

'''simple docstring'''
import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases




def 	UpperCamelCase   (       lowercase_						:    Dict     ,					lowercase_						:    List[Any]	)		->	Any:




  '''simple docstring'''



  assert isinstance(lowercase_     ,					lowercase_	)
  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 	UpperCamelCase   (       lowercase_						:    str     ,					lowercase_						:    int     ,					lowercase_						:    Optional[Any]	)		->	Tuple:




  '''simple docstring'''



  lowercase									=tmp_path / '''cache'''
  lowercase									={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    lowercase									=ParquetDatasetReader(lowercase_     ,					cache_dir=lowercase_     ,					keep_in_memory=lowercase_	).read()
  _check_parquet_dataset(lowercase_     ,					lowercase_	)




@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 	UpperCamelCase   (       lowercase_						:    List[Any]     ,					lowercase_						:    str     ,					lowercase_						:    Any	)		->	int:




  '''simple docstring'''



  lowercase									=tmp_path / '''cache'''
  lowercase									={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  lowercase									=features.copy() if features else default_expected_features
  lowercase									=(
      Features({feature: Value(lowercase_	) for feature, dtype in features.items()}	) if features is not None else None
  )
  lowercase									=ParquetDatasetReader(lowercase_     ,					features=lowercase_     ,					cache_dir=lowercase_	).read()
  _check_parquet_dataset(lowercase_     ,					lowercase_	)




@pytest.mark.parametrize('''split'''     ,					[None, NamedSplit('''train'''	), '''train''', '''test''']	)
def 	UpperCamelCase   (       lowercase_						:    Optional[int]     ,					lowercase_						:    List[str]     ,					lowercase_						:    List[Any]	)		->	Optional[int]:




  '''simple docstring'''



  lowercase									=tmp_path / '''cache'''
  lowercase									={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  lowercase									=ParquetDatasetReader(lowercase_     ,					cache_dir=lowercase_     ,					split=lowercase_	).read()
  _check_parquet_dataset(lowercase_     ,					lowercase_	)
  assert dataset.split == split if split else "train"




@pytest.mark.parametrize('''path_type'''     ,					[str, list]	)
def 	UpperCamelCase   (       lowercase_						:    Optional[Any]     ,					lowercase_						:    Tuple     ,					lowercase_						:    Optional[Any]	)		->	Any:




  '''simple docstring'''



  if issubclass(lowercase_     ,					lowercase_	):
    lowercase									=parquet_path
  elif issubclass(lowercase_     ,					lowercase_	):
    lowercase									=[parquet_path]
  lowercase									=tmp_path / '''cache'''
  lowercase									={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  lowercase									=ParquetDatasetReader(lowercase_     ,					cache_dir=lowercase_	).read()
  _check_parquet_dataset(lowercase_     ,					lowercase_	)




def 	UpperCamelCase   (       lowercase_						:    int     ,					lowercase_						:    List[Any]     ,					lowercase_						:    Any=("train",)	)		->	Optional[Any]:




  '''simple docstring'''



  assert isinstance(lowercase_     ,					lowercase_	)
  for split in splits:
    lowercase									=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 	UpperCamelCase   (       lowercase_						:    Dict     ,					lowercase_						:    Dict     ,					lowercase_						:    Optional[int]	)		->	int:




  '''simple docstring'''



  lowercase									=tmp_path / '''cache'''
  lowercase									={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    lowercase									=ParquetDatasetReader(
        {'''train''': parquet_path}     ,					cache_dir=lowercase_     ,					keep_in_memory=lowercase_	).read()
  _check_parquet_datasetdict(lowercase_     ,					lowercase_	)




@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 	UpperCamelCase   (       lowercase_						:    Union[str, Any]     ,					lowercase_						:    List[Any]     ,					lowercase_						:    Optional[int]	)		->	List[str]:




  '''simple docstring'''



  lowercase									=tmp_path / '''cache'''
  lowercase									={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  lowercase									=features.copy() if features else default_expected_features
  lowercase									=(
      Features({feature: Value(lowercase_	) for feature, dtype in features.items()}	) if features is not None else None
  )
  lowercase									=ParquetDatasetReader({'''train''': parquet_path}     ,					features=lowercase_     ,					cache_dir=lowercase_	).read()
  _check_parquet_datasetdict(lowercase_     ,					lowercase_	)




@pytest.mark.parametrize('''split'''     ,					[None, NamedSplit('''train'''	), '''train''', '''test''']	)
def 	UpperCamelCase   (       lowercase_						:    List[str]     ,					lowercase_						:    Union[str, Any]     ,					lowercase_						:    Tuple	)		->	Optional[int]:




  '''simple docstring'''



  if split:
    lowercase									={split: parquet_path}
  else:
    lowercase									='''train'''
    lowercase									={'''train''': parquet_path, '''test''': parquet_path}
  lowercase									=tmp_path / '''cache'''
  lowercase									={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  lowercase									=ParquetDatasetReader(lowercase_     ,					cache_dir=lowercase_	).read()
  _check_parquet_datasetdict(lowercase_     ,					lowercase_     ,					splits=list(path.keys()	)	)
  assert all(dataset[split].split == split for split in path.keys()	)




def 	UpperCamelCase   (       lowercase_						:    Optional[int]     ,					lowercase_						:    Optional[Any]	)		->	Optional[int]:




  '''simple docstring'''



  lowercase									=ParquetDatasetWriter(lowercase_     ,					tmp_path / '''foo.parquet'''	)
  assert writer.write() > 0
  lowercase									=pq.ParquetFile(tmp_path / '''foo.parquet'''	)
  lowercase									=pf.read()
  assert dataset.data.table == output_table




def 	UpperCamelCase   (       lowercase_						:    Tuple     ,					lowercase_						:    List[str]	)		->	str:




  '''simple docstring'''



  lowercase									=str(shared_datadir / '''test_image_rgb.jpg'''	)
  lowercase									={'''image''': [image_path]}
  lowercase									=Features({'''image''': Image()}	)
  lowercase									=Dataset.from_dict(lowercase_     ,					features=lowercase_	)
  lowercase									=ParquetDatasetWriter(lowercase_     ,					tmp_path / '''foo.parquet'''	)
  assert writer.write() > 0

  lowercase									=Dataset.from_parquet(str(tmp_path / '''foo.parquet'''	)	)
  assert dataset.features == reloaded_dataset.features

  lowercase									=ParquetDatasetReader(str(tmp_path / '''foo.parquet'''	)     ,					streaming=lowercase_	).read()
  assert dataset.features == reloaded_iterable_dataset.features




@pytest.mark.parametrize(
    '''feature, expected'''     ,					[
        (Features({'''foo''': Value('''int32'''	)}	), None),
        (Features({'''image''': Image(), '''foo''': Value('''int32'''	)}	), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({'''nested''': Sequence(Audio()	)}	), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ]     ,					)
def 	UpperCamelCase   (       lowercase_						:    str     ,					lowercase_						:    List[Any]	)		->	List[Any]:




  '''simple docstring'''



  assert get_writer_batch_size(lowercase_	) == expected








import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()





def 		__UpperCAmelCase     (	a_				,	a_				,	a_				,	a_="attention"):
							snake_case_           =				params[f'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
							snake_case_           =				params[f'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
							snake_case_           =				params[f'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
							snake_case_           =				params[f'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
							return k, o, q, v





def 		__UpperCAmelCase     (	a_				,	a_				,	a_				,	a_=False):
							if split_mlp_wi:
														snake_case_           =				params[f'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
														snake_case_           =				params[f'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
														snake_case_           =				(wi_a, wi_a)
							else:
														snake_case_           =				params[f'''{prefix}/layers_{i}/mlp/wi/kernel''']

							snake_case_           =				params[f'''{prefix}/layers_{i}/mlp/wo/kernel''']
							return wi, wo





def 		__UpperCAmelCase     (	a_				,	a_				,	a_				,	a_):
							return params[f'''{prefix}/layers_{i}/{layer_name}/scale''']





def 		__UpperCAmelCase     (	a_				,	*, a_				,	a_):
							snake_case_           =				traverse_util.flatten_dict(variables['target'])
							snake_case_           =				{'/'.join(a_): v for k, v in old.items()}

							# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
							snake_case_           =				'encoder/layers_0/mlp/wi_0/kernel' in old
							print('Split MLP:'				,	a_)

							snake_case_           =				collections.OrderedDict()

							# Shared embeddings.
							snake_case_           =				old['token_embedder/embedding']

							# Encoder.
							for i in range(a_):
														# Block i, layer 0 (Self Attention).
														snake_case_           =				tax_layer_norm_lookup(a_				,	a_				,	'encoder'				,	'pre_attention_layer_norm')
														snake_case_     ,						snake_case_     ,						snake_case_     ,						snake_case_           =				tax_attention_lookup(a_				,	a_				,	'encoder'				,	'attention')
														snake_case_           =				layer_norm
														snake_case_           =				k.T
														snake_case_           =				o.T
														snake_case_           =				q.T
														snake_case_           =				v.T

														# Block i, layer 1 (MLP).
														snake_case_           =				tax_layer_norm_lookup(a_				,	a_				,	'encoder'				,	'pre_mlp_layer_norm')
														snake_case_     ,						snake_case_           =				tax_mlp_lookup(a_				,	a_				,	'encoder'				,	a_)
														snake_case_           =				layer_norm
														if split_mlp_wi:
																					snake_case_           =				wi[0].T
																					snake_case_           =				wi[1].T
														else:
																					snake_case_           =				wi.T
														snake_case_           =				wo.T

							snake_case_           =				old[
							    'encoder/relpos_bias/rel_embedding'
							].T
							snake_case_           =				old['encoder/encoder_norm/scale']

							if not is_encoder_only:
														# Decoder.
														for i in range(a_):
																					# Block i, layer 0 (Self Attention).
																					snake_case_           =				tax_layer_norm_lookup(a_				,	a_				,	'decoder'				,	'pre_self_attention_layer_norm')
																					snake_case_     ,						snake_case_     ,						snake_case_     ,						snake_case_           =				tax_attention_lookup(a_				,	a_				,	'decoder'				,	'self_attention')
																					snake_case_           =				layer_norm
																					snake_case_           =				k.T
																					snake_case_           =				o.T
																					snake_case_           =				q.T
																					snake_case_           =				v.T

																					# Block i, layer 1 (Cross Attention).
																					snake_case_           =				tax_layer_norm_lookup(a_				,	a_				,	'decoder'				,	'pre_cross_attention_layer_norm')
																					snake_case_     ,						snake_case_     ,						snake_case_     ,						snake_case_           =				tax_attention_lookup(a_				,	a_				,	'decoder'				,	'encoder_decoder_attention')
																					snake_case_           =				layer_norm
																					snake_case_           =				k.T
																					snake_case_           =				o.T
																					snake_case_           =				q.T
																					snake_case_           =				v.T

																					# Block i, layer 2 (MLP).
																					snake_case_           =				tax_layer_norm_lookup(a_				,	a_				,	'decoder'				,	'pre_mlp_layer_norm')
																					snake_case_     ,						snake_case_           =				tax_mlp_lookup(a_				,	a_				,	'decoder'				,	a_)
																					snake_case_           =				layer_norm
																					if split_mlp_wi:
																												snake_case_           =				wi[0].T
																												snake_case_           =				wi[1].T
																					else:
																												snake_case_           =				wi.T
																					snake_case_           =				wo.T

														snake_case_           =				old['decoder/decoder_norm/scale']
														snake_case_           =				old[
														    'decoder/relpos_bias/rel_embedding'
														].T

														# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
														if "decoder/logits_dense/kernel" in old:
																					snake_case_           =				old['decoder/logits_dense/kernel'].T

							return new





def 		__UpperCAmelCase     (	a_				,	a_):
							snake_case_           =				collections.OrderedDict([(k, torch.from_numpy(v.copy())) for (k, v) in converted_params.items()])

							# Add what is missing.
							if "encoder.embed_tokens.weight" not in state_dict:
														snake_case_           =				state_dict['shared.weight']

							if not is_encoder_only:
														if "decoder.embed_tokens.weight" not in state_dict:
																					snake_case_           =				state_dict['shared.weight']

														if "lm_head.weight" not in state_dict:  # For old 1.0 models.
																					print('Using shared word embeddings as lm_head.')
																					snake_case_           =				state_dict['shared.weight']

							return state_dict





def 		__UpperCAmelCase     (	a_				,	a_				,	a_				,	a_):
							snake_case_           =				checkpoints.load_tax_checkpoint(a_)
							snake_case_           =				convert_tax_to_pytorch(a_				,	num_layers=config.num_layers				,	is_encoder_only=a_)
							snake_case_           =				make_state_dict(a_				,	a_)
							model.load_state_dict(a_				,	strict=a_)





def 		__UpperCAmelCase     (	a_				,	a_				,	a_				,	a_ = False):
							snake_case_           =				TaConfig.from_json_file(a_)
							print(f'''Building PyTorch model from configuration: {config}''')
							# Non-v1.1 checkpoints could also use T5Model, but this works for all.
							# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
							if is_encoder_only:
														snake_case_           =				TaEncoderModel(a_)
							else:
														snake_case_           =				TaForConditionalGeneration(a_)

							# Load weights from tf checkpoint
							load_tax_weights_in_ta(a_				,	a_				,	a_				,	a_)

							# Save pytorch-model
							print(f'''Save PyTorch model to {pytorch_dump_path}''')
							model.save_pretrained(a_)

							# Verify that we can load the checkpoint.
							model.from_pretrained(a_)
							print('Done')


if __name__ == "__main__":
	lowercase    				=   argparse.ArgumentParser(description="Converts a native T5X checkpoint into a PyTorch checkpoint.")
	# Required parameters
	parser.add_argument(
	    "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path to the T5X checkpoint."
	)
	parser.add_argument(
	    "--config_file",
	    default=None,
	    type=str,
	    required=True,
	    help="The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.",
	)
	parser.add_argument(
	    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
	)
	parser.add_argument(
	    "--is_encoder_only", action="store_true", help="Check if the model is encoder-decoder model", default=False
	)
	lowercase    				=   parser.parse_args()
	convert_tax_checkpoint_to_pytorch(
	    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
	)





