






import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()






def   a			(						lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_				):






				'''simple docstring'''
				return params[F"""{prefix}/{prefix}/relpos_bias/rel_embedding"""][:, i, :]






def   a			(						lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_="attention"				):






				'''simple docstring'''
				lowercase__    =			lowercase__    =			np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/key/kernel"""][:, i, :, :]				)
				lowercase__    =			k_tmp.reshape(k_tmp.shape[0]		,   k_tmp.shape[1] * k_tmp.shape[2]				)
				lowercase__    =			np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/out/kernel"""][:, i, :, :]				)
				lowercase__    =			o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]		,   o_tmp.shape[2]				)
				lowercase__    =			np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/query/kernel"""][:, i, :, :]				)
				lowercase__    =			q_tmp.reshape(q_tmp.shape[0]		,   q_tmp.shape[1] * q_tmp.shape[2]				)
				lowercase__    =			np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/value/kernel"""][:, i, :, :]				)
				lowercase__    =			v_tmp.reshape(v_tmp.shape[0]		,   v_tmp.shape[1] * v_tmp.shape[2]				)
				return k, o, q, v






def   a			(						lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_=False				):






				'''simple docstring'''
				if split_mlp_wi:
								lowercase__    =			params[F"""{prefix}/{prefix}/mlp/wi_0/kernel"""][:, i, :]
								lowercase__    =			params[F"""{prefix}/{prefix}/mlp/wi_1/kernel"""][:, i, :]
								lowercase__    =			(wi_a, wi_a)
				else:
								lowercase__    =			params[F"""{prefix}/{prefix}/mlp/wi/kernel"""][:, i, :]

				lowercase__    =			params[F"""{prefix}/{prefix}/mlp/wo/kernel"""][:, i, :]
				return wi, wo






def   a			(						lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_				):






				'''simple docstring'''
				return params[F"""{prefix}/{prefix}/{layer_name}/scale"""][:, i]






def   a			(						lowerCamelCase_		,   *, lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_ = False				):






				'''simple docstring'''
				lowercase__    =			traverse_util.flatten_dict(variables['''target''']				)
				lowercase__    =			{'''/'''.join(lowerCamelCase_				): v for k, v in old.items()}

				# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
				lowercase__    =			'''encoder/encoder/mlp/wi_0/kernel''' in old
				print('''Split MLP:'''		,   lowerCamelCase_				)

				lowercase__    =			collections.OrderedDict()

				# Shared embeddings.
				lowercase__    =			old['''token_embedder/embedding''']

				# Encoder.
				for i in range(lowerCamelCase_				):
								# Block i, layer 0 (Self Attention).
								lowercase__    =			tax_layer_norm_lookup(lowerCamelCase_		,   lowerCamelCase_		,   '''encoder'''		,   '''pre_attention_layer_norm'''				)
								lowercase__ ,						lowercase__ ,						lowercase__ ,						lowercase__    =			tax_attention_lookup(lowerCamelCase_		,   lowerCamelCase_		,   '''encoder'''		,   '''attention'''				)
								lowercase__    =			layer_norm
								lowercase__    =			k.T
								lowercase__    =			o.T
								lowercase__    =			q.T
								lowercase__    =			v.T

								# Block i, layer 1 (MLP).
								lowercase__    =			tax_layer_norm_lookup(lowerCamelCase_		,   lowerCamelCase_		,   '''encoder'''		,   '''pre_mlp_layer_norm'''				)
								lowercase__ ,						lowercase__    =			tax_mlp_lookup(lowerCamelCase_		,   lowerCamelCase_		,   '''encoder'''		,   lowerCamelCase_				)
								lowercase__    =			layer_norm
								if split_mlp_wi:
												lowercase__    =			wi[0].T
												lowercase__    =			wi[1].T
								else:
												lowercase__    =			wi.T
								lowercase__    =			wo.T
								if scalable_attention:
												# convert the rel_embedding of each layer
												lowercase__    =			tax_relpos_bias_lookup(
												    lowerCamelCase_		,   lowerCamelCase_		,   '''encoder'''				).T

				lowercase__    =			old['''encoder/encoder_norm/scale''']

				if not scalable_attention:
								lowercase__    =			tax_relpos_bias_lookup(
								    lowerCamelCase_		,   0		,   '''encoder'''				).T
								lowercase__    =			tax_relpos_bias_lookup(
								    lowerCamelCase_		,   0		,   '''decoder'''				).T

				if not is_encoder_only:
								# Decoder.
								for i in range(lowerCamelCase_				):
												# Block i, layer 0 (Self Attention).
												lowercase__    =			tax_layer_norm_lookup(lowerCamelCase_		,   lowerCamelCase_		,   '''decoder'''		,   '''pre_self_attention_layer_norm'''				)
												lowercase__ ,						lowercase__ ,						lowercase__ ,						lowercase__    =			tax_attention_lookup(lowerCamelCase_		,   lowerCamelCase_		,   '''decoder'''		,   '''self_attention'''				)
												lowercase__    =			layer_norm
												lowercase__    =			k.T
												lowercase__    =			o.T
												lowercase__    =			q.T
												lowercase__    =			v.T

												# Block i, layer 1 (Cross Attention).
												lowercase__    =			tax_layer_norm_lookup(lowerCamelCase_		,   lowerCamelCase_		,   '''decoder'''		,   '''pre_cross_attention_layer_norm'''				)
												lowercase__ ,						lowercase__ ,						lowercase__ ,						lowercase__    =			tax_attention_lookup(lowerCamelCase_		,   lowerCamelCase_		,   '''decoder'''		,   '''encoder_decoder_attention'''				)
												lowercase__    =			layer_norm
												lowercase__    =			k.T
												lowercase__    =			o.T
												lowercase__    =			q.T
												lowercase__    =			v.T

												# Block i, layer 2 (MLP).
												lowercase__    =			tax_layer_norm_lookup(lowerCamelCase_		,   lowerCamelCase_		,   '''decoder'''		,   '''pre_mlp_layer_norm'''				)
												lowercase__ ,						lowercase__    =			tax_mlp_lookup(lowerCamelCase_		,   lowerCamelCase_		,   '''decoder'''		,   lowerCamelCase_				)
												lowercase__    =			layer_norm
												if split_mlp_wi:
																lowercase__    =			wi[0].T
																lowercase__    =			wi[1].T
												else:
																lowercase__    =			wi.T
												lowercase__    =			wo.T

												if scalable_attention:
																# convert the rel_embedding of each layer
																lowercase__    =			tax_relpos_bias_lookup(lowerCamelCase_		,   lowerCamelCase_		,   '''decoder'''				).T

								lowercase__    =			old['''decoder/decoder_norm/scale''']

								# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
								if "decoder/logits_dense/kernel" in old:
												lowercase__    =			old['''decoder/logits_dense/kernel'''].T

				return new






def   a			(						lowerCamelCase_		,   lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			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:
								lowercase__    =			state_dict['''shared.weight''']

				if not is_encoder_only:
								if "decoder.embed_tokens.weight" not in state_dict:
												lowercase__    =			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.'''				)
												lowercase__    =			state_dict['''shared.weight''']

				return state_dict






def   a			(						lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			checkpoints.load_tax_checkpoint(lowerCamelCase_				)
				lowercase__    =			convert_tax_to_pytorch(
				    lowerCamelCase_		,   num_layers=config.num_layers		,   is_encoder_only=lowerCamelCase_		,   scalable_attention=lowerCamelCase_				)
				lowercase__    =			make_state_dict(lowerCamelCase_		,   lowerCamelCase_				)
				model.load_state_dict(lowerCamelCase_		,   strict=lowerCamelCase_				)






def   a			(						lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_ = False		,   lowerCamelCase_ = False		,   ):






				'''simple docstring'''
				lowercase__    =			MTaConfig.from_json_file(lowerCamelCase_				)
				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:
								lowercase__    =			UMTaEncoderModel(lowerCamelCase_				)
				else:
								lowercase__    =			UMTaForConditionalGeneration(lowerCamelCase_				)

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

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

				# Verify that we can load the checkpoint.
				model.from_pretrained(lowerCamelCase_				)
				print('''Done'''				)


if __name__ == "__main__":
							A__	:			int			   = 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
							)
							parser.add_argument(
							    '--scalable_attention',
							    action='store_true',
							    help='Whether the model uses scaled attention (umt5 model)',
							    default=False,
							)
							A__	:			Any			   = parser.parse_args()
							convert_tax_checkpoint_to_pytorch(
							    args.tax_checkpoint_path,
							    args.config_file,
							    args.pytorch_dump_path,
							    args.is_encoder_only,
							    args.scalable_attention,
							)












from typing import Any, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler

from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
    Adafactor,
    AdamW,
    get_constant_schedule,
    get_constant_schedule_with_warmup,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available


if is_fairscale_available():
							from fairscale.optim import OSS


A__	:			Tuple			   = logging.get_logger(__name__)

A__	:			int			   = {
    'linear': get_linear_schedule_with_warmup,
    'cosine': get_cosine_schedule_with_warmup,
    'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
    'polynomial': get_polynomial_decay_schedule_with_warmup,
    'constant': get_constant_schedule,
    'constant_w_warmup': get_constant_schedule_with_warmup,
}
class       _UpperCAmelCase					(       A__	):

				"""simple docstring"""




				def __init__(     self						:    Optional[int],  lowerCamelCase						:    int=None,  lowerCamelCase						:    int=None,  *lowerCamelCase						:    List[Any],  **lowerCamelCase						:    Any  ):

								'''simple docstring'''
								super().__init__(*lowerCamelCase,  **lowerCamelCase  )

								if config is None:
												assert isinstance(self.model,  lowerCamelCase  ), (
												    "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
												    F""" {self.model.__class__}"""
												)
												lowercase__    =			self.model.config
								else:
												lowercase__    =			config

								lowercase__    =			data_args
								lowercase__    =			self.config.tgt_vocab_size if isinstance(self.config,  lowerCamelCase  ) else self.config.vocab_size

								if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
												assert self.config.pad_token_id is not None, (
												    "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
												    " calculation or doing label smoothing."
												)

								if self.config.pad_token_id is None and self.config.eos_token_id is not None:
												logger.warning(
												    F"""The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for"""
												    ''' padding..'''  )

								if self.args.label_smoothing == 0:
												lowercase__    =			torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id  )
								else:
												# dynamically import label_smoothed_nll_loss
												from utils import label_smoothed_nll_loss

												lowercase__    =			label_smoothed_nll_loss




				def 				lowercase__   (     self						:    List[Any],  lowerCamelCase						:    int  ):

								'''simple docstring'''
								if self.optimizer is None:
												lowercase__    =			['''bias''', '''LayerNorm.weight''']
												lowercase__    =			[
												    {
												        '''params''': [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay  )],
												        '''weight_decay''': self.args.weight_decay,
												    },
												    {
												        '''params''': [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay  )],
												        '''weight_decay''': 0.0,
												    },
												]
												lowercase__    =			Adafactor if self.args.adafactor else AdamW
												if self.args.adafactor:
																lowercase__    =			Adafactor
																lowercase__    =			{'''scale_parameter''': False, '''relative_step''': False}
												else:
																lowercase__    =			AdamW
																lowercase__    =			{
																    '''betas''': (self.args.adam_betaa, self.args.adam_betaa),
																    '''eps''': self.args.adam_epsilon,
																}
												lowercase__    =			self.args.learning_rate
												if self.sharded_ddp:
																lowercase__    =			OSS(
																    params=lowerCamelCase,  optim=lowerCamelCase,  **lowerCamelCase,  )
												else:
																lowercase__    =			optimizer_cls(lowerCamelCase,  **lowerCamelCase  )

								if self.lr_scheduler is None:
												lowercase__    =			self._get_lr_scheduler(lowerCamelCase  )
								else:  # ignoring --lr_scheduler
												logger.warning('''scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.'''  )




				def 				lowercase__   (     self						:    List[str],  lowerCamelCase						:    Optional[int]  ):

								'''simple docstring'''
								lowercase__    =			arg_to_scheduler[self.args.lr_scheduler]
								if self.args.lr_scheduler == "constant":
												lowercase__    =			schedule_func(self.optimizer  )
								elif self.args.lr_scheduler == "constant_w_warmup":
												lowercase__    =			schedule_func(self.optimizer,  num_warmup_steps=self.args.warmup_steps  )
								else:
												lowercase__    =			schedule_func(
												    self.optimizer,  num_warmup_steps=self.args.warmup_steps,  num_training_steps=lowerCamelCase  )
								return scheduler




				def 				lowercase__   (     self						:    List[Any]  ):

								'''simple docstring'''
								if isinstance(self.train_dataset,  torch.utils.data.IterableDataset  ):
												return None
								elif is_torch_tpu_available():
												return get_tpu_sampler(self.train_dataset  )
								else:
												if self.args.sortish_sampler:
																self.train_dataset.make_sortish_sampler(
																    self.args.per_device_train_batch_size,  distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED),  )

												return (
												    RandomSampler(self.train_dataset  )
												    if self.args.local_rank == -1
												    else DistributedSampler(self.train_dataset  )
												)




				def 				lowercase__   (     self						:    Any,  lowerCamelCase						:    List[Any],  lowerCamelCase						:    str,  lowerCamelCase						:    Union[str, Any]  ):

								'''simple docstring'''
								if self.args.label_smoothing == 0:
												if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
																# force training to ignore pad token
																lowercase__    =			model(**lowerCamelCase,  use_cache=lowerCamelCase  )[0]
																lowercase__    =			self.loss_fn(logits.view(-1,  logits.shape[-1]  ),  labels.view(-1  )  )
												else:
																# compute usual loss via models
																lowercase__ ,						lowercase__    =			model(**lowerCamelCase,  labels=lowerCamelCase,  use_cache=lowerCamelCase  )[:2]
								else:
												# compute label smoothed loss
												lowercase__    =			model(**lowerCamelCase,  use_cache=lowerCamelCase  )[0]
												lowercase__    =			torch.nn.functional.log_softmax(lowerCamelCase,  dim=-1  )
												lowercase__ ,						lowercase__    =			self.loss_fn(lowerCamelCase,  lowerCamelCase,  self.args.label_smoothing,  ignore_index=self.config.pad_token_id  )
								return loss, logits




				def 				lowercase__   (     self						:    List[str],  lowerCamelCase						:    Optional[Any],  lowerCamelCase						:    Tuple  ):

								'''simple docstring'''
								lowercase__    =			inputs.pop('''labels'''  )
								lowercase__ ,						lowercase__    =			self._compute_loss(lowerCamelCase,  lowerCamelCase,  lowerCamelCase  )
								return loss




				def 				lowercase__   (     self						:    str,  lowerCamelCase						:    nn.Module,  lowerCamelCase						:    Dict[str, Union[torch.Tensor, Any]],  lowerCamelCase						:    bool,  lowerCamelCase						:    Optional[List[str]] = None,  ):

								'''simple docstring'''
								lowercase__    =			self._prepare_inputs(lowerCamelCase  )

								lowercase__    =			{
								    '''max_length''': self.data_args.val_max_target_length
								    if self.data_args is not None
								    else self.config.max_length,
								    '''num_beams''': self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
								}

								if self.args.predict_with_generate and not self.args.prediction_loss_only:
												lowercase__    =			self.model.generate(
												    inputs['''input_ids'''],  attention_mask=inputs['''attention_mask'''],  **lowerCamelCase,  )
												# in case the batch is shorter than max length, the output should be padded
												if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
																lowercase__    =			self._pad_tensors_to_max_len(lowerCamelCase,  gen_kwargs['''max_length''']  )

								lowercase__    =			inputs.pop('''labels'''  )
								with torch.no_grad():
												# compute loss on predict data
												lowercase__ ,						lowercase__    =			self._compute_loss(lowerCamelCase,  lowerCamelCase,  lowerCamelCase  )

								lowercase__    =			loss.mean().detach()
								if self.args.prediction_loss_only:
												return (loss, None, None)

								lowercase__    =			generated_tokens if self.args.predict_with_generate else logits

								if labels.shape[-1] < gen_kwargs["max_length"]:
												lowercase__    =			self._pad_tensors_to_max_len(lowerCamelCase,  gen_kwargs['''max_length''']  )

								return (loss, logits, labels)




				def 				lowercase__   (     self						:    List[Any],  lowerCamelCase						:    str,  lowerCamelCase						:    Any  ):

								'''simple docstring'''
								# If PAD token is not defined at least EOS token has to be defined
								lowercase__    =			self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id

								if pad_token_id is None:
												raise ValueError(
												    '''Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be'''
												    F""" padded to `max_length`={max_length}"""  )

								lowercase__    =			pad_token_id * torch.ones(
								    (tensor.shape[0], max_length),  dtype=tensor.dtype,  device=tensor.device  )
								lowercase__    =			tensor
								return padded_tensor




