
"""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()

lowerCamelCase__           =	[
    """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__           =	[
    """mlp.dense_4h_to_h.weight""",
    """self_attention.dense.weight""",
]
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		Union[str, 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
	__lowerCAmelCase    :		List[Any]		    =   int(re.match(r'.*layer_(\d*).*' ,						_UpperCamelCase	)[1]	)
	layer_number -= 3
	return F"h.{layer_number}." + key
def 					__lowerCAmelCase       (_UpperCamelCase	):
	if dtype == torch.bool:
		return 1 / 8
	__lowerCAmelCase    :		Dict		    =   re.search(r'[^\d](\d+)$' ,						str(_UpperCamelCase	)	)
	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 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	# Construct model
	if bloom_config_file == "":
		__lowerCAmelCase    :		Optional[Any]		    =   BloomConfig()
	else:
		__lowerCAmelCase    :		str		    =   BloomConfig.from_json_file(_UpperCamelCase	)

	if shard_model:
		__lowerCAmelCase    :		Tuple		    =   os.listdir(_UpperCamelCase	)
		__lowerCAmelCase    :		str		    =   sorted(filter(lambda _UpperCamelCase	: s.startswith('layer'	) and "model_00" in s ,						_UpperCamelCase	)	)

		__lowerCAmelCase    :		Tuple		    =   {'weight_map': {}, 'metadata': {}}
		__lowerCAmelCase    :		List[Any]		    =   0

		__lowerCAmelCase    :		Optional[int]		    =   None

		__lowerCAmelCase    :		Union[str, Any]		    =   BloomConfig()

		for j, file in enumerate(_UpperCamelCase	):
			print('Processing file: {}'.format(_UpperCamelCase	)	)
			__lowerCAmelCase    :		Tuple		    =   None

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

				# Rename keys in the transformers names
				__lowerCAmelCase    :		Union[str, Any]		    =   list(temp.keys()	)
				for key in keys:
					__lowerCAmelCase    :		Optional[int]		    =   temp.pop(_UpperCamelCase	)

				if tensors is None:
					__lowerCAmelCase    :		Tuple		    =   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
							__lowerCAmelCase    :		Optional[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    :		int		    =   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	):
					__lowerCAmelCase    :		List[Any]		    =   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():
				__lowerCAmelCase    :		int		    =   tensors[key]
				total_size += value.numel() * get_dtype_size(value.dtype	)
				if key not in index_dict["weight_map"]:
					__lowerCAmelCase    :		Any		    =   'pytorch_model_{}-of-{}.bin'.format(
					    str(j + 1	).zfill(5	) ,						str(len(_UpperCamelCase	)	).zfill(5	)	)

		__lowerCAmelCase    :		Optional[Any]		    =   BloomConfig()
		__lowerCAmelCase    :		Tuple		    =   pytorch_dump_folder_path + '/' + CONFIG_NAME
		__lowerCAmelCase    :		Optional[int]		    =   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:
			__lowerCAmelCase    :		Any		    =   json.dumps(_UpperCamelCase ,						indent=2 ,						sort_keys=_UpperCamelCase	) + '\n'
			f.write(_UpperCamelCase	)
	else:
		__lowerCAmelCase    :		Union[str, Any]		    =   BloomModel(_UpperCamelCase	)

		__lowerCAmelCase    :		Union[str, Any]		    =   os.listdir(_UpperCamelCase	)
		__lowerCAmelCase    :		str		    =   sorted(filter(lambda _UpperCamelCase	: s.startswith('layer'	) and "model_00" in s ,						_UpperCamelCase	)	)

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

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

				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(_UpperCamelCase	) for end in WEIGHTS_TO_AVERAGE_ENDSWITH	):
							tensors[key] += temp[key]
						else:
							# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
							__lowerCAmelCase    :		Union[str, 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    :		str		    =   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	):
					__lowerCAmelCase    :		List[str]		    =   tensors[key] / pretraining_tp

			__lowerCAmelCase    :		int		    =   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:
				__lowerCAmelCase    :		Dict		    =   set(other_keys.missing_keys	)
			else:
				__lowerCAmelCase    :		List[str]		    =   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	)
		__lowerCAmelCase    :		int		    =   pytorch_dump_folder_path + '/' + WEIGHTS_NAME
		__lowerCAmelCase    :		int		    =   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    :		Dict		    =   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__           =	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__           =	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,
							)

"""simple docstring"""







from __future__ import annotations

from collections import Counter
from random import random




class 				A__ :

		def __init__(   self				):
			__lowerCAmelCase    :		Any		    =   {}

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Dict		    =   {}

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			if nodea not in self.connections:
				self.add_node(_SCREAMING_SNAKE_CASE				)
			if nodea not in self.connections:
				self.add_node(_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Optional[Any]		    =   probability

		def 							__lowerCamelCase							(   self				):
			return list(self.connections				)







		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		List[str]		    =   0
			__lowerCAmelCase    :		List[Any]		    =   random()

			for dest in self.connections[node]:
				current_probability += self.connections[node][dest]
				if current_probability > random_value:
					return dest
			return ""



def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		Optional[Any]		    =   MarkovChainGraphUndirectedUnweighted()

	for nodea, nodea, probability in transitions:
		graph.add_transition_probability(_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	)

	__lowerCAmelCase    :		str		    =   Counter(graph.get_nodes()	)
	__lowerCAmelCase    :		Tuple		    =   start

	for _ in range(_UpperCamelCase	):
		__lowerCAmelCase    :		int		    =   graph.transition(_UpperCamelCase	)
		visited[node] += 1

	return visited


if __name__ == "__main__":
							import doctest

							doctest.testmod()