





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


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

snake_case 				=       {
    """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 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Optional[Any]     )      ->						Dict:
  _lowercase							= list(state_dict.keys()     )
  for name in state_dict_keys:
    _lowercase							= state_dict.pop(snake_case__     )
    # emb -> embedding
    if name.startswith('emb.'     ):
      _lowercase							= name.replace('emb.'			,							'embeddings.'     )
    # ln_0 -> pre_ln (only present at block 0)
    if name.startswith('blocks.0.ln0'     ):
      _lowercase							= name.replace('blocks.0.ln0'			,							'blocks.0.pre_ln'     )
    # att -> attention
    _lowercase							= re.sub(R'blocks\.(\d+)\.att'			,							R'blocks.\1.attention'			,							snake_case__     )
    # ffn -> feed_forward
    _lowercase							= re.sub(R'blocks\.(\d+)\.ffn'			,							R'blocks.\1.feed_forward'			,							snake_case__     )
    # time_mix_k -> time_mix_key and reshape
    if name.endswith('.time_mix_k'     ):
      _lowercase							= name.replace('.time_mix_k'			,							'.time_mix_key'     )
    # time_mix_v -> time_mix_value and reshape
    if name.endswith('.time_mix_v'     ):
      _lowercase							= name.replace('.time_mix_v'			,							'.time_mix_value'     )
    # time_mix_r -> time_mix_key and reshape
    if name.endswith('.time_mix_r'     ):
      _lowercase							= name.replace('.time_mix_r'			,							'.time_mix_receptance'     )

    if name != "head.weight":
      _lowercase							= 'rwkv.' + name

    _lowercase							= weight
  return state_dict

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Union[str, Any]			,							snake_case__  :Optional[int]			,							snake_case__  :List[str]			,							snake_case__  :Any=None			,							snake_case__  :Any=None			,							snake_case__  :Dict=False			,							snake_case__  :Optional[int]=None     )      ->						Tuple:
  # 1. If possible, build the tokenizer.
  if tokenizer_file is None:
    print('No `--tokenizer_file` provided, we will use the default tokenizer.'     )
    _lowercase							= 5_0277
    _lowercase							= AutoTokenizer.from_pretrained('EleutherAI/gpt-neox-20b'     )
  else:
    _lowercase							= PreTrainedTokenizerFast(tokenizer_file=snake_case__     )
    _lowercase							= len(snake_case__     )
  tokenizer.save_pretrained(snake_case__     )

  # 2. Build the config
  _lowercase							= 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:
        _lowercase							= 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}."""     )

  _lowercase							= RwkvConfig(
      vocab_size=snake_case__			,							num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]			,							hidden_size=HIDEN_SIZE_MAPPING[size]			,							)
  config.save_pretrained(snake_case__     )

  # 3. Download model file then convert state_dict
  _lowercase							= hf_hub_download(snake_case__			,							snake_case__     )
  _lowercase							= torch.load(snake_case__			,							map_location='cpu'     )
  _lowercase							= convert_state_dict(snake_case__     )

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

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

    # 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.'     )
    _lowercase							= list(shards.keys()     )

    del state_dict
    del shards
    gc.collect()

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

  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.'     )
    _lowercase							= AutoModelForCausalLM.from_pretrained(snake_case__     )
    model.push_to_hub(snake_case__			,							max_shard_size='2GB'     )
    tokenizer.push_to_hub(snake_case__     )


if __name__ == "__main__":
      snake_case 				=       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.""",
      )

      snake_case 				=       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 inspect
import unittest

from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
      import jax
      import jax.numpy as jnp

      from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor





class 				A_				(      unittest.TestCase	):







       """simple docstring"""





       def __init__(    self	:	List[str]						,__A	:	str						,__A	:	Dict=3						,__A	:	str=32						,__A	:	int=3						,__A	:	Union[str, Any]=10						,__A	:	Optional[Any]=[10, 20, 30, 40]						,__A	:	List[str]=[1, 1, 2, 1]						,__A	:	Optional[int]=True						,__A	:	Optional[Any]=True						,__A	:	int="relu"						,__A	:	Union[str, Any]=3						,__A	:	List[Any]=None						,)						->					int:
         _lowercase							= parent
         _lowercase							= batch_size
         _lowercase							= image_size
         _lowercase							= num_channels
         _lowercase							= embeddings_size
         _lowercase							= hidden_sizes
         _lowercase							= depths
         _lowercase							= is_training
         _lowercase							= use_labels
         _lowercase							= hidden_act
         _lowercase							= num_labels
         _lowercase							= scope
         _lowercase							= len(__A  )







       def    __UpperCAmelCase (    self	:	Optional[Any]  )						->					Any:
         _lowercase							= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

         _lowercase							= self.get_config()

         return config, pixel_values







       def    __UpperCAmelCase (    self	:	Any  )						->					int:
         return RegNetConfig(
             num_channels=self.num_channels						,embeddings_size=self.embeddings_size						,hidden_sizes=self.hidden_sizes						,depths=self.depths						,hidden_act=self.hidden_act						,num_labels=self.num_labels						,image_size=self.image_size						,)







       def    __UpperCAmelCase (    self	:	Tuple						,__A	:	Any						,__A	:	Union[str, Any]  )						->					List[Any]:
         _lowercase							= FlaxRegNetModel(config=__A  )
         _lowercase							= model(__A  )

         # Output shape (b, c, h, w)
         self.parent.assertEqual(
             result.last_hidden_state.shape						,(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)						,)







       def    __UpperCAmelCase (    self	:	List[Any]						,__A	:	Optional[int]						,__A	:	Dict  )						->					Any:
         _lowercase							= self.num_labels
         _lowercase							= FlaxRegNetForImageClassification(config=__A  )
         _lowercase							= model(__A  )
         self.parent.assertEqual(result.logits.shape						,(self.batch_size, self.num_labels)  )







       def    __UpperCAmelCase (    self	:	Tuple  )						->					Any:
         _lowercase							= self.prepare_config_and_inputs()
         _lowercase						,		_lowercase							= config_and_inputs
         _lowercase							= {'pixel_values': pixel_values}
         return config, inputs_dict





@require_flax
class 				A_				(      UpperCAmelCase					,	unittest.TestCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : Tuple      =  (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

       SCREAMING_SNAKE_CASE_      : Union[str, Any]      =  False
       SCREAMING_SNAKE_CASE_      : Any      =  False
       SCREAMING_SNAKE_CASE_      : Union[str, Any]      =  False







       def    __UpperCAmelCase (    self	:	Dict  )						->					None:
         _lowercase							= FlaxRegNetModelTester(self  )
         _lowercase							= ConfigTester(self						,config_class=__A						,has_text_modality=__A  )







       def    __UpperCAmelCase (    self	:	List[str]  )						->					Union[str, Any]:
         self.create_and_test_config_common_properties()
         self.config_tester.create_and_test_config_to_json_string()
         self.config_tester.create_and_test_config_to_json_file()
         self.config_tester.create_and_test_config_from_and_save_pretrained()
         self.config_tester.create_and_test_config_with_num_labels()
         self.config_tester.check_config_can_be_init_without_params()
         self.config_tester.check_config_arguments_init()







       def    __UpperCAmelCase (    self	:	Optional[int]  )						->					Any:
         return







       def    __UpperCAmelCase (    self	:	Dict  )						->					int:
         _lowercase							= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__A  )







       def    __UpperCAmelCase (    self	:	Optional[Any]  )						->					Any:
         _lowercase							= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*__A  )







       @unittest.skip(reason='RegNet does not use inputs_embeds'  )
       def    __UpperCAmelCase (    self	:	List[str]  )						->					Optional[int]:
         pass







       @unittest.skip(reason='RegNet does not support input and output embeddings'  )
       def    __UpperCAmelCase (    self	:	Tuple  )						->					Any:
         pass







       def    __UpperCAmelCase (    self	:	Dict  )						->					List[str]:
         _lowercase						,		_lowercase							= self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           _lowercase							= model_class(__A  )
           _lowercase							= inspect.signature(model.__call__  )
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           _lowercase							= [*signature.parameters.keys()]

           _lowercase							= ['pixel_values']
           self.assertListEqual(arg_names[:1]						,__A  )







       def    __UpperCAmelCase (    self	:	Union[str, Any]  )						->					List[str]:
         def check_hidden_states_output(__A	:	Optional[Any]						,__A	:	Optional[int]						,__A	:	str  ):
           _lowercase							= model_class(__A  )

           _lowercase							= model(**self._prepare_for_class(__A						,__A  )  )

           _lowercase							= outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

           _lowercase							= self.model_tester.num_stages
           self.assertEqual(len(__A  )						,expected_num_stages + 1  )

         _lowercase						,		_lowercase							= self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           _lowercase							= True
           check_hidden_states_output(__A						,__A						,__A  )

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           _lowercase							= True

           check_hidden_states_output(__A						,__A						,__A  )







       def    __UpperCAmelCase (    self	:	Optional[Any]  )						->					int:
         _lowercase						,		_lowercase							= self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__  ):
             _lowercase							= self._prepare_for_class(__A						,__A  )
             _lowercase							= model_class(__A  )

             @jax.jit
             def model_jitted(__A	:	Any						,**__A	:	Union[str, Any]  ):
               return model(pixel_values=__A						,**__A  )

             with self.subTest('JIT Enabled'  ):
               _lowercase							= model_jitted(**__A  ).to_tuple()

             with self.subTest('JIT Disabled'  ):
               with jax.disable_jit():
                 _lowercase							= model_jitted(**__A  ).to_tuple()

             self.assertEqual(len(__A  )						,len(__A  )  )
             for jitted_output, output in zip(__A						,__A  ):
               self.assertEqual(jitted_output.shape						,output.shape  )

def 							SCREAMING_SNAKE_CASE__       (  )      ->						str:
  _lowercase							= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
  return image







@require_flax
class 				A_				(      unittest.TestCase	):







       """simple docstring"""





       @cached_property
       def    __UpperCAmelCase (    self	:	str  )						->					Any:
         return AutoImageProcessor.from_pretrained('facebook/regnet-y-040'  ) if is_vision_available() else None







       @slow
       def    __UpperCAmelCase (    self	:	Optional[Any]  )						->					Any:
         _lowercase							= FlaxRegNetForImageClassification.from_pretrained('facebook/regnet-y-040'  )

         _lowercase							= self.default_image_processor
         _lowercase							= prepare_img()
         _lowercase							= image_processor(images=__A						,return_tensors='np'  )

         _lowercase							= model(**__A  )

         # verify the logits
         _lowercase							= (1, 1000)
         self.assertEqual(outputs.logits.shape						,__A  )

         _lowercase							= jnp.array([-0.4180, -1.5051, -3.4836]  )

         self.assertTrue(jnp.allclose(outputs.logits[0, :3]						,__A						,atol=1e-4  )  )