



import faiss  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import requests  # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn  # noqa: F401 # Here to have a nice missing dependency error message early on
import tqdm  # noqa: F401 # Here to have a nice missing dependency error message early on
from mauve import compute_mauve  # From: mauve-text

import datasets


_lowercase		:				Dict      		="\\n@inproceedings{pillutla-etal:mauve:neurips2021,\n  title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},\n  author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},\n  booktitle = {NeurIPS},\n  year      = {2021}\n}\n\n"

_lowercase		:				Any      		="\\nMAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.\n\nMAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.\n\nFor details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).\n\nThis metrics is a wrapper around the official implementation of MAUVE:\nhttps://github.com/krishnap25/mauve\n"

_lowercase		:				Optional[int]      		="\nCalculates MAUVE scores between two lists of generated text and reference text.\nArgs:\n    predictions: list of generated text to score. Each predictions\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\nOptional Args:\n    num_buckets: the size of the histogram to quantize P and Q. Options: 'auto' (default) or an integer\n    pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1\n    kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9\n    kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5\n    kmeans_max_iter: maximum number of k-means iterations. Default 500\n    featurize_model_name: name of the model from which features are obtained. Default 'gpt2-large' Use one of ['gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'].\n    device_id: Device for featurization. Supply a GPU id (e.g. 0 or 3) to use GPU. If no GPU with this id is found, use CPU\n    max_text_length: maximum number of tokens to consider. Default 1024\n    divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25\n    mauve_scaling_factor: \"c\" from the paper. Default 5.\n    verbose: If True (default), print running time updates\n    seed: random seed to initialize k-means cluster assignments.\nReturns:\n    mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,\n    frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,\n    divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,\n    p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,\n    q_hist: same as above, but with q_text.\nExamples:\n\n    >>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest\n    >>> import datasets\n    >>> mauve = datasets.load_metric('mauve')\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP\n    >>> print(out.mauve) # doctest: +SKIP\n    1.0\n"

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,     _KWARGS_DESCRIPTION		)
class        snake_case__    (datasets.Metric		):







						"""simple docstring"""




						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				int:
									"""simple docstring"""




									return datasets.MetricInfo(
									    description=_DESCRIPTION ,       citation=_CITATION ,       homepage="""https://github.com/krishnap25/mauve""" ,       inputs_description=_KWARGS_DESCRIPTION ,       features=datasets.Features(
									        {
									            """predictions""": datasets.Value("""string""" ,       id="""sequence"""  ),
									            """references""": datasets.Value("""string""" ,       id="""sequence"""  ),
									        }  ) ,       codebase_urls=["""https://github.com/krishnap25/mauve"""] ,       reference_urls=[
									        """https://arxiv.org/abs/2102.01454""",
									        """https://github.com/krishnap25/mauve""",
									    ] ,       )

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase ,       __lowercase=None ,       __lowercase=None ,       __lowercase=None ,       __lowercase=None ,       __lowercase="auto" ,       __lowercase=-1 ,       __lowercase=0.9 ,       __lowercase=5 ,       __lowercase=5_0_0 ,       __lowercase="gpt2-large" ,       __lowercase=-1 ,       __lowercase=1_0_2_4 ,       __lowercase=2_5 ,       __lowercase=5 ,       __lowercase=True ,       __lowercase=2_5 ,       ) ->				Any:
									"""simple docstring"""




									a__       :						int							=  compute_mauve(
									    p_text=__lowercase ,       q_text=__lowercase ,       p_features=__lowercase ,       q_features=__lowercase ,       p_tokens=__lowercase ,       q_tokens=__lowercase ,       num_buckets=__lowercase ,       pca_max_data=__lowercase ,       kmeans_explained_var=__lowercase ,       kmeans_num_redo=__lowercase ,       kmeans_max_iter=__lowercase ,       featurize_model_name=__lowercase ,       device_id=__lowercase ,       max_text_length=__lowercase ,       divergence_curve_discretization_size=__lowercase ,       mauve_scaling_factor=__lowercase ,       verbose=__lowercase ,       seed=__lowercase ,       )
									return out









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_					(  _lowercase    :       List[str])							->	Union[str, Any]:







			"""simple docstring"""
			a__       :						List[str]							=  [
			    """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(_lowercase						,       _lowercase)

def      lowerCAmelCase_					(  _lowercase    :       List[Any])							->	Optional[Any]:







			"""simple docstring"""
			a__	,						a__       :						Union[str, Any]							=  emb.weight.shape
			a__       :						str							=  nn.Linear(_lowercase						,       _lowercase						,       bias=_lowercase)
			a__       :						Any							=  emb.weight.data
			return lin_layer

def      lowerCAmelCase_					(  _lowercase    :       int						,       _lowercase    :       int=None)							->	List[Any]:







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

def      lowerCAmelCase_					(  _lowercase    :       Tuple						,       _lowercase    :       Optional[int]						,       _lowercase    :       Any						,       _lowercase    :       Dict						,       _lowercase    :       str = WEIGHTS_NAME)							->	Tuple:







			"""simple docstring"""
			a__       :						Tuple							=  []
			a__       :						Optional[Any]							=  0
			os.makedirs(_lowercase						,       exist_ok=_lowercase)

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

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

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

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

			return metadata, index


if __name__ == "__main__":
		_lowercase		:				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		:				Tuple      		=parser.parse_args()
		_lowercase		,					_lowercase		:				List[Any]      		=shard_on_the_fly(
		    args.nllb_moe_checkpoint_path,
		    args.pytorch_dump_folder_path,
		    128,
		    args.dtype,
		)

		_lowercase		:				int      		=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		:				List[str]      		=NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
		print("Done")
		model.save_pretrained(args.pytorch_dump_folder_path)




