




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)












from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


snake_case_ : List[str]    		=       logging.get_logger(__name__)


snake_case_ : Tuple    		=       {
    "Salesforce/codegen-350M-nl": "https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json",
    "Salesforce/codegen-350M-multi": "https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json",
    "Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json",
    "Salesforce/codegen-2B-nl": "https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json",
    "Salesforce/codegen-2B-multi": "https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json",
    "Salesforce/codegen-2B-mono": "https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json",
    "Salesforce/codegen-6B-nl": "https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json",
    "Salesforce/codegen-6B-multi": "https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json",
    "Salesforce/codegen-6B-mono": "https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json",
    "Salesforce/codegen-16B-nl": "https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json",
    "Salesforce/codegen-16B-multi": "https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json",
    "Salesforce/codegen-16B-mono": "https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json",
}







class __snake_case			(			a		):
   UpperCAmelCase__  :    str          =  '''codegen'''
   UpperCAmelCase__  :    int          =  {
       '''max_position_embeddings''': '''n_positions''',
       '''hidden_size''': '''n_embd''',
       '''num_attention_heads''': '''n_head''',
       '''num_hidden_layers''': '''n_layer''',
   }







   def __init__(      self		:						Union[str, Any]					,						_snake_case		:						Union[str, Any]=50400					,						_snake_case		:						Optional[int]=2048					,						_snake_case		:						Union[str, Any]=2048					,						_snake_case		:						List[str]=4096					,						_snake_case		:						Any=28					,						_snake_case		:						List[str]=16					,						_snake_case		:						int=64					,						_snake_case		:						Tuple=None					,						_snake_case		:						Dict="gelu_new"					,						_snake_case		:						Union[str, Any]=0.0					,						_snake_case		:						Optional[Any]=0.0					,						_snake_case		:						List[Any]=0.0					,						_snake_case		:						List[Any]=1e-5					,						_snake_case		:						List[str]=0.0_2					,						_snake_case		:						Optional[Any]=True					,						_snake_case		:						int=50256					,						_snake_case		:						Tuple=50256					,						_snake_case		:						int=False					,						**_snake_case		:						Any					,						):

        """simple docstring"""


        UpperCAmelCase_          =							vocab_size
        UpperCAmelCase_          =							n_ctx
        UpperCAmelCase_          =							n_positions
        UpperCAmelCase_          =							n_embd
        UpperCAmelCase_          =							n_layer
        UpperCAmelCase_          =							n_head
        UpperCAmelCase_          =							n_inner
        UpperCAmelCase_          =							rotary_dim
        UpperCAmelCase_          =							activation_function
        UpperCAmelCase_          =							resid_pdrop
        UpperCAmelCase_          =							embd_pdrop
        UpperCAmelCase_          =							attn_pdrop
        UpperCAmelCase_          =							layer_norm_epsilon
        UpperCAmelCase_          =							initializer_range
        UpperCAmelCase_          =							use_cache

        UpperCAmelCase_          =							bos_token_id
        UpperCAmelCase_          =							eos_token_id

        super().__init__(
            bos_token_id=_snake_case					,						eos_token_id=_snake_case					,						tie_word_embeddings=_snake_case					,						**_snake_case)







class __snake_case			(			a		):







   def __init__(      self		:						Tuple					,						_snake_case		:						PretrainedConfig					,						_snake_case		:						str = "default"					,						_snake_case		:						List[PatchingSpec] = None					,						_snake_case		:						bool = False					,						):

        """simple docstring"""


        super().__init__(_snake_case					,						task=_snake_case					,						patching_specs=_snake_case					,						use_past=_snake_case)
        if not getattr(self._config					,						'''pad_token_id'''					,						_snake_case):
             # TODO: how to do that better?
             UpperCAmelCase_          =							0







   @property
   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}})
        if self.use_past:
             self.fill_with_past_key_values_(_snake_case					,						direction='''inputs''')
             UpperCAmelCase_          =							{0: '''batch''', 1: '''past_sequence + sequence'''}
        else:
             UpperCAmelCase_          =							{0: '''batch''', 1: '''sequence'''}

        return common_inputs







   @property
   def lowerCamelCase						(      self		:						List[str]):

        """simple docstring"""


        return self._config.n_layer







   @property
   def lowerCamelCase						(      self		:						int):

        """simple docstring"""


        return self._config.n_head







   def lowerCamelCase						(      self		:						Optional[int]					,						_snake_case		:						PreTrainedTokenizer					,						_snake_case		:						int = -1					,						_snake_case		:						int = -1					,						_snake_case		:						bool = False					,						_snake_case		:						Optional[TensorType] = None					,						):

        """simple docstring"""


        UpperCAmelCase_          =							super(_snake_case					,						self).generate_dummy_inputs(
            _snake_case					,						batch_size=_snake_case					,						seq_length=_snake_case					,						is_pair=_snake_case					,						framework=_snake_case)

        # We need to order the input in the way they appears in the forward()
        UpperCAmelCase_          =							OrderedDict({'''input_ids''': common_inputs['''input_ids''']})

        # Need to add the past_keys
        if self.use_past:
             if not is_torch_available():
                  raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''')
             else:
                  import torch

                  UpperCAmelCase_     ,       UpperCAmelCase_          =							common_inputs['''input_ids'''].shape
                  # Not using the same length for past_key_values
                  UpperCAmelCase_          =							seqlen + 2
                  UpperCAmelCase_          =							(
                      batch,
                      self.num_attention_heads,
                      past_key_values_length,
                      self._config.hidden_size // self.num_attention_heads,
                  )
                  UpperCAmelCase_          =							[
                      (torch.zeros(_snake_case), torch.zeros(_snake_case)) for _ in range(self.num_layers)
                  ]

        UpperCAmelCase_          =							common_inputs['''attention_mask''']
        if self.use_past:
             UpperCAmelCase_          =							ordered_inputs['''attention_mask'''].dtype
             UpperCAmelCase_          =							torch.cat(
                 [ordered_inputs['''attention_mask'''], torch.ones(_snake_case					,						_snake_case					,						dtype=_snake_case)]					,						dim=1)

        return ordered_inputs







   @property
   def lowerCamelCase						(      self		:						Union[str, Any]):

        """simple docstring"""


        return 13






