"""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


_UpperCamelCase     :		Union[str, Any]											=					{
    "169M": 12,
    "430M": 24,
    "1B5": 24,
    "3B": 32,
    "7B": 32,
    "14B": 40,
}

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





def       a_  (   _lowerCAmelCase						:				str  ):





       '''simple docstring'''

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

              if name != "head.weight":
                     lowercase__    :	List[str]     					=			"rwkv." + name

              lowercase__    :	Optional[Any]     					=			weight
       return state_dict





def       a_  (   _lowerCAmelCase						:				str     ,				_lowerCAmelCase						:				int     ,				_lowerCAmelCase						:				Union[str, Any]     ,				_lowerCAmelCase						:				List[Any]=None     ,				_lowerCAmelCase						:				Optional[Any]=None     ,				_lowerCAmelCase						:				int=False     ,				_lowerCAmelCase						:				Dict=None  ):





       '''simple docstring'''

       if tokenizer_file is None:
              print('No `--tokenizer_file` provided, we will use the default tokenizer.'  )
              lowercase__    :	str     					=			5_0277
              lowercase__    :	Optional[Any]     					=			AutoTokenizer.from_pretrained('EleutherAI/gpt-neox-20b'  )
       else:
              lowercase__    :	Tuple     					=			PreTrainedTokenizerFast(tokenizer_file=__lowerCAmelCase  )
              lowercase__    :	List[Any]     					=			len(__lowerCAmelCase  )
       tokenizer.save_pretrained(__lowerCAmelCase  )

       # 2. Build the config
       lowercase__    :	Optional[int]     					=			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__    :	Optional[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}."""  )

       lowercase__    :	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
       lowercase__    :	str     					=			hf_hub_download(__lowerCAmelCase     ,				__lowerCAmelCase  )
       lowercase__    :	Optional[int]     					=			torch.load(__lowerCAmelCase     ,				map_location='cpu'  )
       lowercase__    :	Any     					=			convert_state_dict(__lowerCAmelCase  )

       # 4. Split in shards and save
       lowercase__    :	List[str]     					=			shard_checkpoint(__lowerCAmelCase  )
       for shard_file, shard in shards.items():
              torch.save(__lowerCAmelCase     ,				os.path.join(__lowerCAmelCase     ,				__lowerCAmelCase  )  )

       if index is not None:
              lowercase__    :	int     					=			os.path.join(__lowerCAmelCase     ,				__lowerCAmelCase  )
              # Save the index as well
              with open(__lowerCAmelCase     ,				'w'     ,				encoding='utf-8'  ) as f:
                     lowercase__    :	int     					=			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.'  )
              lowercase__    :	Union[str, Any]     					=			list(shards.keys()  )

              del state_dict
              del shards
              gc.collect()

              for shard_file in shard_files:
                     lowercase__    :	Union[str, Any]     					=			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.'  )
              lowercase__    :	Optional[Any]     					=			AutoModelForCausalLM.from_pretrained(__lowerCAmelCase  )
              model.push_to_hub(__lowerCAmelCase     ,				max_shard_size='2GB'  )
              tokenizer.push_to_hub(__lowerCAmelCase  )


if __name__ == "__main__":
       _UpperCamelCase     :		int											=					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     :		Optional[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,
       )


"""simple docstring"""





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, prepare_image_inputs


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import MobileViTImageProcessor


class   UpperCAmelCase_			(					unittest.TestCase):


      def __init__(     self	,    a	,    a=7	,    a=3	,    a=1_8	,    a=3_0	,    a=4_0_0	,    a=True	,    a=None	,    a=True	,    a=None	,    a=True	,    ) ->      List[str]:
             lowercase__    :	Tuple     					=			size if size is not None else {'shortest_edge': 2_0}
             lowercase__    :	Union[str, Any]     					=			crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
             lowercase__    :	Optional[int]     					=			parent
             lowercase__    :	Optional[int]     					=			batch_size
             lowercase__    :	str     					=			num_channels
             lowercase__    :	Any     					=			image_size
             lowercase__    :	Optional[Any]     					=			min_resolution
             lowercase__    :	int     					=			max_resolution
             lowercase__    :	List[Any]     					=			do_resize
             lowercase__    :	List[str]     					=			size
             lowercase__    :	str     					=			do_center_crop
             lowercase__    :	List[Any]     					=			crop_size
             lowercase__    :	Union[str, Any]     					=			do_flip_channel_order


      def 	_UpperCAmelCase (     self							) ->      int:
             return {
                 "do_resize": self.do_resize,
                 "size": self.size,
                 "do_center_crop": self.do_center_crop,
                 "crop_size": self.crop_size,
                 "do_flip_channel_order": self.do_flip_channel_order,
             }





@require_torch
@require_vision
class   UpperCAmelCase_			(					_a				,							unittest.TestCase):
      lowerCamelCase__	:					Optional[Any]										=						MobileViTImageProcessor if is_vision_available() else None


      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             lowercase__    :	Tuple     					=			MobileViTImageProcessingTester(self							)


      @property
      def 	_UpperCAmelCase (     self							) ->      int:
             return self.image_processor_tester.prepare_image_processor_dict()


      def 	_UpperCAmelCase (     self							) ->      Optional[int]:
             lowercase__    :	List[str]     					=			self.image_processing_class(**self.image_processor_dict							)
             self.assertTrue(hasattr(a	,    'do_resize'							)							)
             self.assertTrue(hasattr(a	,    'size'							)							)
             self.assertTrue(hasattr(a	,    'do_center_crop'							)							)
             self.assertTrue(hasattr(a	,    'center_crop'							)							)
             self.assertTrue(hasattr(a	,    'do_flip_channel_order'							)							)


      def 	_UpperCAmelCase (     self							) ->      List[str]:
             lowercase__    :	Optional[int]     					=			self.image_processing_class.from_dict(self.image_processor_dict							)
             self.assertEqual(image_processor.size	,    {'shortest_edge': 2_0}							)
             self.assertEqual(image_processor.crop_size	,    {'height': 1_8, 'width': 1_8}							)

             lowercase__    :	str     					=			self.image_processing_class.from_dict(self.image_processor_dict	,    size=4_2	,    crop_size=8_4							)
             self.assertEqual(image_processor.size	,    {'shortest_edge': 4_2}							)
             self.assertEqual(image_processor.crop_size	,    {'height': 8_4, 'width': 8_4}							)


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             pass


      def 	_UpperCAmelCase (     self							) ->      str:
             # Initialize image_processing
             lowercase__    :	Any     					=			self.image_processing_class(**self.image_processor_dict							)
             # create random PIL images
             lowercase__    :	List[str]     					=			prepare_image_inputs(self.image_processor_tester	,    equal_resolution=a							)
             for image in image_inputs:
                    self.assertIsInstance(a	,    Image.Image							)

             # Test not batched input
             lowercase__    :	Union[str, Any]     					=			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__    :	List[Any]     					=			image_processing(a	,    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							) ->      Tuple:
             # Initialize image_processing
             lowercase__    :	Dict     					=			self.image_processing_class(**self.image_processor_dict							)
             # create random numpy tensors
             lowercase__    :	Tuple     					=			prepare_image_inputs(self.image_processor_tester	,    equal_resolution=a	,    numpify=a							)
             for image in image_inputs:
                    self.assertIsInstance(a	,    np.ndarray							)

             # Test not batched input
             lowercase__    :	List[Any]     					=			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__    :	Any     					=			image_processing(a	,    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							) ->      Dict:
             # Initialize image_processing
             lowercase__    :	str     					=			self.image_processing_class(**self.image_processor_dict							)
             # create random PyTorch tensors
             lowercase__    :	Optional[Any]     					=			prepare_image_inputs(self.image_processor_tester	,    equal_resolution=a	,    torchify=a							)
             for image in image_inputs:
                    self.assertIsInstance(a	,    torch.Tensor							)

             # Test not batched input
             lowercase__    :	Union[str, Any]     					=			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__    :	Tuple     					=			image_processing(a	,    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'],
                 )	,    )

