"""simple docstring"""


import argparse
import json
import re
import string
import sys
from collections import Counter







def        __a (		A   ):






     '''simple docstring'''



     def remove_articles(A   ):
          return re.sub(r"\b(a|an|the)\b"     ,					" "     ,					A   )

     def white_space_fix(A   ):
          return " ".join(text.split()   )

     def remove_punc(A   ):
          lowercase__								=				set(string.punctuation   )
          return "".join(ch for ch in text if ch not in exclude   )

     def lower(A   ):
          return text.lower()

     return white_space_fix(remove_articles(remove_punc(lower(A   )   )   )   )







def        __a (		A     ,					A   ):






     '''simple docstring'''



     lowercase__								=				normalize_answer(A   ).split()
     lowercase__								=				normalize_answer(A   ).split()
     lowercase__								=				Counter(A   ) & Counter(A   )
     lowercase__								=				sum(common.values()   )
     if num_same == 0:
          return 0
     lowercase__								=				1.0 * num_same / len(A   )
     lowercase__								=				1.0 * num_same / len(A   )
     lowercase__								=				(2 * precision * recall) / (precision + recall)
     return fa







def        __a (		A     ,					A   ):






     '''simple docstring'''



     return normalize_answer(A   ) == normalize_answer(A   )







def        __a (		A     ,					A     ,					A   ):






     '''simple docstring'''



     lowercase__								=				[]
     for ground_truth in ground_truths:
          lowercase__								=				metric_fn(A     ,					A   )
          scores_for_ground_truths.append(A   )
     return max(A   )







def        __a (		A     ,					A   ):






     '''simple docstring'''



     lowercase__								=				lowercase__								=				lowercase__								=				0
     for article in dataset:
          for paragraph in article["paragraphs"]:
               for qa in paragraph["qas"]:
                    total += 1
                    if qa["id"] not in predictions:
                         lowercase__								=				"Unanswered question " + qa["id"] + " will receive score 0."
                         print(A     ,					file=sys.stderr   )
                         continue
                    lowercase__								=				[x["text"] for x in qa["answers"]]
                    lowercase__								=				predictions[qa["id"]]
                    exact_match += metric_max_over_ground_truths(A     ,					A     ,					A   )
                    fa += metric_max_over_ground_truths(A     ,					A     ,					A   )

     lowercase__								=				100.0 * exact_match / total
     lowercase__								=				100.0 * fa / total

     return {"exact_match": exact_match, "f1": fa}


if __name__ == "__main__":
 lowerCAmelCase_:       Union[str, Any]			     =       "1.1"
 lowerCAmelCase_:       Tuple			     =       argparse.ArgumentParser(description="Evaluation for SQuAD " + expected_version)
 parser.add_argument("dataset_file", help="Dataset file")
 parser.add_argument("prediction_file", help="Prediction File")
 lowerCAmelCase_:       Optional[int]			     =       parser.parse_args()
 with open(args.dataset_file) as dataset_file:
  lowerCAmelCase_:       Tuple			     =       json.load(dataset_file)
  if dataset_json["version"] != expected_version:
   print(
       "Evaluation expects v-" + expected_version + ", but got dataset with v-" + dataset_json["version"],
       file=sys.stderr,
   )
  lowerCAmelCase_:       Any			     =       dataset_json["data"]
 with open(args.prediction_file) as prediction_file:
  lowerCAmelCase_:       str			     =       json.load(prediction_file)
 print(json.dumps(evaluate(dataset, predictions)))





"""simple docstring"""


import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig







def        __a (		A     ,					A     ,					A   ):






     '''simple docstring'''



     lowercase__								=				TaConfig.from_pretrained(A   )
     lowercase__								=				FlaxTaForConditionalGeneration(config=A   )
     lowercase__								=				checkpoints.load_tax_checkpoint(A   )

     lowercase__								=				"wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

     # Encoder
     for layer_index in range(config.num_layers   ):
          lowercase__								=				f'''layers_{str(A   )}'''

          # Self-Attention
          lowercase__								=				tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
          lowercase__								=				tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
          lowercase__								=				tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
          lowercase__								=				tax_model["target"]["encoder"][layer_name]["attention"]["value"]["kernel"]

          # Layer Normalization
          lowercase__								=				tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

          if split_mlp_wi:
               lowercase__								=				tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
               lowercase__								=				tax_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
          else:
               lowercase__								=				tax_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]

          lowercase__								=				tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

          # Layer Normalization
          lowercase__								=				tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

          # Assigning
          lowercase__								=				tax_attention_key
          lowercase__								=				tax_attention_out
          lowercase__								=				tax_attention_query
          lowercase__								=				tax_attention_value

          lowercase__								=				tax_attention_layer_norm

          if split_mlp_wi:
               lowercase__								=				tax_mlp_wi_a
               lowercase__								=				tax_mlp_wi_a
          else:
               lowercase__								=				tax_mlp_wi

          lowercase__								=				tax_mlp_wo
          lowercase__								=				tax_mlp_layer_norm

     # Only for layer 0:
     lowercase__								=				tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
     lowercase__								=				tax_encoder_rel_embedding

     # Assigning
     lowercase__								=				tax_model["target"]["encoder"]["encoder_norm"]["scale"]
     lowercase__								=				tax_encoder_norm

     # Decoder
     for layer_index in range(config.num_decoder_layers   ):
          lowercase__								=				f'''layers_{str(A   )}'''

          # Self-Attention
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"]
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"]

          # Layer Normalization
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
              "scale"
          ]

          # Encoder-Decoder-Attention
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["key"][
              "kernel"
          ]
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["out"][
              "kernel"
          ]
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["query"][
              "kernel"
          ]
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["value"][
              "kernel"
          ]

          # Layer Normalization
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

          # MLP
          if split_mlp_wi:
               lowercase__								=				tax_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"]
               lowercase__								=				tax_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"]
          else:
               lowercase__								=				tax_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"]

          lowercase__								=				tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

          # Layer Normalization
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

          # Assigning
          lowercase__								=				tax_attention_key
          lowercase__								=				tax_attention_out
          lowercase__								=				tax_attention_query
          lowercase__								=				tax_attention_value

          lowercase__								=				tax_pre_attention_layer_norm

          lowercase__								=				tax_enc_dec_attention_key
          lowercase__								=				tax_enc_dec_attention_out
          lowercase__								=				tax_enc_dec_attention_query
          lowercase__								=				tax_enc_dec_attention_value

          lowercase__								=				tax_cross_layer_norm

          if split_mlp_wi:
               lowercase__								=				tax_mlp_wi_a
               lowercase__								=				tax_mlp_wi_a
          else:
               lowercase__								=				tax_mlp_wi

          lowercase__								=				tax_mlp_wo

          lowercase__								=				txa_mlp_layer_norm

     # Decoder Normalization
     lowercase__								=				tax_model["target"]["decoder"]["decoder_norm"]["scale"]
     lowercase__								=				txa_decoder_norm

     # Only for layer 0:
     lowercase__								=				tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
     lowercase__								=				tax_decoder_rel_embedding

     # Token Embeddings
     lowercase__								=				tax_model["target"]["token_embedder"]["embedding"]
     lowercase__								=				txa_token_embeddings

     # LM Head (only in v1.1 checkpoints)
     if "logits_dense" in tax_model["target"]["decoder"]:
          lowercase__								=				tax_model["target"]["decoder"]["logits_dense"]["kernel"]

     flax_model.save_pretrained(A   )
     print("T5X Model was sucessfully converted!"   )


if __name__ == "__main__":
 lowerCAmelCase_:       List[Any]			     =       argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path the TX5 checkpoint."
 )
 parser.add_argument("--config_name", default=None, type=str, required=True, help="Config name of T5 model.")
 parser.add_argument(
     "--flax_dump_folder_path", default=None, type=str, required=True, help="Path to the output FLAX model."
 )
 lowerCAmelCase_:       Dict			     =       parser.parse_args()
 convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)




