






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


__a:     Any   					=					{
    '''169M''': 12,
    '''430M''': 24,
    '''1B5''': 24,
    '''3B''': 32,
    '''7B''': 32,
    '''14B''': 40,
}

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




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						Union[str, Any]:
       _UpperCAmelCase							=    list(state_dict.keys() )
       for name in state_dict_keys:
              _UpperCAmelCase							=    state_dict.pop(__snake_case )
              # 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"""   ,      __snake_case )
              # ffn -> feed_forward
              _UpperCAmelCase							=    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""" ):
                     _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       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case=None   ,      __snake_case=None   ,      __snake_case=False   ,      __snake_case=None )				->						Optional[Any]:
       # 1. If possible, build the tokenizer.
       if tokenizer_file is None:
              print("""No `--tokenizer_file` provided, we will use the default tokenizer.""" )
              _UpperCAmelCase							=    5_0_2_7_7
              _UpperCAmelCase							=    AutoTokenizer.from_pretrained("""EleutherAI/gpt-neox-20b""" )
       else:
              _UpperCAmelCase							=    PreTrainedTokenizerFast(tokenizer_file=__snake_case )
              _UpperCAmelCase							=    len(__snake_case )
       tokenizer.save_pretrained(__snake_case )

       # 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=__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
       _UpperCAmelCase							=    hf_hub_download(__snake_case   ,      __snake_case )
       _UpperCAmelCase							=    torch.load(__snake_case   ,      map_location="""cpu""" )
       _UpperCAmelCase							=    convert_state_dict(__snake_case )

       # 4. Split in shards and save
       _UpperCAmelCase	,			_UpperCAmelCase							=    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:
              _UpperCAmelCase							=    os.path.join(__snake_case   ,      __snake_case )
              # Save the index as well
              with open(__snake_case   ,      """w"""   ,      encoding="""utf-8""" ) as f:
                     _UpperCAmelCase							=    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.""" )
              _UpperCAmelCase							=    list(shards.keys() )

              del state_dict
              del shards
              gc.collect()

              for shard_file in shard_files:
                     _UpperCAmelCase							=    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.""" )
              _UpperCAmelCase							=    AutoModelForCausalLM.from_pretrained(__snake_case )
              model.push_to_hub(__snake_case   ,      max_shard_size="""2GB""" )
              tokenizer.push_to_hub(__snake_case )


if __name__ == "__main__":
  __a:     Dict   					=					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.''',
  )

  __a:     Union[str, Any]   					=					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 ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
  from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor
class 		SCREAMING_SNAKE_CASE__	:

  '''simple docstring'''







  def __init__(      self		:			Dict       ,				lowerCamelCase		:			List[str]       ,				lowerCamelCase		:			Optional[Any]=13       ,				lowerCamelCase		:			Dict=32       ,				lowerCamelCase		:			Optional[int]=3       ,				lowerCamelCase		:			List[Any]=4       ,				lowerCamelCase		:			Tuple=[10, 20, 30, 40]       ,				lowerCamelCase		:			str=[2, 2, 3, 2]       ,				lowerCamelCase		:			Optional[int]=True       ,				lowerCamelCase		:			List[Any]=True       ,				lowerCamelCase		:			Union[str, Any]=37       ,				lowerCamelCase		:			Any="gelu"       ,				lowerCamelCase		:			Tuple=10       ,				lowerCamelCase		:			Optional[Any]=0.02       ,				lowerCamelCase		:			Union[str, Any]=["stage2", "stage3", "stage4"]       ,				lowerCamelCase		:			Any=[2, 3, 4]       ,				lowerCamelCase		:			Tuple=None       ,				)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    parent
         _UpperCAmelCase							=    batch_size
         _UpperCAmelCase							=    image_size
         _UpperCAmelCase							=    num_channels
         _UpperCAmelCase							=    num_stages
         _UpperCAmelCase							=    hidden_sizes
         _UpperCAmelCase							=    depths
         _UpperCAmelCase							=    is_training
         _UpperCAmelCase							=    use_labels
         _UpperCAmelCase							=    intermediate_size
         _UpperCAmelCase							=    hidden_act
         _UpperCAmelCase							=    num_labels
         _UpperCAmelCase							=    initializer_range
         _UpperCAmelCase							=    out_features
         _UpperCAmelCase							=    out_indices
         _UpperCAmelCase							=    scope


  def  lowerCamelCase       (      self		:			Union[str, Any]							)						->	Union[str, Any]:


         """simple docstring"""
         _UpperCAmelCase							=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

         _UpperCAmelCase							=    None
         if self.use_labels:
                _UpperCAmelCase							=    ids_tensor([self.batch_size]       ,				self.num_labels							)

         _UpperCAmelCase							=    self.get_config()
         return config, pixel_values, labels


  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	Any:


         """simple docstring"""
         return ConvNextConfig(
             num_channels=self.num_channels       ,				hidden_sizes=self.hidden_sizes       ,				depths=self.depths       ,				num_stages=self.num_stages       ,				hidden_act=self.hidden_act       ,				is_decoder=lowerCamelCase       ,				initializer_range=self.initializer_range       ,				out_features=self.out_features       ,				out_indices=self.out_indices       ,				num_labels=self.num_labels       ,				)


  def  lowerCamelCase       (      self		:			Union[str, Any]       ,				lowerCamelCase		:			List[str]       ,				lowerCamelCase		:			Optional[int]       ,				lowerCamelCase		:			Any							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    ConvNextModel(config=lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    model(lowerCamelCase							)
         # expected last hidden states: B, C, H // 32, W // 32
         self.parent.assertEqual(
             result.last_hidden_state.shape       ,				(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)       ,				)


  def  lowerCamelCase       (      self		:			Dict       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			List[Any]							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    ConvNextForImageClassification(lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    model(lowerCamelCase       ,				labels=lowerCamelCase							)
         self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.num_labels)							)


  def  lowerCamelCase       (      self		:			Dict       ,				lowerCamelCase		:			List[Any]       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			Dict							)						->	Dict:


         """simple docstring"""
         _UpperCAmelCase							=    ConvNextBackbone(config=lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    model(lowerCamelCase							)

         # verify hidden states
         self.parent.assertEqual(len(result.feature_maps							)       ,				len(config.out_features							)							)
         self.parent.assertListEqual(list(result.feature_maps[0].shape							)       ,				[self.batch_size, self.hidden_sizes[1], 4, 4]							)

         # verify channels
         self.parent.assertEqual(len(model.channels							)       ,				len(config.out_features							)							)
         self.parent.assertListEqual(model.channels       ,				config.hidden_sizes[1:]							)

         # verify backbone works with out_features=None
         _UpperCAmelCase							=    None
         _UpperCAmelCase							=    ConvNextBackbone(config=lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    model(lowerCamelCase							)

         # verify feature maps
         self.parent.assertEqual(len(result.feature_maps							)       ,				1							)
         self.parent.assertListEqual(list(result.feature_maps[0].shape							)       ,				[self.batch_size, self.hidden_sizes[-1], 1, 1]							)

         # verify channels
         self.parent.assertEqual(len(model.channels							)       ,				1							)
         self.parent.assertListEqual(model.channels       ,				[config.hidden_sizes[-1]]							)





  def  lowerCamelCase       (      self		:			str							)						->	Union[str, Any]:


         """simple docstring"""
         _UpperCAmelCase							=    self.prepare_config_and_inputs()
         _UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase							=    config_and_inputs
         _UpperCAmelCase							=    {"""pixel_values""": pixel_values}
         return config, inputs_dict



@require_torch
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase					,      UpperCAmelCase					,      unittest.TestCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  (
      (
          ConvNextModel,
          ConvNextForImageClassification,
          ConvNextBackbone,
      )
      if is_torch_available()
      else ()
  )
  _lowerCamelCase    =  (
      {'''feature-extraction''': ConvNextModel, '''image-classification''': ConvNextForImageClassification}
      if is_torch_available()
      else {}
  )

  _lowerCamelCase    =  True
  _lowerCamelCase    =  False
  _lowerCamelCase    =  False
  _lowerCamelCase    =  False
  _lowerCamelCase    =  False


  def  lowerCamelCase       (      self		:			Dict							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    ConvNextModelTester(self							)
         _UpperCAmelCase							=    ConfigTester(self       ,				config_class=lowerCamelCase       ,				has_text_modality=lowerCamelCase       ,				hidden_size=37							)


  def  lowerCamelCase       (      self		:			int							)						->	int:


         """simple docstring"""
         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  lowerCamelCase       (      self		:			str							)						->	Union[str, Any]:


         """simple docstring"""
         return


  @unittest.skip(reason="""ConvNext does not use inputs_embeds"""							)
  def  lowerCamelCase       (      self		:			List[Any]							)						->	Any:


         """simple docstring"""
         pass


  @unittest.skip(reason="""ConvNext does not support input and output embeddings"""							)
  def  lowerCamelCase       (      self		:			int							)						->	int:


         """simple docstring"""
         pass


  @unittest.skip(reason="""ConvNext does not use feedforward chunking"""							)
  def  lowerCamelCase       (      self		:			Optional[int]							)						->	int:


         """simple docstring"""
         pass


  def  lowerCamelCase       (      self		:			List[Any]							)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase	,			_UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs_for_common()

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

                _UpperCAmelCase							=    ["""pixel_values"""]
                self.assertListEqual(arg_names[:1]       ,				lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Tuple							)						->	Any:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Union[str, Any]							)						->	Optional[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_backbone(*lowerCamelCase							)


  def  lowerCamelCase       (      self		:			int							)						->	Any:


         """simple docstring"""
         def check_hidden_states_output(lowerCamelCase		:			List[Any]       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			Optional[int]							):
                _UpperCAmelCase							=    model_class(lowerCamelCase							)
                model.to(lowerCamelCase							)
                model.eval()

                with torch.no_grad():
                       _UpperCAmelCase							=    model(**self._prepare_for_class(lowerCamelCase       ,				lowerCamelCase							)							)

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

                _UpperCAmelCase							=    self.model_tester.num_stages
                self.assertEqual(len(lowerCamelCase							)       ,				expected_num_stages + 1							)

                # ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
                self.assertListEqual(
                    list(hidden_states[0].shape[-2:]							)       ,				[self.model_tester.image_size // 4, self.model_tester.image_size // 4]       ,				)

         _UpperCAmelCase	,			_UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
                _UpperCAmelCase							=    True
                check_hidden_states_output(lowerCamelCase       ,				lowerCamelCase       ,				lowerCamelCase							)

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

                check_hidden_states_output(lowerCamelCase       ,				lowerCamelCase       ,				lowerCamelCase							)


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


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*lowerCamelCase							)





  @slow
  def  lowerCamelCase       (      self		:			Optional[int]							)						->	List[Any]:


         """simple docstring"""
         for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                _UpperCAmelCase							=    ConvNextModel.from_pretrained(lowerCamelCase							)
                self.assertIsNotNone(lowerCamelCase							)




def       _SCREAMING_SNAKE_CASE (							)				->						Tuple:
       _UpperCAmelCase							=    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
       return image
@require_torch
@require_vision
class 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase     ):

  '''simple docstring'''







  @cached_property
  def  lowerCamelCase       (      self		:			Dict							)						->	int:


         """simple docstring"""
         return AutoImageProcessor.from_pretrained("""facebook/convnext-tiny-224"""							) if is_vision_available() else None





  @slow
  def  lowerCamelCase       (      self		:			List[Any]							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    ConvNextForImageClassification.from_pretrained("""facebook/convnext-tiny-224"""							).to(lowerCamelCase							)

         _UpperCAmelCase							=    self.default_image_processor
         _UpperCAmelCase							=    prepare_img()
         _UpperCAmelCase							=    image_processor(images=lowerCamelCase       ,				return_tensors="""pt"""							).to(lowerCamelCase							)

         # forward pass
         with torch.no_grad():
                _UpperCAmelCase							=    model(**lowerCamelCase							)

         # verify the logits
         _UpperCAmelCase							=    torch.Size((1, 1000)							)
         self.assertEqual(outputs.logits.shape       ,				lowerCamelCase							)

         _UpperCAmelCase							=    torch.tensor([-0.0260, -0.4739, 0.1911]							).to(lowerCamelCase							)

         self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,				lowerCamelCase       ,				atol=1E-4							)							)



@require_torch
class 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase					,      UpperCAmelCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  (ConvNextBackbone,) if is_torch_available() else ()
  _lowerCamelCase    =  ConvNextConfig

  _lowerCamelCase    =  False


  def  lowerCamelCase       (      self		:			List[Any]							)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    ConvNextModelTester(self							)