


'''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      (      lowerCAmelCase    :     Any		):





	"""simple docstring"""







	__magic_name__	:   Dict					     =					[
	    '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(lowerCAmelCase		,		lowerCAmelCase		)





def lowerCamelCase      (      lowerCAmelCase    :     List[str]		):





	"""simple docstring"""







	__magic_name__     ,    __magic_name__	:   Union[str, Any]					     =					emb.weight.shape
	__magic_name__	:   Any					     =					nn.Linear(lowerCAmelCase		,		lowerCAmelCase		,		bias=lowerCAmelCase		)
	__magic_name__	:   Optional[Any]					     =					emb.weight.data
	return lin_layer





def lowerCamelCase      (      lowerCAmelCase    :     List[str]		,		lowerCAmelCase    :     Union[str, Any]=None		):





	"""simple docstring"""







	__magic_name__	:   Dict					     =					{}
	for old_key in state_dict.keys():
		__magic_name__	:   Any					     =					old_key
		if "moe_layer.experts." in key:
			if expert_idx is not None:
				__magic_name__	:   Optional[Any]					     =					key.replace('moe_layer.experts.0'		,		f'ffn.experts.expert_{expert_idx}'		)
			else:
				__magic_name__	:   int					     =					key.replace('moe_layer.experts.'		,		'ffn.experts.expert_'		)
		if "gate" in key:
			__magic_name__	:   List[str]					     =					key.replace('.moe_layer.gate.wg'		,		'.ffn.router.classifier'		)
		if "fc2" and "experts" not in key:
			__magic_name__	:   Union[str, Any]					     =					key.replace('.fc2.'		,		'.ffn.fc2.'		)
		if "fc1" and "experts" not in key:
			__magic_name__	:   Tuple					     =					key.replace('.fc1.'		,		'.ffn.fc1.'		)
		if ".encoder_attn." in key:
			__magic_name__	:   Tuple					     =					key.replace('.encoder_attn.'		,		'.cross_attention.'		)
		if "encoder_attn_layer_norm" in key:
			__magic_name__	:   Dict					     =					key.replace('encoder_attn_layer_norm'		,		'cross_attention_layer_norm'		)
		if "final_layer_norm" in key:
			__magic_name__	:   Any					     =					key.replace('final_layer_norm'		,		'ff_layer_norm'		)
		__magic_name__	:   Optional[Any]					     =					state_dict[old_key]
	return new_dict





def lowerCamelCase      (      lowerCAmelCase    :     str		,		lowerCAmelCase    :     Tuple		,		lowerCAmelCase    :     str		,		lowerCAmelCase    :     Union[str, Any]		,		lowerCAmelCase    :     str = WEIGHTS_NAME		):





	"""simple docstring"""







	__magic_name__	:   List[str]					     =					[]
	__magic_name__	:   Union[str, Any]					     =					0
	os.makedirs(lowerCAmelCase		,		exist_ok=lowerCAmelCase		)

	for expert in range(lowerCAmelCase		):
		__magic_name__	:   List[str]					     =					switch_checkpoint_path + f'-rank-{expert}.pt'
		if os.path.isfile(lowerCAmelCase		):
			__magic_name__	:   int					     =					torch.load(lowerCAmelCase		)['model']
			remove_ignore_keys_(lowerCAmelCase		)
			__magic_name__	:   Optional[Any]					     =					rename_fairseq_keys(lowerCAmelCase		,		lowerCAmelCase		)
			__magic_name__	:   Dict					     =					os.path.join(
			    lowerCAmelCase		,		weights_name.replace('.bin'		,		f'-{len(lowerCAmelCase		)+1:05d}-of-???.bin'		)		)
			torch.save(lowerCAmelCase		,		lowerCAmelCase		)
			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(lowerCAmelCase		)[0]].dtype		)

    # Add the last block
	__magic_name__	:   Dict					     =					os.path.join(lowerCAmelCase		,		weights_name.replace('.bin'		,		f'-{len(lowerCAmelCase		)+1:05d}-of-???.bin'		)		)
	__magic_name__	:   Optional[Any]					     =					torch.load(switch_checkpoint_path + '-shared.pt'		)['model']
	remove_ignore_keys_(lowerCAmelCase		)
	__magic_name__	:   Tuple					     =					rename_fairseq_keys(lowerCAmelCase		,		lowerCAmelCase		)
	__magic_name__	:   Tuple					     =					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(lowerCAmelCase		) == 1:
		__magic_name__	:   Dict					     =					os.path.join(lowerCAmelCase		,		lowerCAmelCase		)
		torch.save(lowerCAmelCase		,		lowerCAmelCase		)
		return {weights_name: sharded_state_dicts[0]}, None
	else:
		torch.save(lowerCAmelCase		,		lowerCAmelCase		)
	# Otherwise, let's build the index
	__magic_name__	:   List[str]					     =					{}
	for idx, shard in enumerate(lowerCAmelCase		):
		__magic_name__	:   Union[str, Any]					     =					weights_name.replace('.bin'		,		f'-{idx+1:05d}-of-{len(lowerCAmelCase		):05d}.bin'		)
		__magic_name__	:   List[str]					     =					os.path.join(lowerCAmelCase		,		weights_name.replace('.bin'		,		f'-{idx+1:05d}-of-???.bin'		)		)
		os.rename(lowerCAmelCase		,		os.path.join(lowerCAmelCase		,		lowerCAmelCase		)		)
		for key in shard:
			__magic_name__	:   int					     =					shard_file

    # Add the metadata
	__magic_name__	:   str					     =					{'total_size': total_size}
	__magic_name__	:   Union[str, Any]					     =					{'metadata': metadata, 'weight_map': weight_map}

	with open(os.path.join(lowerCAmelCase		,		lowerCAmelCase		)		,		'w'		,		encoding='utf-8'		) as f:
		__magic_name__	:   Union[str, Any]					     =					json.dumps(lowerCAmelCase		,		indent=2		,		sort_keys=lowerCAmelCase		) + '\n'
		f.write(lowerCAmelCase		)

	return metadata, index


if __name__ == "__main__":
							lowerCAmelCase				:Any        =      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				:List[str]        =      parser.parse_args()
							lowerCAmelCase     ,   lowerCAmelCase				:str        =      shard_on_the_fly(
							    args.nllb_moe_checkpoint_path,
							    args.pytorch_dump_folder_path,
							    1_2_8,
							    args.dtype,
							)

							lowerCAmelCase				:Dict        =      NllbMoeConfig.from_pretrained(
							    '''facebook/nllb-200-3.3B''', encoder_sparse_step=4, decoder_sparse_step=4, num_experts=1_2_8
							)
							config.save_pretrained(args.pytorch_dump_folder_path)
							lowerCAmelCase				:List[str]        =      NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
							print('''Done''')
							model.save_pretrained(args.pytorch_dump_folder_path)



'''simple docstring'''

# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
							from PIL import Image
class _lowerCamelCase	(						lowercase__							):



		'''simple docstring'''







		A_     :      Any				   =    """Salesforce/blip-image-captioning-base"""
		A_     :      Any				   =    (
		    """This is a tool that generates a description of an image. It takes an input named `image` which should be the """
		    """image to caption, and returns a text that contains the description in English."""
		)
		A_     :      List[str]				   =    """image_captioner"""
		A_     :      Optional[Any]				   =    AutoModelForVisionaSeq

		A_     :      Any				   =    ["""image"""]
		A_     :      Union[str, Any]				   =    ["""text"""]







		def __init__( self		:						Dict  ,					*_A		:						Dict  ,					**_A		:						Any      )  ->			Dict:
			requires_backends(self  ,					['vision']      )
			super().__init__(*_A  ,					**_A      )







		def        __lowerCAmelCase		( self		:						Tuple  ,					_A		:						"Image"      )  ->			Union[str, Any]:
			return self.pre_processor(images=_A  ,					return_tensors='pt'      )







		def        __lowerCAmelCase		( self		:						Optional[Any]  ,					_A		:						str      )  ->			List[Any]:
			return self.model.generate(**_A      )







		def        __lowerCAmelCase		( self		:						int  ,					_A		:						Optional[int]      )  ->			Union[str, Any]:
			return self.pre_processor.batch_decode(_A  ,					skip_special_tokens=_A      )[0].strip()