


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	):


		"""simple docstring"""




		lowercase__				= [
		    '''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	):


		"""simple docstring"""




		lowercase__	,						lowercase__				= emb.weight.shape
		lowercase__				= nn.Linear(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							bias=SCREAMING_SNAKE_CASE	)
		lowercase__				= emb.weight.data
		return lin_layer
def      _a					( SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE=None	):


		"""simple docstring"""




		lowercase__				= {}
		for old_key in state_dict.keys():
				lowercase__				= old_key
				if "moe_layer.experts." in key:
						if expert_idx is not None:
								lowercase__				= key.replace('''moe_layer.experts.0'''		,							f'ffn.experts.expert_{expert_idx}'	)
						else:
								lowercase__				= key.replace('''moe_layer.experts.'''		,							'''ffn.experts.expert_'''	)
				if "gate" in key:
						lowercase__				= key.replace('''.moe_layer.gate.wg'''		,							'''.ffn.router.classifier'''	)
				if "fc2" and "experts" not in key:
						lowercase__				= key.replace('''.fc2.'''		,							'''.ffn.fc2.'''	)
				if "fc1" and "experts" not in key:
						lowercase__				= key.replace('''.fc1.'''		,							'''.ffn.fc1.'''	)
				if ".encoder_attn." in key:
						lowercase__				= key.replace('''.encoder_attn.'''		,							'''.cross_attention.'''	)
				if "encoder_attn_layer_norm" in key:
						lowercase__				= key.replace('''encoder_attn_layer_norm'''		,							'''cross_attention_layer_norm'''	)
				if "final_layer_norm" in key:
						lowercase__				= key.replace('''final_layer_norm'''		,							'''ff_layer_norm'''	)
				lowercase__				= 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	):


		"""simple docstring"""




		lowercase__				= []
		lowercase__				= 0
		os.makedirs(SCREAMING_SNAKE_CASE		,							exist_ok=SCREAMING_SNAKE_CASE	)

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

    # Add the metadata
		lowercase__				= {'''total_size''': total_size}
		lowercase__				= {'''metadata''': metadata, '''weight_map''': weight_map}

		with open(os.path.join(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	)		,							'''w'''		,							encoding='''utf-8'''	) as f:
				lowercase__				= json.dumps(SCREAMING_SNAKE_CASE		,							indent=2		,							sort_keys=SCREAMING_SNAKE_CASE	) + '''\n'''
				f.write(SCREAMING_SNAKE_CASE	)

		return metadata, index


if __name__ == "__main__":
			lowerCAmelCase			=				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.',
			)
			lowerCAmelCase			=				parser.parse_args()
			lowerCAmelCase,		lowerCAmelCase			=				shard_on_the_fly(
			    args.nllb_moe_checkpoint_path,
			    args.pytorch_dump_folder_path,
			    128,
			    args.dtype,
			)

			lowerCAmelCase			=				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)
			lowerCAmelCase			=				NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
			print('Done')
			model.save_pretrained(args.pytorch_dump_folder_path)








'''simple docstring'''





from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


UpperCamelCase_		:     Tuple          =		'''\
@article{wang2019superglue,
  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
  author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},
  journal={arXiv preprint arXiv:1905.00537},
  year={2019}
}
'''

UpperCamelCase_		:     Optional[Any]          =		'''\
SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after
GLUE with a new set of more difficult language understanding tasks, improved
resources, and a new public leaderboard.
'''

UpperCamelCase_		:     Dict          =		'''
Compute SuperGLUE evaluation metric associated to each SuperGLUE dataset.
Args:
    predictions: list of predictions to score. Depending on the SuperGlUE subset:
        - for \'record\': list of question-answer dictionaries with the following keys:
            - \'idx\': index of the question as specified by the dataset
            - \'prediction_text\': the predicted answer text
        - for \'multirc\': list of question-answer dictionaries with the following keys:
            - \'idx\': index of the question-answer pair as specified by the dataset
            - \'prediction\': the predicted answer label
        - otherwise: list of predicted labels
    references: list of reference labels. Depending on the SuperGLUE subset:
        - for \'record\': list of question-answers dictionaries with the following keys:
            - \'idx\': index of the question as specified by the dataset
            - \'answers\': list of possible answers
        - otherwise: list of reference labels
Returns: depending on the SuperGLUE subset:
    - for \'record\':
        - \'exact_match\': Exact match between answer and gold answer
        - \'f1\': F1 score
    - for \'multirc\':
        - \'exact_match\': Exact match between answer and gold answer
        - \'f1_m\': Per-question macro-F1 score
        - \'f1_a\': Average F1 score over all answers
    - for \'axb\':
        \'matthews_correlation\': Matthew Correlation
    - for \'cb\':
        - \'accuracy\': Accuracy
        - \'f1\': F1 score
    - for all others:
        - \'accuracy\': Accuracy
Examples:

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'copa\')  # any of ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0}

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'cb\')
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0, \'f1\': 1.0}

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'record\')
    >>> predictions = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'prediction_text\': \'answer\'}]
    >>> references = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'answers\': [\'answer\', \'another_answer\']}]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'exact_match\': 1.0, \'f1\': 1.0}

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'multirc\')
    >>> predictions = [{\'idx\': {\'answer\': 0, \'paragraph\': 0, \'question\': 0}, \'prediction\': 0}, {\'idx\': {\'answer\': 1, \'paragraph\': 2, \'question\': 3}, \'prediction\': 1}]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'exact_match\': 1.0, \'f1_m\': 1.0, \'f1_a\': 1.0}

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'axb\')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'matthews_correlation\': 1.0}
'''





def       __a      (  _UpperCamelCase:       Tuple   ,		_UpperCamelCase:       int       )		->					Optional[Any]:

     """simple docstring"""




     return float((preds == labels).mean()       )





def       __a      (  _UpperCamelCase:       Union[str, Any]   ,		_UpperCamelCase:       Optional[int]   ,		_UpperCamelCase:       int="binary"       )		->					Any:

     """simple docstring"""




     _snake_case				= simple_accuracy(_UpperCamelCase   ,		_UpperCamelCase       )
     _snake_case				= float(fa_score(y_true=_UpperCamelCase   ,		y_pred=_UpperCamelCase   ,		average=_UpperCamelCase       )       )
     return {
         "accuracy": acc,
         "f1": fa,
     }





def       __a      (  _UpperCamelCase:       str   ,		_UpperCamelCase:       Any       )		->					Tuple:

     """simple docstring"""




     _snake_case				= {}
     for id_pred, label in zip(_UpperCamelCase   ,		_UpperCamelCase       ):
          _snake_case				= F"""{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}"""
          _snake_case				= id_pred["prediction"]
          if question_id in question_map:
               question_map[question_id].append((pred, label)       )
          else:
               _snake_case				= [(pred, label)]
     _snake_case				,   _snake_case				= [], []
     for question, preds_labels in question_map.items():
          _snake_case				,   _snake_case				= zip(*_UpperCamelCase       )
          _snake_case				= fa_score(y_true=_UpperCamelCase   ,		y_pred=_UpperCamelCase   ,		average="macro"       )
          fas.append(_UpperCamelCase       )
          _snake_case				= int(sum(pred == label for pred, label in preds_labels       ) == len(_UpperCamelCase       )       )
          ems.append(_UpperCamelCase       )
     _snake_case				= float(sum(_UpperCamelCase       ) / len(_UpperCamelCase       )       )
     _snake_case				= sum(_UpperCamelCase       ) / len(_UpperCamelCase       )
     _snake_case				= float(fa_score(y_true=_UpperCamelCase   ,		y_pred=[id_pred["prediction"] for id_pred in ids_preds]       )       )
     return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}





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






     def 							_lowercase      (					self       )				->     Union[str, Any]:
          if self.config_name not in [
              "boolq",
              "cb",
              "copa",
              "multirc",
              "record",
              "rte",
              "wic",
              "wsc",
              "wsc.fixed",
              "axb",
              "axg",
          ]:
               raise KeyError(
                   "You should supply a configuration name selected in "
                   "[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]"       )
          return datasets.MetricInfo(
              description=_DESCRIPTION   ,citation=_CITATION   ,inputs_description=_KWARGS_DESCRIPTION   ,features=datasets.Features(self._get_feature_types()       )   ,codebase_urls=[]   ,reference_urls=[]   ,format="numpy" if not self.config_name == "record" and not self.config_name == "multirc" else None   ,)






     def 							_lowercase      (					self       )				->     Union[str, Any]:
          if self.config_name == "record":
               return {
                   "predictions": {
                       "idx": {
                           "passage": datasets.Value("int64"       ),
                           "query": datasets.Value("int64"       ),
                       },
                       "prediction_text": datasets.Value("string"       ),
                   },
                   "references": {
                       "idx": {
                           "passage": datasets.Value("int64"       ),
                           "query": datasets.Value("int64"       ),
                       },
                       "answers": datasets.Sequence(datasets.Value("string"       )       ),
                   },
               }
          elif self.config_name == "multirc":
               return {
                   "predictions": {
                       "idx": {
                           "answer": datasets.Value("int64"       ),
                           "paragraph": datasets.Value("int64"       ),
                           "question": datasets.Value("int64"       ),
                       },
                       "prediction": datasets.Value("int64"       ),
                   },
                   "references": datasets.Value("int64"       ),
               }
          else:
               return {
                   "predictions": datasets.Value("int64"       ),
                   "references": datasets.Value("int64"       ),
               }






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )				->     Optional[Any]:
          if self.config_name == "axb":
               return {"matthews_correlation": matthews_corrcoef(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )}
          elif self.config_name == "cb":
               return acc_and_fa(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,fa_avg="macro"       )
          elif self.config_name == "record":
               _snake_case				= [
                   {
                       "qas": [
                           {"id": ref["idx"]["query"], "answers": [{"text": ans} for ans in ref["answers"]]}
                           for ref in references
                       ]
                   }
               ]
               _snake_case				= {pred["idx"]["query"]: pred["prediction_text"] for pred in predictions}
               return evaluate_record(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )[0]
          elif self.config_name == "multirc":
               return evaluate_multirc(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )
          elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
               return {"accuracy": simple_accuracy(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )}
          else:
               raise KeyError(
                   "You should supply a configuration name selected in "
                   "[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]"       )


