



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 	__lowercase   ( a__							,   a__							,   a__=None   )				-> List[str]:
							assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
							__SCREAMING_SNAKE_CASE									=					nn.Parameter(__A   )
							if bias is not None:
														assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
														__SCREAMING_SNAKE_CASE									=					nn.Parameter(__A   )






def 	__lowercase   ( a__							,   a__							,   a__   )				-> Dict:
							__SCREAMING_SNAKE_CASE									=					np.asarray(weights[0]   )
							__SCREAMING_SNAKE_CASE									=					np.asarray(weights[1]   )
							__SCREAMING_SNAKE_CASE									=					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 	__lowercase   ( a__							,   a__							,   a__   )				-> Optional[Any]:
							__SCREAMING_SNAKE_CASE									=					np.asarray(weights[0]   )
							__SCREAMING_SNAKE_CASE									=					np.asarray(weights[1]   )
							__SCREAMING_SNAKE_CASE									=					np.asarray(weights[2]   )
							__SCREAMING_SNAKE_CASE									=					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 	__lowercase   ( a__							,   a__							,   a__   )				-> Dict:
							__SCREAMING_SNAKE_CASE									=					weights[0][0][0]
							__SCREAMING_SNAKE_CASE									=					np.asarray(layer_norm_a[0]   )
							__SCREAMING_SNAKE_CASE									=					np.asarray(layer_norm_a[1]   )
							set_param(
							    torch_block.attention.layer_norm							,   torch.tensor(__A   )							,   torch.tensor(__A   )							,   )

							# lsh weights + output
							__SCREAMING_SNAKE_CASE									=					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
							__SCREAMING_SNAKE_CASE									=					weights[2][0][1][2]

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

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

							# intermediate dense
							__SCREAMING_SNAKE_CASE									=					np.asarray(intermediate_weights[1][0]   )
							__SCREAMING_SNAKE_CASE									=					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
							__SCREAMING_SNAKE_CASE									=					np.asarray(intermediate_weights[4][0]   )
							__SCREAMING_SNAKE_CASE									=					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 	__lowercase   ( a__							,   a__							,   a__   )				-> str:
							__SCREAMING_SNAKE_CASE									=					torch_model.reformer

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

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

							__SCREAMING_SNAKE_CASE									=					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   ):
														__SCREAMING_SNAKE_CASE									=					trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
														set_block_weights_in_torch(__A							,   __A							,   __A   )

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

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






def 	__lowercase   ( a__							,   a__							,   a__   )				-> str:
							__SCREAMING_SNAKE_CASE									=					ReformerConfig.from_json_file(__A   )
							print(f"""Building PyTorch model from configuration: {config}"""   )
							__SCREAMING_SNAKE_CASE									=					ReformerModelWithLMHead(__A   )

							with open(__A							,   'rb'   ) as f:
														__SCREAMING_SNAKE_CASE									=					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__":
			lowerCAmelCase__		:     List[str]       						=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)







import json
import os
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCamelCase__											=					logging.get_logger(__name__)


UpperCamelCase__											=					{
    'vocab_file': 'vocab.json',
    'tokenizer_config_file': 'tokenizer_config.json',
    'merges_file': 'merges.txt',
}

UpperCamelCase__											=					{
    'vocab_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json'
        ),
    },
    'tokenizer_config_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json'
        ),
    },
    'merges_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt'
        ),
    },
}

UpperCamelCase__											=					'</w>'
UpperCamelCase__											=					'@@ '




def    lowerCAmelCase_ (     __A							)     ->				str:







      '''simple docstring'''


      UpperCAmelCase__								=  set()
      UpperCAmelCase__								=  word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)							)
            UpperCAmelCase__								=  char
      return pairs


# Speech2Text2 has no max input length
UpperCamelCase__											=					{'facebook/s2t-wav2vec2-large-en-de': 1_0_2_4}







class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : str      =			VOCAB_FILES_NAMES
   __UpperCAmelCase : str      =			PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase : Union[str, Any]      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase : Dict      =			['input_ids', 'attention_mask']


   def __init__(self     :						Tuple       ,  __UpperCAmelCase     :						List[Any]       ,  __UpperCAmelCase     :						Dict="<s>"       ,  __UpperCAmelCase     :						Tuple="<pad>"       ,  __UpperCAmelCase     :						str="</s>"       ,  __UpperCAmelCase     :						int="<unk>"       ,  __UpperCAmelCase     :						List[str]=False       ,  __UpperCAmelCase     :						str=None       ,  **__UpperCAmelCase     :						Optional[Any]       ,  )						->				Tuple:
         """simple docstring"""



         super().__init__(
             unk_token=__UpperCAmelCase       ,  bos_token=__UpperCAmelCase       ,  eos_token=__UpperCAmelCase       ,  pad_token=__UpperCAmelCase       ,  do_lower_case=__UpperCAmelCase       ,  **__UpperCAmelCase       ,  )

         UpperCAmelCase__								=  do_lower_case

         with open(__UpperCAmelCase       ,  encoding="utf-8"		) as vocab_handle:
               UpperCAmelCase__								=  json.load(__UpperCAmelCase		)
         UpperCAmelCase__								=  {v: k for k, v in self.encoder.items()}

         if merges_file is None:
               logger.info(f"""No merges files provided. {self.__class__.__name__} can only be used for decoding."""		)

               UpperCAmelCase__								=  None
               UpperCAmelCase__								=  None
         else:
               with open(__UpperCAmelCase       ,  encoding="utf-8"		) as merges_handle:
                     UpperCAmelCase__								=  merges_handle.read().split("\n"		)[:-1]

               UpperCAmelCase__								=  [tuple(merge.split()[:2]		) for merge in merges]
               UpperCAmelCase__								=  dict(zip(__UpperCAmelCase       ,  range(len(__UpperCAmelCase		)		)		)		)
               UpperCAmelCase__								=  {}


   @property
   def 			lowercase_      (self     :						List[str]		)						->				int:
         """simple docstring"""



         return len(self.decoder		)


   def 			lowercase_      (self     :						Union[str, Any]		)						->				Dict:
         """simple docstring"""



         return dict(self.encoder       ,  **self.added_tokens_encoder		)


   def 			lowercase_      (self     :						Dict       ,  __UpperCAmelCase     :						Union[str, Any]		)						->				str:
         """simple docstring"""



         UpperCAmelCase__								=  tuple(token[:-1]		) + (token[-1] + BPE_TOKEN_MERGES,)
         if token in self.cache:
               return self.cache[token]
         UpperCAmelCase__								=  get_pairs(__UpperCAmelCase		)

         if not pairs:
               return token

         while True:
               UpperCAmelCase__								=  min(__UpperCAmelCase       ,  key=lambda __UpperCAmelCase		: self.bpe_ranks.get(__UpperCAmelCase       ,  float("inf"		)		)		)
               if bigram not in self.bpe_ranks:
                     break
               UpperCAmelCase__		,      UpperCAmelCase__								=  bigram
               UpperCAmelCase__								=  []
               UpperCAmelCase__								=  0
               while i < len(__UpperCAmelCase		):
                     try:
                           UpperCAmelCase__								=  word.index(__UpperCAmelCase       ,  __UpperCAmelCase		)
                     except ValueError:
                           new_word.extend(word[i:]		)
                           break
                     else:
                           new_word.extend(word[i:j]		)
                           UpperCAmelCase__								=  j

                     if word[i] == first and i < len(__UpperCAmelCase		) - 1 and word[i + 1] == second:
                           new_word.append(first + second		)
                           i += 2
                     else:
                           new_word.append(word[i]		)
                           i += 1
               UpperCAmelCase__								=  tuple(__UpperCAmelCase		)
               UpperCAmelCase__								=  new_word
               if len(__UpperCAmelCase		) == 1:
                     break
               else:
                     UpperCAmelCase__								=  get_pairs(__UpperCAmelCase		)
         UpperCAmelCase__								=  " ".join(__UpperCAmelCase		)
         if word == "\n  " + BPE_TOKEN_MERGES:
               UpperCAmelCase__								=  "\n" + BPE_TOKEN_MERGES

         if word.endswith(__UpperCAmelCase		):
               UpperCAmelCase__								=  word.replace(__UpperCAmelCase       ,  ""		)

         UpperCAmelCase__								=  word.replace(" "       ,  __UpperCAmelCase		)
         UpperCAmelCase__								=  word
         return word


   def 			lowercase_      (self     :						Tuple       ,  __UpperCAmelCase     :						int		)						->				Optional[int]:
         """simple docstring"""



         if self.bpe_ranks is None:
               raise ValueError(
                   "This tokenizer was instantiated without a `merges.txt` file, so"
                   " that it can only be used for decoding, not for encoding."
                   "Make sure to provide `merges.txt` file at instantiation to enable "
                   "encoding."		)

         if self.do_lower_case:
               UpperCAmelCase__								=  text.lower()

         UpperCAmelCase__								=  text.split()

         UpperCAmelCase__								=  []
         for token in text:
               if token:
                     split_tokens.extend(list(self.bpe(__UpperCAmelCase		).split(" "		)		)		)

         return split_tokens


   def 			lowercase_      (self     :						Union[str, Any]       ,  __UpperCAmelCase     :						str		)						->				int:
         """simple docstring"""



         return self.encoder.get(__UpperCAmelCase       ,  self.encoder.get(self.unk_token		)		)


   def 			lowercase_      (self     :						Any       ,  __UpperCAmelCase     :						int		)						->				str:
         """simple docstring"""



         UpperCAmelCase__								=  self.decoder.get(__UpperCAmelCase       ,  self.unk_token		)
         return result


   def 			lowercase_      (self     :						Dict       ,  __UpperCAmelCase     :						List[str]		)						->				str:
         """simple docstring"""



         UpperCAmelCase__								=  " ".join(__UpperCAmelCase		)

         # make sure @@ tokens are concatenated
         UpperCAmelCase__								=  "".join(string.split(__UpperCAmelCase		)		)

         return string





   def 			lowercase_      (self     :						Union[str, Any]       ,  __UpperCAmelCase     :						str       ,  __UpperCAmelCase     :						Optional[str] = None		)						->				Tuple[str]:
         """simple docstring"""



         if not os.path.isdir(__UpperCAmelCase		):
               logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""		)
               return
         UpperCAmelCase__								=  os.path.join(
             __UpperCAmelCase       ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)
         UpperCAmelCase__								=  os.path.join(
             __UpperCAmelCase       ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]		)

         with open(__UpperCAmelCase       ,  "w"       ,  encoding="utf-8"		) as f:
               f.write(json.dumps(self.encoder       ,  indent=2       ,  sort_keys=__UpperCAmelCase       ,  ensure_ascii=__UpperCAmelCase		) + "\n"		)

         UpperCAmelCase__								=  0
         if self.bpe_ranks is None:
               return (vocab_file,)

         with open(__UpperCAmelCase       ,  "w"       ,  encoding="utf-8"		) as writer:
               for bpe_tokens, token_index in sorted(self.bpe_ranks.items()       ,  key=lambda __UpperCAmelCase		: kv[1]		):
                     if index != token_index:
                           logger.warning(
                               f"""Saving vocabulary to {merges_file}: BPE merge indices are not consecutive."""
                               " Please check that the tokenizer is not corrupted!"		)
                           UpperCAmelCase__								=  token_index
                     writer.write(" ".join(__UpperCAmelCase		) + "\n"		)
                     index += 1

         return (vocab_file, merges_file)



