






"""simple docstring"""



import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
						import torch





class 					_UpperCAmelCase       ( _A							):
					SCREAMING_SNAKE_CASE_		:				str							    =   "char"
					SCREAMING_SNAKE_CASE_		:				Any							    =   "bpe"
					SCREAMING_SNAKE_CASE_		:				Optional[int]							    =   "wp"
__A		:       int  				=		(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)





class 					_UpperCAmelCase       ( _A							):
					SCREAMING_SNAKE_CASE_		:				Any							    =   ["image_processor", "char_tokenizer"]
					SCREAMING_SNAKE_CASE_		:				Any							    =   "ViTImageProcessor"
					SCREAMING_SNAKE_CASE_		:				Optional[int]							    =   "MgpstrTokenizer"




					def __init__(				self     :						List[str]    ,						A     :						Union[str, Any]=None    ,						A     :						str=None    ,						**A     :						Optional[Any]   )  ->  str:
							lowercase_ : Optional[Any]							       =   None
							if "feature_extractor" in kwargs:
									warnings.warn(
									    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
									    ''' instead.'''    ,						A    ,						)
									lowercase_ : List[Any]							       =   kwargs.pop('''feature_extractor'''   )

							lowercase_ : str							       =   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`.'''   )

							lowercase_ : List[str]							       =   tokenizer
							lowercase_ : Union[str, Any]							       =   AutoTokenizer.from_pretrained('''gpt2'''   )
							lowercase_ : List[Any]							       =   AutoTokenizer.from_pretrained('''bert-base-uncased'''   )

							super().__init__(A    ,						A   )




					def __call__(				self     :						List[Any]    ,						A     :						int=None    ,						A     :						Tuple=None    ,						A     :						List[Any]=None    ,						**A     :						Union[str, Any]   )  ->  int:
							if images is None and text is None:
									raise ValueError('''You need to specify either an `images` or `text` input to process.'''   )

							if images is not None:
									lowercase_ : Optional[Any]							       =   self.image_processor(A    ,						return_tensors=A    ,						**A   )
							if text is not None:
									lowercase_ : Union[str, Any]							       =   self.char_tokenizer(A    ,						return_tensors=A    ,						**A   )

							if text is None:
									return inputs
							elif images is None:
									return encodings
							else:
									lowercase_ : List[str]							       =   encodings['''input_ids''']
									return inputs




					def   A  (				self     :						Optional[Any]    ,						A     :						int   )  ->  Optional[int]:
							lowercase_       ,					lowercase_       ,					lowercase_ : Any							       =   sequences
							lowercase_ : Tuple							       =   char_preds.size(0   )

							lowercase_       ,					lowercase_ : Optional[int]							       =   self._decode_helper(A    ,						'''char'''   )
							lowercase_       ,					lowercase_ : Dict							       =   self._decode_helper(A    ,						'''bpe'''   )
							lowercase_       ,					lowercase_ : Optional[int]							       =   self._decode_helper(A    ,						'''wp'''   )

							lowercase_ : int							       =   []
							lowercase_ : Optional[int]							       =   []
							for i in range(A   ):
									lowercase_ : Dict							       =   [char_scores[i], bpe_scores[i], wp_scores[i]]
									lowercase_ : List[Any]							       =   [char_strs[i], bpe_strs[i], wp_strs[i]]
									lowercase_ : Dict							       =   scores.index(max(A   )   )
									final_strs.append(strs[max_score_index]   )
									final_scores.append(scores[max_score_index]   )

							lowercase_ : List[Any]							       =   {}
							lowercase_ : Any							       =   final_strs
							lowercase_ : List[Any]							       =   final_scores
							lowercase_ : Optional[Any]							       =   char_strs
							lowercase_ : Union[str, Any]							       =   bpe_strs
							lowercase_ : Dict							       =   wp_strs
							return out




					def   A  (				self     :						Dict    ,						A     :						List[Any]    ,						A     :						Dict   )  ->  Optional[Any]:
							if format == DecodeType.CHARACTER:
									lowercase_ : List[str]							       =   self.char_decode
									lowercase_ : str							       =   1
									lowercase_ : str							       =   '''[s]'''
							elif format == DecodeType.BPE:
									lowercase_ : Optional[int]							       =   self.bpe_decode
									lowercase_ : List[Any]							       =   2
									lowercase_ : Any							       =   '''#'''
							elif format == DecodeType.WORDPIECE:
									lowercase_ : Optional[Any]							       =   self.wp_decode
									lowercase_ : Optional[int]							       =   1_02
									lowercase_ : str							       =   '''[SEP]'''
							else:
									raise ValueError(F'''Format {format} is not supported.'''   )

							lowercase_       ,					lowercase_ : Union[str, Any]							       =   [], []
							lowercase_ : Dict							       =   pred_logits.size(0   )
							lowercase_ : Dict							       =   pred_logits.size(1   )
							lowercase_       ,					lowercase_ : Dict							       =   pred_logits.topk(1    ,						dim=-1    ,						largest=A    ,						sorted=A   )
							lowercase_ : List[str]							       =   preds_index.view(-1    ,						A   )[:, 1:]
							lowercase_ : Dict							       =   decoder(A   )
							lowercase_       ,					lowercase_ : Dict							       =   torch.nn.functional.softmax(A    ,						dim=2   ).max(dim=2   )
							lowercase_ : Optional[int]							       =   preds_max_prob[:, 1:]

							for index in range(A   ):
									lowercase_ : Union[str, Any]							       =   preds_str[index].find(A   )
									lowercase_ : Union[str, Any]							       =   preds_str[index][:pred_eos]
									lowercase_ : Dict							       =   preds_index[index].cpu().tolist()
									lowercase_ : int							       =   pred_index.index(A   ) if eos_token in pred_index else -1
									lowercase_ : List[str]							       =   preds_max_prob[index][: pred_eos_index + 1]
									lowercase_ : Optional[int]							       =   pred_max_prob.cumprod(dim=0   )[-1] if pred_max_prob.nelement() != 0 else 0.0
									dec_strs.append(A   )
									conf_scores.append(A   )

							return dec_strs, conf_scores




					def   A  (				self     :						str    ,						A     :						Optional[int]   )  ->  Any:
							lowercase_ : Tuple							       =   [seq.replace(''' '''    ,						''''''   ) for seq in self.char_tokenizer.batch_decode(A   )]
							return decode_strs




					def   A  (				self     :						Tuple    ,						A     :						Union[str, Any]   )  ->  Optional[int]:
							return self.bpe_tokenizer.batch_decode(A   )






					def   A  (				self     :						Dict    ,						A     :						int   )  ->  str:
							lowercase_ : str							       =   [seq.replace(''' '''    ,						''''''   ) for seq in self.wp_tokenizer.batch_decode(A   )]
							return decode_strs









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)






