





import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()






def lowerCamelCase__    (							snake_case_	:  List[str]       ,      snake_case_	:  List[str]       ,      snake_case_	:  Tuple=None       )      ->				Optional[int]:
   # set parameter of one layer
   assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
   __snake_case							       =   nn.Parameter(snake_case_       )
   if bias is not None:
      assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
      __snake_case							       =   nn.Parameter(snake_case_       )






def lowerCamelCase__    (							snake_case_	:  Union[str, Any]       ,      snake_case_	:  Dict       ,      snake_case_	:  Dict       )      ->				Dict:
   # set torch weights for 1-to-1 comparison
   __snake_case							       =   np.asarray(weights[0]       )
   __snake_case							       =   np.asarray(weights[1]       )
   __snake_case							       =   np.asarray(weights[2]       )

   set_param(
       torch_layer.self_attention.query_key       ,      torch.tensor(snake_case_       ).transpose(1       ,      2       ).contiguous().view(-1       ,      snake_case_       )       ,      )
   set_param(
       torch_layer.self_attention.value       ,      torch.tensor(snake_case_       ).transpose(1       ,      2       ).contiguous().view(-1       ,      snake_case_       )       ,      )
   set_param(
       torch_layer.output.dense       ,      torch.tensor(snake_case_       ).view(-1       ,      snake_case_       ).contiguous().transpose(0       ,      1       )       ,      )






def lowerCamelCase__    (							snake_case_	:  List[Any]       ,      snake_case_	:  Dict       ,      snake_case_	:  Dict       )      ->				Union[str, Any]:
   # set torch weights for 1-to-1 comparison
   __snake_case							       =   np.asarray(weights[0]       )
   __snake_case							       =   np.asarray(weights[1]       )
   __snake_case							       =   np.asarray(weights[2]       )
   __snake_case							       =   np.asarray(weights[3]       )

   set_param(
       torch_layer.self_attention.query       ,      torch.tensor(snake_case_       ).transpose(1       ,      2       ).contiguous().view(-1       ,      snake_case_       )       ,      )
   set_param(
       torch_layer.self_attention.key       ,      torch.tensor(snake_case_       ).transpose(1       ,      2       ).contiguous().view(-1       ,      snake_case_       )       ,      )
   set_param(
       torch_layer.self_attention.value       ,      torch.tensor(snake_case_       ).transpose(1       ,      2       ).contiguous().view(-1       ,      snake_case_       )       ,      )
   set_param(
       torch_layer.output.dense       ,      torch.tensor(snake_case_       ).view(-1       ,      snake_case_       ).contiguous().transpose(0       ,      1       )       ,      )






def lowerCamelCase__    (							snake_case_	:  Union[str, Any]       ,      snake_case_	:  int       ,      snake_case_	:  Optional[Any]       )      ->				List[Any]:
   # layernorm 1
   __snake_case							       =   weights[0][0][0]
   __snake_case							       =   np.asarray(layer_norm_a[0]       )
   __snake_case							       =   np.asarray(layer_norm_a[1]       )
   set_param(
       torch_block.attention.layer_norm       ,      torch.tensor(snake_case_       )       ,      torch.tensor(snake_case_       )       ,      )

   # lsh weights + output
   __snake_case							       =   weights[0][1]
   if len(snake_case_       ) < 4:
      set_layer_weights_in_torch_lsh(snake_case_       ,      torch_block.attention       ,      snake_case_       )
   else:
      set_layer_weights_in_torch_local(snake_case_       ,      torch_block.attention       ,      snake_case_       )

   # intermediate weighs
   __snake_case							       =   weights[2][0][1][2]

   # Chunked Feed Forward
   if len(snake_case_       ) == 4:
      __snake_case							       =   intermediate_weights[2]

   # layernorm 2
   __snake_case							       =   np.asarray(intermediate_weights[0][0]       )
   __snake_case							       =   np.asarray(intermediate_weights[0][1]       )
   set_param(
       torch_block.feed_forward.layer_norm       ,      torch.tensor(snake_case_       )       ,      torch.tensor(snake_case_       )       ,      )

   # intermediate dense
   __snake_case							       =   np.asarray(intermediate_weights[1][0]       )
   __snake_case							       =   np.asarray(intermediate_weights[1][1]       )
   set_param(
       torch_block.feed_forward.dense.dense       ,      torch.tensor(snake_case_       ).transpose(0       ,      1       ).contiguous()       ,      torch.tensor(snake_case_       )       ,      )

   # intermediate out
   __snake_case							       =   np.asarray(intermediate_weights[4][0]       )
   __snake_case							       =   np.asarray(intermediate_weights[4][1]       )
   set_param(
       torch_block.feed_forward.output.dense       ,      torch.tensor(snake_case_       ).transpose(0       ,      1       ).contiguous()       ,      torch.tensor(snake_case_       )       ,      )






def lowerCamelCase__    (							snake_case_	:  List[Any]       ,      snake_case_	:  List[str]       ,      snake_case_	:  Union[str, Any]       )      ->				str:
   # reformer model
   __snake_case							       =   torch_model.reformer

   # word embeds
   __snake_case							       =   np.asarray(weights[1]       )
   set_param(
       torch_model_reformer.embeddings.word_embeddings       ,      torch.tensor(snake_case_       )       ,      )

   if isinstance(weights[3]       ,      snake_case_       ):
      __snake_case							       =   torch_model_reformer.embeddings.position_embeddings
      for emb_idx in range(len(position_embeddings.weights       )       ):
         __snake_case							       =   np.asarray(weights[3][emb_idx][0]       )
         assert (
             position_embeddings.weights[emb_idx].shape == emb_weights.shape
         ), f"""{position_embeddings[emb_idx]} emb does not match"""
         __snake_case							       =   nn.Parameter(torch.tensor(snake_case_       )       )

   __snake_case							       =   weights[5]
   assert len(torch_model_reformer.encoder.layers       ) * 4 == len(
       snake_case_       ), "HF and trax model do not have the same number of layers"
   for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers       ):
      __snake_case							       =   trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
      set_block_weights_in_torch(snake_case_       ,      snake_case_       ,      snake_case_       )

   # output layer norm
   __snake_case							       =   np.asarray(weights[7][0]       )
   __snake_case							       =   np.asarray(weights[7][1]       )
   set_param(
       torch_model_reformer.encoder.layer_norm       ,      torch.tensor(snake_case_       )       ,      torch.tensor(snake_case_       )       ,      )

   # output embeddings
   __snake_case							       =   np.asarray(weights[9][0]       )
   __snake_case							       =   np.asarray(weights[9][1]       )
   set_param(
       torch_model.lm_head.decoder       ,      torch.tensor(snake_case_       ).transpose(0       ,      1       ).contiguous()       ,      torch.tensor(snake_case_       )       ,      )






def lowerCamelCase__    (							snake_case_	:  List[str]       ,      snake_case_	:  Optional[Any]       ,      snake_case_	:  Tuple       )      ->				Tuple:
   # Initialise PyTorch model
   __snake_case							       =   ReformerConfig.from_json_file(snake_case_       )
   print(f"""Building PyTorch model from configuration: {config}"""       )
   __snake_case							       =   ReformerModelWithLMHead(snake_case_       )

   with open(snake_case_       ,      '''rb'''       ) as f:
      __snake_case							       =   pickle.load(snake_case_       )['''weights''']

   set_model_weights_in_torch(snake_case_       ,      snake_case_       ,      config.hidden_size       )

   # Save pytorch-model
   print(f"""Save PyTorch model to {pytorch_dump_path}"""       )
   torch.save(model.state_dict()       ,      snake_case_       )


if __name__ == "__main__":
    snake_case_				=			argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--trax_model_pkl_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
    )
    parser.add_argument(
        '--config_file',
        default=None,
        type=str,
        required=True,
        help=(
            'The config json file corresponding to the pre-trained Reformer model. \n'
            'This specifies the model architecture.'
        ),
    )
    parser.add_argument(
        '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
    )
    snake_case_				=			parser.parse_args()
    convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A		       =						{
    'configuration_jukebox': [
        'JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'JukeboxConfig',
        'JukeboxPriorConfig',
        'JukeboxVQVAEConfig',
    ],
    'tokenization_jukebox': ['JukeboxTokenizer'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						[
       'JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST',
       'JukeboxModel',
       'JukeboxPreTrainedModel',
       'JukeboxVQVAE',
       'JukeboxPrior',
   ]

if TYPE_CHECKING:
   from .configuration_jukebox import (
       JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP,
       JukeboxConfig,
       JukeboxPriorConfig,
       JukeboxVQVAEConfig,
   )
   from .tokenization_jukebox import JukeboxTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_jukebox import (
          JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST,
          JukeboxModel,
          JukeboxPreTrainedModel,
          JukeboxPrior,
          JukeboxVQVAE,
      )

else:
   import sys

   _A		       =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





