from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCAmelCase       =		{
    "configuration_swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"],
}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowerCAmelCase       =		[
					    "SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "Swinv2ForImageClassification",
					    "Swinv2ForMaskedImageModeling",
					    "Swinv2Model",
					    "Swinv2PreTrainedModel",
					]


if TYPE_CHECKING:
					from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_swinva import (
										    SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    SwinvaForImageClassification,
										    SwinvaForMaskedImageModeling,
										    SwinvaModel,
										    SwinvaPreTrainedModel,
										)


else:
					import sys

					_lowerCAmelCase       =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





'''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()

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 a_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					Any:





			"""simple docstring"""







			snake_case:    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
			snake_case:    Dict      							=int(re.match(R'.*layer_(\d*).*'				,							__UpperCAmelCase					)[1]					)
			layer_number -= 3
			return f'''h.{layer_number}.''' + key







def a_     (    __UpperCAmelCase					)				->					Union[str, Any]:





			"""simple docstring"""







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







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)				->					Tuple:





			"""simple docstring"""







			if bloom_config_file == "":
						snake_case:    Tuple      							=BloomConfig()
			else:
						snake_case:    List[Any]      							=BloomConfig.from_json_file(__UpperCAmelCase					)

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

						snake_case:    int      							={'weight_map': {}, 'metadata': {}}
						snake_case:    Dict      							=0

						snake_case:    List[str]      							=None

						snake_case:    Any      							=BloomConfig()

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

									for i in range(__UpperCAmelCase					):
												# load all TP files
												snake_case:    Optional[int]      							=file.replace('model_00'				,							f'''model_0{i}'''					)
												snake_case:    List[str]      							=torch.load(os.path.join(__UpperCAmelCase				,							__UpperCAmelCase					)				,							map_location='cpu'					)

												# Rename keys in the transformers names
												snake_case:    int      							=list(temp.keys()					)
												for key in keys:
															snake_case:    List[Any]      							=temp.pop(__UpperCAmelCase					)

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

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

						snake_case:    Any      							=os.listdir(__UpperCAmelCase					)
						snake_case:    Optional[int]      							=sorted(filter(lambda __UpperCAmelCase					: s.startswith('layer'					) and "model_00" in s				,							__UpperCAmelCase					)					)

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

												# Rename keys in the transformers names
												snake_case:    List[str]      							=list(temp.keys()					)
												for key in keys:
															snake_case:    int      							=temp.pop(__UpperCAmelCase					)

												if tensors is None:
															snake_case:    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(__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:    List[Any]      							=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:    Tuple      							=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:    int      							=tensors[key] / pretraining_tp

									snake_case:    Optional[Any]      							=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:    Optional[Any]      							=set(other_keys.missing_keys					)
									else:
												snake_case:    Any      							=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:    Optional[int]      							=pytorch_dump_folder_path + '/' + WEIGHTS_NAME
						snake_case:    Any      							=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:    Optional[Any]      							=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,
	)




