"""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[str]     ):





       '''simple docstring'''

       lowercase__			:		Optional[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  : Optional[Any]     ):





       '''simple docstring'''

       lowercase__			:		Optional[Any]       =	emb.weight.shape
       lowercase__			:		Optional[int]       =	nn.Linear(_lowerCAmelCase   , _lowerCAmelCase   , bias=_lowerCAmelCase     )
       lowercase__			:		List[Any]       =	emb.weight.data
       return lin_layer





def 				a_     (      _lowerCAmelCase  : Dict   , _lowerCAmelCase  : Any=None     ):





       '''simple docstring'''

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





def 				a_     (      _lowerCAmelCase  : Optional[int]   , _lowerCAmelCase  : Tuple   , _lowerCAmelCase  : List[str]   , _lowerCAmelCase  : List[Any]   , _lowerCAmelCase  : List[str] = WEIGHTS_NAME     ):





       '''simple docstring'''

       lowercase__			:		Optional[Any]       =	[]
       lowercase__			:		Optional[int]       =	0
       os.makedirs(_lowerCAmelCase   , exist_ok=_lowerCAmelCase     )

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

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

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

       return metadata, index


if __name__ == "__main__":
       _UpperCamelCase   :						Any      =    argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--nllb_moe_checkpoint_path",
           default="/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000",
           type=str,
           required=False,
           help="Path to a directory containing a folder per layer. Follows the original Google format.",
       )
       parser.add_argument("--dtype", default="float32", type=str, required=False, help="dtype of the saved model")
       parser.add_argument(
           "--pytorch_dump_folder_path",
           default="/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b",
           type=str,
           required=False,
           help="Path to the output pytorch model.",
       )
       _UpperCamelCase   :						Dict      =    parser.parse_args()
       _UpperCamelCase   :						int      =    shard_on_the_fly(
           args.nllb_moe_checkpoint_path,
           args.pytorch_dump_folder_path,
           1_28,
           args.dtype,
       )

       _UpperCamelCase   :						Union[str, Any]      =    NllbMoeConfig.from_pretrained(
           "facebook/nllb-200-3.3B", encoder_sparse_step=4, decoder_sparse_step=4, num_experts=1_28
       )
       config.save_pretrained(args.pytorch_dump_folder_path)
       _UpperCamelCase   :						Dict      =    NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
       print("Done")
       model.save_pretrained(args.pytorch_dump_folder_path)









'''simple docstring'''







import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import MobileNetVaForImageClassification, MobileNetVaModel
			from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import MobileNetVaImageProcessor

class _lowercase	(				_lowercase		):




							def 		lowerCamelCase_				(							self:				Any     ):
														lowerCamelCase__					:	str         =       self.config_class(**self.inputs_dict     )
														self.parent.assertTrue(hasattr(UpperCamelCase__			,							"""tf_padding"""     )     )
														self.parent.assertTrue(hasattr(UpperCamelCase__			,							"""depth_multiplier"""     )     )



class _lowercase	:




							def __init__(							self:				str			,							UpperCamelCase__:				Dict			,							UpperCamelCase__:				Tuple=13			,							UpperCamelCase__:				Optional[int]=3			,							UpperCamelCase__:				List[Any]=32			,							UpperCamelCase__:				Optional[Any]=0.25			,							UpperCamelCase__:				int=8			,							UpperCamelCase__:				Any=True			,							UpperCamelCase__:				Dict=1_024			,							UpperCamelCase__:				Optional[int]=32			,							UpperCamelCase__:				Tuple="relu6"			,							UpperCamelCase__:				int=0.1			,							UpperCamelCase__:				List[Any]=0.02			,							UpperCamelCase__:				Optional[Any]=True			,							UpperCamelCase__:				Union[str, Any]=True			,							UpperCamelCase__:				Union[str, Any]=10			,							UpperCamelCase__:				str=None			,							):
														lowerCamelCase__					:	Optional[Any]         =       parent
														lowerCamelCase__					:	List[str]         =       batch_size
														lowerCamelCase__					:	Optional[int]         =       num_channels
														lowerCamelCase__					:	Optional[int]         =       image_size
														lowerCamelCase__					:	Optional[Any]         =       depth_multiplier
														lowerCamelCase__					:	Union[str, Any]         =       min_depth
														lowerCamelCase__					:	Optional[Any]         =       tf_padding
														lowerCamelCase__					:	str         =       int(last_hidden_size * depth_multiplier     )
														lowerCamelCase__					:	Any         =       output_stride
														lowerCamelCase__					:	int         =       hidden_act
														lowerCamelCase__					:	Tuple         =       classifier_dropout_prob
														lowerCamelCase__					:	Dict         =       use_labels
														lowerCamelCase__					:	Tuple         =       is_training
														lowerCamelCase__					:	Optional[Any]         =       num_labels
														lowerCamelCase__					:	Union[str, Any]         =       initializer_range
														lowerCamelCase__					:	Optional[Any]         =       scope




							def 		lowerCamelCase_				(							self:				List[str]     ):
														lowerCamelCase__					:	Optional[Any]         =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

														lowerCamelCase__					:	Optional[Any]         =       None
														lowerCamelCase__					:	Dict         =       None
														if self.use_labels:
																					lowerCamelCase__					:	Union[str, Any]         =       ids_tensor([self.batch_size]			,							self.num_labels     )
																					lowerCamelCase__					:	Dict         =       ids_tensor([self.batch_size, self.image_size, self.image_size]			,							self.num_labels     )

														lowerCamelCase__					:	Dict         =       self.get_config()

														return config, pixel_values, labels, pixel_labels




							def 		lowerCamelCase_				(							self:				str     ):
														return MobileNetVaConfig(
														    num_channels=self.num_channels			,							image_size=self.image_size			,							depth_multiplier=self.depth_multiplier			,							min_depth=self.min_depth			,							tf_padding=self.tf_padding			,							hidden_act=self.hidden_act			,							classifier_dropout_prob=self.classifier_dropout_prob			,							initializer_range=self.initializer_range			,							)




							def 		lowerCamelCase_				(							self:				Tuple			,							UpperCamelCase__:				int			,							UpperCamelCase__:				str			,							UpperCamelCase__:				Any			,							UpperCamelCase__:				Optional[int]     ):
														lowerCamelCase__					:	List[str]         =       MobileNetVaModel(config=UpperCamelCase__     )
														model.to(UpperCamelCase__     )
														model.eval()
														lowerCamelCase__					:	List[str]         =       model(UpperCamelCase__     )
														self.parent.assertEqual(
														    result.last_hidden_state.shape			,							(
														        self.batch_size,
														        self.last_hidden_size,
														        self.image_size // self.output_stride,
														        self.image_size // self.output_stride,
														    )			,							)




							def 		lowerCamelCase_				(							self:				str			,							UpperCamelCase__:				Tuple			,							UpperCamelCase__:				Optional[int]			,							UpperCamelCase__:				List[Any]			,							UpperCamelCase__:				Union[str, Any]     ):
														lowerCamelCase__					:	List[str]         =       self.num_labels
														lowerCamelCase__					:	Optional[Any]         =       MobileNetVaForImageClassification(UpperCamelCase__     )
														model.to(UpperCamelCase__     )
														model.eval()
														lowerCamelCase__					:	List[Any]         =       model(UpperCamelCase__			,							labels=UpperCamelCase__     )
														self.parent.assertEqual(result.logits.shape			,							(self.batch_size, self.num_labels)     )




							def 		lowerCamelCase_				(							self:				Optional[Any]     ):
														lowerCamelCase__					:	str         =       self.prepare_config_and_inputs()
														lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__					:	int         =       config_and_inputs
														lowerCamelCase__					:	Optional[int]         =       {"""pixel_values""": pixel_values}
														return config, inputs_dict



@require_torch
class _lowercase	(				_lowercase				,      _lowercase				,      unittest.TestCase		):
							a				      =       (MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
							a				      =       (
							    {"""feature-extraction""": MobileNetVaModel, """image-classification""": MobileNetVaForImageClassification}
							    if is_torch_available()
							    else {}
							)

							a				      =       False
							a				      =       False
							a				      =       False
							a				      =       False




							def 		lowerCamelCase_				(							self:				List[str]     ):
														lowerCamelCase__					:	Optional[int]         =       MobileNetVaModelTester(self     )
														lowerCamelCase__					:	List[str]         =       MobileNetVaConfigTester(self			,							config_class=UpperCamelCase__			,							has_text_modality=UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Union[str, Any]     ):
														self.config_tester.run_common_tests()




							@unittest.skip(reason="""MobileNetV1 does not use inputs_embeds"""     )
							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														pass




							@unittest.skip(reason="""MobileNetV1 does not support input and output embeddings"""     )
							def 		lowerCamelCase_				(							self:				Optional[Any]     ):
														pass




							@unittest.skip(reason="""MobileNetV1 does not output attentions"""     )
							def 		lowerCamelCase_				(							self:				Any     ):
														pass




							def 		lowerCamelCase_				(							self:				Any     ):
														lowerCamelCase__  ,		lowerCamelCase__					:	Optional[Any]         =       self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCamelCase__					:	Dict         =       model_class(UpperCamelCase__     )
																					lowerCamelCase__					:	Dict         =       inspect.signature(model.forward     )
																					# signature.parameters is an OrderedDict => so arg_names order is deterministic
																					lowerCamelCase__					:	List[Any]         =       [*signature.parameters.keys()]

																					lowerCamelCase__					:	Dict         =       ["""pixel_values"""]
																					self.assertListEqual(arg_names[:1]			,							UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				str     ):
														lowerCamelCase__					:	Tuple         =       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				str     ):
														def check_hidden_states_output(UpperCamelCase__:				List[Any]			,							UpperCamelCase__:				Dict			,							UpperCamelCase__:				List[Any]     ):
																					lowerCamelCase__					:	str         =       model_class(UpperCamelCase__     )
																					model.to(UpperCamelCase__     )
																					model.eval()

																					with torch.no_grad():
																												lowerCamelCase__					:	Union[str, Any]         =       model(**self._prepare_for_class(UpperCamelCase__			,							UpperCamelCase__     )     )

																					lowerCamelCase__					:	List[Any]         =       outputs.hidden_states

																					lowerCamelCase__					:	Tuple         =       26
																					self.assertEqual(len(UpperCamelCase__     )			,							UpperCamelCase__     )

														lowerCamelCase__  ,		lowerCamelCase__					:	List[str]         =       self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCamelCase__					:	List[Any]         =       True
																					check_hidden_states_output(UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__     )

																					# check that output_hidden_states also work using config
																					del inputs_dict["output_hidden_states"]
																					lowerCamelCase__					:	Optional[Any]         =       True

																					check_hidden_states_output(UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Dict     ):
														lowerCamelCase__					:	int         =       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__     )




							@slow
							def 		lowerCamelCase_				(							self:				List[str]     ):
														for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					lowerCamelCase__					:	Dict         =       MobileNetVaModel.from_pretrained(UpperCamelCase__     )
																					self.assertIsNotNone(UpperCamelCase__     )





def  SCREAMING_SNAKE_CASE_       ()      ->       Union[str, Any]:
							lowerCamelCase__					:	Optional[int]         =       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
							return image



@require_torch
@require_vision
class _lowercase	(				unittest.TestCase		):




							@cached_property
							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														return (
														    MobileNetVaImageProcessor.from_pretrained("""google/mobilenet_v1_1.0_224"""     ) if is_vision_available() else None
														)




							@slow
							def 		lowerCamelCase_				(							self:				Dict     ):
														lowerCamelCase__					:	List[Any]         =       MobileNetVaForImageClassification.from_pretrained("""google/mobilenet_v1_1.0_224"""     ).to(UpperCamelCase__     )

														lowerCamelCase__					:	Dict         =       self.default_image_processor
														lowerCamelCase__					:	int         =       prepare_img()
														lowerCamelCase__					:	List[Any]         =       image_processor(images=UpperCamelCase__			,							return_tensors="""pt"""     ).to(UpperCamelCase__     )

														# forward pass
														with torch.no_grad():
																					lowerCamelCase__					:	str         =       model(**UpperCamelCase__     )

														# verify the logits
														lowerCamelCase__					:	List[str]         =       torch.Size((1, 1_001)     )
														self.assertEqual(outputs.logits.shape			,							UpperCamelCase__     )

														lowerCamelCase__					:	List[str]         =       torch.tensor([-4.1_739, -1.1_233, 3.1_205]     ).to(UpperCamelCase__     )

														self.assertTrue(torch.allclose(outputs.logits[0, :3]			,							UpperCamelCase__			,							atol=1e-4     )     )

