

import argparse
import logging
import math
import time

import torch

from transformers import TransfoXLCorpus, TransfoXLLMHeadModel


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
__UpperCAmelCase      = logging.getLogger(__name__)





def 	snake_case_      ()			->	Dict:
   __lowerCAmelCase  :			Optional[Any]					    =						argparse.ArgumentParser(description="""PyTorch Transformer Language Model"""     )
   parser.add_argument("""--model_name"""					,   type=__A					,   default="""transfo-xl-wt103"""					,   help="""pretrained model name"""     )
   parser.add_argument(
       """--split"""					,   type=__A					,   default="""test"""					,   choices=["""all""", """valid""", """test"""]					,   help="""which split to evaluate"""     )
   parser.add_argument("""--batch_size"""					,   type=__A					,   default=1_0					,   help="""batch size"""     )
   parser.add_argument("""--tgt_len"""					,   type=__A					,   default=1_2_8					,   help="""number of tokens to predict"""     )
   parser.add_argument("""--ext_len"""					,   type=__A					,   default=0					,   help="""length of the extended context"""     )
   parser.add_argument("""--mem_len"""					,   type=__A					,   default=1_6_0_0					,   help="""length of the retained previous heads"""     )
   parser.add_argument("""--clamp_len"""					,   type=__A					,   default=1_0_0_0					,   help="""max positional embedding index"""     )
   parser.add_argument("""--no_cuda"""					,   action="""store_true"""					,   help="""Do not use CUDA even though CUA is available"""     )
   parser.add_argument("""--work_dir"""					,   type=__A					,   required=__A					,   help="""path to the work_dir"""     )
   parser.add_argument("""--no_log"""					,   action="""store_true"""					,   help="""do not log the eval result"""     )
   parser.add_argument("""--same_length"""					,   action="""store_true"""					,   help="""set same length attention with masking"""     )
   parser.add_argument("""--server_ip"""					,   type=__A					,   default=""""""					,   help="""Can be used for distant debugging."""     )
   parser.add_argument("""--server_port"""					,   type=__A					,   default=""""""					,   help="""Can be used for distant debugging."""     )
   __lowerCAmelCase  :			List[str]					    =						parser.parse_args()
   assert args.ext_len >= 0, "extended context length must be non-negative"

   if args.server_ip and args.server_port:
      # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
      import ptvsd

      print("""Waiting for debugger attach"""     )
      ptvsd.enable_attach(address=(args.server_ip, args.server_port)					,   redirect_output=__A     )
      ptvsd.wait_for_attach()

   __lowerCAmelCase  :			List[Any]					    =						torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""     )
   logger.info("""device: {}""".format(__A     )     )

   # Load a pre-processed dataset
   # You can also build the corpus yourself using TransfoXLCorpus methods
   # The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax
   # and tokenizing the dataset
   # The pre-processed corpus is a convertion (using the conversion script )
   __lowerCAmelCase  :			str					    =						TransfoXLCorpus.from_pretrained(args.model_name     )

   __lowerCAmelCase  :			List[Any]					    =						corpus.get_iterator("""valid"""					,   args.batch_size					,   args.tgt_len					,   device=__A					,   ext_len=args.ext_len     )
   __lowerCAmelCase  :			Optional[int]					    =						corpus.get_iterator("""test"""					,   args.batch_size					,   args.tgt_len					,   device=__A					,   ext_len=args.ext_len     )

   # Load a pre-trained model
   __lowerCAmelCase  :			List[str]					    =						TransfoXLLMHeadModel.from_pretrained(args.model_name     )
   model.to(__A     )

   logger.info(
       """Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}""".format(
           args.batch_size					,   args.tgt_len					,   args.ext_len					,   args.mem_len					,   args.clamp_len     )     )

   model.reset_memory_length(args.mem_len     )
   if args.clamp_len > 0:
      __lowerCAmelCase  :			Optional[int]					    =						args.clamp_len
   if args.same_length:
      __lowerCAmelCase  :			int					    =						True

   ###############################################################################
   # Evaluation code
   ###############################################################################
   def evaluate(__A     :						Union[str, Any]     ):
      # Turn on evaluation mode which disables dropout.
      model.eval()
      __lowerCAmelCase    ,__lowerCAmelCase  :			Optional[Any]					    =						0, 0.0
      __lowerCAmelCase  :			List[Any]					    =						time.time()
      with torch.no_grad():
         __lowerCAmelCase  :			Dict					    =						None
         for idx, (data, target, seq_len) in enumerate(__A     ):
            __lowerCAmelCase  :			Any					    =						model(__A					,   lm_labels=__A					,   mems=__A     )
            __lowerCAmelCase    ,__lowerCAmelCase    ,__lowerCAmelCase  :			Optional[int]					    =						ret
            __lowerCAmelCase  :			Optional[int]					    =						loss.mean()
            total_loss += seq_len * loss.item()
            total_len += seq_len
         __lowerCAmelCase  :			Union[str, Any]					    =						time.time() - start_time
      logger.info("""Time : {:.2f}s, {:.2f}ms/segment""".format(__A					,   1_0_0_0 * total_time / (idx + 1)     )     )
      return total_loss / total_len

   # Run on test data.
   if args.split == "all":
      __lowerCAmelCase  :			List[Any]					    =						evaluate(__A     )
      __lowerCAmelCase  :			Union[str, Any]					    =						evaluate(__A     )
   elif args.split == "valid":
      __lowerCAmelCase  :			Any					    =						evaluate(__A     )
      __lowerCAmelCase  :			Any					    =						None
   elif args.split == "test":
      __lowerCAmelCase  :			Dict					    =						evaluate(__A     )
      __lowerCAmelCase  :			List[str]					    =						None

   def format_log(__A     :						Dict					,   __A     :						Union[str, Any]     ):
      __lowerCAmelCase  :			int					    =						"""| {0} loss {1:5.2f} | {0} ppl {2:9.3f} """.format(__A					,   __A					,   math.exp(__A     )     )
      return log_str

   __lowerCAmelCase  :			Tuple					    =						""""""
   if valid_loss is not None:
      log_str += format_log(__A					,   """valid"""     )
   if test_loss is not None:
      log_str += format_log(__A					,   """test"""     )

   logger.info("""=""" * 1_0_0     )
   logger.info(__A     )
   logger.info("""=""" * 1_0_0     )


if __name__ == "__main__":
   main()



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		(						lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	):
       # Initialise PyTorch model
       lowerCAmelCase__     	=					FNetConfig.from_json_file(lowerCAmelCase__	)
       print(F"""Building PyTorch model from configuration: {config}"""	)
       lowerCAmelCase__     	=					FNetForPreTraining(lowerCAmelCase__	)

       lowerCAmelCase__     	=					restore_checkpoint(lowerCAmelCase__	,  lowerCAmelCase__	)
       lowerCAmelCase__     	=					checkpoint_dict['target']

       # Embeddings
       # Position IDs
       lowerCAmelCase__     	=					fnet_pretraining_model.state_dict()

       lowerCAmelCase__     	=					state_dict['fnet.embeddings.position_ids']
       lowerCAmelCase__     	=					{'fnet.embeddings.position_ids': position_ids}
       # Embedding Layers
       lowerCAmelCase__     	=					torch.tensor(
           pretrained_model_params['encoder']['embedder']['word']['embedding']	)
       lowerCAmelCase__     	=					torch.tensor(
           pretrained_model_params['encoder']['embedder']['position']['embedding'][0]	)
       lowerCAmelCase__     	=					torch.tensor(
           pretrained_model_params['encoder']['embedder']['type']['embedding']	)
       lowerCAmelCase__     	=					torch.tensor(
           pretrained_model_params['encoder']['embedder']['hidden_mapping_in']['kernel']	).T
       lowerCAmelCase__     	=					torch.tensor(
           pretrained_model_params['encoder']['embedder']['hidden_mapping_in']['bias']	)
       lowerCAmelCase__     	=					torch.tensor(
           pretrained_model_params['encoder']['embedder']['layer_norm']['scale']	)
       lowerCAmelCase__     	=					torch.tensor(
           pretrained_model_params['encoder']['embedder']['layer_norm']['bias']	)

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

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

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

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

       # Pooler Layers
       lowerCAmelCase__     	=					torch.tensor(pretrained_model_params['encoder']['pooler']['kernel']	).T
       lowerCAmelCase__     	=					torch.tensor(pretrained_model_params['encoder']['pooler']['bias']	)

       # Masked LM Layers
       lowerCAmelCase__     	=					torch.tensor(
           pretrained_model_params['predictions_dense']['kernel']	).T
       lowerCAmelCase__     	=					torch.tensor(
           pretrained_model_params['predictions_dense']['bias']	)
       lowerCAmelCase__     	=					torch.tensor(
           pretrained_model_params['predictions_layer_norm']['scale']	)
       lowerCAmelCase__     	=					torch.tensor(
           pretrained_model_params['predictions_layer_norm']['bias']	)
       lowerCAmelCase__     	=					torch.tensor(
           pretrained_model_params['encoder']['embedder']['word']['embedding']	)
       lowerCAmelCase__     	=					torch.tensor(
           pretrained_model_params['predictions_output']['output_bias']	)
       lowerCAmelCase__     	=					torch.tensor(pretrained_model_params['predictions_output']['output_bias']	)

       # Seq Relationship Layers
       lowerCAmelCase__     	=					torch.tensor(
           pretrained_model_params['classification']['output_kernel']	)
       lowerCAmelCase__     	=					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)
