



"""simple docstring"""







import argparse
import collections
import json
import os
import re
import string
import sys

import numpy as np


lowercase__		:				List[str]          =	re.compile(R"""\b(a|an|the)\b""", re.UNICODE)

lowercase__		:				Dict          =	None

def 				UpperCamelCase_    (    )						->   Any:

  """simple docstring"""




  lowerCAmelCase_   :				Optional[Any]								= argparse.ArgumentParser('Official evaluation script for SQuAD version 2.0.'						)
  parser.add_argument('data_file'    ,   metavar='data.json'    ,   help='Input data JSON file.'						)
  parser.add_argument('pred_file'    ,   metavar='pred.json'    ,   help='Model predictions.'						)
  parser.add_argument(
      '--out-file'    ,   '-o'    ,   metavar='eval.json'    ,   help='Write accuracy metrics to file (default is stdout).'						)
  parser.add_argument(
      '--na-prob-file'    ,   '-n'    ,   metavar='na_prob.json'    ,   help='Model estimates of probability of no answer.'						)
  parser.add_argument(
      '--na-prob-thresh'    ,   '-t'    ,   type=lowerCAmelCase__    ,   default=1.0    ,   help='Predict \"\" if no-answer probability exceeds this (default = 1.0).'    ,   )
  parser.add_argument(
      '--out-image-dir'    ,   '-p'    ,   metavar='out_images'    ,   default=lowerCAmelCase__    ,   help='Save precision-recall curves to directory.'						)
  parser.add_argument('--verbose'    ,   '-v'    ,   action='store_true'						)
  if len(sys.argv						) == 1:
    parser.print_help()
    sys.exit(1						)
  return parser.parse_args()

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Tuple						)						->   Optional[int]:

  """simple docstring"""




  lowerCAmelCase_   :				str								= {}
  for article in dataset:
    for p in article["paragraphs"]:
      for qa in p["qas"]:
        lowerCAmelCase_   :				Optional[Any]								= bool(qa['answers']['text']						)
  return qid_to_has_ans

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Optional[Any]						)						->   str:

  """simple docstring"""




  def remove_articles(lowerCAmelCase__      :				List[Any]						):
    return ARTICLES_REGEX.sub(' '    ,   lowerCAmelCase__						)

  def white_space_fix(lowerCAmelCase__      :				Optional[Any]						):
    return " ".join(text.split()						)

  def remove_punc(lowerCAmelCase__      :				List[str]						):
    lowerCAmelCase_   :				List[str]								= set(string.punctuation						)
    return "".join(ch for ch in text if ch not in exclude						)

  def lower(lowerCAmelCase__      :				List[Any]						):
    return text.lower()

  return white_space_fix(remove_articles(remove_punc(lower(lowerCAmelCase__						)						)						)						)

def 				UpperCamelCase_    (    lowerCAmelCase__      :				str						)						->   Optional[Any]:

  """simple docstring"""




  if not s:
    return []
  return normalize_answer(lowerCAmelCase__						).split()

def 				UpperCamelCase_    (    lowerCAmelCase__      :				List[Any]    ,   lowerCAmelCase__      :				int						)						->   List[str]:

  """simple docstring"""




  return int(normalize_answer(lowerCAmelCase__						) == normalize_answer(lowerCAmelCase__						)						)

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Union[str, Any]    ,   lowerCAmelCase__      :				Any						)						->   Optional[Any]:

  """simple docstring"""




  lowerCAmelCase_   :				Tuple								= get_tokens(lowerCAmelCase__						)
  lowerCAmelCase_   :				Optional[Any]								= get_tokens(lowerCAmelCase__						)
  lowerCAmelCase_   :				str								= collections.Counter(lowerCAmelCase__						) & collections.Counter(lowerCAmelCase__						)
  lowerCAmelCase_   :				Union[str, Any]								= sum(common.values()						)
  if len(lowerCAmelCase__						) == 0 or len(lowerCAmelCase__						) == 0:
    # If either is no-answer, then F1 is 1 if they agree, 0 otherwise
    return int(gold_toks == pred_toks						)
  if num_same == 0:
    return 0
  lowerCAmelCase_   :				str								= 1.0 * num_same / len(lowerCAmelCase__						)
  lowerCAmelCase_   :				List[str]								= 1.0 * num_same / len(lowerCAmelCase__						)
  lowerCAmelCase_   :				List[str]								= (2 * precision * recall) / (precision + recall)
  return fa

def 				UpperCamelCase_    (    lowerCAmelCase__      :				int    ,   lowerCAmelCase__      :				List[str]						)						->   List[Any]:

  """simple docstring"""




  lowerCAmelCase_   :				Any								= {}
  lowerCAmelCase_   :				Dict								= {}
  for article in dataset:
    for p in article["paragraphs"]:
      for qa in p["qas"]:
        lowerCAmelCase_   :				Tuple								= qa["id"]
        lowerCAmelCase_   :				Dict								= [t for t in qa["answers"]["text"] if normalize_answer(lowerCAmelCase__						)]
        if not gold_answers:
          # For unanswerable questions, only correct answer is empty string
          lowerCAmelCase_   :				Any								= [""]
        if qid not in preds:
          print(f"Missing prediction for {qid}"						)
          continue
        lowerCAmelCase_   :				str								= preds[qid]
        # Take max over all gold answers
        lowerCAmelCase_   :				str								= max(compute_exact(lowerCAmelCase__    ,   lowerCAmelCase__						) for a in gold_answers						)
        lowerCAmelCase_   :				int								= max(compute_fa(lowerCAmelCase__    ,   lowerCAmelCase__						) for a in gold_answers						)
  return exact_scores, fa_scores

def 				UpperCamelCase_    (    lowerCAmelCase__      :				List[str]    ,   lowerCAmelCase__      :				Dict    ,   lowerCAmelCase__      :				List[str]    ,   lowerCAmelCase__      :				Dict						)						->   List[Any]:

  """simple docstring"""




  lowerCAmelCase_   :				Optional[int]								= {}
  for qid, s in scores.items():
    lowerCAmelCase_   :				Optional[int]								= na_probs[qid] > na_prob_thresh
    if pred_na:
      lowerCAmelCase_   :				Optional[int]								= float(not qid_to_has_ans[qid]						)
    else:
      lowerCAmelCase_   :				Optional[int]								= s
  return new_scores

def 				UpperCamelCase_    (    lowerCAmelCase__      :				List[Any]    ,   lowerCAmelCase__      :				Dict    ,   lowerCAmelCase__      :				Optional[Any]=None						)						->   List[str]:

  """simple docstring"""




  if not qid_list:
    lowerCAmelCase_   :				Optional[Any]								= len(lowerCAmelCase__						)
    return collections.OrderedDict(
        [
            ('exact', 100.0 * sum(exact_scores.values()						) / total),
            ('f1', 100.0 * sum(fa_scores.values()						) / total),
            ('total', total),
        ]						)
  else:
    lowerCAmelCase_   :				Any								= len(lowerCAmelCase__						)
    return collections.OrderedDict(
        [
            ('exact', 100.0 * sum(exact_scores[k] for k in qid_list						) / total),
            ('f1', 100.0 * sum(fa_scores[k] for k in qid_list						) / total),
            ('total', total),
        ]						)

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Union[str, Any]    ,   lowerCAmelCase__      :				List[Any]    ,   lowerCAmelCase__      :				List[Any]						)						->   Optional[Any]:

  """simple docstring"""




  for k in new_eval:
    lowerCAmelCase_   :				Union[str, Any]								= new_eval[k]

def 				UpperCamelCase_    (    lowerCAmelCase__      :				str    ,   lowerCAmelCase__      :				Any    ,   lowerCAmelCase__      :				str    ,   lowerCAmelCase__      :				Union[str, Any]						)						->   str:

  """simple docstring"""




  plt.step(lowerCAmelCase__    ,   lowerCAmelCase__    ,   color='b'    ,   alpha=0.2    ,   where='post'						)
  plt.fill_between(lowerCAmelCase__    ,   lowerCAmelCase__    ,   step='post'    ,   alpha=0.2    ,   color='b'						)
  plt.xlabel('Recall'						)
  plt.ylabel('Precision'						)
  plt.xlim([0.0, 1.05]						)
  plt.ylim([0.0, 1.05]						)
  plt.title(lowerCAmelCase__						)
  plt.savefig(lowerCAmelCase__						)
  plt.clf()

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Optional[Any]    ,   lowerCAmelCase__      :				int    ,   lowerCAmelCase__      :				int    ,   lowerCAmelCase__      :				List[Any]    ,   lowerCAmelCase__      :				List[Any]=None    ,   lowerCAmelCase__      :				Union[str, Any]=None						)						->   List[str]:

  """simple docstring"""




  lowerCAmelCase_   :				List[Any]								= sorted(lowerCAmelCase__    ,   key=lambda lowerCAmelCase__						: na_probs[k]						)
  lowerCAmelCase_   :				List[str]								= 0.0
  lowerCAmelCase_   :				int								= 1.0
  lowerCAmelCase_   :				List[Any]								= 0.0
  lowerCAmelCase_   :				Optional[Any]								= [1.0]
  lowerCAmelCase_   :				List[str]								= [0.0]
  lowerCAmelCase_   :				Optional[int]								= 0.0
  for i, qid in enumerate(lowerCAmelCase__						):
    if qid_to_has_ans[qid]:
      true_pos += scores[qid]
    lowerCAmelCase_   :				Tuple								= true_pos / float(i + 1						)
    lowerCAmelCase_   :				List[str]								= true_pos / float(lowerCAmelCase__						)
    if i == len(lowerCAmelCase__						) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
      # i.e., if we can put a threshold after this point
      avg_prec += cur_p * (cur_r - recalls[-1])
      precisions.append(lowerCAmelCase__						)
      recalls.append(lowerCAmelCase__						)
  if out_image:
    plot_pr_curve(lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__						)
  return {"ap": 100.0 * avg_prec}

def 				UpperCamelCase_    (    lowerCAmelCase__      :				List[Any]    ,   lowerCAmelCase__      :				Any    ,   lowerCAmelCase__      :				List[Any]    ,   lowerCAmelCase__      :				str    ,   lowerCAmelCase__      :				Optional[Any]    ,   lowerCAmelCase__      :				Optional[int]						)						->   Optional[Any]:

  """simple docstring"""




  if out_image_dir and not os.path.exists(lowerCAmelCase__						):
    os.makedirs(lowerCAmelCase__						)
  lowerCAmelCase_   :				int								= sum(1 for v in qid_to_has_ans.values() if v						)
  if num_true_pos == 0:
    return
  lowerCAmelCase_   :				List[Any]								= make_precision_recall_eval(
      lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   out_image=os.path.join(lowerCAmelCase__    ,   'pr_exact.png'						)    ,   title='Precision-Recall curve for Exact Match score'    ,   )
  lowerCAmelCase_   :				int								= make_precision_recall_eval(
      lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   out_image=os.path.join(lowerCAmelCase__    ,   'pr_f1.png'						)    ,   title='Precision-Recall curve for F1 score'    ,   )
  lowerCAmelCase_   :				Dict								= {k: float(lowerCAmelCase__						) for k, v in qid_to_has_ans.items()}
  lowerCAmelCase_   :				Any								= make_precision_recall_eval(
      lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   out_image=os.path.join(lowerCAmelCase__    ,   'pr_oracle.png'						)    ,   title='Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)'    ,   )
  merge_eval(lowerCAmelCase__    ,   lowerCAmelCase__    ,   'pr_exact'						)
  merge_eval(lowerCAmelCase__    ,   lowerCAmelCase__    ,   'pr_f1'						)
  merge_eval(lowerCAmelCase__    ,   lowerCAmelCase__    ,   'pr_oracle'						)

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Optional[int]    ,   lowerCAmelCase__      :				Tuple    ,   lowerCAmelCase__      :				Optional[Any]    ,   lowerCAmelCase__      :				List[Any]						)						->   Optional[Any]:

  """simple docstring"""




  if not qid_list:
    return
  lowerCAmelCase_   :				int								= [na_probs[k] for k in qid_list]
  lowerCAmelCase_   :				List[Any]								= np.ones_like(lowerCAmelCase__						) / float(len(lowerCAmelCase__						)						)
  plt.hist(lowerCAmelCase__    ,   weights=lowerCAmelCase__    ,   bins=20    ,   range=(0.0, 1.0)						)
  plt.xlabel('Model probability of no-answer'						)
  plt.ylabel('Proportion of dataset'						)
  plt.title(f"Histogram of no-answer probability: {name}"						)
  plt.savefig(os.path.join(lowerCAmelCase__    ,   f"na_prob_hist_{name}.png"						)						)
  plt.clf()

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Dict    ,   lowerCAmelCase__      :				Union[str, Any]    ,   lowerCAmelCase__      :				int    ,   lowerCAmelCase__      :				Tuple						)						->   Optional[int]:

  """simple docstring"""




  lowerCAmelCase_   :				Optional[int]								= sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k]						)
  lowerCAmelCase_   :				List[str]								= num_no_ans
  lowerCAmelCase_   :				List[str]								= cur_score
  lowerCAmelCase_   :				List[Any]								= 0.0
  lowerCAmelCase_   :				List[str]								= sorted(lowerCAmelCase__    ,   key=lambda lowerCAmelCase__						: na_probs[k]						)
  for i, qid in enumerate(lowerCAmelCase__						):
    if qid not in scores:
      continue
    if qid_to_has_ans[qid]:
      lowerCAmelCase_   :				Tuple								= scores[qid]
    else:
      if preds[qid]:
        lowerCAmelCase_   :				int								= -1
      else:
        lowerCAmelCase_   :				str								= 0
    cur_score += diff
    if cur_score > best_score:
      lowerCAmelCase_   :				Optional[int]								= cur_score
      lowerCAmelCase_   :				str								= na_probs[qid]
  return 100.0 * best_score / len(lowerCAmelCase__						), best_thresh

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Any    ,   lowerCAmelCase__      :				str    ,   lowerCAmelCase__      :				List[str]    ,   lowerCAmelCase__      :				str    ,   lowerCAmelCase__      :				Union[str, Any]    ,   lowerCAmelCase__      :				Any						)						->   str:

  """simple docstring"""




  lowerCAmelCase_   :				Dict								= find_best_thresh(lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__						)
  lowerCAmelCase_   :				Union[str, Any]								= find_best_thresh(lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__						)
  lowerCAmelCase_   :				List[Any]								= best_exact
  lowerCAmelCase_   :				Dict								= exact_thresh
  lowerCAmelCase_   :				List[str]								= best_fa
  lowerCAmelCase_   :				int								= fa_thresh

def 				UpperCamelCase_    (    )						->   str:

  """simple docstring"""




  with open(OPTS.data_file						) as f:
    lowerCAmelCase_   :				int								= json.load(lowerCAmelCase__						)
    lowerCAmelCase_   :				Union[str, Any]								= dataset_json["data"]
  with open(OPTS.pred_file						) as f:
    lowerCAmelCase_   :				int								= json.load(lowerCAmelCase__						)
  if OPTS.na_prob_file:
    with open(OPTS.na_prob_file						) as f:
      lowerCAmelCase_   :				Union[str, Any]								= json.load(lowerCAmelCase__						)
  else:
    lowerCAmelCase_   :				str								= {k: 0.0 for k in preds}
  lowerCAmelCase_   :				str								= make_qid_to_has_ans(lowerCAmelCase__						)  # maps qid to True/False
  lowerCAmelCase_   :				int								= [k for k, v in qid_to_has_ans.items() if v]
  lowerCAmelCase_   :				List[str]								= [k for k, v in qid_to_has_ans.items() if not v]
  lowerCAmelCase_   :				Optional[int]								= get_raw_scores(lowerCAmelCase__    ,   lowerCAmelCase__						)
  lowerCAmelCase_   :				Tuple								= apply_no_ans_threshold(lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   OPTS.na_prob_thresh						)
  lowerCAmelCase_   :				Any								= apply_no_ans_threshold(lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   OPTS.na_prob_thresh						)
  lowerCAmelCase_   :				List[str]								= make_eval_dict(lowerCAmelCase__    ,   lowerCAmelCase__						)
  if has_ans_qids:
    lowerCAmelCase_   :				int								= make_eval_dict(lowerCAmelCase__    ,   lowerCAmelCase__    ,   qid_list=lowerCAmelCase__						)
    merge_eval(lowerCAmelCase__    ,   lowerCAmelCase__    ,   'HasAns'						)
  if no_ans_qids:
    lowerCAmelCase_   :				Union[str, Any]								= make_eval_dict(lowerCAmelCase__    ,   lowerCAmelCase__    ,   qid_list=lowerCAmelCase__						)
    merge_eval(lowerCAmelCase__    ,   lowerCAmelCase__    ,   'NoAns'						)
  if OPTS.na_prob_file:
    find_all_best_thresh(lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__						)
  if OPTS.na_prob_file and OPTS.out_image_dir:
    run_precision_recall_analysis(lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   OPTS.out_image_dir						)
    histogram_na_prob(lowerCAmelCase__    ,   lowerCAmelCase__    ,   OPTS.out_image_dir    ,   'hasAns'						)
    histogram_na_prob(lowerCAmelCase__    ,   lowerCAmelCase__    ,   OPTS.out_image_dir    ,   'noAns'						)
  if OPTS.out_file:
    with open(OPTS.out_file    ,   'w'						) as f:
      json.dump(lowerCAmelCase__    ,   lowerCAmelCase__						)
  else:
    print(json.dumps(lowerCAmelCase__    ,   indent=2						)						)


if __name__ == "__main__":
     lowercase__		:				List[Any]          =	parse_args()
     if OPTS.out_image_dir:
          import matplotlib

          matplotlib.use("""Agg""")
          import matplotlib.pyplot as plt
     main()






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
__lowerCamelCase					:	Any       =  {
    '''/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       _snake_case	(   lowerCAmelCase							:   List[str]	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		int				      =    list(s_dict.keys()	)
		for key in keys:
				SCREAMING_SNAKE_CASE_  :		int				      =    R".*/layers_(\d+)"
				SCREAMING_SNAKE_CASE_  :		List[Any]				      =    key
				if re.match(lowerCAmelCase   , lowerCAmelCase	):
						SCREAMING_SNAKE_CASE_  :		Any				      =    re.sub(R"layers_(\d+)"   , R"block/\1/layer"   , lowerCAmelCase	)

				SCREAMING_SNAKE_CASE_  :		List[Any]				      =    R"(encoder|decoder)\/"

				if re.match(lowerCAmelCase   , lowerCAmelCase	):
						SCREAMING_SNAKE_CASE_  :		str				      =    re.match(lowerCAmelCase   , lowerCAmelCase	).groups()
						if groups[0] == "encoder":
								SCREAMING_SNAKE_CASE_  :		Any				      =    re.sub(R"/mlp/"   , R"/1/mlp/"   , lowerCAmelCase	)
								SCREAMING_SNAKE_CASE_  :		List[Any]				      =    re.sub(R"/pre_mlp_layer_norm/"   , R"/1/layer_norm/"   , lowerCAmelCase	)

						elif groups[0] == "decoder":
								SCREAMING_SNAKE_CASE_  :		List[str]				      =    re.sub(R"/mlp/"   , R"/2/mlp/"   , lowerCAmelCase	)
								SCREAMING_SNAKE_CASE_  :		Dict				      =    re.sub(R"/pre_mlp_layer_norm/"   , R"/2/layer_norm/"   , lowerCAmelCase	)

        # 2. Convert other classic mappings
				for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
						if old_key in new_key:
								SCREAMING_SNAKE_CASE_  :		List[Any]				      =    new_key.replace(lowerCAmelCase   , lowerCAmelCase	)

				print(f'{key} -> {new_key}'	)
				SCREAMING_SNAKE_CASE_  :		List[Any]				      =    s_dict.pop(lowerCAmelCase	)

		if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
				SCREAMING_SNAKE_CASE_  :		str				      =    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:
				SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    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:
						SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    s_dict[key].shape[0]
						SCREAMING_SNAKE_CASE_  :		List[Any]				      =    s_dict[key]
						for idx in range(lowerCAmelCase	):
								SCREAMING_SNAKE_CASE_  :		Tuple				      =    expert_weihts[idx]
								print(f'{key} -> {key.replace("expert/"   , "nested fstring"	)}'	)

						s_dict.pop(lowerCAmelCase	)

		return s_dict


__lowerCamelCase					:	List[Any]       =  {
    '''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       _snake_case	(   lowerCAmelCase							:   Dict   , lowerCAmelCase							:   Optional[Any]	):
		"""simple docstring"""







		import regex as re

		with open(lowerCAmelCase   , "r"	) as f:
				SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    f.read()

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

		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    re.findall(R"(.*activations) = \(\'(.*)\',\)"   , lowerCAmelCase	)[0]
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    str(activation[1]	)

		SCREAMING_SNAKE_CASE_  :		str				      =    num_experts
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    SwitchTransformersConfig(**lowerCAmelCase	)
		return config




def       _snake_case	(   lowerCAmelCase							:   str   , lowerCAmelCase							:   str   , lowerCAmelCase							:   str=None   , lowerCAmelCase							:   Optional[Any]="./"   , lowerCAmelCase							:   Dict=8	):
		"""simple docstring"""







		print(f'Loading flax weights from : {flax_checkpoint_path}'	)
		SCREAMING_SNAKE_CASE_  :		int				      =    checkpoints.load_tax_checkpoint(lowerCAmelCase	)

		if gin_file is not None:
				SCREAMING_SNAKE_CASE_  :		int				      =    convert_gin_to_config(lowerCAmelCase   , lowerCAmelCase	)
		else:
				SCREAMING_SNAKE_CASE_  :		Dict				      =    SwitchTransformersConfig.from_pretrained(lowerCAmelCase	)

		SCREAMING_SNAKE_CASE_  :		str				      =    SwitchTransformersForConditionalGeneration(lowerCAmelCase	)

		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    flax_params["target"]
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    flatten_dict(lowerCAmelCase   , sep="/"	)
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    rename_keys(lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    unflatten_dict(lowerCAmelCase   , sep="/"	)

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

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


if __name__ == "__main__":
		__lowerCamelCase					:	List[str]       =  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''')
		__lowerCamelCase					:	Any       =  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,
		)
