


'''simple docstring'''



import argparse
import re

from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints

from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
A__		:					Optional[Any]												=  {
    '''/attention/''': '''/0/SelfAttention/''',
    '''/self_attention/''': '''/0/SelfAttention/''',
    '''/encoder_decoder_attention/''': '''/1/EncDecAttention/''',
    '''value''': '''v''',
    '''query''': '''q''',
    '''key''': '''k''',
    '''out''': '''o''',
    '''pre_self_attention_layer_norm''': '''0/layer_norm''',
    '''pre_cross_attention_layer_norm''': '''1/layer_norm''',
    '''pre_attention_layer_norm''': '''0/layer_norm''',  # previously 1, but seems wrong
    '''token_embedder''': '''shared''',
    '''encoder_norm''': '''final_layer_norm''',
    '''decoder_norm''': '''final_layer_norm''',
    '''relpos_bias/rel_embedding''': '''block/0/layer/0/SelfAttention/relative_attention_bias/weight''',
    '''router/router_weights/w/''': '''router/classifier/''',
    '''roer/roer_weights/w/''': '''router/classifier/''',
    '''logits_dense''': '''lm_head''',
}



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Optional[Any]							)     ->		Tuple:
     # 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
     # the original model
     __lowerCamelCase   :	Tuple								=		list(s_dict.keys()							)
     for key in keys:
          __lowerCamelCase   :	Union[str, Any]								=		R'''.*/layers_(\d+)'''
          __lowerCamelCase   :	Optional[int]								=		key
          if re.match(__UpperCamelCase      ,    __UpperCamelCase							):
               __lowerCamelCase   :	Tuple								=		re.sub(R'layers_(\d+)'      ,    R'block/\1/layer'      ,    __UpperCamelCase							)

          __lowerCamelCase   :	List[Any]								=		R'''(encoder|decoder)\/'''

          if re.match(__UpperCamelCase      ,    __UpperCamelCase							):
               __lowerCamelCase   :	Union[str, Any]								=		re.match(__UpperCamelCase      ,    __UpperCamelCase							).groups()
               if groups[0] == "encoder":
                    __lowerCamelCase   :	List[Any]								=		re.sub(R'/mlp/'      ,    R'/1/mlp/'      ,    __UpperCamelCase							)
                    __lowerCamelCase   :	Optional[int]								=		re.sub(R'/pre_mlp_layer_norm/'      ,    R'/1/layer_norm/'      ,    __UpperCamelCase							)

               elif groups[0] == "decoder":
                    __lowerCamelCase   :	Dict								=		re.sub(R'/mlp/'      ,    R'/2/mlp/'      ,    __UpperCamelCase							)
                    __lowerCamelCase   :	List[Any]								=		re.sub(R'/pre_mlp_layer_norm/'      ,    R'/2/layer_norm/'      ,    __UpperCamelCase							)

        # 2. Convert other classic mappings
          for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
               if old_key in new_key:
                    __lowerCamelCase   :	Any								=		new_key.replace(__UpperCamelCase      ,    __UpperCamelCase							)

          print(F'{key} -> {new_key}'							)
          __lowerCamelCase   :	Optional[int]								=		s_dict.pop(__UpperCamelCase							)

     if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
          __lowerCamelCase   :	List[str]								=		s_dict[
              '''encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'''
          ].T
     if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
          __lowerCamelCase   :	Any								=		s_dict[
              '''decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'''
          ].T

     # 3. Take extra care of the EXPERTS layer
     for key in list(s_dict.keys()							):
          if "expert" in key:
               __lowerCamelCase   :	Tuple								=		s_dict[key].shape[0]
               __lowerCamelCase   :	str								=		s_dict[key]
               for idx in range(__UpperCamelCase							):
                    __lowerCamelCase   :	Tuple								=		expert_weihts[idx]
                    print(F'{key} -> {key.replace("expert/"      ,    "nested fstring"							)}'							)

               s_dict.pop(__UpperCamelCase							)

     return s_dict


A__		:					Any												=  {
    '''NUM_ENCODER_LAYERS''': '''num_layers''',
    '''NUM_DECODER_LAYERS''': '''num_decoder_layers''',
    '''NUM_HEADS''': '''num_heads''',
    '''HEAD_DIM''': '''d_kv''',
    '''EMBED_DIM''': '''d_model''',
    '''MLP_DIM''': '''d_ff''',
    '''NUM_SELECTED_EXPERTS''': '''num_selected_experts''',
    '''NUM_ENCODER_SPARSE_LAYERS''': '''num_sparse_encoder_layers''',
    '''NUM_DECODER_SPARSE_LAYERS''': '''num_sparse_decoder_layers''',
    '''dense.MlpBlock.activations''': '''feed_forward_proj''',
}



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				int      ,    UpperCAmelCase_    :				str							)     ->		Tuple:
     # Convert a google style config to the hugging face fromat
     import regex as re

     with open(__UpperCamelCase      ,    'r'							) as f:
          __lowerCamelCase   :	int								=		f.read()

     __lowerCamelCase   :	str								=		re.findall(R'(.*) = ([0-9.]*)'      ,    __UpperCamelCase							)
     __lowerCamelCase   :	Dict								=		{}
     for param, value in regex_match:
          if param in GIN_TO_CONFIG_MAPPING and value != "":
               __lowerCamelCase   :	Dict								=		float(__UpperCamelCase							) if '''.''' in value else int(__UpperCamelCase							)

     __lowerCamelCase   :	Any								=		re.findall(R'(.*activations) = \(\'(.*)\',\)'      ,    __UpperCamelCase							)[0]
     __lowerCamelCase   :	List[str]								=		str(activation[1]							)

     __lowerCamelCase   :	Dict								=		num_experts
     __lowerCamelCase   :	Dict								=		SwitchTransformersConfig(**__UpperCamelCase							)
     return config



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Union[str, Any]      ,    UpperCAmelCase_    :				Union[str, Any]      ,    UpperCAmelCase_    :				Any=None      ,    UpperCAmelCase_    :				Optional[int]="./"      ,    UpperCAmelCase_    :				str=8							)     ->		Dict:
     # Initialise PyTorch model

     print(F'Loading flax weights from : {flax_checkpoint_path}'							)
     __lowerCamelCase   :	Optional[int]								=		checkpoints.load_tax_checkpoint(__UpperCamelCase							)

     if gin_file is not None:
          __lowerCamelCase   :	Tuple								=		convert_gin_to_config(__UpperCamelCase      ,    __UpperCamelCase							)
     else:
          __lowerCamelCase   :	Tuple								=		SwitchTransformersConfig.from_pretrained(__UpperCamelCase							)

     __lowerCamelCase   :	int								=		SwitchTransformersForConditionalGeneration(__UpperCamelCase							)

     __lowerCamelCase   :	Tuple								=		flax_params['''target''']
     __lowerCamelCase   :	Optional[Any]								=		flatten_dict(__UpperCamelCase      ,    sep='/'							)
     __lowerCamelCase   :	Tuple								=		rename_keys(__UpperCamelCase							)
     __lowerCamelCase   :	Tuple								=		unflatten_dict(__UpperCamelCase      ,    sep='/'							)

     # Load the flax params in the PT model
     load_flax_weights_in_pytorch_model(__UpperCamelCase      ,    __UpperCamelCase							)

     print(F'Save PyTorch model to {pytorch_dump_path}'							)
     pt_model.save_pretrained(__UpperCamelCase							)


if __name__ == "__main__":
      A__		:					Tuple												=  argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--switch_t5x_checkpoint_path""",
          default=None,
          type=str,
          required=True,
          help=(
              """The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the"""
              """ model architecture. If not provided, a `gin_file` has to be provided."""
          ),
      )
      parser.add_argument(
          """--gin_file""",
          default=None,
          type=str,
          required=False,
          help="""Path to the gin config file. If not provided, a `config_file` has to be passed   """,
      )
      parser.add_argument(
          """--config_name""", default=None, type=str, required=False, help="""Config name of SwitchTransformers model."""
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output pytorch model."""
      )
      parser.add_argument("""--num_experts""", default=8, type=int, required=False, help="""Number of experts""")
      A__		:					Union[str, Any]												=  parser.parse_args()
      convert_flax_checkpoint_to_pytorch(
          args.switch_tax_checkpoint_path,
          args.config_name,
          args.gin_file,
          args.pytorch_dump_folder_path,
          args.num_experts,
      )











import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer


A__  :  str								=					logging.get_logger(__name__)

A__  :  Any								=					{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

A__  :  str								=					{
    '''vocab_file''': {
        '''google/realm-cc-news-pretrained-embedder''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt'''
        ),
        '''google/realm-cc-news-pretrained-encoder''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt'''
        ),
        '''google/realm-cc-news-pretrained-scorer''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt'''
        ),
        '''google/realm-cc-news-pretrained-openqa''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt'''
        ),
        '''google/realm-orqa-nq-openqa''': '''https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt''',
        '''google/realm-orqa-nq-reader''': '''https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt''',
        '''google/realm-orqa-wq-openqa''': '''https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt''',
        '''google/realm-orqa-wq-reader''': '''https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt''',
    },
    '''tokenizer_file''': {
        '''google/realm-cc-news-pretrained-embedder''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont'''
        ),
        '''google/realm-cc-news-pretrained-encoder''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json'''
        ),
        '''google/realm-cc-news-pretrained-scorer''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json'''
        ),
        '''google/realm-cc-news-pretrained-openqa''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json'''
        ),
        '''google/realm-orqa-nq-openqa''': (
            '''https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json'''
        ),
        '''google/realm-orqa-nq-reader''': (
            '''https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json'''
        ),
        '''google/realm-orqa-wq-openqa''': (
            '''https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json'''
        ),
        '''google/realm-orqa-wq-reader''': (
            '''https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json'''
        ),
    },
}

A__  :  Union[str, Any]								=					{
    '''google/realm-cc-news-pretrained-embedder''': 512,
    '''google/realm-cc-news-pretrained-encoder''': 512,
    '''google/realm-cc-news-pretrained-scorer''': 512,
    '''google/realm-cc-news-pretrained-openqa''': 512,
    '''google/realm-orqa-nq-openqa''': 512,
    '''google/realm-orqa-nq-reader''': 512,
    '''google/realm-orqa-wq-openqa''': 512,
    '''google/realm-orqa-wq-reader''': 512,
}

A__  :  Dict								=					{
    '''google/realm-cc-news-pretrained-embedder''': {'''do_lower_case''': True},
    '''google/realm-cc-news-pretrained-encoder''': {'''do_lower_case''': True},
    '''google/realm-cc-news-pretrained-scorer''': {'''do_lower_case''': True},
    '''google/realm-cc-news-pretrained-openqa''': {'''do_lower_case''': True},
    '''google/realm-orqa-nq-openqa''': {'''do_lower_case''': True},
    '''google/realm-orqa-nq-reader''': {'''do_lower_case''': True},
    '''google/realm-orqa-wq-openqa''': {'''do_lower_case''': True},
    '''google/realm-orqa-wq-reader''': {'''do_lower_case''': True},
}



class 				__snake_case						(     UpperCamelCase_						):
							_a			      = VOCAB_FILES_NAMES
							_a			      = PRETRAINED_VOCAB_FILES_MAP
							_a			      = PRETRAINED_INIT_CONFIGURATION
							_a			      = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_a			      = RealmTokenizer




							def __init__(       self     :       int				,				A_     :       Optional[int]=None				,				A_     :       Optional[Any]=None				,				A_     :       Optional[Any]=True				,				A_     :       Optional[int]="[UNK]"				,				A_     :       List[Any]="[SEP]"				,				A_     :       List[Any]="[PAD]"				,				A_     :       Optional[Any]="[CLS]"				,				A_     :       Dict="[MASK]"				,				A_     :       List[Any]=True				,				A_     :       List[str]=None				,				**A_     :       List[str]				,				):
									super().__init__(
									    A_				,				tokenizer_file=A_				,				do_lower_case=A_				,				unk_token=A_				,				sep_token=A_				,				pad_token=A_				,				cls_token=A_				,				mask_token=A_				,				tokenize_chinese_chars=A_				,				strip_accents=A_				,				**A_				,				)

									lowerCAmelCase_			:     Optional[int]									= json.loads(self.backend_tokenizer.normalizer.__getstate__())
									if (
									    normalizer_state.get('''lowercase'''				,				A_) != do_lower_case
									    or normalizer_state.get('''strip_accents'''				,				A_) != strip_accents
									    or normalizer_state.get('''handle_chinese_chars'''				,				A_) != tokenize_chinese_chars
									):
											lowerCAmelCase_			:     int									= getattr(A_				,				normalizer_state.pop('''type'''))
											lowerCAmelCase_			:     str									= do_lower_case
											lowerCAmelCase_			:     Dict									= strip_accents
											lowerCAmelCase_			:     Optional[Any]									= tokenize_chinese_chars
											lowerCAmelCase_			:     Union[str, Any]									= normalizer_class(**A_)

									lowerCAmelCase_			:     Any									= do_lower_case




							def    UpperCAmelCase__			(       self     :       Optional[Any]				,				A_     :       Optional[Any]				,				**A_     :       Tuple):
									lowerCAmelCase_			:     List[str]									= PaddingStrategy.MAX_LENGTH

									lowerCAmelCase_			:     str									= text
									lowerCAmelCase_			:     int									= kwargs.pop('''text_pair'''				,				A_)
									lowerCAmelCase_			:     str									= kwargs.pop('''return_tensors'''				,				A_)

									lowerCAmelCase_			:     int									= {
									    '''input_ids''': [],
									    '''attention_mask''': [],
									    '''token_type_ids''': [],
									}

									for idx, candidate_text in enumerate(A_):
											if batch_text_pair is not None:
													lowerCAmelCase_			:     List[Any]									= batch_text_pair[idx]
											else:
													lowerCAmelCase_			:     List[Any]									= None

											lowerCAmelCase_			:     int									= super().__call__(A_				,				A_				,				return_tensors=A_				,				**A_)

											lowerCAmelCase_			:     Optional[Any]									= encoded_candidates.get('''input_ids''')
											lowerCAmelCase_			:     List[str]									= encoded_candidates.get('''attention_mask''')
											lowerCAmelCase_			:     Optional[Any]									= encoded_candidates.get('''token_type_ids''')

											if encoded_input_ids is not None:
													output_data["input_ids"].append(A_)
											if encoded_attention_mask is not None:
													output_data["attention_mask"].append(A_)
											if encoded_token_type_ids is not None:
													output_data["token_type_ids"].append(A_)

									lowerCAmelCase_			:     List[str]									= {key: item for key, item in output_data.items() if len(A_) != 0}

									return BatchEncoding(A_				,				tensor_type=A_)




							def    UpperCAmelCase__			(       self     :       List[str]				,				A_     :       Tuple				,				A_     :       List[Any]=None):
									lowerCAmelCase_			:     Optional[Any]									= [self.cls_token_id] + token_ids_a + [self.sep_token_id]

									if token_ids_a:
											output += token_ids_a + [self.sep_token_id]

									return output




							def    UpperCAmelCase__			(       self     :       Tuple				,				A_     :       List[int]				,				A_     :       Optional[List[int]] = None):
									lowerCAmelCase_			:     Tuple									= [self.sep_token_id]
									lowerCAmelCase_			:     Union[str, Any]									= [self.cls_token_id]
									if token_ids_a is None:
											return len(cls + token_ids_a + sep) * [0]
									return len(cls + token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1]







							def    UpperCAmelCase__			(       self     :       List[str]				,				A_     :       str				,				A_     :       Optional[str] = None):
									lowerCAmelCase_			:     List[str]									= self._tokenizer.model.save(A_				,				name=A_)
									return tuple(A_)





