





'''simple docstring'''






import unittest

import numpy as np

from transformers import is_flax_available
from transformers.testing_utils import require_flax

from ..test_modeling_flax_common import ids_tensor


if is_flax_available():
	import jax
	import jax.numpy as jnp

	from transformers.generation import (
	    FlaxForcedBOSTokenLogitsProcessor,
	    FlaxForcedEOSTokenLogitsProcessor,
	    FlaxLogitsProcessorList,
	    FlaxMinLengthLogitsProcessor,
	    FlaxTemperatureLogitsWarper,
	    FlaxTopKLogitsWarper,
	    FlaxTopPLogitsWarper,
	)




@require_flax
class  _A (					unittest.TestCase		):

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


					"""simple docstring"""

					__snake_case					:      Dict							      =     jnp.ones((batch_size, length)			) / length
					return scores

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


					"""simple docstring"""

					__snake_case					:      List[str]							      =     None
					__snake_case					:      Tuple							      =     20

					__snake_case					:      Dict							      =     self._get_uniform_logits(batch_size=2  ,      length=__magic_name__			)

					# tweak scores to not be uniform anymore
					__snake_case					:      int							      =     scores.at[1, 5].set((1 / length) + 0.1			)  # peak, 1st batch
					__snake_case					:      Optional[int]							      =     scores.at[1, 10].set((1 / length) - 0.4			)  # valley, 1st batch

					# compute softmax
					__snake_case					:      str							      =     jax.nn.softmax(__magic_name__  ,      axis=-1			)

					__snake_case					:      Optional[Any]							      =     FlaxTemperatureLogitsWarper(temperature=0.5			)
					__snake_case					:      Tuple							      =     FlaxTemperatureLogitsWarper(temperature=1.3			)

					__snake_case					:      Optional[Any]							      =     jax.nn.softmax(temp_dist_warper_sharper(__magic_name__  ,      scores.copy()  ,      cur_len=__magic_name__			)  ,      axis=-1			)
					__snake_case					:      Optional[Any]							      =     jax.nn.softmax(temp_dist_warper_smoother(__magic_name__  ,      scores.copy()  ,      cur_len=__magic_name__			)  ,      axis=-1			)

					# uniform distribution stays uniform
					self.assertTrue(jnp.allclose(probs[0, :]  ,      warped_prob_sharp[0, :]  ,      atol=1E-3			)			)
					self.assertTrue(jnp.allclose(probs[0, :]  ,      warped_prob_smooth[0, :]  ,      atol=1E-3			)			)

					# sharp peaks get higher, valleys get lower
					self.assertLess(probs[1, :].max()  ,      warped_prob_sharp[1, :].max()			)
					self.assertGreater(probs[1, :].min()  ,      warped_prob_sharp[1, :].min()			)

					# smooth peaks get lower, valleys get higher
					self.assertGreater(probs[1, :].max()  ,      warped_prob_smooth[1, :].max()			)
					self.assertLess(probs[1, :].min()  ,      warped_prob_smooth[1, :].min()			)

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


					"""simple docstring"""

					__snake_case					:      int							      =     None
					__snake_case					:      List[str]							      =     10
					__snake_case					:      List[Any]							      =     2

					# create ramp distribution
					__snake_case					:      Optional[Any]							      =     np.broadcast_to(np.arange(__magic_name__			)[None, :]  ,      (batch_size, vocab_size)			).copy()
					__snake_case					:      Dict							      =     ramp_logits[1:, : vocab_size // 2] + vocab_size

					__snake_case					:      List[str]							      =     FlaxTopKLogitsWarper(3			)

					__snake_case					:      Dict							      =     top_k_warp(__magic_name__  ,      __magic_name__  ,      cur_len=__magic_name__			)

					# check that correct tokens are filtered
					self.assertListEqual(jnp.isinf(scores[0]			).tolist()  ,      7 * [True] + 3 * [False]			)
					self.assertListEqual(jnp.isinf(scores[1]			).tolist()  ,      2 * [True] + 3 * [False] + 5 * [True]			)

					# check special case
					__snake_case					:      Any							      =     5
					__snake_case					:      Dict							      =     FlaxTopKLogitsWarper(top_k=1  ,      filter_value=0.0  ,      min_tokens_to_keep=3			)

					__snake_case					:      Any							      =     np.broadcast_to(np.arange(__magic_name__			)[None, :]  ,      (batch_size, length)			).copy()
					__snake_case					:      List[str]							      =     top_k_warp_safety_check(__magic_name__  ,      __magic_name__  ,      cur_len=__magic_name__			)

					# min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
					self.assertListEqual((scores == 0.0).sum(axis=-1			).tolist()  ,      [2, 2]			)

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


					"""simple docstring"""

					__snake_case					:      List[Any]							      =     None
					__snake_case					:      Any							      =     10
					__snake_case					:      List[str]							      =     2

					# create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
					__snake_case					:      Any							      =     np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]]			)			)

					__snake_case					:      Any							      =     FlaxTopPLogitsWarper(0.8			)
					__snake_case					:      Tuple							      =     np.exp(top_p_warp(__magic_name__  ,      __magic_name__  ,      cur_len=__magic_name__			)			)

					# dist should be filtered to keep min num values so that sum is >= top_p
					# exp (-inf) => 0
					__snake_case					:      Any							      =     np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]]			)
					self.assertTrue(np.allclose(__magic_name__  ,      __magic_name__  ,      atol=1E-3			)			)

					# check edge cases with negative and extreme logits
					__snake_case					:      Optional[Any]							      =     np.broadcast_to(np.arange(__magic_name__			)[None, :]  ,      (batch_size, vocab_size)			).copy() - (
					    vocab_size // 2
					)

					# make ramp_logits more extreme
					__snake_case					:      Optional[int]							      =     ramp_logits[1] * 100.0

					# make sure at least 2 tokens are kept
					__snake_case					:      Union[str, Any]							      =     FlaxTopPLogitsWarper(0.9  ,      min_tokens_to_keep=2  ,      filter_value=0.0			)
					__snake_case					:      Union[str, Any]							      =     top_p_warp(__magic_name__  ,      __magic_name__  ,      cur_len=__magic_name__			)

					# first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
					self.assertListEqual((filtered_dist != 0.0).sum(axis=-1			).tolist()  ,      [3, 2]			)

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


					"""simple docstring"""

					__snake_case					:      Any							      =     20
					__snake_case					:      Tuple							      =     4
					__snake_case					:      List[Any]							      =     0

					__snake_case					:      Tuple							      =     FlaxMinLengthLogitsProcessor(min_length=10  ,      eos_token_id=__magic_name__			)

					# check that min length is applied at length 5
					__snake_case					:      Union[str, Any]							      =     ids_tensor((batch_size, 20)  ,      vocab_size=20			)
					__snake_case					:      int							      =     5
					__snake_case					:      Dict							      =     self._get_uniform_logits(__magic_name__  ,      __magic_name__			)
					__snake_case					:      Any							      =     min_dist_processor(__magic_name__  ,      __magic_name__  ,      cur_len=__magic_name__			)
					self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist()  ,      4 * [-float("""inf"""			)]			)

					# check that min length is not applied anymore at length 15
					__snake_case					:      str							      =     self._get_uniform_logits(__magic_name__  ,      __magic_name__			)
					__snake_case					:      List[Any]							      =     15
					__snake_case					:      str							      =     min_dist_processor(__magic_name__  ,      __magic_name__  ,      cur_len=__magic_name__			)
					self.assertFalse(jnp.isinf(__magic_name__			).any()			)

		def 	lowercase__     (   self					:   Dict			)					->    str:


					"""simple docstring"""

					__snake_case					:      Dict							      =     20
					__snake_case					:      List[str]							      =     4
					__snake_case					:      Tuple							      =     0

					__snake_case					:      Optional[int]							      =     FlaxForcedBOSTokenLogitsProcessor(bos_token_id=__magic_name__			)

					# check that all scores are -inf except the bos_token_id score
					__snake_case					:      List[Any]							      =     ids_tensor((batch_size, 1)  ,      vocab_size=20			)
					__snake_case					:      Optional[int]							      =     1
					__snake_case					:      Tuple							      =     self._get_uniform_logits(__magic_name__  ,      __magic_name__			)
					__snake_case					:      List[str]							      =     logits_processor(__magic_name__  ,      __magic_name__  ,      cur_len=__magic_name__			)
					self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :]			).all()			)
					self.assertListEqual(scores[:, bos_token_id].tolist()  ,      4 * [0]			)  # score for bos_token_id shold be zero

					# check that bos_token_id is not forced if current length is greater than 1
					__snake_case					:      Any							      =     3
					__snake_case					:      Union[str, Any]							      =     self._get_uniform_logits(__magic_name__  ,      __magic_name__			)
					__snake_case					:      Any							      =     logits_processor(__magic_name__  ,      __magic_name__  ,      cur_len=__magic_name__			)
					self.assertFalse(jnp.isinf(__magic_name__			).any()			)

		def 	lowercase__     (   self					:   Any			)					->    str:


					"""simple docstring"""

					__snake_case					:      List[Any]							      =     20
					__snake_case					:      Union[str, Any]							      =     4
					__snake_case					:      Dict							      =     0
					__snake_case					:      Optional[int]							      =     5

					__snake_case					:      List[Any]							      =     FlaxForcedEOSTokenLogitsProcessor(max_length=__magic_name__  ,      eos_token_id=__magic_name__			)

					# check that all scores are -inf except the eos_token_id when max_length is reached
					__snake_case					:      Optional[int]							      =     ids_tensor((batch_size, 4)  ,      vocab_size=20			)
					__snake_case					:      int							      =     4
					__snake_case					:      Any							      =     self._get_uniform_logits(__magic_name__  ,      __magic_name__			)
					__snake_case					:      Dict							      =     logits_processor(__magic_name__  ,      __magic_name__  ,      cur_len=__magic_name__			)
					self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :]			).all()			)
					self.assertListEqual(scores[:, eos_token_id].tolist()  ,      4 * [0]			)  # score for eos_token_id should be zero

					# check that eos_token_id is not forced if max_length is not reached
					__snake_case					:      Optional[Any]							      =     3
					__snake_case					:      Optional[Any]							      =     self._get_uniform_logits(__magic_name__  ,      __magic_name__			)
					__snake_case					:      Any							      =     logits_processor(__magic_name__  ,      __magic_name__  ,      cur_len=__magic_name__			)
					self.assertFalse(jnp.isinf(__magic_name__			).any()			)

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


					"""simple docstring"""

					__snake_case					:      Optional[int]							      =     4
					__snake_case					:      int							      =     10
					__snake_case					:      List[Any]							      =     15
					__snake_case					:      Union[str, Any]							      =     2
					__snake_case					:      List[str]							      =     1
					__snake_case					:      Optional[Any]							      =     15

					# dummy input_ids and scores
					__snake_case					:      Dict							      =     ids_tensor((batch_size, sequence_length)  ,      __magic_name__			)
					__snake_case					:      Any							      =     input_ids.copy()

					__snake_case					:      List[Any]							      =     self._get_uniform_logits(__magic_name__  ,      __magic_name__			)
					__snake_case					:      str							      =     scores.copy()

					# instantiate all dist processors
					__snake_case					:      Tuple							      =     FlaxTemperatureLogitsWarper(temperature=0.5			)
					__snake_case					:      Union[str, Any]							      =     FlaxTopKLogitsWarper(3			)
					__snake_case					:      List[str]							      =     FlaxTopPLogitsWarper(0.8			)

					# instantiate all logits processors
					__snake_case					:      Union[str, Any]							      =     FlaxMinLengthLogitsProcessor(min_length=10  ,      eos_token_id=__magic_name__			)
					__snake_case					:      Union[str, Any]							      =     FlaxForcedBOSTokenLogitsProcessor(bos_token_id=__magic_name__			)
					__snake_case					:      str							      =     FlaxForcedEOSTokenLogitsProcessor(max_length=__magic_name__  ,      eos_token_id=__magic_name__			)

					__snake_case					:      Tuple							      =     10

					# no processor list
					__snake_case					:      Dict							      =     temp_dist_warp(__magic_name__  ,      __magic_name__  ,      cur_len=__magic_name__			)
					__snake_case					:      Any							      =     top_k_warp(__magic_name__  ,      __magic_name__  ,      cur_len=__magic_name__			)
					__snake_case					:      str							      =     top_p_warp(__magic_name__  ,      __magic_name__  ,      cur_len=__magic_name__			)
					__snake_case					:      Optional[int]							      =     min_dist_proc(__magic_name__  ,      __magic_name__  ,      cur_len=__magic_name__			)
					__snake_case					:      Optional[Any]							      =     bos_dist_proc(__magic_name__  ,      __magic_name__  ,      cur_len=__magic_name__			)
					__snake_case					:      Optional[Any]							      =     eos_dist_proc(__magic_name__  ,      __magic_name__  ,      cur_len=__magic_name__			)

					# with processor list
					__snake_case					:      List[Any]							      =     FlaxLogitsProcessorList(
					    [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]			)
					__snake_case					:      int							      =     processor(__magic_name__  ,      __magic_name__  ,      cur_len=__magic_name__			)

					# scores should be equal
					self.assertTrue(jnp.allclose(__magic_name__  ,      __magic_name__  ,      atol=1E-3			)			)

					# input_ids should never be changed
					self.assertListEqual(input_ids.tolist()  ,      input_ids_comp.tolist()			)

		def 	lowercase__     (   self					:   Any			)					->    Any:


					"""simple docstring"""

					__snake_case					:      Union[str, Any]							      =     4
					__snake_case					:      Optional[int]							      =     10
					__snake_case					:      Optional[Any]							      =     15
					__snake_case					:      Dict							      =     2
					__snake_case					:      Tuple							      =     1
					__snake_case					:      Optional[int]							      =     15

					# dummy input_ids and scores
					__snake_case					:      List[Any]							      =     ids_tensor((batch_size, sequence_length)  ,      __magic_name__			)
					__snake_case					:      str							      =     input_ids.copy()

					__snake_case					:      str							      =     self._get_uniform_logits(__magic_name__  ,      __magic_name__			)
					__snake_case					:      int							      =     scores.copy()

					# instantiate all dist processors
					__snake_case					:      Optional[int]							      =     FlaxTemperatureLogitsWarper(temperature=0.5			)
					__snake_case					:      Optional[Any]							      =     FlaxTopKLogitsWarper(3			)
					__snake_case					:      str							      =     FlaxTopPLogitsWarper(0.8			)

					# instantiate all logits processors
					__snake_case					:      Optional[Any]							      =     FlaxMinLengthLogitsProcessor(min_length=10  ,      eos_token_id=__magic_name__			)
					__snake_case					:      Union[str, Any]							      =     FlaxForcedBOSTokenLogitsProcessor(bos_token_id=__magic_name__			)
					__snake_case					:      Optional[Any]							      =     FlaxForcedEOSTokenLogitsProcessor(max_length=__magic_name__  ,      eos_token_id=__magic_name__			)

					__snake_case					:      Dict							      =     10

					# no processor list
					def run_no_processor_list(__magic_name__					:   str  ,      __magic_name__					:   List[str]  ,      __magic_name__					:   int			):
								__snake_case					:      Union[str, Any]							      =     temp_dist_warp(__magic_name__  ,      __magic_name__  ,      cur_len=__magic_name__			)
								__snake_case					:      Tuple							      =     top_k_warp(__magic_name__  ,      __magic_name__  ,      cur_len=__magic_name__			)
								__snake_case					:      List[Any]							      =     top_p_warp(__magic_name__  ,      __magic_name__  ,      cur_len=__magic_name__			)
								__snake_case					:      Union[str, Any]							      =     min_dist_proc(__magic_name__  ,      __magic_name__  ,      cur_len=__magic_name__			)
								__snake_case					:      List[Any]							      =     bos_dist_proc(__magic_name__  ,      __magic_name__  ,      cur_len=__magic_name__			)
								__snake_case					:      List[str]							      =     eos_dist_proc(__magic_name__  ,      __magic_name__  ,      cur_len=__magic_name__			)
								return scores

					# with processor list
					def run_processor_list(__magic_name__					:   int  ,      __magic_name__					:   Any  ,      __magic_name__					:   Dict			):
								__snake_case					:      Optional[int]							      =     FlaxLogitsProcessorList(
								    [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]			)
								__snake_case					:      Optional[Any]							      =     processor(__magic_name__  ,      __magic_name__  ,      cur_len=__magic_name__			)
								return scores

					__snake_case					:      int							      =     jax.jit(__magic_name__			)
					__snake_case					:      List[Any]							      =     jax.jit(__magic_name__			)

					__snake_case					:      List[str]							      =     jitted_run_no_processor_list(__magic_name__  ,      __magic_name__  ,      __magic_name__			)
					__snake_case					:      Tuple							      =     jitted_run_processor_list(__magic_name__  ,      __magic_name__  ,      __magic_name__			)

					# scores should be equal
					self.assertTrue(jnp.allclose(__magic_name__  ,      __magic_name__  ,      atol=1E-3			)			)

					# input_ids should never be changed
					self.assertListEqual(input_ids.tolist()  ,      input_ids_comp.tolist()			)









'''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)


