






"""simple docstring"""







import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_):
					a__          =  old_name

					if "patch_embed" in old_name:
										a__       ,a__       ,a__          =  old_name.split('''.''')

										if layer == "0":
															a__          =  old_name.replace('''0''' ,     '''convolution1''')
										elif layer == "1":
															a__          =  old_name.replace('''1''' ,     '''batchnorm_before''')
										elif layer == "3":
															a__          =  old_name.replace('''3''' ,     '''convolution2''')
										else:
															a__          =  old_name.replace('''4''' ,     '''batchnorm_after''')

					if "network" in old_name and re.search(r'''\d\.\d''' ,     SCREAMING_SNAKE_CASE_):
										a__          =  r'''\b\d{2}\b'''
										if bool(re.search(SCREAMING_SNAKE_CASE_ ,     SCREAMING_SNAKE_CASE_)):
															a__          =  re.search(r'''\d\.\d\d.''' ,     SCREAMING_SNAKE_CASE_).group()
										else:
															a__          =  re.search(r'''\d\.\d.''' ,     SCREAMING_SNAKE_CASE_).group()
										if int(match[0]) < 6:
															a__          =  old_name.replace(SCREAMING_SNAKE_CASE_ ,     '''''')
															a__          =  trimmed_name.replace('''network''' ,     match[0] + '''.meta4D_layers.blocks.''' + match[2:-1])
															a__          =  '''intermediate_stages.''' + trimmed_name
										else:
															a__          =  old_name.replace(SCREAMING_SNAKE_CASE_ ,     '''''')
															if int(match[2]) < num_meta4D_last_stage:
																				a__          =  trimmed_name.replace('''network''' ,     '''meta4D_layers.blocks.''' + match[2])
															else:
																				a__          =  str(int(match[2]) - num_meta4D_last_stage)
																				a__          =  trimmed_name.replace('''network''' ,     '''meta3D_layers.blocks.''' + layer_index)
																				if "norm1" in old_name:
																									a__          =  trimmed_name.replace('''norm1''' ,     '''layernorm1''')
																				elif "norm2" in old_name:
																									a__          =  trimmed_name.replace('''norm2''' ,     '''layernorm2''')
																				elif "fc1" in old_name:
																									a__          =  trimmed_name.replace('''fc1''' ,     '''linear_in''')
																				elif "fc2" in old_name:
																									a__          =  trimmed_name.replace('''fc2''' ,     '''linear_out''')

															a__          =  '''last_stage.''' + trimmed_name

					elif "network" in old_name and re.search(r'''.\d.''' ,     SCREAMING_SNAKE_CASE_):
										a__          =  old_name.replace('''network''' ,     '''intermediate_stages''')

					if "fc" in new_name:
										a__          =  new_name.replace('''fc''' ,     '''convolution''')
					elif ("norm1" in new_name) and ("layernorm1" not in new_name):
										a__          =  new_name.replace('''norm1''' ,     '''batchnorm_before''')
					elif ("norm2" in new_name) and ("layernorm2" not in new_name):
										a__          =  new_name.replace('''norm2''' ,     '''batchnorm_after''')
					if "proj" in new_name:
										a__          =  new_name.replace('''proj''' ,     '''projection''')
					if "dist_head" in new_name:
										a__          =  new_name.replace('''dist_head''' ,     '''distillation_classifier''')
					elif "head" in new_name:
										a__          =  new_name.replace('''head''' ,     '''classifier''')
					elif "patch_embed" in new_name:
										a__          =  '''efficientformer.''' + new_name
					elif new_name == "norm.weight" or new_name == "norm.bias":
										a__          =  new_name.replace('''norm''' ,     '''layernorm''')
										a__          =  '''efficientformer.''' + new_name
					else:
										a__          =  '''efficientformer.encoder.''' + new_name

					return new_name





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_):
					for key in checkpoint.copy().keys():
										a__          =  checkpoint.pop(SCREAMING_SNAKE_CASE_)
										a__          =  val

					return checkpoint





def        SCREAMING_SNAKE_CASE       (      ):
					a__          =  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
					a__          =  Image.open(requests.get(SCREAMING_SNAKE_CASE_ ,     stream=SCREAMING_SNAKE_CASE_).raw)

					return image





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					a__          =  torch.load(SCREAMING_SNAKE_CASE_ ,     map_location='''cpu''')['''model''']
					a__          =  EfficientFormerConfig.from_json_file(SCREAMING_SNAKE_CASE_)
					a__          =  EfficientFormerForImageClassificationWithTeacher(SCREAMING_SNAKE_CASE_)
					a__          =  '''_'''.join(checkpoint_path.split('''/''')[-1].split('''.''')[0].split('''_''')[:-1])

					a__          =  config.depths[-1] - config.num_metaad_blocks + 1
					a__          =  convert_torch_checkpoint(SCREAMING_SNAKE_CASE_ ,     SCREAMING_SNAKE_CASE_)

					model.load_state_dict(SCREAMING_SNAKE_CASE_)
					model.eval()

					a__          =  {
					    '''bilinear''': PILImageResampling.BILINEAR,
					    '''bicubic''': PILImageResampling.BICUBIC,
					    '''nearest''': PILImageResampling.NEAREST,
					}

					# prepare image
					a__          =  prepare_img()
					a__          =  256
					a__          =  224
					a__          =  EfficientFormerImageProcessor(
					    size={'''shortest_edge''': image_size} ,     crop_size={'''height''': crop_size, '''width''': crop_size} ,     resample=pillow_resamplings['''bicubic'''] ,     )
					a__          =  processor(images=SCREAMING_SNAKE_CASE_ ,     return_tensors='''pt''').pixel_values

					# original processing pipeline
					a__          =  Compose(
					    [
					        Resize(SCREAMING_SNAKE_CASE_ ,     interpolation=pillow_resamplings['''bicubic''']),
					        CenterCrop(SCREAMING_SNAKE_CASE_),
					        ToTensor(),
					        Normalize(SCREAMING_SNAKE_CASE_ ,     SCREAMING_SNAKE_CASE_),
					    ])
					a__          =  image_transforms(SCREAMING_SNAKE_CASE_).unsqueeze(0)

					assert torch.allclose(SCREAMING_SNAKE_CASE_ ,     SCREAMING_SNAKE_CASE_)

					a__          =  model(SCREAMING_SNAKE_CASE_)
					a__          =  outputs.logits

					a__          =  (1, 1000)

					if "l1" in model_name:
										a__          =  torch.Tensor(
										    [-0.1312, 0.4353, -1.0499, -0.5124, 0.4183, -0.6793, -1.3777, -0.0893, -0.7358, -2.4328])
										assert torch.allclose(logits[0, :10] ,     SCREAMING_SNAKE_CASE_ ,     atol=1E-3)
										assert logits.shape == expected_shape
					elif "l3" in model_name:
										a__          =  torch.Tensor(
										    [-1.3150, -1.5456, -1.2556, -0.8496, -0.7127, -0.7897, -0.9728, -0.3052, 0.3751, -0.3127])
										assert torch.allclose(logits[0, :10] ,     SCREAMING_SNAKE_CASE_ ,     atol=1E-3)
										assert logits.shape == expected_shape
					elif "l7" in model_name:
										a__          =  torch.Tensor(
										    [-1.0283, -1.4131, -0.5644, -1.3115, -0.5785, -1.2049, -0.7528, 0.1992, -0.3822, -0.0878])
										assert logits.shape == expected_shape
					else:
										raise ValueError(
										    f'Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7')

					# Save Checkpoints
					Path(SCREAMING_SNAKE_CASE_).mkdir(exist_ok=SCREAMING_SNAKE_CASE_)
					model.save_pretrained(SCREAMING_SNAKE_CASE_)
					print(f'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}')
					processor.save_pretrained(SCREAMING_SNAKE_CASE_)
					print(f'Processor successfuly saved at {pytorch_dump_path}')

					if push_to_hub:
										print('''Pushing model to the hub...''')

										model.push_to_hub(
										    repo_id=f'Bearnardd/{pytorch_dump_path}' ,     commit_message='''Add model''' ,     use_temp_dir=SCREAMING_SNAKE_CASE_ ,     )
										processor.push_to_hub(
										    repo_id=f'Bearnardd/{pytorch_dump_path}' ,     commit_message='''Add image processor''' ,     use_temp_dir=SCREAMING_SNAKE_CASE_ ,     )


if __name__ == "__main__":
	__a       :	List[str]             = argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--pytorch_model_path',
	    default=None,
	    type=str,
	    required=True,
	    help='Path to EfficientFormer pytorch checkpoint.',
	)
	parser.add_argument(
	    '--config_file',
	    default=None,
	    type=str,
	    required=True,
	    help='The json file for EfficientFormer model config.',
	)
	parser.add_argument(
	    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
	)

	parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
	parser.add_argument(
	    '--no-push_to_hub',
	    dest='push_to_hub',
	    action='store_false',
	    help='Do not push model and image processor to the hub',
	)
	parser.set_defaults(push_to_hub=True)

	__a       :	str             = parser.parse_args()
	convert_efficientformer_checkpoint(
	    checkpoint_path=args.pytorch_model_path,
	    efficientformer_config_file=args.config_file,
	    pytorch_dump_path=args.pytorch_dump_path,
	    push_to_hub=args.push_to_hub,
	)










"""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'
	)
	__a       :	Union[str, Any]             = None
__a       :	Union[str, Any]             = {
    '7B': 1_1008,
    '13B': 1_3824,
    '30B': 1_7920,
    '65B': 2_2016,
    '70B': 2_8672,
}
__a       :	List[Any]             = {
    '7B': 1,
    '7Bf': 1,
    '13B': 2,
    '13Bf': 2,
    '30B': 4,
    '65B': 8,
    '70B': 8,
    '70Bf': 8,
}





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





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_):
					with open(lowerCamelCase_ ,     '''r''') as f:
										return json.load(lowerCamelCase_)





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_):
					with open(lowerCamelCase_ ,     '''w''') as f:
										json.dump(lowerCamelCase_ ,     lowerCamelCase_)





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_=True):
					os.makedirs(lowerCamelCase_ ,     exist_ok=lowerCamelCase_)
					a__          =  os.path.join(lowerCamelCase_ ,     '''tmp''')
					os.makedirs(lowerCamelCase_ ,     exist_ok=lowerCamelCase_)

					a__          =  read_json(os.path.join(lowerCamelCase_ ,     '''params.json'''))
					a__          =  NUM_SHARDS[model_size]
					a__          =  params['''n_layers''']
					a__          =  params['''n_heads''']
					a__          =  n_heads // num_shards
					a__          =  params['''dim''']
					a__          =  dim // n_heads
					a__          =  10000.0
					a__          =  1.0 / (base ** (torch.arange(0 ,     lowerCamelCase_ ,     2).float() / dims_per_head))

					if "n_kv_heads" in params:
										a__          =  params['''n_kv_heads''']  # for GQA / MQA
										a__          =  n_heads_per_shard // num_key_value_heads
										a__          =  dim // num_key_value_heads
					else:  # compatibility with other checkpoints
										a__          =  n_heads
										a__          =  n_heads_per_shard
										a__          =  dim

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

					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.)
										a__          =  torch.load(os.path.join(lowerCamelCase_ ,     '''consolidated.00.pth''') ,     map_location='''cpu''')
					else:
										# Sharded
										a__          =  [
										    torch.load(os.path.join(lowerCamelCase_ ,     f'consolidated.{i:02d}.pth') ,     map_location='''cpu''')
										    for i in range(lowerCamelCase_)
										]
					a__          =  0
					a__          =  {'''weight_map''': {}}
					for layer_i in range(lowerCamelCase_):
										a__          =  f'pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin'
										if model_size == "7B":
															# Unsharded
															a__          =  {
															    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.

															a__          =  {
															    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(),
															}
															a__          =  permute(
															    torch.cat(
															        [
															            loaded[i][f'layers.{layer_i}.attention.wq.weight'].view(lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_)
															            for i in range(lowerCamelCase_)
															        ] ,     dim=0 ,     ).reshape(lowerCamelCase_ ,     lowerCamelCase_))
															a__          =  permute(
															    torch.cat(
															        [
															            loaded[i][f'layers.{layer_i}.attention.wk.weight'].view(
															                lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_)
															            for i in range(lowerCamelCase_)
															        ] ,     dim=0 ,     ).reshape(lowerCamelCase_ ,     lowerCamelCase_) ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     )
															a__          =  torch.cat(
															    [
															        loaded[i][f'layers.{layer_i}.attention.wv.weight'].view(
															            lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_)
															        for i in range(lowerCamelCase_)
															    ] ,     dim=0 ,     ).reshape(lowerCamelCase_ ,     lowerCamelCase_)

															a__          =  torch.cat(
															    [loaded[i][f'layers.{layer_i}.attention.wo.weight'] for i in range(lowerCamelCase_)] ,     dim=1)
															a__          =  torch.cat(
															    [loaded[i][f'layers.{layer_i}.feed_forward.w1.weight'] for i in range(lowerCamelCase_)] ,     dim=0)
															a__          =  torch.cat(
															    [loaded[i][f'layers.{layer_i}.feed_forward.w2.weight'] for i in range(lowerCamelCase_)] ,     dim=1)
															a__          =  torch.cat(
															    [loaded[i][f'layers.{layer_i}.feed_forward.w3.weight'] for i in range(lowerCamelCase_)] ,     dim=0)

										a__          =  inv_freq
										for k, v in state_dict.items():
															a__          =  filename
															param_count += v.numel()
										torch.save(lowerCamelCase_ ,     os.path.join(lowerCamelCase_ ,     lowerCamelCase_))

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

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

					# Write configs
					a__          =  {'''total_size''': param_count * 2}
					write_json(lowerCamelCase_ ,     os.path.join(lowerCamelCase_ ,     '''pytorch_model.bin.index.json'''))
					a__          =  params['''ffn_dim_multiplier'''] if '''ffn_dim_multiplier''' in params else 1
					a__          =  params['''multiple_of'''] if '''multiple_of''' in params else 256
					a__          =  LlamaConfig(
					    hidden_size=lowerCamelCase_ ,     intermediate_size=compute_intermediate_size(lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_) ,     num_attention_heads=params['''n_heads'''] ,     num_hidden_layers=params['''n_layers'''] ,     rms_norm_eps=params['''norm_eps'''] ,     num_key_value_heads=lowerCamelCase_ ,     )
					config.save_pretrained(lowerCamelCase_)

					# 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.''')
					a__          =  LlamaForCausalLM.from_pretrained(lowerCamelCase_ ,     torch_dtype=torch.floataa ,     low_cpu_mem_usage=lowerCamelCase_)
					# Avoid saving this as part of the config.
					del model.config._name_or_path

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





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





def        SCREAMING_SNAKE_CASE       (      ):
					a__          =  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=lowerCamelCase_ ,     help='''Whether or not to save using `safetensors`.''')
					a__          =  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 ,     )
					a__          =  os.path.join(args.input_dir ,     '''tokenizer.model''')
					write_tokenizer(args.output_dir ,     lowerCamelCase_)


if __name__ == "__main__":
	main()


