





import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


__lowerCAmelCase :Tuple     =    logging.get_logger(__name__)






@dataclass
class        _a:
		lowerCamelCase__     :str   				=		field(metadata={'help': 'The name of the task to train on: ' + ', '.join(glue_processors.keys()    )}    )
		lowerCamelCase__     :str   				=		field(
		    metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}    )
		lowerCamelCase__     :int   				=		field(
		    default=128	,   metadata={
		        'help': (
		            'The maximum total input sequence length after tokenization. Sequences longer '
		            'than this will be truncated, sequences shorter will be padded.'
		        )
		    }	,   )
		lowerCamelCase__     :bool   				=		field(
		    default=__A	,   metadata={'help': 'Overwrite the cached training and evaluation sets'}    )







		def        lowercase       (    self				)      ->      Optional[Any]:


							'''simple docstring'''

							_snake_case       :					Dict				       =   self.task_name.lower()






class        _a(    __A    ):
		lowerCamelCase__     :Optional[Any]   				=		'train'
		lowerCamelCase__     :List[str]   				=		'dev'
		lowerCamelCase__     :Any   				=		'test'






class        _a(    __A    ):
		lowerCamelCase__     :GlueDataTrainingArguments
		lowerCamelCase__     :str
		lowerCamelCase__     :List[InputFeatures]







		def __init__(    self ,						__snake_case ,						__snake_case ,						__snake_case = None ,						__snake_case = Split.train ,						__snake_case = None ,						)      ->      Any:


							'''simple docstring'''

							warnings.warn(
							    "This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets "
							    "library. You can have a look at this example script for pointers: "
							    "https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py" ,						__snake_case ,						)
							_snake_case       :					Optional[Any]				       =   args
							_snake_case       :					Optional[int]				       =   glue_processors[args.task_name]()
							_snake_case       :					int				       =   glue_output_modes[args.task_name]
							if isinstance(__snake_case ,						__snake_case				):
												try:
																	_snake_case       :					Tuple				       =   Split[mode]
												except KeyError:
																	raise KeyError("mode is not a valid split name"				)
        # Load data features from cache or dataset file
							_snake_case       :					List[str]				       =   os.path.join(
							    cache_dir if cache_dir is not None else args.data_dir ,						f"""cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}""" ,						)
							_snake_case       :					List[Any]				       =   self.processor.get_labels()
							if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
							    "RobertaTokenizer",
							    "RobertaTokenizerFast",
							    "XLMRobertaTokenizer",
							    "BartTokenizer",
							    "BartTokenizerFast",
							):
												# HACK(label indices are swapped in RoBERTa pretrained model)
												_snake_case       , _snake_case       :					int				       =   label_list[2], label_list[1]
							_snake_case       :					str				       =   label_list

							# Make sure only the first process in distributed training processes the dataset,
							# and the others will use the cache.
							_snake_case       :					Optional[Any]				       =   cached_features_file + ".lock"
							with FileLock(__snake_case				):
												if os.path.exists(__snake_case				) and not args.overwrite_cache:
																	_snake_case       :					List[str]				       =   time.time()
																	_snake_case       :					str				       =   torch.load(__snake_case				)
																	logger.info(
																	    f"""Loading features from cached file {cached_features_file} [took %.3f s]""" ,						time.time() - start				)
												else:
																	logger.info(f"""Creating features from dataset file at {args.data_dir}"""				)

																	if mode == Split.dev:
																						_snake_case       :					List[Any]				       =   self.processor.get_dev_examples(args.data_dir				)
																	elif mode == Split.test:
																						_snake_case       :					List[Any]				       =   self.processor.get_test_examples(args.data_dir				)
																	else:
																						_snake_case       :					str				       =   self.processor.get_train_examples(args.data_dir				)
																	if limit_length is not None:
																						_snake_case       :					Tuple				       =   examples[:limit_length]
																	_snake_case       :					List[str]				       =   glue_convert_examples_to_features(
																	    __snake_case ,						__snake_case ,						max_length=args.max_seq_length ,						label_list=__snake_case ,						output_mode=self.output_mode ,						)
																	_snake_case       :					Optional[Any]				       =   time.time()
																	torch.save(self.features ,						__snake_case				)
																	# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
																	logger.info(
																	    f"""Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"""				)







		def __len__(    self				)      ->      List[str]:


							'''simple docstring'''

							return len(self.features				)







		def __getitem__(    self ,						__snake_case				)      ->      InputFeatures:


							'''simple docstring'''

							return self.features[i]







		def        lowercase       (    self				)      ->      Optional[Any]:


							'''simple docstring'''

							return self.label_list






import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase="attention"				):
					_snake_case       :					Any				       =   params[F"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
					_snake_case       :					Tuple				       =   params[F"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
					_snake_case       :					str				       =   params[F"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
					_snake_case       :					List[Any]				       =   params[F"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
					return k, o, q, v







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase=False				):
					if split_mlp_wi:
										_snake_case       :					List[Any]				       =   params[F"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
										_snake_case       :					Tuple				       =   params[F"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
										_snake_case       :					Union[str, Any]				       =   (wi_a, wi_a)
					else:
										_snake_case       :					Optional[int]				       =   params[F"""{prefix}/layers_{i}/mlp/wi/kernel"""]

					_snake_case       :					List[str]				       =   params[F"""{prefix}/layers_{i}/mlp/wo/kernel"""]
					return wi, wo







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				):
					return params[F"""{prefix}/layers_{i}/{layer_name}/scale"""]







def    A				(							UpperCAmelCase  ,  *, UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					Any				       =   traverse_util.flatten_dict(variables["target"]				)
					_snake_case       :					str				       =   {"/".join(UpperCAmelCase				): v for k, v in old.items()}

					# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
					_snake_case       :					Tuple				       =   "encoder/layers_0/mlp/wi_0/kernel" in old
					print("Split MLP:"  ,  UpperCAmelCase				)

					_snake_case       :					List[Any]				       =   collections.OrderedDict()

					# Shared embeddings.
					_snake_case       :					Tuple				       =   old["token_embedder/embedding"]

					# Encoder.
					for i in range(UpperCAmelCase				):
										# Block i, layer 0 (Self Attention).
										_snake_case       :					Optional[Any]				       =   tax_layer_norm_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "encoder"  ,  "pre_attention_layer_norm"				)
										_snake_case       , _snake_case       , _snake_case       , _snake_case       :					Union[str, Any]				       =   tax_attention_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "encoder"  ,  "attention"				)
										_snake_case       :					Optional[Any]				       =   layer_norm
										_snake_case       :					int				       =   k.T
										_snake_case       :					Optional[int]				       =   o.T
										_snake_case       :					Dict				       =   q.T
										_snake_case       :					Any				       =   v.T

										# Block i, layer 1 (MLP).
										_snake_case       :					Any				       =   tax_layer_norm_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "encoder"  ,  "pre_mlp_layer_norm"				)
										_snake_case       , _snake_case       :					Tuple				       =   tax_mlp_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "encoder"  ,  UpperCAmelCase				)
										_snake_case       :					Any				       =   layer_norm
										if split_mlp_wi:
															_snake_case       :					Union[str, Any]				       =   wi[0].T
															_snake_case       :					List[str]				       =   wi[1].T
										else:
															_snake_case       :					List[Any]				       =   wi.T
										_snake_case       :					Dict				       =   wo.T

					_snake_case       :					Dict				       =   old[
					    "encoder/relpos_bias/rel_embedding"
					].T
					_snake_case       :					Dict				       =   old["encoder/encoder_norm/scale"]

					if not is_encoder_only:
										# Decoder.
										for i in range(UpperCAmelCase				):
															# Block i, layer 0 (Self Attention).
															_snake_case       :					str				       =   tax_layer_norm_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "decoder"  ,  "pre_self_attention_layer_norm"				)
															_snake_case       , _snake_case       , _snake_case       , _snake_case       :					Optional[Any]				       =   tax_attention_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "decoder"  ,  "self_attention"				)
															_snake_case       :					Optional[Any]				       =   layer_norm
															_snake_case       :					Dict				       =   k.T
															_snake_case       :					List[str]				       =   o.T
															_snake_case       :					List[Any]				       =   q.T
															_snake_case       :					List[Any]				       =   v.T

															# Block i, layer 1 (Cross Attention).
															_snake_case       :					Tuple				       =   tax_layer_norm_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "decoder"  ,  "pre_cross_attention_layer_norm"				)
															_snake_case       , _snake_case       , _snake_case       , _snake_case       :					Union[str, Any]				       =   tax_attention_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "decoder"  ,  "encoder_decoder_attention"				)
															_snake_case       :					Tuple				       =   layer_norm
															_snake_case       :					Union[str, Any]				       =   k.T
															_snake_case       :					str				       =   o.T
															_snake_case       :					List[str]				       =   q.T
															_snake_case       :					List[Any]				       =   v.T

															# Block i, layer 2 (MLP).
															_snake_case       :					Dict				       =   tax_layer_norm_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "decoder"  ,  "pre_mlp_layer_norm"				)
															_snake_case       , _snake_case       :					Union[str, Any]				       =   tax_mlp_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "decoder"  ,  UpperCAmelCase				)
															_snake_case       :					Optional[Any]				       =   layer_norm
															if split_mlp_wi:
																				_snake_case       :					str				       =   wi[0].T
																				_snake_case       :					Union[str, Any]				       =   wi[1].T
															else:
																				_snake_case       :					Optional[Any]				       =   wi.T
															_snake_case       :					int				       =   wo.T

										_snake_case       :					Optional[int]				       =   old["decoder/decoder_norm/scale"]
										_snake_case       :					int				       =   old[
										    "decoder/relpos_bias/rel_embedding"
										].T

										# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
										if "decoder/logits_dense/kernel" in old:
															_snake_case       :					Tuple				       =   old["decoder/logits_dense/kernel"].T

					return new







def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					Tuple				       =   collections.OrderedDict([(k, torch.from_numpy(v.copy()				)) for (k, v) in converted_params.items()]				)

					# Add what is missing.
					if "encoder.embed_tokens.weight" not in state_dict:
										_snake_case       :					Tuple				       =   state_dict["shared.weight"]

					if not is_encoder_only:
										if "decoder.embed_tokens.weight" not in state_dict:
															_snake_case       :					List[Any]				       =   state_dict["shared.weight"]

										if "lm_head.weight" not in state_dict:  # For old 1.0 models.
															print("Using shared word embeddings as lm_head."				)
															_snake_case       :					Optional[int]				       =   state_dict["shared.weight"]

					return state_dict







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					List[str]				       =   checkpoints.load_tax_checkpoint(UpperCAmelCase				)
					_snake_case       :					Dict				       =   convert_tax_to_pytorch(UpperCAmelCase  ,  num_layers=config.num_layers  ,  is_encoder_only=UpperCAmelCase				)
					_snake_case       :					Dict				       =   make_state_dict(UpperCAmelCase  ,  UpperCAmelCase				)
					model.load_state_dict(UpperCAmelCase  ,  strict=UpperCAmelCase				)







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase = False				):
					_snake_case       :					str				       =   TaConfig.from_json_file(UpperCAmelCase				)
					print(F"""Building PyTorch model from configuration: {config}"""				)
					# Non-v1.1 checkpoints could also use T5Model, but this works for all.
					# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
					if is_encoder_only:
										_snake_case       :					List[Any]				       =   TaEncoderModel(UpperCAmelCase				)
					else:
										_snake_case       :					List[str]				       =   TaForConditionalGeneration(UpperCAmelCase				)

					# Load weights from tf checkpoint
					load_tax_weights_in_ta(UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				)

					# Save pytorch-model
					print(F"""Save PyTorch model to {pytorch_dump_path}"""				)
					model.save_pretrained(UpperCAmelCase				)

					# Verify that we can load the checkpoint.
					model.from_pretrained(UpperCAmelCase				)
					print("Done"				)


if __name__ == "__main__":
							__lowerCAmelCase :Any     =    argparse.ArgumentParser(description='Converts a native T5X checkpoint into a PyTorch checkpoint.')
							# Required parameters
							parser.add_argument(
							    '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path to the T5X checkpoint.'
							)
							parser.add_argument(
							    '--config_file',
							    default=None,
							    type=str,
							    required=True,
							    help='The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.',
							)
							parser.add_argument(
							    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
							)
							parser.add_argument(
							    '--is_encoder_only', action='store_true', help='Check if the model is encoder-decoder model', default=False
							)
							__lowerCAmelCase :Optional[int]     =    parser.parse_args()
							convert_tax_checkpoint_to_pytorch(
							    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
							)