


'''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     UpperCamelCase_      (   A__ :       Tuple							,   A__ :       Dict							,   A__ :       int							,   A__ :       str="attention" ):
  '''simple docstring'''





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







def     UpperCamelCase_      (   A__ :       Tuple							,   A__ :       List[str]							,   A__ :       Tuple							,   A__ :       List[Any]=False ):
  '''simple docstring'''





  if split_mlp_wi:
    lowerCAmelCase_      :				Optional[Any]      =       params[f'{prefix}/layers_{i}/mlp/wi_0/kernel']
    lowerCAmelCase_      :				List[Any]      =       params[f'{prefix}/layers_{i}/mlp/wi_1/kernel']
    lowerCAmelCase_      :				Union[str, Any]      =       (wi_a, wi_a)
  else:
    lowerCAmelCase_      :				List[Any]      =       params[f'{prefix}/layers_{i}/mlp/wi/kernel']

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







def     UpperCamelCase_      (   A__ :       str							,   A__ :       List[Any]							,   A__ :       int							,   A__ :       Dict ):
  '''simple docstring'''





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







def     UpperCamelCase_      (   A__ :       dict							,   *, A__ :       int							,   A__ :       bool ):
  '''simple docstring'''





  lowerCAmelCase_      :				str      =       traverse_util.flatten_dict(variables["""target"""] )
  lowerCAmelCase_      :				List[Any]      =       {"""/""".join(A__ ): v for k, v in old.items()}

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

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

  # Shared embeddings.
  lowerCAmelCase_      :				int      =       old["""token_embedder/embedding"""]

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

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

  lowerCAmelCase_      :				Optional[int]      =       old[
      """encoder/relpos_bias/rel_embedding"""
  ].T
  lowerCAmelCase_      :				Optional[int]      =       old["""encoder/encoder_norm/scale"""]

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

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

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

    lowerCAmelCase_      :				str      =       old["""decoder/decoder_norm/scale"""]
    lowerCAmelCase_      :				Union[str, Any]      =       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:
      lowerCAmelCase_      :				Optional[Any]      =       old["""decoder/logits_dense/kernel"""].T

  return new







def     UpperCamelCase_      (   A__ :       Union[str, Any]							,   A__ :       bool ):
  '''simple docstring'''





  lowerCAmelCase_      :				int      =       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:
    lowerCAmelCase_      :				Dict      =       state_dict["""shared.weight"""]

  if not is_encoder_only:
    if "decoder.embed_tokens.weight" not in state_dict:
      lowerCAmelCase_      :				Tuple      =       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.""" )
      lowerCAmelCase_      :				List[str]      =       state_dict["""shared.weight"""]

  return state_dict







def     UpperCamelCase_      (   A__ :       List[Any]							,   A__ :       Union[str, Any]							,   A__ :       Optional[int]							,   A__ :       str ):
  '''simple docstring'''





  lowerCAmelCase_      :				Optional[Any]      =       checkpoints.load_tax_checkpoint(A__ )
  lowerCAmelCase_      :				Any      =       convert_tax_to_pytorch(A__							,   num_layers=config.num_layers							,   is_encoder_only=A__ )
  lowerCAmelCase_      :				Dict      =       make_state_dict(A__							,   A__ )
  model.load_state_dict(A__							,   strict=A__ )







def     UpperCamelCase_      (   A__ :       Any							,   A__ :       Any							,   A__ :       List[str]							,   A__ :       bool = False ):
  '''simple docstring'''





  lowerCAmelCase_      :				Dict      =       TaConfig.from_json_file(A__ )
  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:
    lowerCAmelCase_      :				int      =       TaEncoderModel(A__ )
  else:
    lowerCAmelCase_      :				str      =       TaForConditionalGeneration(A__ )

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

  # Save pytorch-model
  print(f'Save PyTorch model to {pytorch_dump_path}' )
  model.save_pretrained(A__ )

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


if __name__ == "__main__":
  __A :  List[str]    							=				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
  )
  __A :  List[Any]    							=				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'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__A :  List[Any]    							=				{
    "configuration_roc_bert": ["ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoCBertConfig"],
    "tokenization_roc_bert": ["RoCBertTokenizer"],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  pass

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A :  Union[str, Any]    							=				[
      "ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "RoCBertForCausalLM",
      "RoCBertForMaskedLM",
      "RoCBertForMultipleChoice",
      "RoCBertForPreTraining",
      "RoCBertForQuestionAnswering",
      "RoCBertForSequenceClassification",
      "RoCBertForTokenClassification",
      "RoCBertLayer",
      "RoCBertModel",
      "RoCBertPreTrainedModel",
      "load_tf_weights_in_roc_bert",
  ]

if TYPE_CHECKING:
  from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
  from .tokenization_roc_bert import RoCBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    raise OptionalDependencyNotAvailable()

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_roc_bert import (
        ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        RoCBertForCausalLM,
        RoCBertForMaskedLM,
        RoCBertForMultipleChoice,
        RoCBertForPreTraining,
        RoCBertForQuestionAnswering,
        RoCBertForSequenceClassification,
        RoCBertForTokenClassification,
        RoCBertLayer,
        RoCBertModel,
        RoCBertPreTrainedModel,
        load_tf_weights_in_roc_bert,
    )


else:
  import sys

  __A :  int    							=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



