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_							=      {
    """/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      (						A_   : List[Any]					)    ->  Optional[Any]:
  # 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
  # the original model
  __UpperCAmelCase						    =       list(s_dict.keys()					)
  for key in keys:
    __UpperCAmelCase						    =       r".*/layers_(\d+)"
    __UpperCAmelCase						    =       key
    if re.match(A_						,	A_					):
      __UpperCAmelCase						    =       re.sub(r"layers_(\d+)"						,	r"block/\1/layer"						,	A_					)

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

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

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

    print(F'''{key} -> {new_key}'''					)
    __UpperCAmelCase						    =       s_dict.pop(A_					)

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

      s_dict.pop(A_					)

  return s_dict


a_							=      {
    """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      (						A_   : List[Any]						,	A_   : Union[str, Any]					)    ->  str:
  # Convert a google style config to the hugging face fromat
  import regex as re

  with open(A_						,	"r"					) as f:
    __UpperCAmelCase						    =       f.read()

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

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

  __UpperCAmelCase						    =       num_experts
  __UpperCAmelCase						    =       SwitchTransformersConfig(**A_					)
  return config




def       __lowerCAmelCase      (						A_   : str						,	A_   : Union[str, Any]						,	A_   : Tuple=None						,	A_   : List[str]="./"						,	A_   : int=8					)    ->  Any:
  # Initialise PyTorch model

  print(F'''Loading flax weights from : {flax_checkpoint_path}'''					)
  __UpperCAmelCase						    =       checkpoints.load_tax_checkpoint(A_					)

  if gin_file is not None:
    __UpperCAmelCase						    =       convert_gin_to_config(A_						,	A_					)
  else:
    __UpperCAmelCase						    =       SwitchTransformersConfig.from_pretrained(A_					)

  __UpperCAmelCase						    =       SwitchTransformersForConditionalGeneration(A_					)

  __UpperCAmelCase						    =       flax_params["target"]
  __UpperCAmelCase						    =       flatten_dict(A_						,	sep="/"					)
  __UpperCAmelCase						    =       rename_keys(A_					)
  __UpperCAmelCase						    =       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_							=      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_							=      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,
   )




from typing import TYPE_CHECKING

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


a_							=      {
    """configuration_altclip""": [
        """ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """AltCLIPConfig""",
        """AltCLIPTextConfig""",
        """AltCLIPVisionConfig""",
    ],
    """processing_altclip""": ["""AltCLIPProcessor"""],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_							=      [
           """ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """AltCLIPPreTrainedModel""",
           """AltCLIPModel""",
           """AltCLIPTextModel""",
           """AltCLIPVisionModel""",
       ]


if TYPE_CHECKING:
       from .configuration_altclip import (
           ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
           AltCLIPConfig,
           AltCLIPTextConfig,
           AltCLIPVisionConfig,
       )
       from .processing_altclip import AltCLIPProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_altclip import (
                  ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
                  AltCLIPModel,
                  AltCLIPPreTrainedModel,
                  AltCLIPTextModel,
                  AltCLIPVisionModel,
              )


else:
       import sys

       a_							=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



