


'''simple docstring'''






import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
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 torch import nn

							from transformers import SwiftFormerForImageClassification, SwiftFormerModel
							from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import ViTImageProcessor
class 						__UpperCamelCase	:





							def __init__(  self		:int  ,_UpperCamelCase		:Optional[int]  ,_UpperCamelCase		:List[Any]=1_3  ,_UpperCamelCase		:Any=3  ,_UpperCamelCase		:Dict=True  ,_UpperCamelCase		:List[Any]=True  ,_UpperCamelCase		:Union[str, Any]=0.1  ,_UpperCamelCase		:int=0.1  ,_UpperCamelCase		:List[Any]=2_2_4  ,_UpperCamelCase		:Optional[Any]=1_0_0_0  ,_UpperCamelCase		:Any=[3, 3, 6, 4]  ,_UpperCamelCase		:Optional[Any]=[4_8, 5_6, 1_1_2, 2_2_0]  ,):
									snake_case_		:							Dict			       =						parent
									snake_case_		:							Optional[Any]			       =						batch_size
									snake_case_		:							Tuple			       =						num_channels
									snake_case_		:							List[str]			       =						is_training
									snake_case_		:							Dict			       =						use_labels
									snake_case_		:							Any			       =						hidden_dropout_prob
									snake_case_		:							List[str]			       =						attention_probs_dropout_prob
									snake_case_		:							Optional[int]			       =						num_labels
									snake_case_		:							str			       =						image_size
									snake_case_		:							Any			       =						layer_depths
									snake_case_		:							Optional[Any]			       =						embed_dims





							def 		a__   (  self		:Optional[int]			):
									snake_case_		:							Optional[int]			       =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

									snake_case_		:							Optional[Any]			       =						None
									if self.use_labels:
											snake_case_		:							Dict			       =						ids_tensor([self.batch_size]  ,self.num_labels			)

									snake_case_		:							Optional[int]			       =						self.get_config()

									return config, pixel_values, labels





							def 		a__   (  self		:int			):
									return SwiftFormerConfig(
									    depths=self.layer_depths  ,embed_dims=self.embed_dims  ,mlp_ratio=4  ,downsamples=[True, True, True, True]  ,hidden_act="""gelu"""  ,num_labels=self.num_labels  ,down_patch_size=3  ,down_stride=2  ,down_pad=1  ,drop_rate=0.0  ,drop_path_rate=0.0  ,use_layer_scale=_UpperCamelCase  ,layer_scale_init_value=1E-5  ,)





							def 		a__   (  self		:Any  ,_UpperCamelCase		:int  ,_UpperCamelCase		:Dict  ,_UpperCamelCase		:List[Any]			):
									snake_case_		:							Dict			       =						SwiftFormerModel(config=_UpperCamelCase			)
									model.to(_UpperCamelCase			)
									model.eval()
									snake_case_		:							str			       =						model(_UpperCamelCase			)
									self.parent.assertEqual(result.last_hidden_state.shape  ,(self.batch_size, self.embed_dims[-1], 7, 7)			)





							def 		a__   (  self		:Optional[int]  ,_UpperCamelCase		:int  ,_UpperCamelCase		:Dict  ,_UpperCamelCase		:Union[str, Any]			):
									snake_case_		:							Any			       =						self.num_labels
									snake_case_		:							Optional[Any]			       =						SwiftFormerForImageClassification(_UpperCamelCase			)
									model.to(_UpperCamelCase			)
									model.eval()
									snake_case_		:							int			       =						model(_UpperCamelCase  ,labels=_UpperCamelCase			)
									self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.num_labels)			)

									snake_case_		:							Dict			       =						SwiftFormerForImageClassification(_UpperCamelCase			)
									model.to(_UpperCamelCase			)
									model.eval()

									snake_case_		:							Tuple			       =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)
									snake_case_		:							str			       =						model(_UpperCamelCase			)
									self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.num_labels)			)





							def 		a__   (  self		:Optional[int]			):
									((snake_case_)       ,      (snake_case_)       ,      (snake_case_))		:							List[str]			       =						self.prepare_config_and_inputs()
									snake_case_		:							List[str]			       =						{"""pixel_values""": pixel_values}
									return config, inputs_dict





@require_torch
class 						__UpperCamelCase	(						lowercase__						,  lowercase__						,  unittest.TestCase							):
							lowercase			:		Optional[int]   		=  (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
							lowercase			:		Dict   		=  (
							    {'feature-extraction': SwiftFormerModel, 'image-classification': SwiftFormerForImageClassification}
							    if is_torch_available()
							    else {}
							)

							lowercase			:		Dict   		=  False
							lowercase			:		int   		=  False
							lowercase			:		Tuple   		=  False
							lowercase			:		Dict   		=  False
							lowercase			:		Any   		=  False





							def 		a__   (  self		:Optional[Any]			):
									snake_case_		:							Tuple			       =						SwiftFormerModelTester(self			)
									snake_case_		:							Any			       =						ConfigTester(
									    self  ,config_class=_UpperCamelCase  ,has_text_modality=_UpperCamelCase  ,hidden_size=3_7  ,num_attention_heads=1_2  ,num_hidden_layers=1_2  ,)





							def 		a__   (  self		:Tuple			):
									self.config_tester.run_common_tests()





							@unittest.skip(reason="""SwiftFormer does not use inputs_embeds"""			)
							def 		a__   (  self		:Any			):
									pass





							def 		a__   (  self		:Dict			):
									snake_case_       ,      snake_case_		:							str			       =						self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											snake_case_		:							Any			       =						model_class(_UpperCamelCase			)
											snake_case_		:							List[Any]			       =						model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(_UpperCamelCase  ,nn.Linear			)			)





							def 		a__   (  self		:Tuple			):
									snake_case_       ,      snake_case_		:							Dict			       =						self.model_tester.prepare_config_and_inputs_for_common()

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

											snake_case_		:							Optional[Any]			       =						["""pixel_values"""]
											self.assertListEqual(arg_names[:1]  ,_UpperCamelCase			)





							def 		a__   (  self		:Dict			):
									snake_case_		:							int			       =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_UpperCamelCase			)





							def 		a__   (  self		:Tuple			):
									snake_case_		:							List[str]			       =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*_UpperCamelCase			)





							@slow
							def 		a__   (  self		:int			):
									for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											snake_case_		:							Optional[Any]			       =						SwiftFormerModel.from_pretrained(_UpperCamelCase			)
											self.assertIsNotNone(_UpperCamelCase			)





							@unittest.skip(reason="""SwiftFormer does not output attentions"""			)
							def 		a__   (  self		:Optional[int]			):
									pass





							def 		a__   (  self		:Tuple			):
									def check_hidden_states_output(_UpperCamelCase		:str  ,_UpperCamelCase		:List[str]  ,_UpperCamelCase		:Any			):
											snake_case_		:							Optional[Any]			       =						model_class(_UpperCamelCase			)
											model.to(_UpperCamelCase			)
											model.eval()

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

											snake_case_		:							Optional[int]			       =						outputs.hidden_states

											snake_case_		:							str			       =						8
											self.assertEqual(len(_UpperCamelCase			)  ,_UpperCamelCase			)  # TODO

											# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
											# with the width and height being successively divided by 2, after every 2 blocks
											for i in range(len(_UpperCamelCase			)			):
													self.assertEqual(
													    hidden_states[i].shape  ,torch.Size(
													        [
													            self.model_tester.batch_size,
													            self.model_tester.embed_dims[i // 2],
													            (self.model_tester.image_size // 4) // 2 ** (i // 2),
													            (self.model_tester.image_size // 4) // 2 ** (i // 2),
													        ]			)  ,)

									snake_case_       ,      snake_case_		:							List[Any]			       =						self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											snake_case_		:							Tuple			       =						True
											check_hidden_states_output(_UpperCamelCase  ,_UpperCamelCase  ,_UpperCamelCase			)

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

											check_hidden_states_output(_UpperCamelCase  ,_UpperCamelCase  ,_UpperCamelCase			)





							def 		a__   (  self		:List[Any]			):
									def _config_zero_init(_UpperCamelCase		:Any			):
											snake_case_		:							Optional[Any]			       =						copy.deepcopy(_UpperCamelCase			)
											for key in configs_no_init.__dict__.keys():
													if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
															setattr(_UpperCamelCase  ,_UpperCamelCase  ,1E-1_0			)
													if isinstance(getattr(_UpperCamelCase  ,_UpperCamelCase  ,_UpperCamelCase			)  ,_UpperCamelCase			):
															snake_case_		:							int			       =						_config_zero_init(getattr(_UpperCamelCase  ,_UpperCamelCase			)			)
															setattr(_UpperCamelCase  ,_UpperCamelCase  ,_UpperCamelCase			)
											return configs_no_init

									snake_case_       ,      snake_case_		:							Dict			       =						self.model_tester.prepare_config_and_inputs_for_common()

									snake_case_		:							int			       =						_config_zero_init(_UpperCamelCase			)
									for model_class in self.all_model_classes:
											snake_case_		:							Union[str, Any]			       =						model_class(config=_UpperCamelCase			)
											for name, param in model.named_parameters():
													if param.requires_grad:
															self.assertIn(
															    ((param.data.mean() * 1E9) / 1E9).round().item()  ,[0.0, 1.0]  ,msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''  ,)





							@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""			)
							def 		a__   (  self		:int			):
									pass





def      UpperCAmelCase      (		):







		'''simple docstring'''
		snake_case_		:							Tuple			       =						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
		return image






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





							@cached_property
							def 		a__   (  self		:Optional[Any]			):
									return ViTImageProcessor.from_pretrained("""MBZUAI/swiftformer-xs"""			) if is_vision_available() else None





							@slow
							def 		a__   (  self		:Union[str, Any]			):
									snake_case_		:							str			       =						SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""			).to(_UpperCamelCase			)

									snake_case_		:							List[str]			       =						self.default_image_processor
									snake_case_		:							Union[str, Any]			       =						prepare_img()
									snake_case_		:							Union[str, Any]			       =						image_processor(images=_UpperCamelCase  ,return_tensors="""pt"""			).to(_UpperCamelCase			)

									# forward pass
									with torch.no_grad():
											snake_case_		:							int			       =						model(**_UpperCamelCase			)

									# verify the logits
									snake_case_		:							str			       =						torch.Size((1, 1_0_0_0)			)
									self.assertEqual(outputs.logits.shape  ,_UpperCamelCase			)

									snake_case_		:							Any			       =						torch.tensor([[-2.1_7_0_3E0_0, 2.1_1_0_7E0_0, -2.0_8_1_1E0_0]]			).to(_UpperCamelCase			)
									self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,_UpperCamelCase  ,atol=1E-4			)			)



'''simple docstring'''






import argparse
import json
import os

import torch
from torch import nn

from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME





def      UpperCAmelCase      (		lowerCamelCase_  :List[Any] ):







		'''simple docstring'''
		snake_case_		:							Tuple			       =						[
		    """encoder.version""",
		    """decoder.version""",
		    """model.encoder.version""",
		    """model.decoder.version""",
		    """decoder.output_projection.weight""",
		    """_float_tensor""",
		    """encoder.embed_positions._float_tensor""",
		    """decoder.embed_positions._float_tensor""",
		]
		for k in ignore_keys:
				state_dict.pop(lowerCamelCase_				,      lowerCamelCase_ )





def      UpperCAmelCase      (		lowerCamelCase_  :List[Any] ):







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





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







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





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







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

		for expert in range(lowerCamelCase_ ):
				snake_case_		:							Optional[Any]			       =						switch_checkpoint_path + F'''-rank-{expert}.pt'''
				if os.path.isfile(lowerCamelCase_ ):
						snake_case_		:							List[Any]			       =						torch.load(lowerCamelCase_ )["""model"""]
						remove_ignore_keys_(lowerCamelCase_ )
						snake_case_		:							List[str]			       =						rename_fairseq_keys(lowerCamelCase_				,      lowerCamelCase_ )
						snake_case_		:							List[str]			       =						os.path.join(
						    lowerCamelCase_				,      weights_name.replace(""".bin"""				,      F'''-{len(lowerCamelCase_ )+1:05d}-of-???.bin''' ) )
						torch.save(lowerCamelCase_				,      lowerCamelCase_ )
						sharded_state_dicts.append(expert_state.keys() )
						total_size += sum([value.numel() for key, value in expert_state.items()] ) * dtype_byte_size(
						    expert_state[list(lowerCamelCase_ )[0]].dtype )

    # Add the last block
		snake_case_		:							Tuple			       =						os.path.join(lowerCamelCase_				,      weights_name.replace(""".bin"""				,      F'''-{len(lowerCamelCase_ )+1:05d}-of-???.bin''' ) )
		snake_case_		:							Tuple			       =						torch.load(switch_checkpoint_path + """-shared.pt""" )["""model"""]
		remove_ignore_keys_(lowerCamelCase_ )
		snake_case_		:							Tuple			       =						rename_fairseq_keys(lowerCamelCase_				,      lowerCamelCase_ )
		snake_case_		:							List[str]			       =						shared_weights["""decoder.embed_tokens.weight"""]
		sharded_state_dicts.append(shared_weights.keys() )

		# If we only have the shared weights (dummy model/experts saved on the same file)
		if len(lowerCamelCase_ ) == 1:
				snake_case_		:							Union[str, Any]			       =						os.path.join(lowerCamelCase_				,      lowerCamelCase_ )
				torch.save(lowerCamelCase_				,      lowerCamelCase_ )
				return {weights_name: sharded_state_dicts[0]}, None
		else:
				torch.save(lowerCamelCase_				,      lowerCamelCase_ )
		# Otherwise, let's build the index
		snake_case_		:							str			       =						{}
		for idx, shard in enumerate(lowerCamelCase_ ):
				snake_case_		:							List[str]			       =						weights_name.replace(""".bin"""				,      F'''-{idx+1:05d}-of-{len(lowerCamelCase_ ):05d}.bin''' )
				snake_case_		:							Optional[int]			       =						os.path.join(lowerCamelCase_				,      weights_name.replace(""".bin"""				,      F'''-{idx+1:05d}-of-???.bin''' ) )
				os.rename(lowerCamelCase_				,      os.path.join(lowerCamelCase_				,      lowerCamelCase_ ) )
				for key in shard:
						snake_case_		:							Optional[int]			       =						shard_file

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

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

		return metadata, index


if __name__ == "__main__":
							__A	:	int     =     argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--nllb_moe_checkpoint_path',
							    default='/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000',
							    type=str,
							    required=False,
							    help='Path to a directory containing a folder per layer. Follows the original Google format.',
							)
							parser.add_argument('--dtype', default='float32', type=str, required=False, help='dtype of the saved model')
							parser.add_argument(
							    '--pytorch_dump_folder_path',
							    default='/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b',
							    type=str,
							    required=False,
							    help='Path to the output pytorch model.',
							)
							__A	:	List[str]     =     parser.parse_args()
							__A,   __A	:	List[Any]     =     shard_on_the_fly(
							    args.nllb_moe_checkpoint_path,
							    args.pytorch_dump_folder_path,
							    128,
							    args.dtype,
							)

							__A	:	Tuple     =     NllbMoeConfig.from_pretrained(
							    'facebook/nllb-200-3.3B', encoder_sparse_step=4, decoder_sparse_step=4, num_experts=128
							)
							config.save_pretrained(args.pytorch_dump_folder_path)
							__A	:	List[str]     =     NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
							print('Done')
							model.save_pretrained(args.pytorch_dump_folder_path)