





'''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       UpperCAmelCase							(     _lowerCamelCase      :  Any     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				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(_lowerCamelCase			,   _lowerCamelCase     )







def       UpperCAmelCase							(     _lowerCamelCase      :  Any     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				str   =					emb.weight.shape
						SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					nn.Linear(_lowerCamelCase			,   _lowerCamelCase			,   bias=_lowerCamelCase     )
						SCREAMING_SNAKE_CASE__     :				List[str]   =					emb.weight.data
						return lin_layer







def       UpperCAmelCase							(     _lowerCamelCase      :  Optional[int]			,   _lowerCamelCase      :  List[str]=None     ):





						'''simple docstring'''


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







def       UpperCAmelCase							(     _lowerCamelCase      :  List[Any]			,   _lowerCamelCase      :  Optional[Any]			,   _lowerCamelCase      :  Optional[int]			,   _lowerCamelCase      :  Union[str, Any]			,   _lowerCamelCase      :  str = WEIGHTS_NAME     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Any   =					[]
						SCREAMING_SNAKE_CASE__     :				str   =					0
						os.makedirs(_lowerCamelCase			,   exist_ok=_lowerCamelCase     )

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

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

    # Add the metadata
						SCREAMING_SNAKE_CASE__     :				List[str]   =					{"total_size": total_size}
						SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					{"metadata": metadata, "weight_map": weight_map}

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

						return metadata, index


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

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






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    _a     (				unittest.TestCase							):






							"""simple docstring"""

							def      A_  (      self				:     Dict    )	->List[Any]:
													# clean up the VRAM after each test
													super().tearDown()
													gc.collect()

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

													SCREAMING_SNAKE_CASE__     :				Dict   =					"bird"
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					jax.device_count()
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					pipe.prepare_text_inputs([prompts] * num_samples    )

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

													SCREAMING_SNAKE_CASE__     :				List[Any]   =					jax.random.PRNGKey(0    )
													SCREAMING_SNAKE_CASE__     :				int   =					jax.random.split(a							,	jax.device_count()    )

													SCREAMING_SNAKE_CASE__     :				List[Any]   =					replicate(a    )
													SCREAMING_SNAKE_CASE__     :				List[str]   =					shard(a    )
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					shard(a    )

													SCREAMING_SNAKE_CASE__     :				Dict   =					pipe(
													    prompt_ids=a							,	image=a							,	params=a							,	prng_seed=a							,	num_inference_steps=50							,	jit=a							,	).images
													assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]    )
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					images[0, 2_53:2_56, 2_53:2_56, -1]

													SCREAMING_SNAKE_CASE__     :				Tuple   =					jnp.asarray(jax.device_get(image_slice.flatten()    )    )
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					jnp.array(
													    [0.16_7969, 0.11_6699, 0.08_1543, 0.15_4297, 0.13_2812, 0.10_8887, 0.16_9922, 0.16_9922, 0.20_5078]    )
													print(f"""output_slice: {output_slice}"""    )
													assert jnp.abs(output_slice - expected_slice    ).max() < 1E-2






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

													SCREAMING_SNAKE_CASE__     :				Any   =					"Chef in the kitchen"
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					jax.device_count()
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					pipe.prepare_text_inputs([prompts] * num_samples    )

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

													SCREAMING_SNAKE_CASE__     :				Any   =					jax.random.PRNGKey(0    )
													SCREAMING_SNAKE_CASE__     :				List[str]   =					jax.random.split(a							,	jax.device_count()    )

													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					replicate(a    )
													SCREAMING_SNAKE_CASE__     :				Tuple   =					shard(a    )
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					shard(a    )

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					pipe(
													    prompt_ids=a							,	image=a							,	params=a							,	prng_seed=a							,	num_inference_steps=50							,	jit=a							,	).images
													assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]    )
													SCREAMING_SNAKE_CASE__     :				str   =					images[0, 2_53:2_56, 2_53:2_56, -1]

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					jnp.asarray(jax.device_get(image_slice.flatten()    )    )
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					jnp.array(
													    [[0.27_1484, 0.26_1719, 0.27_5391, 0.27_7344, 0.27_9297, 0.29_1016, 0.29_4922, 0.30_2734, 0.30_2734]]    )
													print(f"""output_slice: {output_slice}"""    )
													assert jnp.abs(output_slice - expected_slice    ).max() < 1E-2