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_           =				None
UpperCAmelCase_           =				{
    '''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_           =				{
    '''7B''': 1,
    '''7Bf''': 1,
    '''13B''': 2,
    '''13Bf''': 2,
    '''30B''': 4,
    '''65B''': 8,
    '''70B''': 8,
    '''70Bf''': 8,
}




def    lowerCAmelCase_  (		lowercase:				Union[str, Any]	,       lowercase:				Optional[Any]=1	,       lowercase:				Any=256		)     ->					Optional[int]:







 '''simple docstring'''
 return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3		)		) + multiple_of - 1) // multiple_of)




def    lowerCAmelCase_  (		lowercase:				Union[str, Any]		)     ->					List[str]:







 '''simple docstring'''
 with open(lowercase	,       '''r'''		) as f:
  return json.load(lowercase		)




def    lowerCAmelCase_  (		lowercase:				str	,       lowercase:				List[Any]		)     ->					Union[str, Any]:







 '''simple docstring'''
 with open(lowercase	,       '''w'''		) as f:
  json.dump(lowercase	,       lowercase		)




def    lowerCAmelCase_  (		lowercase:				List[Any]	,       lowercase:				int	,       lowercase:				Tuple	,       lowercase:				Tuple=True		)     ->					Any:







 '''simple docstring'''
 os.makedirs(lowercase	,       exist_ok=lowercase		)
 _UpperCamelCase:  Tuple    						=			os.path.join(lowercase	,       '''tmp'''		)
 os.makedirs(lowercase	,       exist_ok=lowercase		)

 _UpperCamelCase:  Dict    						=			read_json(os.path.join(lowercase	,       '''params.json'''		)		)
 _UpperCamelCase:  str    						=			NUM_SHARDS[model_size]
 _UpperCamelCase:  int    						=			params['''n_layers''']
 _UpperCamelCase:  List[Any]    						=			params['''n_heads''']
 _UpperCamelCase:  Any    						=			n_heads // num_shards
 _UpperCamelCase:  int    						=			params['''dim''']
 _UpperCamelCase:  Tuple    						=			dim // n_heads
 _UpperCamelCase:  int    						=			10000.0
 _UpperCamelCase:  List[Any]    						=			1.0 / (base ** (torch.arange(0	,       lowercase	,       2		).float() / dims_per_head))

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

 # permute for sliced rotary
 def permute(lowercase:				Tuple	,       lowercase:				Optional[Any]=n_heads	,       lowercase:				int=dim	,       lowercase:				Optional[int]=dim		):
  return w.view(lowercase	,       dima // n_heads // 2	,       2	,       lowercase		).transpose(1	,       2		).reshape(lowercase	,       lowercase		)

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

   _UpperCamelCase:  Tuple    						=			{
       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(),
   }
   _UpperCamelCase:  Tuple    						=			permute(
       torch.cat(
           [
               loaded[i][F"""layers.{layer_i}.attention.wq.weight"""].view(lowercase	,       lowercase	,       lowercase		)
               for i in range(lowercase		)
           ]	,       dim=0	,       ).reshape(lowercase	,       lowercase		)		)
   _UpperCamelCase:  Dict    						=			permute(
       torch.cat(
           [
               loaded[i][F"""layers.{layer_i}.attention.wk.weight"""].view(
                   lowercase	,       lowercase	,       lowercase		)
               for i in range(lowercase		)
           ]	,       dim=0	,       ).reshape(lowercase	,       lowercase		)	,       lowercase	,       lowercase	,       lowercase	,       )
   _UpperCamelCase:  Optional[int]    						=			torch.cat(
       [
           loaded[i][F"""layers.{layer_i}.attention.wv.weight"""].view(
               lowercase	,       lowercase	,       lowercase		)
           for i in range(lowercase		)
       ]	,       dim=0	,       ).reshape(lowercase	,       lowercase		)

   _UpperCamelCase:  Any    						=			torch.cat(
       [loaded[i][F"""layers.{layer_i}.attention.wo.weight"""] for i in range(lowercase		)]	,       dim=1		)
   _UpperCamelCase:  Union[str, Any]    						=			torch.cat(
       [loaded[i][F"""layers.{layer_i}.feed_forward.w1.weight"""] for i in range(lowercase		)]	,       dim=0		)
   _UpperCamelCase:  Dict    						=			torch.cat(
       [loaded[i][F"""layers.{layer_i}.feed_forward.w2.weight"""] for i in range(lowercase		)]	,       dim=1		)
   _UpperCamelCase:  List[Any]    						=			torch.cat(
       [loaded[i][F"""layers.{layer_i}.feed_forward.w3.weight"""] for i in range(lowercase		)]	,       dim=0		)

  _UpperCamelCase:  Any    						=			inv_freq
  for k, v in state_dict.items():
   _UpperCamelCase:  int    						=			filename
   param_count += v.numel()
  torch.save(lowercase	,       os.path.join(lowercase	,       lowercase		)		)

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

 for k, v in state_dict.items():
  _UpperCamelCase:  List[str]    						=			filename
  param_count += v.numel()
 torch.save(lowercase	,       os.path.join(lowercase	,       lowercase		)		)

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

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

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




def    lowerCAmelCase_  (		lowercase:				Union[str, Any]	,       lowercase:				Optional[int]		)     ->					Any:







 '''simple docstring'''
 # Initialize the tokenizer based on the `spm` model
 _UpperCamelCase:  Optional[Any]    						=			LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
 print(F"""Saving a {tokenizer_class.__name__} to {tokenizer_path}."""		)
 _UpperCamelCase:  str    						=			tokenizer_class(lowercase		)
 tokenizer.save_pretrained(lowercase		)




def    lowerCAmelCase_  (		)     ->					Tuple:







 '''simple docstring'''
 _UpperCamelCase:  Optional[Any]    						=			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=lowercase	,       help='''Whether or not to save using `safetensors`.'''		)
 _UpperCamelCase:  List[Any]    						=			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	,       )
 _UpperCamelCase:  Dict    						=			os.path.join(args.input_dir	,       '''tokenizer.model'''		)
 write_tokenizer(args.output_dir	,       lowercase		)


if __name__ == "__main__":
  main()
import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class 			__magic_name__	(		__a   ,						__a   ,						unittest.TestCase     ):





   """simple docstring"""




   lowerCAmelCase   :     List[str]	      = StableDiffusionSAGPipeline
   lowerCAmelCase   :     List[Any]	      = TEXT_TO_IMAGE_PARAMS
   lowerCAmelCase   :     List[str]	      = TEXT_TO_IMAGE_BATCH_PARAMS
   lowerCAmelCase   :     str	      = TEXT_TO_IMAGE_IMAGE_PARAMS
   lowerCAmelCase   :     int	      = TEXT_TO_IMAGE_IMAGE_PARAMS
   lowerCAmelCase   :     int	      = False



   def 	lowerCAmelCase    (						self       :	Optional[Any]				):



    """simple docstring"""



    torch.manual_seed(0				)
    _UpperCamelCase:  Dict    						=			UNetaDConditionModel(
        block_out_channels=(32, 64)		,	layers_per_block=2		,	sample_size=32		,	in_channels=4		,	out_channels=4		,	down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')		,	up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')		,	cross_attention_dim=32		,	)
    _UpperCamelCase:  List[Any]    						=			DDIMScheduler(
        beta_start=0.00085		,	beta_end=0.012		,	beta_schedule='''scaled_linear'''		,	clip_sample=_lowercase		,	set_alpha_to_one=_lowercase		,	)
    torch.manual_seed(0				)
    _UpperCamelCase:  Optional[int]    						=			AutoencoderKL(
        block_out_channels=[32, 64]		,	in_channels=3		,	out_channels=3		,	down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']		,	up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']		,	latent_channels=4		,	)
    torch.manual_seed(0				)
    _UpperCamelCase:  int    						=			CLIPTextConfig(
        bos_token_id=0		,	eos_token_id=2		,	hidden_size=32		,	intermediate_size=37		,	layer_norm_eps=1E-05		,	num_attention_heads=4		,	num_hidden_layers=5		,	pad_token_id=1		,	vocab_size=1_000		,	)
    _UpperCamelCase:  Any    						=			CLIPTextModel(_lowercase				)
    _UpperCamelCase:  List[str]    						=			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

    _UpperCamelCase:  int    						=			{
        '''unet''': unet,
        '''scheduler''': scheduler,
        '''vae''': vae,
        '''text_encoder''': text_encoder,
        '''tokenizer''': tokenizer,
        '''safety_checker''': None,
        '''feature_extractor''': None,
    }
    return components



   def 	lowerCAmelCase    (						self       :	Dict		,	_lowercase       :	List[Any]		,	_lowercase       :	int=0				):



    """simple docstring"""



    if str(_lowercase				).startswith('''mps'''				):
     _UpperCamelCase:  Tuple    						=			torch.manual_seed(_lowercase				)
    else:
     _UpperCamelCase:  List[str]    						=			torch.Generator(device=_lowercase				).manual_seed(_lowercase				)
    _UpperCamelCase:  Dict    						=			{
        '''prompt''': '''.''',
        '''generator''': generator,
        '''num_inference_steps''': 2,
        '''guidance_scale''': 1.0,
        '''sag_scale''': 1.0,
        '''output_type''': '''numpy''',
    }
    return inputs



   def 	lowerCAmelCase    (						self       :	Dict				):



    """simple docstring"""



    super().test_inference_batch_single_identical(expected_max_diff=3E-3				)




@slow
@require_torch_gpu
class 			__magic_name__	(		unittest.TestCase     ):





   """simple docstring"""




   def 	lowerCAmelCase    (						self       :	Union[str, Any]				):



    """simple docstring"""



    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()



   def 	lowerCAmelCase    (						self       :	int				):



    """simple docstring"""



    _UpperCamelCase:  Any    						=			StableDiffusionSAGPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''				)
    _UpperCamelCase:  Any    						=			sag_pipe.to(_lowercase				)
    sag_pipe.set_progress_bar_config(disable=_lowercase				)

    _UpperCamelCase:  Tuple    						=			'''.'''
    _UpperCamelCase:  Tuple    						=			torch.manual_seed(0				)
    _UpperCamelCase:  Union[str, Any]    						=			sag_pipe(
        [prompt]		,	generator=_lowercase		,	guidance_scale=7.5		,	sag_scale=1.0		,	num_inference_steps=20		,	output_type='''np'''				)

    _UpperCamelCase:  Optional[Any]    						=			output.images

    _UpperCamelCase:  Tuple    						=			image[0, -3:, -3:, -1]

    assert image.shape == (1, 512, 512, 3)
    _UpperCamelCase:  Any    						=			np.array([0.1568, 0.1738, 0.1695, 0.1693, 0.1507, 0.1705, 0.1547, 0.1751, 0.1949]				)

    assert np.abs(image_slice.flatten() - expected_slice				).max() < 5E-2



   def 	lowerCAmelCase    (						self       :	Dict				):



    """simple docstring"""



    _UpperCamelCase:  Optional[Any]    						=			StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''				)
    _UpperCamelCase:  int    						=			sag_pipe.to(_lowercase				)
    sag_pipe.set_progress_bar_config(disable=_lowercase				)

    _UpperCamelCase:  List[Any]    						=			'''.'''
    _UpperCamelCase:  str    						=			torch.manual_seed(0				)
    _UpperCamelCase:  Dict    						=			sag_pipe(
        [prompt]		,	generator=_lowercase		,	guidance_scale=7.5		,	sag_scale=1.0		,	num_inference_steps=20		,	output_type='''np'''				)

    _UpperCamelCase:  Union[str, Any]    						=			output.images

    _UpperCamelCase:  List[str]    						=			image[0, -3:, -3:, -1]

    assert image.shape == (1, 512, 512, 3)
    _UpperCamelCase:  int    						=			np.array([0.3459, 0.2876, 0.2537, 0.3002, 0.2671, 0.2160, 0.3026, 0.2262, 0.2371]				)

    assert np.abs(image_slice.flatten() - expected_slice				).max() < 5E-2



   def 	lowerCAmelCase    (						self       :	Any				):



    """simple docstring"""



    _UpperCamelCase:  Tuple    						=			StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''				)
    _UpperCamelCase:  int    						=			sag_pipe.to(_lowercase				)
    sag_pipe.set_progress_bar_config(disable=_lowercase				)

    _UpperCamelCase:  Any    						=			'''.'''
    _UpperCamelCase:  List[Any]    						=			torch.manual_seed(0				)
    _UpperCamelCase:  str    						=			sag_pipe(
        [prompt]		,	width=768		,	height=512		,	generator=_lowercase		,	guidance_scale=7.5		,	sag_scale=1.0		,	num_inference_steps=20		,	output_type='''np'''		,	)

    _UpperCamelCase:  str    						=			output.images

    assert image.shape == (1, 512, 768, 3)