
from typing import TYPE_CHECKING

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


_lowerCamelCase        ={"""configuration_t5""": ["""T5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """T5Config""", """T5OnnxConfig"""]}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCamelCase        =["""T5Tokenizer"""]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCamelCase        =["""T5TokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCamelCase        =[
          """T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """T5EncoderModel""",
          """T5ForConditionalGeneration""",
          """T5Model""",
          """T5PreTrainedModel""",
          """load_tf_weights_in_t5""",
          """T5ForQuestionAnswering""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCamelCase        =[
          """TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFT5EncoderModel""",
          """TFT5ForConditionalGeneration""",
          """TFT5Model""",
          """TFT5PreTrainedModel""",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCamelCase        =[
          """FlaxT5EncoderModel""",
          """FlaxT5ForConditionalGeneration""",
          """FlaxT5Model""",
          """FlaxT5PreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_ta import TaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_ta_fast import TaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_ta import (
                T5_PRETRAINED_MODEL_ARCHIVE_LIST,
                TaEncoderModel,
                TaForConditionalGeneration,
                TaForQuestionAnswering,
                TaModel,
                TaPreTrainedModel,
                load_tf_weights_in_ta,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_ta import (
                TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFTaEncoderModel,
                TFTaForConditionalGeneration,
                TFTaModel,
                TFTaPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_ta import (
                FlaxTaEncoderModel,
                FlaxTaForConditionalGeneration,
                FlaxTaModel,
                FlaxTaPreTrainedModel,
            )


else:
      import sys

      _lowerCamelCase        =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






import argparse

import torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()





def    _a (						lowerCamelCase,     lowerCamelCase,     lowerCamelCase							):
 # Initialise PyTorch model
 lowerCamelCase		:     List[str]          =				FNetConfig.from_json_file(lowerCamelCase							)
 print(F'''Building PyTorch model from configuration: {config}'''							)
 lowerCamelCase		:     Any          =				FNetForPreTraining(lowerCamelCase							)

 lowerCamelCase		:     List[Any]          =				restore_checkpoint(lowerCamelCase,     lowerCamelCase							)
 lowerCamelCase		:     List[str]          =				checkpoint_dict["""target"""]

 # Embeddings
 # Position IDs
 lowerCamelCase		:     Optional[Any]          =				fnet_pretraining_model.state_dict()

 lowerCamelCase		:     str          =				state_dict["""fnet.embeddings.position_ids"""]
 lowerCamelCase		:     List[str]          =				{"""fnet.embeddings.position_ids""": position_ids}
 # Embedding Layers
 lowerCamelCase		:     Tuple          =				torch.tensor(
     pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]							)
 lowerCamelCase		:     List[Any]          =				torch.tensor(
     pretrained_model_params["""encoder"""]["""embedder"""]["""position"""]["""embedding"""][0]							)
 lowerCamelCase		:     Optional[Any]          =				torch.tensor(
     pretrained_model_params["""encoder"""]["""embedder"""]["""type"""]["""embedding"""]							)
 lowerCamelCase		:     Optional[int]          =				torch.tensor(
     pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""kernel"""]							).T
 lowerCamelCase		:     int          =				torch.tensor(
     pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""bias"""]							)
 lowerCamelCase		:     str          =				torch.tensor(
     pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""scale"""]							)
 lowerCamelCase		:     Optional[int]          =				torch.tensor(
     pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""bias"""]							)

 # Encoder Layers
 for layer in range(config.num_hidden_layers							):
  lowerCamelCase		:     List[Any]          =				torch.tensor(
      pretrained_model_params["""encoder"""][F'''encoder_{layer}''']["""mixing_layer_norm"""]["""scale"""]							)
  lowerCamelCase		:     Any          =				torch.tensor(
      pretrained_model_params["""encoder"""][F'''encoder_{layer}''']["""mixing_layer_norm"""]["""bias"""]							)

  lowerCamelCase		:     int          =				torch.tensor(
      pretrained_model_params["""encoder"""][F'''feed_forward_{layer}''']["""intermediate"""]["""kernel"""]							).T
  lowerCamelCase		:     List[str]          =				torch.tensor(
      pretrained_model_params["""encoder"""][F'''feed_forward_{layer}''']["""intermediate"""]["""bias"""]							)

  lowerCamelCase		:     List[Any]          =				torch.tensor(
      pretrained_model_params["""encoder"""][F'''feed_forward_{layer}''']["""output"""]["""kernel"""]							).T
  lowerCamelCase		:     str          =				torch.tensor(
      pretrained_model_params["""encoder"""][F'''feed_forward_{layer}''']["""output"""]["""bias"""]							)

  lowerCamelCase		:     Tuple          =				torch.tensor(
      pretrained_model_params["""encoder"""][F'''encoder_{layer}''']["""output_layer_norm"""]["""scale"""]							)
  lowerCamelCase		:     Optional[int]          =				torch.tensor(
      pretrained_model_params["""encoder"""][F'''encoder_{layer}''']["""output_layer_norm"""]["""bias"""]							)

 # Pooler Layers
 lowerCamelCase		:     Tuple          =				torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""kernel"""]							).T
 lowerCamelCase		:     List[Any]          =				torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""bias"""]							)

 # Masked LM Layers
 lowerCamelCase		:     Optional[Any]          =				torch.tensor(
     pretrained_model_params["""predictions_dense"""]["""kernel"""]							).T
 lowerCamelCase		:     Tuple          =				torch.tensor(
     pretrained_model_params["""predictions_dense"""]["""bias"""]							)
 lowerCamelCase		:     Tuple          =				torch.tensor(
     pretrained_model_params["""predictions_layer_norm"""]["""scale"""]							)
 lowerCamelCase		:     int          =				torch.tensor(
     pretrained_model_params["""predictions_layer_norm"""]["""bias"""]							)
 lowerCamelCase		:     Dict          =				torch.tensor(
     pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]							)
 lowerCamelCase		:     Dict          =				torch.tensor(
     pretrained_model_params["""predictions_output"""]["""output_bias"""]							)
 lowerCamelCase		:     Any          =				torch.tensor(pretrained_model_params["""predictions_output"""]["""output_bias"""]							)

 # Seq Relationship Layers
 lowerCamelCase		:     int          =				torch.tensor(
     pretrained_model_params["""classification"""]["""output_kernel"""]							)
 lowerCamelCase		:     Optional[Any]          =				torch.tensor(
     pretrained_model_params["""classification"""]["""output_bias"""]							)

 # Load State Dict
 fnet_pretraining_model.load_state_dict(lowerCamelCase							)

 # Save PreTrained
 print(F'''Saving pretrained model to {save_path}'''							)
 fnet_pretraining_model.save_pretrained(lowerCamelCase							)


if __name__ == "__main__":
      _lowerCamelCase        =argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--flax_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
      )
      parser.add_argument(
          """--fnet_config_file""",
          default=None,
          type=str,
          required=True,
          help=(
              """The config json file corresponding to the pre-trained FNet model. \n"""
              """This specifies the model architecture."""
          ),
      )
      parser.add_argument("""--save_path""", default=None, type=str, required=True, help="""Path to the output model.""")
      _lowerCamelCase        =parser.parse_args()
      convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)




