


'''simple docstring'''






import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List

from ..utils import logging
from . import BaseTransformersCLICommand


try:
  from cookiecutter.main import cookiecutter

  __A					:  List[Any]											=      True
except ImportError:
  __A					:  int											=      False

__A					:  str											=      logging.get_logger(__name__)  # pylint: disable=invalid-name







def 				UpperCamelCase_			(    A__					:   Namespace					):
  '''simple docstring'''





  return AddNewModelCommand(args.testing							,    args.testing_file							,    path=args.path					)






class    __snake_case       (   _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    @staticmethod
    def __lowercase   (   lowerCamelCase    :  ArgumentParser		)   ->  int:
      lowerCAmelCase_			:      Optional[int]			=   parser.add_parser("""add-new-model"""		)
      add_new_model_parser.add_argument("""--testing"""    ,						action="""store_true"""    ,						help="""If in testing mode."""		)
      add_new_model_parser.add_argument("""--testing_file"""    ,						type=lowerCamelCase    ,						help="""Configuration file on which to run."""		)
      add_new_model_parser.add_argument(
          """--path"""    ,						type=lowerCamelCase    ,						help="""Path to cookiecutter. Should only be used for testing purposes."""		)
      add_new_model_parser.set_defaults(func=lowerCamelCase		)

    def __init__(   self    :  List[str]    ,						lowerCamelCase    :  bool    ,						lowerCamelCase    :  str    ,						lowerCamelCase    :  Any=None    ,						*lowerCamelCase    :  List[str]		)   ->  Optional[Any]:
      lowerCAmelCase_			:      int			=   testing
      lowerCAmelCase_			:      Union[str, Any]			=   testing_file
      lowerCAmelCase_			:      Tuple			=   path





    def __lowercase   (   self    :  Tuple		)   ->  int:
      warnings.warn(
          """The command `transformers-cli add-new-model` is deprecated and will be removed in v5 of Transformers. """
          """It is not actively maintained anymore, so might give a result that won't pass all tests and quality """
          """checks, you should use `transformers-cli add-new-model-like` instead."""		)
      if not _has_cookiecutter:
        raise ImportError(
            """Model creation dependencies are required to use the `add_new_model` command. Install them by running """
            """the following at the root of your `transformers` clone:\n\n\t$ pip install -e .[modelcreation]\n"""		)
      # Ensure that there is no other `cookiecutter-template-xxx` directory in the current working directory
      lowerCAmelCase_			:      int			=   [directory for directory in os.listdir() if """cookiecutter-template-""" == directory[:22]]
      if len(lowerCamelCase		) > 0:
        raise ValueError(
            """Several directories starting with `cookiecutter-template-` in current working directory. """
            """Please clean your directory by removing all folders starting with `cookiecutter-template-` or """
            """change your working directory."""		)

      lowerCAmelCase_			:      List[Any]			=   (
          Path(lowerCamelCase		).parent.parent.parent.parent if self._path is None else Path(self._path		).parent.parent
      )
      lowerCAmelCase_			:      Dict			=   path_to_transformer_root / """templates""" / """adding_a_new_model"""

      # Execute cookiecutter
      if not self._testing:
        cookiecutter(str(lowerCamelCase		)		)
      else:
        with open(self._testing_file    ,						"""r"""		) as configuration_file:
          lowerCAmelCase_			:      Tuple			=   json.load(lowerCamelCase		)

        cookiecutter(
            str(path_to_cookiecutter if self._path is None else self._path		)    ,						no_input=lowerCamelCase    ,						extra_context=lowerCamelCase    ,						)

      lowerCAmelCase_			:      List[str]			=   [directory for directory in os.listdir() if """cookiecutter-template-""" in directory[:22]][0]

      # Retrieve configuration
      with open(directory + """/configuration.json"""    ,						"""r"""		) as configuration_file:
        lowerCAmelCase_			:      Tuple			=   json.load(lowerCamelCase		)

      lowerCAmelCase_			:      str			=   configuration["""lowercase_modelname"""]
      lowerCAmelCase_			:      List[str]			=   configuration["""generate_tensorflow_pytorch_and_flax"""]
      os.remove(F'{directory}/configuration.json'		)

      lowerCAmelCase_			:      Dict			=   """PyTorch""" in generate_tensorflow_pytorch_and_flax
      lowerCAmelCase_			:      Optional[int]			=   """TensorFlow""" in generate_tensorflow_pytorch_and_flax
      lowerCAmelCase_			:      List[str]			=   """Flax""" in generate_tensorflow_pytorch_and_flax

      lowerCAmelCase_			:      Union[str, Any]			=   F'{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}'
      os.makedirs(lowerCamelCase    ,						exist_ok=lowerCamelCase		)
      os.makedirs(F'{path_to_transformer_root}/tests/models/{lowercase_model_name}'    ,						exist_ok=lowerCamelCase		)

      # Tests require submodules as they have parent imports
      with open(F'{path_to_transformer_root}/tests/models/{lowercase_model_name}/__init__.py'    ,						"""w"""		):
        pass

      shutil.move(
          F'{directory}/__init__.py'    ,						F'{model_dir}/__init__.py'    ,						)
      shutil.move(
          F'{directory}/configuration_{lowercase_model_name}.py'    ,						F'{model_dir}/configuration_{lowercase_model_name}.py'    ,						)

      def remove_copy_lines(lowerCamelCase    :  Any		):
        with open(lowerCamelCase    ,						"""r"""		) as f:
          lowerCAmelCase_			:      List[str]			=   f.readlines()
        with open(lowerCamelCase    ,						"""w"""		) as f:
          for line in lines:
            if "# Copied from transformers." not in line:
              f.write(lowerCamelCase		)

      if output_pytorch:
        if not self._testing:
          remove_copy_lines(F'{directory}/modeling_{lowercase_model_name}.py'		)

        shutil.move(
            F'{directory}/modeling_{lowercase_model_name}.py'    ,						F'{model_dir}/modeling_{lowercase_model_name}.py'    ,						)

        shutil.move(
            F'{directory}/test_modeling_{lowercase_model_name}.py'    ,						F'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_{lowercase_model_name}.py'    ,						)
      else:
        os.remove(F'{directory}/modeling_{lowercase_model_name}.py'		)
        os.remove(F'{directory}/test_modeling_{lowercase_model_name}.py'		)

      if output_tensorflow:
        if not self._testing:
          remove_copy_lines(F'{directory}/modeling_tf_{lowercase_model_name}.py'		)

        shutil.move(
            F'{directory}/modeling_tf_{lowercase_model_name}.py'    ,						F'{model_dir}/modeling_tf_{lowercase_model_name}.py'    ,						)

        shutil.move(
            F'{directory}/test_modeling_tf_{lowercase_model_name}.py'    ,						F'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_tf_{lowercase_model_name}.py'    ,						)
      else:
        os.remove(F'{directory}/modeling_tf_{lowercase_model_name}.py'		)
        os.remove(F'{directory}/test_modeling_tf_{lowercase_model_name}.py'		)

      if output_flax:
        if not self._testing:
          remove_copy_lines(F'{directory}/modeling_flax_{lowercase_model_name}.py'		)

        shutil.move(
            F'{directory}/modeling_flax_{lowercase_model_name}.py'    ,						F'{model_dir}/modeling_flax_{lowercase_model_name}.py'    ,						)

        shutil.move(
            F'{directory}/test_modeling_flax_{lowercase_model_name}.py'    ,						F'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_flax_{lowercase_model_name}.py'    ,						)
      else:
        os.remove(F'{directory}/modeling_flax_{lowercase_model_name}.py'		)
        os.remove(F'{directory}/test_modeling_flax_{lowercase_model_name}.py'		)

      shutil.move(
          F'{directory}/{lowercase_model_name}.md'    ,						F'{path_to_transformer_root}/docs/source/en/model_doc/{lowercase_model_name}.md'    ,						)

      shutil.move(
          F'{directory}/tokenization_{lowercase_model_name}.py'    ,						F'{model_dir}/tokenization_{lowercase_model_name}.py'    ,						)

      shutil.move(
          F'{directory}/tokenization_fast_{lowercase_model_name}.py'    ,						F'{model_dir}/tokenization_{lowercase_model_name}_fast.py'    ,						)

      from os import fdopen, remove
      from shutil import copymode, move
      from tempfile import mkstemp

      def replace(lowerCamelCase    :  str    ,						lowerCamelCase    :  str    ,						lowerCamelCase    :  List[str]		):
        # Create temp file
        lowerCAmelCase_,							lowerCAmelCase_			:      int			=   mkstemp()
        lowerCAmelCase_			:      List[Any]			=   False
        with fdopen(lowerCamelCase    ,						"""w"""		) as new_file:
          with open(lowerCamelCase		) as old_file:
            for line in old_file:
              new_file.write(lowerCamelCase		)
              if line_to_copy_below in line:
                lowerCAmelCase_			:      List[str]			=   True
                for line_to_copy in lines_to_copy:
                  new_file.write(lowerCamelCase		)

        if not line_found:
          raise ValueError(F'Line {line_to_copy_below} was not found in file.'		)

        # Copy the file permissions from the old file to the new file
        copymode(lowerCamelCase    ,						lowerCamelCase		)
        # Remove original file
        remove(lowerCamelCase		)
        # Move new file
        move(lowerCamelCase    ,						lowerCamelCase		)

      def skip_units(lowerCamelCase    :  Optional[int]		):
        return (
            ("generating PyTorch" in line and not output_pytorch)
            or ("generating TensorFlow" in line and not output_tensorflow)
            or ("generating Flax" in line and not output_flax)
        )

      def replace_in_files(lowerCamelCase    :  Any		):
        with open(lowerCamelCase		) as datafile:
          lowerCAmelCase_			:      Dict			=   []
          lowerCAmelCase_			:      List[str]			=   False
          lowerCAmelCase_			:      str			=   False
          for line in datafile:
            if "# To replace in: " in line and "##" not in line:
              lowerCAmelCase_			:      Dict			=   line.split("""\""""		)[1]
              lowerCAmelCase_			:      int			=   skip_units(lowerCamelCase		)
            elif "# Below: " in line and "##" not in line:
              lowerCAmelCase_			:      Any			=   line.split("""\""""		)[1]
              lowerCAmelCase_			:      Tuple			=   skip_units(lowerCamelCase		)
            elif "# End." in line and "##" not in line:
              if not skip_file and not skip_snippet:
                replace(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase		)

              lowerCAmelCase_			:      Dict			=   []
            elif "# Replace with" in line and "##" not in line:
              lowerCAmelCase_			:      int			=   []
            elif "##" not in line:
              lines_to_copy.append(lowerCamelCase		)

        remove(lowerCamelCase		)

      replace_in_files(F'{directory}/to_replace_{lowercase_model_name}.py'		)
      os.rmdir(lowerCamelCase		)







'''simple docstring'''






import argparse
import json
import os
import re

import torch

from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging


logging.set_verbosity_info()

__A					:  Any											=      [
    "word_embeddings_layernorm.weight",
    "word_embeddings_layernorm.bias",
    "input_layernorm.weight",
    "input_layernorm.bias",
    "post_attention_layernorm.weight",
    "post_attention_layernorm.bias",
    "self_attention.dense.bias",
    "mlp.dense_4h_to_h.bias",
    "ln_f.weight",
    "ln_f.bias",
]

__A					:  Any											=      [
    "mlp.dense_4h_to_h.weight",
    "self_attention.dense.weight",
]







def 				UpperCamelCase_			(    A__					:   str							,    A__					:   str					):
  '''simple docstring'''





  lowerCAmelCase_			:      Dict			=   {
      """word_embeddings.weight""": """word_embeddings.weight""",
      """word_embeddings.norm.weight""": """word_embeddings_layernorm.weight""",
      """word_embeddings.norm.bias""": """word_embeddings_layernorm.bias""",
      """weight""": """ln_f.weight""",
      """bias""": """ln_f.bias""",
  }

  if key in layer_rename_map:
    return layer_rename_map[key]

  # Handle transformer blocks
  lowerCAmelCase_			:      List[str]			=   int(re.match(R""".*layer_(\d*).*"""							,    A__					)[1]					)
  layer_number -= 3
  return f'h.{layer_number}.' + key







def 				UpperCamelCase_			(    A__					:   List[str]					):
  '''simple docstring'''





  if dtype == torch.bool:
    return 1 / 8
  lowerCAmelCase_			:      List[str]			=   re.search(R"""[^\d](\d+)$"""							,    str(A__					)					)
  if bit_search is None:
    raise ValueError(f'`dtype` is not a valid dtype: {dtype}.'					)
  lowerCAmelCase_			:      Dict			=   int(bit_search.groups()[0]					)
  return bit_size // 8







def 				UpperCamelCase_			(    A__					:   int							,    A__					:   Tuple							,    A__					:   Any							,    A__					:   Tuple							,    A__					:   List[str]					):
  '''simple docstring'''





  if bloom_config_file == "":
    lowerCAmelCase_			:      Any			=   BloomConfig()
  else:
    lowerCAmelCase_			:      Any			=   BloomConfig.from_json_file(A__					)

  if shard_model:
    lowerCAmelCase_			:      Any			=   os.listdir(A__					)
    lowerCAmelCase_			:      Optional[int]			=   sorted(filter(lambda A__					: s.startswith("""layer"""					) and "model_00" in s							,    A__					)					)

    lowerCAmelCase_			:      Optional[Any]			=   {"""weight_map""": {}, """metadata""": {}}
    lowerCAmelCase_			:      Optional[Any]			=   0

    lowerCAmelCase_			:      List[str]			=   None

    lowerCAmelCase_			:      Optional[int]			=   BloomConfig()

    for j, file in enumerate(A__					):
      print("""Processing file: {}""".format(A__					)					)
      lowerCAmelCase_			:      List[str]			=   None

      for i in range(A__					):
        # load all TP files
        lowerCAmelCase_			:      Optional[Any]			=   file.replace("""model_00"""							,    f'model_0{i}'					)
        lowerCAmelCase_			:      Optional[int]			=   torch.load(os.path.join(A__							,    A__					)							,    map_location="""cpu"""					)

        # Rename keys in the transformers names
        lowerCAmelCase_			:      str			=   list(temp.keys()					)
        for key in keys:
          lowerCAmelCase_			:      Optional[int]			=   temp.pop(A__					)

        if tensors is None:
          lowerCAmelCase_			:      str			=   temp
        else:
          for key in tensors.keys():
            if any(key.endswith(A__					) for end in WEIGHTS_TO_AVERAGE_ENDSWITH					):
              # We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
              tensors[key] += temp[key]
            else:
              # Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
              lowerCAmelCase_			:      int			=   1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN					) else 0
              # We concatenate these weights accross TP ranks
              lowerCAmelCase_			:      List[str]			=   torch.cat([tensors[key], temp[key]]							,    dim=A__					)

            # Divide by the number of TP the weights we want to average
      for key in tensors.keys():
        if any(key.endswith(A__					) for end in WEIGHTS_TO_AVERAGE_ENDSWITH					):
          lowerCAmelCase_			:      Optional[int]			=   tensors[key] / pretraining_tp
      torch.save(
          A__							,    os.path.join(
              A__							,    """pytorch_model_{}-of-{}.bin""".format(str(j + 1					).zfill(5					)							,    str(len(A__					)					).zfill(5					)					)							,    )							,    )

      for key in tensors.keys():
        lowerCAmelCase_			:      str			=   tensors[key]
        total_size += value.numel() * get_dtype_size(value.dtype					)
        if key not in index_dict["weight_map"]:
          lowerCAmelCase_			:      Tuple			=   """pytorch_model_{}-of-{}.bin""".format(
              str(j + 1					).zfill(5					)							,    str(len(A__					)					).zfill(5					)					)

    lowerCAmelCase_			:      str			=   BloomConfig()
    lowerCAmelCase_			:      List[Any]			=   pytorch_dump_folder_path + """/""" + CONFIG_NAME
    lowerCAmelCase_			:      List[Any]			=   total_size
    with open(A__							,    """w"""							,    encoding="""utf-8"""					) as f:
      f.write(config.to_json_string()					)
    with open(os.path.join(A__							,    WEIGHTS_NAME + """.index.json"""					)							,    """w"""							,    encoding="""utf-8"""					) as f:
      lowerCAmelCase_			:      Optional[int]			=   json.dumps(A__							,    indent=2							,    sort_keys=A__					) + """\n"""
      f.write(A__					)
  else:
    lowerCAmelCase_			:      int			=   BloomModel(A__					)

    lowerCAmelCase_			:      Union[str, Any]			=   os.listdir(A__					)
    lowerCAmelCase_			:      Tuple			=   sorted(filter(lambda A__					: s.startswith("""layer"""					) and "model_00" in s							,    A__					)					)

    lowerCAmelCase_			:      List[Any]			=   None
    for i, file in enumerate(A__					):
      lowerCAmelCase_			:      List[Any]			=   None
      for i in range(A__					):
        # load all TP files
        lowerCAmelCase_			:      str			=   file.replace("""model_00"""							,    f'model_0{i}'					)
        lowerCAmelCase_			:      Optional[Any]			=   torch.load(os.path.join(A__							,    A__					)							,    map_location="""cpu"""					)

        # Rename keys in the transformers names
        lowerCAmelCase_			:      Union[str, Any]			=   list(temp.keys()					)
        for key in keys:
          lowerCAmelCase_			:      str			=   temp.pop(A__					)

        if tensors is None:
          lowerCAmelCase_			:      int			=   temp
        else:
          for key in tensors.keys():
            # We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
            if any(key.endswith(A__					) for end in WEIGHTS_TO_AVERAGE_ENDSWITH					):
              tensors[key] += temp[key]
            else:
              # Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
              lowerCAmelCase_			:      Optional[Any]			=   1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN					) else 0
              # We concatenate these weights accross TP ranks
              lowerCAmelCase_			:      Optional[Any]			=   torch.cat([tensors[key], temp[key]]							,    dim=A__					)

            # Divide by the number of TP the weights we want to average
      for key in tensors.keys():
        if any(key.endswith(A__					) for end in WEIGHTS_TO_AVERAGE_ENDSWITH					):
          lowerCAmelCase_			:      Union[str, Any]			=   tensors[key] / pretraining_tp

      lowerCAmelCase_			:      Optional[int]			=   model.load_state_dict(A__							,    strict=A__					)
      assert not other_keys.unexpected_keys, f'The keys {other_keys.unexpected_keys} are unexpected'
      if missing_keys is None:
        lowerCAmelCase_			:      Any			=   set(other_keys.missing_keys					)
      else:
        lowerCAmelCase_			:      List[Any]			=   missing_keys.intersection(set(other_keys.missing_keys					)					)

    assert not missing_keys, f'The keys {missing_keys} are missing'

    # Save pytorch-model
    os.makedirs(A__							,    exist_ok=A__					)
    lowerCAmelCase_			:      Dict			=   pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
    lowerCAmelCase_			:      Tuple			=   pytorch_dump_folder_path + """/""" + CONFIG_NAME
    print(f'Save PyTorch model to {pytorch_weights_dump_path} with dtype {config.torch_dtype}'					)
    if config.torch_dtype is not None:
      lowerCAmelCase_			:      Any			=   model.to(config.torch_dtype					)
    torch.save(model.state_dict()							,    A__					)
    print(f'Save configuration file to {pytorch_config_dump_path}'					)
    with open(A__							,    """w"""							,    encoding="""utf-8"""					) as f:
      f.write(config.to_json_string()					)


if __name__ == "__main__":
  __A					:  Tuple											=      argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--bloom_checkpoint_path",
      default=None,
      type=str,
      required=True,
      help="Path to the Megatron-LM checkpoint path.",
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
  )
  parser.add_argument(
      "--bloom_config_file",
      default="",
      type=str,
      help=(
          "An optional config json file corresponding to the pre-trained model. \n"
          "This specifies the model architecture."
      ),
  )
  parser.add_argument(
      "--shard_model",
      action="store_true",
      help="An optional setting to shard the output model \nThis enables sharding the converted checkpoint",
  )
  parser.add_argument(
      "--pretraining_tp",
      default=4,
      type=int,
      help="Pretraining TP rank that has been used when training the model in Megatron-LM \n",
  )
  __A					:  Dict											=      parser.parse_args()
  convert_bloom_checkpoint_to_pytorch(
      args.bloom_checkpoint_path,
      args.bloom_config_file,
      args.pytorch_dump_folder_path,
      args.shard_model,
      args.pretraining_tp,
  )



