



"""simple docstring"""




from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig


UpperCAmelCase_							:      Dict      =  logging.get_logger(__name__)

# General docstring
UpperCAmelCase_							:      Optional[Any]      =  "RegNetConfig"

# Base docstring
UpperCAmelCase_							:      List[str]      =  "facebook/regnet-y-040"
UpperCAmelCase_							:      Optional[int]      =  [1, 1_088, 7, 7]

# Image classification docstring
UpperCAmelCase_							:      Dict      =  "facebook/regnet-y-040"
UpperCAmelCase_							:      Any      =  "tabby, tabby cat"

UpperCAmelCase_							:      int      =  [
    "facebook/regnet-y-040",
    # See all regnet models at https://huggingface.co/models?filter=regnet
]






class      UpperCamelCase    (						tf.keras.layers.Layer     ):



	def __init__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__ = 3			,	UpperCAmelCase__ = 1			,	UpperCAmelCase__ = 1			,	UpperCAmelCase__ = "relu"			,	**UpperCAmelCase__			,	):
							super().__init__(**UpperCAmelCase__		)
							# The padding and conv has been verified in
							# https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
							A__							=       tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2		)
							A__							=       tf.keras.layers.ConvaD(
							    filters=UpperCAmelCase__			,	kernel_size=UpperCAmelCase__			,	strides=UpperCAmelCase__			,	padding="VALID"			,	groups=UpperCAmelCase__			,	use_bias=UpperCAmelCase__			,	name="convolution"			,	)
							A__							=       tf.keras.layers.BatchNormalization(epsilon=1e-5			,	momentum=0.9			,	name="normalization"		)
							A__							=       ACTaFN[activation] if activation is not None else tf.identity



	def 			__A							(       self			,	UpperCAmelCase__		):
							A__							=       self.convolution(self.padding(UpperCAmelCase__		)		)
							A__							=       self.normalization(UpperCAmelCase__		)
							A__							=       self.activation(UpperCAmelCase__		)
							return hidden_state






class      UpperCamelCase    (						tf.keras.layers.Layer     ):



	def __init__(       self			,	UpperCAmelCase__			,	**UpperCAmelCase__		):
							super().__init__(**UpperCAmelCase__		)
							A__							=       config.num_channels
							A__							=       TFRegNetConvLayer(
							    out_channels=config.embedding_size			,	kernel_size=3			,	stride=2			,	activation=config.hidden_act			,	name="embedder"			,	)



	def 			__A							(       self			,	UpperCAmelCase__		):
							A__							=       shape_list(UpperCAmelCase__		)[1]
							if tf.executing_eagerly() and num_channels != self.num_channels:
													raise ValueError(
													    "Make sure that the channel dimension of the pixel values match with the one set in the configuration."		)

							# When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
							# So change the input format from `NCHW` to `NHWC`.
							# shape = (batch_size, in_height, in_width, in_channels=num_channels)
							A__							=       tf.transpose(UpperCAmelCase__			,	perm=(0, 2, 3, 1)		)
							A__							=       self.embedder(UpperCAmelCase__		)
							return hidden_state






class      UpperCamelCase    (						tf.keras.layers.Layer     ):



	def __init__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__ = 2			,	**UpperCAmelCase__		):
							super().__init__(**UpperCAmelCase__		)
							A__							=       tf.keras.layers.ConvaD(
							    filters=UpperCAmelCase__			,	kernel_size=1			,	strides=UpperCAmelCase__			,	use_bias=UpperCAmelCase__			,	name="convolution"		)
							A__							=       tf.keras.layers.BatchNormalization(epsilon=1e-5			,	momentum=0.9			,	name="normalization"		)



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__ = False		):
							return self.normalization(self.convolution(UpperCAmelCase__		)			,	training=UpperCAmelCase__		)






class      UpperCamelCase    (						tf.keras.layers.Layer     ):



	def __init__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	**UpperCAmelCase__		):
							super().__init__(**UpperCAmelCase__		)
							A__							=       tf.keras.layers.GlobalAveragePoolingaD(keepdims=UpperCAmelCase__			,	name="pooler"		)
							A__							=       [
							    tf.keras.layers.ConvaD(filters=UpperCAmelCase__			,	kernel_size=1			,	activation="relu"			,	name="attention.0"		),
							    tf.keras.layers.ConvaD(filters=UpperCAmelCase__			,	kernel_size=1			,	activation="sigmoid"			,	name="attention.2"		),
							]



	def 			__A							(       self			,	UpperCAmelCase__		):
							# [batch_size, h, w, num_channels] -> [batch_size, 1, 1, num_channels]
							A__							=       self.pooler(UpperCAmelCase__		)
							for layer_module in self.attention:
													A__							=       layer_module(UpperCAmelCase__		)
							A__							=       hidden_state * pooled
							return hidden_state






class      UpperCamelCase    (						tf.keras.layers.Layer     ):



	def __init__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__ = 1			,	**UpperCAmelCase__		):
							super().__init__(**UpperCAmelCase__		)
							A__							=       in_channels != out_channels or stride != 1
							A__							=       max(1			,	out_channels // config.groups_width		)
							A__							=       (
							    TFRegNetShortCut(UpperCAmelCase__			,	stride=UpperCAmelCase__			,	name="shortcut"		)
							    if should_apply_shortcut
							    else tf.keras.layers.Activation("linear"			,	name="shortcut"		)
							)
							# `self.layers` instead of `self.layer` because that is a reserved argument.
							A__							=       [
							    TFRegNetConvLayer(UpperCAmelCase__			,	kernel_size=1			,	activation=config.hidden_act			,	name="layer.0"		),
							    TFRegNetConvLayer(
							        UpperCAmelCase__			,	stride=UpperCAmelCase__			,	groups=UpperCAmelCase__			,	activation=config.hidden_act			,	name="layer.1"		),
							    TFRegNetConvLayer(UpperCAmelCase__			,	kernel_size=1			,	activation=UpperCAmelCase__			,	name="layer.2"		),
							]
							A__							=       ACTaFN[config.hidden_act]



	def 			__A							(       self			,	UpperCAmelCase__		):
							A__							=       hidden_state
							for layer_module in self.layers:
													A__							=       layer_module(UpperCAmelCase__		)
							A__							=       self.shortcut(UpperCAmelCase__		)
							hidden_state += residual
							A__							=       self.activation(UpperCAmelCase__		)
							return hidden_state






class      UpperCamelCase    (						tf.keras.layers.Layer     ):



	def __init__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__ = 1			,	**UpperCAmelCase__		):
							super().__init__(**UpperCAmelCase__		)
							A__							=       in_channels != out_channels or stride != 1
							A__							=       max(1			,	out_channels // config.groups_width		)
							A__							=       (
							    TFRegNetShortCut(UpperCAmelCase__			,	stride=UpperCAmelCase__			,	name="shortcut"		)
							    if should_apply_shortcut
							    else tf.keras.layers.Activation("linear"			,	name="shortcut"		)
							)
							A__							=       [
							    TFRegNetConvLayer(UpperCAmelCase__			,	kernel_size=1			,	activation=config.hidden_act			,	name="layer.0"		),
							    TFRegNetConvLayer(
							        UpperCAmelCase__			,	stride=UpperCAmelCase__			,	groups=UpperCAmelCase__			,	activation=config.hidden_act			,	name="layer.1"		),
							    TFRegNetSELayer(UpperCAmelCase__			,	reduced_channels=int(round(in_channels / 4		)		)			,	name="layer.2"		),
							    TFRegNetConvLayer(UpperCAmelCase__			,	kernel_size=1			,	activation=UpperCAmelCase__			,	name="layer.3"		),
							]
							A__							=       ACTaFN[config.hidden_act]



	def 			__A							(       self			,	UpperCAmelCase__		):
							A__							=       hidden_state
							for layer_module in self.layers:
													A__							=       layer_module(UpperCAmelCase__		)
							A__							=       self.shortcut(UpperCAmelCase__		)
							hidden_state += residual
							A__							=       self.activation(UpperCAmelCase__		)
							return hidden_state






class      UpperCamelCase    (						tf.keras.layers.Layer     ):



	def __init__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__ = 2			,	UpperCAmelCase__ = 2			,	**UpperCAmelCase__		):
							super().__init__(**UpperCAmelCase__		)

							A__							=       TFRegNetXLayer if config.layer_type == "x" else TFRegNetYLayer
							A__							=       [
							    # downsampling is done in the first layer with stride of 2
							    layer(UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	stride=UpperCAmelCase__			,	name="layers.0"		),
							    *[layer(UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	name=F"""layers.{i+1}"""		) for i in range(depth - 1		)],
							]



	def 			__A							(       self			,	UpperCAmelCase__		):
							for layer_module in self.layers:
													A__							=       layer_module(UpperCAmelCase__		)
							return hidden_state






class      UpperCamelCase    (						tf.keras.layers.Layer     ):



	def __init__(       self			,	UpperCAmelCase__			,	**UpperCAmelCase__		):
							super().__init__(**UpperCAmelCase__		)
							A__							=       []
							# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
							self.stages.append(
							    TFRegNetStage(
							        UpperCAmelCase__			,	config.embedding_size			,	config.hidden_sizes[0]			,	stride=2 if config.downsample_in_first_stage else 1			,	depth=config.depths[0]			,	name="stages.0"			,	)		)
							A__							=       zip(config.hidden_sizes			,	config.hidden_sizes[1:]		)
							for i, ((in_channels, out_channels), depth) in enumerate(zip(UpperCAmelCase__			,	config.depths[1:]		)		):
													self.stages.append(TFRegNetStage(UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	depth=UpperCAmelCase__			,	name=F"""stages.{i+1}"""		)		)



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__ = False			,	UpperCAmelCase__ = True		):
							A__							=       () if output_hidden_states else None

							for stage_module in self.stages:
													if output_hidden_states:
																			A__							=       hidden_states + (hidden_state,)

													A__							=       stage_module(UpperCAmelCase__		)

							if output_hidden_states:
													A__							=       hidden_states + (hidden_state,)

							if not return_dict:
													return tuple(v for v in [hidden_state, hidden_states] if v is not None		)

							return TFBaseModelOutputWithNoAttention(last_hidden_state=UpperCAmelCase__			,	hidden_states=UpperCAmelCase__		)






@keras_serializable
class      UpperCamelCase    (						tf.keras.layers.Layer     ):
	lowerCAmelCase	:   Dict				=	RegNetConfig



	def __init__(       self			,	UpperCAmelCase__			,	**UpperCAmelCase__		):
							super().__init__(**UpperCAmelCase__		)
							A__							=       config
							A__							=       TFRegNetEmbeddings(UpperCAmelCase__			,	name="embedder"		)
							A__							=       TFRegNetEncoder(UpperCAmelCase__			,	name="encoder"		)
							A__							=       tf.keras.layers.GlobalAveragePoolingaD(keepdims=UpperCAmelCase__			,	name="pooler"		)



	@unpack_inputs
	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = False			,	):
							A__							=       (
							    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
							)
							A__							=       return_dict if return_dict is not None else self.config.use_return_dict

							A__							=       self.embedder(UpperCAmelCase__			,	training=UpperCAmelCase__		)

							A__							=       self.encoder(
							    UpperCAmelCase__			,	output_hidden_states=UpperCAmelCase__			,	return_dict=UpperCAmelCase__			,	training=UpperCAmelCase__		)

							A__							=       encoder_outputs[0]
							A__							=       self.pooler(UpperCAmelCase__		)

							# Change to NCHW output format have uniformity in the modules
							A__							=       tf.transpose(UpperCAmelCase__			,	perm=(0, 3, 1, 2)		)
							A__							=       tf.transpose(UpperCAmelCase__			,	perm=(0, 3, 1, 2)		)

							# Change the other hidden state outputs to NCHW as well
							if output_hidden_states:
													A__							=       tuple([tf.transpose(UpperCAmelCase__			,	perm=(0, 3, 1, 2)		) for h in encoder_outputs[1]]		)

							if not return_dict:
													return (last_hidden_state, pooled_output) + encoder_outputs[1:]

							return TFBaseModelOutputWithPoolingAndNoAttention(
							    last_hidden_state=UpperCAmelCase__			,	pooler_output=UpperCAmelCase__			,	hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states			,	)






class      UpperCamelCase    (						_UpperCAmelCase     ):
	lowerCAmelCase	:   str				=	RegNetConfig
	lowerCAmelCase	:   str				=	"""regnet"""
	lowerCAmelCase	:   str				=	"""pixel_values"""



	@property
	def 			__A							(       self		):
							return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224)			,	dtype=tf.floataa		)}





UpperCAmelCase_							:      Union[str, Any]      =  R"\n    Parameters:\n    This model is a Tensorflow\n    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a\n    regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and\n    behavior.\n        config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n"

UpperCAmelCase_							:      str      =  R"\n    Args:\n        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConveNextImageProcessor.__call__`] for details.\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n"






@add_start_docstrings(
    """The bare RegNet model outputting raw features without any specific head on top."""       ,    _UpperCAmelCase       ,    )
class      UpperCamelCase    (						_UpperCAmelCase     ):



	def __init__(       self			,	UpperCAmelCase__			,	*UpperCAmelCase__			,	**UpperCAmelCase__		):
							super().__init__(UpperCAmelCase__			,	*UpperCAmelCase__			,	**UpperCAmelCase__		)
							A__							=       TFRegNetMainLayer(UpperCAmelCase__			,	name="regnet"		)



	@unpack_inputs
	@add_start_docstrings_to_model_forward(UpperCAmelCase__		)
	@add_code_sample_docstrings(
	    checkpoint=_CHECKPOINT_FOR_DOC			,	output_type=UpperCAmelCase__			,	config_class=_CONFIG_FOR_DOC			,	modality="vision"			,	expected_output=_EXPECTED_OUTPUT_SHAPE			,	)
	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	UpperCAmelCase__=False			,	):
							A__							=       (
							    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
							)
							A__							=       return_dict if return_dict is not None else self.config.use_return_dict

							A__							=       self.regnet(
							    pixel_values=UpperCAmelCase__			,	output_hidden_states=UpperCAmelCase__			,	return_dict=UpperCAmelCase__			,	training=UpperCAmelCase__			,	)
							if not return_dict:
													return (outputs[0],) + outputs[1:]

							return TFBaseModelOutputWithPoolingAndNoAttention(
							    last_hidden_state=outputs.last_hidden_state			,	pooler_output=outputs.pooler_output			,	hidden_states=outputs.hidden_states			,	)






@add_start_docstrings(
    """
    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    """       ,    _UpperCAmelCase       ,    )
class      UpperCamelCase    (						_UpperCAmelCase       ,    _UpperCAmelCase     ):



	def __init__(       self			,	UpperCAmelCase__			,	*UpperCAmelCase__			,	**UpperCAmelCase__		):
							super().__init__(UpperCAmelCase__			,	*UpperCAmelCase__			,	**UpperCAmelCase__		)
							A__							=       config.num_labels
							A__							=       TFRegNetMainLayer(UpperCAmelCase__			,	name="regnet"		)
							# classification head
							A__							=       [
							    tf.keras.layers.Flatten(),
							    tf.keras.layers.Dense(config.num_labels			,	name="classifier.1"		) if config.num_labels > 0 else tf.identity,
							]



	@unpack_inputs
	@add_start_docstrings_to_model_forward(UpperCAmelCase__		)
	@add_code_sample_docstrings(
	    checkpoint=_IMAGE_CLASS_CHECKPOINT			,	output_type=UpperCAmelCase__			,	config_class=_CONFIG_FOR_DOC			,	expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT			,	)
	def 			__A							(       self			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	UpperCAmelCase__=False			,	):
							A__							=       (
							    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
							)
							A__							=       return_dict if return_dict is not None else self.config.use_return_dict

							A__							=       self.regnet(
							    UpperCAmelCase__			,	output_hidden_states=UpperCAmelCase__			,	return_dict=UpperCAmelCase__			,	training=UpperCAmelCase__		)

							A__							=       outputs.pooler_output if return_dict else outputs[1]

							A__							=       self.classifier[0](UpperCAmelCase__		)
							A__							=       self.classifier[1](UpperCAmelCase__		)

							A__							=       None if labels is None else self.hf_compute_loss(labels=UpperCAmelCase__			,	logits=UpperCAmelCase__		)

							if not return_dict:
													A__							=       (logits,) + outputs[2:]
													return ((loss,) + output) if loss is not None else output

							return TFSequenceClassifierOutput(loss=UpperCAmelCase__			,	logits=UpperCAmelCase__			,	hidden_states=outputs.hidden_states		)










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						(       _A			:				Tuple    )->       Dict:





						"""simple docstring"""





						A__							=       [
						    "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(_A			,							_A    )
def  UpperCamelCase						(       _A			:				int    )->       Optional[Any]:





						"""simple docstring"""





						A__						,       A__							=       emb.weight.shape
						A__							=       nn.Linear(_A			,							_A			,							bias=_A    )
						A__							=       emb.weight.data
						return lin_layer
def  UpperCamelCase						(       _A			:				str			,							_A			:				Optional[Any]=None    )->       str:





						"""simple docstring"""





						A__							=       {}
						for old_key in state_dict.keys():
												A__							=       old_key
												if "moe_layer.experts." in key:
																		if expert_idx is not None:
																								A__							=       key.replace("moe_layer.experts.0"			,							f"""ffn.experts.expert_{expert_idx}"""    )
																		else:
																								A__							=       key.replace("moe_layer.experts."			,							"ffn.experts.expert_"    )
												if "gate" in key:
																		A__							=       key.replace(".moe_layer.gate.wg"			,							".ffn.router.classifier"    )
												if "fc2" and "experts" not in key:
																		A__							=       key.replace(".fc2."			,							".ffn.fc2."    )
												if "fc1" and "experts" not in key:
																		A__							=       key.replace(".fc1."			,							".ffn.fc1."    )
												if ".encoder_attn." in key:
																		A__							=       key.replace(".encoder_attn."			,							".cross_attention."    )
												if "encoder_attn_layer_norm" in key:
																		A__							=       key.replace("encoder_attn_layer_norm"			,							"cross_attention_layer_norm"    )
												if "final_layer_norm" in key:
																		A__							=       key.replace("final_layer_norm"			,							"ff_layer_norm"    )
												A__							=       state_dict[old_key]
						return new_dict
def  UpperCamelCase						(       _A			:				Tuple			,							_A			:				Tuple			,							_A			:				int			,							_A			:				str			,							_A			:				str = WEIGHTS_NAME    )->       List[str]:





						"""simple docstring"""





						A__							=       []
						A__							=       0
						os.makedirs(_A			,							exist_ok=_A    )

						for expert in range(_A    ):
												A__							=       switch_checkpoint_path + f"""-rank-{expert}.pt"""
												if os.path.isfile(_A    ):
																		A__							=       torch.load(_A    )["model"]
																		remove_ignore_keys_(_A    )
																		A__							=       rename_fairseq_keys(_A			,							_A    )
																		A__							=       os.path.join(
																		    _A			,							weights_name.replace(".bin"			,							f"""-{len(_A    )+1:05d}-of-???.bin"""    )    )
																		torch.save(_A			,							_A    )
																		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(_A    )[0]].dtype    )

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

    # Add the metadata
						A__							=       {"total_size": total_size}
						A__							=       {"metadata": metadata, "weight_map": weight_map}

						with open(os.path.join(_A			,							_A    )			,							"w"			,							encoding="utf-8"    ) as f:
												A__							=       json.dumps(_A			,							indent=2			,							sort_keys=_A    ) + "\n"
												f.write(_A    )

						return metadata, index


if __name__ == "__main__":
						UpperCAmelCase_							:      str      =  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.",
						)
						UpperCAmelCase_							:      Union[str, Any]      =  parser.parse_args()
						UpperCAmelCase_   ,	UpperCAmelCase_							:      Optional[int]      =  shard_on_the_fly(
						    args.nllb_moe_checkpoint_path,
						    args.pytorch_dump_folder_path,
						    128,
						    args.dtype,
						)

						UpperCAmelCase_							:      Any      =  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)
						UpperCAmelCase_							:      Tuple      =  NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
						print("Done")
						model.save_pretrained(args.pytorch_dump_folder_path)





