


"""simple docstring"""







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 					lowercase      (    A_  ,       A_  ,       A_	)->				int:
  '''simple docstring'''







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


def 					lowercase      (    A_  ,       A_  ,       A_  ,       A_="attention"	)->				Dict:
  '''simple docstring'''







  a						:		Optional[Any]											=      np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/key/kernel'''][:, i, :, :]	)
  a						:		List[str]											=      k_tmp.reshape(k_tmp.shape[0]  ,       k_tmp.shape[1] * k_tmp.shape[2]	)
  a						:		str											=      np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/out/kernel'''][:, i, :, :]	)
  a						:		Dict											=      o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]  ,       o_tmp.shape[2]	)
  a						:		Any											=      np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/query/kernel'''][:, i, :, :]	)
  a						:		Optional[Any]											=      q_tmp.reshape(q_tmp.shape[0]  ,       q_tmp.shape[1] * q_tmp.shape[2]	)
  a						:		Dict											=      np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/value/kernel'''][:, i, :, :]	)
  a						:		Union[str, Any]											=      v_tmp.reshape(v_tmp.shape[0]  ,       v_tmp.shape[1] * v_tmp.shape[2]	)
  return k, o, q, v


def 					lowercase      (    A_  ,       A_  ,       A_  ,       A_=False	)->				List[str]:
  '''simple docstring'''







  if split_mlp_wi:
    a						:		int											=      params[F'''{prefix}/{prefix}/mlp/wi_0/kernel'''][:, i, :]
    a						:		Any											=      params[F'''{prefix}/{prefix}/mlp/wi_1/kernel'''][:, i, :]
    a						:		Tuple											=      (wi_a, wi_a)
  else:
    a						:		Optional[Any]											=      params[F'''{prefix}/{prefix}/mlp/wi/kernel'''][:, i, :]

  a						:		Any											=      params[F'''{prefix}/{prefix}/mlp/wo/kernel'''][:, i, :]
  return wi, wo


def 					lowercase      (    A_  ,       A_  ,       A_  ,       A_	)->				Any:
  '''simple docstring'''







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


def 					lowercase      (    A_  ,       *, A_  ,       A_  ,       A_ = False	)->				str:
  '''simple docstring'''







  a						:		Optional[int]											=      traverse_util.flatten_dict(variables["target"]	)
  a						:		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
  a						:		Optional[Any]											=      "encoder/encoder/mlp/wi_0/kernel" in old
  print("Split MLP:"  ,       A_	)

  a						:		Optional[int]											=      collections.OrderedDict()

  # Shared embeddings.
  a						:		List[str]											=      old["token_embedder/embedding"]

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

    # Block i, layer 1 (MLP).
    a						:		Dict											=      tax_layer_norm_lookup(A_  ,       A_  ,       "encoder"  ,       "pre_mlp_layer_norm"	)
    a						:		Dict											=      tax_mlp_lookup(A_  ,       A_  ,       "encoder"  ,       A_	)
    a						:		Union[str, Any]											=      layer_norm
    if split_mlp_wi:
      a						:		Union[str, Any]											=      wi[0].T
      a						:		Union[str, Any]											=      wi[1].T
    else:
      a						:		Any											=      wi.T
    a						:		Dict											=      wo.T
    if scalable_attention:
      # convert the rel_embedding of each layer
      a						:		List[Any]											=      tax_relpos_bias_lookup(
          A_  ,       A_  ,       "encoder"	).T

  a						:		Tuple											=      old["encoder/encoder_norm/scale"]

  if not scalable_attention:
    a						:		Optional[int]											=      tax_relpos_bias_lookup(
        A_  ,       0  ,       "encoder"	).T
    a						:		Any											=      tax_relpos_bias_lookup(
        A_  ,       0  ,       "decoder"	).T

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

      # Block i, layer 1 (Cross Attention).
      a						:		List[str]											=      tax_layer_norm_lookup(A_  ,       A_  ,       "decoder"  ,       "pre_cross_attention_layer_norm"	)
      a						:		str											=      tax_attention_lookup(A_  ,       A_  ,       "decoder"  ,       "encoder_decoder_attention"	)
      a						:		int											=      layer_norm
      a						:		Dict											=      k.T
      a						:		int											=      o.T
      a						:		Tuple											=      q.T
      a						:		Tuple											=      v.T

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

      if scalable_attention:
        # convert the rel_embedding of each layer
        a						:		int											=      tax_relpos_bias_lookup(A_  ,       A_  ,       "decoder"	).T

    a						:		int											=      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:
      a						:		str											=      old["decoder/logits_dense/kernel"].T

  return new


def 					lowercase      (    A_  ,       A_	)->				Optional[Any]:
  '''simple docstring'''







  a						:		List[Any]											=      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:
    a						:		str											=      state_dict["shared.weight"]

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

  return state_dict


def 					lowercase      (    A_  ,       A_  ,       A_  ,       A_  ,       A_	)->				Dict:
  '''simple docstring'''







  a						:		Dict											=      checkpoints.load_tax_checkpoint(A_	)
  a						:		Any											=      convert_tax_to_pytorch(
      A_  ,       num_layers=config.num_layers  ,       is_encoder_only=A_  ,       scalable_attention=A_	)
  a						:		Dict											=      make_state_dict(A_  ,       A_	)
  model.load_state_dict(A_  ,       strict=A_	)


def 					lowercase      (    A_  ,       A_  ,       A_  ,       A_ = False  ,       A_ = False  ,       )->				Tuple:
  '''simple docstring'''







  a						:		List[Any]											=      MTaConfig.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:
    a						:		str											=      UMTaEncoderModel(A_	)
  else:
    a						:		List[str]											=      UMTaForConditionalGeneration(A_	)

  # Load weights from tf checkpoint
  load_tax_weights_in_ta(A_  ,       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__":
 __lowercase    =				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,
 )
 __lowercase    =				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,
 )



"""simple docstring"""







import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer


def 					lowercase      (    A_  ,       A_  ,       A_  ,       A_=5	)->				Union[str, Any]:
  '''simple docstring'''







  assert masked_input.count("<mask>"	) == 1
  a						:		List[str]											=      torch.tensor(tokenizer.encode(A_  ,       add_special_tokens=A_	)	).unsqueeze(0	)  # Batch size 1
  a						:		Dict											=      model(A_	)[0]  # The last hidden-state is the first element of the output tuple
  a						:		int											=      (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
  a						:		Optional[Any]											=      logits[0, masked_index, :]
  a						:		Dict											=      logits.softmax(dim=0	)
  a							,		a						:		Any											=      prob.topk(k=A_  ,       dim=0	)
  a						:		Optional[Any]											=      " ".join(
      [tokenizer.convert_ids_to_tokens(indices[i].item()	) for i in range(len(A_	)	)]	)
  a						:		str											=      tokenizer.mask_token
  a						:		Any											=      []
  for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(" "	)	):
    a						:		Dict											=      predicted_token_bpe.replace("\u2581"  ,       " "	)
    if " {0}".format(A_	) in masked_input:
      topk_filled_outputs.append(
          (
              masked_input.replace(" {0}".format(A_	)  ,       A_	),
              values[index].item(),
              predicted_token,
          )	)
    else:
      topk_filled_outputs.append(
          (
              masked_input.replace(A_  ,       A_	),
              values[index].item(),
              predicted_token,
          )	)
  return topk_filled_outputs


__lowercase    =				CamembertTokenizer.from_pretrained("""camembert-base""")
__lowercase    =				CamembertForMaskedLM.from_pretrained("""camembert-base""")
model.eval()

__lowercase    =				"""Le camembert est <mask> :)"""







print(fill_mask(masked_input, model, tokenizer, topk=3))






