




import argparse
import json
import os
import re
import shutil

import torch

from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

_lowerCamelCase      			=						2



class       a					:

     '''simple docstring'''





     def __init__(     self       :      int ,   *,  # begin keyword-only arguments
         __snake_case       :      Union[str, Any]="<s>" ,   __snake_case       :      List[str]="<pad>" ,   __snake_case       :      Union[str, Any]="</s>" ,   __snake_case       :      Optional[int]="<unk>" ,   __snake_case       :      Optional[Any]=None ,   ):
         UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_										=					bos, unk, pad, eos
         UpperCAmelCase_										=					[]
         UpperCAmelCase_										=					[]
         UpperCAmelCase_										=					{}
         UpperCAmelCase_										=					self.add_symbol(__snake_case    )
         UpperCAmelCase_										=					self.add_symbol(__snake_case    )
         UpperCAmelCase_										=					self.add_symbol(__snake_case    )
         UpperCAmelCase_										=					self.add_symbol(__snake_case    )
         if extra_special_symbols:
             for s in extra_special_symbols:
                 self.add_symbol(__snake_case    )
         UpperCAmelCase_										=					len(self.symbols    )
     def __eq__(     self       :      Any ,   __snake_case       :      str    ):
         return self.indices == other.indices
     def __getitem__(     self       :      Dict ,   __snake_case       :      Optional[int]    ):
         if idx < len(self.symbols    ):
             return self.symbols[idx]
         return self.unk_word
     def __len__(     self       :      Any    ):
         return len(self.symbols    )
     def __contains__(     self       :      Any ,   __snake_case       :      Tuple    ):
         return sym in self.indices
     @classmethod
     def   lowerCamelCase_       (     cls       :      Any ,   __snake_case       :      Optional[int]    ):
         UpperCAmelCase_										=					cls()
         d.add_from_file(__snake_case    )
         return d
     def   lowerCamelCase_       (     self       :      Any ,   __snake_case       :      Optional[int] ,   __snake_case       :      Optional[Any]=1 ,   __snake_case       :      Optional[Any]=False    ):
         if word in self.indices and not overwrite:
             UpperCAmelCase_										=					self.indices[word]
             UpperCAmelCase_										=					self.count[idx] + n
             return idx
         else:
             UpperCAmelCase_										=					len(self.symbols    )
             UpperCAmelCase_										=					idx
             self.symbols.append(__snake_case    )
             self.count.append(__snake_case    )
             return idx
     def   lowerCamelCase_       (     self       :      str ,   __snake_case       :      Optional[Any]    ):
         return 0




     def   lowerCamelCase_       (     self       :      List[Any] ,   __snake_case       :      Dict    ):
         if isinstance(__snake_case ,   __snake_case    ):
             try:
                 with open(__snake_case ,   '''r''' ,   encoding='''utf-8'''    ) as fd:
                     self.add_from_file(__snake_case    )
             except FileNotFoundError as fnfe:
                 raise fnfe
             except UnicodeError:
                 raise Exception('''Incorrect encoding detected in {}, please rebuild the dataset'''.format(__snake_case    )    )
             return

         UpperCAmelCase_										=					f.readlines()
         UpperCAmelCase_										=					self._load_meta(__snake_case    )

         for line in lines[indices_start_line:]:
             try:
                 UpperCAmelCase_			,							UpperCAmelCase_										=					line.rstrip().rsplit(''' ''' ,   1    )
                 if field == "#fairseq:overwrite":
                     UpperCAmelCase_										=					True
                     UpperCAmelCase_			,							UpperCAmelCase_										=					line.rsplit(''' ''' ,   1    )
                 else:
                     UpperCAmelCase_										=					False
                 UpperCAmelCase_										=					int(__snake_case    )
                 UpperCAmelCase_										=					line
                 if word in self and not overwrite:
                     raise RuntimeError(
                         '''Duplicate word found when loading Dictionary: \'{}\'. '''
                         '''Duplicate words can overwrite earlier ones by adding the '''
                         '''#fairseq:overwrite flag at the end of the corresponding row '''
                         '''in the dictionary file. If using the Camembert model, please '''
                         '''download an updated copy of the model file.'''.format(__snake_case    )    )
                 self.add_symbol(__snake_case ,   n=__snake_case ,   overwrite=__snake_case    )
             except ValueError:
                 raise ValueError('''Incorrect dictionary format, expected \'<token> <cnt> [flags]\''''    )




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						int				)  ->							Any:
    # (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
    # e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
    UpperCAmelCase_										=					dict((re.sub(R'''@@$'''	,					''''''	,					__UpperCamelCase				), v) if k.endswith('''@@'''				) else (re.sub(R'''$'''	,					'''</w>'''	,					__UpperCamelCase				), v) for k, v in d.items()				)
    UpperCAmelCase_										=					'''<s> <pad> </s> <unk>'''.split()
    # restore the special tokens
    for k in keep_keys:
        del da[f'{k}</w>']
        UpperCAmelCase_										=					d[k]  # restore
    return da




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Optional[int]	,					__UpperCamelCase      :						List[Any]				)  ->							Optional[Any]:
    # prep
    if not os.path.exists(__UpperCamelCase				):
        raise ValueError(f'path {biogpt_checkpoint_path} does not exist!'				)
    os.makedirs(__UpperCamelCase	,					exist_ok=__UpperCamelCase				)
    print(f'Writing results to {pytorch_dump_folder_path}'				)

    # handle various types of models

    UpperCAmelCase_										=					os.path.join(__UpperCamelCase	,					'''checkpoint.pt'''				)
    if not os.path.isfile(__UpperCamelCase				):
        raise ValueError(f'path to the file {checkpoint_file} does not exist!'				)
    UpperCAmelCase_										=					torch.load(__UpperCamelCase	,					map_location='''cpu'''				)

    UpperCAmelCase_										=					chkpt['''cfg''']['''model''']

    # dicts
    UpperCAmelCase_										=					os.path.join(__UpperCamelCase	,					'''dict.txt'''				)
    if not os.path.isfile(__UpperCamelCase				):
        raise ValueError(f'path to the file {dict_file} does not exist!'				)
    UpperCAmelCase_										=					Dictionary.load(__UpperCamelCase				)
    UpperCAmelCase_										=					rewrite_dict_keys(src_dict.indices				)
    UpperCAmelCase_										=					len(__UpperCamelCase				)
    UpperCAmelCase_										=					os.path.join(__UpperCamelCase	,					VOCAB_FILES_NAMES['''vocab_file''']				)
    print(f'Generating {src_vocab_file} of {src_vocab_size} records'				)
    with open(__UpperCamelCase	,					'''w'''	,					encoding='''utf-8'''				) as f:
        f.write(json.dumps(__UpperCamelCase	,					ensure_ascii=__UpperCamelCase	,					indent=__UpperCamelCase				)				)

    # merges_file (bpecodes)
    UpperCAmelCase_										=					os.path.join(__UpperCamelCase	,					'''bpecodes'''				)
    if not os.path.isfile(__UpperCamelCase				):
        raise ValueError(f'path to the file {bpecodes_file} does not exist!'				)

    UpperCAmelCase_										=					os.path.join(__UpperCamelCase	,					VOCAB_FILES_NAMES['''merges_file''']				)
    shutil.copyfile(__UpperCamelCase	,					__UpperCamelCase				)

    # model config
    UpperCAmelCase_										=					os.path.join(__UpperCamelCase	,					'''config.json'''				)

    UpperCAmelCase_										=					{
        '''activation_dropout''': args['''activation_dropout'''],
        '''architectures''': ['''BioGptForCausalLM'''],
        '''attention_probs_dropout_prob''': args['''attention_dropout'''],
        '''bos_token_id''': 0,
        '''eos_token_id''': 2,
        '''hidden_act''': args['''activation_fn'''],
        '''hidden_dropout_prob''': args['''dropout'''],
        '''hidden_size''': args['''decoder_embed_dim'''],
        '''initializer_range''': 0.02,
        '''intermediate_size''': args['''decoder_ffn_embed_dim'''],
        '''layer_norm_eps''': 1e-12,
        '''layerdrop''': args['''decoder_layerdrop'''],
        '''max_position_embeddings''': args['''max_target_positions'''],
        '''model_type''': '''biogpt''',
        '''num_attention_heads''': args['''decoder_attention_heads'''],
        '''num_hidden_layers''': args['''decoder_layers'''],
        '''pad_token_id''': 1,
        '''scale_embedding''': not args['''no_scale_embedding'''],
        '''tie_word_embeddings''': args['''share_decoder_input_output_embed'''],
        '''vocab_size''': src_vocab_size,
    }

    # good hparam defaults to start with

    print(f'Generating {biogpt_model_config_file}'				)
    with open(__UpperCamelCase	,					'''w'''	,					encoding='''utf-8'''				) as f:
        f.write(json.dumps(__UpperCamelCase	,					ensure_ascii=__UpperCamelCase	,					indent=__UpperCamelCase				)				)

    # tokenizer config
    UpperCAmelCase_										=					os.path.join(__UpperCamelCase	,					__UpperCamelCase				)

    UpperCAmelCase_										=					{
        '''bos_token''': '''<s>''',
        '''eos_token''': '''</s>''',
        '''model_max_length''': 1024,
        '''pad_token''': '''<pad>''',
        '''special_tokens_map_file''': None,
        '''tokenizer_class''': '''BioGptTokenizer''',
        '''unk_token''': '''<unk>''',
    }

    print(f'Generating {biogpt_tokenizer_config_file}'				)
    with open(__UpperCamelCase	,					'''w'''	,					encoding='''utf-8'''				) as f:
        f.write(json.dumps(__UpperCamelCase	,					ensure_ascii=__UpperCamelCase	,					indent=__UpperCamelCase				)				)

    # model
    UpperCAmelCase_										=					chkpt['''model''']

    # remove unneeded keys
    UpperCAmelCase_										=					[
        '''decoder.version''',
    ]
    for k in ignore_keys:
        model_state_dict.pop(__UpperCamelCase	,					__UpperCamelCase				)

    UpperCAmelCase_										=					list(model_state_dict.keys()				)
    for layer_name in layer_names:
        if layer_name.endswith('''output_projection.weight'''				):
            UpperCAmelCase_										=					model_state_dict.pop(__UpperCamelCase				)
        else:
            UpperCAmelCase_										=					model_state_dict.pop(__UpperCamelCase				)

    UpperCAmelCase_										=					BioGptConfig.from_pretrained(__UpperCamelCase				)
    UpperCAmelCase_										=					BioGptForCausalLM(__UpperCamelCase				)

    # check that it loads ok
    model_new.load_state_dict(__UpperCamelCase				)

    # save
    UpperCAmelCase_										=					os.path.join(__UpperCamelCase	,					__UpperCamelCase				)
    print(f'Generating {pytorch_weights_dump_path}'				)
    torch.save(__UpperCamelCase	,					__UpperCamelCase				)

    print('''Conversion is done!'''				)


if __name__ == "__main__":
 _lowerCamelCase      			=						argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--biogpt_checkpoint_path',
     default=None,
     type=str,
     required=True,
     help=(
         'Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,'
         ' bpecodes, etc.'
     ),
 )
 parser.add_argument(
     '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
 )
 _lowerCamelCase      			=						parser.parse_args()
 convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)











import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Tuple	,					__UpperCamelCase      :						List[str]	,					__UpperCamelCase      :						str				)  ->							str:
    UpperCAmelCase_										=					AutoConfig.from_pretrained(__UpperCamelCase				)
    UpperCAmelCase_										=					FlaxAutoModelForSeqaSeqLM.from_config(config=__UpperCamelCase				)
    UpperCAmelCase_										=					checkpoints.load_tax_checkpoint(__UpperCamelCase				)

    UpperCAmelCase_										=					'''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

    if config.model_type == "t5":
        UpperCAmelCase_										=					'''SelfAttention'''
    if config.model_type == "longt5" and config.encoder_attention_type == "local":
        UpperCAmelCase_										=					'''LocalSelfAttention'''
    elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
        UpperCAmelCase_										=					'''TransientGlobalSelfAttention'''
    else:
        raise ValueError(
            '''Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'''
            ''' attribute with a value from [\'local\', \'transient-global].'''				)

    # Encoder
    for layer_index in range(config.num_layers				):
        UpperCAmelCase_										=					f'layers_{str(__UpperCamelCase				)}'

        # Self-Attention
        UpperCAmelCase_										=					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
        UpperCAmelCase_										=					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
        UpperCAmelCase_										=					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
        UpperCAmelCase_										=					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

        # Global input layer norm
        if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            UpperCAmelCase_										=					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

        # Layer Normalization
        UpperCAmelCase_										=					tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

        if split_mlp_wi:
            UpperCAmelCase_										=					tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
            UpperCAmelCase_										=					tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
        else:
            UpperCAmelCase_										=					tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

        UpperCAmelCase_										=					tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

        # Layer Normalization
        UpperCAmelCase_										=					tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

        # Assigning
        UpperCAmelCase_										=					flax_model.params['''encoder''']['''block'''][str(__UpperCamelCase				)]['''layer''']
        UpperCAmelCase_										=					tax_attention_key
        UpperCAmelCase_										=					tax_attention_out
        UpperCAmelCase_										=					tax_attention_query
        UpperCAmelCase_										=					tax_attention_value

        UpperCAmelCase_										=					tax_attention_layer_norm

        # Global input layer norm
        if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            UpperCAmelCase_										=					tax_global_layer_norm

        if split_mlp_wi:
            UpperCAmelCase_										=					tax_mlp_wi_a
            UpperCAmelCase_										=					tax_mlp_wi_a
        else:
            UpperCAmelCase_										=					tax_mlp_wi

        UpperCAmelCase_										=					tax_mlp_wo
        UpperCAmelCase_										=					tax_mlp_layer_norm

        UpperCAmelCase_										=					flax_model_encoder_layer_block

    # Only for layer 0:
    UpperCAmelCase_										=					tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
    UpperCAmelCase_										=					tax_encoder_rel_embedding

    # Side/global relative position_bias + layer norm
    if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
        UpperCAmelCase_										=					tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
        UpperCAmelCase_										=					tax_encoder_global_rel_embedding

    # Assigning
    UpperCAmelCase_										=					tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
    UpperCAmelCase_										=					tax_encoder_norm

    # Decoder
    for layer_index in range(config.num_layers				):
        UpperCAmelCase_										=					f'layers_{str(__UpperCamelCase				)}'

        # Self-Attention
        UpperCAmelCase_										=					tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
        UpperCAmelCase_										=					tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
        UpperCAmelCase_										=					tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
        UpperCAmelCase_										=					tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

        # Layer Normalization
        UpperCAmelCase_										=					tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
            '''scale'''
        ]

        # Encoder-Decoder-Attention
        UpperCAmelCase_										=					tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']
        UpperCAmelCase_										=					tax_enc_dec_attention_module['''key''']['''kernel''']
        UpperCAmelCase_										=					tax_enc_dec_attention_module['''out''']['''kernel''']
        UpperCAmelCase_										=					tax_enc_dec_attention_module['''query''']['''kernel''']
        UpperCAmelCase_										=					tax_enc_dec_attention_module['''value''']['''kernel''']

        # Layer Normalization
        UpperCAmelCase_										=					tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

        # MLP
        if split_mlp_wi:
            UpperCAmelCase_										=					tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
            UpperCAmelCase_										=					tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
        else:
            UpperCAmelCase_										=					tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

        UpperCAmelCase_										=					tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

        # Layer Normalization
        UpperCAmelCase_										=					tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

        # Assigning
        UpperCAmelCase_										=					flax_model.params['''decoder''']['''block'''][str(__UpperCamelCase				)]['''layer''']
        UpperCAmelCase_										=					tax_attention_key
        UpperCAmelCase_										=					tax_attention_out
        UpperCAmelCase_										=					tax_attention_query
        UpperCAmelCase_										=					tax_attention_value

        UpperCAmelCase_										=					tax_pre_attention_layer_norm

        UpperCAmelCase_										=					tax_enc_dec_attention_key
        UpperCAmelCase_										=					tax_enc_dec_attention_out
        UpperCAmelCase_										=					tax_enc_dec_attention_query
        UpperCAmelCase_										=					tax_enc_dec_attention_value

        UpperCAmelCase_										=					tax_cross_layer_norm

        if split_mlp_wi:
            UpperCAmelCase_										=					tax_mlp_wi_a
            UpperCAmelCase_										=					tax_mlp_wi_a
        else:
            UpperCAmelCase_										=					tax_mlp_wi

        UpperCAmelCase_										=					tax_mlp_wo

        UpperCAmelCase_										=					txa_mlp_layer_norm

        UpperCAmelCase_										=					flax_model_decoder_layer_block

    # Decoder Normalization
    UpperCAmelCase_										=					tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
    UpperCAmelCase_										=					txa_decoder_norm

    # Only for layer 0:
    UpperCAmelCase_										=					tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
    UpperCAmelCase_										=					tax_decoder_rel_embedding

    # Token Embeddings
    UpperCAmelCase_										=					tax_model['''target''']['''token_embedder''']['''embedding''']
    UpperCAmelCase_										=					txa_token_embeddings

    # LM Head (only in v1.1 and LongT5 checkpoints)
    if "logits_dense" in tax_model["target"]["decoder"]:
        UpperCAmelCase_										=					tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

    flax_model.save_pretrained(__UpperCamelCase				)
    print('''T5X Model was sucessfully converted!'''				)


if __name__ == "__main__":
 _lowerCamelCase      			=						argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path the T5X checkpoint.'
 )
 parser.add_argument('--config_name', default=None, type=str, required=True, help='Config name of LongT5/T5 model.')
 parser.add_argument(
     '--flax_dump_folder_path', default=None, type=str, required=True, help='Path to the output FLAX model.'
 )
 _lowerCamelCase      			=						parser.parse_args()
 convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)





