
"""simple docstring"""







import argparse
import json
import os

import torch
from torch import nn

from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def 					__lowerCAmelCase       (_UpperCamelCase	):
	__lowerCAmelCase    :		Union[str, Any]		    =   [
	    'encoder.version',
	    'decoder.version',
	    'model.encoder.version',
	    'model.decoder.version',
	    'decoder.output_projection.weight',
	    '_float_tensor',
	    'encoder.embed_positions._float_tensor',
	    'decoder.embed_positions._float_tensor',
	]
	for k in ignore_keys:
		state_dict.pop(_UpperCamelCase ,						_UpperCamelCase	)
def 					__lowerCAmelCase       (_UpperCamelCase	):
	__lowerCAmelCase    ,     __lowerCAmelCase    :		str		    =   emb.weight.shape
	__lowerCAmelCase    :		Optional[int]		    =   nn.Linear(_UpperCamelCase ,						_UpperCamelCase ,						bias=_UpperCamelCase	)
	__lowerCAmelCase    :		Optional[Any]		    =   emb.weight.data
	return lin_layer
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase=None	):
	__lowerCAmelCase    :		List[Any]		    =   {}
	for old_key in state_dict.keys():
		__lowerCAmelCase    :		Dict		    =   old_key
		if "moe_layer.experts." in key:
			if expert_idx is not None:
				__lowerCAmelCase    :		Union[str, Any]		    =   key.replace('moe_layer.experts.0' ,						F"ffn.experts.expert_{expert_idx}"	)
			else:
				__lowerCAmelCase    :		Any		    =   key.replace('moe_layer.experts.' ,						'ffn.experts.expert_'	)
		if "gate" in key:
			__lowerCAmelCase    :		Tuple		    =   key.replace('.moe_layer.gate.wg' ,						'.ffn.router.classifier'	)
		if "fc2" and "experts" not in key:
			__lowerCAmelCase    :		int		    =   key.replace('.fc2.' ,						'.ffn.fc2.'	)
		if "fc1" and "experts" not in key:
			__lowerCAmelCase    :		Tuple		    =   key.replace('.fc1.' ,						'.ffn.fc1.'	)
		if ".encoder_attn." in key:
			__lowerCAmelCase    :		Union[str, Any]		    =   key.replace('.encoder_attn.' ,						'.cross_attention.'	)
		if "encoder_attn_layer_norm" in key:
			__lowerCAmelCase    :		List[str]		    =   key.replace('encoder_attn_layer_norm' ,						'cross_attention_layer_norm'	)
		if "final_layer_norm" in key:
			__lowerCAmelCase    :		int		    =   key.replace('final_layer_norm' ,						'ff_layer_norm'	)
		__lowerCAmelCase    :		str		    =   state_dict[old_key]
	return new_dict
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase = WEIGHTS_NAME	):
	__lowerCAmelCase    :		Any		    =   []
	__lowerCAmelCase    :		Tuple		    =   0
	os.makedirs(_UpperCamelCase ,						exist_ok=_UpperCamelCase	)

	for expert in range(_UpperCamelCase	):
		__lowerCAmelCase    :		int		    =   switch_checkpoint_path + F"-rank-{expert}.pt"
		if os.path.isfile(_UpperCamelCase	):
			__lowerCAmelCase    :		Union[str, Any]		    =   torch.load(_UpperCamelCase	)['model']
			remove_ignore_keys_(_UpperCamelCase	)
			__lowerCAmelCase    :		Optional[int]		    =   rename_fairseq_keys(_UpperCamelCase ,						_UpperCamelCase	)
			__lowerCAmelCase    :		Dict		    =   os.path.join(
			    _UpperCamelCase ,						weights_name.replace('.bin' ,						F"-{len(_UpperCamelCase	)+1:05d}-of-???.bin"	)	)
			torch.save(_UpperCamelCase ,						_UpperCamelCase	)
			sharded_state_dicts.append(expert_state.keys()	)
			total_size += sum([value.numel() for key, value in expert_state.items()]	) * dtype_byte_size(
			    expert_state[list(_UpperCamelCase	)[0]].dtype	)

    # Add the last block
	__lowerCAmelCase    :		List[str]		    =   os.path.join(_UpperCamelCase ,						weights_name.replace('.bin' ,						F"-{len(_UpperCamelCase	)+1:05d}-of-???.bin"	)	)
	__lowerCAmelCase    :		List[Any]		    =   torch.load(switch_checkpoint_path + '-shared.pt'	)['model']
	remove_ignore_keys_(_UpperCamelCase	)
	__lowerCAmelCase    :		Tuple		    =   rename_fairseq_keys(_UpperCamelCase ,						_UpperCamelCase	)
	__lowerCAmelCase    :		Optional[Any]		    =   shared_weights['decoder.embed_tokens.weight']
	sharded_state_dicts.append(shared_weights.keys()	)

	# If we only have the shared weights (dummy model/experts saved on the same file)
	if len(_UpperCamelCase	) == 1:
		__lowerCAmelCase    :		Optional[int]		    =   os.path.join(_UpperCamelCase ,						_UpperCamelCase	)
		torch.save(_UpperCamelCase ,						_UpperCamelCase	)
		return {weights_name: sharded_state_dicts[0]}, None
	else:
		torch.save(_UpperCamelCase ,						_UpperCamelCase	)
	# Otherwise, let's build the index
	__lowerCAmelCase    :		int		    =   {}
	for idx, shard in enumerate(_UpperCamelCase	):
		__lowerCAmelCase    :		Union[str, Any]		    =   weights_name.replace('.bin' ,						F"-{idx+1:05d}-of-{len(_UpperCamelCase	):05d}.bin"	)
		__lowerCAmelCase    :		str		    =   os.path.join(_UpperCamelCase ,						weights_name.replace('.bin' ,						F"-{idx+1:05d}-of-???.bin"	)	)
		os.rename(_UpperCamelCase ,						os.path.join(_UpperCamelCase ,						_UpperCamelCase	)	)
		for key in shard:
			__lowerCAmelCase    :		Union[str, Any]		    =   shard_file

    # Add the metadata
	__lowerCAmelCase    :		int		    =   {'total_size': total_size}
	__lowerCAmelCase    :		List[str]		    =   {'metadata': metadata, 'weight_map': weight_map}

	with open(os.path.join(_UpperCamelCase ,						_UpperCamelCase	) ,						'w' ,						encoding='utf-8'	) as f:
		__lowerCAmelCase    :		Dict		    =   json.dumps(_UpperCamelCase ,						indent=2 ,						sort_keys=_UpperCamelCase	) + '\n'
		f.write(_UpperCamelCase	)

	return metadata, index


if __name__ == "__main__":
							lowerCamelCase__           =	argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--nllb_moe_checkpoint_path""",
							    default="""/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000""",
							    type=str,
							    required=False,
							    help="""Path to a directory containing a folder per layer. Follows the original Google format.""",
							)
							parser.add_argument("""--dtype""", default="""float32""", type=str, required=False, help="""dtype of the saved model""")
							parser.add_argument(
							    """--pytorch_dump_folder_path""",
							    default="""/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b""",
							    type=str,
							    required=False,
							    help="""Path to the output pytorch model.""",
							)
							lowerCamelCase__           =	parser.parse_args()
							lowerCamelCase__      ,		lowerCamelCase__           =	shard_on_the_fly(
							    args.nllb_moe_checkpoint_path,
							    args.pytorch_dump_folder_path,
							    128,
							    args.dtype,
							)

							lowerCamelCase__           =	NllbMoeConfig.from_pretrained(
							    """facebook/nllb-200-3.3B""", encoder_sparse_step=4, decoder_sparse_step=4, num_experts=128
							)
							config.save_pretrained(args.pytorch_dump_folder_path)
							lowerCamelCase__           =	NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
							print("""Done""")
							model.save_pretrained(args.pytorch_dump_folder_path)

"""simple docstring"""







from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 				A__ (			_lowerCamelCase):
		A_		: Any            =			['image_processor', 'tokenizer']
		A_		: Optional[Any]            =			'BlipImageProcessor'
		A_		: int            =			'AutoTokenizer'

		def __init__(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Union[str, Any]		    =   False
			super().__init__(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		List[Any]		    =   self.image_processor

		def __call__(   self ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = True ,	_SCREAMING_SNAKE_CASE = False ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = 0 ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = False ,	_SCREAMING_SNAKE_CASE = False ,	_SCREAMING_SNAKE_CASE = False ,	_SCREAMING_SNAKE_CASE = False ,	_SCREAMING_SNAKE_CASE = False ,	_SCREAMING_SNAKE_CASE = True ,	_SCREAMING_SNAKE_CASE = None ,	**_SCREAMING_SNAKE_CASE ,	):
			if images is None and text is None:
				raise ValueError('You have to specify either images or text.'				)

			# Get only text
			if images is None:
				__lowerCAmelCase    :		Optional[int]		    =   self.tokenizer
				__lowerCAmelCase    :		List[str]		    =   self.tokenizer(
				    text=_SCREAMING_SNAKE_CASE ,	add_special_tokens=_SCREAMING_SNAKE_CASE ,	padding=_SCREAMING_SNAKE_CASE ,	truncation=_SCREAMING_SNAKE_CASE ,	max_length=_SCREAMING_SNAKE_CASE ,	stride=_SCREAMING_SNAKE_CASE ,	pad_to_multiple_of=_SCREAMING_SNAKE_CASE ,	return_attention_mask=_SCREAMING_SNAKE_CASE ,	return_overflowing_tokens=_SCREAMING_SNAKE_CASE ,	return_special_tokens_mask=_SCREAMING_SNAKE_CASE ,	return_offsets_mapping=_SCREAMING_SNAKE_CASE ,	return_token_type_ids=_SCREAMING_SNAKE_CASE ,	return_length=_SCREAMING_SNAKE_CASE ,	verbose=_SCREAMING_SNAKE_CASE ,	return_tensors=_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE ,	)
				return text_encoding

			# add pixel_values
			__lowerCAmelCase    :		List[Any]		    =   self.image_processor(_SCREAMING_SNAKE_CASE ,	return_tensors=_SCREAMING_SNAKE_CASE				)

			if text is not None:
				__lowerCAmelCase    :		Optional[int]		    =   self.tokenizer(
				    text=_SCREAMING_SNAKE_CASE ,	add_special_tokens=_SCREAMING_SNAKE_CASE ,	padding=_SCREAMING_SNAKE_CASE ,	truncation=_SCREAMING_SNAKE_CASE ,	max_length=_SCREAMING_SNAKE_CASE ,	stride=_SCREAMING_SNAKE_CASE ,	pad_to_multiple_of=_SCREAMING_SNAKE_CASE ,	return_attention_mask=_SCREAMING_SNAKE_CASE ,	return_overflowing_tokens=_SCREAMING_SNAKE_CASE ,	return_special_tokens_mask=_SCREAMING_SNAKE_CASE ,	return_offsets_mapping=_SCREAMING_SNAKE_CASE ,	return_token_type_ids=_SCREAMING_SNAKE_CASE ,	return_length=_SCREAMING_SNAKE_CASE ,	verbose=_SCREAMING_SNAKE_CASE ,	return_tensors=_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE ,	)
			else:
				__lowerCAmelCase    :		int		    =   None

			if text_encoding is not None:
				encoding_image_processor.update(_SCREAMING_SNAKE_CASE				)

			return encoding_image_processor

		def 							__lowerCamelCase							(   self ,	*_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				):
			return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self ,	*_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				):
			return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)







		@property
		# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		int		    =   self.tokenizer.model_input_names
			__lowerCAmelCase    :		Union[str, Any]		    =   self.image_processor.model_input_names
			return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)