



import argparse
import gc
import json
import os
import re

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint


SCREAMING_SNAKE_CASE_:List[str]					=						{
    """169M""": 12,
    """430M""": 24,
    """1B5""": 24,
    """3B""": 32,
    """7B""": 32,
    """14B""": 40,
}

SCREAMING_SNAKE_CASE_:str					=						{
    """169M""": 768,
    """430M""": 1_024,
    """1B5""": 2_048,
    """3B""": 2_560,
    """7B""": 4_096,
    """14B""": 5_120,
}




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  Union[str, Any]:



    """simple docstring"""






    A			:	str										=       list(state_dict.keys()		)
    for name in state_dict_keys:
        A			:	Optional[Any]										=       state_dict.pop(_lowerCAmelCase		)
        # emb -> embedding
        if name.startswith("""emb."""		):
            A			:	Tuple										=       name.replace("""emb."""				,					"""embeddings."""		)
        # ln_0 -> pre_ln (only present at block 0)
        if name.startswith("""blocks.0.ln0"""		):
            A			:	Dict										=       name.replace("""blocks.0.ln0"""				,					"""blocks.0.pre_ln"""		)
        # att -> attention
        A			:	Union[str, Any]										=       re.sub(R"""blocks\.(\d+)\.att"""				,					R"""blocks.\1.attention"""				,					_lowerCAmelCase		)
        # ffn -> feed_forward
        A			:	Optional[int]										=       re.sub(R"""blocks\.(\d+)\.ffn"""				,					R"""blocks.\1.feed_forward"""				,					_lowerCAmelCase		)
        # time_mix_k -> time_mix_key and reshape
        if name.endswith(""".time_mix_k"""		):
            A			:	Tuple										=       name.replace(""".time_mix_k"""				,					""".time_mix_key"""		)
        # time_mix_v -> time_mix_value and reshape
        if name.endswith(""".time_mix_v"""		):
            A			:	Tuple										=       name.replace(""".time_mix_v"""				,					""".time_mix_value"""		)
        # time_mix_r -> time_mix_key and reshape
        if name.endswith(""".time_mix_r"""		):
            A			:	Optional[Any]										=       name.replace(""".time_mix_r"""				,					""".time_mix_receptance"""		)

        if name != "head.weight":
            A			:	List[Any]										=       """rwkv.""" + name

        A			:	Optional[int]										=       weight
    return state_dict




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase=None				,					_lowerCAmelCase=None				,					_lowerCAmelCase=False				,					_lowerCAmelCase=None		)	->  Dict:



    """simple docstring"""






    if tokenizer_file is None:
        print("""No `--tokenizer_file` provided, we will use the default tokenizer."""		)
        A			:	Optional[int]										=       5_0277
        A			:	Dict										=       AutoTokenizer.from_pretrained("""EleutherAI/gpt-neox-20b"""		)
    else:
        A			:	List[Any]										=       PreTrainedTokenizerFast(tokenizer_file=_lowerCAmelCase		)
        A			:	Tuple										=       len(_lowerCAmelCase		)
    tokenizer.save_pretrained(_lowerCAmelCase		)

    # 2. Build the config
    A			:	List[str]										=       list(NUM_HIDDEN_LAYERS_MAPPING.keys()		)
    if size is None:
        # Try to infer size from the checkpoint name
        for candidate in possible_sizes:
            if candidate in checkpoint_file:
                A			:	Tuple										=       candidate
                break
        if size is None:
            raise ValueError("""Could not infer the size, please provide it with the `--size` argument."""		)
    if size not in possible_sizes:
        raise ValueError(f'''`size` should be one of {possible_sizes}, got {size}.'''		)

    A			:	int										=       RwkvConfig(
        vocab_size=_lowerCAmelCase				,					num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]				,					hidden_size=HIDEN_SIZE_MAPPING[size]				,					)
    config.save_pretrained(_lowerCAmelCase		)

    # 3. Download model file then convert state_dict
    A			:	Dict										=       hf_hub_download(_lowerCAmelCase				,					_lowerCAmelCase		)
    A			:	Any										=       torch.load(_lowerCAmelCase				,					map_location="""cpu"""		)
    A			:	str										=       convert_state_dict(_lowerCAmelCase		)

    # 4. Split in shards and save
    A					,							A			:	Any										=       shard_checkpoint(_lowerCAmelCase		)
    for shard_file, shard in shards.items():
        torch.save(_lowerCAmelCase				,					os.path.join(_lowerCAmelCase				,					_lowerCAmelCase		)		)

    if index is not None:
        A			:	Tuple										=       os.path.join(_lowerCAmelCase				,					_lowerCAmelCase		)
        # Save the index as well
        with open(_lowerCAmelCase				,					"""w"""				,					encoding="""utf-8"""		) as f:
            A			:	int										=       json.dumps(_lowerCAmelCase				,					indent=2				,					sort_keys=_lowerCAmelCase		) + """\n"""
            f.write(_lowerCAmelCase		)

        # 5. Clean up shards (for some reason the file PyTorch saves take the same space as the whole state_dict
        print(
            """Cleaning up shards. This may error with an OOM error, it this is the case don't worry you still have converted the model."""		)
        A			:	str										=       list(shards.keys()		)

        del state_dict
        del shards
        gc.collect()

        for shard_file in shard_files:
            A			:	Any										=       torch.load(os.path.join(_lowerCAmelCase				,					_lowerCAmelCase		)		)
            torch.save({k: v.cpu().clone() for k, v in state_dict.items()}				,					os.path.join(_lowerCAmelCase				,					_lowerCAmelCase		)		)

    del state_dict
    gc.collect()

    if push_to_hub:
        if model_name is None:
            raise ValueError("""Please provide a `model_name` to push the model to the Hub."""		)
        A			:	Any										=       AutoModelForCausalLM.from_pretrained(_lowerCAmelCase		)
        model.push_to_hub(_lowerCAmelCase				,					max_shard_size="""2GB"""		)
        tokenizer.push_to_hub(_lowerCAmelCase		)


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_:Optional[Any]					=						argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--repo_id""", default=None, type=str, required=True, help="""Repo ID from which to pull the checkpoint."""
     )
     parser.add_argument(
         """--checkpoint_file""", default=None, type=str, required=True, help="""Name of the checkpoint file in the repo."""
     )
     parser.add_argument(
         """--output_dir""", default=None, type=str, required=True, help="""Where to save the converted model."""
     )
     parser.add_argument(
         """--tokenizer_file""",
         default=None,
         type=str,
         help="""Path to the tokenizer file to use (if not provided, only the model is converted).""",
     )
     parser.add_argument(
         """--size""",
         default=None,
         type=str,
         help="""Size of the model. Will be inferred from the `checkpoint_file` if not passed.""",
     )
     parser.add_argument(
         """--push_to_hub""",
         action="""store_true""",
         help="""Push to the Hub the converted model.""",
     )
     parser.add_argument(
         """--model_name""",
         default=None,
         type=str,
         help="""Name of the pushed model on the Hub, including the username / organization.""",
     )

     SCREAMING_SNAKE_CASE_:int					=						parser.parse_args()
     convert_rmkv_checkpoint_to_hf_format(
         args.repo_id,
         args.checkpoint_file,
         args.output_dir,
         size=args.size,
         tokenizer_file=args.tokenizer_file,
         push_to_hub=args.push_to_hub,
         model_name=args.model_name,
     )







import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
SCREAMING_SNAKE_CASE_:Any					=						"""src/diffusers"""

# Matches is_xxx_available()
SCREAMING_SNAKE_CASE_:Optional[Any]					=						re.compile(R"""is\_([a-z_]*)_available\(\)""")
# Matches from xxx import bla
SCREAMING_SNAKE_CASE_:Union[str, Any]					=						re.compile(R"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")


SCREAMING_SNAKE_CASE_:List[Any]					=						"""
{0} = None
"""

SCREAMING_SNAKE_CASE_:Optional[Any]					=						"""
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, {1})

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, {1})
"""


SCREAMING_SNAKE_CASE_:int					=						"""
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  Tuple:



    """simple docstring"""






    A			:	Union[str, Any]										=       _re_backend.findall(_lowerCAmelCase		)
    if len(_lowerCAmelCase		) == 0:
        return None

    return "_and_".join(_lowerCAmelCase		)




def      __UpperCamelCase     (  )	->  str:



    """simple docstring"""






    with open(os.path.join(_lowerCAmelCase				,					"""__init__.py"""		)				,					"""r"""				,					encoding="""utf-8"""				,					newline="""\n"""		) as f:
        A			:	Dict										=       f.readlines()

    # Get to the point we do the actual imports for type checking
    A			:	Dict										=       0
    A			:	List[Any]										=       {}
    # Go through the end of the file
    while line_index < len(_lowerCAmelCase		):
        # If the line contains is_backend_available, we grab all objects associated with the `else` block
        A			:	Optional[int]										=       find_backend(lines[line_index]		)
        if backend is not None:
            while not lines[line_index].startswith("""else:"""		):
                line_index += 1
            line_index += 1
            A			:	str										=       []
            # Until we unindent, add backend objects to the list
            while line_index < len(_lowerCAmelCase		) and len(lines[line_index]		) > 1:
                A			:	Tuple										=       lines[line_index]
                A			:	List[str]										=       _re_single_line_import.search(_lowerCAmelCase		)
                if single_line_import_search is not None:
                    objects.extend(single_line_import_search.groups()[0].split(""", """		)		)
                elif line.startswith(""" """ * 8		):
                    objects.append(line[8:-2]		)
                line_index += 1

            if len(_lowerCAmelCase		) > 0:
                A			:	List[str]										=       objects
        else:
            line_index += 1

    return backend_specific_objects




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase		)	->  str:



    """simple docstring"""






    if name.isupper():
        return DUMMY_CONSTANT.format(_lowerCAmelCase		)
    elif name.islower():
        return DUMMY_FUNCTION.format(_lowerCAmelCase				,					_lowerCAmelCase		)
    else:
        return DUMMY_CLASS.format(_lowerCAmelCase				,					_lowerCAmelCase		)




def      __UpperCamelCase     (  _lowerCAmelCase=None		)	->  Tuple:



    """simple docstring"""






    if backend_specific_objects is None:
        A			:	Union[str, Any]										=       read_init()
    # For special correspondence backend to module name as used in the function requires_modulename
    A			:	Any										=       {}

    for backend, objects in backend_specific_objects.items():
        A			:	str										=       """[""" + """, """.join(f'''"{b}"''' for b in backend.split("""_and_"""		)		) + """]"""
        A			:	Any										=       """# This file is autogenerated by the command `make fix-copies`, do not edit.\n"""
        dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
        dummy_file += "\n".join([create_dummy_object(_lowerCAmelCase				,					_lowerCAmelCase		) for o in objects]		)
        A			:	Optional[Any]										=       dummy_file

    return dummy_files




def      __UpperCamelCase     (  _lowerCAmelCase=False		)	->  str:



    """simple docstring"""






    A			:	str										=       create_dummy_files()
    # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
    A			:	List[str]										=       {"""torch""": """pt"""}

    # Locate actual dummy modules and read their content.
    A			:	Union[str, Any]										=       os.path.join(_lowerCAmelCase				,					"""utils"""		)
    A			:	Any										=       {
        backend: os.path.join(_lowerCAmelCase				,					f'''dummy_{short_names.get(_lowerCAmelCase				,					_lowerCAmelCase		)}_objects.py'''		)
        for backend in dummy_files.keys()
    }

    A			:	List[Any]										=       {}
    for backend, file_path in dummy_file_paths.items():
        if os.path.isfile(_lowerCAmelCase		):
            with open(_lowerCAmelCase				,					"""r"""				,					encoding="""utf-8"""				,					newline="""\n"""		) as f:
                A			:	Dict										=       f.read()
        else:
            A			:	str										=       """"""

    for backend in dummy_files.keys():
        if dummy_files[backend] != actual_dummies[backend]:
            if overwrite:
                print(
                    f'''Updating diffusers.utils.dummy_{short_names.get(_lowerCAmelCase				,					_lowerCAmelCase		)}_objects.py as the main '''
                    """__init__ has new objects."""		)
                with open(dummy_file_paths[backend]				,					"""w"""				,					encoding="""utf-8"""				,					newline="""\n"""		) as f:
                    f.write(dummy_files[backend]		)
            else:
                raise ValueError(
                    """The main __init__ has objects that are not present in """
                    f'''diffusers.utils.dummy_{short_names.get(_lowerCAmelCase				,					_lowerCAmelCase		)}_objects.py. Run `make fix-copies` '''
                    """to fix this."""		)


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_:List[Any]					=						argparse.ArgumentParser()
     parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
     SCREAMING_SNAKE_CASE_:Any					=						parser.parse_args()

     check_dummies(args.fix_and_overwrite)


