






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': 12,
    '430M': 24,
    '1B5': 24,
    '3B': 32,
    '7B': 32,
    '14B': 40,
}

UpperCamelCase    							=      {
    '169M': 768,
    '430M': 1024,
    '1B5': 2048,
    '3B': 2560,
    '7B': 4096,
    '14B': 5120,
}


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Optional[Any]							)     ->  str:
   __UpperCamelCase	:			Optional[int]					=					list(state_dict.keys()							)
   for name in state_dict_keys:
      __UpperCamelCase	:			Dict					=					state_dict.pop(__lowerCAmelCase							)
      # emb -> embedding
      if name.startswith("""emb."""							):
         __UpperCamelCase	:			str					=					name.replace("""emb."""					,							"""embeddings."""							)
      # ln_0 -> pre_ln (only present at block 0)
      if name.startswith("""blocks.0.ln0"""							):
         __UpperCamelCase	:			Dict					=					name.replace("""blocks.0.ln0"""					,							"""blocks.0.pre_ln"""							)
      # att -> attention
      __UpperCamelCase	:			List[Any]					=					re.sub(R"""blocks\.(\d+)\.att"""					,							R"""blocks.\1.attention"""					,							__lowerCAmelCase							)
      # ffn -> feed_forward
      __UpperCamelCase	:			List[str]					=					re.sub(R"""blocks\.(\d+)\.ffn"""					,							R"""blocks.\1.feed_forward"""					,							__lowerCAmelCase							)
      # 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	:			Dict					=					name.replace(""".time_mix_r"""					,							""".time_mix_receptance"""							)

      if name != "head.weight":
         __UpperCamelCase	:			str					=					"""rwkv.""" + name

      __UpperCamelCase	:			List[Any]					=					weight
   return state_dict


def 				__lowerCamelCase     (							__lowerCAmelCase							:					str					,							__lowerCAmelCase							:					int					,							__lowerCAmelCase							:					Dict					,							__lowerCAmelCase							:					Union[str, Any]=None					,							__lowerCAmelCase							:					str=None					,							__lowerCAmelCase							:					Optional[Any]=False					,							__lowerCAmelCase							:					List[str]=None							)     ->  int:
   # 1. If possible, build the tokenizer.
   if tokenizer_file is None:
      print("""No `--tokenizer_file` provided, we will use the default tokenizer."""							)
      __UpperCamelCase	:			Union[str, Any]					=					50277
      __UpperCamelCase	:			Union[str, Any]					=					AutoTokenizer.from_pretrained("""EleutherAI/gpt-neox-20b"""							)
   else:
      __UpperCamelCase	:			Dict					=					PreTrainedTokenizerFast(tokenizer_file=__lowerCAmelCase							)
      __UpperCamelCase	:			Tuple					=					len(__lowerCAmelCase							)
   tokenizer.save_pretrained(__lowerCAmelCase							)

   # 2. Build the config
   __UpperCamelCase	:			Dict					=					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	:			Any					=					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	:			Any					=					RwkvConfig(
       vocab_size=__lowerCAmelCase					,							num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]					,							hidden_size=HIDEN_SIZE_MAPPING[size]					,							)
   config.save_pretrained(__lowerCAmelCase							)

   # 3. Download model file then convert state_dict
   __UpperCamelCase	:			Dict					=					hf_hub_download(__lowerCAmelCase					,							__lowerCAmelCase							)
   __UpperCamelCase	:			int					=					torch.load(__lowerCAmelCase					,							map_location="""cpu"""							)
   __UpperCamelCase	:			int					=					convert_state_dict(__lowerCAmelCase							)

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

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

      # 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	:			Any					=					list(shards.keys()							)

      del state_dict
      del shards
      gc.collect()

      for shard_file in shard_files:
         __UpperCamelCase	:			List[str]					=					torch.load(os.path.join(__lowerCAmelCase					,							__lowerCAmelCase							)							)
         torch.save({k: v.cpu().clone() for k, v in state_dict.items()}					,							os.path.join(__lowerCAmelCase					,							__lowerCAmelCase							)							)

   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	:			Any					=					AutoModelForCausalLM.from_pretrained(__lowerCAmelCase							)
      model.push_to_hub(__lowerCAmelCase					,							max_shard_size="""2GB"""							)
      tokenizer.push_to_hub(__lowerCAmelCase							)


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,
       )











import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class 		_A						( UpperCAmelCase_	,				unittest.TestCase ):
  lowercase_						:			int						=					KandinskyVaaPipeline
  lowercase_						:			Union[str, Any]						=					[
      '''image_embeds''',
      '''negative_image_embeds''',
  ]
  lowercase_						:			int						=					['''image_embeds''', '''negative_image_embeds''']
  lowercase_						:			str						=					[
      '''generator''',
      '''height''',
      '''width''',
      '''latents''',
      '''guidance_scale''',
      '''num_inference_steps''',
      '''return_dict''',
      '''guidance_scale''',
      '''num_images_per_prompt''',
      '''output_type''',
      '''return_dict''',
  ]
  lowercase_						:			str						=					False

  @property
  def    a					(    self						:	int			):
     """simple docstring"""

     return 32

  @property
  def    a					(    self						:	str			):
     """simple docstring"""

     return 32

  @property
  def    a					(    self						:	Optional[int]			):
     """simple docstring"""

     return self.time_input_dim

  @property
  def    a					(    self						:	Optional[int]			):
     """simple docstring"""

     return self.time_input_dim * 4

  @property
  def    a					(    self						:	Optional[Any]			):
     """simple docstring"""

     return 1_00

  @property
  def    a					(    self						:	str			):
     """simple docstring"""

     torch.manual_seed(0			)

     __UpperCamelCase	:			int					=					{
         """in_channels""": 4,
         # Out channels is double in channels because predicts mean and variance
         """out_channels""": 8,
         """addition_embed_type""": """image""",
         """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
         """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
         """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
         """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
         """layers_per_block""": 1,
         """encoder_hid_dim""": self.text_embedder_hidden_size,
         """encoder_hid_dim_type""": """image_proj""",
         """cross_attention_dim""": self.cross_attention_dim,
         """attention_head_dim""": 4,
         """resnet_time_scale_shift""": """scale_shift""",
         """class_embed_type""": None,
     }

     __UpperCamelCase	:			Any					=					UNetaDConditionModel(**lowerCamelCase__			)
     return model

  @property
  def    a					(    self						:	Union[str, Any]			):
     """simple docstring"""

     return {
         "block_out_channels": [32, 64],
         "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
         "in_channels": 3,
         "latent_channels": 4,
         "layers_per_block": 1,
         "norm_num_groups": 8,
         "norm_type": "spatial",
         "num_vq_embeddings": 12,
         "out_channels": 3,
         "up_block_types": [
             "AttnUpDecoderBlock2D",
             "UpDecoderBlock2D",
         ],
         "vq_embed_dim": 4,
     }

  @property
  def    a					(    self						:	int			):
     """simple docstring"""

     torch.manual_seed(0			)
     __UpperCamelCase	:			Tuple					=					VQModel(**self.dummy_movq_kwargs			)
     return model

  def    a					(    self						:	Tuple			):
     """simple docstring"""

     __UpperCamelCase	:			str					=					self.dummy_unet
     __UpperCamelCase	:			Optional[int]					=					self.dummy_movq

     __UpperCamelCase	:			List[str]					=					DDIMScheduler(
         num_train_timesteps=10_00   ,				beta_schedule="""linear"""   ,				beta_start=0.0_0085   ,				beta_end=0.012   ,				clip_sample=lowerCamelCase__   ,				set_alpha_to_one=lowerCamelCase__   ,				steps_offset=1   ,				prediction_type="""epsilon"""   ,				thresholding=lowerCamelCase__   ,				)

     __UpperCamelCase	:			str					=					{
         """unet""": unet,
         """scheduler""": scheduler,
         """movq""": movq,
     }
     return components

  def    a					(    self						:	Optional[int]   ,				lowerCamelCase__						:	Optional[Any]   ,				lowerCamelCase__						:	Any=0			):
     """simple docstring"""

     __UpperCamelCase	:			Tuple					=					floats_tensor((1, self.text_embedder_hidden_size)   ,				rng=random.Random(lowerCamelCase__			)			).to(lowerCamelCase__			)
     __UpperCamelCase	:			Optional[int]					=					floats_tensor((1, self.text_embedder_hidden_size)   ,				rng=random.Random(seed + 1			)			).to(
         lowerCamelCase__			)
     if str(lowerCamelCase__			).startswith("""mps"""			):
        __UpperCamelCase	:			Optional[int]					=					torch.manual_seed(lowerCamelCase__			)
     else:
        __UpperCamelCase	:			int					=					torch.Generator(device=lowerCamelCase__			).manual_seed(lowerCamelCase__			)
     __UpperCamelCase	:			Dict					=					{
         """image_embeds""": image_embeds,
         """negative_image_embeds""": negative_image_embeds,
         """generator""": generator,
         """height""": 64,
         """width""": 64,
         """guidance_scale""": 4.0,
         """num_inference_steps""": 2,
         """output_type""": """np""",
     }
     return inputs

  def    a					(    self						:	Dict			):
     """simple docstring"""

     __UpperCamelCase	:			Dict					=					"""cpu"""

     __UpperCamelCase	:			Dict					=					self.get_dummy_components()

     __UpperCamelCase	:			Union[str, Any]					=					self.pipeline_class(**lowerCamelCase__			)
     __UpperCamelCase	:			int					=					pipe.to(lowerCamelCase__			)

     pipe.set_progress_bar_config(disable=lowerCamelCase__			)

     __UpperCamelCase	:			List[Any]					=					pipe(**self.get_dummy_inputs(lowerCamelCase__			)			)
     __UpperCamelCase	:			List[Any]					=					output.images

     __UpperCamelCase	:			int					=					pipe(
         **self.get_dummy_inputs(lowerCamelCase__			)   ,				return_dict=lowerCamelCase__   ,				)[0]

     __UpperCamelCase	:			List[str]					=					image[0, -3:, -3:, -1]
     __UpperCamelCase	:			Tuple					=					image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)

     __UpperCamelCase	:			int					=					np.array(
         [0.623_7976, 1.0, 0.3644_1332, 1.0, 0.7063_9634, 0.2987_7186, 0.8565_2125, 0.521_6843, 0.5445_4046]			)

     assert (
         np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
     ), f' expected_slice {expected_slice}, but got {image_slice.flatten()}'

     assert (
         np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1e-2
     ), f' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'







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

  def    a					(    self						:	Optional[int]			):
     """simple docstring"""

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

  def    a					(    self						:	List[Any]			):
     """simple docstring"""

     __UpperCamelCase	:			int					=					load_numpy(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
         """/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy"""			)

     __UpperCamelCase	:			List[str]					=					KandinskyVaaPriorPipeline.from_pretrained(
         """kandinsky-community/kandinsky-2-2-prior"""   ,				torch_dtype=torch.floataa			)
     pipe_prior.to(lowerCamelCase__			)

     __UpperCamelCase	:			int					=					KandinskyVaaPipeline.from_pretrained(
         """kandinsky-community/kandinsky-2-2-decoder"""   ,				torch_dtype=torch.floataa			)
     __UpperCamelCase	:			Optional[int]					=					pipeline.to(lowerCamelCase__			)
     pipeline.set_progress_bar_config(disable=lowerCamelCase__			)

     __UpperCamelCase	:			Optional[Any]					=					"""red cat, 4k photo"""

     __UpperCamelCase	:			Tuple					=					torch.Generator(device="""cuda"""			).manual_seed(0			)
     __UpperCamelCase	,     __UpperCamelCase	:			Union[str, Any]					=					pipe_prior(
         lowerCamelCase__   ,				generator=lowerCamelCase__   ,				num_inference_steps=5   ,				negative_prompt=""""""   ,				).to_tuple()

     __UpperCamelCase	:			Any					=					torch.Generator(device="""cuda"""			).manual_seed(0			)
     __UpperCamelCase	:			Union[str, Any]					=					pipeline(
         image_embeds=lowerCamelCase__   ,				negative_image_embeds=lowerCamelCase__   ,				generator=lowerCamelCase__   ,				num_inference_steps=1_00   ,				output_type="""np"""   ,				)

     __UpperCamelCase	:			Any					=					output.images[0]

     assert image.shape == (5_12, 5_12, 3)

     assert_mean_pixel_difference(lowerCamelCase__   ,				lowerCamelCase__			)



