'''simple docstring'''






import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union

import tqdm
from filelock import FileLock

from transformers import (
    BartTokenizer,
    BartTokenizerFast,
    DataProcessor,
    PreTrainedTokenizer,
    RobertaTokenizer,
    RobertaTokenizerFast,
    XLMRobertaTokenizer,
    is_tf_available,
    is_torch_available,
)


__a										=					logging.getLogger(__name__)





@dataclass(frozen=UpperCamelCase   )
class     A__							:





   """simple docstring"""





   UpperCamelCase_       :						str
   UpperCamelCase_       :						str
   UpperCamelCase_       :						Optional[str]    				=     None
   UpperCamelCase_       :						Optional[str]    				=     None
   UpperCamelCase_       :						Optional[str]    				=     None





@dataclass(frozen=UpperCamelCase   )
class     A__							:





   """simple docstring"""





   UpperCamelCase_       :						List[int]
   UpperCamelCase_       :						Optional[List[int]]    				=     None
   UpperCamelCase_       :						Optional[List[int]]    				=     None
   UpperCamelCase_       :						Optional[Union[int, float]]    				=     None
   UpperCamelCase_       :						Optional[int]    				=     None


if is_torch_available():
     import torch
     from torch.utils.data import Dataset





     class     A__							(       UpperCamelCase   ):





        """simple docstring"""





        UpperCamelCase_       :						List[InputFeatures]




        def __init__( self     :		int						,     lowerCAmelCase__     :		str						,     lowerCAmelCase__     :		PreTrainedTokenizer						,     lowerCAmelCase__     :		str						,     lowerCAmelCase__     :		Optional[int] = None						,     lowerCAmelCase__     :		Any=False						,     lowerCAmelCase__     :		bool = False						,     )       ->    Union[str, Any]:







           """simple docstring"""




           _UpperCAmelCase							:	Optional[int]							=   hans_processors[task]()

           _UpperCAmelCase							:	Optional[int]							=   os.path.join(
               lowerCAmelCase__						,     "cached_{}_{}_{}_{}".format(
                   "dev" if evaluate else "train"						,     tokenizer.__class__.__name__						,     str(lowerCAmelCase__			)						,     lowerCAmelCase__						,     )						,     )
           _UpperCAmelCase							:	List[Any]							=   processor.get_labels()
           if tokenizer.__class__ in (
               RobertaTokenizer,
               RobertaTokenizerFast,
               XLMRobertaTokenizer,
               BartTokenizer,
               BartTokenizerFast,
           ):
              # HACK(label indices are swapped in RoBERTa pretrained model)
              _UpperCAmelCase							,       _UpperCAmelCase							:	Any							=   label_list[2], label_list[1]
           _UpperCAmelCase							:	str							=   label_list

           # Make sure only the first process in distributed training processes the dataset,
           # and the others will use the cache.
           _UpperCAmelCase							:	Dict							=   cached_features_file + ".lock"
           with FileLock(lowerCAmelCase__			):
              if os.path.exists(lowerCAmelCase__			) and not overwrite_cache:
                 logger.info(F"""Loading features from cached file {cached_features_file}"""			)
                 _UpperCAmelCase							:	Any							=   torch.load(lowerCAmelCase__			)
              else:
                 logger.info(F"""Creating features from dataset file at {data_dir}"""			)

                 _UpperCAmelCase							:	Tuple							=   (
                     processor.get_dev_examples(lowerCAmelCase__			) if evaluate else processor.get_train_examples(lowerCAmelCase__			)
                 )

                 logger.info("Training examples: %s"						,     len(lowerCAmelCase__			)			)
                 _UpperCAmelCase							:	Any							=   hans_convert_examples_to_features(lowerCAmelCase__						,     lowerCAmelCase__						,     lowerCAmelCase__						,     lowerCAmelCase__			)
                 logger.info("Saving features into cached file %s"						,     lowerCAmelCase__			)
                 torch.save(self.features						,     lowerCAmelCase__			)




        def __len__( self     :		List[Any]			)       ->    Dict:







           """simple docstring"""




           return len(self.features			)




        def __getitem__( self     :		Optional[Any]						,     lowerCAmelCase__     :		Tuple			)       ->    Union[str, Any]:







           """simple docstring"""




           return self.features[i]




        def 			_lowerCAmelCase		( self     :		Optional[int]			)       ->    List[Any]:







           """simple docstring"""




           return self.label_list


if is_tf_available():
     import tensorflow as tf





     class     A__							:





        """simple docstring"""





        UpperCamelCase_       :						List[InputFeatures]




        def __init__( self     :		Optional[Any]						,     lowerCAmelCase__     :		str						,     lowerCAmelCase__     :		PreTrainedTokenizer						,     lowerCAmelCase__     :		str						,     lowerCAmelCase__     :		Optional[int] = 1_2_8						,     lowerCAmelCase__     :		Tuple=False						,     lowerCAmelCase__     :		bool = False						,     )       ->    List[Any]:







           """simple docstring"""




           _UpperCAmelCase							:	Union[str, Any]							=   hans_processors[task]()
           _UpperCAmelCase							:	Union[str, Any]							=   processor.get_labels()
           if tokenizer.__class__ in (
               RobertaTokenizer,
               RobertaTokenizerFast,
               XLMRobertaTokenizer,
               BartTokenizer,
               BartTokenizerFast,
           ):
              # HACK(label indices are swapped in RoBERTa pretrained model)
              _UpperCAmelCase							,       _UpperCAmelCase							:	Optional[Any]							=   label_list[2], label_list[1]
           _UpperCAmelCase							:	str							=   label_list

           _UpperCAmelCase							:	List[str]							=   processor.get_dev_examples(lowerCAmelCase__			) if evaluate else processor.get_train_examples(lowerCAmelCase__			)
           _UpperCAmelCase							:	List[str]							=   hans_convert_examples_to_features(lowerCAmelCase__						,     lowerCAmelCase__						,     lowerCAmelCase__						,     lowerCAmelCase__			)

           def gen():
              for ex_index, ex in tqdm.tqdm(enumerate(self.features			)						,     desc="convert examples to features"			):
                 if ex_index % 1_0_0_0_0 == 0:
                    logger.info("Writing example %d of %d" % (ex_index, len(lowerCAmelCase__			))			)

                 yield (
                     {
                         "example_id": 0,
                         "input_ids": ex.input_ids,
                         "attention_mask": ex.attention_mask,
                         "token_type_ids": ex.token_type_ids,
                     },
                     ex.label,
                 )

           _UpperCAmelCase							:	int							=   tf.data.Dataset.from_generator(
               lowerCAmelCase__						,     (
                   {
                       "example_id": tf.intaa,
                       "input_ids": tf.intaa,
                       "attention_mask": tf.intaa,
                       "token_type_ids": tf.intaa,
                   },
                   tf.intaa,
               )						,     (
                   {
                       "example_id": tf.TensorShape([]			),
                       "input_ids": tf.TensorShape([None, None]			),
                       "attention_mask": tf.TensorShape([None, None]			),
                       "token_type_ids": tf.TensorShape([None, None]			),
                   },
                   tf.TensorShape([]			),
               )						,     )




        def 			_lowerCAmelCase		( self     :		str			)       ->    Optional[Any]:







           """simple docstring"""




           return self.dataset




        def __len__( self     :		str			)       ->    Any:







           """simple docstring"""




           return len(self.features			)




        def __getitem__( self     :		Dict						,     lowerCAmelCase__     :		Tuple			)       ->    Dict:







           """simple docstring"""




           return self.features[i]




        def 			_lowerCAmelCase		( self     :		List[str]			)       ->    Any:







           """simple docstring"""




           return self.label_list





class     A__							(       UpperCamelCase   ):





   """simple docstring"""





   def 			_lowerCAmelCase		( self     :		Tuple						,     lowerCAmelCase__     :		Tuple			)       ->    List[str]:







      """simple docstring"""




      return self._create_examples(self._read_tsv(os.path.join(lowerCAmelCase__						,     "heuristics_train_set.txt"			)			)						,     "train"			)




   def 			_lowerCAmelCase		( self     :		Tuple						,     lowerCAmelCase__     :		List[str]			)       ->    Dict:







      """simple docstring"""




      return self._create_examples(self._read_tsv(os.path.join(lowerCAmelCase__						,     "heuristics_evaluation_set.txt"			)			)						,     "dev"			)




   def 			_lowerCAmelCase		( self     :		Tuple			)       ->    List[Any]:







      """simple docstring"""




      return ["contradiction", "entailment", "neutral"]




   def 			_lowerCAmelCase		( self     :		int						,     lowerCAmelCase__     :		str						,     lowerCAmelCase__     :		int			)       ->    str:







      """simple docstring"""




      _UpperCAmelCase							:	int							=   []
      for i, line in enumerate(lowerCAmelCase__			):
         if i == 0:
            continue
         _UpperCAmelCase							:	Optional[Any]							=   "%s-%s" % (set_type, line[0])
         _UpperCAmelCase							:	Optional[int]							=   line[5]
         _UpperCAmelCase							:	Any							=   line[6]
         _UpperCAmelCase							:	Optional[Any]							=   line[7][2:] if line[7].startswith("ex"			) else line[7]
         _UpperCAmelCase							:	Tuple							=   line[0]
         examples.append(InputExample(guid=lowerCAmelCase__						,     text_a=lowerCAmelCase__						,     text_b=lowerCAmelCase__						,     label=lowerCAmelCase__						,     pairID=lowerCAmelCase__			)			)
      return examples






def    __UpperCAmelCase			(  a_:       List[InputExample],					a_:       List[str],					a_:       int,					a_:       PreTrainedTokenizer,					):
   _UpperCAmelCase							:	int							=   {label: i for i, label in enumerate(__lowercase							)}

   _UpperCAmelCase							:	str							=   []
   for ex_index, example in tqdm.tqdm(enumerate(__lowercase							),					desc="convert examples to features"							):
      if ex_index % 10_000 == 0:
         logger.info("Writing example %d" % (ex_index)							)

      _UpperCAmelCase							:	List[Any]							=   tokenizer(
          example.text_a,					example.text_b,					add_special_tokens=__lowercase,					max_length=__lowercase,					padding="max_length",					truncation=__lowercase,					return_overflowing_tokens=__lowercase,					)

      _UpperCAmelCase							:	Any							=   label_map[example.label] if example.label in label_map else 0

      _UpperCAmelCase							:	int							=   int(example.pairID							)

      features.append(InputFeatures(**__lowercase,					label=__lowercase,					pairID=__lowercase							)							)

   for i, example in enumerate(examples[:5]							):
      logger.info("*** Example ***"							)
      logger.info(f"""guid: {example}"""							)
      logger.info(f"""features: {features[i]}"""							)

   return features


__a										=					{
    '''hans''': 3,
}






__a										=					{
    '''hans''': HansProcessor,
}

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_		(       __lowercase   :	Dict       ,					__lowercase   :	int       ,					__lowercase   :	Optional[Any]=None ) ->  Any:
			# set parameter of one layer
			assert torch_layer.weight.shape == weight.shape, f'''{torch_layer} layer.weight does not match'''
			_snake_case		=			nn.Parameter(__lowercase )
			if bias is not None:
						assert torch_layer.bias.shape == bias.shape, f'''{torch_layer} layer.bias does not match'''
						_snake_case		=			nn.Parameter(__lowercase )






def        a_		(       __lowercase   :	Any       ,					__lowercase   :	Dict       ,					__lowercase   :	Union[str, Any] ) ->  Optional[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] )

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






def        a_		(       __lowercase   :	str       ,					__lowercase   :	Tuple       ,					__lowercase   :	Any ) ->  Optional[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(__lowercase ).transpose(1       ,					2 ).contiguous().view(-1       ,					__lowercase )       ,					)
			set_param(
			    torch_layer.self_attention.key       ,					torch.tensor(__lowercase ).transpose(1       ,					2 ).contiguous().view(-1       ,					__lowercase )       ,					)
			set_param(
			    torch_layer.self_attention.value       ,					torch.tensor(__lowercase ).transpose(1       ,					2 ).contiguous().view(-1       ,					__lowercase )       ,					)
			set_param(
			    torch_layer.output.dense       ,					torch.tensor(__lowercase ).view(-1       ,					__lowercase ).contiguous().transpose(0       ,					1 )       ,					)






def        a_		(       __lowercase   :	Dict       ,					__lowercase   :	List[str]       ,					__lowercase   :	Union[str, Any] ) ->  Optional[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(__lowercase )       ,					torch.tensor(__lowercase )       ,					)

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

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

			# Chunked Feed Forward
			if len(__lowercase ) == 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(__lowercase )       ,					torch.tensor(__lowercase )       ,					)

			# 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(__lowercase ).transpose(0       ,					1 ).contiguous()       ,					torch.tensor(__lowercase )       ,					)

			# 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(__lowercase ).transpose(0       ,					1 ).contiguous()       ,					torch.tensor(__lowercase )       ,					)






def        a_		(       __lowercase   :	Tuple       ,					__lowercase   :	Tuple       ,					__lowercase   :	Dict ) ->  Optional[int]:
			# 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(__lowercase )       ,					)

			if isinstance(weights[3]       ,					__lowercase ):
						_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(__lowercase ) )

			_snake_case		=			weights[5]
			assert len(torch_model_reformer.encoder.layers ) * 4 == len(
			    __lowercase ), "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(__lowercase       ,					__lowercase       ,					__lowercase )

			# 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(__lowercase )       ,					torch.tensor(__lowercase )       ,					)

			# 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(__lowercase ).transpose(0       ,					1 ).contiguous()       ,					torch.tensor(__lowercase )       ,					)






def        a_		(       __lowercase   :	Optional[Any]       ,					__lowercase   :	Dict       ,					__lowercase   :	List[Any] ) ->  Optional[int]:
			# Initialise PyTorch model
			_snake_case		=			ReformerConfig.from_json_file(__lowercase )
			print(f'''Building PyTorch model from configuration: {config}''' )
			_snake_case		=			ReformerModelWithLMHead(__lowercase )

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

			set_model_weights_in_torch(__lowercase       ,					__lowercase       ,					config.hidden_size )

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


if __name__ == "__main__":
			_lowerCamelCase       :				Union[str, 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.'''
			)
			_lowerCamelCase       :				List[Any]       =			parser.parse_args()
			convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)