






"""simple docstring"""







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


a			:    List[Any]				 =		logging.getLogger(__name__)


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						__lowerCamelCase    	= "token-classification"




						def __init__(       self	,  snake_case__      ):
												'''simple docstring'''
												if type(snake_case__      ) == dict:
																		lowercase__    :				str=	Namespace(**snake_case__      )
												lowercase__    :				Optional[Any]=	import_module("tasks"      )
												try:
																		lowercase__    :				List[Any]=	getattr(snake_case__	,  hparams.task_type      )
																		lowercase__    :				TokenClassificationTask=	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__    :				List[Any]=	self.token_classification_task.get_labels(hparams.labels      )
												lowercase__    :				Dict=	CrossEntropyLoss().ignore_index
												super().__init__(snake_case__	,  len(self.labels      )	,  self.mode      )




						def 		UpperCAmelCase_	(       self	,  **snake_case__      ):
												'''simple docstring'''
												return self.model(**snake_case__      )




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

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




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Union[str, Any]=	self.hparams
												for mode in ["train", "dev", "test"]:
																		lowercase__    :				Any=	self._feature_file(snake_case__      )
																		if os.path.exists(snake_case__      ) and not args.overwrite_cache:
																								logger.info("Loading features from cached file %s"	,  snake_case__      )
																								lowercase__    :				Optional[int]=	torch.load(snake_case__      )
																		else:
																								logger.info("Creating features from dataset file at %s"	,  args.data_dir      )
																								lowercase__    :				List[str]=	self.token_classification_task.read_examples_from_file(args.data_dir	,  snake_case__      )
																								lowercase__    :				Optional[Any]=	self.token_classification_task.convert_examples_to_features(
																								    snake_case__	,  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=snake_case__	,  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"	,  snake_case__      )
																								torch.save(snake_case__	,  snake_case__      )




						def 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__	,  snake_case__ = False      ):
												'''simple docstring'''
												lowercase__    :				List[Any]=	self._feature_file(snake_case__      )
												logger.info("Loading features from cached file %s"	,  snake_case__      )
												lowercase__    :				Tuple=	torch.load(snake_case__      )
												lowercase__    :				Tuple=	torch.tensor([f.input_ids for f in features]	,  dtype=torch.long      )
												lowercase__    :				Union[str, Any]=	torch.tensor([f.attention_mask for f in features]	,  dtype=torch.long      )
												if features[0].token_type_ids is not None:
																		lowercase__    :				Tuple=	torch.tensor([f.token_type_ids for f in features]	,  dtype=torch.long      )
												else:
																		lowercase__    :				Union[str, Any]=	torch.tensor([0 for f in features]	,  dtype=torch.long      )
																		# HACK(we will not use this anymore soon)
												lowercase__    :				Optional[Any]=	torch.tensor([f.label_ids for f in features]	,  dtype=torch.long      )
												return DataLoader(
												    TensorDataset(snake_case__	,  snake_case__	,  snake_case__	,  snake_case__      )	,  batch_size=snake_case__      )




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




						def 		UpperCAmelCase_	(       self	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				Union[str, Any]=	torch.stack([x["val_loss"] for x in outputs]      ).mean()
												lowercase__    :				Optional[Any]=	np.concatenate([x["pred"] for x in outputs]	,  axis=0      )
												lowercase__    :				List[str]=	np.argmax(snake_case__	,  axis=2      )
												lowercase__    :				Optional[Any]=	np.concatenate([x["target"] for x in outputs]	,  axis=0      )

												lowercase__    :				str=	dict(enumerate(self.labels      )      )
												lowercase__    :				int=	[[] for _ in range(out_label_ids.shape[0]      )]
												lowercase__    :				List[str]=	[[] 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__    :				List[str]=	{
												    "val_loss": val_loss_mean,
												    "accuracy_score": accuracy_score(snake_case__	,  snake_case__      ),
												    "precision": precision_score(snake_case__	,  snake_case__      ),
												    "recall": recall_score(snake_case__	,  snake_case__      ),
												    "f1": fa_score(snake_case__	,  snake_case__      ),
												}

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




						def 		UpperCAmelCase_	(       self	,  snake_case__      ):
												'''simple docstring'''
												# when stable
												lowercase__,     lowercase__,     lowercase__    :				Any=	self._eval_end(snake_case__      )
												lowercase__    :				Any=	ret["log"]
												return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}




						def 		UpperCAmelCase_	(       self	,  snake_case__      ):
												'''simple docstring'''
												# updating to test_epoch_end instead of deprecated test_end
												lowercase__,     lowercase__,     lowercase__    :				Union[str, Any]=	self._eval_end(snake_case__      )

												# Converting to the dict required by pl
												# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
												# pytorch_lightning/trainer/logging.py#L139
												lowercase__    :				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 		UpperCAmelCase_	(       snake_case__	,  snake_case__      ):
												'''simple docstring'''
												# Add NER specific options
												BaseTransformer.add_model_specific_args(snake_case__	,  snake_case__      )
												parser.add_argument(
												    "--task_type"	,  default="NER"	,  type=snake_case__	,  help="Task type to fine tune in training (e.g. NER, POS, etc)"      )
												parser.add_argument(
												    "--max_seq_length"	,  default=128	,  type=snake_case__	,  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=snake_case__	,  help="Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."	,  )
												parser.add_argument(
												    "--gpus"	,  default=0	,  type=snake_case__	,  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__":
			a			:    Union[str, Any]				 =		argparse.ArgumentParser()
			add_generic_args(parser, os.getcwd())
			a			:    Optional[Any]				 =		NERTransformer.add_model_specific_args(parser, os.getcwd())
			a			:    Tuple				 =		parser.parse_args()
			a			:    Optional[Any]				 =		NERTransformer(args)
			a			:    Optional[Any]				 =		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
						a			:    Any				 =		sorted(glob.glob(os.path.join(args.output_dir, """checkpoint-epoch=*.ckpt"""), recursive=True))
						a			:    Optional[Any]				 =		model.load_from_checkpoint(checkpoints[-1])
						trainer.test(model)














"""simple docstring"""







import argparse
import os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)





def 						lowercase__(A						)    ->Dict:




						"""simple docstring"""



						lowercase__    :				Dict=	checkpoints.load_tax_checkpoint(A						)
						lowercase__    :				List[Any]=	flatten_dict(A						)
						return flax_params





def 						lowercase__(A						)    ->Tuple:




						"""simple docstring"""



						lowercase__    :				Union[str, Any]=	{}

						lowercase__    :				Any=	{
						    "token_embedder": "embeddings",
						    "encoder_norm": "layernorm",
						    "kernel": "weight",
						    ".out": ".output",
						    "scale": "weight",
						    "embedders_0.pos_embedding": "row_embedder.weight",
						    "embedders_1.pos_embedding": "column_embedder.weight",
						}

						lowercase__    :				Tuple=	{
						    "query": "attention.query",
						    "key": "attention.key",
						    "value": "attention.value",
						    "output.dense": "output",
						    "encoder_decoder_attention.o": "encoder_decoder_attention.attention.o",
						    "pre_self_attention_layer_norm": "self_attention.layer_norm",
						    "pre_cross_attention_layer_norm": "encoder_decoder_attention.layer_norm",
						    "mlp.": "mlp.DenseReluDense.",
						    "pre_mlp_layer_norm": "mlp.layer_norm",
						    "self_attention.o": "self_attention.attention.o",
						    "decoder.embeddings.embedding": "decoder.embed_tokens.weight",
						    "decoder.relpos_bias.rel_embedding": "decoder.layer.0.self_attention.attention.relative_attention_bias.weight",
						    "decoder.decoder_norm.weight": "decoder.final_layer_norm.weight",
						    "decoder.logits_dense.weight": "decoder.lm_head.weight",
						}

						for key in flax_dict.keys():
												if "target" in key:
																		# remove the first prefix from the key
																		lowercase__    :				Any=	".".join(key[1:]						)

																		# rename the key
																		for old, new in CONVERSION_MAPPING.items():
																								lowercase__    :				List[Any]=	new_key.replace(A ,		A						)

																		if "decoder" in new_key:
																								for old, new in DECODER_CONVERSION_MAPPING.items():
																														lowercase__    :				List[str]=	new_key.replace(A ,		A						)

																		if "layers" in new_key and "decoder" not in new_key:
																								# use regex to replace the layer number
																								lowercase__    :				List[str]=	re.sub(R"layers_(\d+)" ,		R"layer.\1" ,		A						)
																								lowercase__    :				Any=	new_key.replace("encoder" ,		"encoder.encoder"						)

																		elif "layers" in new_key and "decoder" in new_key:
																								# use regex to replace the layer number
																								lowercase__    :				str=	re.sub(R"layers_(\d+)" ,		R"layer.\1" ,		A						)

																		lowercase__    :				Dict=	flax_dict[key]

						lowercase__    :				str=	{}
						# convert converted_dict into torch format
						for key in converted_dict.keys():
												if ("embed_tokens" not in key) and ("embedder" not in key):
																		lowercase__    :				Dict=	torch.from_numpy(converted_dict[key].T						)
												else:
																		lowercase__    :				Any=	torch.from_numpy(converted_dict[key]						)

						return converted_torch_dict





def 						lowercase__(A ,		A ,		A=False ,		A=False						)    ->Union[str, Any]:




						"""simple docstring"""



						lowercase__    :				Tuple=	get_flax_param(A						)

						if not use_large:
												lowercase__    :				int=	PixaStructVisionConfig()
												lowercase__    :				str=	PixaStructTextConfig()
						else:
												lowercase__    :				List[Any]=	PixaStructVisionConfig(
												    hidden_size=1_536 ,		d_ff=3_968 ,		num_attention_heads=24 ,		num_hidden_layers=18						)
												lowercase__    :				Optional[Any]=	PixaStructTextConfig(hidden_size=1_536 ,		d_ff=3_968 ,		num_heads=24 ,		num_layers=18						)
						lowercase__    :				Any=	PixaStructConfig(
						    vision_config=encoder_config.to_dict() ,		text_config=decoder_config.to_dict() ,		is_vqa=A						)

						lowercase__    :				Optional[Any]=	PixaStructForConditionalGeneration(A						)

						lowercase__    :				Tuple=	rename_and_convert_flax_params(A						)
						model.load_state_dict(A						)

						lowercase__    :				List[str]=	AutoTokenizer.from_pretrained("ybelkada/test-pix2struct-tokenizer"						)
						lowercase__    :				List[str]=	PixaStructImageProcessor()
						lowercase__    :				Dict=	PixaStructProcessor(image_processor=A ,		tokenizer=A						)

						if use_large:
												lowercase__    :				Tuple=	4_096

						lowercase__    :				List[Any]=	True

						# mkdir if needed
						os.makedirs(A ,		exist_ok=A						)

						model.save_pretrained(A						)
						processor.save_pretrained(A						)

						print("Model saved in {}".format(A						)						)


if __name__ == "__main__":
			a			:    int				 =		argparse.ArgumentParser()
			parser.add_argument("""--t5x_checkpoint_path""", default=None, type=str, help="""Path to the original T5x checkpoint.""")
			parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
			parser.add_argument("""--use_large""", action="""store_true""", help="""Use large model.""")
			parser.add_argument("""--is_vqa""", action="""store_true""", help="""Use large model.""")
			a			:    Optional[int]				 =		parser.parse_args()

			convert_pixastruct_original_pytorch_checkpoint_to_hf(
			    args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
			)






