





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__							(   SCREAMING_SNAKE_CASE__)       ->   List[str]:
  __snake_case:						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(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)







def       A__							(   SCREAMING_SNAKE_CASE__)       ->   Dict:
  __snake_case  ,       __snake_case:						Tuple			  =						emb.weight.shape
  __snake_case:						str			  =						nn.Linear(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , bias=SCREAMING_SNAKE_CASE__)
  __snake_case:						Any			  =						emb.weight.data
  return lin_layer







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







def       A__							(   SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = WEIGHTS_NAME)       ->   Optional[Any]:
  __snake_case:						str			  =						[]
  __snake_case:						Optional[int]			  =						0
  os.makedirs(SCREAMING_SNAKE_CASE__ , exist_ok=SCREAMING_SNAKE_CASE__)

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

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

    # Add the metadata
  __snake_case:						Dict			  =						{"""total_size""": total_size}
  __snake_case:						Optional[int]			  =						{"""metadata""": metadata, """weight_map""": weight_map}

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

  return metadata, index


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

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













import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
      import onnxruntime as ort







class    __snake_case	(							__lowerCamelCase       ,      unittest.TestCase      ):





       '''simple docstring'''







       pass







@nightly
@require_onnxruntime
@require_torch_gpu
class    __snake_case	(							unittest.TestCase      ):





       '''simple docstring'''







       @property
       def 							UpperCAmelCase__			(  self			:   Dict		):
         return (
             "CUDAExecutionProvider",
             {
                 "gpu_mem_limit": "15000000000",  # 15GB
                 "arena_extend_strategy": "kSameAsRequested",
             },
         )




       @property
       def 							UpperCAmelCase__			(  self			:   Dict		):
         __snake_case:						Optional[int]			  =						ort.SessionOptions()
         __snake_case:						List[Any]			  =						False
         return options




       def 							UpperCAmelCase__			(  self			:   Optional[Any]		):
         __snake_case:						Optional[Any]			  =						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/in_paint/overture-creations-5sI6fQgYIuo.png"""		)
         __snake_case:						Any			  =						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""		)
         __snake_case:						List[str]			  =						OnnxStableDiffusionInpaintPipeline.from_pretrained(
             """runwayml/stable-diffusion-inpainting"""							,    revision="""onnx"""							,    safety_checker=A							,    feature_extractor=A							,    provider=self.gpu_provider							,    sess_options=self.gpu_options							,    )
         pipe.set_progress_bar_config(disable=A		)

         __snake_case:						int			  =						"""A red cat sitting on a park bench"""

         __snake_case:						Any			  =						np.random.RandomState(0		)
         __snake_case:						Optional[Any]			  =						pipe(
             prompt=A							,    image=A							,    mask_image=A							,    guidance_scale=7.5							,    num_inference_steps=10							,    generator=A							,    output_type="""np"""							,    )
         __snake_case:						List[Any]			  =						output.images
         __snake_case:						str			  =						images[0, 255:258, 255:258, -1]

         assert images.shape == (1, 512, 512, 3)
         __snake_case:						Any			  =						np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464]		)

         assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-3






       def 							UpperCAmelCase__			(  self			:   Optional[Any]		):
         __snake_case:						Optional[Any]			  =						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/in_paint/overture-creations-5sI6fQgYIuo.png"""		)
         __snake_case:						Optional[int]			  =						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""		)
         __snake_case:						Optional[int]			  =						LMSDiscreteScheduler.from_pretrained(
             """runwayml/stable-diffusion-inpainting"""							,    subfolder="""scheduler"""							,    revision="""onnx"""		)
         __snake_case:						List[Any]			  =						OnnxStableDiffusionInpaintPipeline.from_pretrained(
             """runwayml/stable-diffusion-inpainting"""							,    revision="""onnx"""							,    scheduler=A							,    safety_checker=A							,    feature_extractor=A							,    provider=self.gpu_provider							,    sess_options=self.gpu_options							,    )
         pipe.set_progress_bar_config(disable=A		)

         __snake_case:						Optional[int]			  =						"""A red cat sitting on a park bench"""

         __snake_case:						Dict			  =						np.random.RandomState(0		)
         __snake_case:						Optional[Any]			  =						pipe(
             prompt=A							,    image=A							,    mask_image=A							,    guidance_scale=7.5							,    num_inference_steps=20							,    generator=A							,    output_type="""np"""							,    )
         __snake_case:						List[str]			  =						output.images
         __snake_case:						str			  =						images[0, 255:258, 255:258, -1]

         assert images.shape == (1, 512, 512, 3)
         __snake_case:						Union[str, Any]			  =						np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125]		)

         assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-3






