



'''simple docstring'''

from typing import TYPE_CHECKING

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


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

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__         =      [
     "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__         =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)







'''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__         =      {
    "/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_     ):
 # 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
 # the original model
 UpperCAmelCase	:     List[Any]						 =			list(s_dict.keys()     )
 for key in keys:
  UpperCAmelCase	:     Union[str, Any]						 =			R'.*/layers_(\d+)'
  UpperCAmelCase	:     List[str]						 =			key
  if re.match(UpperCAmelCase_  ,	UpperCAmelCase_     ):
   UpperCAmelCase	:     Union[str, Any]						 =			re.sub(R'layers_(\d+)'  ,	R'block/\1/layer'  ,	UpperCAmelCase_     )

  UpperCAmelCase	:     str						 =			R'(encoder|decoder)\/'

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

   elif groups[0] == "decoder":
    UpperCAmelCase	:     Tuple						 =			re.sub(R'/mlp/'  ,	R'/2/mlp/'  ,	UpperCAmelCase_     )
    UpperCAmelCase	:     Union[str, 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:
    UpperCAmelCase	:     List[str]						 =			new_key.replace(UpperCAmelCase_  ,	UpperCAmelCase_     )

  print(F"""{key} -> {new_key}"""     )
  UpperCAmelCase	:     List[Any]						 =			s_dict.pop(UpperCAmelCase_     )

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

   s_dict.pop(UpperCAmelCase_     )

 return s_dict


lowercase__         =      {
    "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_  ,	UpperCAmelCase_     ):
 # Convert a google style config to the hugging face fromat
 import regex as re

 with open(UpperCAmelCase_  ,	'r'     ) as f:
  UpperCAmelCase	:     Union[str, Any]						 =			f.read()

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

 UpperCAmelCase	:     str						 =			re.findall(R'(.*activations) = \(\'(.*)\',\)'  ,	UpperCAmelCase_     )[0]
 UpperCAmelCase	:     Union[str, Any]						 =			str(activation[1]     )

 UpperCAmelCase	:     Optional[int]						 =			num_experts
 UpperCAmelCase	:     List[str]						 =			SwitchTransformersConfig(**UpperCAmelCase_     )
 return config




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_=None  ,	UpperCAmelCase_="./"  ,	UpperCAmelCase_=8     ):
 # Initialise PyTorch model

 print(F"""Loading flax weights from : {flax_checkpoint_path}"""     )
 UpperCAmelCase	:     List[Any]						 =			checkpoints.load_tax_checkpoint(UpperCAmelCase_     )

 if gin_file is not None:
  UpperCAmelCase	:     List[Any]						 =			convert_gin_to_config(UpperCAmelCase_  ,	UpperCAmelCase_     )
 else:
  UpperCAmelCase	:     str						 =			SwitchTransformersConfig.from_pretrained(UpperCAmelCase_     )

 UpperCAmelCase	:     str						 =			SwitchTransformersForConditionalGeneration(UpperCAmelCase_     )

 UpperCAmelCase	:     str						 =			flax_params['target']
 UpperCAmelCase	:     Union[str, Any]						 =			flatten_dict(UpperCAmelCase_  ,	sep='/'     )
 UpperCAmelCase	:     Tuple						 =			rename_keys(UpperCAmelCase_     )
 UpperCAmelCase	:     Optional[int]						 =			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__":
 lowercase__         =      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__         =      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,
 )




