

import argparse
import re

from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints

from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
_a		       =   {
    "/attention/": "/0/SelfAttention/",
    "/self_attention/": "/0/SelfAttention/",
    "/encoder_decoder_attention/": "/1/EncDecAttention/",
    "value": "v",
    "query": "q",
    "key": "k",
    "out": "o",
    "pre_self_attention_layer_norm": "0/layer_norm",
    "pre_cross_attention_layer_norm": "1/layer_norm",
    "pre_attention_layer_norm": "0/layer_norm",  # previously 1, but seems wrong
    "token_embedder": "shared",
    "encoder_norm": "final_layer_norm",
    "decoder_norm": "final_layer_norm",
    "relpos_bias/rel_embedding": "block/0/layer/0/SelfAttention/relative_attention_bias/weight",
    "router/router_weights/w/": "router/classifier/",
    "roer/roer_weights/w/": "router/classifier/",
    "logits_dense": "lm_head",
}







def _a (       SCREAMING_SNAKE_CASE		:			int		)    ->    Union[str, Any]:

 """simple docstring"""






 __lowerCAmelCase:   Optional[int]   	=      list(s_dict.keys()		)
 for key in keys:
  __lowerCAmelCase:   Tuple   	=      R'.*/layers_(\d+)'
  __lowerCAmelCase:   Any   	=      key
  if re.match(A__    ,					A__		):
   __lowerCAmelCase:   Union[str, Any]   	=      re.sub(R'layers_(\d+)'    ,					R'block/\1/layer'    ,					A__		)

  __lowerCAmelCase:   List[str]   	=      R'(encoder|decoder)\/'

  if re.match(A__    ,					A__		):
   __lowerCAmelCase:   Any   	=      re.match(A__    ,					A__		).groups()
   if groups[0] == "encoder":
    __lowerCAmelCase:   Any   	=      re.sub(R'/mlp/'    ,					R'/1/mlp/'    ,					A__		)
    __lowerCAmelCase:   Union[str, Any]   	=      re.sub(R'/pre_mlp_layer_norm/'    ,					R'/1/layer_norm/'    ,					A__		)

   elif groups[0] == "decoder":
    __lowerCAmelCase:   List[str]   	=      re.sub(R'/mlp/'    ,					R'/2/mlp/'    ,					A__		)
    __lowerCAmelCase:   Optional[Any]   	=      re.sub(R'/pre_mlp_layer_norm/'    ,					R'/2/layer_norm/'    ,					A__		)

        # 2. Convert other classic mappings
  for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
   if old_key in new_key:
    __lowerCAmelCase:   Tuple   	=      new_key.replace(A__    ,					A__		)

  print(f'''{key} -> {new_key}'''		)
  __lowerCAmelCase:   Tuple   	=      s_dict.pop(A__		)

 if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
  __lowerCAmelCase:   Optional[Any]   	=      s_dict[
      'encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'
  ].T
 if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
  __lowerCAmelCase:   List[str]   	=      s_dict[
      'decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'
  ].T

 # 3. Take extra care of the EXPERTS layer
 for key in list(s_dict.keys()		):
  if "expert" in key:
   __lowerCAmelCase:   Union[str, Any]   	=      s_dict[key].shape[0]
   __lowerCAmelCase:   List[Any]   	=      s_dict[key]
   for idx in range(A__		):
    __lowerCAmelCase:   Any   	=      expert_weihts[idx]
    print(f'''{key} -> {key.replace('expert/'    ,					'nested fstring'		)}'''		)

   s_dict.pop(A__		)

 return s_dict


_a		       =   {
    "NUM_ENCODER_LAYERS": "num_layers",
    "NUM_DECODER_LAYERS": "num_decoder_layers",
    "NUM_HEADS": "num_heads",
    "HEAD_DIM": "d_kv",
    "EMBED_DIM": "d_model",
    "MLP_DIM": "d_ff",
    "NUM_SELECTED_EXPERTS": "num_selected_experts",
    "NUM_ENCODER_SPARSE_LAYERS": "num_sparse_encoder_layers",
    "NUM_DECODER_SPARSE_LAYERS": "num_sparse_decoder_layers",
    "dense.MlpBlock.activations": "feed_forward_proj",
}







def _a (       SCREAMING_SNAKE_CASE		:			Optional[Any]    ,					SCREAMING_SNAKE_CASE		:			str		)    ->    Optional[int]:

 """simple docstring"""






 import regex as re

 with open(A__    ,					'r'		) as f:
  __lowerCAmelCase:   List[Any]   	=      f.read()

 __lowerCAmelCase:   List[Any]   	=      re.findall(R'(.*) = ([0-9.]*)'    ,					A__		)
 __lowerCAmelCase:   int   	=      {}
 for param, value in regex_match:
  if param in GIN_TO_CONFIG_MAPPING and value != "":
   __lowerCAmelCase:   List[str]   	=      float(A__		) if '.' in value else int(A__		)

 __lowerCAmelCase:   str   	=      re.findall(R'(.*activations) = \(\'(.*)\',\)'    ,					A__		)[0]
 __lowerCAmelCase:   Optional[int]   	=      str(activation[1]		)

 __lowerCAmelCase:   List[Any]   	=      num_experts
 __lowerCAmelCase:   int   	=      SwitchTransformersConfig(**A__		)
 return config







def _a (       SCREAMING_SNAKE_CASE		:			Tuple    ,					SCREAMING_SNAKE_CASE		:			int    ,					SCREAMING_SNAKE_CASE		:			int=None    ,					SCREAMING_SNAKE_CASE		:			Dict="./"    ,					SCREAMING_SNAKE_CASE		:			str=8		)    ->    Dict:

 """simple docstring"""






 print(f'''Loading flax weights from : {flax_checkpoint_path}'''		)
 __lowerCAmelCase:   Optional[Any]   	=      checkpoints.load_tax_checkpoint(A__		)

 if gin_file is not None:
  __lowerCAmelCase:   str   	=      convert_gin_to_config(A__    ,					A__		)
 else:
  __lowerCAmelCase:   Dict   	=      SwitchTransformersConfig.from_pretrained(A__		)

 __lowerCAmelCase:   Union[str, Any]   	=      SwitchTransformersForConditionalGeneration(A__		)

 __lowerCAmelCase:   Tuple   	=      flax_params['target']
 __lowerCAmelCase:   Union[str, Any]   	=      flatten_dict(A__    ,					sep='/'		)
 __lowerCAmelCase:   Dict   	=      rename_keys(A__		)
 __lowerCAmelCase:   Any   	=      unflatten_dict(A__    ,					sep='/'		)

 # Load the flax params in the PT model
 load_flax_weights_in_pytorch_model(A__    ,					A__		)

 print(f'''Save PyTorch model to {pytorch_dump_path}'''		)
 pt_model.save_pretrained(A__		)


if __name__ == "__main__":
  _a		       =   argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--switch_t5x_checkpoint_path''',
      default=None,
      type=str,
      required=True,
      help=(
          '''The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the'''
          ''' model architecture. If not provided, a `gin_file` has to be provided.'''
      ),
  )
  parser.add_argument(
      '''--gin_file''',
      default=None,
      type=str,
      required=False,
      help='''Path to the gin config file. If not provided, a `config_file` has to be passed   ''',
  )
  parser.add_argument(
      '''--config_name''', default=None, type=str, required=False, help='''Config name of SwitchTransformers model.'''
  )
  parser.add_argument(
      '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output pytorch model.'''
  )
  parser.add_argument('''--num_experts''', default=8, type=int, required=False, help='''Number of experts''')
  _a		       =   parser.parse_args()
  convert_flax_checkpoint_to_pytorch(
      args.switch_tax_checkpoint_path,
      args.config_name,
      args.gin_file,
      args.pytorch_dump_folder_path,
      args.num_experts,
  )










'''simple docstring'''


import absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # 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 six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


_lowerCamelCase	:       List[str]      	=	"\\n@inproceedings{lin-2004-rouge,\n    title = \"{ROUGE}: A Package for Automatic Evaluation of Summaries\",\n    author = \"Lin, Chin-Yew\",\n    booktitle = \"Text Summarization Branches Out\",\n    month = jul,\n    year = \"2004\",\n    address = \"Barcelona, Spain\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W04-1013\",\n    pages = \"74--81\",\n}\n"

_lowerCamelCase	:       Optional[int]      	=	"\\nROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for\nevaluating automatic summarization and machine translation software in natural language processing.\nThe metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.\n\nNote that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.\n\nThis metrics is a wrapper around Google Research reimplementation of ROUGE:\nhttps://github.com/google-research/google-research/tree/master/rouge\n"

_lowerCamelCase	:       str      	=	"\nCalculates average rouge scores for a list of hypotheses and references\nArgs:\n    predictions: list of predictions to score. Each prediction\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.\n    rouge_types: A list of rouge types to calculate.\n        Valid names:\n        `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,\n        `\"rougeL\"`: Longest common subsequence based scoring.\n        `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.\n        See details in https://github.com/huggingface/datasets/issues/617\n    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.\n    use_aggregator: Return aggregates if this is set to True\nReturns:\n    rouge1: rouge_1 (precision, recall, f1),\n    rouge2: rouge_2 (precision, recall, f1),\n    rougeL: rouge_l (precision, recall, f1),\n    rougeLsum: rouge_lsum (precision, recall, f1)\nExamples:\n\n    >>> rouge = datasets.load_metric('rouge')\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> results = rouge.compute(predictions=predictions, references=references)\n    >>> print(list(results.keys()))\n    ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']\n    >>> print(results[\"rouge1\"])\n    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))\n    >>> print(results[\"rouge1\"].mid.fmeasure)\n    1.0\n"

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






    """simple docstring"""





    def  A       (  self      :					Union[str, Any] ):


       """simple docstring"""







       return datasets.MetricInfo(
           description=_DESCRIPTION  ,						citation=_CITATION  ,						inputs_description=_KWARGS_DESCRIPTION  ,						features=datasets.Features(
               {
                   'predictions': datasets.Value('string'  ,						id='sequence' ),
                   'references': datasets.Value('string'  ,						id='sequence' ),
               } )  ,						codebase_urls=['https://github.com/google-research/google-research/tree/master/rouge']  ,						reference_urls=[
               'https://en.wikipedia.org/wiki/ROUGE_(metric)',
               'https://github.com/google-research/google-research/tree/master/rouge',
           ]  ,						)






    def  A       (  self      :					Tuple  ,						UpperCamelCase__      :					Tuple  ,						UpperCamelCase__      :					Dict  ,						UpperCamelCase__      :					List[str]=None  ,						UpperCamelCase__      :					List[Any]=True  ,						UpperCamelCase__      :					Optional[Any]=False ):


       """simple docstring"""







       if rouge_types is None:
          UpperCamelCase					=  ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']

       UpperCamelCase					=  rouge_scorer.RougeScorer(rouge_types=UpperCamelCase__  ,						use_stemmer=UpperCamelCase__ )
       if use_aggregator:
          UpperCamelCase					=  scoring.BootstrapAggregator()
       else:
          UpperCamelCase					=  []

       for ref, pred in zip(UpperCamelCase__  ,						UpperCamelCase__ ):
          UpperCamelCase					=  scorer.score(UpperCamelCase__  ,						UpperCamelCase__ )
          if use_aggregator:
             aggregator.add_scores(UpperCamelCase__ )
          else:
             scores.append(UpperCamelCase__ )

       if use_aggregator:
          UpperCamelCase					=  aggregator.aggregate()
       else:
          UpperCamelCase					=  {}
          for key in scores[0]:
             UpperCamelCase					=  [score[key] for score in scores]

       return result

