


'''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       ):







       """simple docstring"""







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






def       lowercase					(_A ,    _A ,    _A ,    _A="attention"       ):







       """simple docstring"""







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







       """simple docstring"""







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

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






def       lowercase					(_A ,    _A ,    _A ,    _A       ):







       """simple docstring"""







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






def       lowercase					(_A ,    *, _A ,    _A ,    _A = False       ):







       """simple docstring"""







       _lowerCAmelCase     :				List[str]       =							traverse_util.flatten_dict(variables['target']       )
       _lowerCAmelCase     :				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     :				int       =							'encoder/encoder/mlp/wi_0/kernel' in old
       print('Split MLP:' ,    _A       )

       _lowerCAmelCase     :				Union[str, Any]       =							collections.OrderedDict()

       # Shared embeddings.
       _lowerCAmelCase     :				Tuple       =							old['token_embedder/embedding']

       # Encoder.
       for i in range(_A       ):
              # Block i, layer 0 (Self Attention).
              _lowerCAmelCase     :				Optional[Any]       =							tax_layer_norm_lookup(_A ,    _A ,    'encoder' ,    'pre_attention_layer_norm'       )
              _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase     :				int       =							tax_attention_lookup(_A ,    _A ,    'encoder' ,    'attention'       )
              _lowerCAmelCase     :				str       =							layer_norm
              _lowerCAmelCase     :				Union[str, Any]       =							k.T
              _lowerCAmelCase     :				List[str]       =							o.T
              _lowerCAmelCase     :				str       =							q.T
              _lowerCAmelCase     :				Union[str, Any]       =							v.T

              # Block i, layer 1 (MLP).
              _lowerCAmelCase     :				Tuple       =							tax_layer_norm_lookup(_A ,    _A ,    'encoder' ,    'pre_mlp_layer_norm'       )
              _lowerCAmelCase      ,  _lowerCAmelCase     :				Dict       =							tax_mlp_lookup(_A ,    _A ,    'encoder' ,    _A       )
              _lowerCAmelCase     :				List[str]       =							layer_norm
              if split_mlp_wi:
                     _lowerCAmelCase     :				List[Any]       =							wi[0].T
                     _lowerCAmelCase     :				List[str]       =							wi[1].T
              else:
                     _lowerCAmelCase     :				Any       =							wi.T
              _lowerCAmelCase     :				Any       =							wo.T
              if scalable_attention:
                     # convert the rel_embedding of each layer
                     _lowerCAmelCase     :				Dict       =							tax_relpos_bias_lookup(
                         _A ,    _A ,    'encoder'       ).T

       _lowerCAmelCase     :				int       =							old['encoder/encoder_norm/scale']

       if not scalable_attention:
              _lowerCAmelCase     :				List[str]       =							tax_relpos_bias_lookup(
                  _A ,    0 ,    'encoder'       ).T
              _lowerCAmelCase     :				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).
                     _lowerCAmelCase     :				Any       =							tax_layer_norm_lookup(_A ,    _A ,    'decoder' ,    'pre_self_attention_layer_norm'       )
                     _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase     :				Any       =							tax_attention_lookup(_A ,    _A ,    'decoder' ,    'self_attention'       )
                     _lowerCAmelCase     :				Optional[Any]       =							layer_norm
                     _lowerCAmelCase     :				Dict       =							k.T
                     _lowerCAmelCase     :				str       =							o.T
                     _lowerCAmelCase     :				Optional[int]       =							q.T
                     _lowerCAmelCase     :				str       =							v.T

                     # Block i, layer 1 (Cross Attention).
                     _lowerCAmelCase     :				Tuple       =							tax_layer_norm_lookup(_A ,    _A ,    'decoder' ,    'pre_cross_attention_layer_norm'       )
                     _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase     :				List[Any]       =							tax_attention_lookup(_A ,    _A ,    'decoder' ,    'encoder_decoder_attention'       )
                     _lowerCAmelCase     :				str       =							layer_norm
                     _lowerCAmelCase     :				Optional[Any]       =							k.T
                     _lowerCAmelCase     :				str       =							o.T
                     _lowerCAmelCase     :				Union[str, Any]       =							q.T
                     _lowerCAmelCase     :				int       =							v.T

                     # Block i, layer 2 (MLP).
                     _lowerCAmelCase     :				Tuple       =							tax_layer_norm_lookup(_A ,    _A ,    'decoder' ,    'pre_mlp_layer_norm'       )
                     _lowerCAmelCase      ,  _lowerCAmelCase     :				Any       =							tax_mlp_lookup(_A ,    _A ,    'decoder' ,    _A       )
                     _lowerCAmelCase     :				Optional[int]       =							layer_norm
                     if split_mlp_wi:
                            _lowerCAmelCase     :				Union[str, Any]       =							wi[0].T
                            _lowerCAmelCase     :				Optional[int]       =							wi[1].T
                     else:
                            _lowerCAmelCase     :				Optional[Any]       =							wi.T
                     _lowerCAmelCase     :				str       =							wo.T

                     if scalable_attention:
                            # convert the rel_embedding of each layer
                            _lowerCAmelCase     :				Dict       =							tax_relpos_bias_lookup(_A ,    _A ,    'decoder'       ).T

              _lowerCAmelCase     :				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:
                     _lowerCAmelCase     :				List[Any]       =							old['decoder/logits_dense/kernel'].T

       return new






def       lowercase					(_A ,    _A       ):







       """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     :				Union[str, Any]       =							state_dict['shared.weight']

       if not is_encoder_only:
              if "decoder.embed_tokens.weight" not in state_dict:
                     _lowerCAmelCase     :				int       =							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     :				Any       =							state_dict['shared.weight']

       return state_dict






def       lowercase					(_A ,    _A ,    _A ,    _A ,    _A       ):







       """simple docstring"""







       _lowerCAmelCase     :				Any       =							checkpoints.load_tax_checkpoint(_A       )
       _lowerCAmelCase     :				Tuple       =							convert_tax_to_pytorch(
           _A ,    num_layers=config.num_layers ,    is_encoder_only=_A ,    scalable_attention=_A       )
       _lowerCAmelCase     :				List[Any]       =							make_state_dict(_A ,    _A       )
       model.load_state_dict(_A ,    strict=_A       )






def       lowercase					(_A ,    _A ,    _A ,    _A = False ,    _A = False ,    ):







       """simple docstring"""







       _lowerCAmelCase     :				Tuple       =							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:
              _lowerCAmelCase     :				Tuple       =							UMTaEncoderModel(_A       )
       else:
              _lowerCAmelCase     :				Any       =							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__":
       lowerCAmelCase     :  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
       )
       parser.add_argument(
           """--scalable_attention""",
           action="""store_true""",
           help="""Whether the model uses scaled attention (umt5 model)""",
           default=False,
       )
       lowerCAmelCase     :  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,
       )





'''simple docstring'''







from typing import TYPE_CHECKING

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


lowerCAmelCase     :  Any											=    {
    """configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
    """tokenization_canine""": ["""CanineTokenizer"""],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase     :  Tuple											=    [
           """CANINE_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """CanineForMultipleChoice""",
           """CanineForQuestionAnswering""",
           """CanineForSequenceClassification""",
           """CanineForTokenClassification""",
           """CanineLayer""",
           """CanineModel""",
           """CaninePreTrainedModel""",
           """load_tf_weights_in_canine""",
       ]


if TYPE_CHECKING:
       from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
       from .tokenization_canine import CanineTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_canine import (
                  CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
                  CanineForMultipleChoice,
                  CanineForQuestionAnswering,
                  CanineForSequenceClassification,
                  CanineForTokenClassification,
                  CanineLayer,
                  CanineModel,
                  CaninePreTrainedModel,
                  load_tf_weights_in_canine,
              )


else:
       import sys

       lowerCAmelCase     :  Optional[Any]											=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

