

'''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 	_lowerCAmelCase (						_UpperCamelCase						:					Optional[Any]							,			_UpperCamelCase						:					int							,			_UpperCamelCase						:					Optional[Any]							,			_UpperCamelCase						:					Tuple="attention"    )				->      str:
     """simple docstring"""




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


def 	_lowerCAmelCase (						_UpperCamelCase						:					str							,			_UpperCamelCase						:					Optional[Any]							,			_UpperCamelCase						:					Union[str, Any]							,			_UpperCamelCase						:					Any=False    )				->      Union[str, Any]:
     """simple docstring"""




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

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


def 	_lowerCAmelCase (						_UpperCamelCase						:					int							,			_UpperCamelCase						:					List[str]							,			_UpperCamelCase						:					Dict							,			_UpperCamelCase						:					Union[str, Any]    )				->      Any:
     """simple docstring"""




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


def 	_lowerCAmelCase (						_UpperCamelCase						:					dict							,			*, _UpperCamelCase						:					int							,			_UpperCamelCase						:					bool    )				->      Dict:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =traverse_util.flatten_dict(variables['target']    )
     _SCREAMING_SNAKE_CASE							   ={'''/'''.join(_UpperCamelCase    ): v for k, v in old.items()}

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

     _SCREAMING_SNAKE_CASE							   =collections.OrderedDict()

     # Shared embeddings.
     _SCREAMING_SNAKE_CASE							   =old['''token_embedder/embedding''']

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

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

     _SCREAMING_SNAKE_CASE							   =old[
         '''encoder/relpos_bias/rel_embedding'''
     ].T
     _SCREAMING_SNAKE_CASE							   =old['''encoder/encoder_norm/scale''']

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

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

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

          _SCREAMING_SNAKE_CASE							   =old['''decoder/decoder_norm/scale''']
          _SCREAMING_SNAKE_CASE							   =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:
               _SCREAMING_SNAKE_CASE							   =old['''decoder/logits_dense/kernel'''].T

     return new


def 	_lowerCAmelCase (						_UpperCamelCase						:					Tuple							,			_UpperCamelCase						:					bool    )				->      Any:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =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:
          _SCREAMING_SNAKE_CASE							   =state_dict['''shared.weight''']

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

     return state_dict


def 	_lowerCAmelCase (						_UpperCamelCase						:					Any							,			_UpperCamelCase						:					List[str]							,			_UpperCamelCase						:					Dict							,			_UpperCamelCase						:					Tuple    )				->      List[str]:
     """simple docstring"""




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


def 	_lowerCAmelCase (						_UpperCamelCase						:					List[str]							,			_UpperCamelCase						:					Optional[int]							,			_UpperCamelCase						:					List[Any]							,			_UpperCamelCase						:					bool = False    )				->      List[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =TaConfig.from_json_file(_UpperCamelCase    )
     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:
          _SCREAMING_SNAKE_CASE							   =TaEncoderModel(_UpperCamelCase    )
     else:
          _SCREAMING_SNAKE_CASE							   =TaForConditionalGeneration(_UpperCamelCase    )

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

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

     # Verify that we can load the checkpoint.
     model.from_pretrained(_UpperCamelCase    )
     print('Done'    )


if __name__ == "__main__":
   lowerCamelCase			:				Union[str, Any] 			=					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'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase			:				Tuple 			=					{"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"]}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				List[Any] 			=					["FNetTokenizer"]

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				Optional[int] 			=					["FNetTokenizerFast"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				str 			=					[
         "FNET_PRETRAINED_MODEL_ARCHIVE_LIST",
         "FNetForMaskedLM",
         "FNetForMultipleChoice",
         "FNetForNextSentencePrediction",
         "FNetForPreTraining",
         "FNetForQuestionAnswering",
         "FNetForSequenceClassification",
         "FNetForTokenClassification",
         "FNetLayer",
         "FNetModel",
         "FNetPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_fnet import FNetTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_fnet_fast import FNetTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_fnet import (
              FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              FNetForMaskedLM,
              FNetForMultipleChoice,
              FNetForNextSentencePrediction,
              FNetForPreTraining,
              FNetForQuestionAnswering,
              FNetForSequenceClassification,
              FNetForTokenClassification,
              FNetLayer,
              FNetModel,
              FNetPreTrainedModel,
          )


else:
     import sys

     lowerCamelCase			:				Dict 			=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
