


'''simple docstring'''




import io
import json

import fsspec
import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases

def 			__UpperCamelCase    (    _A    :				Union[str, Any]					,   _A    :				Optional[int]				)    ->      int:




	"""simple docstring"""





	assert isinstance(_A					,   _A				)
	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    (    _A    :				Dict					,   _A    :				int					,   _A    :				Any				)    ->      str:




	"""simple docstring"""





	lowerCAmelCase					:    str      							= tmp_path / 'cache'
	lowerCAmelCase					:    str      							= {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
	with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
		lowerCAmelCase					:    List[str]      							= JsonDatasetReader(_A					,   cache_dir=_A					,   keep_in_memory=_A				).read()
	_check_json_dataset(_A					,   _A				)

@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    (    _A    :				int					,   _A    :				List[Any]					,   _A    :				Optional[Any]				)    ->      str:




	"""simple docstring"""





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

@pytest.mark.parametrize(
    'features'					,   [
        None,
        {'col_3': 'float64', 'col_1': 'string', 'col_2': 'int64'},
    ]					,   )
def 			__UpperCamelCase    (    _A    :				Any					,   _A    :				Dict					,   _A    :				Optional[Any]				)    ->      Dict:




	"""simple docstring"""





	lowerCAmelCase					:    str      							= tmp_path / 'cache'
	lowerCAmelCase					:    Optional[int]      							= {'col_3': 'float64', 'col_1': 'string', 'col_2': 'int64'}
	lowerCAmelCase					:    Any      							= features.copy() if features else default_expected_features
	lowerCAmelCase					:    List[Any]      							= (
	    Features({feature: Value(_A				) for feature, dtype in features.items()}				) if features is not None else None
	)
	lowerCAmelCase					:    Optional[int]      							= JsonDatasetReader(_A					,   features=_A					,   cache_dir=_A				).read()
	assert isinstance(_A					,   _A				)
	assert dataset.num_rows == 2
	assert dataset.num_columns == 3
	assert dataset.column_names == ["col_3", "col_1", "col_2"]
	for feature, expected_dtype in expected_features.items():
		assert dataset.features[feature].dtype == expected_dtype

def 			__UpperCamelCase    (    _A    :				Dict					,   _A    :				Union[str, Any]				)    ->      Dict:




	"""simple docstring"""





	lowerCAmelCase					:    int      							= {'col_2': 'int64', 'col_3': 'float64', 'col_1': 'string'}
	lowerCAmelCase					:    Optional[int]      							= features.copy()
	lowerCAmelCase					:    int      							= (
	    Features({feature: Value(_A				) for feature, dtype in features.items()}				) if features is not None else None
	)
	lowerCAmelCase					:    Any      							= tmp_path / 'cache'
	lowerCAmelCase					:    Dict      							= JsonDatasetReader(_A					,   features=_A					,   cache_dir=_A				).read()
	assert isinstance(_A					,   _A				)
	assert dataset.num_rows == 2
	assert dataset.num_columns == 3
	assert dataset.column_names == ["col_2", "col_3", "col_1"]
	for feature, expected_dtype in expected_features.items():
		assert dataset.features[feature].dtype == expected_dtype

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




	"""simple docstring"""





	lowerCAmelCase					:    Optional[Any]      							= tmp_path / 'cache'
	lowerCAmelCase					:    Dict      							= {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
	lowerCAmelCase					:    int      							= JsonDatasetReader(_A					,   cache_dir=_A					,   split=_A				).read()
	_check_json_dataset(_A					,   _A				)
	assert dataset.split == split if split else "train"

@pytest.mark.parametrize('path_type'					,   [str, list]				)
def 			__UpperCamelCase    (    _A    :				Any					,   _A    :				str					,   _A    :				Dict				)    ->      List[str]:




	"""simple docstring"""





	if issubclass(_A					,   _A				):
		lowerCAmelCase					:    Optional[int]      							= jsonl_path
	elif issubclass(_A					,   _A				):
		lowerCAmelCase					:    Optional[Any]      							= [jsonl_path]
	lowerCAmelCase					:    Any      							= tmp_path / 'cache'
	lowerCAmelCase					:    Union[str, Any]      							= {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
	lowerCAmelCase					:    Dict      							= JsonDatasetReader(_A					,   cache_dir=_A				).read()
	_check_json_dataset(_A					,   _A				)

def 			__UpperCamelCase    (    _A    :				int					,   _A    :				int					,   _A    :				Optional[int]=("train",)				)    ->      int:




	"""simple docstring"""





	assert isinstance(_A					,   _A				)
	for split in splits:
		lowerCAmelCase					:    str      							= 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    (    _A    :				Optional[Any]					,   _A    :				int					,   _A    :				List[str]				)    ->      List[Any]:




	"""simple docstring"""





	lowerCAmelCase					:    Optional[int]      							= tmp_path / 'cache'
	lowerCAmelCase					:    List[Any]      							= {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
	with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
		lowerCAmelCase					:    Optional[Any]      							= JsonDatasetReader({'train': jsonl_path}					,   cache_dir=_A					,   keep_in_memory=_A				).read()
	_check_json_datasetdict(_A					,   _A				)

@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    (    _A    :				Optional[Any]					,   _A    :				Any					,   _A    :				Any				)    ->      Tuple:




	"""simple docstring"""





	lowerCAmelCase					:    List[Any]      							= tmp_path / 'cache'
	lowerCAmelCase					:    Union[str, Any]      							= {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
	lowerCAmelCase					:    Dict      							= features.copy() if features else default_expected_features
	lowerCAmelCase					:    Dict      							= (
	    Features({feature: Value(_A				) for feature, dtype in features.items()}				) if features is not None else None
	)
	lowerCAmelCase					:    Dict      							= JsonDatasetReader({'train': jsonl_path}					,   features=_A					,   cache_dir=_A				).read()
	_check_json_datasetdict(_A					,   _A				)

@pytest.mark.parametrize('split'					,   [None, NamedSplit('train'				), 'train', 'test']				)
def 			__UpperCamelCase    (    _A    :				Any					,   _A    :				Optional[Any]					,   _A    :				Union[str, Any]				)    ->      str:




	"""simple docstring"""





	if split:
		lowerCAmelCase					:    Optional[Any]      							= {split: jsonl_path}
	else:
		lowerCAmelCase					:    Any      							= 'train'
		lowerCAmelCase					:    Tuple      							= {'train': jsonl_path, 'test': jsonl_path}
	lowerCAmelCase					:    str      							= tmp_path / 'cache'
	lowerCAmelCase					:    Optional[Any]      							= {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
	lowerCAmelCase					:    str      							= JsonDatasetReader(_A					,   cache_dir=_A				).read()
	_check_json_datasetdict(_A					,   _A					,   splits=list(path.keys()				)				)
	assert all(dataset[split].split == split for split in path.keys()				)

def 			__UpperCamelCase    (    _A    :				Union[str, Any]				)    ->      Optional[int]:




	"""simple docstring"""





	return json.load(_A				)

def 			__UpperCamelCase    (    _A    :				Optional[int]				)    ->      List[str]:




	"""simple docstring"""





	return [json.loads(_A				) for line in buffer]






class  lowerCAmelCase				:



	@pytest.mark.parametrize('lines, load_json_function' ,					[(True, load_json_lines), (False, load_json)]    )
	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__    ):
		with io.BytesIO() as buffer:
			JsonDatasetWriter(snake_case__ ,					snake_case__ ,					lines=snake_case__    ).write()
			buffer.seek(0    )
			lowerCAmelCase					:    Optional[Any]      							= load_json_function(snake_case__    )
		assert isinstance(snake_case__ ,					snake_case__    )
		assert isinstance(exported_content[0] ,					snake_case__    )
		assert len(snake_case__    ) == 10



	@pytest.mark.parametrize(
	    'orient, container, keys, len_at' ,					[
	        ('records', list, {'tokens', 'labels', 'answers', 'id'}, None),
	        ('split', dict, {'columns', 'data'}, 'data'),
	        ('index', dict, set('0123456789'    ), None),
	        ('columns', dict, {'tokens', 'labels', 'answers', 'id'}, 'tokens'),
	        ('values', list, None, None),
	        ('table', dict, {'schema', 'data'}, 'data'),
	    ] ,					)
	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__    ):
		with io.BytesIO() as buffer:
			JsonDatasetWriter(snake_case__ ,					snake_case__ ,					lines=snake_case__ ,					orient=snake_case__    ).write()
			buffer.seek(0    )
			lowerCAmelCase					:    List[str]      							= load_json(snake_case__    )
		assert isinstance(snake_case__ ,					snake_case__    )
		if keys:
			if container is dict:
				assert exported_content.keys() == keys
			else:
				assert exported_content[0].keys() == keys
		else:
			assert not hasattr(snake_case__ ,					'keys'    ) and not hasattr(exported_content[0] ,					'keys'    )
		if len_at:
			assert len(exported_content[len_at]    ) == 10
		else:
			assert len(snake_case__    ) == 10



	@pytest.mark.parametrize('lines, load_json_function' ,					[(True, load_json_lines), (False, load_json)]    )
	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__    ):
		with io.BytesIO() as buffer:
			JsonDatasetWriter(snake_case__ ,					snake_case__ ,					lines=snake_case__ ,					num_proc=2    ).write()
			buffer.seek(0    )
			lowerCAmelCase					:    Any      							= load_json_function(snake_case__    )
		assert isinstance(snake_case__ ,					snake_case__    )
		assert isinstance(exported_content[0] ,					snake_case__    )
		assert len(snake_case__    ) == 10



	@pytest.mark.parametrize(
	    'orient, container, keys, len_at' ,					[
	        ('records', list, {'tokens', 'labels', 'answers', 'id'}, None),
	        ('split', dict, {'columns', 'data'}, 'data'),
	        ('index', dict, set('0123456789'    ), None),
	        ('columns', dict, {'tokens', 'labels', 'answers', 'id'}, 'tokens'),
	        ('values', list, None, None),
	        ('table', dict, {'schema', 'data'}, 'data'),
	    ] ,					)
	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__    ):
		with io.BytesIO() as buffer:
			JsonDatasetWriter(snake_case__ ,					snake_case__ ,					lines=snake_case__ ,					orient=snake_case__ ,					num_proc=2    ).write()
			buffer.seek(0    )
			lowerCAmelCase					:    List[Any]      							= load_json(snake_case__    )
		assert isinstance(snake_case__ ,					snake_case__    )
		if keys:
			if container is dict:
				assert exported_content.keys() == keys
			else:
				assert exported_content[0].keys() == keys
		else:
			assert not hasattr(snake_case__ ,					'keys'    ) and not hasattr(exported_content[0] ,					'keys'    )
		if len_at:
			assert len(exported_content[len_at]    ) == 10
		else:
			assert len(snake_case__    ) == 10



	def 				lowercase     (					self ,					snake_case__    ):
		with pytest.raises(snake_case__    ):
			with io.BytesIO() as buffer:
				JsonDatasetWriter(snake_case__ ,					snake_case__ ,					num_proc=0    )



	@pytest.mark.parametrize('compression, extension' ,					[('gzip', 'gz'), ('bz2', 'bz2'), ('xz', 'xz')]    )
	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__    ):
		lowerCAmelCase					:    int      							= tmp_path_factory.mktemp('data'    ) / f"test.json.{extension}"
		lowerCAmelCase					:    Optional[int]      							= str(shared_datadir / f"test_file.json.{extension}"    )
		JsonDatasetWriter(snake_case__ ,					snake_case__ ,					compression=snake_case__    ).write()

		with fsspec.open(snake_case__ ,					'rb' ,					compression='infer'    ) as f:
			lowerCAmelCase					:    Union[str, Any]      							= f.read()
		with fsspec.open(snake_case__ ,					'rb' ,					compression='infer'    ) as f:
			lowerCAmelCase					:    Optional[int]      							= f.read()
		assert exported_content == original_content




'''simple docstring'''




import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
_lowerCAmelCase  :		List[str]		     =	logging.get_logger('transformers.models.speecht5')

def 			__UpperCamelCase    (    _A    :				Any					,   _A    :				Dict					,   _A    :				Any				)    ->      Union[str, Any]:




	"""simple docstring"""





	hf_model.apply_weight_norm()

	lowerCAmelCase					:    int      							= checkpoint['input_conv.weight_g']
	lowerCAmelCase					:    Optional[int]      							= checkpoint['input_conv.weight_v']
	lowerCAmelCase					:    Dict      							= checkpoint['input_conv.bias']

	for i in range(len(config.upsample_rates				)				):
		lowerCAmelCase					:    Optional[Any]      							= checkpoint[F"upsamples.{i}.1.weight_g"]
		lowerCAmelCase					:    str      							= checkpoint[F"upsamples.{i}.1.weight_v"]
		lowerCAmelCase					:    str      							= checkpoint[F"upsamples.{i}.1.bias"]

	for i in range(len(config.upsample_rates				) * len(config.resblock_kernel_sizes				)				):
		for j in range(len(config.resblock_dilation_sizes				)				):
			lowerCAmelCase					:    int      							= checkpoint[F"blocks.{i}.convs1.{j}.1.weight_g"]
			lowerCAmelCase					:    str      							= checkpoint[F"blocks.{i}.convs1.{j}.1.weight_v"]
			lowerCAmelCase					:    int      							= checkpoint[F"blocks.{i}.convs1.{j}.1.bias"]

			lowerCAmelCase					:    Optional[Any]      							= checkpoint[F"blocks.{i}.convs2.{j}.1.weight_g"]
			lowerCAmelCase					:    Tuple      							= checkpoint[F"blocks.{i}.convs2.{j}.1.weight_v"]
			lowerCAmelCase					:    Tuple      							= checkpoint[F"blocks.{i}.convs2.{j}.1.bias"]

	lowerCAmelCase					:    List[Any]      							= checkpoint['output_conv.1.weight_g']
	lowerCAmelCase					:    List[str]      							= checkpoint['output_conv.1.weight_v']
	lowerCAmelCase					:    Optional[Any]      							= checkpoint['output_conv.1.bias']

	hf_model.remove_weight_norm()

@torch.no_grad()
def 			__UpperCamelCase    (    _A    :				Dict					,   _A    :				Union[str, Any]					,   _A    :				List[Any]					,   _A    :				Any=None					,   _A    :				Any=None					,   )    ->      Dict:




	"""simple docstring"""





	if config_path is not None:
		lowerCAmelCase					:    Dict      							= SpeechTaHifiGanConfig.from_pretrained(_A				)
	else:
		lowerCAmelCase					:    Union[str, Any]      							= SpeechTaHifiGanConfig()

	lowerCAmelCase					:    List[Any]      							= SpeechTaHifiGan(_A				)

	lowerCAmelCase					:    List[str]      							= torch.load(_A				)
	load_weights(orig_checkpoint['model']['generator']					,   _A					,   _A				)

	lowerCAmelCase					:    Tuple      							= np.load(_A				)
	lowerCAmelCase					:    List[Any]      							= stats[0].reshape(-1				)
	lowerCAmelCase					:    int      							= stats[1].reshape(-1				)
	lowerCAmelCase					:    Union[str, Any]      							= torch.from_numpy(_A				).float()
	lowerCAmelCase					:    int      							= torch.from_numpy(_A				).float()

	model.save_pretrained(_A				)

	if repo_id:
		print('Pushing to the hub...'				)
		model.push_to_hub(_A				)


if __name__ == "__main__":
				_lowerCAmelCase  :		List[Any]		     =	argparse.ArgumentParser()
				parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to original checkpoint')
				parser.add_argument('--stats_path', required=True, default=None, type=str, help='Path to stats.npy file')
				parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
				parser.add_argument(
				    '--pytorch_dump_folder_path', required=True, default=None, type=str, help='Path to the output PyTorch model.'
				)
				parser.add_argument(
				    '--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
				)

				_lowerCAmelCase  :		Union[str, Any]		     =	parser.parse_args()
				convert_hifigan_checkpoint(
				    args.checkpoint_path,
				    args.stats_path,
				    args.pytorch_dump_folder_path,
				    args.config_path,
				    args.push_to_hub,
				)
