



'''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 							lowerCamelCase	(					_snake_case	:    Tuple							):





    '''simple docstring'''


    lowercase__									=    [
        "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 							lowerCamelCase	(					_snake_case	:    Dict							):





    '''simple docstring'''


    lowercase__							,							lowercase__									=    emb.weight.shape
    lowercase__									=    nn.Linear(_snake_case  ,_snake_case  ,bias=_snake_case							)
    lowercase__									=    emb.weight.data
    return lin_layer




def 							lowerCamelCase	(					_snake_case	:    str  ,_snake_case	:    List[Any]=None							):





    '''simple docstring'''


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




def 							lowerCamelCase	(					_snake_case	:    Optional[Any]  ,_snake_case	:    Optional[int]  ,_snake_case	:    Union[str, Any]  ,_snake_case	:    Tuple  ,_snake_case	:    str = WEIGHTS_NAME							):





    '''simple docstring'''


    lowercase__									=    []
    lowercase__									=    0
    os.makedirs(_snake_case  ,exist_ok=_snake_case							)

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

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

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

    return metadata, index


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE__          =  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.",
 )
 SCREAMING_SNAKE_CASE__          =  parser.parse_args()
 SCREAMING_SNAKE_CASE__       ,    SCREAMING_SNAKE_CASE__          =  shard_on_the_fly(
     args.nllb_moe_checkpoint_path,
     args.pytorch_dump_folder_path,
     128,
     args.dtype,
 )

 SCREAMING_SNAKE_CASE__          =  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)
 SCREAMING_SNAKE_CASE__          =  NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
 print("Done")
 model.save_pretrained(args.pytorch_dump_folder_path)









'''simple docstring'''





import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
 import jax
 import jax.numpy as jnp
 from flax.jax_utils import replicate
 from flax.training.common_utils import shard


@slow
@require_flax
class 					snake_case			(unittest.TestCase ):



       def 				_a   (					self   )	->				Tuple:
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()



       def 				_a   (					self   )	->				List[Any]:
           lowercase__							,							lowercase__									=    FlaxControlNetModel.from_pretrained(
               "lllyasviel/sd-controlnet-canny"  ,from_pt=UpperCAmelCase_  ,dtype=jnp.bfloataa   )
           lowercase__							,							lowercase__									=    FlaxStableDiffusionControlNetPipeline.from_pretrained(
               "runwayml/stable-diffusion-v1-5"  ,controlnet=UpperCAmelCase_  ,from_pt=UpperCAmelCase_  ,dtype=jnp.bfloataa   )
           lowercase__									=    controlnet_params

           lowercase__									=    "bird"
           lowercase__									=    jax.device_count()
           lowercase__									=    pipe.prepare_text_inputs([prompts] * num_samples   )

           lowercase__									=    load_image(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"   )
           lowercase__									=    pipe.prepare_image_inputs([canny_image] * num_samples   )

           lowercase__									=    jax.random.PRNGKey(0   )
           lowercase__									=    jax.random.split(UpperCAmelCase_  ,jax.device_count()   )

           lowercase__									=    replicate(UpperCAmelCase_   )
           lowercase__									=    shard(UpperCAmelCase_   )
           lowercase__									=    shard(UpperCAmelCase_   )

           lowercase__									=    pipe(
               prompt_ids=UpperCAmelCase_  ,image=UpperCAmelCase_  ,params=UpperCAmelCase_  ,prng_seed=UpperCAmelCase_  ,num_inference_steps=50  ,jit=UpperCAmelCase_  ,).images
           assert images.shape == (jax.device_count(), 1, 768, 512, 3)

           lowercase__									=    images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]   )
           lowercase__									=    images[0, 253:256, 253:256, -1]

           lowercase__									=    jnp.asarray(jax.device_get(image_slice.flatten()   )   )
           lowercase__									=    jnp.array(
               [0.16_79_69, 0.11_66_99, 0.08_15_43, 0.15_42_97, 0.13_28_12, 0.10_88_87, 0.16_99_22, 0.16_99_22, 0.20_50_78]   )
           print(F'''output_slice: {output_slice}'''   )
           assert jnp.abs(output_slice - expected_slice   ).max() < 1E-2







       def 				_a   (					self   )	->				List[Any]:
           lowercase__							,							lowercase__									=    FlaxControlNetModel.from_pretrained(
               "lllyasviel/sd-controlnet-openpose"  ,from_pt=UpperCAmelCase_  ,dtype=jnp.bfloataa   )
           lowercase__							,							lowercase__									=    FlaxStableDiffusionControlNetPipeline.from_pretrained(
               "runwayml/stable-diffusion-v1-5"  ,controlnet=UpperCAmelCase_  ,from_pt=UpperCAmelCase_  ,dtype=jnp.bfloataa   )
           lowercase__									=    controlnet_params

           lowercase__									=    "Chef in the kitchen"
           lowercase__									=    jax.device_count()
           lowercase__									=    pipe.prepare_text_inputs([prompts] * num_samples   )

           lowercase__									=    load_image(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"   )
           lowercase__									=    pipe.prepare_image_inputs([pose_image] * num_samples   )

           lowercase__									=    jax.random.PRNGKey(0   )
           lowercase__									=    jax.random.split(UpperCAmelCase_  ,jax.device_count()   )

           lowercase__									=    replicate(UpperCAmelCase_   )
           lowercase__									=    shard(UpperCAmelCase_   )
           lowercase__									=    shard(UpperCAmelCase_   )

           lowercase__									=    pipe(
               prompt_ids=UpperCAmelCase_  ,image=UpperCAmelCase_  ,params=UpperCAmelCase_  ,prng_seed=UpperCAmelCase_  ,num_inference_steps=50  ,jit=UpperCAmelCase_  ,).images
           assert images.shape == (jax.device_count(), 1, 768, 512, 3)

           lowercase__									=    images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]   )
           lowercase__									=    images[0, 253:256, 253:256, -1]

           lowercase__									=    jnp.asarray(jax.device_get(image_slice.flatten()   )   )
           lowercase__									=    jnp.array(
               [[0.27_14_84, 0.26_17_19, 0.27_53_91, 0.27_73_44, 0.27_92_97, 0.29_10_16, 0.29_49_22, 0.30_27_34, 0.30_27_34]]   )
           print(F'''output_slice: {output_slice}'''   )
           assert jnp.abs(output_slice - expected_slice   ).max() < 1E-2




