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_    					=       [
    '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_    					=       [
    'mlp.dense_4h_to_h.weight',
    'self_attention.dense.weight',
]





def    __lowercase     (						lowerCamelCase					:     Union[str, Any]  ,  lowerCamelCase					:     Optional[Any]	):
 UpperCamelCase_					:						Optional[Any] =					{
     '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
 UpperCamelCase_					:						Any =					int(re.match(R'.*layer_(\d*).*'  ,  lowerCamelCase	)[1]	)
 layer_number -= 3
 return F"h.{layer_number}." + key





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





def    __lowercase     (						lowerCamelCase					:     Optional[int]  ,  lowerCamelCase					:     Union[str, Any]  ,  lowerCamelCase					:     Optional[int]  ,  lowerCamelCase					:     int  ,  lowerCamelCase					:     Tuple	):
 # Construct model
 if bloom_config_file == "":
  UpperCamelCase_					:						Optional[int] =					BloomConfig()
 else:
  UpperCamelCase_					:						Tuple =					BloomConfig.from_json_file(lowerCamelCase	)

 if shard_model:
  UpperCamelCase_					:						Tuple =					os.listdir(lowerCamelCase	)
  UpperCamelCase_					:						List[str] =					sorted(filter(lambda lowerCamelCase	: s.startswith('layer'	) and "model_00" in s  ,  lowerCamelCase	)	)

  UpperCamelCase_					:						List[str] =					{'weight_map': {}, 'metadata': {}}
  UpperCamelCase_					:						Union[str, Any] =					0

  UpperCamelCase_					:						Dict =					None

  UpperCamelCase_					:						Optional[int] =					BloomConfig()

  for j, file in enumerate(lowerCamelCase	):
   print('Processing file: {}'.format(lowerCamelCase	)	)
   UpperCamelCase_					:						int =					None

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

    # Rename keys in the transformers names
    UpperCamelCase_					:						int =					list(temp.keys()	)
    for key in keys:
     UpperCamelCase_					:						Tuple =					temp.pop(lowerCamelCase	)

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

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

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

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

  UpperCamelCase_					:						int =					os.listdir(lowerCamelCase	)
  UpperCamelCase_					:						Any =					sorted(filter(lambda lowerCamelCase	: s.startswith('layer'	) and "model_00" in s  ,  lowerCamelCase	)	)

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

    # Rename keys in the transformers names
    UpperCamelCase_					:						List[str] =					list(temp.keys()	)
    for key in keys:
     UpperCamelCase_					:						List[str] =					temp.pop(lowerCamelCase	)

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

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

   UpperCamelCase_					:						Optional[Any] =					model.load_state_dict(lowerCamelCase  ,  strict=lowerCamelCase	)
   assert not other_keys.unexpected_keys, F"The keys {other_keys.unexpected_keys} are unexpected"
   if missing_keys is None:
    UpperCamelCase_					:						Any =					set(other_keys.missing_keys	)
   else:
    UpperCamelCase_					:						Dict =					missing_keys.intersection(set(other_keys.missing_keys	)	)

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

  # Save pytorch-model
  os.makedirs(lowerCamelCase  ,  exist_ok=lowerCamelCase	)
  UpperCamelCase_					:						int =					pytorch_dump_folder_path + '/' + WEIGHTS_NAME
  UpperCamelCase_					:						List[str] =					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:
   UpperCamelCase_					:						int =					model.to(config.torch_dtype	)
  torch.save(model.state_dict()  ,  lowerCamelCase	)
  print(F"Save configuration file to {pytorch_config_dump_path}"	)
  with open(lowerCamelCase  ,  'w'  ,  encoding='utf-8'	) as f:
   f.write(config.to_json_string()	)


if __name__ == "__main__":
   a_    					=       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_    					=       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

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
   a_    					=       argparse.ArgumentParser(
       description=(
           'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned'
           ' Distillation'
       )
   )
   parser.add_argument('--model_type', default='roberta', choices=['roberta', 'gpt2'])
   parser.add_argument('--model_name', default='roberta-large', type=str)
   parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_roberta_048131723.pth', type=str)
   parser.add_argument('--vocab_transform', action='store_true')
   a_    					=       parser.parse_args()

   if args.model_type == "roberta":
      a_    					=       RobertaForMaskedLM.from_pretrained(args.model_name)
      a_    					=       'roberta'
   elif args.model_type == "gpt2":
      a_    					=       GPTaLMHeadModel.from_pretrained(args.model_name)
      a_    					=       'transformer'

   a_    					=       model.state_dict()
   a_    					=       {}

   # Embeddings #
   if args.model_type == "gpt2":
      for param_name in ["wte.weight", "wpe.weight"]:
         a_    					=       state_dict[F"""{prefix}.{param_name}"""]
   else:
      for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
         a_    					=       F"""{prefix}.embeddings.{w}.weight"""
         a_    					=       state_dict[param_name]
      for w in ["weight", "bias"]:
         a_    					=       F"""{prefix}.embeddings.LayerNorm.{w}"""
         a_    					=       state_dict[param_name]

    # Transformer Blocks #
   a_    					=       0
   for teacher_idx in [0, 2, 4, 7, 9, 11]:
      if args.model_type == "gpt2":
         for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
            for w in ["weight", "bias"]:
               a_    					=       state_dict[
                   F"""{prefix}.h.{teacher_idx}.{layer}.{w}"""
               ]
         a_    					=       state_dict[F"""{prefix}.h.{teacher_idx}.attn.bias"""]
      else:
         for layer in [
             "attention.self.query",
             "attention.self.key",
             "attention.self.value",
             "attention.output.dense",
             "attention.output.LayerNorm",
             "intermediate.dense",
             "output.dense",
             "output.LayerNorm",
         ]:
            for w in ["weight", "bias"]:
               a_    					=       state_dict[
                   F"""{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}"""
               ]
      std_idx += 1

   # Language Modeling Head ###s
   if args.model_type == "roberta":
      for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
         a_    					=       state_dict[F"""{layer}"""]
      if args.vocab_transform:
         for w in ["weight", "bias"]:
            a_    					=       state_dict[F"""lm_head.dense.{w}"""]
            a_    					=       state_dict[F"""lm_head.layer_norm.{w}"""]
   elif args.model_type == "gpt2":
      for w in ["weight", "bias"]:
         a_    					=       state_dict[F"""{prefix}.ln_f.{w}"""]
      a_    					=       state_dict['lm_head.weight']

   print(F"""N layers selected for distillation: {std_idx}""")
   print(F"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")

   print(F"""Save transferred checkpoint to {args.dump_checkpoint}.""")
   torch.save(compressed_sd, args.dump_checkpoint)



