase										={'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

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

								_lowerCAmelCase										=self(**__A				)
								_lowerCAmelCase										=outputs[0]

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






		def 		UpperCamelCase__       (   self				) ->     Any:
								_lowerCAmelCase										=self.hparams
								_lowerCAmelCase										=processors[args.task]()
								_lowerCAmelCase										=processor.get_labels()

								for mode in ["train", "dev"]:
														_lowerCAmelCase										=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				)
																				_lowerCAmelCase										=(
																				    processor.get_dev_examples(args.data_dir				)
																				    if mode == 'dev'
																				    else processor.get_train_examples(args.data_dir				)
																				)
																				_lowerCAmelCase										=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 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A = False				) ->     DataLoader:
								_lowerCAmelCase										='dev' if mode == 'test' else mode

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

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






		def 		UpperCamelCase__       (   self		,						__A		,						__A				) ->     List[str]:
								_lowerCAmelCase										={'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

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

								_lowerCAmelCase										=self(**__A				)
								_lowerCAmelCase						,				_lowerCAmelCase										=outputs[:2]
								_lowerCAmelCase										=logits.detach().cpu().numpy()
								_lowerCAmelCase										=inputs['labels'].detach().cpu().numpy()

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






		def 		UpperCamelCase__       (   self		,						__A				) ->     tuple:
								_lowerCAmelCase										=torch.stack([x['val_loss'] for x in outputs]				).mean().detach().cpu().item()
								_lowerCAmelCase										=np.concatenate([x['pred'] for x in outputs]		,						axis=0				)

								if self.hparams.glue_output_mode == "classification":
														_lowerCAmelCase										=np.argmax(__A		,						axis=1				)
								elif self.hparams.glue_output_mode == "regression":
														_lowerCAmelCase										=np.squeeze(__A				)

								_lowerCAmelCase										=np.concatenate([x['target'] for x in outputs]		,						axis=0				)
								_lowerCAmelCase										=[[] for _ in range(out_label_ids.shape[0]				)]
								_lowerCAmelCase										=[[] for _ in range(out_label_ids.shape[0]				)]

								_lowerCAmelCase										={**{'val_loss': val_loss_mean}, **compute_metrics(self.hparams.task		,						__A		,						__A				)}

								_lowerCAmelCase										=dict(results.items()				)
								_lowerCAmelCase										=results
								return ret, preds_list, out_label_list






		def 		UpperCamelCase__       (   self		,						__A				) ->     dict:
								_lowerCAmelCase						,				_lowerCAmelCase						,				_lowerCAmelCase										=self._eval_end(__A				)
								_lowerCAmelCase										=ret['log']
								return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}






		def 		UpperCamelCase__       (   self		,						__A				) ->     dict:
								_lowerCAmelCase						,				_lowerCAmelCase						,				_lowerCAmelCase										=self._eval_end(__A				)
								_lowerCAmelCase										=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 		UpperCamelCase__       (   __A		,						__A				) ->     Any:
								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 							UpperCamelCase__			(			):


						'''simple docstring'''






						_lowerCAmelCase										=argparse.ArgumentParser()
						add_generic_args(a__			,      os.getcwd()      )
						_lowerCAmelCase										=GLUETransformer.add_model_specific_args(a__			,      os.getcwd()      )
						_lowerCAmelCase										=parser.parse_args()

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

						_lowerCAmelCase										=GLUETransformer(a__      )
						_lowerCAmelCase										=generic_train(a__			,      a__      )

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


if __name__ == "__main__":
							main()





