






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


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

_SCREAMING_SNAKE_CASE   =  {
    "169M": 7_68,
    "430M": 10_24,
    "1B5": 20_48,
    "3B": 25_60,
    "7B": 40_96,
    "14B": 51_20,
}





def        _snake_case   (_snake_case				:       Optional[Any])					->	Tuple:
   _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        _snake_case   (_snake_case				:       int      ,    _snake_case				:       Tuple      ,    _snake_case				:       Any      ,    _snake_case				:       Dict=None      ,    _snake_case				:       Tuple=None      ,    _snake_case				:       Tuple=False      ,    _snake_case				:       Optional[Any]=None)					->	Any:
   # 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__":
    _SCREAMING_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.",
    )

    _SCREAMING_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 unittest

import numpy as np
import requests

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, prepare_image_inputs


if is_torch_available():
    import torch

    from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
    _SCREAMING_SNAKE_CASE   =  False

if is_vision_available():
    from PIL import Image

    from transformers import PixaStructImageProcessor






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


       def __init__(				self	:str,      snake_case	:int,      snake_case	:Dict=7,      snake_case	:List[Any]=3,      snake_case	:int=18,      snake_case	:List[str]=30,      snake_case	:str=400,      snake_case	:int=None,      snake_case	:List[Any]=True,      snake_case	:Optional[int]=True,      snake_case	:Union[str, Any]=None,      ):



          """simple docstring"""

          _lowercase              =size if size is not None else {'height': 20, 'width': 20}
          _lowercase              =parent
          _lowercase              =batch_size
          _lowercase              =num_channels
          _lowercase              =image_size
          _lowercase              =min_resolution
          _lowercase              =max_resolution
          _lowercase              =size
          _lowercase              =do_normalize
          _lowercase              =do_convert_rgb
          _lowercase              =[512, 1024, 2048, 4096]
          _lowercase              =patch_size if patch_size is not None else {'height': 16, 'width': 16}





       def 				UpperCamelCase__						(				self	:Any):



          """simple docstring"""

          return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}





       def 				UpperCamelCase__						(				self	:int):



          """simple docstring"""

          _lowercase              ='https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
          _lowercase              =Image.open(requests.get(snake_case,      stream=snake_case).raw).convert('RGB')
          return raw_image






@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11  ,						reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''  ,						)
@require_torch
@require_vision
class 					SCREAMING_SNAKE_CASE_  (       _a  ,						unittest.TestCase ):
       """simple docstring"""


       __lowerCAmelCase    :		Tuple					=PixaStructImageProcessor if is_vision_available() else None





       def 				UpperCamelCase__						(				self	:str):



          """simple docstring"""

          _lowercase              =PixaStructImageProcessingTester(self)





       @property
       def 				UpperCamelCase__						(				self	:Optional[int]):



          """simple docstring"""

          return self.image_processor_tester.prepare_image_processor_dict()





       def 				UpperCamelCase__						(				self	:str):



          """simple docstring"""

          _lowercase              =self.image_processing_class(**self.image_processor_dict)
          self.assertTrue(hasattr(snake_case,      'do_normalize'))
          self.assertTrue(hasattr(snake_case,      'do_convert_rgb'))





       def 				UpperCamelCase__						(				self	:Any):



          """simple docstring"""

          _lowercase              =self.image_processor_tester.prepare_dummy_image()

          _lowercase              =self.image_processing_class(**self.image_processor_dict)
          _lowercase              =2048

          _lowercase              =image_processor(snake_case,      return_tensors='pt',      max_patches=snake_case)
          self.assertTrue(torch.allclose(inputs.flattened_patches.mean(),      torch.tensor(0.0_6_0_6),      atol=1e-3,      rtol=1e-3))





       def 				UpperCamelCase__						(				self	:int):



          """simple docstring"""

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

          # Test not batched input
          _lowercase              =(
              (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
              * self.image_processor_tester.num_channels
          ) + 2

          for max_patch in self.image_processor_tester.max_patches:
             # Test not batched input
             _lowercase              =image_processor(
                 image_inputs[0],      return_tensors='pt',      max_patches=snake_case).flattened_patches
             self.assertEqual(
                 encoded_images.shape,      (1, max_patch, expected_hidden_dim),      )

             # Test batched
             _lowercase              =image_processor(
                 snake_case,      return_tensors='pt',      max_patches=snake_case).flattened_patches
             self.assertEqual(
                 encoded_images.shape,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),      )





       def 				UpperCamelCase__						(				self	:Union[str, Any]):



          """simple docstring"""

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

          # Test not batched input
          _lowercase              =(
              (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
              * self.image_processor_tester.num_channels
          ) + 2

          _lowercase              =True

          for max_patch in self.image_processor_tester.max_patches:
             # Test not batched input
             with self.assertRaises(snake_case):
                _lowercase              =image_processor(
                    image_inputs[0],      return_tensors='pt',      max_patches=snake_case).flattened_patches

             _lowercase              ='Hello'

             _lowercase              =image_processor(
                 image_inputs[0],      return_tensors='pt',      max_patches=snake_case,      header_text=snake_case).flattened_patches
             self.assertEqual(
                 encoded_images.shape,      (1, max_patch, expected_hidden_dim),      )

             # Test batched
             _lowercase              =image_processor(
                 snake_case,      return_tensors='pt',      max_patches=snake_case,      header_text=snake_case).flattened_patches
             self.assertEqual(
                 encoded_images.shape,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),      )





       def 				UpperCamelCase__						(				self	:int):



          """simple docstring"""

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

          _lowercase              =(
              (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
              * self.image_processor_tester.num_channels
          ) + 2

          for max_patch in self.image_processor_tester.max_patches:
             # Test not batched input
             _lowercase              =image_processor(
                 image_inputs[0],      return_tensors='pt',      max_patches=snake_case).flattened_patches
             self.assertEqual(
                 encoded_images.shape,      (1, max_patch, expected_hidden_dim),      )

             # Test batched
             _lowercase              =image_processor(
                 snake_case,      return_tensors='pt',      max_patches=snake_case).flattened_patches
             self.assertEqual(
                 encoded_images.shape,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),      )





       def 				UpperCamelCase__						(				self	:Any):



          """simple docstring"""

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

          # Test not batched input
          _lowercase              =(
              (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
              * self.image_processor_tester.num_channels
          ) + 2

          for max_patch in self.image_processor_tester.max_patches:
             # Test not batched input
             _lowercase              =image_processor(
                 image_inputs[0],      return_tensors='pt',      max_patches=snake_case).flattened_patches
             self.assertEqual(
                 encoded_images.shape,      (1, max_patch, expected_hidden_dim),      )

             # Test batched
             _lowercase              =image_processor(
                 snake_case,      return_tensors='pt',      max_patches=snake_case).flattened_patches
             self.assertEqual(
                 encoded_images.shape,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),      )






@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11  ,						reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''  ,						)
@require_torch
@require_vision
class 					SCREAMING_SNAKE_CASE_  (       _a  ,						unittest.TestCase ):
       """simple docstring"""


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





       def 				UpperCamelCase__						(				self	:Optional[int]):



          """simple docstring"""

          _lowercase              =PixaStructImageProcessingTester(self,      num_channels=4)
          _lowercase              =3





       @property
       def 				UpperCamelCase__						(				self	:str):



          """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_normalize'))
          self.assertTrue(hasattr(snake_case,      'do_convert_rgb'))





       def 				UpperCamelCase__						(				self	:Optional[Any]):



          """simple docstring"""

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

          # Test not batched input
          _lowercase              =(
              (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
              * (self.image_processor_tester.num_channels - 1)
          ) + 2

          for max_patch in self.image_processor_tester.max_patches:
             # Test not batched input
             _lowercase              =image_processor(
                 image_inputs[0],      return_tensors='pt',      max_patches=snake_case).flattened_patches
             self.assertEqual(
                 encoded_images.shape,      (1, max_patch, expected_hidden_dim),      )

             # Test batched
             _lowercase              =image_processor(
                 snake_case,      return_tensors='pt',      max_patches=snake_case).flattened_patches
             self.assertEqual(
                 encoded_images.shape,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),      )


