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()

__lowerCamelCase			: 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''',
]

__lowerCamelCase			: Optional[Any]							   =		[
    '''mlp.dense_4h_to_h.weight''',
    '''self_attention.dense.weight''',
]



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       int ,     __UpperCamelCase    :       str	)							->					int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      {
      """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
  SCREAMING_SNAKE_CASE__										=      int(re.match(r""".*layer_(\d*).*""" ,     __UpperCamelCase	)[1]	)
  layer_number -= 3
  return f"""h.{layer_number}.""" + key



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       str	)							->					List[str]:







  """simple docstring"""





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



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Optional[Any] ,     __UpperCamelCase    :       Union[str, Any] ,     __UpperCamelCase    :       str ,     __UpperCamelCase    :       List[str] ,     __UpperCamelCase    :       Optional[int]	)							->					Tuple:







  """simple docstring"""





  if bloom_config_file == "":
    SCREAMING_SNAKE_CASE__										=      BloomConfig()
  else:
    SCREAMING_SNAKE_CASE__										=      BloomConfig.from_json_file(__UpperCamelCase	)

  if shard_model:
    SCREAMING_SNAKE_CASE__										=      os.listdir(__UpperCamelCase	)
    SCREAMING_SNAKE_CASE__										=      sorted(filter(lambda __UpperCamelCase	: s.startswith("""layer"""	) and "model_00" in s ,     __UpperCamelCase	)	)

    SCREAMING_SNAKE_CASE__										=      {"""weight_map""": {}, """metadata""": {}}
    SCREAMING_SNAKE_CASE__										=      0

    SCREAMING_SNAKE_CASE__										=      None

    SCREAMING_SNAKE_CASE__										=      BloomConfig()

    for j, file in enumerate(__UpperCamelCase	):
      print("""Processing file: {}""".format(__UpperCamelCase	)	)
      SCREAMING_SNAKE_CASE__										=      None

      for i in range(__UpperCamelCase	):
        # load all TP files
        SCREAMING_SNAKE_CASE__										=      file.replace("""model_00""" ,     f"""model_0{i}"""	)
        SCREAMING_SNAKE_CASE__										=      torch.load(os.path.join(__UpperCamelCase ,     __UpperCamelCase	) ,     map_location="""cpu"""	)

        # Rename keys in the transformers names
        SCREAMING_SNAKE_CASE__										=      list(temp.keys()	)
        for key in keys:
          SCREAMING_SNAKE_CASE__										=      temp.pop(__UpperCamelCase	)

        if tensors is None:
          SCREAMING_SNAKE_CASE__										=      temp
        else:
          for key in tensors.keys():
            if any(key.endswith(__UpperCamelCase	) 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
              SCREAMING_SNAKE_CASE__										=      1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN	) else 0
              # We concatenate these weights accross TP ranks
              SCREAMING_SNAKE_CASE__										=      torch.cat([tensors[key], temp[key]] ,     dim=__UpperCamelCase	)

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

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

    SCREAMING_SNAKE_CASE__										=      BloomConfig()
    SCREAMING_SNAKE_CASE__										=      pytorch_dump_folder_path + """/""" + CONFIG_NAME
    SCREAMING_SNAKE_CASE__										=      total_size
    with open(__UpperCamelCase ,     """w""" ,     encoding="""utf-8"""	) as f:
      f.write(config.to_json_string()	)
    with open(os.path.join(__UpperCamelCase ,     WEIGHTS_NAME + """.index.json"""	) ,     """w""" ,     encoding="""utf-8"""	) as f:
      SCREAMING_SNAKE_CASE__										=      json.dumps(__UpperCamelCase ,     indent=2 ,     sort_keys=__UpperCamelCase	) + """\n"""
      f.write(__UpperCamelCase	)
  else:
    SCREAMING_SNAKE_CASE__										=      BloomModel(__UpperCamelCase	)

    SCREAMING_SNAKE_CASE__										=      os.listdir(__UpperCamelCase	)
    SCREAMING_SNAKE_CASE__										=      sorted(filter(lambda __UpperCamelCase	: s.startswith("""layer"""	) and "model_00" in s ,     __UpperCamelCase	)	)

    SCREAMING_SNAKE_CASE__										=      None
    for i, file in enumerate(__UpperCamelCase	):
      SCREAMING_SNAKE_CASE__										=      None
      for i in range(__UpperCamelCase	):
        # load all TP files
        SCREAMING_SNAKE_CASE__										=      file.replace("""model_00""" ,     f"""model_0{i}"""	)
        SCREAMING_SNAKE_CASE__										=      torch.load(os.path.join(__UpperCamelCase ,     __UpperCamelCase	) ,     map_location="""cpu"""	)

        # Rename keys in the transformers names
        SCREAMING_SNAKE_CASE__										=      list(temp.keys()	)
        for key in keys:
          SCREAMING_SNAKE_CASE__										=      temp.pop(__UpperCamelCase	)

        if tensors is None:
          SCREAMING_SNAKE_CASE__										=      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(__UpperCamelCase	) for end in WEIGHTS_TO_AVERAGE_ENDSWITH	):
              tensors[key] += temp[key]
            else:
              # Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
              SCREAMING_SNAKE_CASE__										=      1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN	) else 0
              # We concatenate these weights accross TP ranks
              SCREAMING_SNAKE_CASE__										=      torch.cat([tensors[key], temp[key]] ,     dim=__UpperCamelCase	)

            # Divide by the number of TP the weights we want to average
      for key in tensors.keys():
        if any(key.endswith(__UpperCamelCase	) for end in WEIGHTS_TO_AVERAGE_ENDSWITH	):
          SCREAMING_SNAKE_CASE__										=      tensors[key] / pretraining_tp

      SCREAMING_SNAKE_CASE__										=      model.load_state_dict(__UpperCamelCase ,     strict=__UpperCamelCase	)
      assert not other_keys.unexpected_keys, f"""The keys {other_keys.unexpected_keys} are unexpected"""
      if missing_keys is None:
        SCREAMING_SNAKE_CASE__										=      set(other_keys.missing_keys	)
      else:
        SCREAMING_SNAKE_CASE__										=      missing_keys.intersection(set(other_keys.missing_keys	)	)

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

    # Save pytorch-model
    os.makedirs(__UpperCamelCase ,     exist_ok=__UpperCamelCase	)
    SCREAMING_SNAKE_CASE__										=      pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
    SCREAMING_SNAKE_CASE__										=      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:
      SCREAMING_SNAKE_CASE__										=      model.to(config.torch_dtype	)
    torch.save(model.state_dict() ,     __UpperCamelCase	)
    print(f"""Save configuration file to {pytorch_config_dump_path}"""	)
    with open(__UpperCamelCase ,     """w""" ,     encoding="""utf-8"""	) as f:
      f.write(config.to_json_string()	)


if __name__ == "__main__":
   __lowerCamelCase			: List[str]							   =		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''',
   )
   __lowerCamelCase			: Tuple							   =		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,
   )

import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('''0.12.2'''):
   raise Exception('''requires fairseq >= 0.12.2''')
if version.parse(fairseq.__version__) > version.parse('''2'''):
   raise Exception('''requires fairseq < v2''')

logging.set_verbosity_info()
__lowerCamelCase			: Dict							   =		logging.get_logger(__name__)

__lowerCamelCase			: Dict							   =		'''Hello, World!'''
__lowerCamelCase			: Optional[Any]							   =		'''en_XX'''



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       str ,     __UpperCamelCase    :       str ,     __UpperCamelCase    :       bool	)							->					Any:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      Path("""data_bin"""	)
  SCREAMING_SNAKE_CASE__										=      FairseqXmodModel.from_pretrained(
      model_name_or_path=str(Path(__UpperCamelCase	).parent	) ,     checkpoint_file=Path(__UpperCamelCase	).name ,     _name="""xmod_base""" ,     arch="""xmod_base""" ,     task="""multilingual_masked_lm""" ,     data_name_or_path=str(__UpperCamelCase	) ,     bpe="""sentencepiece""" ,     sentencepiece_model=str(Path(__UpperCamelCase	).parent / """sentencepiece.bpe.model"""	) ,     src_dict=str(data_dir / """dict.txt"""	) ,     )
  xmod.eval()  # disable dropout
  print(__UpperCamelCase	)

  SCREAMING_SNAKE_CASE__										=      xmod.model.encoder.sentence_encoder
  SCREAMING_SNAKE_CASE__										=      XmodConfig(
      vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings ,     hidden_size=xmod.cfg.model.encoder_embed_dim ,     num_hidden_layers=xmod.cfg.model.encoder_layers ,     num_attention_heads=xmod.cfg.model.encoder_attention_heads ,     intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim ,     max_position_embeddings=5_14 ,     type_vocab_size=1 ,     layer_norm_eps=1E-5 ,     pre_norm=xmod.cfg.model.encoder_normalize_before ,     adapter_reduction_factor=getattr(xmod.cfg.model ,     """bottleneck""" ,     2	) ,     adapter_layer_norm=xmod.cfg.model.adapter_layer_norm ,     adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm ,     ln_before_adapter=xmod.cfg.model.ln_before_adapter ,     languages=xmod.cfg.model.languages ,     )
  if classification_head:
    SCREAMING_SNAKE_CASE__										=      xmod.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

  print("""Our X-MOD config:""" ,     __UpperCamelCase	)

  SCREAMING_SNAKE_CASE__										=      XmodForSequenceClassification(__UpperCamelCase	) if classification_head else XmodForMaskedLM(__UpperCamelCase	)
  model.eval()

  # Now let's copy all the weights.
  # Embeddings
  SCREAMING_SNAKE_CASE__										=      xmod_sent_encoder.embed_tokens.weight
  SCREAMING_SNAKE_CASE__										=      xmod_sent_encoder.embed_positions.weight
  SCREAMING_SNAKE_CASE__										=      torch.zeros_like(
      model.roberta.embeddings.token_type_embeddings.weight	)  # just zero them out b/c xmod doesn't use them.

  SCREAMING_SNAKE_CASE__										=      xmod_sent_encoder.layernorm_embedding.weight
  SCREAMING_SNAKE_CASE__										=      xmod_sent_encoder.layernorm_embedding.bias

  for i in range(config.num_hidden_layers	):
    # Encoder: start of layer
    SCREAMING_SNAKE_CASE__										=      model.roberta.encoder.layer[i]
    SCREAMING_SNAKE_CASE__										=      xmod_sent_encoder.layers[i]

    # self attention
    SCREAMING_SNAKE_CASE__										=      layer.attention.self
    if not (
        xmod_layer.self_attn.k_proj.weight.data.shape
        == xmod_layer.self_attn.q_proj.weight.data.shape
        == xmod_layer.self_attn.v_proj.weight.data.shape
        == torch.Size((config.hidden_size, config.hidden_size)	)
    ):
      raise AssertionError("""Dimensions of self-attention weights do not match."""	)

    SCREAMING_SNAKE_CASE__										=      xmod_layer.self_attn.q_proj.weight
    SCREAMING_SNAKE_CASE__										=      xmod_layer.self_attn.q_proj.bias
    SCREAMING_SNAKE_CASE__										=      xmod_layer.self_attn.k_proj.weight
    SCREAMING_SNAKE_CASE__										=      xmod_layer.self_attn.k_proj.bias
    SCREAMING_SNAKE_CASE__										=      xmod_layer.self_attn.v_proj.weight
    SCREAMING_SNAKE_CASE__										=      xmod_layer.self_attn.v_proj.bias

    # self-attention output
    SCREAMING_SNAKE_CASE__										=      layer.attention.output
    if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
      raise AssertionError("""Dimensions of self-attention output weights do not match."""	)
    SCREAMING_SNAKE_CASE__										=      xmod_layer.self_attn.out_proj.weight
    SCREAMING_SNAKE_CASE__										=      xmod_layer.self_attn.out_proj.bias
    SCREAMING_SNAKE_CASE__										=      xmod_layer.self_attn_layer_norm.weight
    SCREAMING_SNAKE_CASE__										=      xmod_layer.self_attn_layer_norm.bias

    # intermediate
    SCREAMING_SNAKE_CASE__										=      layer.intermediate
    if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
      raise AssertionError("""Dimensions of intermediate weights do not match."""	)
    SCREAMING_SNAKE_CASE__										=      xmod_layer.fca.weight
    SCREAMING_SNAKE_CASE__										=      xmod_layer.fca.bias

    # output
    SCREAMING_SNAKE_CASE__										=      layer.output
    if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
      raise AssertionError("""Dimensions of feed-forward weights do not match."""	)
    SCREAMING_SNAKE_CASE__										=      xmod_layer.fca.weight
    SCREAMING_SNAKE_CASE__										=      xmod_layer.fca.bias
    SCREAMING_SNAKE_CASE__										=      xmod_layer.final_layer_norm.weight
    SCREAMING_SNAKE_CASE__										=      xmod_layer.final_layer_norm.bias
    if bert_output.adapter_layer_norm is not None:
      SCREAMING_SNAKE_CASE__										=      xmod_layer.adapter_layer_norm.weight
      SCREAMING_SNAKE_CASE__										=      xmod_layer.adapter_layer_norm.bias

    if sorted(bert_output.adapter_modules.keys()	) != sorted(xmod_layer.adapter_modules.keys()	):
      raise AssertionError("""Lists of language adapters do not match."""	)
    for lang_code, adapter in xmod_layer.adapter_modules.items():
      SCREAMING_SNAKE_CASE__										=      bert_output.adapter_modules[lang_code]
      SCREAMING_SNAKE_CASE__										=      xmod_layer.adapter_modules[lang_code]
      SCREAMING_SNAKE_CASE__										=      from_adapter.fca.weight
      SCREAMING_SNAKE_CASE__										=      from_adapter.fca.bias
      SCREAMING_SNAKE_CASE__										=      from_adapter.fca.weight
      SCREAMING_SNAKE_CASE__										=      from_adapter.fca.bias

    # end of layer

  if xmod_sent_encoder.layer_norm is not None:
    SCREAMING_SNAKE_CASE__										=      xmod_sent_encoder.layer_norm.weight
    SCREAMING_SNAKE_CASE__										=      xmod_sent_encoder.layer_norm.bias

  if classification_head:
    SCREAMING_SNAKE_CASE__										=      xmod.model.classification_heads["""mnli"""].dense.weight
    SCREAMING_SNAKE_CASE__										=      xmod.model.classification_heads["""mnli"""].dense.bias
    SCREAMING_SNAKE_CASE__										=      xmod.model.classification_heads["""mnli"""].out_proj.weight
    SCREAMING_SNAKE_CASE__										=      xmod.model.classification_heads["""mnli"""].out_proj.bias
  else:
    # LM Head
    SCREAMING_SNAKE_CASE__										=      xmod.model.encoder.lm_head.dense.weight
    SCREAMING_SNAKE_CASE__										=      xmod.model.encoder.lm_head.dense.bias
    SCREAMING_SNAKE_CASE__										=      xmod.model.encoder.lm_head.layer_norm.weight
    SCREAMING_SNAKE_CASE__										=      xmod.model.encoder.lm_head.layer_norm.bias
    SCREAMING_SNAKE_CASE__										=      xmod.model.encoder.lm_head.weight
    SCREAMING_SNAKE_CASE__										=      xmod.model.encoder.lm_head.bias

  # Let's check that we get the same results.
  SCREAMING_SNAKE_CASE__										=      xmod.encode(__UpperCamelCase	).unsqueeze(0	)  # batch of size 1
  model.roberta.set_default_language(__UpperCamelCase	)

  SCREAMING_SNAKE_CASE__										=      model(__UpperCamelCase	)[0]
  if classification_head:
    SCREAMING_SNAKE_CASE__										=      xmod.model.classification_heads["""mnli"""](xmod.extract_features(__UpperCamelCase	)	)
  else:
    SCREAMING_SNAKE_CASE__										=      xmod.model(__UpperCamelCase ,     lang_id=[SAMPLE_LANGUAGE]	)[0]
  print(our_output.shape ,     their_output.shape	)
  SCREAMING_SNAKE_CASE__										=      torch.max(torch.abs(our_output - their_output	)	).item()
  print(f"""max_absolute_diff = {max_absolute_diff}"""	)  # ~ 1e-7
  SCREAMING_SNAKE_CASE__										=      torch.allclose(__UpperCamelCase ,     __UpperCamelCase ,     atol=1E-3	)
  print("""Do both models output the same tensors?""" ,     """🔥""" if success else """💩"""	)
  if not success:
    raise Exception("""Something went wRoNg"""	)

  Path(__UpperCamelCase	).mkdir(parents=__UpperCamelCase ,     exist_ok=__UpperCamelCase	)
  print(f"""Saving model to {pytorch_dump_folder_path}"""	)
  model.save_pretrained(__UpperCamelCase	)


if __name__ == "__main__":
   __lowerCamelCase			: Dict							   =		argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--xmod_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
   )
   parser.add_argument(
       '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
   )
   parser.add_argument(
       '''--classification_head''', action='''store_true''', help='''Whether to convert a final classification head.'''
   )
   __lowerCamelCase			: str							   =		parser.parse_args()
   convert_xmod_checkpoint_to_pytorch(
       args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
   )
