





'''simple docstring'''






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   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE="attention"   ):


		"""simple docstring"""

		_UpperCamelCase       			=params[f'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
		_UpperCamelCase       			=params[f'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
		_UpperCamelCase       			=params[f'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
		_UpperCamelCase       			=params[f'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
		return k, o, q, v


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE=False   ):


		"""simple docstring"""

		if split_mlp_wi:
				_UpperCamelCase       			=params[f'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
				_UpperCamelCase       			=params[f'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
				_UpperCamelCase       			=(wi_a, wi_a)
		else:
				_UpperCamelCase       			=params[f'''{prefix}/layers_{i}/mlp/wi/kernel''']

		_UpperCamelCase       			=params[f'''{prefix}/layers_{i}/mlp/wo/kernel''']
		return wi, wo


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


		"""simple docstring"""

		return params[f'''{prefix}/layers_{i}/{layer_name}/scale''']


def 					_a   (__SCREAMING_SNAKE_CASE  ,      *, __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


		"""simple docstring"""

		_UpperCamelCase       			=traverse_util.flatten_dict(variables['''target''']   )
		_UpperCamelCase       			={"/".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
		_UpperCamelCase       			="encoder/layers_0/mlp/wi_0/kernel" in old
		print('''Split MLP:'''  ,      lowerCamelCase__   )

		_UpperCamelCase       			=collections.OrderedDict()

		# Shared embeddings.
		_UpperCamelCase       			=old["token_embedder/embedding"]

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

				# Block i, layer 1 (MLP).
				_UpperCamelCase       			=tax_layer_norm_lookup(lowerCamelCase__  ,      lowerCamelCase__  ,      '''encoder'''  ,      '''pre_mlp_layer_norm'''   )
				_UpperCamelCase       			=tax_mlp_lookup(lowerCamelCase__  ,      lowerCamelCase__  ,      '''encoder'''  ,      lowerCamelCase__   )
				_UpperCamelCase       			=layer_norm
				if split_mlp_wi:
						_UpperCamelCase       			=wi[0].T
						_UpperCamelCase       			=wi[1].T
				else:
						_UpperCamelCase       			=wi.T
				_UpperCamelCase       			=wo.T

		_UpperCamelCase       			=old[
		    "encoder/relpos_bias/rel_embedding"
		].T
		_UpperCamelCase       			=old["encoder/encoder_norm/scale"]

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

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

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

				_UpperCamelCase       			=old["decoder/decoder_norm/scale"]
				_UpperCamelCase       			=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:
						_UpperCamelCase       			=old["decoder/logits_dense/kernel"].T

		return new


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


		"""simple docstring"""

		_UpperCamelCase       			=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:
				_UpperCamelCase       			=state_dict["shared.weight"]

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

		return state_dict


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


		"""simple docstring"""

		_UpperCamelCase       			=checkpoints.load_tax_checkpoint(lowerCamelCase__   )
		_UpperCamelCase       			=convert_tax_to_pytorch(lowerCamelCase__  ,      num_layers=config.num_layers  ,      is_encoder_only=lowerCamelCase__   )
		_UpperCamelCase       			=make_state_dict(lowerCamelCase__  ,      lowerCamelCase__   )
		model.load_state_dict(lowerCamelCase__  ,      strict=lowerCamelCase__   )


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE = False   ):


		"""simple docstring"""

		_UpperCamelCase       			=TaConfig.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:
				_UpperCamelCase       			=TaEncoderModel(lowerCamelCase__   )
		else:
				_UpperCamelCase       			=TaForConditionalGeneration(lowerCamelCase__   )

		# Load weights from tf checkpoint
		load_tax_weights_in_ta(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__":
						__lowerCamelCase						: Dict		=   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						: int		=   parser.parse_args()
						convert_tax_checkpoint_to_pytorch(
						    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
						)













'''simple docstring'''






import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


__lowerCamelCase						: Union[str, Any]		=   logging.get_logger(__name__)







class 		UpperCAmelCase   :


  """simple docstring"""







  lowerCAmelCase_					=				None




@experimental
def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  if ParallelBackendConfig.backend_name is None:
    return _map_with_multiprocessing_pool(
        __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )

  return _map_with_joblib(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=num_proc if num_proc <= len(__SCREAMING_SNAKE_CASE   ) else len(__SCREAMING_SNAKE_CASE   )
  _UpperCamelCase       			=[]  # We organize the splits ourselve (contiguous splits)
  for index in range(__SCREAMING_SNAKE_CASE   ):
    _UpperCamelCase       			=len(__SCREAMING_SNAKE_CASE   ) // num_proc
    _UpperCamelCase       			=len(__SCREAMING_SNAKE_CASE   ) % num_proc
    _UpperCamelCase       			=div * index + min(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )
    _UpperCamelCase       			=start + div + (1 if index < mod else 0)
    split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc)   )

  if len(__SCREAMING_SNAKE_CASE   ) != sum(len(i[1]   ) for i in split_kwds   ):
    raise ValueError(
        f'''Error dividing inputs iterable among processes. '''
        f'''Total number of objects {len(__SCREAMING_SNAKE_CASE   )}, '''
        f'''length: {sum(len(i[1]   ) for i in split_kwds   )}'''   )

  logger.info(
      f'''Spawning {num_proc} processes for {len(__SCREAMING_SNAKE_CASE   )} objects in slices of {[len(i[1]   ) for i in split_kwds]}'''   )
  _UpperCamelCase			,     _UpperCamelCase       			=None, None
  if not disable_tqdm:
    _UpperCamelCase			,     _UpperCamelCase       			=(RLock(),), tqdm.set_lock
  with Pool(__SCREAMING_SNAKE_CASE  ,      initargs=__SCREAMING_SNAKE_CASE  ,      initializer=__SCREAMING_SNAKE_CASE   ) as pool:
    _UpperCamelCase       			=pool.map(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )
  logger.info(f'''Finished {num_proc} processes'''   )
  _UpperCamelCase       			=[obj for proc_res in mapped for obj in proc_res]
  logger.info(f'''Unpacked {len(__SCREAMING_SNAKE_CASE   )} objects'''   )

  return mapped


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  import joblib

  with joblib.parallel_backend(ParallelBackendConfig.backend_name  ,      n_jobs=__SCREAMING_SNAKE_CASE   ):
    return joblib.Parallel()(
        joblib.delayed(__SCREAMING_SNAKE_CASE   )((function, obj, types, None, True, None)   ) for obj in iterable   )


@experimental
@contextlib.contextmanager
def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=backend_name

  if backend_name == "spark":
    from joblibspark import register_spark

    register_spark()

    # TODO: call create_cache_and_write_probe if "download" in steps
    # TODO: raise NotImplementedError when Dataset.map etc is called

  try:
    yield
  finally:
    _UpperCamelCase       			=None






