
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask


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







class 				lowercase		(					SCREAMING_SNAKE_CASE__		):
							lowercase_       :       Optional[Any]       					='''token-classification'''




							def __init__(				self   ,A__):
												if type(A__) == dict:
																	lowercase							=						Namespace(**A__)
												lowercase							=						import_module('''tasks''')
												try:
																	lowercase							=						getattr(A__   ,hparams.task_type)
																	lowercase							=						token_classification_task_clazz()
												except AttributeError:
																	raise ValueError(
																	    f'Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '
																	    f'Available tasks classes are: {TokenClassificationTask.__subclasses__()}')
												lowercase							=						self.token_classification_task.get_labels(hparams.labels)
												lowercase							=						CrossEntropyLoss().ignore_index
												super().__init__(A__   ,len(self.labels)   ,self.mode)




							def  A__      (				self   ,**A__):
												return self.model(**A__)




							def  A__      (				self   ,A__   ,A__):
												lowercase							=						{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
												if self.config.model_type != "distilbert":
																	lowercase							=						(
																	    batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
																	)  # XLM and RoBERTa don"t use token_type_ids

												lowercase							=						self(**A__)
												lowercase							=						outputs[0]
												# tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
												return {"loss": loss}




							def  A__      (				self):
												lowercase							=						self.hparams
												for mode in ["train", "dev", "test"]:
																	lowercase							=						self._feature_file(A__)
																	if os.path.exists(A__) and not args.overwrite_cache:
																						logger.info('''Loading features from cached file %s'''   ,A__)
																						lowercase							=						torch.load(A__)
																	else:
																						logger.info('''Creating features from dataset file at %s'''   ,args.data_dir)
																						lowercase							=						self.token_classification_task.read_examples_from_file(args.data_dir   ,A__)
																						lowercase							=						self.token_classification_task.convert_examples_to_features(
																						    A__   ,self.labels   ,args.max_seq_length   ,self.tokenizer   ,cls_token_at_end=bool(self.config.model_type in ['''xlnet'''])   ,cls_token=self.tokenizer.cls_token   ,cls_token_segment_id=2 if self.config.model_type in ['''xlnet'''] else 0   ,sep_token=self.tokenizer.sep_token   ,sep_token_extra=A__   ,pad_on_left=bool(self.config.model_type in ['''xlnet'''])   ,pad_token=self.tokenizer.pad_token_id   ,pad_token_segment_id=self.tokenizer.pad_token_type_id   ,pad_token_label_id=self.pad_token_label_id   ,)
																						logger.info('''Saving features into cached file %s'''   ,A__)
																						torch.save(A__   ,A__)




							def  A__      (				self   ,A__   ,A__   ,A__ = False):
												lowercase							=						self._feature_file(A__)
												logger.info('''Loading features from cached file %s'''   ,A__)
												lowercase							=						torch.load(A__)
												lowercase							=						torch.tensor([f.input_ids for f in features]   ,dtype=torch.long)
												lowercase							=						torch.tensor([f.attention_mask for f in features]   ,dtype=torch.long)
												if features[0].token_type_ids is not None:
																	lowercase							=						torch.tensor([f.token_type_ids for f in features]   ,dtype=torch.long)
												else:
																	lowercase							=						torch.tensor([0 for f in features]   ,dtype=torch.long)
																	# HACK(we will not use this anymore soon)
												lowercase							=						torch.tensor([f.label_ids for f in features]   ,dtype=torch.long)
												return DataLoader(
												    TensorDataset(A__   ,A__   ,A__   ,A__)   ,batch_size=A__)




							def  A__      (				self   ,A__   ,A__):
												"""Compute validation""" ""
												lowercase							=						{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
												if self.config.model_type != "distilbert":
																	lowercase							=						(
																	    batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
																	)  # XLM and RoBERTa don"t use token_type_ids
												lowercase							=						self(**A__)
												lowercase	,						lowercase							=						outputs[:2]
												lowercase							=						logits.detach().cpu().numpy()
												lowercase							=						inputs['''labels'''].detach().cpu().numpy()
												return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}




							def  A__      (				self   ,A__):
												lowercase							=						torch.stack([x['''val_loss'''] for x in outputs]).mean()
												lowercase							=						np.concatenate([x['''pred'''] for x in outputs]   ,axis=0)
												lowercase							=						np.argmax(A__   ,axis=2)
												lowercase							=						np.concatenate([x['''target'''] for x in outputs]   ,axis=0)

												lowercase							=						dict(enumerate(self.labels))
												lowercase							=						[[] for _ in range(out_label_ids.shape[0])]
												lowercase							=						[[] for _ in range(out_label_ids.shape[0])]

												for i in range(out_label_ids.shape[0]):
																	for j in range(out_label_ids.shape[1]):
																						if out_label_ids[i, j] != self.pad_token_label_id:
																											out_label_list[i].append(label_map[out_label_ids[i][j]])
																											preds_list[i].append(label_map[preds[i][j]])

												lowercase							=						{
												    '''val_loss''': val_loss_mean,
												    '''accuracy_score''': accuracy_score(A__   ,A__),
												    '''precision''': precision_score(A__   ,A__),
												    '''recall''': recall_score(A__   ,A__),
												    '''f1''': fa_score(A__   ,A__),
												}

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




							def  A__      (				self   ,A__):
												# when stable
												lowercase	,						lowercase	,						lowercase							=						self._eval_end(A__)
												lowercase							=						ret['''log''']
												return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}




							def  A__      (				self   ,A__):
												# updating to test_epoch_end instead of deprecated test_end
												lowercase	,						lowercase	,						lowercase							=						self._eval_end(A__)

												# Converting to the dict required by pl
												# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
												# pytorch_lightning/trainer/logging.py#L139
												lowercase							=						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  A__      (				A__   ,A__):
												# Add NER specific options
												BaseTransformer.add_model_specific_args(A__   ,A__)
												parser.add_argument(
												    '''--task_type'''   ,default='''NER'''   ,type=A__   ,help='''Task type to fine tune in training (e.g. NER, POS, etc)''')
												parser.add_argument(
												    '''--max_seq_length'''   ,default=1_2_8   ,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(
												    '''--labels'''   ,default=''''''   ,type=A__   ,help='''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''   ,)
												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


if __name__ == "__main__":
							lowercase__		:List[Any]   =    argparse.ArgumentParser()
							add_generic_args(parser, os.getcwd())
							lowercase__		:int   =    NERTransformer.add_model_specific_args(parser, os.getcwd())
							lowercase__		:Optional[int]   =    parser.parse_args()
							lowercase__		:Any   =    NERTransformer(args)
							lowercase__		:Tuple   =    generic_train(model, args)

							if args.do_predict:
														# See https://github.com/huggingface/transformers/issues/3159
														# pl use this default format to create a checkpoint:
														# https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
														# /pytorch_lightning/callbacks/model_checkpoint.py#L322
														lowercase__		:Any   =    sorted(glob.glob(os.path.join(args.output_dir, "checkpoint-epoch=*.ckpt"), recursive=True))
														lowercase__		:Union[str, Any]   =    model.load_from_checkpoint(checkpoints[-1])
														trainer.test(model)






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 				lowercase		(					SCREAMING_SNAKE_CASE__       ,  unittest.TestCase		):
							lowercase_       :       Tuple       					=ShapEPipeline
							lowercase_       :       List[Any]       					=['''prompt''']
							lowercase_       :       int       					=['''prompt''']
							lowercase_       :       Union[str, Any]       					=[
							    '''num_images_per_prompt''',
							    '''num_inference_steps''',
							    '''generator''',
							    '''latents''',
							    '''guidance_scale''',
							    '''frame_size''',
							    '''output_type''',
							    '''return_dict''',
							]
							lowercase_       :       Optional[int]       					=False




							@property
							def  A__      (				self):
												return 3_2




							@property
							def  A__      (				self):
												return 3_2




							@property
							def  A__      (				self):
												return self.time_input_dim * 4




							@property
							def  A__      (				self):
												return 8




							@property
							def  A__      (				self):
												lowercase							=						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')
												return tokenizer




							@property
							def  A__      (				self):
												torch.manual_seed(0)
												lowercase							=						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=3_7   ,layer_norm_eps=1E-05   ,num_attention_heads=4   ,num_hidden_layers=5   ,pad_token_id=1   ,vocab_size=1_0_0_0   ,)
												return CLIPTextModelWithProjection(A__)




							@property
							def  A__      (				self):
												torch.manual_seed(0)

												lowercase							=						{
												    '''num_attention_heads''': 2,
												    '''attention_head_dim''': 1_6,
												    '''embedding_dim''': self.time_input_dim,
												    '''num_embeddings''': 3_2,
												    '''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,
												}

												lowercase							=						PriorTransformer(**A__)
												return model




							@property
							def  A__      (				self):
												torch.manual_seed(0)

												lowercase							=						{
												    '''param_shapes''': (
												        (self.renderer_dim, 9_3),
												        (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''': 1_2,
												    '''background''': (
												        0.1,
												        0.1,
												        0.1,
												    ),
												}
												lowercase							=						ShapERenderer(**A__)
												return model




							def  A__      (				self):
												lowercase							=						self.dummy_prior
												lowercase							=						self.dummy_text_encoder
												lowercase							=						self.dummy_tokenizer
												lowercase							=						self.dummy_renderer

												lowercase							=						HeunDiscreteScheduler(
												    beta_schedule='''exp'''   ,num_train_timesteps=1_0_2_4   ,prediction_type='''sample'''   ,use_karras_sigmas=A__   ,clip_sample=A__   ,clip_sample_range=1.0   ,)
												lowercase							=						{
												    '''prior''': prior,
												    '''text_encoder''': text_encoder,
												    '''tokenizer''': tokenizer,
												    '''renderer''': renderer,
												    '''scheduler''': scheduler,
												}

												return components




							def  A__      (				self   ,A__   ,A__=0):
												if str(A__).startswith('''mps'''):
																	lowercase							=						torch.manual_seed(A__)
												else:
																	lowercase							=						torch.Generator(device=A__).manual_seed(A__)
												lowercase							=						{
												    '''prompt''': '''horse''',
												    '''generator''': generator,
												    '''num_inference_steps''': 1,
												    '''frame_size''': 3_2,
												    '''output_type''': '''np''',
												}
												return inputs




							def  A__      (				self):
												lowercase							=						'''cpu'''

												lowercase							=						self.get_dummy_components()

												lowercase							=						self.pipeline_class(**A__)
												lowercase							=						pipe.to(A__)

												pipe.set_progress_bar_config(disable=A__)

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

												assert image.shape == (2_0, 3_2, 3_2, 3)

												lowercase							=						np.array(
												    [
												        0.00039216,
												        0.00039216,
												        0.00039216,
												        0.00039216,
												        0.00039216,
												        0.00039216,
												        0.00039216,
												        0.00039216,
												        0.00039216,
												    ])

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




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




							def  A__      (				self):
												lowercase							=						torch_device == '''cpu'''
												lowercase							=						True

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





							def  A__      (				self):
												lowercase							=						self.get_dummy_components()
												lowercase							=						self.pipeline_class(**A__)
												lowercase							=						pipe.to(A__)
												pipe.set_progress_bar_config(disable=A__)

												lowercase							=						1
												lowercase							=						2

												lowercase							=						self.get_dummy_inputs(A__)

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

												lowercase							=						pipe(**A__   ,num_images_per_prompt=A__)[0]

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







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




							def  A__      (				self):
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()





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

												lowercase							=						torch.Generator(device=A__).manual_seed(0)

												lowercase							=						pipe(
												    '''a shark'''   ,generator=A__   ,guidance_scale=15.0   ,num_inference_steps=6_4   ,frame_size=6_4   ,output_type='''np'''   ,).images[0]

												assert images.shape == (2_0, 6_4, 6_4, 3)

												assert_mean_pixel_difference(A__   ,A__)




