





'''simple docstring'''






import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import BridgeTowerImageProcessor




class  _A (					unittest.TestCase		):

		def __init__(   self					:   Tuple  ,      __magic_name__					:   List[Any]  ,      __magic_name__					:   bool = True  ,      __magic_name__					:   Dict[str, int] = None  ,      __magic_name__					:   int = 32  ,      __magic_name__					:   bool = True  ,      __magic_name__					:   Union[int, float] = 1 / 2_55  ,      __magic_name__					:   bool = True  ,      __magic_name__					:   bool = True  ,      __magic_name__					:   Optional[Union[float, List[float]]] = [0.48145466, 0.4578275, 0.40821073]  ,      __magic_name__					:   Optional[Union[float, List[float]]] = [0.26862954, 0.26130258, 0.27577711]  ,      __magic_name__					:   bool = True  ,      __magic_name__					:   Any=7  ,      __magic_name__					:   Tuple=30  ,      __magic_name__					:   List[Any]=4_00  ,      __magic_name__					:   Optional[Any]=3  ,      )					->    Tuple:


					"""simple docstring"""

					__snake_case					:      int							      =     parent
					__snake_case					:      Optional[Any]							      =     do_resize
					__snake_case					:      List[str]							      =     size if size is not None else {"""shortest_edge""": 2_88}
					__snake_case					:      Any							      =     size_divisor
					__snake_case					:      List[str]							      =     do_rescale
					__snake_case					:      Optional[int]							      =     rescale_factor
					__snake_case					:      Any							      =     do_normalize
					__snake_case					:      Any							      =     do_center_crop
					__snake_case					:      Tuple							      =     image_mean
					__snake_case					:      Tuple							      =     image_std
					__snake_case					:      int							      =     do_pad
					__snake_case					:      Union[str, Any]							      =     batch_size
					__snake_case					:      Union[str, Any]							      =     num_channels
					__snake_case					:      Optional[int]							      =     min_resolution
					__snake_case					:      str							      =     max_resolution

		def 	lowercase__     (   self					:   List[Any]			)					->    Any:


					"""simple docstring"""

					return {
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "do_normalize": self.do_normalize,
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "size_divisor": self.size_divisor,
					}

		def 	lowercase__     (   self					:   str  ,      __magic_name__					:   Optional[Any]  ,      __magic_name__					:   Union[str, Any]=False			)					->    Any:


					"""simple docstring"""

					if not batched:
								__snake_case					:      Optional[Any]							      =     self.size["""shortest_edge"""]
								__snake_case					:      Optional[int]							      =     image_inputs[0]
								if isinstance(__magic_name__  ,      Image.Image			):
											__snake_case      ,     __snake_case					:      List[Any]							      =     image.size
								else:
											__snake_case      ,     __snake_case					:      List[str]							      =     image.shape[1], image.shape[2]
								__snake_case					:      Optional[Any]							      =     size / min(__magic_name__  ,      __magic_name__			)
								if h < w:
											__snake_case      ,     __snake_case					:      List[str]							      =     size, scale * w
								else:
											__snake_case      ,     __snake_case					:      int							      =     scale * h, size

								__snake_case					:      str							      =     int((13_33 / 8_00) * size			)
								if max(__magic_name__  ,      __magic_name__			) > max_size:
											__snake_case					:      Dict							      =     max_size / max(__magic_name__  ,      __magic_name__			)
											__snake_case					:      str							      =     newh * scale
											__snake_case					:      List[Any]							      =     neww * scale

								__snake_case      ,     __snake_case					:      int							      =     int(newh + 0.5			), int(neww + 0.5			)
								__snake_case      ,     __snake_case					:      Tuple							      =     (
								    newh // self.size_divisor * self.size_divisor,
								    neww // self.size_divisor * self.size_divisor,
								)

					else:
								__snake_case					:      Tuple							      =     []
								for image in image_inputs:
											__snake_case      ,     __snake_case					:      Tuple							      =     self.get_expected_values([image]			)
											expected_values.append((expected_height, expected_width)			)
								__snake_case					:      Dict							      =     max(__magic_name__  ,      key=lambda __magic_name__			: item[0]			)[0]
								__snake_case					:      Union[str, Any]							      =     max(__magic_name__  ,      key=lambda __magic_name__			: item[1]			)[1]

					return expected_height, expected_width





@require_torch
@require_vision
class  _A (					__lowercase						,					unittest.TestCase		):
		lowercase__:   Any				     =   BridgeTowerImageProcessor if is_vision_available() else None

		def 	lowercase__     (   self					:   List[str]			)					->    Dict:


					"""simple docstring"""

					__snake_case					:      Optional[int]							      =     BridgeTowerImageProcessingTester(self			)

		@property
		def 	lowercase__     (   self					:   Optional[Any]			)					->    Any:


					"""simple docstring"""

					return self.image_processor_tester.prepare_image_processor_dict()

		def 	lowercase__     (   self					:   int			)					->    Union[str, Any]:


					"""simple docstring"""

					__snake_case					:      Optional[int]							      =     self.image_processing_class(**self.image_processor_dict			)
					self.assertTrue(hasattr(__magic_name__  ,      """image_mean"""			)			)
					self.assertTrue(hasattr(__magic_name__  ,      """image_std"""			)			)
					self.assertTrue(hasattr(__magic_name__  ,      """do_normalize"""			)			)
					self.assertTrue(hasattr(__magic_name__  ,      """do_resize"""			)			)
					self.assertTrue(hasattr(__magic_name__  ,      """size"""			)			)
					self.assertTrue(hasattr(__magic_name__  ,      """size_divisor"""			)			)

		def 	lowercase__     (   self					:   Any			)					->    Optional[Any]:


					"""simple docstring"""

					pass

		def 	lowercase__     (   self					:   Dict			)					->    List[str]:


					"""simple docstring"""

					__snake_case					:      Dict							      =     self.image_processing_class(**self.image_processor_dict			)
					# create random PIL images
					__snake_case					:      Optional[Any]							      =     prepare_image_inputs(self.image_processor_tester  ,      equal_resolution=__magic_name__			)
					for image in image_inputs:
								self.assertIsInstance(__magic_name__  ,      Image.Image			)

					# Test not batched input
					__snake_case					:      Any							      =     image_processing(image_inputs[0]  ,      return_tensors="""pt"""			).pixel_values

					__snake_case      ,     __snake_case					:      int							      =     self.image_processor_tester.get_expected_values(__magic_name__			)
					self.assertEqual(
					    encoded_images.shape  ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,      )

					# Test batched
					__snake_case					:      Dict							      =     image_processing(__magic_name__  ,      return_tensors="""pt"""			).pixel_values

					__snake_case      ,     __snake_case					:      Optional[int]							      =     self.image_processor_tester.get_expected_values(__magic_name__  ,      batched=__magic_name__			)
					self.assertEqual(
					    encoded_images.shape  ,      (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )  ,      )

		def 	lowercase__     (   self					:   Any			)					->    str:


					"""simple docstring"""

					__snake_case					:      str							      =     self.image_processing_class(**self.image_processor_dict			)
					# create random numpy tensors
					__snake_case					:      Optional[Any]							      =     prepare_image_inputs(self.image_processor_tester  ,      equal_resolution=__magic_name__  ,      numpify=__magic_name__			)
					for image in image_inputs:
								self.assertIsInstance(__magic_name__  ,      np.ndarray			)

					# Test not batched input
					__snake_case					:      int							      =     image_processing(image_inputs[0]  ,      return_tensors="""pt"""			).pixel_values

					__snake_case      ,     __snake_case					:      Optional[int]							      =     self.image_processor_tester.get_expected_values(__magic_name__			)
					self.assertEqual(
					    encoded_images.shape  ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,      )

					# Test batched
					__snake_case					:      Any							      =     image_processing(__magic_name__  ,      return_tensors="""pt"""			).pixel_values

					__snake_case      ,     __snake_case					:      Tuple							      =     self.image_processor_tester.get_expected_values(__magic_name__  ,      batched=__magic_name__			)
					self.assertEqual(
					    encoded_images.shape  ,      (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )  ,      )

		def 	lowercase__     (   self					:   str			)					->    Any:


					"""simple docstring"""

					__snake_case					:      Union[str, Any]							      =     self.image_processing_class(**self.image_processor_dict			)
					# create random PyTorch tensors
					__snake_case					:      Dict							      =     prepare_image_inputs(self.image_processor_tester  ,      equal_resolution=__magic_name__  ,      torchify=__magic_name__			)
					for image in image_inputs:
								self.assertIsInstance(__magic_name__  ,      torch.Tensor			)

					# Test not batched input
					__snake_case					:      str							      =     image_processing(image_inputs[0]  ,      return_tensors="""pt"""			).pixel_values

					__snake_case      ,     __snake_case					:      List[Any]							      =     self.image_processor_tester.get_expected_values(__magic_name__			)
					self.assertEqual(
					    encoded_images.shape  ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,      )

					# Test batched
					__snake_case					:      Optional[int]							      =     image_processing(__magic_name__  ,      return_tensors="""pt"""			).pixel_values

					__snake_case      ,     __snake_case					:      List[Any]							      =     self.image_processor_tester.get_expected_values(__magic_name__  ,      batched=__magic_name__			)
					self.assertEqual(
					    encoded_images.shape  ,      (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )  ,      )









'''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						(       _lowerCamelCase ) ->				List[Any]:



			"""simple docstring"""


			__snake_case					:      Union[str, 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 					_a						(       _lowerCamelCase ) ->				List[str]:



			"""simple docstring"""


			__snake_case      ,     __snake_case					:      Dict							      =     emb.weight.shape
			__snake_case					:      Optional[int]							      =     nn.Linear(_lowerCamelCase    ,	_lowerCamelCase    ,	bias=_lowerCamelCase )
			__snake_case					:      Union[str, Any]							      =     emb.weight.data
			return lin_layer







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase=None ) ->				Union[str, Any]:



			"""simple docstring"""


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







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase = WEIGHTS_NAME ) ->				Dict:



			"""simple docstring"""


			__snake_case					:      Optional[int]							      =     []
			__snake_case					:      Dict							      =     0
			os.makedirs(_lowerCamelCase    ,	exist_ok=_lowerCamelCase )

			for expert in range(_lowerCamelCase ):
						__snake_case					:      Tuple							      =     switch_checkpoint_path + F'''-rank-{expert}.pt'''
						if os.path.isfile(_lowerCamelCase ):
									__snake_case					:      Dict							      =     torch.load(_lowerCamelCase )["""model"""]
									remove_ignore_keys_(_lowerCamelCase )
									__snake_case					:      Optional[Any]							      =     rename_fairseq_keys(_lowerCamelCase    ,	_lowerCamelCase )
									__snake_case					:      List[Any]							      =     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
			__snake_case					:      Optional[Any]							      =     os.path.join(_lowerCamelCase    ,	weights_name.replace(""".bin"""    ,	F'''-{len(_lowerCamelCase )+1:05d}-of-???.bin''' ) )
			__snake_case					:      str							      =     torch.load(switch_checkpoint_path + """-shared.pt""" )["""model"""]
			remove_ignore_keys_(_lowerCamelCase )
			__snake_case					:      Optional[Any]							      =     rename_fairseq_keys(_lowerCamelCase    ,	_lowerCamelCase )
			__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(_lowerCamelCase ) == 1:
						__snake_case					:      Optional[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
			__snake_case					:      Tuple							      =     {}
			for idx, shard in enumerate(_lowerCamelCase ):
						__snake_case					:      Any							      =     weights_name.replace(""".bin"""    ,	F'''-{idx+1:05d}-of-{len(_lowerCamelCase ):05d}.bin''' )
						__snake_case					:      int							      =     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:
									__snake_case					:      str							      =     shard_file

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

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

			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)


