
import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()


def    A (      lowercase					, lowercase					, lowercase				)     ->						Optional[Any]:
		'''simple docstring'''

		return params[f'''{prefix}/{prefix}/relpos_bias/rel_embedding'''][:, i, :]


def    A (      lowercase					, lowercase					, lowercase					, lowercase="attention"				)     ->						List[Any]:
		'''simple docstring'''

		UpperCamelCase						=  UpperCamelCase						=  np.ascontiguousarray(params[f'''{prefix}/{prefix}/{layer_name}/key/kernel'''][:, i, :, :]				)
		UpperCamelCase						=  k_tmp.reshape(k_tmp.shape[0]					, k_tmp.shape[1] * k_tmp.shape[2]				)
		UpperCamelCase						=  np.ascontiguousarray(params[f'''{prefix}/{prefix}/{layer_name}/out/kernel'''][:, i, :, :]				)
		UpperCamelCase						=  o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]					, o_tmp.shape[2]				)
		UpperCamelCase						=  np.ascontiguousarray(params[f'''{prefix}/{prefix}/{layer_name}/query/kernel'''][:, i, :, :]				)
		UpperCamelCase						=  q_tmp.reshape(q_tmp.shape[0]					, q_tmp.shape[1] * q_tmp.shape[2]				)
		UpperCamelCase						=  np.ascontiguousarray(params[f'''{prefix}/{prefix}/{layer_name}/value/kernel'''][:, i, :, :]				)
		UpperCamelCase						=  v_tmp.reshape(v_tmp.shape[0]					, v_tmp.shape[1] * v_tmp.shape[2]				)
		return k, o, q, v


def    A (      lowercase					, lowercase					, lowercase					, lowercase=False				)     ->						Tuple:
		'''simple docstring'''

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

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


def    A (      lowercase					, lowercase					, lowercase					, lowercase				)     ->						Any:
		'''simple docstring'''

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


def    A (      lowercase					, *, lowercase					, lowercase					, lowercase = False				)     ->						Tuple:
		'''simple docstring'''

		UpperCamelCase						=  traverse_util.flatten_dict(variables['target']				)
		UpperCamelCase						=  {'/'.join(lowercase				): v for k, v in old.items()}

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

		UpperCamelCase						=  collections.OrderedDict()

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

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

				# Block i, layer 1 (MLP).
				UpperCamelCase						=  tax_layer_norm_lookup(lowercase					, lowercase					, 'encoder'					, 'pre_mlp_layer_norm'				)
				UpperCamelCase			,						UpperCamelCase						=  tax_mlp_lookup(lowercase					, lowercase					, 'encoder'					, lowercase				)
				UpperCamelCase						=  layer_norm
				if split_mlp_wi:
						UpperCamelCase						=  wi[0].T
						UpperCamelCase						=  wi[1].T
				else:
						UpperCamelCase						=  wi.T
				UpperCamelCase						=  wo.T
				if scalable_attention:
						# convert the rel_embedding of each layer
						UpperCamelCase						=  tax_relpos_bias_lookup(
						    lowercase					, lowercase					, 'encoder'				).T

		UpperCamelCase						=  old['encoder/encoder_norm/scale']

		if not scalable_attention:
				UpperCamelCase						=  tax_relpos_bias_lookup(
				    lowercase					, 0					, 'encoder'				).T
				UpperCamelCase						=  tax_relpos_bias_lookup(
				    lowercase					, 0					, 'decoder'				).T

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

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

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

						if scalable_attention:
								# convert the rel_embedding of each layer
								UpperCamelCase						=  tax_relpos_bias_lookup(lowercase					, lowercase					, 'decoder'				).T

				UpperCamelCase						=  old['decoder/decoder_norm/scale']

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

		return new


def    A (      lowercase					, lowercase				)     ->						Optional[Any]:
		'''simple docstring'''

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

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

		return state_dict


def    A (      lowercase					, lowercase					, lowercase					, lowercase					, lowercase				)     ->						Dict:
		'''simple docstring'''

		UpperCamelCase						=  checkpoints.load_tax_checkpoint(lowercase				)
		UpperCamelCase						=  convert_tax_to_pytorch(
		    lowercase					, num_layers=config.num_layers					, is_encoder_only=lowercase					, scalable_attention=lowercase				)
		UpperCamelCase						=  make_state_dict(lowercase					, lowercase				)
		model.load_state_dict(lowercase					, strict=lowercase				)


def    A (      lowercase					, lowercase					, lowercase					, lowercase = False					, lowercase = False					, )     ->						List[Any]:
		'''simple docstring'''

		UpperCamelCase						=  MTaConfig.from_json_file(lowercase				)
		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:
				UpperCamelCase						=  UMTaEncoderModel(lowercase				)
		else:
				UpperCamelCase						=  UMTaForConditionalGeneration(lowercase				)

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

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

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


if __name__ == "__main__":
					_UpperCAmelCase      :				int          = 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
					)
					parser.add_argument(
					    "--scalable_attention",
					    action="store_true",
					    help="Whether the model uses scaled attention (umt5 model)",
					    default=False,
					)
					_UpperCAmelCase      :				Any          = parser.parse_args()
					convert_tax_checkpoint_to_pytorch(
					    args.tax_checkpoint_path,
					    args.config_file,
					    args.pytorch_dump_path,
					    args.is_encoder_only,
					    args.scalable_attention,
					)


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    A (      lowercase					, lowercase				)     ->						Optional[int]:
		'''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    A (      lowercase					, lowercase					, lowercase				)     ->						Tuple:
		'''simple docstring'''

		UpperCamelCase						=  tmp_path / 'cache'
		UpperCamelCase						=  {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
		with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
				UpperCamelCase						=  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    A (      lowercase					, lowercase					, lowercase				)     ->						Tuple:
		'''simple docstring'''

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


@pytest.mark.parametrize('split'					, [None, NamedSplit('train'				), 'train', 'test']				)
def    A (      lowercase					, lowercase					, lowercase				)     ->						Optional[int]:
		'''simple docstring'''

		UpperCamelCase						=  tmp_path / 'cache'
		UpperCamelCase						=  {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
		UpperCamelCase						=  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    A (      lowercase					, lowercase					, lowercase				)     ->						Union[str, Any]:
		'''simple docstring'''

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


def    A (      lowercase					, lowercase					, lowercase=("train",)				)     ->						Tuple:
		'''simple docstring'''

		assert isinstance(lowercase					, lowercase				)
		for split in splits:
				UpperCamelCase						=  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    A (      lowercase					, lowercase					, lowercase				)     ->						Optional[Any]:
		'''simple docstring'''

		UpperCamelCase						=  tmp_path / 'cache'
		UpperCamelCase						=  {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
		with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
				UpperCamelCase						=  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    A (      lowercase					, lowercase					, lowercase				)     ->						List[Any]:
		'''simple docstring'''

		UpperCamelCase						=  tmp_path / 'cache'
		UpperCamelCase						=  {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
		UpperCamelCase						=  features.copy() if features else default_expected_features
		UpperCamelCase						=  (
		    Features({feature: Value(lowercase				) for feature, dtype in features.items()}				) if features is not None else None
		)
		UpperCamelCase						=  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    A (      lowercase					, lowercase					, lowercase				)     ->						Union[str, Any]:
		'''simple docstring'''

		if split:
				UpperCamelCase						=  {split: parquet_path}
		else:
				UpperCamelCase						=  'train'
				UpperCamelCase						=  {'train': parquet_path, 'test': parquet_path}
		UpperCamelCase						=  tmp_path / 'cache'
		UpperCamelCase						=  {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
		UpperCamelCase						=  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    A (      lowercase					, lowercase				)     ->						List[Any]:
		'''simple docstring'''

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


def    A (      lowercase					, lowercase				)     ->						Optional[int]:
		'''simple docstring'''

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

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

		UpperCamelCase						=  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    A (      lowercase					, lowercase				)     ->						Union[str, Any]:
		'''simple docstring'''

		assert get_writer_batch_size(lowercase				) == expected
