

"""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       (     snake_case__    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      [
			    """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(snake_case__		,				snake_case__    )

def 	A       (     snake_case__    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      emb.weight.shape
			SCREAMING_SNAKE_CASE__								=      nn.Linear(snake_case__		,				snake_case__		,				bias=snake_case__    )
			SCREAMING_SNAKE_CASE__								=      emb.weight.data
			return lin_layer

def 	A       (     snake_case__		,				snake_case__=None    ):




			'''simple docstring'''


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

def 	A       (     snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__ = WEIGHTS_NAME    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      []
			SCREAMING_SNAKE_CASE__								=      0
			os.makedirs(snake_case__		,				exist_ok=snake_case__    )

			for expert in range(snake_case__    ):
						SCREAMING_SNAKE_CASE__								=      switch_checkpoint_path + f"""-rank-{expert}.pt"""
						if os.path.isfile(snake_case__    ):
									SCREAMING_SNAKE_CASE__								=      torch.load(snake_case__    )["""model"""]
									remove_ignore_keys_(snake_case__    )
									SCREAMING_SNAKE_CASE__								=      rename_fairseq_keys(snake_case__		,				snake_case__    )
									SCREAMING_SNAKE_CASE__								=      os.path.join(
									    snake_case__		,				weights_name.replace(""".bin"""		,				f"""-{len(snake_case__    )+1:05d}-of-???.bin"""    )    )
									torch.save(snake_case__		,				snake_case__    )
									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(snake_case__    )[0]].dtype    )

    # Add the last block
			SCREAMING_SNAKE_CASE__								=      os.path.join(snake_case__		,				weights_name.replace(""".bin"""		,				f"""-{len(snake_case__    )+1:05d}-of-???.bin"""    )    )
			SCREAMING_SNAKE_CASE__								=      torch.load(switch_checkpoint_path + """-shared.pt"""    )["""model"""]
			remove_ignore_keys_(snake_case__    )
			SCREAMING_SNAKE_CASE__								=      rename_fairseq_keys(snake_case__		,				snake_case__    )
			SCREAMING_SNAKE_CASE__								=      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(snake_case__    ) == 1:
						SCREAMING_SNAKE_CASE__								=      os.path.join(snake_case__		,				snake_case__    )
						torch.save(snake_case__		,				snake_case__    )
						return {weights_name: sharded_state_dicts[0]}, None
			else:
						torch.save(snake_case__		,				snake_case__    )
			# Otherwise, let's build the index
			SCREAMING_SNAKE_CASE__								=      {}
			for idx, shard in enumerate(snake_case__    ):
						SCREAMING_SNAKE_CASE__								=      weights_name.replace(""".bin"""		,				f"""-{idx+1:05d}-of-{len(snake_case__    ):05d}.bin"""    )
						SCREAMING_SNAKE_CASE__								=      os.path.join(snake_case__		,				weights_name.replace(""".bin"""		,				f"""-{idx+1:05d}-of-???.bin"""    )    )
						os.rename(snake_case__		,				os.path.join(snake_case__		,				snake_case__    )    )
						for key in shard:
									SCREAMING_SNAKE_CASE__								=      shard_file

    # Add the metadata
			SCREAMING_SNAKE_CASE__								=      {"""total_size""": total_size}
			SCREAMING_SNAKE_CASE__								=      {"""metadata""": metadata, """weight_map""": weight_map}

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

			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_							:			List[str]											= parser.parse_args()
						A_							:			Optional[Any]											= shard_on_the_fly(
						    args.nllb_moe_checkpoint_path,
						    args.pytorch_dump_folder_path,
						    128,
						    args.dtype,
						)

						A_							:			Optional[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)
						A_							:			Dict											= 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_							:			Union[str, Any]											= logging.get_logger(__name__)

A_							:			int											= {
    "caidas/swin2sr-classicalsr-x2-64": (
        "https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json"
    ),
}





class    lowerCamelCase						(A__			):
			lowerCamelCase__   :		List[str]							=							'swin2sr'

			lowerCamelCase__   :		Optional[Any]							=							{
			    'hidden_size': 'embed_dim',
			    'num_attention_heads': 'num_heads',
			    'num_hidden_layers': 'num_layers',
			}

			def __init__(					self				:						Tuple    ,				__UpperCAmelCase				:						Optional[int]=6_4    ,				__UpperCAmelCase				:						Tuple=1    ,				__UpperCAmelCase				:						Optional[Any]=3    ,				__UpperCAmelCase				:						List[str]=1_8_0    ,				__UpperCAmelCase				:						List[str]=[6, 6, 6, 6, 6, 6]    ,				__UpperCAmelCase				:						Optional[Any]=[6, 6, 6, 6, 6, 6]    ,				__UpperCAmelCase				:						Tuple=8    ,				__UpperCAmelCase				:						Union[str, Any]=2.0    ,				__UpperCAmelCase				:						int=True    ,				__UpperCAmelCase				:						Optional[Any]=0.0    ,				__UpperCAmelCase				:						str=0.0    ,				__UpperCAmelCase				:						Dict=0.1    ,				__UpperCAmelCase				:						Dict="gelu"    ,				__UpperCAmelCase				:						Tuple=False    ,				__UpperCAmelCase				:						List[str]=0.02    ,				__UpperCAmelCase				:						List[Any]=1e-5    ,				__UpperCAmelCase				:						str=2    ,				__UpperCAmelCase				:						Optional[int]=1.0    ,				__UpperCAmelCase				:						Union[str, Any]="1conv"    ,				__UpperCAmelCase				:						List[Any]="pixelshuffle"    ,				**__UpperCAmelCase				:						Any    ,				)   ->    Optional[int]:
						super().__init__(**__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      image_size
						SCREAMING_SNAKE_CASE__								=      patch_size
						SCREAMING_SNAKE_CASE__								=      num_channels
						SCREAMING_SNAKE_CASE__								=      embed_dim
						SCREAMING_SNAKE_CASE__								=      depths
						SCREAMING_SNAKE_CASE__								=      len(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      num_heads
						SCREAMING_SNAKE_CASE__								=      window_size
						SCREAMING_SNAKE_CASE__								=      mlp_ratio
						SCREAMING_SNAKE_CASE__								=      qkv_bias
						SCREAMING_SNAKE_CASE__								=      hidden_dropout_prob
						SCREAMING_SNAKE_CASE__								=      attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE__								=      drop_path_rate
						SCREAMING_SNAKE_CASE__								=      hidden_act
						SCREAMING_SNAKE_CASE__								=      use_absolute_embeddings
						SCREAMING_SNAKE_CASE__								=      layer_norm_eps
						SCREAMING_SNAKE_CASE__								=      initializer_range
						SCREAMING_SNAKE_CASE__								=      upscale
						SCREAMING_SNAKE_CASE__								=      img_range
						SCREAMING_SNAKE_CASE__								=      resi_connection
						SCREAMING_SNAKE_CASE__								=      upsampler


