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_   : Optional[int]					)    ->  Dict:
  __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[str]						,	A_   : Tuple					)    ->  List[str]:
  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_   : Optional[Any]						,	A_   : Optional[Any]						,	A_   : Dict=None						,	A_   : List[Any]="./"						,	A_   : Optional[int]=8					)    ->  str:
  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 ...configuration_utils import PretrainedConfig
from ...utils import logging


a_							=      logging.get_logger(__name__)

a_							=      {
    """facebook/vit-mae-base""": """https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json""",
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}


class 			UpperCAmelCase__    (		snake_case					):



  """simple docstring"""






  lowerCAmelCase__		:	Dict    		= 'vit_mae'






  def __init__(				self:			List[Any]				,				__lowerCAmelCase:			Any=768				,				__lowerCAmelCase:			List[str]=12				,				__lowerCAmelCase:			Optional[int]=12				,				__lowerCAmelCase:			Tuple=3_072				,				__lowerCAmelCase:			List[Any]="gelu"				,				__lowerCAmelCase:			Dict=0.0				,				__lowerCAmelCase:			Tuple=0.0				,				__lowerCAmelCase:			Any=0.02				,				__lowerCAmelCase:			List[Any]=1E-12				,				__lowerCAmelCase:			List[str]=224				,				__lowerCAmelCase:			Optional[Any]=16				,				__lowerCAmelCase:			Union[str, Any]=3				,				__lowerCAmelCase:			Tuple=True				,				__lowerCAmelCase:			Union[str, Any]=16				,				__lowerCAmelCase:			Optional[int]=512				,				__lowerCAmelCase:			int=8				,				__lowerCAmelCase:			int=2_048				,				__lowerCAmelCase:			str=0.75				,				__lowerCAmelCase:			Union[str, Any]=False				,				**__lowerCAmelCase:			List[Any]				,				) -> Optional[int]:




    '''simple docstring'''




    super().__init__(**__lowerCAmelCase   )

    __UpperCAmelCase						    =       hidden_size
    __UpperCAmelCase						    =       num_hidden_layers
    __UpperCAmelCase						    =       num_attention_heads
    __UpperCAmelCase						    =       intermediate_size
    __UpperCAmelCase						    =       hidden_act
    __UpperCAmelCase						    =       hidden_dropout_prob
    __UpperCAmelCase						    =       attention_probs_dropout_prob
    __UpperCAmelCase						    =       initializer_range
    __UpperCAmelCase						    =       layer_norm_eps
    __UpperCAmelCase						    =       image_size
    __UpperCAmelCase						    =       patch_size
    __UpperCAmelCase						    =       num_channels
    __UpperCAmelCase						    =       qkv_bias
    __UpperCAmelCase						    =       decoder_num_attention_heads
    __UpperCAmelCase						    =       decoder_hidden_size
    __UpperCAmelCase						    =       decoder_num_hidden_layers
    __UpperCAmelCase						    =       decoder_intermediate_size
    __UpperCAmelCase						    =       mask_ratio
    __UpperCAmelCase						    =       norm_pix_loss



