


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__			:      Tuple								=[
    '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__			:      Optional[Any]								=[
    'mlp.dense_4h_to_h.weight',
    'self_attention.dense.weight',
]






def 						a__			(				A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     str					=					{
			    '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
			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					int(re.match(r'.*layer_(\d*).*',      A__				)[1]				)
			layer_number -= 3
			return F'''h.{layer_number}.''' + key






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






def 						a__			(				A__,      A__,      A__,      A__,      A__				):
			# Construct model
			if bloom_config_file == "":
						SCREAMING_SNAKE_CASE_		:     Optional[int]					=					BloomConfig()
			else:
						SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					BloomConfig.from_json_file(A__				)

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

						SCREAMING_SNAKE_CASE_		:     Tuple					=					{'weight_map': {}, 'metadata': {}}
						SCREAMING_SNAKE_CASE_		:     int					=					0

						SCREAMING_SNAKE_CASE_		:     str					=					None

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

						for j, file in enumerate(A__				):
									print('Processing file: {}'.format(A__				)				)
									SCREAMING_SNAKE_CASE_		:     Dict					=					None

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

												# Rename keys in the transformers names
												SCREAMING_SNAKE_CASE_		:     Dict					=					list(temp.keys()				)
												for key in keys:
															SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					temp.pop(A__				)

												if tensors is None:
															SCREAMING_SNAKE_CASE_		:     Tuple					=					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
																					SCREAMING_SNAKE_CASE_		:     int					=					1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN				) else 0
																					# We concatenate these weights accross TP ranks
																					SCREAMING_SNAKE_CASE_		:     Optional[int]					=					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				):
															SCREAMING_SNAKE_CASE_		:     Optional[int]					=					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():
												SCREAMING_SNAKE_CASE_		:     str					=					tensors[key]
												total_size += value.numel() * get_dtype_size(value.dtype				)
												if key not in index_dict["weight_map"]:
															SCREAMING_SNAKE_CASE_		:     List[Any]					=					'pytorch_model_{}-of-{}.bin'.format(
															    str(j + 1				).zfill(5				),      str(len(A__				)				).zfill(5				)				)

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

						SCREAMING_SNAKE_CASE_		:     int					=					os.listdir(A__				)
						SCREAMING_SNAKE_CASE_		:     Tuple					=					sorted(filter(lambda A__				: s.startswith('layer'				) and "model_00" in s,      A__				)				)

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

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

												if tensors is None:
															SCREAMING_SNAKE_CASE_		:     List[Any]					=					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
																					SCREAMING_SNAKE_CASE_		:     Dict					=					1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN				) else 0
																					# We concatenate these weights accross TP ranks
																					SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					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				):
															SCREAMING_SNAKE_CASE_		:     Any					=					tensors[key] / pretraining_tp

									SCREAMING_SNAKE_CASE_		:     Any					=					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:
												SCREAMING_SNAKE_CASE_		:     Tuple					=					set(other_keys.missing_keys				)
									else:
												SCREAMING_SNAKE_CASE_		:     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(A__,      exist_ok=A__				)
						SCREAMING_SNAKE_CASE_		:     Any					=					pytorch_dump_folder_path + '/' + WEIGHTS_NAME
						SCREAMING_SNAKE_CASE_		:     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:
									SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					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__":
				lowerCAmelCase__			:      Optional[Any]								=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__			:      List[str]								=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 PIL import Image





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )		->		Image:

			def brightness(SCREAMING_SNAKE_CASE_     ) -> float:
						return 128 + level + (c - 128)

			if not -255.0 <= level <= 255.0:
						raise ValueError('level must be between -255.0 (black) and 255.0 (white)'     )
			return img.point(SCREAMING_SNAKE_CASE_     )


if __name__ == "__main__":
							# Load image
							with Image.open("image_data/lena.jpg") as img:
														# Change brightness to 100
														_SCREAMING_SNAKE_CASE			=						change_brightness(img, 1_00)
														brigt_img.save("image_data/lena_brightness.png", format="png")




