




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_							:    Any					):
     __lowerCAmelCase           =	[
         "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_							:    Tuple					):
     __lowerCAmelCase       ,							__lowerCAmelCase           =	emb.weight.shape
     __lowerCAmelCase           =	nn.Linear(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_				,				bias=SCREAMING_SNAKE_CASE_					)
     __lowerCAmelCase           =	emb.weight.data
     return lin_layer







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







def      _a				(						SCREAMING_SNAKE_CASE_							:    Any				,				SCREAMING_SNAKE_CASE_							:    int				,				SCREAMING_SNAKE_CASE_							:    Optional[Any]				,				SCREAMING_SNAKE_CASE_							:    str				,				SCREAMING_SNAKE_CASE_							:    str = WEIGHTS_NAME					):
     __lowerCAmelCase           =	[]
     __lowerCAmelCase           =	0
     os.makedirs(SCREAMING_SNAKE_CASE_				,				exist_ok=SCREAMING_SNAKE_CASE_					)

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

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

     with open(os.path.join(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)				,				"w"				,				encoding="utf-8"					) as f:
          __lowerCAmelCase           =	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__         =      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__         =      parser.parse_args()
     UpperCamelCase__      ,     UpperCamelCase__         =      shard_on_the_fly(
         args.nllb_moe_checkpoint_path,
         args.pytorch_dump_folder_path,
         128,
         args.dtype,
     )

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










import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image
else:
					class       a__   :






											@staticmethod
											def     __SCREAMING_SNAKE_CASE(       *_A		,      **_A      ):




																"""simple docstring"""



																pass







def      _a				(						SCREAMING_SNAKE_CASE_							:    Image					):
					__lowerCAmelCase           =	hashlib.mda(image.tobytes()					)
					return m.hexdigest()


@is_pipeline_test
@require_vision
@require_timm
@require_torch
class       a__   ( unittest.TestCase				):
						_a							:   Tuple =   MODEL_FOR_DEPTH_ESTIMATION_MAPPING






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	DepthEstimationPipeline(model=_A		,      image_processor=_A      )
											return depth_estimator, [
											    "./tests/fixtures/tests_samples/COCO/000000039769.png",
											    "./tests/fixtures/tests_samples/COCO/000000039769.png",
											]






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
											self.assertEqual({"predicted_depth": ANY(torch.Tensor      ), "depth": ANY(Image.Image      )}		,      _A      )
											import datasets

											__lowerCAmelCase           =	datasets.load_dataset("hf-internal-testing/fixtures_image_utils"		,      "image"		,      split="test"      )
											__lowerCAmelCase           =	depth_estimator(
											    [
											        Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      ),
											        "http://images.cocodataset.org/val2017/000000039769.jpg",
											        # RGBA
											        dataset[0]["file"],
											        # LA
											        dataset[1]["file"],
											        # L
											        dataset[2]["file"],
											    ]      )
											self.assertEqual(
											    [
											        {"predicted_depth": ANY(torch.Tensor      ), "depth": ANY(Image.Image      )},
											        {"predicted_depth": ANY(torch.Tensor      ), "depth": ANY(Image.Image      )},
											        {"predicted_depth": ANY(torch.Tensor      ), "depth": ANY(Image.Image      )},
											        {"predicted_depth": ANY(torch.Tensor      ), "depth": ANY(Image.Image      )},
											        {"predicted_depth": ANY(torch.Tensor      ), "depth": ANY(Image.Image      )},
											    ]		,      _A		,      )






						@require_tf
						@unittest.skip("Depth estimation is not implemented in TF"      )
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											pass






						@slow
						@require_torch
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	"Intel/dpt-large"
											__lowerCAmelCase           =	pipeline("depth-estimation"		,      model=_A      )
											__lowerCAmelCase           =	depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg"      )
											__lowerCAmelCase           =	hashimage(outputs["depth"]      )

											# This seems flaky.
											# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
											self.assertEqual(nested_simplify(outputs["predicted_depth"].max().item()      )		,      29.3_04      )
											self.assertEqual(nested_simplify(outputs["predicted_depth"].min().item()      )		,      2.6_62      )






						@require_torch
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											self.skipTest("There is not hf-internal-testing tiny model for either GLPN nor DPT"      )




