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__    							= {
    '169M': 1_2,
    '430M': 2_4,
    '1B5': 2_4,
    '3B': 3_2,
    '7B': 3_2,
    '14B': 4_0,
}

UpperCamelCase__    							= {
    '169M': 7_6_8,
    '430M': 1_0_2_4,
    '1B5': 2_0_4_8,
    '3B': 2_5_6_0,
    '7B': 4_0_9_6,
    '14B': 5_1_2_0,
}




def        lowerCAmelCase_			(					__A							)					->    Dict:







      '''simple docstring'''


      UpperCAmelCase__								=  list(state_dict.keys()							)
      for name in state_dict_keys:
            UpperCAmelCase__								=  state_dict.pop(__A							)
            # emb -> embedding
            if name.startswith("emb."							):
                  UpperCAmelCase__								=  name.replace("emb.",  "embeddings."							)
            # ln_0 -> pre_ln (only present at block 0)
            if name.startswith("blocks.0.ln0"							):
                  UpperCAmelCase__								=  name.replace("blocks.0.ln0",  "blocks.0.pre_ln"							)
            # att -> attention
            UpperCAmelCase__								=  re.sub(r"blocks\.(\d+)\.att",  r"blocks.\1.attention",  __A							)
            # ffn -> feed_forward
            UpperCAmelCase__								=  re.sub(r"blocks\.(\d+)\.ffn",  r"blocks.\1.feed_forward",  __A							)
            # time_mix_k -> time_mix_key and reshape
            if name.endswith(".time_mix_k"							):
                  UpperCAmelCase__								=  name.replace(".time_mix_k",  ".time_mix_key"							)
            # time_mix_v -> time_mix_value and reshape
            if name.endswith(".time_mix_v"							):
                  UpperCAmelCase__								=  name.replace(".time_mix_v",  ".time_mix_value"							)
            # time_mix_r -> time_mix_key and reshape
            if name.endswith(".time_mix_r"							):
                  UpperCAmelCase__								=  name.replace(".time_mix_r",  ".time_mix_receptance"							)

            if name != "head.weight":
                  UpperCAmelCase__								=  "rwkv." + name

            UpperCAmelCase__								=  weight
      return state_dict




def        lowerCAmelCase_			(					__A,  __A,  __A,  __A=None,  __A=None,  __A=False,  __A=None							)					->    Union[str, Any]:







      '''simple docstring'''


      if tokenizer_file is None:
            print("No `--tokenizer_file` provided, we will use the default tokenizer."							)
            UpperCAmelCase__								=  50_277
            UpperCAmelCase__								=  AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b"							)
      else:
            UpperCAmelCase__								=  PreTrainedTokenizerFast(tokenizer_file=__A							)
            UpperCAmelCase__								=  len(__A							)
      tokenizer.save_pretrained(__A							)

      # 2. Build the config
      UpperCAmelCase__								=  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__								=  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__								=  RwkvConfig(
          vocab_size=__A,  num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size],  hidden_size=HIDEN_SIZE_MAPPING[size],  )
      config.save_pretrained(__A							)

      # 3. Download model file then convert state_dict
      UpperCAmelCase__								=  hf_hub_download(__A,  __A							)
      UpperCAmelCase__								=  torch.load(__A,  map_location="cpu"							)
      UpperCAmelCase__								=  convert_state_dict(__A							)

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

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

            # 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__								=  list(shards.keys()							)

            del state_dict
            del shards
            gc.collect()

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

      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__								=  AutoModelForCausalLM.from_pretrained(__A							)
            model.push_to_hub(__A,  max_shard_size="2GB"							)
            tokenizer.push_to_hub(__A							)


if __name__ == "__main__":
      UpperCamelCase__    							= 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__    							= 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,
      )




from typing import Any, Callable, Dict, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker


UpperCamelCase__    							= 'CompVis/stable-diffusion-v1-1'
UpperCamelCase__    							= 'CompVis/stable-diffusion-v1-2'
UpperCamelCase__    							= 'CompVis/stable-diffusion-v1-3'
UpperCamelCase__    							= 'CompVis/stable-diffusion-v1-4'





class 					A							(    UpperCAmelCase_					):


   def __init__(self					:						Union[str, Any]	,  __UpperCAmelCase					:						AutoencoderKL	,  __UpperCAmelCase					:						CLIPTextModel	,  __UpperCAmelCase					:						CLIPTokenizer	,  __UpperCAmelCase					:						UNetaDConditionModel	,  __UpperCAmelCase					:						Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]	,  __UpperCAmelCase					:						StableDiffusionSafetyChecker	,  __UpperCAmelCase					:						CLIPImageProcessor	,  __UpperCAmelCase					:						bool = True	,  )						->    Tuple:
         """simple docstring"""



         super()._init_()

         UpperCAmelCase__								=  StableDiffusionPipeline.from_pretrained(__UpperCAmelCase		)
         UpperCAmelCase__								=  StableDiffusionPipeline.from_pretrained(__UpperCAmelCase		)
         UpperCAmelCase__								=  StableDiffusionPipeline.from_pretrained(__UpperCAmelCase		)
         UpperCAmelCase__								=  StableDiffusionPipeline(
             vae=__UpperCAmelCase	,  text_encoder=__UpperCAmelCase	,  tokenizer=__UpperCAmelCase	,  unet=__UpperCAmelCase	,  scheduler=__UpperCAmelCase	,  safety_checker=__UpperCAmelCase	,  feature_extractor=__UpperCAmelCase	,  requires_safety_checker=__UpperCAmelCase	,  )

         self.register_modules(pipelinea=self.pipea	,  pipelinea=self.pipea	,  pipelinea=self.pipea	,  pipelinea=self.pipea		)


   @property
   def       lowercase_				(self					:						int		)						->    Dict[str, Any]:
         """simple docstring"""



         return {k: getattr(self	,  __UpperCAmelCase		) for k in self.config.keys() if not k.startswith("_"		)}


   def       lowercase_				(self					:						Union[str, Any]	,  __UpperCAmelCase					:						Optional[Union[str, int]] = "auto"		)						->    Optional[int]:
         """simple docstring"""



         if slice_size == "auto":
               # half the attention head size is usually a good trade-off between
               # speed and memory
               UpperCAmelCase__								=  self.unet.config.attention_head_dim // 2
         self.unet.set_attention_slice(__UpperCAmelCase		)


   def       lowercase_				(self					:						int		)						->    Optional[int]:
         """simple docstring"""



         self.enable_attention_slicing(__UpperCAmelCase		)


   @torch.no_grad()
   def       lowercase_				(self					:						Optional[int]	,  __UpperCAmelCase					:						Union[str, List[str]]	,  __UpperCAmelCase					:						int = 5_1_2	,  __UpperCAmelCase					:						int = 5_1_2	,  __UpperCAmelCase					:						int = 5_0	,  __UpperCAmelCase					:						float = 7.5	,  __UpperCAmelCase					:						Optional[Union[str, List[str]]] = None	,  __UpperCAmelCase					:						Optional[int] = 1	,  __UpperCAmelCase					:						float = 0.0	,  __UpperCAmelCase					:						Optional[torch.Generator] = None	,  __UpperCAmelCase					:						Optional[torch.FloatTensor] = None	,  __UpperCAmelCase					:						Optional[str] = "pil"	,  __UpperCAmelCase					:						bool = True	,  __UpperCAmelCase					:						Optional[Callable[[int, int, torch.FloatTensor], None]] = None	,  __UpperCAmelCase					:						int = 1	,  **__UpperCAmelCase					:						Any	,  )						->    Dict:
         """simple docstring"""



         return self.pipea(
             prompt=__UpperCAmelCase	,  height=__UpperCAmelCase	,  width=__UpperCAmelCase	,  num_inference_steps=__UpperCAmelCase	,  guidance_scale=__UpperCAmelCase	,  negative_prompt=__UpperCAmelCase	,  num_images_per_prompt=__UpperCAmelCase	,  eta=__UpperCAmelCase	,  generator=__UpperCAmelCase	,  latents=__UpperCAmelCase	,  output_type=__UpperCAmelCase	,  return_dict=__UpperCAmelCase	,  callback=__UpperCAmelCase	,  callback_steps=__UpperCAmelCase	,  **__UpperCAmelCase	,  )


   @torch.no_grad()
   def       lowercase_				(self					:						List[str]	,  __UpperCAmelCase					:						Union[str, List[str]]	,  __UpperCAmelCase					:						int = 5_1_2	,  __UpperCAmelCase					:						int = 5_1_2	,  __UpperCAmelCase					:						int = 5_0	,  __UpperCAmelCase					:						float = 7.5	,  __UpperCAmelCase					:						Optional[Union[str, List[str]]] = None	,  __UpperCAmelCase					:						Optional[int] = 1	,  __UpperCAmelCase					:						float = 0.0	,  __UpperCAmelCase					:						Optional[torch.Generator] = None	,  __UpperCAmelCase					:						Optional[torch.FloatTensor] = None	,  __UpperCAmelCase					:						Optional[str] = "pil"	,  __UpperCAmelCase					:						bool = True	,  __UpperCAmelCase					:						Optional[Callable[[int, int, torch.FloatTensor], None]] = None	,  __UpperCAmelCase					:						int = 1	,  **__UpperCAmelCase					:						Optional[Any]	,  )						->    Any:
         """simple docstring"""



         return self.pipea(
             prompt=__UpperCAmelCase	,  height=__UpperCAmelCase	,  width=__UpperCAmelCase	,  num_inference_steps=__UpperCAmelCase	,  guidance_scale=__UpperCAmelCase	,  negative_prompt=__UpperCAmelCase	,  num_images_per_prompt=__UpperCAmelCase	,  eta=__UpperCAmelCase	,  generator=__UpperCAmelCase	,  latents=__UpperCAmelCase	,  output_type=__UpperCAmelCase	,  return_dict=__UpperCAmelCase	,  callback=__UpperCAmelCase	,  callback_steps=__UpperCAmelCase	,  **__UpperCAmelCase	,  )


   @torch.no_grad()
   def       lowercase_				(self					:						List[str]	,  __UpperCAmelCase					:						Union[str, List[str]]	,  __UpperCAmelCase					:						int = 5_1_2	,  __UpperCAmelCase					:						int = 5_1_2	,  __UpperCAmelCase					:						int = 5_0	,  __UpperCAmelCase					:						float = 7.5	,  __UpperCAmelCase					:						Optional[Union[str, List[str]]] = None	,  __UpperCAmelCase					:						Optional[int] = 1	,  __UpperCAmelCase					:						float = 0.0	,  __UpperCAmelCase					:						Optional[torch.Generator] = None	,  __UpperCAmelCase					:						Optional[torch.FloatTensor] = None	,  __UpperCAmelCase					:						Optional[str] = "pil"	,  __UpperCAmelCase					:						bool = True	,  __UpperCAmelCase					:						Optional[Callable[[int, int, torch.FloatTensor], None]] = None	,  __UpperCAmelCase					:						int = 1	,  **__UpperCAmelCase					:						Any	,  )						->    Dict:
         """simple docstring"""



         return self.pipea(
             prompt=__UpperCAmelCase	,  height=__UpperCAmelCase	,  width=__UpperCAmelCase	,  num_inference_steps=__UpperCAmelCase	,  guidance_scale=__UpperCAmelCase	,  negative_prompt=__UpperCAmelCase	,  num_images_per_prompt=__UpperCAmelCase	,  eta=__UpperCAmelCase	,  generator=__UpperCAmelCase	,  latents=__UpperCAmelCase	,  output_type=__UpperCAmelCase	,  return_dict=__UpperCAmelCase	,  callback=__UpperCAmelCase	,  callback_steps=__UpperCAmelCase	,  **__UpperCAmelCase	,  )


   @torch.no_grad()
   def       lowercase_				(self					:						Union[str, Any]	,  __UpperCAmelCase					:						Union[str, List[str]]	,  __UpperCAmelCase					:						int = 5_1_2	,  __UpperCAmelCase					:						int = 5_1_2	,  __UpperCAmelCase					:						int = 5_0	,  __UpperCAmelCase					:						float = 7.5	,  __UpperCAmelCase					:						Optional[Union[str, List[str]]] = None	,  __UpperCAmelCase					:						Optional[int] = 1	,  __UpperCAmelCase					:						float = 0.0	,  __UpperCAmelCase					:						Optional[torch.Generator] = None	,  __UpperCAmelCase					:						Optional[torch.FloatTensor] = None	,  __UpperCAmelCase					:						Optional[str] = "pil"	,  __UpperCAmelCase					:						bool = True	,  __UpperCAmelCase					:						Optional[Callable[[int, int, torch.FloatTensor], None]] = None	,  __UpperCAmelCase					:						int = 1	,  **__UpperCAmelCase					:						Union[str, Any]	,  )						->    List[str]:
         """simple docstring"""



         return self.pipea(
             prompt=__UpperCAmelCase	,  height=__UpperCAmelCase	,  width=__UpperCAmelCase	,  num_inference_steps=__UpperCAmelCase	,  guidance_scale=__UpperCAmelCase	,  negative_prompt=__UpperCAmelCase	,  num_images_per_prompt=__UpperCAmelCase	,  eta=__UpperCAmelCase	,  generator=__UpperCAmelCase	,  latents=__UpperCAmelCase	,  output_type=__UpperCAmelCase	,  return_dict=__UpperCAmelCase	,  callback=__UpperCAmelCase	,  callback_steps=__UpperCAmelCase	,  **__UpperCAmelCase	,  )





   @torch.no_grad()
   def       lowercase_				(self					:						int	,  __UpperCAmelCase					:						Union[str, List[str]]	,  __UpperCAmelCase					:						int = 5_1_2	,  __UpperCAmelCase					:						int = 5_1_2	,  __UpperCAmelCase					:						int = 5_0	,  __UpperCAmelCase					:						float = 7.5	,  __UpperCAmelCase					:						Optional[Union[str, List[str]]] = None	,  __UpperCAmelCase					:						Optional[int] = 1	,  __UpperCAmelCase					:						float = 0.0	,  __UpperCAmelCase					:						Optional[torch.Generator] = None	,  __UpperCAmelCase					:						Optional[torch.FloatTensor] = None	,  __UpperCAmelCase					:						Optional[str] = "pil"	,  __UpperCAmelCase					:						bool = True	,  __UpperCAmelCase					:						Optional[Callable[[int, int, torch.FloatTensor], None]] = None	,  __UpperCAmelCase					:						int = 1	,  **__UpperCAmelCase					:						Any	,  )						->    Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__								=  "cuda" if torch.cuda.is_available() else "cpu"
         self.to(__UpperCAmelCase		)

         # Checks if the height and width are divisible by 8 or not
         if height % 8 != 0 or width % 8 != 0:
               raise ValueError(f"""`height` and `width` must be divisible by 8 but are {height} and {width}."""		)

         # Get first result from Stable Diffusion Checkpoint v1.1
         UpperCAmelCase__								=  self.textaimg_sda_a(
             prompt=__UpperCAmelCase	,  height=__UpperCAmelCase	,  width=__UpperCAmelCase	,  num_inference_steps=__UpperCAmelCase	,  guidance_scale=__UpperCAmelCase	,  negative_prompt=__UpperCAmelCase	,  num_images_per_prompt=__UpperCAmelCase	,  eta=__UpperCAmelCase	,  generator=__UpperCAmelCase	,  latents=__UpperCAmelCase	,  output_type=__UpperCAmelCase	,  return_dict=__UpperCAmelCase	,  callback=__UpperCAmelCase	,  callback_steps=__UpperCAmelCase	,  **__UpperCAmelCase	,  )

         # Get first result from Stable Diffusion Checkpoint v1.2
         UpperCAmelCase__								=  self.textaimg_sda_a(
             prompt=__UpperCAmelCase	,  height=__UpperCAmelCase	,  width=__UpperCAmelCase	,  num_inference_steps=__UpperCAmelCase	,  guidance_scale=__UpperCAmelCase	,  negative_prompt=__UpperCAmelCase	,  num_images_per_prompt=__UpperCAmelCase	,  eta=__UpperCAmelCase	,  generator=__UpperCAmelCase	,  latents=__UpperCAmelCase	,  output_type=__UpperCAmelCase	,  return_dict=__UpperCAmelCase	,  callback=__UpperCAmelCase	,  callback_steps=__UpperCAmelCase	,  **__UpperCAmelCase	,  )

         # Get first result from Stable Diffusion Checkpoint v1.3
         UpperCAmelCase__								=  self.textaimg_sda_a(
             prompt=__UpperCAmelCase	,  height=__UpperCAmelCase	,  width=__UpperCAmelCase	,  num_inference_steps=__UpperCAmelCase	,  guidance_scale=__UpperCAmelCase	,  negative_prompt=__UpperCAmelCase	,  num_images_per_prompt=__UpperCAmelCase	,  eta=__UpperCAmelCase	,  generator=__UpperCAmelCase	,  latents=__UpperCAmelCase	,  output_type=__UpperCAmelCase	,  return_dict=__UpperCAmelCase	,  callback=__UpperCAmelCase	,  callback_steps=__UpperCAmelCase	,  **__UpperCAmelCase	,  )

         # Get first result from Stable Diffusion Checkpoint v1.4
         UpperCAmelCase__								=  self.textaimg_sda_a(
             prompt=__UpperCAmelCase	,  height=__UpperCAmelCase	,  width=__UpperCAmelCase	,  num_inference_steps=__UpperCAmelCase	,  guidance_scale=__UpperCAmelCase	,  negative_prompt=__UpperCAmelCase	,  num_images_per_prompt=__UpperCAmelCase	,  eta=__UpperCAmelCase	,  generator=__UpperCAmelCase	,  latents=__UpperCAmelCase	,  output_type=__UpperCAmelCase	,  return_dict=__UpperCAmelCase	,  callback=__UpperCAmelCase	,  callback_steps=__UpperCAmelCase	,  **__UpperCAmelCase	,  )

         # Get all result images into a single list and pass it via StableDiffusionPipelineOutput for final result
         return StableDiffusionPipelineOutput([resa[0], resa[0], resa[0], resa[0]]		)



