






"""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
UpperCAmelCase_   : str   			= {
    """/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 					_A			(__a							) ->       Tuple:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  int    						=		list(s_dict.keys()							)
    for key in keys:
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		R'''.*/layers_(\d+)'''
        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		key
        if re.match(__a    ,				__a							):
            SCREAMING_SNAKE_CASE_      :  Tuple    						=		re.sub(R'''layers_(\d+)'''    ,				R'''block/\1/layer'''    ,				__a							)

        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		R'''(encoder|decoder)\/'''

        if re.match(__a    ,				__a							):
            SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		re.match(__a    ,				__a							).groups()
            if groups[0] == "encoder":
                SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		re.sub(R'''/mlp/'''    ,				R'''/1/mlp/'''    ,				__a							)
                SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		re.sub(R'''/pre_mlp_layer_norm/'''    ,				R'''/1/layer_norm/'''    ,				__a							)

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

        # 2. Convert other classic mappings
        for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
            if old_key in new_key:
                SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		new_key.replace(__a    ,				__a							)

        print(f'{key} -> {new_key}'							)
        SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		s_dict.pop(__a							)

    if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		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:
        SCREAMING_SNAKE_CASE_      :  List[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:
            SCREAMING_SNAKE_CASE_      :  Any    						=		s_dict[key].shape[0]
            SCREAMING_SNAKE_CASE_      :  Any    						=		s_dict[key]
            for idx in range(__a							):
                SCREAMING_SNAKE_CASE_      :  str    						=		expert_weihts[idx]
                print(f'{key} -> {key.replace("expert/"    ,				"nested fstring"							)}'							)

            s_dict.pop(__a							)

    return s_dict


UpperCAmelCase_   : Tuple   			= {
    """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 					_A			(__a    ,				__a							) ->       List[Any]:
    """simple docstring"""






    import regex as re

    with open(__a    ,				'''r'''							) as f:
        SCREAMING_SNAKE_CASE_      :  List[Any]    						=		f.read()

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

    SCREAMING_SNAKE_CASE_      :  Dict    						=		re.findall(R'''(.*activations) = \(\'(.*)\',\)'''    ,				__a							)[0]
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		str(activation[1]							)

    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		num_experts
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		SwitchTransformersConfig(**__a							)
    return config



def 					_A			(__a    ,				__a    ,				__a=None    ,				__a="./"    ,				__a=8							) ->       Union[str, Any]:
    """simple docstring"""






    print(f'Loading flax weights from : {flax_checkpoint_path}'							)
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		checkpoints.load_tax_checkpoint(__a							)

    if gin_file is not None:
        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		convert_gin_to_config(__a    ,				__a							)
    else:
        SCREAMING_SNAKE_CASE_      :  int    						=		SwitchTransformersConfig.from_pretrained(__a							)

    SCREAMING_SNAKE_CASE_      :  Dict    						=		SwitchTransformersForConditionalGeneration(__a							)

    SCREAMING_SNAKE_CASE_      :  Dict    						=		flax_params['''target''']
    SCREAMING_SNAKE_CASE_      :  Any    						=		flatten_dict(__a    ,				sep='''/'''							)
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		rename_keys(__a							)
    SCREAMING_SNAKE_CASE_      :  int    						=		unflatten_dict(__a    ,				sep='''/'''							)

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

    print(f'Save PyTorch model to {pytorch_dump_path}'							)
    pt_model.save_pretrained(__a							)


if __name__ == "__main__":
  UpperCAmelCase_   : List[Any]   			= 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""")
  UpperCAmelCase_   : 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__								=						logging.get_logger(__name__)

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

A__								=						{
    '''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__								=						{
    '''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__								=						{
    '''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       a							(			__lowerCamelCase				):
		__lowerCAmelCase	:    List[Any]											=					VOCAB_FILES_NAMES
		__lowerCAmelCase	:    Optional[Any]											=					PRETRAINED_VOCAB_FILES_MAP
		__lowerCAmelCase	:    Optional[Any]											=					PRETRAINED_INIT_CONFIGURATION
		__lowerCAmelCase	:    Optional[Any]											=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__lowerCAmelCase	:    Optional[Any]											=					RealmTokenizer



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

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

						snake_case__	:      Tuple     		=	do_lower_case



		def 	__lowerCamelCase			(     self     :Union[str, Any]   ,__lowercase     :Union[str, Any]   ,**__lowercase     :Any  ):
						snake_case__	:      Dict     		=	PaddingStrategy.MAX_LENGTH

						snake_case__	:      List[str]     		=	text
						snake_case__	:      int     		=	kwargs.pop('''text_pair'''   ,__lowercase  )
						snake_case__	:      Optional[int]     		=	kwargs.pop('''return_tensors'''   ,__lowercase  )

						snake_case__	:      str     		=	{
						    '''input_ids''': [],
						    '''attention_mask''': [],
						    '''token_type_ids''': [],
						}

						for idx, candidate_text in enumerate(__lowercase  ):
										if batch_text_pair is not None:
														snake_case__	:      Optional[int]     		=	batch_text_pair[idx]
										else:
														snake_case__	:      Tuple     		=	None

										snake_case__	:      List[str]     		=	super().__call__(__lowercase   ,__lowercase   ,return_tensors=__lowercase   ,**__lowercase  )

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

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

						snake_case__	:      Any     		=	{key: item for key, item in output_data.items() if len(__lowercase  ) != 0}

						return BatchEncoding(__lowercase   ,tensor_type=__lowercase  )



		def 	__lowerCamelCase			(     self     :List[Any]   ,__lowercase     :Tuple   ,__lowercase     :Tuple=None  ):
						snake_case__	:      List[str]     		=	[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 	__lowerCamelCase			(     self     :List[str]   ,__lowercase     :List[int]   ,__lowercase     :Optional[List[int]] = None  ):
						snake_case__	:      Tuple     		=	[self.sep_token_id]
						snake_case__	:      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 	__lowerCamelCase			(     self     :Optional[Any]   ,__lowercase     :str   ,__lowercase     :Optional[str] = None  ):
						snake_case__	:      Tuple     		=	self._tokenizer.model.save(__lowercase   ,name=__lowercase  )
						return tuple(__lowercase  )

