






"""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     __lowercase					(    a :				Union[str, Any] )					->					List[Any]:
      __snake_case      :    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(a       ,		a )



def     __lowercase					(    a :				int )					->					Tuple:
      __snake_case			,				__snake_case      :    Any  			=emb.weight.shape
      __snake_case      :    Optional[Any]  			=nn.Linear(a       ,		a       ,		bias=a )
      __snake_case      :    Any  			=emb.weight.data
      return lin_layer



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



def     __lowercase					(    a :				int       ,		a :				Optional[int]       ,		a :				int       ,		a :				int       ,		a :				str = WEIGHTS_NAME )					->					Optional[int]:
      __snake_case      :    Optional[Any]  			=[]
      __snake_case      :    List[str]  			=0
      os.makedirs(a       ,		exist_ok=a )

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

    # Add the last block
      __snake_case      :    int  			=os.path.join(a       ,		weights_name.replace('''.bin'''       ,		f'''-{len(a )+1:05d}-of-???.bin''' ) )
      __snake_case      :    Tuple  			=torch.load(switch_checkpoint_path + '''-shared.pt''' )['''model''']
      remove_ignore_keys_(a )
      __snake_case      :    Optional[Any]  			=rename_fairseq_keys(a       ,		a )
      __snake_case      :    Dict  			=shared_weights['''decoder.embed_tokens.weight''']
      sharded_state_dicts.append(shared_weights.keys() )

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

    # Add the metadata
      __snake_case      :    Optional[int]  			={'''total_size''': total_size}
      __snake_case      :    str  			={'''metadata''': metadata, '''weight_map''': weight_map}

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

      return metadata, index


if __name__ == "__main__":
    UpperCamelCase_	:				List[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_	:				List[Any]										= parser.parse_args()
    UpperCamelCase_							,							UpperCamelCase_	:				int										= shard_on_the_fly(
        args.nllb_moe_checkpoint_path,
        args.pytorch_dump_folder_path,
        128,
        args.dtype,
    )

    UpperCamelCase_	:				int										= NllbMoeConfig.from_pretrained(
        """facebook/nllb-200-3.3B""", encoder_sparse_step=4, decoder_sparse_step=4, num_experts=128
    )
    config.save_pretrained(args.pytorch_dump_folder_path)
    UpperCamelCase_	:				Tuple										= NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
    print("""Done""")
    model.save_pretrained(args.pytorch_dump_folder_path)










"""simple docstring"""





import unittest

import numpy as np

from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import DonutImageProcessor


class      _lowercase (     unittest.TestCase						):


      def __init__(    self				:	List[Any]							,   a				:	int							,   a				:	Dict=7							,   a				:	int=3							,   a				:	int=1_8							,   a				:	Dict=3_0							,   a				:	Dict=4_0_0							,   a				:	Optional[Any]=True							,   a				:	Dict=None							,   a				:	int=True							,   a				:	Dict=False							,   a				:	int=True							,   a				:	str=True							,   a				:	List[str]=[0.5, 0.5, 0.5]							,   a				:	Optional[Any]=[0.5, 0.5, 0.5]							,   ):





            """simple docstring"""







            __snake_case      :    List[Any]  			=parent
            __snake_case      :    List[Any]  			=batch_size
            __snake_case      :    str  			=num_channels
            __snake_case      :    Dict  			=image_size
            __snake_case      :    str  			=min_resolution
            __snake_case      :    Tuple  			=max_resolution
            __snake_case      :    str  			=do_resize
            __snake_case      :    Any  			=size if size is not None else {'''height''': 1_8, '''width''': 2_0}
            __snake_case      :    List[Any]  			=do_thumbnail
            __snake_case      :    Tuple  			=do_align_axis
            __snake_case      :    Any  			=do_pad
            __snake_case      :    Dict  			=do_normalize
            __snake_case      :    List[Any]  			=image_mean
            __snake_case      :    Any  			=image_std







      def _UpperCamelCase						(    self				:	str						):





            """simple docstring"""







            return {
                "do_resize": self.do_resize,
                "size": self.size,
                "do_thumbnail": self.do_thumbnail,
                "do_align_long_axis": self.do_align_axis,
                "do_pad": self.do_pad,
                "do_normalize": self.do_normalize,
                "image_mean": self.image_mean,
                "image_std": self.image_std,
            }



@require_torch
@require_vision
class      _lowercase (     lowerCAmelCase		,  unittest.TestCase						):
      _a   :     str								=   DonutImageProcessor if is_vision_available() else None


      def _UpperCamelCase						(    self				:	Optional[int]						):





            """simple docstring"""







            __snake_case      :    Optional[Any]  			=DonutImageProcessingTester(self						)


      @property
      def _UpperCamelCase						(    self				:	Optional[int]						):





            """simple docstring"""







            return self.image_processor_tester.prepare_image_processor_dict()


      def _UpperCamelCase						(    self				:	List[str]						):





            """simple docstring"""







            __snake_case      :    Optional[Any]  			=self.image_processing_class(**self.image_processor_dict						)
            self.assertTrue(hasattr(a							,   '''do_resize'''						)						)
            self.assertTrue(hasattr(a							,   '''size'''						)						)
            self.assertTrue(hasattr(a							,   '''do_thumbnail'''						)						)
            self.assertTrue(hasattr(a							,   '''do_align_long_axis'''						)						)
            self.assertTrue(hasattr(a							,   '''do_pad'''						)						)
            self.assertTrue(hasattr(a							,   '''do_normalize'''						)						)
            self.assertTrue(hasattr(a							,   '''image_mean'''						)						)
            self.assertTrue(hasattr(a							,   '''image_std'''						)						)


      def _UpperCamelCase						(    self				:	Tuple						):





            """simple docstring"""







            __snake_case      :    Dict  			=self.image_processing_class.from_dict(self.image_processor_dict						)
            self.assertEqual(image_processor.size							,   {'''height''': 1_8, '''width''': 2_0}						)

            __snake_case      :    List[Any]  			=self.image_processing_class.from_dict(self.image_processor_dict							,   size=4_2						)
            self.assertEqual(image_processor.size							,   {'''height''': 4_2, '''width''': 4_2}						)

            # Previous config had dimensions in (width, height) order
            __snake_case      :    Tuple  			=self.image_processing_class.from_dict(self.image_processor_dict							,   size=(4_2, 8_4)						)
            self.assertEqual(image_processor.size							,   {'''height''': 8_4, '''width''': 4_2}						)


      def _UpperCamelCase						(    self				:	Any						):





            """simple docstring"""







            pass


      @is_flaky()
      def _UpperCamelCase						(    self				:	Union[str, Any]						):





            """simple docstring"""







            __snake_case      :    List[Any]  			=self.image_processing_class(**self.image_processor_dict						)
            # create random PIL images
            __snake_case      :    List[Any]  			=prepare_image_inputs(self.image_processor_tester							,   equal_resolution=a						)
            for image in image_inputs:
                  self.assertIsInstance(a							,   Image.Image						)

            # Test not batched input
            __snake_case      :    str  			=image_processing(image_inputs[0]							,   return_tensors='''pt'''						).pixel_values
            self.assertEqual(
                encoded_images.shape							,   (
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size['''height'''],
                    self.image_processor_tester.size['''width'''],
                )							,   )

            # Test batched
            __snake_case      :    Optional[int]  			=image_processing(a							,   return_tensors='''pt'''						).pixel_values
            self.assertEqual(
                encoded_images.shape							,   (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size['''height'''],
                    self.image_processor_tester.size['''width'''],
                )							,   )


      @is_flaky()
      def _UpperCamelCase						(    self				:	Tuple						):





            """simple docstring"""







            __snake_case      :    Tuple  			=self.image_processing_class(**self.image_processor_dict						)
            # create random numpy tensors
            __snake_case      :    Optional[int]  			=prepare_image_inputs(self.image_processor_tester							,   equal_resolution=a							,   numpify=a						)
            for image in image_inputs:
                  self.assertIsInstance(a							,   np.ndarray						)

            # Test not batched input
            __snake_case      :    List[Any]  			=image_processing(image_inputs[0]							,   return_tensors='''pt'''						).pixel_values
            self.assertEqual(
                encoded_images.shape							,   (
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size['''height'''],
                    self.image_processor_tester.size['''width'''],
                )							,   )

            # Test batched
            __snake_case      :    int  			=image_processing(a							,   return_tensors='''pt'''						).pixel_values
            self.assertEqual(
                encoded_images.shape							,   (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size['''height'''],
                    self.image_processor_tester.size['''width'''],
                )							,   )







      @is_flaky()
      def _UpperCamelCase						(    self				:	List[str]						):





            """simple docstring"""







            __snake_case      :    Dict  			=self.image_processing_class(**self.image_processor_dict						)
            # create random PyTorch tensors
            __snake_case      :    Dict  			=prepare_image_inputs(self.image_processor_tester							,   equal_resolution=a							,   torchify=a						)
            for image in image_inputs:
                  self.assertIsInstance(a							,   torch.Tensor						)

            # Test not batched input
            __snake_case      :    Dict  			=image_processing(image_inputs[0]							,   return_tensors='''pt'''						).pixel_values
            self.assertEqual(
                encoded_images.shape							,   (
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size['''height'''],
                    self.image_processor_tester.size['''width'''],
                )							,   )

            # Test batched
            __snake_case      :    Optional[int]  			=image_processing(a							,   return_tensors='''pt'''						).pixel_values
            self.assertEqual(
                encoded_images.shape							,   (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size['''height'''],
                    self.image_processor_tester.size['''width'''],
                )							,   )


