'''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
_lowercase		:				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 							lowerCamelCase			(   UpperCAmelCase__ :      Optional[int]				)    ->   Any:
       lowercase_      :    Optional[Any]       		= list(s_dict.keys()				)
       for key in keys:
              lowercase_      :    List[str]       		= R""".*/layers_(\d+)"""
              lowercase_      :    Optional[Any]       		= key
              if re.match(A__   ,   A__				):
                     lowercase_      :    Optional[int]       		= re.sub(R"""layers_(\d+)"""   ,   R"""block/\1/layer"""   ,   A__				)

              lowercase_      :    Optional[int]       		= R"""(encoder|decoder)\/"""

              if re.match(A__   ,   A__				):
                     lowercase_      :    List[str]       		= re.match(A__   ,   A__				).groups()
                     if groups[0] == "encoder":
                            lowercase_      :    List[Any]       		= re.sub(R"""/mlp/"""   ,   R"""/1/mlp/"""   ,   A__				)
                            lowercase_      :    Tuple       		= re.sub(R"""/pre_mlp_layer_norm/"""   ,   R"""/1/layer_norm/"""   ,   A__				)

                     elif groups[0] == "decoder":
                            lowercase_      :    Tuple       		= re.sub(R"""/mlp/"""   ,   R"""/2/mlp/"""   ,   A__				)
                            lowercase_      :    str       		= 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:
                            lowercase_      :    Dict       		= new_key.replace(A__   ,   A__				)

              print(F'''{key} -> {new_key}'''				)
              lowercase_      :    int       		= s_dict.pop(A__				)

       if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
              lowercase_      :    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:
              lowercase_      :    Optional[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:
                     lowercase_      :    Tuple       		= s_dict[key].shape[0]
                     lowercase_      :    Optional[int]       		= s_dict[key]
                     for idx in range(A__				):
                            lowercase_      :    int       		= expert_weihts[idx]
                            print(F'''{key} -> {key.replace('expert/'   ,   'nested fstring'				)}'''				)

                     s_dict.pop(A__				)

       return s_dict


_lowercase		:				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 							lowerCamelCase			(   UpperCAmelCase__ :      List[str]   ,   UpperCAmelCase__ :      Tuple				)    ->   List[Any]:
       import regex as re

       with open(A__   ,   """r"""				) as f:
              lowercase_      :    Optional[int]       		= f.read()

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

       lowercase_      :    Any       		= re.findall(R"""(.*activations) = \(\'(.*)\',\)"""   ,   A__				)[0]
       lowercase_      :    str       		= str(activation[1]				)

       lowercase_      :    Union[str, Any]       		= num_experts
       lowercase_      :    Union[str, Any]       		= SwitchTransformersConfig(**A__				)
       return config




def 							lowerCamelCase			(   UpperCAmelCase__ :      Optional[Any]   ,   UpperCAmelCase__ :      Optional[Any]   ,   UpperCAmelCase__ :      Dict=None   ,   UpperCAmelCase__ :      List[Any]="./"   ,   UpperCAmelCase__ :      Optional[int]=8				)    ->   Union[str, Any]:
       print(F'''Loading flax weights from : {flax_checkpoint_path}'''				)
       lowercase_      :    str       		= checkpoints.load_tax_checkpoint(A__				)

       if gin_file is not None:
              lowercase_      :    Dict       		= convert_gin_to_config(A__   ,   A__				)
       else:
              lowercase_      :    List[str]       		= SwitchTransformersConfig.from_pretrained(A__				)

       lowercase_      :    Optional[Any]       		= SwitchTransformersForConditionalGeneration(A__				)

       lowercase_      :    List[Any]       		= flax_params["""target"""]
       lowercase_      :    int       		= flatten_dict(A__   ,   sep="""/"""				)
       lowercase_      :    Tuple       		= rename_keys(A__				)
       lowercase_      :    Optional[Any]       		= 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__":
      _lowercase		:				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")
      _lowercase		:				List[str]	       =		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,
      )






'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_lowercase		:				Dict	       =		{
    "configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"],
}
try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase		:				Dict	       =		["BloomTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase		:				Dict	       =		[
						    "BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "BloomForCausalLM",
						    "BloomModel",
						    "BloomPreTrainedModel",
						    "BloomForSequenceClassification",
						    "BloomForTokenClassification",
						    "BloomForQuestionAnswering",
						]

if TYPE_CHECKING:
						from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_bloom_fast import BloomTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_bloom import (
												    BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BloomForCausalLM,
												    BloomForQuestionAnswering,
												    BloomForSequenceClassification,
												    BloomForTokenClassification,
												    BloomModel,
												    BloomPreTrainedModel,
												)

else:
						import sys

						_lowercase		:				Optional[int]	       =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





