



'''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    lowerCamelCase_     (    lowercase__):
 lowerCamelCase__  =     list(s_dict.keys())
 for key in keys:
  lowerCamelCase__  =     r".*/layers_(\d+)"
  lowerCamelCase__  =     key
  if re.match(A__				,     A__):
   lowerCamelCase__  =     re.sub(r"layers_(\d+)"				,     r"block/\1/layer"				,     A__)

  lowerCamelCase__  =     r"(encoder|decoder)\/"

  if re.match(A__				,     A__):
   lowerCamelCase__  =     re.match(A__				,     A__).groups()
   if groups[0] == "encoder":
    lowerCamelCase__  =     re.sub(r"/mlp/"				,     r"/1/mlp/"				,     A__)
    lowerCamelCase__  =     re.sub(r"/pre_mlp_layer_norm/"				,     r"/1/layer_norm/"				,     A__)

   elif groups[0] == "decoder":
    lowerCamelCase__  =     re.sub(r"/mlp/"				,     r"/2/mlp/"				,     A__)
    lowerCamelCase__  =     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:
    lowerCamelCase__  =     new_key.replace(A__				,     A__)

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

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

   s_dict.pop(A__)

 return s_dict


__A							:							Union[str, 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_     (    lowercase__				,     lowercase__):
 import regex as re

 with open(A__				,     "r") as f:
  lowerCamelCase__  =     f.read()

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

 lowerCamelCase__  =     re.findall(r"(.*activations) = \(\'(.*)\',\)"				,     A__)[0]
 lowerCamelCase__  =     str(activation[1])

 lowerCamelCase__  =     num_experts
 lowerCamelCase__  =     SwitchTransformersConfig(**A__)
 return config






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__=None				,     lowercase__="./"				,     lowercase__=8):
 print(F'''Loading flax weights from : {flax_checkpoint_path}''')
 lowerCamelCase__  =     checkpoints.load_tax_checkpoint(A__)

 if gin_file is not None:
  lowerCamelCase__  =     convert_gin_to_config(A__				,     A__)
 else:
  lowerCamelCase__  =     SwitchTransformersConfig.from_pretrained(A__)

 lowerCamelCase__  =     SwitchTransformersForConditionalGeneration(A__)

 lowerCamelCase__  =     flax_params["target"]
 lowerCamelCase__  =     flatten_dict(A__				,     sep="/")
 lowerCamelCase__  =     rename_keys(A__)
 lowerCamelCase__  =     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__":
 __A							:							List[str]  							=		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							:							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,
 )









'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__A							:							Optional[int]  							=		logging.get_logger(__name__)

__A							:							Tuple  							=		{
    """google/mobilenet_v1_1.0_224""": """https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v1_0.75_192""": """https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json""",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}
class 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							"mobilenet_v1"

    def __init__(					self     :     Union[str, Any]				,     __lowerCamelCase     :     Tuple=3				,     __lowerCamelCase     :     Any=224				,     __lowerCamelCase     :     Tuple=1.0				,     __lowerCamelCase     :     Optional[int]=8				,     __lowerCamelCase     :     str="relu6"				,     __lowerCamelCase     :     Any=True				,     __lowerCamelCase     :     Union[str, Any]=0.9_9_9				,     __lowerCamelCase     :     List[Any]=0.0_2				,     __lowerCamelCase     :     str=0.0_0_1				,     **__lowerCamelCase     :     str				,     )							->							Dict:

     '''simple docstring'''




     super().__init__(**__lowerCamelCase     )

     if depth_multiplier <= 0:
      raise ValueError("depth_multiplier must be greater than zero."     )

     lowerCamelCase__  =     num_channels
     lowerCamelCase__  =     image_size
     lowerCamelCase__  =     depth_multiplier
     lowerCamelCase__  =     min_depth
     lowerCamelCase__  =     hidden_act
     lowerCamelCase__  =     tf_padding
     lowerCamelCase__  =     classifier_dropout_prob
     lowerCamelCase__  =     initializer_range
     lowerCamelCase__  =     layer_norm_eps
class 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							version.parse("1.11"      )

    @property
    def    a__				(					self     :     List[str]     )							->							Mapping[str, Mapping[int, str]]:

     '''simple docstring'''




     return OrderedDict([("pixel_values", {0: "batch"})]     )

    @property
    def    a__				(					self     :     str     )							->							Mapping[str, Mapping[int, str]]:

     '''simple docstring'''




     if self.task == "image-classification":
      return OrderedDict([("logits", {0: "batch"})]     )
     else:
      return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]     )




    @property
    def    a__				(					self     :     List[Any]     )							->							float:

     '''simple docstring'''




     return 1E-4

