





'''simple docstring'''






from __future__ import annotations

import unittest

from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers import (
	    TFFunnelBaseModel,
	    TFFunnelForMaskedLM,
	    TFFunnelForMultipleChoice,
	    TFFunnelForPreTraining,
	    TFFunnelForQuestionAnswering,
	    TFFunnelForSequenceClassification,
	    TFFunnelForTokenClassification,
	    TFFunnelModel,
	)




class  _A :

		def __init__(   self					:   Optional[Any]  ,      __magic_name__					:   Optional[int]  ,      __magic_name__					:   List[str]=13  ,      __magic_name__					:   List[Any]=7  ,      __magic_name__					:   str=True  ,      __magic_name__					:   Union[str, Any]=True  ,      __magic_name__					:   Optional[Any]=True  ,      __magic_name__					:   Optional[Any]=True  ,      __magic_name__					:   Any=99  ,      __magic_name__					:   List[Any]=[1, 1, 2]  ,      __magic_name__					:   Optional[Any]=1  ,      __magic_name__					:   str=32  ,      __magic_name__					:   Optional[Any]=4  ,      __magic_name__					:   Tuple=8  ,      __magic_name__					:   int=37  ,      __magic_name__					:   str="gelu_new"  ,      __magic_name__					:   Any=0.1  ,      __magic_name__					:   List[Any]=0.1  ,      __magic_name__					:   Optional[Any]=0.0  ,      __magic_name__					:   List[Any]=5_12  ,      __magic_name__					:   Any=3  ,      __magic_name__					:   Any=0.02  ,      __magic_name__					:   Tuple=3  ,      __magic_name__					:   Optional[int]=4  ,      __magic_name__					:   Any=None  ,      __magic_name__					:   int=False  ,      )					->    Union[str, Any]:


					"""simple docstring"""

					__snake_case					:      Any							      =     parent
					__snake_case					:      Optional[int]							      =     batch_size
					__snake_case					:      List[str]							      =     seq_length
					__snake_case					:      Optional[int]							      =     is_training
					__snake_case					:      List[str]							      =     use_input_mask
					__snake_case					:      Union[str, Any]							      =     use_token_type_ids
					__snake_case					:      Optional[int]							      =     use_labels
					__snake_case					:      Any							      =     vocab_size
					__snake_case					:      Union[str, Any]							      =     block_sizes
					__snake_case					:      List[str]							      =     num_decoder_layers
					__snake_case					:      List[str]							      =     d_model
					__snake_case					:      List[Any]							      =     n_head
					__snake_case					:      Tuple							      =     d_head
					__snake_case					:      Union[str, Any]							      =     d_inner
					__snake_case					:      Union[str, Any]							      =     hidden_act
					__snake_case					:      Optional[Any]							      =     hidden_dropout
					__snake_case					:      Union[str, Any]							      =     attention_dropout
					__snake_case					:      Any							      =     activation_dropout
					__snake_case					:      List[str]							      =     max_position_embeddings
					__snake_case					:      Any							      =     type_vocab_size
					__snake_case					:      Tuple							      =     2
					__snake_case					:      int							      =     num_labels
					__snake_case					:      List[Any]							      =     num_choices
					__snake_case					:      Any							      =     scope
					__snake_case					:      str							      =     initializer_std

					# Used in the tests to check the size of the first attention layer
					__snake_case					:      Any							      =     n_head
					# Used in the tests to check the size of the first hidden state
					__snake_case					:      Tuple							      =     self.d_model
					# Used in the tests to check the number of output hidden states/attentions
					__snake_case					:      int							      =     sum(self.block_sizes			) + (0 if base else self.num_decoder_layers)
					# FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
					# the last hidden state of the first block (which is the first hidden state of the decoder).
					if not base:
								__snake_case					:      Dict							      =     self.num_hidden_layers + 2

		def 	lowercase__     (   self					:   Optional[int]			)					->    Tuple:


					"""simple docstring"""

					__snake_case					:      Tuple							      =     ids_tensor([self.batch_size, self.seq_length]  ,      self.vocab_size			)

					__snake_case					:      str							      =     None
					if self.use_input_mask:
								__snake_case					:      int							      =     random_attention_mask([self.batch_size, self.seq_length]			)

					__snake_case					:      Optional[int]							      =     None
					if self.use_token_type_ids:
								__snake_case					:      Optional[Any]							      =     ids_tensor([self.batch_size, self.seq_length]  ,      self.type_vocab_size			)

					__snake_case					:      Union[str, Any]							      =     None
					__snake_case					:      Optional[int]							      =     None
					__snake_case					:      Tuple							      =     None
					if self.use_labels:
								__snake_case					:      int							      =     ids_tensor([self.batch_size]  ,      self.type_sequence_label_size			)
								__snake_case					:      Optional[int]							      =     ids_tensor([self.batch_size, self.seq_length]  ,      self.num_labels			)
								__snake_case					:      int							      =     ids_tensor([self.batch_size]  ,      self.num_choices			)

					__snake_case					:      List[Any]							      =     FunnelConfig(
					    vocab_size=self.vocab_size  ,      block_sizes=self.block_sizes  ,      num_decoder_layers=self.num_decoder_layers  ,      d_model=self.d_model  ,      n_head=self.n_head  ,      d_head=self.d_head  ,      d_inner=self.d_inner  ,      hidden_act=self.hidden_act  ,      hidden_dropout=self.hidden_dropout  ,      attention_dropout=self.attention_dropout  ,      activation_dropout=self.activation_dropout  ,      max_position_embeddings=self.max_position_embeddings  ,      type_vocab_size=self.type_vocab_size  ,      initializer_std=self.initializer_std  ,      )

					return (
					    config,
					    input_ids,
					    token_type_ids,
					    input_mask,
					    sequence_labels,
					    token_labels,
					    choice_labels,
					)

		def 	lowercase__     (   self					:   Any  ,      __magic_name__					:   Tuple  ,      __magic_name__					:   Any  ,      __magic_name__					:   Any  ,      __magic_name__					:   str  ,      __magic_name__					:   Dict  ,      __magic_name__					:   str  ,      __magic_name__					:   List[Any]  ,      )					->    Optional[Any]:


					"""simple docstring"""

					__snake_case					:      Dict							      =     TFFunnelModel(config=__magic_name__			)
					__snake_case					:      List[Any]							      =     {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
					__snake_case					:      Optional[Any]							      =     model(__magic_name__			)

					__snake_case					:      Tuple							      =     [input_ids, input_mask]
					__snake_case					:      Tuple							      =     model(__magic_name__			)

					__snake_case					:      Optional[Any]							      =     model(__magic_name__			)
					self.parent.assertEqual(result.last_hidden_state.shape  ,      (self.batch_size, self.seq_length, self.d_model)			)

					__snake_case					:      str							      =     False
					__snake_case					:      Dict							      =     TFFunnelModel(config=__magic_name__			)
					__snake_case					:      str							      =     model(__magic_name__			)
					self.parent.assertEqual(result.last_hidden_state.shape  ,      (self.batch_size, self.seq_length, self.d_model)			)

					__snake_case					:      List[Any]							      =     False
					__snake_case					:      Any							      =     TFFunnelModel(config=__magic_name__			)
					__snake_case					:      List[str]							      =     model(__magic_name__			)
					self.parent.assertEqual(result.last_hidden_state.shape  ,      (self.batch_size, self.seq_length, self.d_model)			)

		def 	lowercase__     (   self					:   Any  ,      __magic_name__					:   Dict  ,      __magic_name__					:   Dict  ,      __magic_name__					:   Union[str, Any]  ,      __magic_name__					:   Any  ,      __magic_name__					:   List[Any]  ,      __magic_name__					:   List[Any]  ,      __magic_name__					:   Dict  ,      )					->    List[Any]:


					"""simple docstring"""

					__snake_case					:      str							      =     TFFunnelBaseModel(config=__magic_name__			)
					__snake_case					:      Tuple							      =     {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
					__snake_case					:      Any							      =     model(__magic_name__			)

					__snake_case					:      List[Any]							      =     [input_ids, input_mask]
					__snake_case					:      Dict							      =     model(__magic_name__			)

					__snake_case					:      Optional[int]							      =     model(__magic_name__			)
					self.parent.assertEqual(result.last_hidden_state.shape  ,      (self.batch_size, 2, self.d_model)			)

					__snake_case					:      str							      =     False
					__snake_case					:      List[str]							      =     TFFunnelBaseModel(config=__magic_name__			)
					__snake_case					:      Optional[int]							      =     model(__magic_name__			)
					self.parent.assertEqual(result.last_hidden_state.shape  ,      (self.batch_size, 3, self.d_model)			)

					__snake_case					:      Tuple							      =     False
					__snake_case					:      Optional[int]							      =     TFFunnelBaseModel(config=__magic_name__			)
					__snake_case					:      Dict							      =     model(__magic_name__			)
					self.parent.assertEqual(result.last_hidden_state.shape  ,      (self.batch_size, 2, self.d_model)			)

		def 	lowercase__     (   self					:   int  ,      __magic_name__					:   List[Any]  ,      __magic_name__					:   Any  ,      __magic_name__					:   Tuple  ,      __magic_name__					:   Optional[int]  ,      __magic_name__					:   List[str]  ,      __magic_name__					:   str  ,      __magic_name__					:   List[Any]  ,      )					->    Optional[Any]:


					"""simple docstring"""

					__snake_case					:      str							      =     TFFunnelForPreTraining(config=__magic_name__			)
					__snake_case					:      int							      =     {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
					__snake_case					:      Tuple							      =     model(__magic_name__			)
					self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.seq_length)			)

		def 	lowercase__     (   self					:   int  ,      __magic_name__					:   List[str]  ,      __magic_name__					:   Any  ,      __magic_name__					:   int  ,      __magic_name__					:   List[Any]  ,      __magic_name__					:   Tuple  ,      __magic_name__					:   Union[str, Any]  ,      __magic_name__					:   List[Any]  ,      )					->    Dict:


					"""simple docstring"""

					__snake_case					:      Optional[Any]							      =     TFFunnelForMaskedLM(config=__magic_name__			)
					__snake_case					:      Any							      =     {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
					__snake_case					:      str							      =     model(__magic_name__			)
					self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.seq_length, self.vocab_size)			)

		def 	lowercase__     (   self					:   List[str]  ,      __magic_name__					:   int  ,      __magic_name__					:   Optional[int]  ,      __magic_name__					:   Union[str, Any]  ,      __magic_name__					:   Tuple  ,      __magic_name__					:   List[Any]  ,      __magic_name__					:   Optional[Any]  ,      __magic_name__					:   Tuple  ,      )					->    Dict:


					"""simple docstring"""

					__snake_case					:      Union[str, Any]							      =     self.num_labels
					__snake_case					:      str							      =     TFFunnelForSequenceClassification(config=__magic_name__			)
					__snake_case					:      Tuple							      =     {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
					__snake_case					:      Union[str, Any]							      =     model(__magic_name__			)
					self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.num_labels)			)

		def 	lowercase__     (   self					:   Optional[int]  ,      __magic_name__					:   Optional[int]  ,      __magic_name__					:   List[str]  ,      __magic_name__					:   str  ,      __magic_name__					:   List[Any]  ,      __magic_name__					:   Optional[int]  ,      __magic_name__					:   Any  ,      __magic_name__					:   int  ,      )					->    List[Any]:


					"""simple docstring"""

					__snake_case					:      List[Any]							      =     self.num_choices
					__snake_case					:      List[str]							      =     TFFunnelForMultipleChoice(config=__magic_name__			)
					__snake_case					:      str							      =     tf.tile(tf.expand_dims(__magic_name__  ,      1			)  ,      (1, self.num_choices, 1)			)
					__snake_case					:      Tuple							      =     tf.tile(tf.expand_dims(__magic_name__  ,      1			)  ,      (1, self.num_choices, 1)			)
					__snake_case					:      int							      =     tf.tile(tf.expand_dims(__magic_name__  ,      1			)  ,      (1, self.num_choices, 1)			)
					__snake_case					:      Dict							      =     {
					    """input_ids""": multiple_choice_inputs_ids,
					    """attention_mask""": multiple_choice_input_mask,
					    """token_type_ids""": multiple_choice_token_type_ids,
					}
					__snake_case					:      Dict							      =     model(__magic_name__			)
					self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.num_choices)			)

		def 	lowercase__     (   self					:   Optional[int]  ,      __magic_name__					:   str  ,      __magic_name__					:   Optional[int]  ,      __magic_name__					:   Tuple  ,      __magic_name__					:   str  ,      __magic_name__					:   Tuple  ,      __magic_name__					:   Union[str, Any]  ,      __magic_name__					:   str  ,      )					->    Any:


					"""simple docstring"""

					__snake_case					:      Optional[int]							      =     self.num_labels
					__snake_case					:      List[str]							      =     TFFunnelForTokenClassification(config=__magic_name__			)
					__snake_case					:      str							      =     {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
					__snake_case					:      Optional[Any]							      =     model(__magic_name__			)
					self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.seq_length, self.num_labels)			)

		def 	lowercase__     (   self					:   Union[str, Any]  ,      __magic_name__					:   List[Any]  ,      __magic_name__					:   int  ,      __magic_name__					:   Dict  ,      __magic_name__					:   List[str]  ,      __magic_name__					:   List[str]  ,      __magic_name__					:   Tuple  ,      __magic_name__					:   Optional[Any]  ,      )					->    Union[str, Any]:


					"""simple docstring"""

					__snake_case					:      int							      =     TFFunnelForQuestionAnswering(config=__magic_name__			)
					__snake_case					:      Union[str, Any]							      =     {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
					__snake_case					:      Any							      =     model(__magic_name__			)
					self.parent.assertEqual(result.start_logits.shape  ,      (self.batch_size, self.seq_length)			)
					self.parent.assertEqual(result.end_logits.shape  ,      (self.batch_size, self.seq_length)			)

		def 	lowercase__     (   self					:   List[str]			)					->    int:


					"""simple docstring"""

					__snake_case					:      List[Any]							      =     self.prepare_config_and_inputs()
					(
					    (
					    __snake_case
					)      ,     (
					    __snake_case
					)      ,     (
					    __snake_case
					)      ,     (
					    __snake_case
					)      ,     (
					    __snake_case
					)      ,     (
					    __snake_case
					)      ,     (
					    __snake_case
					)      ,     
					)					:      Optional[Any]							      =     config_and_inputs
					__snake_case					:      List[str]							      =     {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
					return config, inputs_dict





@require_tf
class  _A (					__lowercase						,					__lowercase						,					unittest.TestCase		):
		lowercase__:   int				     =   (
		    (
		        TFFunnelModel,
		        TFFunnelForMaskedLM,
		        TFFunnelForPreTraining,
		        TFFunnelForQuestionAnswering,
		        TFFunnelForTokenClassification,
		    )
		    if is_tf_available()
		    else ()
		)
		lowercase__:   List[Any]				     =   (
		    {
		        '''feature-extraction''': (TFFunnelBaseModel, TFFunnelModel),
		        '''fill-mask''': TFFunnelForMaskedLM,
		        '''question-answering''': TFFunnelForQuestionAnswering,
		        '''text-classification''': TFFunnelForSequenceClassification,
		        '''token-classification''': TFFunnelForTokenClassification,
		        '''zero-shot''': TFFunnelForSequenceClassification,
		    }
		    if is_tf_available()
		    else {}
		)
		lowercase__:   List[Any]				     =   False
		lowercase__:   str				     =   False

		def 	lowercase__     (   self					:   Union[str, Any]			)					->    Any:


					"""simple docstring"""

					__snake_case					:      Dict							      =     TFFunnelModelTester(self			)
					__snake_case					:      Tuple							      =     ConfigTester(self  ,      config_class=__magic_name__			)

		def 	lowercase__     (   self					:   List[str]			)					->    List[str]:


					"""simple docstring"""

					self.config_tester.run_common_tests()

		def 	lowercase__     (   self					:   Optional[int]			)					->    List[str]:


					"""simple docstring"""

					__snake_case					:      int							      =     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*__magic_name__			)

		def 	lowercase__     (   self					:   Union[str, Any]			)					->    List[str]:


					"""simple docstring"""

					__snake_case					:      Optional[Any]							      =     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_pretraining(*__magic_name__			)

		def 	lowercase__     (   self					:   Any			)					->    Union[str, Any]:


					"""simple docstring"""

					__snake_case					:      Optional[Any]							      =     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_masked_lm(*__magic_name__			)

		def 	lowercase__     (   self					:   List[Any]			)					->    Union[str, Any]:


					"""simple docstring"""

					__snake_case					:      List[Any]							      =     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_token_classification(*__magic_name__			)

		def 	lowercase__     (   self					:   Optional[int]			)					->    List[Any]:


					"""simple docstring"""

					__snake_case					:      Any							      =     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_question_answering(*__magic_name__			)





@require_tf
class  _A (					__lowercase						,					unittest.TestCase		):
		lowercase__:   Dict				     =   (
		    (TFFunnelBaseModel, TFFunnelForMultipleChoice, TFFunnelForSequenceClassification) if is_tf_available() else ()
		)
		lowercase__:   List[str]				     =   False
		lowercase__:   List[str]				     =   False

		def 	lowercase__     (   self					:   Tuple			)					->    Dict:


					"""simple docstring"""

					__snake_case					:      Any							      =     TFFunnelModelTester(self  ,      base=__magic_name__			)
					__snake_case					:      List[Any]							      =     ConfigTester(self  ,      config_class=__magic_name__			)

		def 	lowercase__     (   self					:   str			)					->    List[str]:


					"""simple docstring"""

					self.config_tester.run_common_tests()

		def 	lowercase__     (   self					:   int			)					->    Tuple:


					"""simple docstring"""

					__snake_case					:      Dict							      =     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_base_model(*__magic_name__			)

		def 	lowercase__     (   self					:   Optional[int]			)					->    Tuple:


					"""simple docstring"""

					__snake_case					:      int							      =     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_sequence_classification(*__magic_name__			)

		def 	lowercase__     (   self					:   Optional[int]			)					->    Optional[Any]:


					"""simple docstring"""

					__snake_case					:      Union[str, Any]							      =     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_multiple_choice(*__magic_name__			)









'''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 					_a						(       _lowerCamelCase ) ->				List[Any]:



			"""simple docstring"""


			__snake_case					:      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(_lowerCamelCase    ,	_lowerCamelCase )







def 					_a						(       _lowerCamelCase ) ->				List[str]:



			"""simple docstring"""


			__snake_case      ,     __snake_case					:      Dict							      =     emb.weight.shape
			__snake_case					:      Optional[int]							      =     nn.Linear(_lowerCamelCase    ,	_lowerCamelCase    ,	bias=_lowerCamelCase )
			__snake_case					:      Union[str, Any]							      =     emb.weight.data
			return lin_layer







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase=None ) ->				Union[str, Any]:



			"""simple docstring"""


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







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase = WEIGHTS_NAME ) ->				Dict:



			"""simple docstring"""


			__snake_case					:      Optional[int]							      =     []
			__snake_case					:      Dict							      =     0
			os.makedirs(_lowerCamelCase    ,	exist_ok=_lowerCamelCase )

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

    # Add the metadata
			__snake_case					:      Optional[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					:      Union[str, Any]							      =     json.dumps(_lowerCamelCase    ,	indent=2    ,	sort_keys=_lowerCamelCase ) + """\n"""
						f.write(_lowerCamelCase )

			return metadata, index


if __name__ == "__main__":
	__UpperCamelCase					    =	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					    =	parser.parse_args()
	__UpperCamelCase    ,		__UpperCamelCase					    =	shard_on_the_fly(
	    args.nllb_moe_checkpoint_path,
	    args.pytorch_dump_folder_path,
	    128,
	    args.dtype,
	)

	__UpperCamelCase					    =	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					    =	NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
	print("Done")
	model.save_pretrained(args.pytorch_dump_folder_path)


