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 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					str		,							__SCREAMING_SNAKE_CASE       :					List[Any]		,							__SCREAMING_SNAKE_CASE       :					Optional[int]   ):







    '''simple docstring'''
    # Initialise PyTorch model
    __snake_case       :				Tuple 			=    FNetConfig.from_json_file(__SCREAMING_SNAKE_CASE   )
    print(F'''Building PyTorch model from configuration: {config}'''   )
    __snake_case       :				Optional[Any] 			=    FNetForPreTraining(__SCREAMING_SNAKE_CASE   )

    __snake_case       :				Tuple 			=    restore_checkpoint(__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   )
    __snake_case       :				Optional[Any] 			=    checkpoint_dict["""target"""]

    # Embeddings
    # Position IDs
    __snake_case       :				Union[str, Any] 			=    fnet_pretraining_model.state_dict()

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

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

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

        __snake_case       :				int 			=    torch.tensor(
            pretrained_model_params["""encoder"""][F'''feed_forward_{layer}''']["""output"""]["""kernel"""]   ).T
        __snake_case       :				Optional[int] 			=    torch.tensor(
            pretrained_model_params["""encoder"""][F'''feed_forward_{layer}''']["""output"""]["""bias"""]   )

        __snake_case       :				Dict 			=    torch.tensor(
            pretrained_model_params["""encoder"""][F'''encoder_{layer}''']["""output_layer_norm"""]["""scale"""]   )
        __snake_case       :				str 			=    torch.tensor(
            pretrained_model_params["""encoder"""][F'''encoder_{layer}''']["""output_layer_norm"""]["""bias"""]   )

    # Pooler Layers
    __snake_case       :				Tuple 			=    torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""kernel"""]   ).T
    __snake_case       :				Union[str, Any] 			=    torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""bias"""]   )

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

    # Seq Relationship Layers
    __snake_case       :				List[str] 			=    torch.tensor(
        pretrained_model_params["""classification"""]["""output_kernel"""]   )
    __snake_case       :				List[str] 			=    torch.tensor(
        pretrained_model_params["""classification"""]["""output_bias"""]   )

    # Load State Dict
    fnet_pretraining_model.load_state_dict(__SCREAMING_SNAKE_CASE   )

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


if __name__ == "__main__":
     lowercase_  							=			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.")
     lowercase_  							=			parser.parse_args()
     convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)




import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):
       A							:	Optional[Any]          =	["image_processor", "tokenizer"]
       A							:	Dict          =	"VideoMAEImageProcessor"
       A							:	Tuple          =	("CLIPTokenizer", "CLIPTokenizerFast")

       def __init__(							self					:     str			,			_lowerCAmelCase					:     Tuple=None			,			_lowerCAmelCase					:     Any=None			,			**_lowerCAmelCase					:     Union[str, Any]			):
           __snake_case       :				int 			=    None
           if "feature_extractor" in kwargs:
               warnings.warn(
                   """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                   """ instead."""			,			_lowerCAmelCase			,			)
               __snake_case       :				Optional[Any] 			=    kwargs.pop("""feature_extractor"""			)

           __snake_case       :				int 			=    image_processor if image_processor is not None else feature_extractor
           if image_processor is None:
               raise ValueError("""You need to specify an `image_processor`."""			)
           if tokenizer is None:
               raise ValueError("""You need to specify a `tokenizer`."""			)

           super().__init__(_lowerCAmelCase			,			_lowerCAmelCase			)
           __snake_case       :				Optional[Any] 			=    self.image_processor

       def __call__(							self					:     str			,			_lowerCAmelCase					:     int=None			,			_lowerCAmelCase					:     Dict=None			,			_lowerCAmelCase					:     Optional[int]=None			,			**_lowerCAmelCase					:     List[str]			):

           if text is None and videos is None:
               raise ValueError("""You have to specify either text or videos. Both cannot be none."""			)

           if text is not None:
               __snake_case       :				Optional[Any] 			=    self.tokenizer(_lowerCAmelCase			,			return_tensors=_lowerCAmelCase			,			**_lowerCAmelCase			)

           if videos is not None:
               __snake_case       :				Optional[int] 			=    self.image_processor(_lowerCAmelCase			,			return_tensors=_lowerCAmelCase			,			**_lowerCAmelCase			)

           if text is not None and videos is not None:
               __snake_case       :				Union[str, Any] 			=    image_features.pixel_values
               return encoding
           elif text is not None:
               return encoding
           else:
               return BatchEncoding(data=dict(**_lowerCAmelCase			)			,			tensor_type=_lowerCAmelCase			)

       def 			snake_case__    (							self					:     Tuple			,			*_lowerCAmelCase					:     str			,			**_lowerCAmelCase					:     List[Any]			):
           return self.tokenizer.batch_decode(*_lowerCAmelCase			,			**_lowerCAmelCase			)

       def 			snake_case__    (							self					:     Tuple			,			*_lowerCAmelCase					:     List[Any]			,			**_lowerCAmelCase					:     Dict			):
           return self.tokenizer.decode(*_lowerCAmelCase			,			**_lowerCAmelCase			)

       @property
       def 			snake_case__    (							self					:     str			):
           return ["input_ids", "attention_mask", "position_ids", "pixel_values"]

       @property
       def 			snake_case__    (							self					:     str			):
           warnings.warn(
               """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""			,			_lowerCAmelCase			,			)
           return self.image_processor_class





       @property
       def 			snake_case__    (							self					:     Union[str, Any]			):
           warnings.warn(
               """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""			,			_lowerCAmelCase			,			)
           return self.image_processor



