






"""simple docstring"""




import argparse
import os

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
_snake_case          =   'src/transformers'
_snake_case          =   'docs/source/en/tasks'

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  with open(UpperCamelCase__     ,   """r"""     ,   encoding="""utf-8"""     ,   newline="""\n"""				) as f:
    _a  :       Optional[int]             =	f.readlines()
  # Find the start prompt.
  _a  :       Tuple             =	0
  while not lines[start_index].startswith(UpperCamelCase__				):
    start_index += 1
  start_index += 1

  _a  :       Union[str, Any]             =	start_index
  while not lines[end_index].startswith(UpperCamelCase__				):
    end_index += 1
  end_index -= 1

  while len(lines[start_index]				) <= 1:
    start_index += 1
  while len(lines[end_index]				) <= 1:
    end_index -= 1
  end_index += 1
  return "".join(lines[start_index:end_index]				), start_index, end_index, lines


# This is to make sure the transformers module imported is the one in the repo.
_snake_case          =   direct_transformers_import(TRANSFORMERS_PATH)

_snake_case          =   {
    'asr.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
    'audio_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
    'language_modeling.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
    'image_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
    'masked_language_modeling.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
    'multiple_choice.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
    'object_detection.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
    'question_answering.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
    'semantic_segmentation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
    'sequence_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
    'summarization.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    'token_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
    'translation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    'video_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
    'document_question_answering.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
    'monocular_depth_estimation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}

# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
_snake_case          =   {
    'summarization.md': ('nllb',),
    'translation.md': ('nllb',),
}

def 			lowerCAmelCase__     (     UpperCamelCase__				):



  '''simple docstring'''
  _a  :       Union[str, Any]             =	TASK_GUIDE_TO_MODELS[task_guide]
  _a  :       Any             =	SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(UpperCamelCase__     ,   set()				)
  _a  :       str             =	{
      code: name
      for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
      if (code in model_maping_names or code in special_model_types)
  }
  return ", ".join([F"""[{name}](../model_doc/{code})""" for code, name in model_names.items()]				) + "\n"

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__=False				):



  '''simple docstring'''
  _a      ,  _a      ,  _a      ,  _a  :       Any             =	_find_text_in_file(
      filename=os.path.join(UpperCamelCase__     ,   UpperCamelCase__				)     ,   start_prompt="""<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->"""     ,   end_prompt="""<!--End of the generated tip-->"""     ,   )

  _a  :       Optional[int]             =	get_model_list_for_task(UpperCamelCase__				)

  if current_list != new_list:
    if overwrite:
      with open(os.path.join(UpperCamelCase__     ,   UpperCamelCase__				)     ,   """w"""     ,   encoding="""utf-8"""     ,   newline="""\n"""				) as f:
        f.writelines(lines[:start_index] + [new_list] + lines[end_index:]				)
    else:
      raise ValueError(
          F"""The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`"""
          """ to fix this."""				)


if __name__ == "__main__":
 _snake_case          =   argparse.ArgumentParser()
 parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
 _snake_case          =   parser.parse_args()

 for task_guide in TASK_GUIDE_TO_MODELS.keys():
  check_model_list_for_task(task_guide, args.fix_and_overwrite)









"""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


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

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

def 			lowerCAmelCase__     (     UpperCamelCase__				):



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

    if name != "head.weight":
      _a  :       Optional[int]             =	"""rwkv.""" + name

    _a  :       Any             =	weight
  return state_dict

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__=None     ,   UpperCamelCase__=None     ,   UpperCamelCase__=False     ,   UpperCamelCase__=None				):



  '''simple docstring'''
  # 1. If possible, build the tokenizer.
  if tokenizer_file is None:
    print("""No `--tokenizer_file` provided, we will use the default tokenizer."""				)
    _a  :       Tuple             =	5_0_2_7_7
    _a  :       str             =	AutoTokenizer.from_pretrained("""EleutherAI/gpt-neox-20b"""				)
  else:
    _a  :       int             =	PreTrainedTokenizerFast(tokenizer_file=UpperCamelCase__				)
    _a  :       int             =	len(UpperCamelCase__				)
  tokenizer.save_pretrained(UpperCamelCase__				)

  # 2. Build the config
  _a  :       Optional[Any]             =	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:
        _a  :       Tuple             =	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}."""				)

  _a  :       List[Any]             =	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
  _a  :       str             =	hf_hub_download(UpperCamelCase__     ,   UpperCamelCase__				)
  _a  :       int             =	torch.load(UpperCamelCase__     ,   map_location="""cpu"""				)
  _a  :       List[str]             =	convert_state_dict(UpperCamelCase__				)

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

  if index is not None:
    _a  :       Optional[Any]             =	os.path.join(UpperCamelCase__     ,   UpperCamelCase__				)
    # Save the index as well
    with open(UpperCamelCase__     ,   """w"""     ,   encoding="""utf-8"""				) as f:
      _a  :       Dict             =	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."""				)
    _a  :       List[Any]             =	list(shards.keys()				)

    del state_dict
    del shards
    gc.collect()

    for shard_file in shard_files:
      _a  :       Any             =	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."""				)
    _a  :       Dict             =	AutoModelForCausalLM.from_pretrained(UpperCamelCase__				)
    model.push_to_hub(UpperCamelCase__     ,   max_shard_size="""2GB"""				)
    tokenizer.push_to_hub(UpperCamelCase__				)


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

 _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,
 )

