
'''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 	_snake_case						(      A_       :    Dict     ,		A_       :    Optional[int]     ,		A_       :    int   ):
  """simple docstring"""







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






def 	_snake_case						(      A_       :    Dict     ,		A_       :    List[Any]     ,		A_       :    List[str]     ,		A_       :    Optional[int]="attention"   ):
  """simple docstring"""







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






def 	_snake_case						(      A_       :    Any     ,		A_       :    List[Any]     ,		A_       :    Dict     ,		A_       :    Dict=False   ):
  """simple docstring"""







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

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






def 	_snake_case						(      A_       :    Dict     ,		A_       :    Optional[int]     ,		A_       :    Tuple     ,		A_       :    List[Any]   ):
  """simple docstring"""







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






def 	_snake_case						(      A_       :    dict     ,		*, A_       :    int     ,		A_       :    bool     ,		A_       :    bool = False   ):
  """simple docstring"""







  a_      :	Any      =							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_      :	Tuple      =							"""encoder/encoder/mlp/wi_0/kernel""" in old
  print("""Split MLP:"""     ,		A_   )

  a_      :	List[str]      =							collections.OrderedDict()

  # Shared embeddings.
  a_      :	List[Any]      =							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_      :	Union[str, Any]      =							tax_attention_lookup(A_     ,		A_     ,		"""encoder"""     ,		"""attention"""   )
    a_      :	Tuple      =							layer_norm
    a_      :	Dict      =							k.T
    a_      :	int      =							o.T
    a_      :	Union[str, Any]      =							q.T
    a_      :	int      =							v.T

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

  a_      :	List[Any]      =							old["""encoder/encoder_norm/scale"""]

  if not scalable_attention:
    a_      :	Dict      =							tax_relpos_bias_lookup(
        A_     ,		0     ,		"""encoder"""   ).T
    a_      :	Union[str, 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_      :	Any      =							tax_layer_norm_lookup(A_     ,		A_     ,		"""decoder"""     ,		"""pre_self_attention_layer_norm"""   )
      a_      :	Dict      =							tax_attention_lookup(A_     ,		A_     ,		"""decoder"""     ,		"""self_attention"""   )
      a_      :	List[str]      =							layer_norm
      a_      :	int      =							k.T
      a_      :	int      =							o.T
      a_      :	Union[str, Any]      =							q.T
      a_      :	Union[str, Any]      =							v.T

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

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

      if scalable_attention:
        # convert the rel_embedding of each layer
        a_      :	List[Any]      =							tax_relpos_bias_lookup(A_     ,		A_     ,		"""decoder"""   ).T

    a_      :	Optional[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_      :	Optional[int]      =							old["""decoder/logits_dense/kernel"""].T

  return new






def 	_snake_case						(      A_       :    Optional[int]     ,		A_       :    bool   ):
  """simple docstring"""







  a_      :	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:
    a_      :	List[Any]      =							state_dict["""shared.weight"""]

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

  return state_dict






def 	_snake_case						(      A_       :    str     ,		A_       :    List[str]     ,		A_       :    Any     ,		A_       :    Optional[Any]     ,		A_       :    int   ):
  """simple docstring"""







  a_      :	str      =							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_      :	str      =							make_state_dict(A_     ,		A_   )
  model.load_state_dict(A_     ,		strict=A_   )






def 	_snake_case						(      A_       :    str     ,		A_       :    Union[str, Any]     ,		A_       :    Optional[Any]     ,		A_       :    bool = False     ,		A_       :    bool = False     ,		):
  """simple docstring"""







  a_      :	Dict      =							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_      :	Dict      =							UMTaEncoderModel(A_   )
  else:
    a_      :	int      =							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__":
    __snake_case:  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
    )
    parser.add_argument(
        "--scalable_attention",
        action="store_true",
        help="Whether the model uses scaled attention (umt5 model)",
        default=False,
    )
    __snake_case:  Tuple								=			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'''

from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


__snake_case:  Tuple								=			logging.get_logger(__name__)

__snake_case:  Tuple								=			{
    "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
    "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
    "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
    "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
    "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}


class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     a_    	=					"t5"
     a_    	=					["past_key_values"]
     a_    	=					{"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}


     def __init__(      self							,		lowerCAmelCase_=3_21_28							,		lowerCAmelCase_=5_12							,		lowerCAmelCase_=64							,		lowerCAmelCase_=20_48							,		lowerCAmelCase_=6							,		lowerCAmelCase_=None							,		lowerCAmelCase_=8							,		lowerCAmelCase_=32							,		lowerCAmelCase_=1_28							,		lowerCAmelCase_=0.1							,		lowerCAmelCase_=1E-6							,		lowerCAmelCase_=1.0							,		lowerCAmelCase_="relu"							,		lowerCAmelCase_=True							,		lowerCAmelCase_=True							,		lowerCAmelCase_=0							,		lowerCAmelCase_=1							,		**lowerCAmelCase_							,		):

       '''simple docstring'''

       a_      :	List[Any]      =							vocab_size
       a_      :	List[str]      =							d_model
       a_      :	Union[str, Any]      =							d_kv
       a_      :	List[Any]      =							d_ff
       a_      :	int      =							num_layers
       a_      :	Any      =							(
           num_decoder_layers if num_decoder_layers is not None else self.num_layers
       )  # default = symmetry
       a_      :	Union[str, Any]      =							num_heads
       a_      :	int      =							relative_attention_num_buckets
       a_      :	Dict      =							relative_attention_max_distance
       a_      :	Union[str, Any]      =							dropout_rate
       a_      :	Union[str, Any]      =							layer_norm_epsilon
       a_      :	List[Any]      =							initializer_factor
       a_      :	Any      =							feed_forward_proj
       a_      :	List[Any]      =							use_cache

       a_      :	Dict      =							self.feed_forward_proj.split("""-""" )
       a_      :	Union[str, Any]      =							act_info[-1]
       a_      :	str      =							act_info[0] == """gated"""

       if len(lowerCAmelCase_ ) > 1 and act_info[0] != "gated" or len(lowerCAmelCase_ ) > 2:
         raise ValueError(
             f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
             """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
             """'gated-gelu' or 'relu'""" )

       # for backwards compatibility
       if feed_forward_proj == "gated-gelu":
         a_      :	List[str]      =							"""gelu_new"""

       super().__init__(
           pad_token_id=lowerCAmelCase_							,		eos_token_id=lowerCAmelCase_							,		is_encoder_decoder=lowerCAmelCase_							,		**lowerCAmelCase_							,		)







class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[Any]      =							{
           """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
           """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
       }
       if self.use_past:
         a_      :	Tuple      =							"""past_encoder_sequence + sequence"""
         a_      :	str      =							{0: """batch"""}
         a_      :	Any      =							{0: """batch""", 1: """past_decoder_sequence + sequence"""}
       else:
         a_      :	List[Any]      =							{0: """batch""", 1: """decoder_sequence"""}
         a_      :	Optional[int]      =							{0: """batch""", 1: """decoder_sequence"""}

       if self.use_past:
         self.fill_with_past_key_values_(lowerCAmelCase_							,		direction="""inputs""" )

       return common_inputs






     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return 13


