



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
SCREAMING_SNAKE_CASE_:int					=						{
    """/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     (  _lowerCAmelCase		)	->  Optional[Any]:



    """simple docstring"""






    A			:	Optional[int]										=       list(s_dict.keys()		)
    for key in keys:
        A			:	List[Any]										=       R""".*/layers_(\d+)"""
        A			:	str										=       key
        if re.match(_lowerCAmelCase				,					_lowerCAmelCase		):
            A			:	Any										=       re.sub(R"""layers_(\d+)"""				,					R"""block/\1/layer"""				,					_lowerCAmelCase		)

        A			:	Optional[Any]										=       R"""(encoder|decoder)\/"""

        if re.match(_lowerCAmelCase				,					_lowerCAmelCase		):
            A			:	Optional[int]										=       re.match(_lowerCAmelCase				,					_lowerCAmelCase		).groups()
            if groups[0] == "encoder":
                A			:	Optional[Any]										=       re.sub(R"""/mlp/"""				,					R"""/1/mlp/"""				,					_lowerCAmelCase		)
                A			:	List[str]										=       re.sub(R"""/pre_mlp_layer_norm/"""				,					R"""/1/layer_norm/"""				,					_lowerCAmelCase		)

            elif groups[0] == "decoder":
                A			:	Any										=       re.sub(R"""/mlp/"""				,					R"""/2/mlp/"""				,					_lowerCAmelCase		)
                A			:	Tuple										=       re.sub(R"""/pre_mlp_layer_norm/"""				,					R"""/2/layer_norm/"""				,					_lowerCAmelCase		)

        # 2. Convert other classic mappings
        for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
            if old_key in new_key:
                A			:	Optional[int]										=       new_key.replace(_lowerCAmelCase				,					_lowerCAmelCase		)

        print(f'''{key} -> {new_key}'''		)
        A			:	Union[str, Any]										=       s_dict.pop(_lowerCAmelCase		)

    if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
        A			:	List[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:
        A			:	List[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:
            A			:	Dict										=       s_dict[key].shape[0]
            A			:	Dict										=       s_dict[key]
            for idx in range(_lowerCAmelCase		):
                A			:	Optional[Any]										=       expert_weihts[idx]
                print(f'''{key} -> {key.replace("expert/"				,					"nested fstring"		)}'''		)

            s_dict.pop(_lowerCAmelCase		)

    return s_dict


SCREAMING_SNAKE_CASE_:Optional[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      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase		)	->  List[str]:



    """simple docstring"""






    import regex as re

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

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

    A			:	List[str]										=       re.findall(R"""(.*activations) = \(\'(.*)\',\)"""				,					_lowerCAmelCase		)[0]
    A			:	Optional[int]										=       str(activation[1]		)

    A			:	Dict										=       num_experts
    A			:	int										=       SwitchTransformersConfig(**_lowerCAmelCase		)
    return config




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase=None				,					_lowerCAmelCase="./"				,					_lowerCAmelCase=8		)	->  Optional[int]:



    """simple docstring"""






    print(f'''Loading flax weights from : {flax_checkpoint_path}'''		)
    A			:	str										=       checkpoints.load_tax_checkpoint(_lowerCAmelCase		)

    if gin_file is not None:
        A			:	Union[str, Any]										=       convert_gin_to_config(_lowerCAmelCase				,					_lowerCAmelCase		)
    else:
        A			:	Tuple										=       SwitchTransformersConfig.from_pretrained(_lowerCAmelCase		)

    A			:	Optional[int]										=       SwitchTransformersForConditionalGeneration(_lowerCAmelCase		)

    A			:	str										=       flax_params["""target"""]
    A			:	Dict										=       flatten_dict(_lowerCAmelCase				,					sep="""/"""		)
    A			:	Dict										=       rename_keys(_lowerCAmelCase		)
    A			:	str										=       unflatten_dict(_lowerCAmelCase				,					sep="""/"""		)

    # Load the flax params in the PT model
    load_flax_weights_in_pytorch_model(_lowerCAmelCase				,					_lowerCAmelCase		)

    print(f'''Save PyTorch model to {pytorch_dump_path}'''		)
    pt_model.save_pretrained(_lowerCAmelCase		)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE_:int					=						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""")
      SCREAMING_SNAKE_CASE_: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,
      )







import inspect
import re
from hashlib import shaaaa
from typing import Dict, List

from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql  # noqa F401
from .text import text




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  str:



    """simple docstring"""






    A			:	int										=       []
    for line in lines:
        A			:	int										=       re.sub(R"""#.*"""				,					""""""				,					_lowerCAmelCase		)  # remove comments
        if line:
            filtered_lines.append(_lowerCAmelCase		)
    A			:	Tuple										=       """\n""".join(_lowerCAmelCase		)

    # Make a hash from all this code
    A			:	Union[str, Any]										=       full_str.encode("""utf-8"""		)
    return shaaaa(_lowerCAmelCase		).hexdigest()


# get importable module names and hash for caching
SCREAMING_SNAKE_CASE_:List[Any]					=						{
    """csv""": (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
    """json""": (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
    """pandas""": (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
    """parquet""": (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
    """arrow""": (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
    """text""": (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
    """imagefolder""": (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
    """audiofolder""": (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}

# Used to infer the module to use based on the data files extensions
SCREAMING_SNAKE_CASE_:Optional[Any]					=						{
    """.csv""": ("""csv""", {}),
    """.tsv""": ("""csv""", {"""sep""": """\t"""}),
    """.json""": ("""json""", {}),
    """.jsonl""": ("""json""", {}),
    """.parquet""": ("""parquet""", {}),
    """.arrow""": ("""arrow""", {}),
    """.txt""": ("""text""", {}),
}
_EXTENSION_TO_MODULE.update({ext: ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
SCREAMING_SNAKE_CASE_:Optional[int]					=						{"""imagefolder""", """audiofolder"""}

# Used to filter data files based on extensions given a module name
SCREAMING_SNAKE_CASE_:Dict[str, List[str]]					=						{}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
     _MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)

_MODULE_TO_EXTENSIONS["imagefolder"].append(""".zip""")




_MODULE_TO_EXTENSIONS["audiofolder"].append(""".zip""")


