



"""simple docstring"""







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


lowerCAmelCase__									={
    "169M": 12,
    "430M": 24,
    "1B5": 24,
    "3B": 32,
    "7B": 32,
    "14B": 40,
}

lowerCAmelCase__									={
    "169M": 768,
    "430M": 1_024,
    "1B5": 2_048,
    "3B": 2_560,
    "7B": 4_096,
    "14B": 5_120,
}






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

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

            __SCREAMING_SNAKE_CASE       		=   weight
      return state_dict






def     _a (	UpperCAmelCase__     ,	UpperCAmelCase__     ,	UpperCAmelCase__     ,	UpperCAmelCase__=None     ,	UpperCAmelCase__=None     ,	UpperCAmelCase__=False     ,	UpperCAmelCase__=None     )						->    Any:
      # 1. If possible, build the tokenizer.
      if tokenizer_file is None:
            print('''No `--tokenizer_file` provided, we will use the default tokenizer.'''     )
            __SCREAMING_SNAKE_CASE       		=   5_02_77
            __SCREAMING_SNAKE_CASE       		=   AutoTokenizer.from_pretrained('''EleutherAI/gpt-neox-20b'''     )
      else:
            __SCREAMING_SNAKE_CASE       		=   PreTrainedTokenizerFast(tokenizer_file=_snake_case     )
            __SCREAMING_SNAKE_CASE       		=   len(_snake_case     )
      tokenizer.save_pretrained(_snake_case     )

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

      __SCREAMING_SNAKE_CASE       		=   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
      __SCREAMING_SNAKE_CASE       		=   hf_hub_download(_snake_case     ,	_snake_case     )
      __SCREAMING_SNAKE_CASE       		=   torch.load(_snake_case     ,	map_location='''cpu'''     )
      __SCREAMING_SNAKE_CASE       		=   convert_state_dict(_snake_case     )

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

            del state_dict
            del shards
            gc.collect()

            for shard_file in shard_files:
                  __SCREAMING_SNAKE_CASE       		=   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.'''     )
            __SCREAMING_SNAKE_CASE       		=   AutoModelForCausalLM.from_pretrained(_snake_case     )
            model.push_to_hub(_snake_case     ,	max_shard_size='''2GB'''     )
            tokenizer.push_to_hub(_snake_case     )


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

       lowerCAmelCase__									=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 unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import ChineseCLIPImageProcessor






class 					SCREAMING_SNAKE_CASE_  (       unittest.TestCase ):
       """simple docstring"""


       def __init__(				self	:Any,      snake_case	:Optional[int],      snake_case	:Optional[Any]=7,      snake_case	:str=3,      snake_case	:Optional[int]=18,      snake_case	:str=30,      snake_case	:List[Any]=400,      snake_case	:Any=True,      snake_case	:Dict=None,      snake_case	:Any=True,      snake_case	:Dict=None,      snake_case	:List[Any]=True,      snake_case	:Optional[int]=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],      snake_case	:Any=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],      snake_case	:int=True,      ):



          """simple docstring"""

          _lowercase              =size if size is not None else {'height': 224, 'width': 224}
          _lowercase              =crop_size if crop_size is not None else {'height': 18, 'width': 18}
          _lowercase              =parent
          _lowercase              =batch_size
          _lowercase              =num_channels
          _lowercase              =image_size
          _lowercase              =min_resolution
          _lowercase              =max_resolution
          _lowercase              =do_resize
          _lowercase              =size
          _lowercase              =do_center_crop
          _lowercase              =crop_size
          _lowercase              =do_normalize
          _lowercase              =image_mean
          _lowercase              =image_std
          _lowercase              =do_convert_rgb





       def 				UpperCamelCase__						(				self	:Any):



          """simple docstring"""

          return {
              "do_resize": self.do_resize,
              "size": self.size,
              "do_center_crop": self.do_center_crop,
              "crop_size": self.crop_size,
              "do_normalize": self.do_normalize,
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_convert_rgb": self.do_convert_rgb,
          }





       def 				UpperCamelCase__						(				self	:Dict,      snake_case	:List[Any]=False,      snake_case	:Any=False,      snake_case	:Union[str, Any]=False):



          """simple docstring"""

          assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

          if equal_resolution:
             _lowercase              =[]
             for i in range(self.batch_size):
                image_inputs.append(
                    np.random.randint(
                        255,      size=(self.num_channels, self.max_resolution, self.max_resolution),      dtype=np.uinta))
          else:
             _lowercase              =[]
             for i in range(self.batch_size):
                _lowercase       ,       _lowercase              =np.random.choice(np.arange(self.min_resolution,      self.max_resolution),      2)
                image_inputs.append(np.random.randint(255,      size=(self.num_channels, width, height),      dtype=np.uinta))

          if not numpify and not torchify:
             # PIL expects the channel dimension as last dimension
             _lowercase              =[Image.fromarray(np.moveaxis(snake_case,      0,      -1)) for x in image_inputs]

          if torchify:
             _lowercase              =[torch.from_numpy(snake_case) for x in image_inputs]

          return image_inputs






@require_torch
@require_vision
class 					SCREAMING_SNAKE_CASE_  (       _a  ,						unittest.TestCase ):
       """simple docstring"""


       __lowerCAmelCase    :		List[Any]					=ChineseCLIPImageProcessor if is_vision_available() else None





       def 				UpperCamelCase__						(				self	:Dict):



          """simple docstring"""

          _lowercase              =ChineseCLIPImageProcessingTester(self,      do_center_crop=snake_case)





       @property
       def 				UpperCamelCase__						(				self	:Dict):



          """simple docstring"""

          return self.image_processor_tester.prepare_image_processor_dict()





       def 				UpperCamelCase__						(				self	:Optional[int]):



          """simple docstring"""

          _lowercase              =self.image_processing_class(**self.image_processor_dict)
          self.assertTrue(hasattr(snake_case,      'do_resize'))
          self.assertTrue(hasattr(snake_case,      'size'))
          self.assertTrue(hasattr(snake_case,      'do_center_crop'))
          self.assertTrue(hasattr(snake_case,      'center_crop'))
          self.assertTrue(hasattr(snake_case,      'do_normalize'))
          self.assertTrue(hasattr(snake_case,      'image_mean'))
          self.assertTrue(hasattr(snake_case,      'image_std'))
          self.assertTrue(hasattr(snake_case,      'do_convert_rgb'))





       def 				UpperCamelCase__						(				self	:List[str]):



          """simple docstring"""

          _lowercase              =self.image_processing_class.from_dict(self.image_processor_dict)
          self.assertEqual(image_processor.size,      {'height': 224, 'width': 224})
          self.assertEqual(image_processor.crop_size,      {'height': 18, 'width': 18})

          _lowercase              =self.image_processing_class.from_dict(self.image_processor_dict,      size=42,      crop_size=84)
          self.assertEqual(image_processor.size,      {'shortest_edge': 42})
          self.assertEqual(image_processor.crop_size,      {'height': 84, 'width': 84})





       def 				UpperCamelCase__						(				self	:Tuple):



          """simple docstring"""

          pass





       def 				UpperCamelCase__						(				self	:Any):



          """simple docstring"""

          _lowercase              =self.image_processing_class(**self.image_processor_dict)
          # create random PIL images
          _lowercase              =self.image_processor_tester.prepare_inputs(equal_resolution=snake_case)
          for image in image_inputs:
             self.assertIsInstance(snake_case,      Image.Image)

          # Test not batched input
          _lowercase              =image_processing(image_inputs[0],      return_tensors='pt').pixel_values
          self.assertEqual(
              encoded_images.shape,      (
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              ),      )

          # Test batched
          _lowercase              =image_processing(snake_case,      return_tensors='pt').pixel_values
          self.assertEqual(
              encoded_images.shape,      (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              ),      )





       def 				UpperCamelCase__						(				self	:List[str]):



          """simple docstring"""

          _lowercase              =self.image_processing_class(**self.image_processor_dict)
          # create random numpy tensors
          _lowercase              =self.image_processor_tester.prepare_inputs(equal_resolution=snake_case,      numpify=snake_case)
          for image in image_inputs:
             self.assertIsInstance(snake_case,      np.ndarray)

          # Test not batched input
          _lowercase              =image_processing(image_inputs[0],      return_tensors='pt').pixel_values
          self.assertEqual(
              encoded_images.shape,      (
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              ),      )

          # Test batched
          _lowercase              =image_processing(snake_case,      return_tensors='pt').pixel_values
          self.assertEqual(
              encoded_images.shape,      (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              ),      )





       def 				UpperCamelCase__						(				self	:Optional[int]):



          """simple docstring"""

          _lowercase              =self.image_processing_class(**self.image_processor_dict)
          # create random PyTorch tensors
          _lowercase              =self.image_processor_tester.prepare_inputs(equal_resolution=snake_case,      torchify=snake_case)
          for image in image_inputs:
             self.assertIsInstance(snake_case,      torch.Tensor)

          # Test not batched input
          _lowercase              =image_processing(image_inputs[0],      return_tensors='pt').pixel_values
          self.assertEqual(
              encoded_images.shape,      (
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              ),      )

          # Test batched
          _lowercase              =image_processing(snake_case,      return_tensors='pt').pixel_values
          self.assertEqual(
              encoded_images.shape,      (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              ),      )






@require_torch
@require_vision
class 					SCREAMING_SNAKE_CASE_  (       _a  ,						unittest.TestCase ):
       """simple docstring"""


       __lowerCAmelCase    :		Optional[Any]					=ChineseCLIPImageProcessor if is_vision_available() else None





       def 				UpperCamelCase__						(				self	:Dict):



          """simple docstring"""

          _lowercase              =ChineseCLIPImageProcessingTester(self,      num_channels=4,      do_center_crop=snake_case)
          _lowercase              =3





       @property
       def 				UpperCamelCase__						(				self	:Tuple):



          """simple docstring"""

          return self.image_processor_tester.prepare_image_processor_dict()





       def 				UpperCamelCase__						(				self	:Tuple):



          """simple docstring"""

          _lowercase              =self.image_processing_class(**self.image_processor_dict)
          self.assertTrue(hasattr(snake_case,      'do_resize'))
          self.assertTrue(hasattr(snake_case,      'size'))
          self.assertTrue(hasattr(snake_case,      'do_center_crop'))
          self.assertTrue(hasattr(snake_case,      'center_crop'))
          self.assertTrue(hasattr(snake_case,      'do_normalize'))
          self.assertTrue(hasattr(snake_case,      'image_mean'))
          self.assertTrue(hasattr(snake_case,      'image_std'))
          self.assertTrue(hasattr(snake_case,      'do_convert_rgb'))





       def 				UpperCamelCase__						(				self	:Dict):



          """simple docstring"""

          pass





       def 				UpperCamelCase__						(				self	:str):



          """simple docstring"""

          _lowercase              =self.image_processing_class(**self.image_processor_dict)
          # create random PIL images
          _lowercase              =self.image_processor_tester.prepare_inputs(equal_resolution=snake_case)
          for image in image_inputs:
             self.assertIsInstance(snake_case,      Image.Image)

          # Test not batched input
          _lowercase              =image_processing(image_inputs[0],      return_tensors='pt').pixel_values
          self.assertEqual(
              encoded_images.shape,      (
                  1,
                  self.expected_encoded_image_num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              ),      )

          # Test batched
          _lowercase              =image_processing(snake_case,      return_tensors='pt').pixel_values
          self.assertEqual(
              encoded_images.shape,      (
                  self.image_processor_tester.batch_size,
                  self.expected_encoded_image_num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              ),      )


