
'''simple docstring'''
import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


__lowerCAmelCase												=	False
__lowerCAmelCase												=	True
__lowerCAmelCase												=	False


if __name__ == "__main__":
			__lowerCAmelCase												=	argparse.ArgumentParser()

			parser.add_argument(
			    '--repo_path',
			    default=None,
			    type=str,
			    required=True,
			    help='The config json file corresponding to the architecture.',
			)

			parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

			__lowerCAmelCase												=	parser.parse_args()

			__lowerCAmelCase												=	{
			    'image_size': 'sample_size',
			    'num_res_blocks': 'layers_per_block',
			    'block_channels': 'block_out_channels',
			    'down_blocks': 'down_block_types',
			    'up_blocks': 'up_block_types',
			    'downscale_freq_shift': 'freq_shift',
			    'resnet_num_groups': 'norm_num_groups',
			    'resnet_act_fn': 'act_fn',
			    'resnet_eps': 'norm_eps',
			    'num_head_channels': 'attention_head_dim',
			}

			__lowerCAmelCase												=	{
			    'time_steps': 'time_proj',
			    'mid': 'mid_block',
			    'downsample_blocks': 'down_blocks',
			    'upsample_blocks': 'up_blocks',
			}

			__lowerCAmelCase												=	'' if has_file(args.repo_path, 'config.json') else 'unet'

			with open(os.path.join(args.repo_path, subfolder, 'config.json'), 'r', encoding='utf-8') as reader:
						__lowerCAmelCase												=	reader.read()
						__lowerCAmelCase												=	json.loads(text)

			if do_only_config:
						for key in config_parameters_to_change.keys():
									config.pop(key, None)

			if has_file(args.repo_path, 'config.json'):
						__lowerCAmelCase												=	UNetaDModel(**config)
			else:
						__lowerCAmelCase												=	UNetaDConditionModel if 'ldm-text2im-large-256' in args.repo_path else UNetaDModel
						__lowerCAmelCase												=	class_name(**config)

			if do_only_config:
						model.save_config(os.path.join(args.repo_path, subfolder))

			__lowerCAmelCase												=	dict(model.config)

			if do_only_renaming:
						for key, value in config_parameters_to_change.items():
									if key in config:
												__lowerCAmelCase												=	config[key]
												del config[key]

						__lowerCAmelCase												=	[k.replace('UNetRes', '') for k in config['down_block_types']]
						__lowerCAmelCase												=	[k.replace('UNetRes', '') for k in config['up_block_types']]

			if do_only_weights:
						__lowerCAmelCase												=	torch.load(os.path.join(args.repo_path, subfolder, 'diffusion_pytorch_model.bin'))

						__lowerCAmelCase												=	{}
						for param_key, param_value in state_dict.items():
									if param_key.endswith('.op.bias') or param_key.endswith('.op.weight'):
												continue
									__lowerCAmelCase												=	False
									for key, new_key in key_parameters_to_change.items():
												if not has_changed and param_key.split('.')[0] == key:
															__lowerCAmelCase												=	param_value
															__lowerCAmelCase												=	True
									if not has_changed:
												__lowerCAmelCase												=	param_value

						model.load_state_dict(new_state_dict)
						model.save_pretrained(os.path.join(args.repo_path, subfolder))

'''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  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							_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*).*"""							, _SCREAMING_SNAKE_CASE				)[1]				)
							layer_number -= 3
							return f"""h.{layer_number}.""" + key






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE				):
							if dtype == torch.bool:
														return 1 / 8
							_snake_case       =  re.search(R"""[^\d](\d+)$"""							, str(_SCREAMING_SNAKE_CASE				)				)
							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  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							# Construct model
							if bloom_config_file == "":
														_snake_case       =  BloomConfig()
							else:
														_snake_case       =  BloomConfig.from_json_file(_SCREAMING_SNAKE_CASE				)

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

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

														_snake_case       =  None

														_snake_case       =  BloomConfig()

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

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

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

																												if tensors is None:
																																			_snake_case       =  temp
																												else:
																																			for key in tensors.keys():
																																										if any(key.endswith(_SCREAMING_SNAKE_CASE				) 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=_SCREAMING_SNAKE_CASE				)

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

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

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

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

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

																												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(_SCREAMING_SNAKE_CASE				) 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=_SCREAMING_SNAKE_CASE				)

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

																					_snake_case       =  model.load_state_dict(_SCREAMING_SNAKE_CASE							, strict=_SCREAMING_SNAKE_CASE				)
																					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(_SCREAMING_SNAKE_CASE							, exist_ok=_SCREAMING_SNAKE_CASE				)
														_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()							, _SCREAMING_SNAKE_CASE				)
														print(f"""Save configuration file to {pytorch_config_dump_path}"""				)
														with open(_SCREAMING_SNAKE_CASE							, """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,
				)