'''simple docstring'''
import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM



def  __lowerCamelCase       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  Optional[int]							,    _UpperCamelCase  :  Optional[Any]					):


      '''simple docstring'''


      UpperCAmelCase_            =  AutoConfig.from_pretrained(_UpperCamelCase					)
      UpperCAmelCase_            =  FlaxAutoModelForSeqaSeqLM.from_config(config=_UpperCamelCase					)
      UpperCAmelCase_            =  checkpoints.load_tax_checkpoint(_UpperCamelCase					)

      UpperCAmelCase_            =  'wi_0' in tax_model['target']['encoder']['layers_0']['mlp']

      if config.model_type == "t5":
            UpperCAmelCase_            =  'SelfAttention'
      if config.model_type == "longt5" and config.encoder_attention_type == "local":
            UpperCAmelCase_            =  'LocalSelfAttention'
      elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            UpperCAmelCase_            =  '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					):
            UpperCAmelCase_            =  F"""layers_{str(_UpperCamelCase					)}"""

            # Self-Attention
            UpperCAmelCase_            =  tax_model['target']['encoder'][layer_name]['attention']['key']['kernel']
            UpperCAmelCase_            =  tax_model['target']['encoder'][layer_name]['attention']['out']['kernel']
            UpperCAmelCase_            =  tax_model['target']['encoder'][layer_name]['attention']['query']['kernel']
            UpperCAmelCase_            =  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":
                  UpperCAmelCase_            =  tax_model['target']['encoder'][layer_name]['attention']['T5LayerNorm_0']['scale']

            # Layer Normalization
            UpperCAmelCase_            =  tax_model['target']['encoder'][layer_name]['pre_attention_layer_norm']['scale']

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

            UpperCAmelCase_            =  tax_model['target']['encoder'][layer_name]['mlp']['wo']['kernel']

            # Layer Normalization
            UpperCAmelCase_            =  tax_model['target']['encoder'][layer_name]['pre_mlp_layer_norm']['scale']

            # Assigning
            UpperCAmelCase_            =  flax_model.params['encoder']['block'][str(_UpperCamelCase					)]['layer']
            UpperCAmelCase_            =  tax_attention_key
            UpperCAmelCase_            =  tax_attention_out
            UpperCAmelCase_            =  tax_attention_query
            UpperCAmelCase_            =  tax_attention_value

            UpperCAmelCase_            =  tax_attention_layer_norm

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

            if split_mlp_wi:
                  UpperCAmelCase_            =  tax_mlp_wi_a
                  UpperCAmelCase_            =  tax_mlp_wi_a
            else:
                  UpperCAmelCase_            =  tax_mlp_wi

            UpperCAmelCase_            =  tax_mlp_wo
            UpperCAmelCase_            =  tax_mlp_layer_norm

            UpperCAmelCase_            =  flax_model_encoder_layer_block

      # Only for layer 0:
      UpperCAmelCase_            =  tax_model['target']['encoder']['relpos_bias']['rel_embedding'].T
      UpperCAmelCase_            =  tax_encoder_rel_embedding

      # Side/global relative position_bias + layer norm
      if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            UpperCAmelCase_            =  tax_model['target']['encoder']['side_relpos_bias']['rel_embedding'].T
            UpperCAmelCase_            =  tax_encoder_global_rel_embedding

      # Assigning
      UpperCAmelCase_            =  tax_model['target']['encoder']['encoder_norm']['scale']
      UpperCAmelCase_            =  tax_encoder_norm

      # Decoder
      for layer_index in range(config.num_layers					):
            UpperCAmelCase_            =  F"""layers_{str(_UpperCamelCase					)}"""

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

            # Layer Normalization
            UpperCAmelCase_            =  tax_model['target']['decoder'][layer_name]['pre_self_attention_layer_norm'][
                'scale'
            ]

            # Encoder-Decoder-Attention
            UpperCAmelCase_            =  tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']
            UpperCAmelCase_            =  tax_enc_dec_attention_module['key']['kernel']
            UpperCAmelCase_            =  tax_enc_dec_attention_module['out']['kernel']
            UpperCAmelCase_            =  tax_enc_dec_attention_module['query']['kernel']
            UpperCAmelCase_            =  tax_enc_dec_attention_module['value']['kernel']

            # Layer Normalization
            UpperCAmelCase_            =  tax_model['target']['decoder'][layer_name]['pre_cross_attention_layer_norm']['scale']

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

            UpperCAmelCase_            =  tax_model['target']['decoder'][layer_name]['mlp']['wo']['kernel']

            # Layer Normalization
            UpperCAmelCase_            =  tax_model['target']['decoder'][layer_name]['pre_mlp_layer_norm']['scale']

            # Assigning
            UpperCAmelCase_            =  flax_model.params['decoder']['block'][str(_UpperCamelCase					)]['layer']
            UpperCAmelCase_            =  tax_attention_key
            UpperCAmelCase_            =  tax_attention_out
            UpperCAmelCase_            =  tax_attention_query
            UpperCAmelCase_            =  tax_attention_value

            UpperCAmelCase_            =  tax_pre_attention_layer_norm

            UpperCAmelCase_            =  tax_enc_dec_attention_key
            UpperCAmelCase_            =  tax_enc_dec_attention_out
            UpperCAmelCase_            =  tax_enc_dec_attention_query
            UpperCAmelCase_            =  tax_enc_dec_attention_value

            UpperCAmelCase_            =  tax_cross_layer_norm

            if split_mlp_wi:
                  UpperCAmelCase_            =  tax_mlp_wi_a
                  UpperCAmelCase_            =  tax_mlp_wi_a
            else:
                  UpperCAmelCase_            =  tax_mlp_wi

            UpperCAmelCase_            =  tax_mlp_wo

            UpperCAmelCase_            =  txa_mlp_layer_norm

            UpperCAmelCase_            =  flax_model_decoder_layer_block

      # Decoder Normalization
      UpperCAmelCase_            =  tax_model['target']['decoder']['decoder_norm']['scale']
      UpperCAmelCase_            =  txa_decoder_norm

      # Only for layer 0:
      UpperCAmelCase_            =  tax_model['target']['decoder']['relpos_bias']['rel_embedding'].T
      UpperCAmelCase_            =  tax_decoder_rel_embedding

      # Token Embeddings
      UpperCAmelCase_            =  tax_model['target']['token_embedder']['embedding']
      UpperCAmelCase_            =  txa_token_embeddings

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

      flax_model.save_pretrained(_UpperCamelCase					)
      print('''T5X Model was sucessfully converted!'''					)


if __name__ == "__main__":
      lowercase__  :      Tuple        =					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."
      )
      lowercase__  :      List[str]        =					parser.parse_args()
      convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)






'''simple docstring'''
import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


lowercase__  :      Tuple        =					pytest.mark.integration



@pytest.mark.parametrize('''path'''							,    ['''paws''', '''csv''']					)
def  __lowerCamelCase       (      _UpperCamelCase  :  Optional[Any]							,    _UpperCamelCase  :  int					):


      '''simple docstring'''


      inspect_dataset(_UpperCamelCase							,    _UpperCamelCase					)
      UpperCAmelCase_            =  path + '''.py'''
      assert script_name in os.listdir(_UpperCamelCase					)
      assert "__pycache__" not in os.listdir(_UpperCamelCase					)



@pytest.mark.filterwarnings('''ignore:inspect_metric is deprecated:FutureWarning'''					)
@pytest.mark.filterwarnings('''ignore:metric_module_factory is deprecated:FutureWarning'''					)
@pytest.mark.parametrize('''path'''							,    ['''accuracy''']					)
def  __lowerCamelCase       (      _UpperCamelCase  :  Tuple							,    _UpperCamelCase  :  Optional[Any]					):


      '''simple docstring'''


      inspect_metric(_UpperCamelCase							,    _UpperCamelCase					)
      UpperCAmelCase_            =  path + '''.py'''
      assert script_name in os.listdir(_UpperCamelCase					)
      assert "__pycache__" not in os.listdir(_UpperCamelCase					)



@pytest.mark.parametrize(
    '''path, config_name, expected_splits'''							,    [
        ('''squad''', '''plain_text''', ['''train''', '''validation''']),
        ('''dalle-mini/wit''', '''dalle-mini--wit''', ['''train''']),
        ('''paws''', '''labeled_final''', ['''train''', '''test''', '''validation''']),
    ]							,    )
def  __lowerCamelCase       (      _UpperCamelCase  :  Optional[Any]							,    _UpperCamelCase  :  Any							,    _UpperCamelCase  :  int					):


      '''simple docstring'''


      UpperCAmelCase_            =  get_dataset_config_info(_UpperCamelCase							,    config_name=_UpperCamelCase					)
      assert info.config_name == config_name
      assert list(info.splits.keys()					) == expected_splits



@pytest.mark.parametrize(
    '''path, config_name, expected_exception'''							,    [
        ('''paws''', None, ValueError),
    ]							,    )
def  __lowerCamelCase       (      _UpperCamelCase  :  Any							,    _UpperCamelCase  :  List[str]							,    _UpperCamelCase  :  Union[str, Any]					):


      '''simple docstring'''


      with pytest.raises(_UpperCamelCase					):
            get_dataset_config_info(_UpperCamelCase							,    config_name=_UpperCamelCase					)



@pytest.mark.parametrize(
    '''path, expected'''							,    [
        ('''squad''', '''plain_text'''),
        ('''acronym_identification''', '''default'''),
        ('''lhoestq/squad''', '''plain_text'''),
        ('''lhoestq/test''', '''default'''),
        ('''lhoestq/demo1''', '''lhoestq--demo1'''),
        ('''dalle-mini/wit''', '''dalle-mini--wit'''),
    ]							,    )
def  __lowerCamelCase       (      _UpperCamelCase  :  Any							,    _UpperCamelCase  :  Optional[Any]					):


      '''simple docstring'''


      UpperCAmelCase_            =  get_dataset_config_names(_UpperCamelCase					)
      assert expected in config_names



@pytest.mark.parametrize(
    '''path, expected_configs, expected_splits_in_first_config'''							,    [
        ('''squad''', ['''plain_text'''], ['''train''', '''validation''']),
        ('''dalle-mini/wit''', ['''dalle-mini--wit'''], ['''train''']),
        ('''paws''', ['''labeled_final''', '''labeled_swap''', '''unlabeled_final'''], ['''train''', '''test''', '''validation''']),
    ]							,    )
def  __lowerCamelCase       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  Optional[int]							,    _UpperCamelCase  :  int					):


      '''simple docstring'''


      UpperCAmelCase_            =  get_dataset_infos(_UpperCamelCase					)
      assert list(infos.keys()					) == expected_configs
      UpperCAmelCase_            =  expected_configs[0]
      assert expected_config in infos
      UpperCAmelCase_            =  infos[expected_config]
      assert info.config_name == expected_config
      assert list(info.splits.keys()					) == expected_splits_in_first_config



@pytest.mark.parametrize(
    '''path, expected_config, expected_splits'''							,    [
        ('''squad''', '''plain_text''', ['''train''', '''validation''']),
        ('''dalle-mini/wit''', '''dalle-mini--wit''', ['''train''']),
        ('''paws''', '''labeled_final''', ['''train''', '''test''', '''validation''']),
    ]							,    )
def  __lowerCamelCase       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  int							,    _UpperCamelCase  :  str					):


      '''simple docstring'''


      UpperCAmelCase_            =  get_dataset_infos(_UpperCamelCase					)
      assert expected_config in infos
      UpperCAmelCase_            =  infos[expected_config]
      assert info.config_name == expected_config
      assert list(info.splits.keys()					) == expected_splits





@pytest.mark.parametrize(
    '''path, config_name, expected_exception'''							,    [
        ('''paws''', None, ValueError),
    ]							,    )
def  __lowerCamelCase       (      _UpperCamelCase  :  Any							,    _UpperCamelCase  :  Optional[int]							,    _UpperCamelCase  :  Optional[int]					):


      '''simple docstring'''


      with pytest.raises(_UpperCamelCase					):
            get_dataset_split_names(_UpperCamelCase							,    config_name=_UpperCamelCase					)





