
"""simple docstring"""




import argparse
import gc
import json
import os
import shutil
import warnings

import torch

from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer


try:
					from transformers import LlamaTokenizerFast
except ImportError as e:
					warnings.warn(e)
					warnings.warn(
					    'The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion'
					)
					UpperCAmelCase__      :    Optional[int]    							= None
UpperCAmelCase__      :    Optional[Any]    							= {
    '7B': 1_1_0_0_8,
    '13B': 1_3_8_2_4,
    '30B': 1_7_9_2_0,
    '65B': 2_2_0_1_6,
    '70B': 2_8_6_7_2,
}
UpperCAmelCase__      :    List[str]    							= {
    '7B': 1,
    '7Bf': 1,
    '13B': 2,
    '13Bf': 2,
    '30B': 4,
    '65B': 8,
    '70B': 8,
    '70Bf': 8,
}

def lowercase_  (   _snake_case   ,_snake_case=1   ,_snake_case=256						):
							return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3						)						) + multiple_of - 1) // multiple_of)

def lowercase_  (   _snake_case						):
							with open(_snake_case   ,"""r"""						) as f:
														return json.load(_snake_case						)

def lowercase_  (   _snake_case   ,_snake_case						):
							with open(_snake_case   ,"""w"""						) as f:
														json.dump(_snake_case   ,_snake_case						)

def lowercase_  (   _snake_case   ,_snake_case   ,_snake_case   ,_snake_case=True						):
							os.makedirs(_snake_case   ,exist_ok=_snake_case						)
							SCREAMING_SNAKE_CASE__						:       Tuple           =   os.path.join(_snake_case   ,"""tmp"""						)
							os.makedirs(_snake_case   ,exist_ok=_snake_case						)

							SCREAMING_SNAKE_CASE__						:       Any           =   read_json(os.path.join(_snake_case   ,"""params.json"""						)						)
							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   NUM_SHARDS[model_size]
							SCREAMING_SNAKE_CASE__						:       Optional[int]           =   params["""n_layers"""]
							SCREAMING_SNAKE_CASE__						:       str           =   params["""n_heads"""]
							SCREAMING_SNAKE_CASE__						:       List[str]           =   n_heads // num_shards
							SCREAMING_SNAKE_CASE__						:       List[str]           =   params["""dim"""]
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   dim // n_heads
							SCREAMING_SNAKE_CASE__						:       str           =   10000.0
							SCREAMING_SNAKE_CASE__						:       List[str]           =   1.0 / (base ** (torch.arange(0   ,_snake_case   ,2						).float() / dims_per_head))

							if "n_kv_heads" in params:
														SCREAMING_SNAKE_CASE__						:       List[str]           =   params["""n_kv_heads"""]  # for GQA / MQA
														SCREAMING_SNAKE_CASE__						:       str           =   n_heads_per_shard // num_key_value_heads
														SCREAMING_SNAKE_CASE__						:       str           =   dim // num_key_value_heads
							else:  # compatibility with other checkpoints
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   n_heads
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   n_heads_per_shard
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   dim

							# permute for sliced rotary
							def permute(_snake_case   ,_snake_case=n_heads   ,_snake_case=dim   ,_snake_case=dim						):
														return w.view(_snake_case   ,dima // n_heads // 2   ,2   ,_snake_case						).transpose(1   ,2						).reshape(_snake_case   ,_snake_case						)

							print(f'''Fetching all parameters from the checkpoint at {input_base_path}.'''						)
							# Load weights
							if model_size == "7B":
														# Not sharded
														# (The sharded implementation would also work, but this is simpler.)
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   torch.load(os.path.join(_snake_case   ,"""consolidated.00.pth"""						)   ,map_location="""cpu"""						)
							else:
														# Sharded
														SCREAMING_SNAKE_CASE__						:       Tuple           =   [
														    torch.load(os.path.join(_snake_case   ,f'''consolidated.{i:02d}.pth'''						)   ,map_location="""cpu"""						)
														    for i in range(_snake_case						)
														]
							SCREAMING_SNAKE_CASE__						:       List[Any]           =   0
							SCREAMING_SNAKE_CASE__						:       List[Any]           =   {"""weight_map""": {}}
							for layer_i in range(_snake_case						):
														SCREAMING_SNAKE_CASE__						:       Optional[int]           =   f'''pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin'''
														if model_size == "7B":
																					# Unsharded
																					SCREAMING_SNAKE_CASE__						:       Dict           =   {
																					    f'''model.layers.{layer_i}.self_attn.q_proj.weight''': permute(
																					        loaded[f'''layers.{layer_i}.attention.wq.weight''']						),
																					    f'''model.layers.{layer_i}.self_attn.k_proj.weight''': permute(
																					        loaded[f'''layers.{layer_i}.attention.wk.weight''']						),
																					    f'''model.layers.{layer_i}.self_attn.v_proj.weight''': loaded[f'''layers.{layer_i}.attention.wv.weight'''],
																					    f'''model.layers.{layer_i}.self_attn.o_proj.weight''': loaded[f'''layers.{layer_i}.attention.wo.weight'''],
																					    f'''model.layers.{layer_i}.mlp.gate_proj.weight''': loaded[f'''layers.{layer_i}.feed_forward.w1.weight'''],
																					    f'''model.layers.{layer_i}.mlp.down_proj.weight''': loaded[f'''layers.{layer_i}.feed_forward.w2.weight'''],
																					    f'''model.layers.{layer_i}.mlp.up_proj.weight''': loaded[f'''layers.{layer_i}.feed_forward.w3.weight'''],
																					    f'''model.layers.{layer_i}.input_layernorm.weight''': loaded[f'''layers.{layer_i}.attention_norm.weight'''],
																					    f'''model.layers.{layer_i}.post_attention_layernorm.weight''': loaded[f'''layers.{layer_i}.ffn_norm.weight'''],
																					}
														else:
																					# Sharded
																					# Note that attention.w{q,k,v,o}, feed_fordward.w[1,2,3], attention_norm.weight and ffn_norm.weight share
																					# the same storage object, saving attention_norm and ffn_norm will save other weights too, which is
																					# redundant as other weights will be stitched from multiple shards. To avoid that, they are cloned.

																					SCREAMING_SNAKE_CASE__						:       int           =   {
																					    f'''model.layers.{layer_i}.input_layernorm.weight''': loaded[0][
																					        f'''layers.{layer_i}.attention_norm.weight'''
																					    ].clone(),
																					    f'''model.layers.{layer_i}.post_attention_layernorm.weight''': loaded[0][
																					        f'''layers.{layer_i}.ffn_norm.weight'''
																					    ].clone(),
																					}
																					SCREAMING_SNAKE_CASE__						:       int           =   permute(
																					    torch.cat(
																					        [
																					            loaded[i][f'''layers.{layer_i}.attention.wq.weight'''].view(_snake_case   ,_snake_case   ,_snake_case						)
																					            for i in range(_snake_case						)
																					        ]   ,dim=0   ,).reshape(_snake_case   ,_snake_case						)						)
																					SCREAMING_SNAKE_CASE__						:       List[Any]           =   permute(
																					    torch.cat(
																					        [
																					            loaded[i][f'''layers.{layer_i}.attention.wk.weight'''].view(
																					                _snake_case   ,_snake_case   ,_snake_case						)
																					            for i in range(_snake_case						)
																					        ]   ,dim=0   ,).reshape(_snake_case   ,_snake_case						)   ,_snake_case   ,_snake_case   ,_snake_case   ,)
																					SCREAMING_SNAKE_CASE__						:       Dict           =   torch.cat(
																					    [
																					        loaded[i][f'''layers.{layer_i}.attention.wv.weight'''].view(
																					            _snake_case   ,_snake_case   ,_snake_case						)
																					        for i in range(_snake_case						)
																					    ]   ,dim=0   ,).reshape(_snake_case   ,_snake_case						)

																					SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   torch.cat(
																					    [loaded[i][f'''layers.{layer_i}.attention.wo.weight'''] for i in range(_snake_case						)]   ,dim=1						)
																					SCREAMING_SNAKE_CASE__						:       Optional[int]           =   torch.cat(
																					    [loaded[i][f'''layers.{layer_i}.feed_forward.w1.weight'''] for i in range(_snake_case						)]   ,dim=0						)
																					SCREAMING_SNAKE_CASE__						:       Dict           =   torch.cat(
																					    [loaded[i][f'''layers.{layer_i}.feed_forward.w2.weight'''] for i in range(_snake_case						)]   ,dim=1						)
																					SCREAMING_SNAKE_CASE__						:       Any           =   torch.cat(
																					    [loaded[i][f'''layers.{layer_i}.feed_forward.w3.weight'''] for i in range(_snake_case						)]   ,dim=0						)

														SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   inv_freq
														for k, v in state_dict.items():
																					SCREAMING_SNAKE_CASE__						:       Optional[int]           =   filename
																					param_count += v.numel()
														torch.save(_snake_case   ,os.path.join(_snake_case   ,_snake_case						)						)

							SCREAMING_SNAKE_CASE__						:       int           =   f'''pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin'''
							if model_size == "7B":
														# Unsharded
														SCREAMING_SNAKE_CASE__						:       Dict           =   {
														    """model.embed_tokens.weight""": loaded["""tok_embeddings.weight"""],
														    """model.norm.weight""": loaded["""norm.weight"""],
														    """lm_head.weight""": loaded["""output.weight"""],
														}
							else:
														SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   {
														    """model.norm.weight""": loaded[0]["""norm.weight"""],
														    """model.embed_tokens.weight""": torch.cat(
														        [loaded[i]["""tok_embeddings.weight"""] for i in range(_snake_case						)]   ,dim=1						),
														    """lm_head.weight""": torch.cat([loaded[i]["""output.weight"""] for i in range(_snake_case						)]   ,dim=0						),
														}

							for k, v in state_dict.items():
														SCREAMING_SNAKE_CASE__						:       Tuple           =   filename
														param_count += v.numel()
							torch.save(_snake_case   ,os.path.join(_snake_case   ,_snake_case						)						)

							# Write configs
							SCREAMING_SNAKE_CASE__						:       str           =   {"""total_size""": param_count * 2}
							write_json(_snake_case   ,os.path.join(_snake_case   ,"""pytorch_model.bin.index.json"""						)						)
							SCREAMING_SNAKE_CASE__						:       Optional[int]           =   params["""ffn_dim_multiplier"""] if """ffn_dim_multiplier""" in params else 1
							SCREAMING_SNAKE_CASE__						:       Tuple           =   params["""multiple_of"""] if """multiple_of""" in params else 256
							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   LlamaConfig(
							    hidden_size=_snake_case   ,intermediate_size=compute_intermediate_size(_snake_case   ,_snake_case   ,_snake_case						)   ,num_attention_heads=params["""n_heads"""]   ,num_hidden_layers=params["""n_layers"""]   ,rms_norm_eps=params["""norm_eps"""]   ,num_key_value_heads=_snake_case   ,)
							config.save_pretrained(_snake_case						)

							# Make space so we can load the model properly now.
							del state_dict
							del loaded
							gc.collect()

							print("""Loading the checkpoint in a Llama model."""						)
							SCREAMING_SNAKE_CASE__						:       List[Any]           =   LlamaForCausalLM.from_pretrained(_snake_case   ,torch_dtype=torch.floataa   ,low_cpu_mem_usage=_snake_case						)
							# Avoid saving this as part of the config.
							del model.config._name_or_path

							print("""Saving in the Transformers format."""						)
							model.save_pretrained(_snake_case   ,safe_serialization=_snake_case						)
							shutil.rmtree(_snake_case						)

def lowercase_  (   _snake_case   ,_snake_case						):
							# Initialize the tokenizer based on the `spm` model
							SCREAMING_SNAKE_CASE__						:       int           =   LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
							print(f'''Saving a {tokenizer_class.__name__} to {tokenizer_path}.'''						)
							SCREAMING_SNAKE_CASE__						:       Tuple           =   tokenizer_class(_snake_case						)
							tokenizer.save_pretrained(_snake_case						)

def lowercase_  (   ):
							SCREAMING_SNAKE_CASE__						:       int           =   argparse.ArgumentParser()
							parser.add_argument(
							    """--input_dir"""   ,help="""Location of LLaMA weights, which contains tokenizer.model and model folders"""   ,)
							parser.add_argument(
							    """--model_size"""   ,choices=["""7B""", """7Bf""", """13B""", """13Bf""", """30B""", """65B""", """70B""", """70Bf""", """tokenizer_only"""]   ,)
							parser.add_argument(
							    """--output_dir"""   ,help="""Location to write HF model and tokenizer"""   ,)
							parser.add_argument("""--safe_serialization"""   ,type=_snake_case   ,help="""Whether or not to save using `safetensors`."""						)
							SCREAMING_SNAKE_CASE__						:       Optional[int]           =   parser.parse_args()
							if args.model_size != "tokenizer_only":
														write_model(
														    model_path=args.output_dir   ,input_base_path=os.path.join(args.input_dir   ,args.model_size						)   ,model_size=args.model_size   ,safe_serialization=args.safe_serialization   ,)
							SCREAMING_SNAKE_CASE__						:       Any           =   os.path.join(args.input_dir   ,"""tokenizer.model"""						)
							write_tokenizer(args.output_dir   ,_snake_case						)


if __name__ == "__main__":
					main()








"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__UpperCamelCase						=      {
    '''configuration_blip''': [
        '''BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlipConfig''',
        '''BlipTextConfig''',
        '''BlipVisionConfig''',
    ],
    '''processing_blip''': ['''BlipProcessor'''],
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCamelCase						=      ['''BlipImageProcessor''']


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCamelCase						=      [
	    '''BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''BlipModel''',
	    '''BlipPreTrainedModel''',
	    '''BlipForConditionalGeneration''',
	    '''BlipForQuestionAnswering''',
	    '''BlipVisionModel''',
	    '''BlipTextModel''',
	    '''BlipForImageTextRetrieval''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCamelCase						=      [
	    '''TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFBlipModel''',
	    '''TFBlipPreTrainedModel''',
	    '''TFBlipForConditionalGeneration''',
	    '''TFBlipForQuestionAnswering''',
	    '''TFBlipVisionModel''',
	    '''TFBlipTextModel''',
	    '''TFBlipForImageTextRetrieval''',
	]

if TYPE_CHECKING:
	from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
	from .processing_blip import BlipProcessor

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .image_processing_blip import BlipImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_blip import (
		    BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BlipForConditionalGeneration,
		    BlipForImageTextRetrieval,
		    BlipForQuestionAnswering,
		    BlipModel,
		    BlipPreTrainedModel,
		    BlipTextModel,
		    BlipVisionModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_blip import (
		    TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFBlipForConditionalGeneration,
		    TFBlipForImageTextRetrieval,
		    TFBlipForQuestionAnswering,
		    TFBlipModel,
		    TFBlipPreTrainedModel,
		    TFBlipTextModel,
		    TFBlipVisionModel,
		)

else:
	import sys

	__UpperCamelCase						=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


