
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_    (     A__				,	A__	)							->							Tuple:
    """simple docstring"""

    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
    snake_case				  =      int(re.match(r".*layer_(\d*).*"				,	__UpperCAmelCase	)[1]	)
    layer_number -= 3
    return F'h.{layer_number}.' + key



def 		lowercase_    (     A__	)							->							Dict:
    """simple docstring"""

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



def 		lowercase_    (     A__				,	A__				,	A__				,	A__				,	A__	)							->							Any:
    """simple docstring"""

    if bloom_config_file == "":
        snake_case				  =      BloomConfig()
    else:
        snake_case				  =      BloomConfig.from_json_file(__UpperCAmelCase	)

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

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

        snake_case				  =      None

        snake_case				  =      BloomConfig()

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

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

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

                if tensors is None:
                    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
                            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
                            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	):
                    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():
                snake_case				  =      tensors[key]
                total_size += value.numel() * get_dtype_size(value.dtype	)
                if key not in index_dict["weight_map"]:
                    snake_case				  =      "pytorch_model_{}-of-{}.bin".format(
                        str(j + 1	).zfill(5	)				,	str(len(__UpperCAmelCase	)	).zfill(5	)	)

        snake_case				  =      BloomConfig()
        snake_case				  =      pytorch_dump_folder_path + "/" + CONFIG_NAME
        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:
            snake_case				  =      json.dumps(__UpperCAmelCase				,	indent=2				,	sort_keys=__UpperCAmelCase	) + "\n"
            f.write(__UpperCAmelCase	)
    else:
        snake_case				  =      BloomModel(__UpperCAmelCase	)

        snake_case				  =      os.listdir(__UpperCAmelCase	)
        snake_case				  =      sorted(filter(lambda A__	: s.startswith("layer"	) and "model_00" in s				,	__UpperCAmelCase	)	)

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

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

                if tensors is None:
                    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
                            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
                            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	):
                    snake_case				  =      tensors[key] / pretraining_tp

            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:
                snake_case				  =      set(other_keys.missing_keys	)
            else:
                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	)
        snake_case				  =      pytorch_dump_folder_path + "/" + WEIGHTS_NAME
        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:
            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__":
   _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,
   )



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_A						      =				logging.get_logger(__name__)

_A						      =				{
    "roberta-base": "https://huggingface.co/roberta-base/resolve/main/config.json",
    "roberta-large": "https://huggingface.co/roberta-large/resolve/main/config.json",
    "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/config.json",
    "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/config.json",
    "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json",
    "roberta-large-openai-detector": "https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json",
}







class   lowerCamelCase	(						A_						):
     UpperCAmelCase__       :							Optional[int]     =	"roberta"





     def __init__(self   :					Union[str, Any]       ,					_A   :					List[Any]=5_0_2_6_5       ,					_A   :					Dict=7_6_8       ,					_A   :					Tuple=1_2       ,					_A   :					Optional[Any]=1_2       ,					_A   :					int=3_0_7_2       ,					_A   :					List[str]="gelu"       ,					_A   :					Tuple=0.1       ,					_A   :					Dict=0.1       ,					_A   :					Optional[int]=5_1_2       ,					_A   :					Dict=2       ,					_A   :					Optional[Any]=0.02       ,					_A   :					Optional[Any]=1E-12       ,					_A   :					str=1       ,					_A   :					Dict=0       ,					_A   :					Optional[int]=2       ,					_A   :					int="absolute"       ,					_A   :					Any=True       ,					_A   :					Union[str, Any]=None       ,					**_A   :					Optional[int]       ,					)		->		Tuple:
         super().__init__(pad_token_id=_A       ,					bos_token_id=_A       ,					eos_token_id=_A       ,					**_A     )

         snake_case				  =      vocab_size
         snake_case				  =      hidden_size
         snake_case				  =      num_hidden_layers
         snake_case				  =      num_attention_heads
         snake_case				  =      hidden_act
         snake_case				  =      intermediate_size
         snake_case				  =      hidden_dropout_prob
         snake_case				  =      attention_probs_dropout_prob
         snake_case				  =      max_position_embeddings
         snake_case				  =      type_vocab_size
         snake_case				  =      initializer_range
         snake_case				  =      layer_norm_eps
         snake_case				  =      position_embedding_type
         snake_case				  =      use_cache
         snake_case				  =      classifier_dropout







class   lowerCamelCase	(						A_						):





     @property
     def 	UpperCAmelCase(self   :					int     )		->		Mapping[str, Mapping[int, str]]:
         if self.task == "multiple-choice":
             snake_case				  =      {0: "batch", 1: "choice", 2: "sequence"}
         else:
             snake_case				  =      {0: "batch", 1: "sequence"}
         return OrderedDict(
             [
                 ("input_ids", dynamic_axis),
                 ("attention_mask", dynamic_axis),
             ]     )

