





'''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


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

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

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

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

  snake_case				=			weight
 return state_dict

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

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

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

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

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

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

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

  del state_dict
  del shards
  gc.collect()

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

 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.'''     )
  snake_case				=			AutoModelForCausalLM.from_pretrained(_UpperCAmelCase     )
  model.push_to_hub(_UpperCAmelCase					,   max_shard_size='''2GB'''     )
  tokenizer.push_to_hub(_UpperCAmelCase     )


if __name__ == "__main__":
   _lowercase       						=			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.',
   )

   _lowercase       						=			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 hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image
else:
   class 			_lowercase						:







        @staticmethod
        def     UpperCamelCase			( *A__    ,   **A__    ) ->	List[Any]:
         pass

def    __UpperCamelCase	(	a    : Image     ) ->str:
 snake_case				=			hashlib.mda(image.tobytes()     )
 return m.hexdigest()






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 			_lowercase						(       unittest.TestCase					):
     _UpperCAmelCase										=					MODEL_FOR_DEPTH_ESTIMATION_MAPPING







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ) ->	Union[str, Any]:
      snake_case				=			DepthEstimationPipeline(model=A__    ,   image_processor=A__    )
      return depth_estimator, [
          "./tests/fixtures/tests_samples/COCO/000000039769.png",
          "./tests/fixtures/tests_samples/COCO/000000039769.png",
      ]







     def     UpperCamelCase			( self    ,   A__    ,   A__    ) ->	List[Any]:
      snake_case				=			depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    )
      self.assertEqual({'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )}    ,   A__    )
      import datasets

      snake_case				=			datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''    ,   '''image'''    ,   split='''test'''    )
      snake_case				=			depth_estimator(
          [
              Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    ),
              '''http://images.cocodataset.org/val2017/000000039769.jpg''',
              # RGBA
              dataset[0]['''file'''],
              # LA
              dataset[1]['''file'''],
              # L
              dataset[2]['''file'''],
          ]    )
      self.assertEqual(
          [
              {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
              {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
              {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
              {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
              {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
          ]    ,   A__    ,   )







     @require_tf
     @unittest.skip('''Depth estimation is not implemented in TF'''    )
     def     UpperCamelCase			( self    ) ->	Optional[Any]:
      pass







     @slow
     @require_torch
     def     UpperCamelCase			( self    ) ->	Dict:
      snake_case				=			'''Intel/dpt-large'''
      snake_case				=			pipeline('''depth-estimation'''    ,   model=A__    )
      snake_case				=			depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg'''    )
      snake_case				=			hashimage(outputs['''depth''']    )

      # This seems flaky.
      # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
      self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item()    )    ,   2_9.3_0_4    )
      self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item()    )    ,   2.6_6_2    )







     @require_torch
     def     UpperCamelCase			( self    ) ->	Any:
      # This is highly irregular to have no small tests.
      self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT'''    )

