
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__		)							->							Dict:
				"""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*).*"			,		A__		)[1]		)
				layer_number -= 3
				return F'h.{layer_number}.' + key



def 						lowercase_      (       A__		)							->							List[str]:
				"""simple docstring"""

				if dtype == torch.bool:
								return 1 / 8
				snake_case								= re.search(r"[^\d](\d+)$"			,		str(A__		)		)
				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__		)							->							Optional[int]:
				"""simple docstring"""

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

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

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

								snake_case								= None

								snake_case								= BloomConfig()

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

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

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

																if tensors is None:
																				snake_case								= 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
																												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=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		):
																				snake_case								= 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():
																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(A__		)		).zfill(5		)		)

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

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

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

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

																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(A__		) 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=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		):
																				snake_case								= tensors[key] / pretraining_tp

												snake_case								= 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:
																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(A__			,		exist_ok=A__		)
								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()			,		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   =			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 __future__ import annotations

from fractions import Fraction
from math import gcd, sqrt



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

    snake_case								= int(number**0.5		)
    return number == sq * sq



def 						lowercase_      (       A__			,		A__			,		A__			,		A__			,		A__			,		A__		)							->							tuple[int, int]:
    """simple docstring"""

    snake_case								= x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
    snake_case								= x_den * y_den * z_den
    snake_case								= gcd(A__			,		A__		)
    top //= hcf
    bottom //= hcf
    return top, bottom



def 						lowercase_      (       A__ = 35		)							->							int:
    """simple docstring"""

    snake_case								= set()
    snake_case								= 42
    snake_case								= Fraction(0		)
    snake_case								= 42

    for x_num in range(1			,		order + 1		):
        for x_den in range(x_num + 1			,		order + 1		):
            for y_num in range(1			,		order + 1		):
                for y_den in range(y_num + 1			,		order + 1		):
                    # n=1
                    snake_case								= x_num * y_den + x_den * y_num
                    snake_case								= x_den * y_den
                    snake_case								= gcd(A__			,		A__		)
                    z_num //= hcf
                    z_den //= hcf
                    if 0 < z_num < z_den <= order:
                        snake_case								= add_three(
                            A__			,		A__			,		A__			,		A__			,		A__			,		A__		)
                        unique_s.add(A__		)

                    # n=2
                    snake_case								= (
                        x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
                    )
                    snake_case								= x_den * x_den * y_den * y_den
                    if is_sq(A__		) and is_sq(A__		):
                        snake_case								= int(sqrt(A__		)		)
                        snake_case								= int(sqrt(A__		)		)
                        snake_case								= gcd(A__			,		A__		)
                        z_num //= hcf
                        z_den //= hcf
                        if 0 < z_num < z_den <= order:
                            snake_case								= add_three(
                                A__			,		A__			,		A__			,		A__			,		A__			,		A__		)
                            unique_s.add(A__		)

                    # n=-1
                    snake_case								= x_num * y_num
                    snake_case								= x_den * y_num + x_num * y_den
                    snake_case								= gcd(A__			,		A__		)
                    z_num //= hcf
                    z_den //= hcf
                    if 0 < z_num < z_den <= order:
                        snake_case								= add_three(
                            A__			,		A__			,		A__			,		A__			,		A__			,		A__		)
                        unique_s.add(A__		)

                    # n=2
                    snake_case								= x_num * x_num * y_num * y_num
                    snake_case								= (
                        x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
                    )
                    if is_sq(A__		) and is_sq(A__		):
                        snake_case								= int(sqrt(A__		)		)
                        snake_case								= int(sqrt(A__		)		)
                        snake_case								= gcd(A__			,		A__		)
                        z_num //= hcf
                        z_den //= hcf
                        if 0 < z_num < z_den <= order:
                            snake_case								= add_three(
                                A__			,		A__			,		A__			,		A__			,		A__			,		A__		)
                            unique_s.add(A__		)

    for num, den in unique_s:
        total += Fraction(A__			,		A__		)

    return total.denominator + total.numerator


if __name__ == "__main__":
   print(f"{solution() = }")

