
"""simple docstring"""






# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


__UpperCAmelCase       =  logging.get_logger(__name__)

__UpperCAmelCase       =  {}
__UpperCAmelCase       =  {}
__UpperCAmelCase       =  {}






def 						_snake_case       (	lowercase__		:  type      ,     lowercase__		:  Optional[str]      ,     lowercase__		:  Optional[List[str]] = None      ,     )  ->				Tuple:


			'''simple docstring'''





			lowerCAmelCase_    :Optional[Any]			     =  aliases if aliases is not None else []
			if format_type in _FORMAT_TYPES:
						logger.warning(
						    f"""Overwriting format type '{format_type}' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})"""		)
			lowerCAmelCase_    :Optional[int]			     =  formatter_cls
			for alias in set(aliases + [format_type]		):
						if alias in _FORMAT_TYPES_ALIASES:
									logger.warning(
									    f"""Overwriting format type alias '{alias}' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})"""		)
						lowerCAmelCase_    :Optional[Any]			     =  format_type






def 						_snake_case       (	lowercase__		:  Exception      ,     lowercase__		:  Optional[str]      ,     lowercase__		:  Optional[List[str]] = None		)  ->				Union[str, Any]:


			'''simple docstring'''





			lowerCAmelCase_    :int			     =  aliases if aliases is not None else []
			for alias in set(aliases + [format_type]		):
						lowerCAmelCase_    :List[Any]			     =  unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=['python'])
_register_formatter(ArrowFormatter, 'arrow', aliases=['pa', 'pyarrow'])
_register_formatter(NumpyFormatter, 'numpy', aliases=['np'])
_register_formatter(PandasFormatter, 'pandas', aliases=['pd'])
_register_formatter(CustomFormatter, 'custom')

if config.TORCH_AVAILABLE:
				from .torch_formatter import TorchFormatter

				_register_formatter(TorchFormatter, 'torch', aliases=['pt', 'pytorch'])
else:
				__UpperCAmelCase       =  ValueError('PyTorch needs to be installed to be able to return PyTorch tensors.')
				_register_unavailable_formatter(_torch_error, 'torch', aliases=['pt', 'pytorch'])

if config.TF_AVAILABLE:
				from .tf_formatter import TFFormatter

				_register_formatter(TFFormatter, 'tensorflow', aliases=['tf'])
else:
				__UpperCAmelCase       =  ValueError('Tensorflow needs to be installed to be able to return Tensorflow tensors.')
				_register_unavailable_formatter(_tf_error, 'tensorflow', aliases=['tf'])

if config.JAX_AVAILABLE:
				from .jax_formatter import JaxFormatter

				_register_formatter(JaxFormatter, 'jax', aliases=[])
else:
				__UpperCAmelCase       =  ValueError('JAX needs to be installed to be able to return JAX arrays.')
				_register_unavailable_formatter(_jax_error, 'jax', aliases=[])






def 						_snake_case       (	lowercase__		:  Optional[str]		)  ->				Optional[str]:


			'''simple docstring'''





			if format_type in _FORMAT_TYPES_ALIASES:
						return _FORMAT_TYPES_ALIASES[format_type]
			else:
						return format_type






def 						_snake_case       (	lowercase__		:  Optional[str]      ,     **lowercase__		:  List[Any]		)  ->				Formatter:


			'''simple docstring'''





			lowerCAmelCase_    :List[Any]			     =  get_format_type_from_alias(__A		)
			if format_type in _FORMAT_TYPES:
						return _FORMAT_TYPES[format_type](**__A		)
			if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
						raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
			else:
						raise ValueError(
						    f"""Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None		)}, but got '{format_type}'"""		)












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    A						(__A   :    Optional[int]   ,				__A   :    int   ,				__A   :    str=None       )   ->      List[Any]:







     """simple docstring"""


     assert torch_layer.weight.shape == weight.shape, F"""{torch_layer} layer.weight does not match"""
     UpperCAmelCase_          =							nn.Parameter(__A       )
     if bias is not None:
          assert torch_layer.bias.shape == bias.shape, F"""{torch_layer} layer.bias does not match"""
          UpperCAmelCase_          =							nn.Parameter(__A       )


def    A						(__A   :    Tuple   ,				__A   :    Dict   ,				__A   :    str       )   ->      Tuple:







     """simple docstring"""


     UpperCAmelCase_          =							np.asarray(weights[0]       )
     UpperCAmelCase_          =							np.asarray(weights[1]       )
     UpperCAmelCase_          =							np.asarray(weights[2]       )

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


def    A						(__A   :    Optional[Any]   ,				__A   :    Any   ,				__A   :    List[Any]       )   ->      int:







     """simple docstring"""


     UpperCAmelCase_          =							np.asarray(weights[0]       )
     UpperCAmelCase_          =							np.asarray(weights[1]       )
     UpperCAmelCase_          =							np.asarray(weights[2]       )
     UpperCAmelCase_          =							np.asarray(weights[3]       )

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


def    A						(__A   :    int   ,				__A   :    Union[str, Any]   ,				__A   :    List[str]       )   ->      List[Any]:







     """simple docstring"""


     UpperCAmelCase_          =							weights[0][0][0]
     UpperCAmelCase_          =							np.asarray(layer_norm_a[0]       )
     UpperCAmelCase_          =							np.asarray(layer_norm_a[1]       )
     set_param(
         torch_block.attention.layer_norm   ,				torch.tensor(__A       )   ,				torch.tensor(__A       )   ,				)

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

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

     # Chunked Feed Forward
     if len(__A       ) == 4:
          UpperCAmelCase_          =							intermediate_weights[2]

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

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

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


def    A						(__A   :    Optional[int]   ,				__A   :    Tuple   ,				__A   :    Any       )   ->      Tuple:







     """simple docstring"""


     UpperCAmelCase_          =							torch_model.reformer

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

     if isinstance(weights[3]   ,				__A       ):
          UpperCAmelCase_          =							torch_model_reformer.embeddings.position_embeddings
          for emb_idx in range(len(position_embeddings.weights       )       ):
               UpperCAmelCase_          =							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"""
               UpperCAmelCase_          =							nn.Parameter(torch.tensor(__A       )       )

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

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

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


def    A						(__A   :    Tuple   ,				__A   :    int   ,				__A   :    str       )   ->      Union[str, Any]:







     """simple docstring"""


     UpperCAmelCase_          =							ReformerConfig.from_json_file(__A       )
     print(F"""Building PyTorch model from configuration: {config}"""       )
     UpperCAmelCase_          =							ReformerModelWithLMHead(__A       )

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

     set_model_weights_in_torch(__A   ,				__A   ,				config.hidden_size       )

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


if __name__ == "__main__":
    snake_case_ : Optional[Any]    		=       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_ : List[Any]    		=       parser.parse_args()
    convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)






