






'''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 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> List[str]:
							lowerCamelCase__      :			Optional[int] 						= [
							    """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 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> int:
							lowerCamelCase__						,	lowerCamelCase__      :			List[str] 						= emb.weight.shape
							lowerCamelCase__      :			int 						= nn.Linear(_A       ,    _A       ,    bias=_A   )
							lowerCamelCase__      :			List[Any] 						= emb.weight.data
							return lin_layer





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





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase = WEIGHTS_NAME   )   -> str:
							lowerCamelCase__      :			Dict 						= []
							lowerCamelCase__      :			int 						= 0
							os.makedirs(_A       ,    exist_ok=_A   )

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

    # Add the metadata
							lowerCamelCase__      :			Tuple 						= {"""total_size""": total_size}
							lowerCamelCase__      :			List[Any] 						= {"""metadata""": metadata, """weight_map""": weight_map}

							with open(os.path.join(_A       ,    _A   )       ,    """w"""       ,    encoding="""utf-8"""   ) as f:
														lowerCamelCase__      :			Optional[Any] 						= json.dumps(_A       ,    indent=2       ,    sort_keys=_A   ) + """\n"""
														f.write(_A   )

							return metadata, index


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

			_A			:    Union[str, Any]					  =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			:    Union[str, Any]					  =NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
			print('''Done''')
			model.save_pretrained(args.pytorch_dump_folder_path)









'''simple docstring'''







from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A			:    Any					  =logging.get_logger(__name__)

_A			:    Dict					  ={
    '''microsoft/trocr-base-handwritten''': (
        '''https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'''
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}

class _lowercase			(       _lowercase	):
							a          = """trocr"""
							a          = ["""past_key_values"""]
							a          = {
							    """num_attention_heads""": """decoder_attention_heads""",
							    """hidden_size""": """d_model""",
							    """num_hidden_layers""": """decoder_layers""",
							}




							def __init__(							self:       Optional[Any]    ,							UpperCamelCase__:       int=50_265    ,							UpperCamelCase__:       int=1_024    ,							UpperCamelCase__:       Optional[Any]=12    ,							UpperCamelCase__:       Dict=16    ,							UpperCamelCase__:       int=4_096    ,							UpperCamelCase__:       Tuple="gelu"    ,							UpperCamelCase__:       int=512    ,							UpperCamelCase__:       Dict=0.1    ,							UpperCamelCase__:       Tuple=0.0    ,							UpperCamelCase__:       str=0.0    ,							UpperCamelCase__:       Any=2    ,							UpperCamelCase__:       Dict=0.02    ,							UpperCamelCase__:       Optional[Any]=0.0    ,							UpperCamelCase__:       str=True    ,							UpperCamelCase__:       Tuple=False    ,							UpperCamelCase__:       Union[str, Any]=True    ,							UpperCamelCase__:       Tuple=True    ,							UpperCamelCase__:       Dict=1    ,							UpperCamelCase__:       List[str]=0    ,							UpperCamelCase__:       Union[str, Any]=2    ,							**UpperCamelCase__:       str    ,							):
														lowerCamelCase__      :			Any 						= vocab_size
														lowerCamelCase__      :			Tuple 						= d_model
														lowerCamelCase__      :			Any 						= decoder_layers
														lowerCamelCase__      :			Dict 						= decoder_attention_heads
														lowerCamelCase__      :			str 						= decoder_ffn_dim
														lowerCamelCase__      :			Tuple 						= activation_function
														lowerCamelCase__      :			Dict 						= max_position_embeddings
														lowerCamelCase__      :			int 						= dropout
														lowerCamelCase__      :			int 						= attention_dropout
														lowerCamelCase__      :			List[Any] 						= activation_dropout
														lowerCamelCase__      :			Union[str, Any] 						= init_std
														lowerCamelCase__      :			Optional[int] 						= decoder_layerdrop
														lowerCamelCase__      :			Dict 						= use_cache
														lowerCamelCase__      :			Any 						= scale_embedding
														lowerCamelCase__      :			Optional[int] 						= use_learned_position_embeddings
														lowerCamelCase__      :			List[str] 						= layernorm_embedding

														super().__init__(
														    pad_token_id=UpperCamelCase__    ,							bos_token_id=UpperCamelCase__    ,							eos_token_id=UpperCamelCase__    ,							decoder_start_token_id=UpperCamelCase__    ,							**UpperCamelCase__    ,							)

