


"""simple docstring"""






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


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

UpperCAmelCase    :     str   						=		{
    '169M': 768,
    '430M': 1024,
    '1B5': 2048,
    '3B': 2560,
    '7B': 4096,
    '14B': 5120,
}
def      lowerCamelCase       (     _UpperCamelCase		:      List[str]							)		->       List[str]:

							'''simple docstring'''



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

														if name != "head.weight":
																					__UpperCAmelCase   :						Optional[int]      							=					"""rwkv.""" + name

														__UpperCAmelCase   :						str      							=					weight
							return state_dict
def      lowerCamelCase       (     _UpperCamelCase		:      List[str]       ,			_UpperCamelCase		:      Optional[int]       ,			_UpperCamelCase		:      int       ,			_UpperCamelCase		:      str=None       ,			_UpperCamelCase		:      List[str]=None       ,			_UpperCamelCase		:      List[Any]=False       ,			_UpperCamelCase		:      str=None							)		->       int:

							'''simple docstring'''



							if tokenizer_file is None:
														print("""No `--tokenizer_file` provided, we will use the default tokenizer."""							)
														__UpperCAmelCase   :						Any      							=					5_0_2_7_7
														__UpperCAmelCase   :						List[Any]      							=					AutoTokenizer.from_pretrained("""EleutherAI/gpt-neox-20b"""							)
							else:
														__UpperCAmelCase   :						Optional[int]      							=					PreTrainedTokenizerFast(tokenizer_file=_UpperCamelCase							)
														__UpperCAmelCase   :						Optional[Any]      							=					len(_UpperCamelCase							)
							tokenizer.save_pretrained(_UpperCamelCase							)

							# 2. Build the config
							__UpperCAmelCase   :						List[Any]      							=					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:
																												__UpperCAmelCase   :						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}.'''							)

							__UpperCAmelCase   :						Optional[int]      							=					RwkvConfig(
							    vocab_size=_UpperCamelCase       ,			num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]       ,			hidden_size=HIDEN_SIZE_MAPPING[size]       ,			)
							config.save_pretrained(_UpperCamelCase							)

							# 3. Download model file then convert state_dict
							__UpperCAmelCase   :						List[Any]      							=					hf_hub_download(_UpperCamelCase       ,			_UpperCamelCase							)
							__UpperCAmelCase   :						List[str]      							=					torch.load(_UpperCamelCase       ,			map_location="""cpu"""							)
							__UpperCAmelCase   :						int      							=					convert_state_dict(_UpperCamelCase							)

							# 4. Split in shards and save
							__UpperCAmelCase							,__UpperCAmelCase   :						Union[str, Any]      							=					shard_checkpoint(_UpperCamelCase							)
							for shard_file, shard in shards.items():
														torch.save(_UpperCamelCase       ,			os.path.join(_UpperCamelCase       ,			_UpperCamelCase							)							)

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

														# 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."""							)
														__UpperCAmelCase   :						Dict      							=					list(shards.keys()							)

														del state_dict
														del shards
														gc.collect()

														for shard_file in shard_files:
																					__UpperCAmelCase   :						Optional[Any]      							=					torch.load(os.path.join(_UpperCamelCase       ,			_UpperCamelCase							)							)
																					torch.save({k: v.cpu().clone() for k, v in state_dict.items()}       ,			os.path.join(_UpperCamelCase       ,			_UpperCamelCase							)							)

							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."""							)
														__UpperCAmelCase   :						Union[str, Any]      							=					AutoModelForCausalLM.from_pretrained(_UpperCamelCase							)
														model.push_to_hub(_UpperCamelCase       ,			max_shard_size="""2GB"""							)
														tokenizer.push_to_hub(_UpperCamelCase							)


if __name__ == "__main__":
							UpperCAmelCase    :     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.',
							)

							UpperCAmelCase    :     List[Any]   						=		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,
							)




"""simple docstring"""






from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
							if not (is_transformers_available() and is_torch_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ...utils.dummy_torch_and_transformers_objects import (
							    ImageTextPipelineOutput,
							    UniDiffuserPipeline,
							)
else:
							from .modeling_text_decoder import UniDiffuserTextDecoder
							from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
							from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline
