
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     __lowerCAmelCase			(			a__  )       ->  Tuple:
				__a										=    [
				    '''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     __lowerCAmelCase			(			a__  )       ->  List[Any]:
				__a							,     __a										=    emb.weight.shape
				__a										=    nn.Linear(a__						,    a__						,    bias=a__  )
				__a										=    emb.weight.data
				return lin_layer


def     __lowerCAmelCase			(			a__						,    a__=None  )       ->  List[Any]:
				__a										=    {}
				for old_key in state_dict.keys():
								__a										=    old_key
								if "moe_layer.experts." in key:
												if expert_idx is not None:
																__a										=    key.replace('''moe_layer.experts.0'''						,    F"""ffn.experts.expert_{expert_idx}"""  )
												else:
																__a										=    key.replace('''moe_layer.experts.'''						,    '''ffn.experts.expert_'''  )
								if "gate" in key:
												__a										=    key.replace('''.moe_layer.gate.wg'''						,    '''.ffn.router.classifier'''  )
								if "fc2" and "experts" not in key:
												__a										=    key.replace('''.fc2.'''						,    '''.ffn.fc2.'''  )
								if "fc1" and "experts" not in key:
												__a										=    key.replace('''.fc1.'''						,    '''.ffn.fc1.'''  )
								if ".encoder_attn." in key:
												__a										=    key.replace('''.encoder_attn.'''						,    '''.cross_attention.'''  )
								if "encoder_attn_layer_norm" in key:
												__a										=    key.replace('''encoder_attn_layer_norm'''						,    '''cross_attention_layer_norm'''  )
								if "final_layer_norm" in key:
												__a										=    key.replace('''final_layer_norm'''						,    '''ff_layer_norm'''  )
								__a										=    state_dict[old_key]
				return new_dict


def     __lowerCAmelCase			(			a__						,    a__						,    a__						,    a__						,    a__ = WEIGHTS_NAME  )       ->  Any:
				__a										=    []
				__a										=    0
				os.makedirs(a__						,    exist_ok=a__  )

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

    # Add the metadata
				__a										=    {'''total_size''': total_size}
				__a										=    {'''metadata''': metadata, '''weight_map''': weight_map}

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

				return metadata, index


if __name__ == "__main__":
					A     :	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.',
					)
					A     :	Optional[Any]    = parser.parse_args()
					A ,   A     :	List[str]    = shard_on_the_fly(
					    args.nllb_moe_checkpoint_path,
					    args.pytorch_dump_folder_path,
					    1_2_8,
					    args.dtype,
					)

					A     :	Dict    = NllbMoeConfig.from_pretrained(
					    'facebook/nllb-200-3.3B', encoder_sparse_step=4, decoder_sparse_step=4, num_experts=1_2_8
					)
					config.save_pretrained(args.pytorch_dump_folder_path)
					A     :	Optional[Any]    = NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
					print('Done')
					model.save_pretrained(args.pytorch_dump_folder_path)





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowerCamelCase				:		Optional[Any]     =   logging.get_logger(__name__)


if is_vision_available():
 import PIL







class   A(						UpperCamelCase   ):



 '''simple docstring'''


 UpperCamelCase						   =						['''pixel_values''']

 def __init__(				self					:  List[Any]						,							A_					:  bool = True						,							A_					:  Dict[str, int] = None						,							A_					:  PILImageResampling = PILImageResampling.BICUBIC						,							A_					:  bool = True						,							A_					:  Dict[str, int] = None						,							A_					:  bool = True						,							A_					:  Union[int, float] = 1 / 255						,							A_					:  bool = True						,							A_					:  Optional[Union[float, List[float]]] = None						,							A_					:  Optional[Union[float, List[float]]] = None						,							A_					:  bool = True						,							**A_					:  Dict						,							)   ->	None:

       """simple docstring"""






       super().__init__(**A_  )
       lowerCamelCase_		     =    size if size is not None else {'shortest_edge': 224}
       lowerCamelCase_		     =    get_size_dict(A_						,							default_to_square=A_  )
       lowerCamelCase_		     =    crop_size if crop_size is not None else {'height': 224, 'width': 224}
       lowerCamelCase_		     =    get_size_dict(A_						,							default_to_square=A_						,							param_name='crop_size'  )

       lowerCamelCase_		     =    do_resize
       lowerCamelCase_		     =    size
       lowerCamelCase_		     =    resample
       lowerCamelCase_		     =    do_center_crop
       lowerCamelCase_		     =    crop_size
       lowerCamelCase_		     =    do_rescale
       lowerCamelCase_		     =    rescale_factor
       lowerCamelCase_		     =    do_normalize
       lowerCamelCase_		     =    image_mean if image_mean is not None else OPENAI_CLIP_MEAN
       lowerCamelCase_		     =    image_std if image_std is not None else OPENAI_CLIP_STD
       lowerCamelCase_		     =    do_convert_rgb

 def 				a__ (				self					:  Dict						,							A_					:  np.ndarray						,							A_					:  Dict[str, int]						,							A_					:  PILImageResampling = PILImageResampling.BICUBIC						,							A_					:  Optional[Union[str, ChannelDimension]] = None						,							**A_					:  Union[str, Any]						,							)   ->	np.ndarray:

       """simple docstring"""






       lowerCamelCase_		     =    get_size_dict(A_						,							default_to_square=A_  )
       if "shortest_edge" not in size:
             raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""  )
       lowerCamelCase_		     =    get_resize_output_image_size(A_						,							size=size['shortest_edge']						,							default_to_square=A_  )
       return resize(A_						,							size=A_						,							resample=A_						,							data_format=A_						,							**A_  )

 def 				a__ (				self					:  Tuple						,							A_					:  np.ndarray						,							A_					:  Dict[str, int]						,							A_					:  Optional[Union[str, ChannelDimension]] = None						,							**A_					:  Dict						,							)   ->	np.ndarray:

       """simple docstring"""






       lowerCamelCase_		     =    get_size_dict(A_  )
       if "height" not in size or "width" not in size:
             raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""  )
       return center_crop(A_						,							size=(size['height'], size['width'])						,							data_format=A_						,							**A_  )

 def 				a__ (				self					:  str						,							A_					:  np.ndarray						,							A_					:  Union[int, float]						,							A_					:  Optional[Union[str, ChannelDimension]] = None						,							**A_					:  Union[str, Any]						,							)   ->	str:

       """simple docstring"""






       return rescale(A_						,							scale=A_						,							data_format=A_						,							**A_  )

 def 				a__ (				self					:  Optional[Any]						,							A_					:  np.ndarray						,							A_					:  Union[float, List[float]]						,							A_					:  Union[float, List[float]]						,							A_					:  Optional[Union[str, ChannelDimension]] = None						,							**A_					:  str						,							)   ->	np.ndarray:

       """simple docstring"""






       return normalize(A_						,							mean=A_						,							std=A_						,							data_format=A_						,							**A_  )





 def 				a__ (				self					:  Any						,							A_					:  ImageInput						,							A_					:  bool = None						,							A_					:  Dict[str, int] = None						,							A_					:  PILImageResampling = None						,							A_					:  bool = None						,							A_					:  int = None						,							A_					:  bool = None						,							A_					:  float = None						,							A_					:  bool = None						,							A_					:  Optional[Union[float, List[float]]] = None						,							A_					:  Optional[Union[float, List[float]]] = None						,							A_					:  bool = None						,							A_					:  Optional[Union[str, TensorType]] = None						,							A_					:  Optional[ChannelDimension] = ChannelDimension.FIRST						,							**A_					:  Dict						,							)   ->	PIL.Image.Image:

       """simple docstring"""






       lowerCamelCase_		     =    do_resize if do_resize is not None else self.do_resize
       lowerCamelCase_		     =    size if size is not None else self.size
       lowerCamelCase_		     =    get_size_dict(A_						,							param_name='size'						,							default_to_square=A_  )
       lowerCamelCase_		     =    resample if resample is not None else self.resample
       lowerCamelCase_		     =    do_center_crop if do_center_crop is not None else self.do_center_crop
       lowerCamelCase_		     =    crop_size if crop_size is not None else self.crop_size
       lowerCamelCase_		     =    get_size_dict(A_						,							param_name='crop_size'						,							default_to_square=A_  )
       lowerCamelCase_		     =    do_rescale if do_rescale is not None else self.do_rescale
       lowerCamelCase_		     =    rescale_factor if rescale_factor is not None else self.rescale_factor
       lowerCamelCase_		     =    do_normalize if do_normalize is not None else self.do_normalize
       lowerCamelCase_		     =    image_mean if image_mean is not None else self.image_mean
       lowerCamelCase_		     =    image_std if image_std is not None else self.image_std
       lowerCamelCase_		     =    do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

       lowerCamelCase_		     =    make_list_of_images(A_  )

       if not valid_images(A_  ):
             raise ValueError(
                 'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                 'torch.Tensor, tf.Tensor or jax.ndarray.'  )

       if do_resize and size is None:
             raise ValueError('Size must be specified if do_resize is True.'  )

       if do_center_crop and crop_size is None:
             raise ValueError('Crop size must be specified if do_center_crop is True.'  )

       if do_rescale and rescale_factor is None:
             raise ValueError('Rescale factor must be specified if do_rescale is True.'  )

       if do_normalize and (image_mean is None or image_std is None):
             raise ValueError('Image mean and std must be specified if do_normalize is True.'  )

       # PIL RGBA images are converted to RGB
       if do_convert_rgb:
             lowerCamelCase_		     =    [convert_to_rgb(A_  ) for image in images]

       # All transformations expect numpy arrays.
       lowerCamelCase_		     =    [to_numpy_array(A_  ) for image in images]

       if do_resize:
             lowerCamelCase_		     =    [self.resize(image=A_						,							size=A_						,							resample=A_  ) for image in images]

       if do_center_crop:
             lowerCamelCase_		     =    [self.center_crop(image=A_						,							size=A_  ) for image in images]

       if do_rescale:
             lowerCamelCase_		     =    [self.rescale(image=A_						,							scale=A_  ) for image in images]

       if do_normalize:
             lowerCamelCase_		     =    [self.normalize(image=A_						,							mean=A_						,							std=A_  ) for image in images]

       lowerCamelCase_		     =    [to_channel_dimension_format(A_						,							A_  ) for image in images]

       lowerCamelCase_		     =    {'pixel_values': images}
       return BatchFeature(data=A_						,							tensor_type=A_  )



