
from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


_UpperCAmelCase      :				int          = logging.get_logger(__name__)


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

		try:
				with open(lowercase					, 'rb'				) as flax_state_f:
						UpperCamelCase						=  from_bytes(lowercase					, flax_state_f.read()				)
		except UnpicklingError as e:
				try:
						with open(lowercase				) as f:
								if f.read().startswith('version'				):
										raise OSError(
										    'You seem to have cloned a repository without having git-lfs installed. Please'
										    ' install git-lfs and run `git lfs install` followed by `git lfs pull` in the'
										    ' folder you cloned.'				)
								else:
										raise ValueError from e
				except (UnicodeDecodeError, ValueError):
						raise EnvironmentError(f'''Unable to convert {model_file} to Flax deserializable object. '''				)

		return load_flax_weights_in_pytorch_model(lowercase					, lowercase				)


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

		try:
				import torch  # noqa: F401
		except ImportError:
				logger.error(
				    'Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see'
				    ' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'
				    ' instructions.'				)
				raise

		# check if we have bf16 weights
		UpperCamelCase						=  flatten_dict(jax.tree_util.tree_map(lambda lowercase				: x.dtype == jnp.bfloataa					, lowercase				)				).values()
		if any(lowercase				):
				# convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

				# and bf16 is not fully supported in PT yet.
				logger.warning(
				    'Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '
				    'before loading those in PyTorch model.'				)
				UpperCamelCase						=  jax.tree_util.tree_map(
				    lambda lowercase				: params.astype(np.floataa				) if params.dtype == jnp.bfloataa else params					, lowercase				)

		UpperCamelCase						=  ''

		UpperCamelCase						=  flatten_dict(lowercase					, sep='.'				)
		UpperCamelCase						=  pt_model.state_dict()

		# keep track of unexpected & missing keys
		UpperCamelCase						=  []
		UpperCamelCase						=  set(pt_model_dict.keys()				)

		for flax_key_tuple, flax_tensor in flax_state_dict.items():
				UpperCamelCase						=  flax_key_tuple.split('.'				)

				if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
						UpperCamelCase						=  flax_key_tuple_array[:-1] + ['weight']
						UpperCamelCase						=  jnp.transpose(lowercase					, (3, 2, 0, 1)				)
				elif flax_key_tuple_array[-1] == "kernel":
						UpperCamelCase						=  flax_key_tuple_array[:-1] + ['weight']
						UpperCamelCase						=  flax_tensor.T
				elif flax_key_tuple_array[-1] == "scale":
						UpperCamelCase						=  flax_key_tuple_array[:-1] + ['weight']

				if "time_embedding" not in flax_key_tuple_array:
						for i, flax_key_tuple_string in enumerate(lowercase				):
								UpperCamelCase						=  (
								    flax_key_tuple_string.replace('_0'					, '.0'				)
								    .replace('_1'					, '.1'				)
								    .replace('_2'					, '.2'				)
								    .replace('_3'					, '.3'				)
								    .replace('_4'					, '.4'				)
								    .replace('_5'					, '.5'				)
								    .replace('_6'					, '.6'				)
								    .replace('_7'					, '.7'				)
								    .replace('_8'					, '.8'				)
								    .replace('_9'					, '.9'				)
								)

				UpperCamelCase						=  '.'.join(lowercase				)

				if flax_key in pt_model_dict:
						if flax_tensor.shape != pt_model_dict[flax_key].shape:
								raise ValueError(
								    f'''Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '''
								    f'''to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.'''				)
						else:
								# add weight to pytorch dict
								UpperCamelCase						=  np.asarray(lowercase				) if not isinstance(lowercase					, np.ndarray				) else flax_tensor
								UpperCamelCase						=  torch.from_numpy(lowercase				)
								# remove from missing keys
								missing_keys.remove(lowercase				)
				else:
						# weight is not expected by PyTorch model
						unexpected_keys.append(lowercase				)

		pt_model.load_state_dict(lowercase				)

		# re-transform missing_keys to list
		UpperCamelCase						=  list(lowercase				)

		if len(lowercase				) > 0:
				logger.warning(
				    'Some weights of the Flax model were not used when initializing the PyTorch model'
				    f''' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'''
				    f''' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'''
				    ' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'
				    f''' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'''
				    ' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'
				    ' FlaxBertForSequenceClassification model).'				)
		if len(lowercase				) > 0:
				logger.warning(
				    f'''Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'''
				    f''' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'''
				    ' use it for predictions and inference.'				)

		return pt_model


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
