'''simple docstring'''






import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : int      ,		__snake_case       : Optional[Any]      ,		__snake_case       : Any							):
     _A								=     TaConfig.from_pretrained(__snake_case							)
     _A								=     FlaxTaForConditionalGeneration(config=__snake_case							)
     _A								=     checkpoints.load_tax_checkpoint(__snake_case							)

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

     # Encoder
     for layer_index in range(config.num_layers							):
          _A								=     F'layers_{str(__snake_case							)}'

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

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

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

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

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

          # Assigning
          _A								=     tax_attention_key
          _A								=     tax_attention_out
          _A								=     tax_attention_query
          _A								=     tax_attention_value

          _A								=     tax_attention_layer_norm

          if split_mlp_wi:
               _A								=     tax_mlp_wi_a
               _A								=     tax_mlp_wi_a
          else:
               _A								=     tax_mlp_wi

          _A								=     tax_mlp_wo
          _A								=     tax_mlp_layer_norm

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

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

     # Decoder
     for layer_index in range(config.num_decoder_layers							):
          _A								=     F'layers_{str(__snake_case							)}'

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

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

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

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

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

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

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

          # Assigning
          _A								=     tax_attention_key
          _A								=     tax_attention_out
          _A								=     tax_attention_query
          _A								=     tax_attention_value

          _A								=     tax_pre_attention_layer_norm

          _A								=     tax_enc_dec_attention_key
          _A								=     tax_enc_dec_attention_out
          _A								=     tax_enc_dec_attention_query
          _A								=     tax_enc_dec_attention_value

          _A								=     tax_cross_layer_norm

          if split_mlp_wi:
               _A								=     tax_mlp_wi_a
               _A								=     tax_mlp_wi_a
          else:
               _A								=     tax_mlp_wi

          _A								=     tax_mlp_wo

          _A								=     txa_mlp_layer_norm

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

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

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

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

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


if __name__ == "__main__":
   _UpperCAmelCase						:    Tuple     					=					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.'''
   )
   _UpperCAmelCase						:    Union[str, Any]     					=					parser.parse_args()
   convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)




'''simple docstring'''






import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Optional[int]      ,		__snake_case       : List[str]							):
     assert isinstance(__snake_case      ,		__snake_case							)
     assert dataset.num_rows == 4
     assert dataset.num_columns == 3
     assert dataset.column_names == ["col_1", "col_2", "col_3"]
     for feature, expected_dtype in expected_features.items():
          assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize('keep_in_memory'      ,		[False, True]							)
def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Union[str, Any]      ,		__snake_case       : List[Any]      ,		__snake_case       : List[str]							):
     _A								=     tmp_path / 'cache'
     _A								=     {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
     with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
          _A								=     CsvDatasetReader(__snake_case      ,		cache_dir=__snake_case      ,		keep_in_memory=__snake_case							).read()
     _check_csv_dataset(__snake_case      ,		__snake_case							)





@pytest.mark.parametrize(
    'features'      ,		[
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ]      ,		)
def    _SCREAMING_SNAKE_CASE		(			__snake_case       : List[Any]      ,		__snake_case       : Any      ,		__snake_case       : Optional[Any]							):
     _A								=     tmp_path / 'cache'
     # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
     _A								=     {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
     _A								=     features.copy() if features else default_expected_features
     _A								=     (
         Features({feature: Value(__snake_case							) for feature, dtype in features.items()}							) if features is not None else None
     )
     _A								=     CsvDatasetReader(__snake_case      ,		features=__snake_case      ,		cache_dir=__snake_case							).read()
     _check_csv_dataset(__snake_case      ,		__snake_case							)





@pytest.mark.parametrize('split'      ,		[None, NamedSplit('train'							), 'train', 'test']							)
def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Tuple      ,		__snake_case       : str      ,		__snake_case       : int							):
     _A								=     tmp_path / 'cache'
     _A								=     {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
     _A								=     CsvDatasetReader(__snake_case      ,		cache_dir=__snake_case      ,		split=__snake_case							).read()
     _check_csv_dataset(__snake_case      ,		__snake_case							)
     assert dataset.split == split if split else "train"





@pytest.mark.parametrize('path_type'      ,		[str, list]							)
def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Tuple      ,		__snake_case       : int      ,		__snake_case       : Optional[int]							):
     if issubclass(__snake_case      ,		__snake_case							):
          _A								=     csv_path
     elif issubclass(__snake_case      ,		__snake_case							):
          _A								=     [csv_path]
     _A								=     tmp_path / 'cache'
     _A								=     {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
     _A								=     CsvDatasetReader(__snake_case      ,		cache_dir=__snake_case							).read()
     _check_csv_dataset(__snake_case      ,		__snake_case							)





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Optional[int]      ,		__snake_case       : int      ,		__snake_case       : int=("train",)							):
     assert isinstance(__snake_case      ,		__snake_case							)
     for split in splits:
          _A								=     dataset_dict[split]
          assert dataset.num_rows == 4
          assert dataset.num_columns == 3
          assert dataset.column_names == ["col_1", "col_2", "col_3"]
          for feature, expected_dtype in expected_features.items():
               assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize('keep_in_memory'      ,		[False, True]							)
def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Dict      ,		__snake_case       : Any      ,		__snake_case       : Optional[Any]							):
     _A								=     tmp_path / 'cache'
     _A								=     {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
     with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
          _A								=     CsvDatasetReader({'train': csv_path}      ,		cache_dir=__snake_case      ,		keep_in_memory=__snake_case							).read()
     _check_csv_datasetdict(__snake_case      ,		__snake_case							)





@pytest.mark.parametrize(
    'features'      ,		[
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ]      ,		)
def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Union[str, Any]      ,		__snake_case       : List[str]      ,		__snake_case       : str							):
     _A								=     tmp_path / 'cache'
     # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
     _A								=     {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
     _A								=     features.copy() if features else default_expected_features
     _A								=     (
         Features({feature: Value(__snake_case							) for feature, dtype in features.items()}							) if features is not None else None
     )
     _A								=     CsvDatasetReader({'train': csv_path}      ,		features=__snake_case      ,		cache_dir=__snake_case							).read()
     _check_csv_datasetdict(__snake_case      ,		__snake_case							)





@pytest.mark.parametrize('split'      ,		[None, NamedSplit('train'							), 'train', 'test']							)
def    _SCREAMING_SNAKE_CASE		(			__snake_case       : List[Any]      ,		__snake_case       : Union[str, Any]      ,		__snake_case       : int							):
     if split:
          _A								=     {split: csv_path}
     else:
          _A								=     {'train': csv_path, 'test': csv_path}
     _A								=     tmp_path / 'cache'
     _A								=     {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
     _A								=     CsvDatasetReader(__snake_case      ,		cache_dir=__snake_case							).read()
     _check_csv_datasetdict(__snake_case      ,		__snake_case      ,		splits=list(path.keys()							)							)
     assert all(dataset[split].split == split for split in path.keys()							)





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : List[str]							):
     with open(__snake_case      ,		encoding='utf-8'							) as csvfile:
          yield from csv.reader(__snake_case							)





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Any      ,		__snake_case       : Optional[Any]							):
     _A								=     tmp_path / 'cache'
     _A								=     os.path.join(__snake_case      ,		'tmp.csv'							)
     _A								=     CsvDatasetReader({'train': csv_path}      ,		cache_dir=__snake_case							).read()
     CsvDatasetWriter(dataset['train']      ,		__snake_case      ,		num_proc=1							).write()

     _A								=     iter_csv_file(__snake_case							)
     _A								=     iter_csv_file(__snake_case							)

     for rowa, rowa in zip(__snake_case      ,		__snake_case							):
          assert rowa == rowa





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Dict      ,		__snake_case       : Optional[Any]							):
     _A								=     tmp_path / 'cache'
     _A								=     os.path.join(__snake_case      ,		'tmp.csv'							)
     _A								=     CsvDatasetReader({'train': csv_path}      ,		cache_dir=__snake_case							).read()
     CsvDatasetWriter(dataset['train']      ,		__snake_case      ,		num_proc=2							).write()

     _A								=     iter_csv_file(__snake_case							)
     _A								=     iter_csv_file(__snake_case							)

     for rowa, rowa in zip(__snake_case      ,		__snake_case							):
          assert rowa == rowa





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Optional[Any]      ,		__snake_case       : Tuple							):
     _A								=     tmp_path / 'cache'
     _A								=     os.path.join(__snake_case      ,		'tmp.csv'							)
     _A								=     CsvDatasetReader({'train': csv_path}      ,		cache_dir=__snake_case							).read()
     with pytest.raises(__snake_case							):
          CsvDatasetWriter(dataset['train']      ,		__snake_case      ,		num_proc=0							)



