


'''simple docstring'''






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
							from ...processing_utils import ProcessorMixin
							from ...utils import TensorType


__A	:	str     =     logging.get_logger(__name__)

__A	:	Optional[int]     =     {
    'microsoft/layoutlmv3-base': 'https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json',
}
class 						__UpperCamelCase	(						lowercase__							):
							lowercase			:		Dict   		=  'layoutlmv3'





							def __init__(  self		:List[Any]  ,_UpperCamelCase		:Union[str, Any]=5_0_2_6_5  ,_UpperCamelCase		:Tuple=7_6_8  ,_UpperCamelCase		:Dict=1_2  ,_UpperCamelCase		:str=1_2  ,_UpperCamelCase		:List[Any]=3_0_7_2  ,_UpperCamelCase		:str="gelu"  ,_UpperCamelCase		:str=0.1  ,_UpperCamelCase		:Dict=0.1  ,_UpperCamelCase		:Optional[Any]=5_1_2  ,_UpperCamelCase		:Any=2  ,_UpperCamelCase		:List[Any]=0.02  ,_UpperCamelCase		:Optional[int]=1E-5  ,_UpperCamelCase		:Union[str, Any]=1  ,_UpperCamelCase		:Any=0  ,_UpperCamelCase		:Union[str, Any]=2  ,_UpperCamelCase		:Optional[int]=1_0_2_4  ,_UpperCamelCase		:List[str]=1_2_8  ,_UpperCamelCase		:str=1_2_8  ,_UpperCamelCase		:Union[str, Any]=True  ,_UpperCamelCase		:Union[str, Any]=3_2  ,_UpperCamelCase		:Union[str, Any]=1_2_8  ,_UpperCamelCase		:Optional[int]=6_4  ,_UpperCamelCase		:Optional[Any]=2_5_6  ,_UpperCamelCase		:Any=True  ,_UpperCamelCase		:Tuple=True  ,_UpperCamelCase		:str=True  ,_UpperCamelCase		:Tuple=2_2_4  ,_UpperCamelCase		:List[Any]=3  ,_UpperCamelCase		:Any=1_6  ,_UpperCamelCase		:str=None  ,**_UpperCamelCase		:Dict  ,):
									super().__init__(
									    vocab_size=_UpperCamelCase  ,hidden_size=_UpperCamelCase  ,num_hidden_layers=_UpperCamelCase  ,num_attention_heads=_UpperCamelCase  ,intermediate_size=_UpperCamelCase  ,hidden_act=_UpperCamelCase  ,hidden_dropout_prob=_UpperCamelCase  ,attention_probs_dropout_prob=_UpperCamelCase  ,max_position_embeddings=_UpperCamelCase  ,type_vocab_size=_UpperCamelCase  ,initializer_range=_UpperCamelCase  ,layer_norm_eps=_UpperCamelCase  ,pad_token_id=_UpperCamelCase  ,bos_token_id=_UpperCamelCase  ,eos_token_id=_UpperCamelCase  ,**_UpperCamelCase  ,)
									snake_case_		:							Any			       =						max_ad_position_embeddings
									snake_case_		:							Optional[Any]			       =						coordinate_size
									snake_case_		:							str			       =						shape_size
									snake_case_		:							int			       =						has_relative_attention_bias
									snake_case_		:							Optional[Any]			       =						rel_pos_bins
									snake_case_		:							str			       =						max_rel_pos
									snake_case_		:							Optional[int]			       =						has_spatial_attention_bias
									snake_case_		:							Optional[Any]			       =						rel_ad_pos_bins
									snake_case_		:							Tuple			       =						max_rel_ad_pos
									snake_case_		:							Dict			       =						text_embed
									snake_case_		:							List[str]			       =						visual_embed
									snake_case_		:							List[Any]			       =						input_size
									snake_case_		:							Optional[Any]			       =						num_channels
									snake_case_		:							List[str]			       =						patch_size
									snake_case_		:							Union[str, Any]			       =						classifier_dropout





class 						__UpperCamelCase	(						lowercase__							):
							lowercase			:		Tuple   		=  version.parse('1.12'							)





							@property
							def 		a__   (  self		:Optional[Any]			):
									# The order of inputs is different for question answering and sequence classification
									if self.task in ["question-answering", "sequence-classification"]:
											return OrderedDict(
											    [
											        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
											        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
											        ("""bbox""", {0: """batch""", 1: """sequence"""}),
											        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
											    ]			)
									else:
											return OrderedDict(
											    [
											        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
											        ("""bbox""", {0: """batch""", 1: """sequence"""}),
											        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
											        ("""pixel_values""", {0: """batch""", 1: """num_channels"""}),
											    ]			)





							@property
							def 		a__   (  self		:List[Any]			):
									return 1E-5





							@property
							def 		a__   (  self		:Optional[int]			):
									return 1_2





							def 		a__   (  self		:List[str]  ,_UpperCamelCase		:"ProcessorMixin"  ,_UpperCamelCase		:int = -1  ,_UpperCamelCase		:int = -1  ,_UpperCamelCase		:bool = False  ,_UpperCamelCase		:Optional["TensorType"] = None  ,_UpperCamelCase		:int = 3  ,_UpperCamelCase		:int = 4_0  ,_UpperCamelCase		:int = 4_0  ,):
									setattr(processor.image_processor  ,"""apply_ocr"""  ,_UpperCamelCase			)

									# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
									snake_case_		:							str			       =						compute_effective_axis_dimension(
									    _UpperCamelCase  ,fixed_dimension=OnnxConfig.default_fixed_batch  ,num_token_to_add=0			)
									# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
									snake_case_		:							List[Any]			       =						processor.tokenizer.num_special_tokens_to_add(_UpperCamelCase			)
									snake_case_		:							Union[str, Any]			       =						compute_effective_axis_dimension(
									    _UpperCamelCase  ,fixed_dimension=OnnxConfig.default_fixed_sequence  ,num_token_to_add=_UpperCamelCase			)
									# Generate dummy inputs according to compute batch and sequence
									snake_case_		:							Optional[Any]			       =						[[""" """.join([processor.tokenizer.unk_token]			) * seq_length]] * batch_size

									# Generate dummy bounding boxes
									snake_case_		:							Union[str, Any]			       =						[[[4_8, 8_4, 7_3, 1_2_8]]] * batch_size

									# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
									# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
									snake_case_		:							Dict			       =						self._generate_dummy_images(_UpperCamelCase  ,_UpperCamelCase  ,_UpperCamelCase  ,_UpperCamelCase			)

									snake_case_		:							int			       =						dict(
									    processor(
									        _UpperCamelCase  ,text=_UpperCamelCase  ,boxes=_UpperCamelCase  ,return_tensors=_UpperCamelCase  ,)			)

									return inputs



'''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      UpperCAmelCase      (		lowerCamelCase_  :List[Any] ):







		'''simple docstring'''
		snake_case_		:							Tuple			       =						[
		    """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      UpperCAmelCase      (		lowerCamelCase_  :List[Any] ):







		'''simple docstring'''
		snake_case_       ,      snake_case_		:							Dict			       =						emb.weight.shape
		snake_case_		:							str			       =						nn.Linear(lowerCamelCase_				,      lowerCamelCase_				,      bias=lowerCamelCase_ )
		snake_case_		:							str			       =						emb.weight.data
		return lin_layer





def      UpperCAmelCase      (		lowerCamelCase_  :List[Any]				,      lowerCamelCase_  :List[str]=None ):







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





def      UpperCAmelCase      (		lowerCamelCase_  :Tuple				,      lowerCamelCase_  :Optional[Any]				,      lowerCamelCase_  :Union[str, Any]				,      lowerCamelCase_  :Any				,      lowerCamelCase_  :str = WEIGHTS_NAME ):







		'''simple docstring'''
		snake_case_		:							Tuple			       =						[]
		snake_case_		:							Dict			       =						0
		os.makedirs(lowerCamelCase_				,      exist_ok=lowerCamelCase_ )

		for expert in range(lowerCamelCase_ ):
				snake_case_		:							Optional[Any]			       =						switch_checkpoint_path + F'''-rank-{expert}.pt'''
				if os.path.isfile(lowerCamelCase_ ):
						snake_case_		:							List[Any]			       =						torch.load(lowerCamelCase_ )["""model"""]
						remove_ignore_keys_(lowerCamelCase_ )
						snake_case_		:							List[str]			       =						rename_fairseq_keys(lowerCamelCase_				,      lowerCamelCase_ )
						snake_case_		:							List[str]			       =						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
		snake_case_		:							Tuple			       =						os.path.join(lowerCamelCase_				,      weights_name.replace(""".bin"""				,      F'''-{len(lowerCamelCase_ )+1:05d}-of-???.bin''' ) )
		snake_case_		:							Tuple			       =						torch.load(switch_checkpoint_path + """-shared.pt""" )["""model"""]
		remove_ignore_keys_(lowerCamelCase_ )
		snake_case_		:							Tuple			       =						rename_fairseq_keys(lowerCamelCase_				,      lowerCamelCase_ )
		snake_case_		:							List[str]			       =						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:
				snake_case_		:							Union[str, Any]			       =						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
		snake_case_		:							str			       =						{}
		for idx, shard in enumerate(lowerCamelCase_ ):
				snake_case_		:							List[str]			       =						weights_name.replace(""".bin"""				,      F'''-{idx+1:05d}-of-{len(lowerCamelCase_ ):05d}.bin''' )
				snake_case_		:							Optional[int]			       =						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:
						snake_case_		:							Optional[int]			       =						shard_file

    # Add the metadata
		snake_case_		:							Any			       =						{"""total_size""": total_size}
		snake_case_		:							int			       =						{"""metadata""": metadata, """weight_map""": weight_map}

		with open(os.path.join(lowerCamelCase_				,      lowerCamelCase_ )				,      """w"""				,      encoding="""utf-8""" ) as f:
				snake_case_		:							List[str]			       =						json.dumps(lowerCamelCase_				,      indent=2				,      sort_keys=lowerCamelCase_ ) + """\n"""
				f.write(lowerCamelCase_ )

		return metadata, index


if __name__ == "__main__":
							__A	:	int     =     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.',
							)
							__A	:	List[str]     =     parser.parse_args()
							__A,   __A	:	List[Any]     =     shard_on_the_fly(
							    args.nllb_moe_checkpoint_path,
							    args.pytorch_dump_folder_path,
							    128,
							    args.dtype,
							)

							__A	:	Tuple     =     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)
							__A	:	List[str]     =     NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
							print('Done')
							model.save_pretrained(args.pytorch_dump_folder_path)