






"""simple docstring"""




import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM

def      lowerCAmelCase__	(	UpperCamelCase__	,  UpperCamelCase__	,  UpperCamelCase__							):



  '''simple docstring'''
  _a    :			Optional[int]			 =      AutoConfig.from_pretrained(__lowerCamelCase							)
  _a    :			Any			 =      FlaxAutoModelForSeqaSeqLM.from_config(config=__lowerCamelCase							)
  _a    :			Dict			 =      checkpoints.load_tax_checkpoint(__lowerCamelCase							)

  _a    :			Union[str, Any]			 =      "wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

  if config.model_type == "t5":
    _a    :			Optional[Any]			 =      "SelfAttention"
  if config.model_type == "longt5" and config.encoder_attention_type == "local":
    _a    :			int			 =      "LocalSelfAttention"
  elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
    _a    :			List[str]			 =      "TransientGlobalSelfAttention"
  else:
    raise ValueError(
        """Given config is expected to have `model_type='t5'`, or `model_type='longt5` with `encoder_attention_type`"""
        """ attribute with a value from ['local', 'transient-global]."""							)

  # Encoder
  for layer_index in range(config.num_layers							):
    _a    :			Tuple			 =      F"""layers_{str(__lowerCamelCase							)}"""

    # Self-Attention
    _a    :			Tuple			 =      tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
    _a    :			Optional[Any]			 =      tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
    _a    :			Dict			 =      tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
    _a    :			str			 =      tax_model["target"]["encoder"][layer_name]["attention"]["value"]["kernel"]

    # Global input layer norm
    if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
      _a    :			Union[str, Any]			 =      tax_model["target"]["encoder"][layer_name]["attention"]["T5LayerNorm_0"]["scale"]

    # Layer Normalization
    _a    :			Union[str, Any]			 =      tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

    if split_mlp_wi:
      _a    :			Dict			 =      tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
      _a    :			Any			 =      tax_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
    else:
      _a    :			int			 =      tax_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]

    _a    :			int			 =      tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

    # Layer Normalization
    _a    :			Tuple			 =      tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

    # Assigning
    _a    :			Optional[Any]			 =      flax_model.params["encoder"]["block"][str(__lowerCamelCase							)]["layer"]
    _a    :			Tuple			 =      tax_attention_key
    _a    :			str			 =      tax_attention_out
    _a    :			Dict			 =      tax_attention_query
    _a    :			Union[str, Any]			 =      tax_attention_value

    _a    :			Tuple			 =      tax_attention_layer_norm

    # Global input layer norm
    if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
      _a    :			str			 =      tax_global_layer_norm

    if split_mlp_wi:
      _a    :			Optional[int]			 =      tax_mlp_wi_a
      _a    :			Union[str, Any]			 =      tax_mlp_wi_a
    else:
      _a    :			List[str]			 =      tax_mlp_wi

    _a    :			Union[str, Any]			 =      tax_mlp_wo
    _a    :			Optional[int]			 =      tax_mlp_layer_norm

    _a    :			List[str]			 =      flax_model_encoder_layer_block

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

  # Side/global relative position_bias + layer norm
  if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
    _a    :			Optional[Any]			 =      tax_model["target"]["encoder"]["side_relpos_bias"]["rel_embedding"].T
    _a    :			Any			 =      tax_encoder_global_rel_embedding

  # Assigning
  _a    :			int			 =      tax_model["target"]["encoder"]["encoder_norm"]["scale"]
  _a    :			List[Any]			 =      tax_encoder_norm

  # Decoder
  for layer_index in range(config.num_layers							):
    _a    :			List[str]			 =      F"""layers_{str(__lowerCamelCase							)}"""

    # Self-Attention
    _a    :			str			 =      tax_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
    _a    :			str			 =      tax_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
    _a    :			int			 =      tax_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"]
    _a    :			Tuple			 =      tax_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"]

    # Layer Normalization
    _a    :			Dict			 =      tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
        "scale"
    ]

    # Encoder-Decoder-Attention
    _a    :			Any			 =      tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]
    _a    :			str			 =      tax_enc_dec_attention_module["key"]["kernel"]
    _a    :			Optional[Any]			 =      tax_enc_dec_attention_module["out"]["kernel"]
    _a    :			List[Any]			 =      tax_enc_dec_attention_module["query"]["kernel"]
    _a    :			int			 =      tax_enc_dec_attention_module["value"]["kernel"]

    # Layer Normalization
    _a    :			Optional[int]			 =      tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

    # MLP
    if split_mlp_wi:
      _a    :			Optional[int]			 =      tax_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"]
      _a    :			List[Any]			 =      tax_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"]
    else:
      _a    :			List[str]			 =      tax_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"]

    _a    :			Tuple			 =      tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

    # Layer Normalization
    _a    :			str			 =      tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

    # Assigning
    _a    :			Union[str, Any]			 =      flax_model.params["decoder"]["block"][str(__lowerCamelCase							)]["layer"]
    _a    :			int			 =      tax_attention_key
    _a    :			int			 =      tax_attention_out
    _a    :			int			 =      tax_attention_query
    _a    :			Optional[int]			 =      tax_attention_value

    _a    :			Optional[Any]			 =      tax_pre_attention_layer_norm

    _a    :			List[Any]			 =      tax_enc_dec_attention_key
    _a    :			Union[str, Any]			 =      tax_enc_dec_attention_out
    _a    :			int			 =      tax_enc_dec_attention_query
    _a    :			int			 =      tax_enc_dec_attention_value

    _a    :			Optional[Any]			 =      tax_cross_layer_norm

    if split_mlp_wi:
      _a    :			str			 =      tax_mlp_wi_a
      _a    :			List[Any]			 =      tax_mlp_wi_a
    else:
      _a    :			List[Any]			 =      tax_mlp_wi

    _a    :			Optional[Any]			 =      tax_mlp_wo

    _a    :			Optional[int]			 =      txa_mlp_layer_norm

    _a    :			Tuple			 =      flax_model_decoder_layer_block

  # Decoder Normalization
  _a    :			Dict			 =      tax_model["target"]["decoder"]["decoder_norm"]["scale"]
  _a    :			Union[str, Any]			 =      txa_decoder_norm

  # Only for layer 0:
  _a    :			Optional[int]			 =      tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
  _a    :			Optional[Any]			 =      tax_decoder_rel_embedding

  # Token Embeddings
  _a    :			Any			 =      tax_model["target"]["token_embedder"]["embedding"]
  _a    :			int			 =      txa_token_embeddings

  # LM Head (only in v1.1 and LongT5 checkpoints)
  if "logits_dense" in tax_model["target"]["decoder"]:
    _a    :			Optional[int]			 =      tax_model["target"]["decoder"]["logits_dense"]["kernel"]

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


if __name__ == "__main__":
 _snake_case        =  argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path the T5X checkpoint.'
 )
 parser.add_argument('--config_name', default=None, type=str, required=True, help='Config name of LongT5/T5 model.')
 parser.add_argument(
     '--flax_dump_folder_path', default=None, type=str, required=True, help='Path to the output FLAX model.'
 )
 _snake_case        =  parser.parse_args()
 convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)



import math_equivalence  # From: git+https://github.com/hendrycks/math.git

import datasets


__lowerCamelCase				=			"""\
@article{hendrycksmath2021,
  title={Measuring Mathematical Problem Solving With the MATH Dataset},
  author={Dan Hendrycks
    and Collin Burns
    and Saurav Kadavath
    and Akul Arora
    and Steven Basart
    and Eric Tang
    and Dawn Song
    and Jacob Steinhardt},
  journal={arXiv preprint arXiv:2103.03874},
  year={2021}
}
"""


__lowerCamelCase				=			"""\
This metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.
It first canonicalizes the inputs (e.g., converting \"1/2\" to \"\\frac{1}{2}\") and then computes accuracy.
"""


__lowerCamelCase				=			R"""
Calculates accuracy after canonicalizing inputs.

Args:
    predictions: list of predictions to score. Each prediction
        is a string that contains natural language and LaTex.
    references: list of reference for each prediction. Each
        reference is a string that contains natural language
        and LaTex.
Returns:
    accuracy: accuracy after canonicalizing inputs
        (e.g., converting \"1/2\" to \"\\frac{1}{2}\")

Examples:
    >>> metric = datasets.load_metric(\"competition_math\")
    >>> results = metric.compute(references=[\"\\frac{1}{2}\"], predictions=[\"1/2\"])
    >>> print(results)
    {'accuracy': 1.0}
"""






@datasets.utils.file_utils.add_end_docstrings(_DESCRIPTION			,_KWARGS_DESCRIPTION					)
class     UpperCAmelCase						(		datasets.Metric					):



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[int]					)						->      Optional[Any]:


          '''simple docstring'''


          return datasets.MetricInfo(
              description=_DESCRIPTION       ,		citation=_CITATION       ,		inputs_description=_KWARGS_DESCRIPTION       ,		features=datasets.Features(
                  {
                      "predictions": datasets.Value("string"					),
                      "references": datasets.Value("string"					),
                  }					)       ,		homepage="https://github.com/hendrycks/math"       ,		codebase_urls=["https://github.com/hendrycks/math"]       ,		)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[int]       ,		snake_case__						:   str       ,		snake_case__						:   List[Any]					)						->      Optional[Any]:


          '''simple docstring'''


          snake_case			:  Optional[int]     							= 0.0
          for i, j in zip(snake_case__       ,		snake_case__					):
                 n_correct += 1.0 if math_equivalence.is_equiv(snake_case__       ,		snake_case__					) else 0.0
          snake_case			:  Optional[int]     							= n_correct / len(snake_case__					)
          return {
              "accuracy": accuracy,
          }





