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        __lowerCAmelCase      (	_A			,_A			,_A			,_A="attention"       ):



							"""simple docstring"""


							_lowercase					=						params[f'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
							_lowercase					=						params[f'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
							_lowercase					=						params[f'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
							_lowercase					=						params[f'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
							return k, o, q, v


def        __lowerCAmelCase      (	_A			,_A			,_A			,_A=False       ):



							"""simple docstring"""


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

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


def        __lowerCAmelCase      (	_A			,_A			,_A			,_A       ):



							"""simple docstring"""


							return params[f'''{prefix}/layers_{i}/{layer_name}/scale''']


def        __lowerCAmelCase      (	_A			,*, _A			,_A       ):



							"""simple docstring"""


							_lowercase					=						traverse_util.flatten_dict(variables["""target"""]       )
							_lowercase					=						{'''/'''.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
							_lowercase					=						'''encoder/layers_0/mlp/wi_0/kernel''' in old
							print("""Split MLP:"""			,_A       )

							_lowercase					=						collections.OrderedDict()

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

							# Encoder.
							for i in range(_A       ):
														# Block i, layer 0 (Self Attention).
														_lowercase					=						tax_layer_norm_lookup(_A			,_A			,"""encoder"""			,"""pre_attention_layer_norm"""       )
														_lowercase					=						tax_attention_lookup(_A			,_A			,"""encoder"""			,"""attention"""       )
														_lowercase					=						layer_norm
														_lowercase					=						k.T
														_lowercase					=						o.T
														_lowercase					=						q.T
														_lowercase					=						v.T

														# Block i, layer 1 (MLP).
														_lowercase					=						tax_layer_norm_lookup(_A			,_A			,"""encoder"""			,"""pre_mlp_layer_norm"""       )
														_lowercase					=						tax_mlp_lookup(_A			,_A			,"""encoder"""			,_A       )
														_lowercase					=						layer_norm
														if split_mlp_wi:
																					_lowercase					=						wi[0].T
																					_lowercase					=						wi[1].T
														else:
																					_lowercase					=						wi.T
														_lowercase					=						wo.T

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

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

																					# Block i, layer 1 (Cross Attention).
																					_lowercase					=						tax_layer_norm_lookup(_A			,_A			,"""decoder"""			,"""pre_cross_attention_layer_norm"""       )
																					_lowercase					=						tax_attention_lookup(_A			,_A			,"""decoder"""			,"""encoder_decoder_attention"""       )
																					_lowercase					=						layer_norm
																					_lowercase					=						k.T
																					_lowercase					=						o.T
																					_lowercase					=						q.T
																					_lowercase					=						v.T

																					# Block i, layer 2 (MLP).
																					_lowercase					=						tax_layer_norm_lookup(_A			,_A			,"""decoder"""			,"""pre_mlp_layer_norm"""       )
																					_lowercase					=						tax_mlp_lookup(_A			,_A			,"""decoder"""			,_A       )
																					_lowercase					=						layer_norm
																					if split_mlp_wi:
																												_lowercase					=						wi[0].T
																												_lowercase					=						wi[1].T
																					else:
																												_lowercase					=						wi.T
																					_lowercase					=						wo.T

														_lowercase					=						old['''decoder/decoder_norm/scale''']
														_lowercase					=						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:
																					_lowercase					=						old['''decoder/logits_dense/kernel'''].T

							return new


def        __lowerCAmelCase      (	_A			,_A       ):



							"""simple docstring"""


							_lowercase					=						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:
														_lowercase					=						state_dict['''shared.weight''']

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

							return state_dict


def        __lowerCAmelCase      (	_A			,_A			,_A			,_A       ):



							"""simple docstring"""


							_lowercase					=						checkpoints.load_tax_checkpoint(_A       )
							_lowercase					=						convert_tax_to_pytorch(_A			,num_layers=config.num_layers			,is_encoder_only=_A       )
							_lowercase					=						make_state_dict(_A			,_A       )
							model.load_state_dict(_A			,strict=_A       )


def        __lowerCAmelCase      (	_A			,_A			,_A			,_A = False       ):



							"""simple docstring"""


							_lowercase					=						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:
														_lowercase					=						TaEncoderModel(_A       )
							else:
														_lowercase					=						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__":
				A_:   List[str]   =      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
				)
				A_:   Optional[Any]   =      parser.parse_args()
				convert_tax_checkpoint_to_pytorch(
				    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
				)










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   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				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   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Tuple   =   tmp_path / '''cache'''
				__magic_name__		:int   =   {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
								__magic_name__		:Optional[Any]   =   ParquetDatasetReader(snake_case,	cache_dir=snake_case,	keep_in_memory=snake_case			).read()
				_check_parquet_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   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:List[str]   =   tmp_path / '''cache'''
				__magic_name__		:int   =   {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				__magic_name__		:Tuple   =   features.copy() if features else default_expected_features
				__magic_name__		:Union[str, Any]   =   (
				    Features({feature: Value(snake_case			) for feature, dtype in features.items()}			) if features is not None else None
				)
				__magic_name__		:int   =   ParquetDatasetReader(snake_case,	features=snake_case,	cache_dir=snake_case			).read()
				_check_parquet_dataset(snake_case,	snake_case			)

@pytest.mark.parametrize('''split''',	[None, NamedSplit('''train'''			), '''train''', '''test''']			)
def   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:str   =   tmp_path / '''cache'''
				__magic_name__		:List[Any]   =   {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				__magic_name__		:int   =   ParquetDatasetReader(snake_case,	cache_dir=snake_case,	split=snake_case			).read()
				_check_parquet_dataset(snake_case,	snake_case			)
				assert dataset.split == split if split else "train"

@pytest.mark.parametrize('''path_type''',	[str, list]			)
def   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				if issubclass(snake_case,	snake_case			):
								__magic_name__		:Union[str, Any]   =   parquet_path
				elif issubclass(snake_case,	snake_case			):
								__magic_name__		:Union[str, Any]   =   [parquet_path]
				__magic_name__		:Optional[int]   =   tmp_path / '''cache'''
				__magic_name__		:Optional[int]   =   {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				__magic_name__		:str   =   ParquetDatasetReader(snake_case,	cache_dir=snake_case			).read()
				_check_parquet_dataset(snake_case,	snake_case			)

def   __lowercase       (			snake_case,	snake_case,	snake_case=("train",)			):







				"""simple docstring"""






				assert isinstance(snake_case,	snake_case			)
				for split in splits:
								__magic_name__		:Optional[Any]   =   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   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Any   =   tmp_path / '''cache'''
				__magic_name__		:Optional[int]   =   {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
								__magic_name__		:Tuple   =   ParquetDatasetReader(
								    {'''train''': parquet_path},	cache_dir=snake_case,	keep_in_memory=snake_case			).read()
				_check_parquet_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   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Optional[Any]   =   tmp_path / '''cache'''
				__magic_name__		:Dict   =   {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				__magic_name__		:int   =   features.copy() if features else default_expected_features
				__magic_name__		:List[Any]   =   (
				    Features({feature: Value(snake_case			) for feature, dtype in features.items()}			) if features is not None else None
				)
				__magic_name__		:Optional[int]   =   ParquetDatasetReader({'''train''': parquet_path},	features=snake_case,	cache_dir=snake_case			).read()
				_check_parquet_datasetdict(snake_case,	snake_case			)

@pytest.mark.parametrize('''split''',	[None, NamedSplit('''train'''			), '''train''', '''test''']			)
def   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






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

def   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:str   =   ParquetDatasetWriter(snake_case,	tmp_path / '''foo.parquet'''			)
				assert writer.write() > 0
				__magic_name__		:List[Any]   =   pq.ParquetFile(tmp_path / '''foo.parquet'''			)
				__magic_name__		:List[Any]   =   pf.read()
				assert dataset.data.table == output_table

def   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






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

				__magic_name__		:List[str]   =   Dataset.from_parquet(str(tmp_path / '''foo.parquet'''			)			)
				assert dataset.features == reloaded_dataset.features

				__magic_name__		:List[str]   =   ParquetDatasetReader(str(tmp_path / '''foo.parquet'''			),	streaming=snake_case			).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   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				assert get_writer_batch_size(snake_case			) == expected






