
"""simple docstring"""

import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch

import multiprocess
import numpy as np
import pytest

from datasets.utils.py_utils import (
    NestedDataStructure,
    asdict,
    iflatmap_unordered,
    map_nested,
    temp_seed,
    temporary_assignment,
    zip_dict,
)

from .utils import require_tf, require_torch
def 		UpperCAmelCase (		a_      ):  # picklable for multiprocessing





		'''simple docstring'''


		return x.sum()
def 		UpperCAmelCase (		a_      ):  # picklable for multiprocessing





		'''simple docstring'''


		return i + 1






@dataclass
class 	_lowercase       :
	lowercase_				=     42
	lowercase_				=     42






class 	_lowercase       (    __UpperCAmelCase		):




	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			lowerCamelCase					:      Union[str, Any]          =   {}
			lowerCamelCase					:      Optional[int]          =   []
			lowerCamelCase					:      Union[str, Any]          =   1
			lowerCamelCase					:      str          =   [1, 2]
			lowerCamelCase					:      Tuple          =   {'a': 1, 'b': 2}
			lowerCamelCase					:      str          =   {'a': [1, 2], 'b': [3, 4]}
			lowerCamelCase					:      List[str]          =   {'a': {'1': 1}, 'b': 2}
			lowerCamelCase					:      int          =   {'a': 1, 'b': 2, 'c': 3, 'd': 4}
			lowerCamelCase					:      Dict          =   {}
			lowerCamelCase					:      Optional[Any]          =   []
			lowerCamelCase					:      Dict          =   2
			lowerCamelCase					:      Union[str, Any]          =   [2, 3]
			lowerCamelCase					:      List[Any]          =   {'a': 2, 'b': 3}
			lowerCamelCase					:      Optional[int]          =   {'a': [2, 3], 'b': [4, 5]}
			lowerCamelCase					:      Optional[int]          =   {'a': {'1': 2}, 'b': 3}
			lowerCamelCase					:      str          =   {'a': 2, 'b': 3, 'c': 4, 'd': 5}
			self.assertEqual(map_nested(UpperCAmelCase_		,			UpperCAmelCase_ )		,			UpperCAmelCase_ )
			self.assertEqual(map_nested(UpperCAmelCase_		,			UpperCAmelCase_ )		,			UpperCAmelCase_ )
			self.assertEqual(map_nested(UpperCAmelCase_		,			UpperCAmelCase_ )		,			UpperCAmelCase_ )
			self.assertEqual(map_nested(UpperCAmelCase_		,			UpperCAmelCase_ )		,			UpperCAmelCase_ )
			self.assertEqual(map_nested(UpperCAmelCase_		,			UpperCAmelCase_ )		,			UpperCAmelCase_ )
			self.assertEqual(map_nested(UpperCAmelCase_		,			UpperCAmelCase_ )		,			UpperCAmelCase_ )
			self.assertEqual(map_nested(UpperCAmelCase_		,			UpperCAmelCase_ )		,			UpperCAmelCase_ )
			self.assertEqual(map_nested(UpperCAmelCase_		,			UpperCAmelCase_ )		,			UpperCAmelCase_ )

			lowerCamelCase					:      str          =   2
			self.assertEqual(map_nested(UpperCAmelCase_		,			UpperCAmelCase_		,			num_proc=UpperCAmelCase_ )		,			UpperCAmelCase_ )
			self.assertEqual(map_nested(UpperCAmelCase_		,			UpperCAmelCase_		,			num_proc=UpperCAmelCase_ )		,			UpperCAmelCase_ )
			self.assertEqual(map_nested(UpperCAmelCase_		,			UpperCAmelCase_		,			num_proc=UpperCAmelCase_ )		,			UpperCAmelCase_ )
			self.assertEqual(map_nested(UpperCAmelCase_		,			UpperCAmelCase_		,			num_proc=UpperCAmelCase_ )		,			UpperCAmelCase_ )
			self.assertEqual(map_nested(UpperCAmelCase_		,			UpperCAmelCase_		,			num_proc=UpperCAmelCase_ )		,			UpperCAmelCase_ )
			self.assertEqual(map_nested(UpperCAmelCase_		,			UpperCAmelCase_		,			num_proc=UpperCAmelCase_ )		,			UpperCAmelCase_ )
			self.assertEqual(map_nested(UpperCAmelCase_		,			UpperCAmelCase_		,			num_proc=UpperCAmelCase_ )		,			UpperCAmelCase_ )
			self.assertEqual(map_nested(UpperCAmelCase_		,			UpperCAmelCase_		,			num_proc=UpperCAmelCase_ )		,			UpperCAmelCase_ )

			lowerCamelCase					:      Optional[Any]          =   {'a': np.eye(2 ), 'b': np.zeros(3 ), 'c': np.ones(2 )}
			lowerCamelCase					:      List[str]          =   {'a': 2, 'b': 0, 'c': 2}
			lowerCamelCase					:      List[str]          =   {
			    'a': np.eye(2 ).astype(UpperCAmelCase_ ),
			    'b': np.zeros(3 ).astype(UpperCAmelCase_ ),
			    'c': np.ones(2 ).astype(UpperCAmelCase_ ),
			}
			self.assertEqual(map_nested(UpperCAmelCase_		,			UpperCAmelCase_		,			map_numpy=UpperCAmelCase_ )		,			UpperCAmelCase_ )
			self.assertEqual(
			    {k: v.tolist() for k, v in map_nested(UpperCAmelCase_		,			UpperCAmelCase_		,			map_numpy=UpperCAmelCase_ ).items()}		,			{k: v.tolist() for k, v in expected_map_nested_sna_int.items()}		,			)
			self.assertEqual(map_nested(UpperCAmelCase_		,			UpperCAmelCase_		,			map_numpy=UpperCAmelCase_		,			num_proc=UpperCAmelCase_ )		,			UpperCAmelCase_ )
			self.assertEqual(
			    {k: v.tolist() for k, v in map_nested(UpperCAmelCase_		,			UpperCAmelCase_		,			map_numpy=UpperCAmelCase_		,			num_proc=UpperCAmelCase_ ).items()}		,			{k: v.tolist() for k, v in expected_map_nested_sna_int.items()}		,			)
			with self.assertRaises(UpperCAmelCase_ ):  # can't pickle a local lambda
					map_nested(lambda UpperCAmelCase_ : x + 1		,			UpperCAmelCase_		,			num_proc=UpperCAmelCase_ )




	def       _UpperCamelCase   (			self )		->							int:
			lowerCamelCase					:      Optional[int]          =   {'a': 1, 'b': 2}
			lowerCamelCase					:      Optional[int]          =   {'a': 3, 'b': 4}
			lowerCamelCase					:      List[str]          =   {'a': 5, 'b': 6}
			lowerCamelCase					:      Dict          =   sorted([('a', (1, 3, 5)), ('b', (2, 4, 6))] )
			self.assertEqual(sorted(zip_dict(UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ ) )		,			UpperCAmelCase_ )




	def       _UpperCamelCase   (			self )		->							Optional[int]:






			class 	_lowercase       :
				lowercase_				=     'bar'


			lowerCamelCase					:      Optional[int]          =   Foo()
			self.assertEqual(foo.my_attr		,			'bar' )
			with temporary_assignment(UpperCAmelCase_		,			'my_attr'		,			'BAR' ):
					self.assertEqual(foo.my_attr		,			'BAR' )
			self.assertEqual(foo.my_attr		,			'bar' )


@pytest.mark.parametrize(
    'iterable_length, num_proc, expected_num_proc',						[
        (1, None, 1),
        (1, 1, 1),
        (2, None, 1),
        (2, 1, 1),
        (2, 2, 1),
        (2, 3, 1),
        (3, 2, 1),
        (16, 16, 16),
        (16, 17, 16),
        (17, 16, 16),
    ],						)
def 		UpperCAmelCase (		a_,						a_,						a_      ):





		'''simple docstring'''


		with patch('datasets.utils.py_utils._single_map_nested'      ) as mock_single_map_nested, patch(
		    'datasets.parallel.parallel.Pool'      ) as mock_multiprocessing_pool:
				lowerCamelCase					:      Tuple          =   {F"""{i}""": i for i in range(a_      )}
				lowerCamelCase					:      Union[str, Any]          =   map_nested(lambda a_      : x + 10,						a_,						num_proc=a_,						parallel_min_length=16      )
				if expected_num_proc == 1:
						assert mock_single_map_nested.called
						assert not mock_multiprocessing_pool.called
				else:
						assert not mock_single_map_nested.called
						assert mock_multiprocessing_pool.called
						assert mock_multiprocessing_pool.call_args[0][0] == expected_num_proc






class 	_lowercase       (    __UpperCAmelCase		):




	@require_tf
	def       _UpperCamelCase   (			self )		->							str:
			import tensorflow as tf
			from tensorflow.keras import layers

			lowerCamelCase					:      List[str]          =   layers.Dense(2 )

			def gen_random_output():
					lowerCamelCase					:      int          =   tf.random.uniform((1, 3) )
					return model(UpperCAmelCase_ ).numpy()

			with temp_seed(42		,			set_tensorflow=UpperCAmelCase_ ):
					lowerCamelCase					:      Union[str, Any]          =   gen_random_output()
			with temp_seed(42		,			set_tensorflow=UpperCAmelCase_ ):
					lowerCamelCase					:      Tuple          =   gen_random_output()
			lowerCamelCase					:      Any          =   gen_random_output()

			np.testing.assert_equal(UpperCAmelCase_		,			UpperCAmelCase_ )
			self.assertGreater(np.abs(outa - outa ).sum()		,			0 )




	@require_torch
	def       _UpperCamelCase   (			self )		->							Dict:
			import torch

			def gen_random_output():
					lowerCamelCase					:      Optional[int]          =   torch.nn.Linear(3		,			2 )
					lowerCamelCase					:      Optional[Any]          =   torch.rand(1		,			3 )
					return model(UpperCAmelCase_ ).detach().numpy()

			with temp_seed(42		,			set_pytorch=UpperCAmelCase_ ):
					lowerCamelCase					:      Optional[int]          =   gen_random_output()
			with temp_seed(42		,			set_pytorch=UpperCAmelCase_ ):
					lowerCamelCase					:      List[str]          =   gen_random_output()
			lowerCamelCase					:      Any          =   gen_random_output()

			np.testing.assert_equal(UpperCAmelCase_		,			UpperCAmelCase_ )
			self.assertGreater(np.abs(outa - outa ).sum()		,			0 )




	def       _UpperCamelCase   (			self )		->							Optional[int]:
			def gen_random_output():
					return np.random.rand(1		,			3 )

			with temp_seed(42 ):
					lowerCamelCase					:      int          =   gen_random_output()
			with temp_seed(42 ):
					lowerCamelCase					:      Optional[int]          =   gen_random_output()
			lowerCamelCase					:      Dict          =   gen_random_output()

			np.testing.assert_equal(UpperCAmelCase_		,			UpperCAmelCase_ )
			self.assertGreater(np.abs(outa - outa ).sum()		,			0 )


@pytest.mark.parametrize('input_data',						[{}]      )
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		lowerCamelCase					:      str          =   NestedDataStructure(a_      ).data
		assert output_data == input_data
@pytest.mark.parametrize(
    'data, expected_output',						[
        ({}, []),
        ([], []),
        ('foo', ['foo']),
        (['foo', 'bar'], ['foo', 'bar']),
        ([['foo', 'bar']], ['foo', 'bar']),
        ([[['foo'], ['bar']]], ['foo', 'bar']),
        ([[['foo'], 'bar']], ['foo', 'bar']),
        ({'a': 1, 'b': 2}, [1, 2]),
        ({'a': [1, 2], 'b': [3, 4]}, [1, 2, 3, 4]),
        ({'a': [[1, 2]], 'b': [[3, 4]]}, [1, 2, 3, 4]),
        ({'a': [[1, 2]], 'b': [3, 4]}, [1, 2, 3, 4]),
        ({'a': [[[1], [2]]], 'b': [[[3], [4]]]}, [1, 2, 3, 4]),
        ({'a': [[[1], [2]]], 'b': [[3, 4]]}, [1, 2, 3, 4]),
        ({'a': [[[1], [2]]], 'b': [3, 4]}, [1, 2, 3, 4]),
        ({'a': [[[1], [2]]], 'b': [3, [4]]}, [1, 2, 3, 4]),
        ({'a': {'1': 1}, 'b': 2}, [1, 2]),
        ({'a': {'1': [1]}, 'b': 2}, [1, 2]),
        ({'a': {'1': [1]}, 'b': [2]}, [1, 2]),
    ],						)
def 		UpperCAmelCase (		a_,						a_      ):





		'''simple docstring'''


		lowerCamelCase					:      Optional[int]          =   NestedDataStructure(a_      ).flatten()
		assert output == expected_output
def 		UpperCAmelCase (		):





		'''simple docstring'''


		lowerCamelCase					:      str          =   A(x=1,						y='foobar'      )
		lowerCamelCase					:      List[Any]          =   {'x': 1, 'y': 'foobar'}
		assert asdict(a_      ) == expected_output

		lowerCamelCase					:      Union[str, Any]          =   {'a': {'b': A(x=10,						y='foo'      )}, 'c': [A(x=20,						y='bar'      )]}
		lowerCamelCase					:      List[str]          =   {'a': {'b': {'x': 10, 'y': 'foo'}}, 'c': [{'x': 20, 'y': 'bar'}]}
		assert asdict(a_      ) == expected_output

		with pytest.raises(a_      ):
				asdict([1, A(x=10,						y='foo'      )]      )
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		return text.split()
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		yield (time.time(), content)
		time.sleep(2      )
		yield (time.time(), content)





def 		UpperCAmelCase (		):





		'''simple docstring'''


		with Pool(2      ) as pool:
				lowerCamelCase					:      Tuple          =   list(iflatmap_unordered(a_,						_split_text,						kwargs_iterable=[{'text': 'hello there'}] * 10      )      )
				assert out.count('hello'      ) == 10
				assert out.count('there'      ) == 10
				assert len(a_      ) == 20

		# check multiprocess from pathos (uses dill for pickling)
		with multiprocess.Pool(2      ) as pool:
				lowerCamelCase					:      List[Any]          =   list(iflatmap_unordered(a_,						_split_text,						kwargs_iterable=[{'text': 'hello there'}] * 10      )      )
				assert out.count('hello'      ) == 10
				assert out.count('there'      ) == 10
				assert len(a_      ) == 20

		# check that we get items as fast as possible
		with Pool(2      ) as pool:
				lowerCamelCase					:      Optional[Any]          =   []
				for yield_time, content in iflatmap_unordered(
				    a_,						_aseconds_generator_of_aitems_with_timing,						kwargs_iterable=[{'content': 'a'}, {'content': 'b'}]      ):
						assert yield_time < time.time() + 0.1, "we should each item directly after it was yielded"
						out.append(a_      )
				assert out.count('a'      ) == 2
				assert out.count('b'      ) == 2
				assert len(a_      ) == 4








"""simple docstring"""

import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
_A						=			logging.get_logger('transformers.models.speecht5')
def 		UpperCAmelCase (		a_,						a_,						a_      ):





		'''simple docstring'''


		hf_model.apply_weight_norm()

		lowerCamelCase					:      str          =   checkpoint['input_conv.weight_g']
		lowerCamelCase					:      int          =   checkpoint['input_conv.weight_v']
		lowerCamelCase					:      Optional[Any]          =   checkpoint['input_conv.bias']

		for i in range(len(config.upsample_rates      )      ):
				lowerCamelCase					:      Tuple          =   checkpoint[F"""upsamples.{i}.1.weight_g"""]
				lowerCamelCase					:      Any          =   checkpoint[F"""upsamples.{i}.1.weight_v"""]
				lowerCamelCase					:      List[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					:      Optional[Any]          =   checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_g"""]
						lowerCamelCase					:      Tuple          =   checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_v"""]
						lowerCamelCase					:      Union[str, Any]          =   checkpoint[F"""blocks.{i}.convs1.{j}.1.bias"""]

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

		lowerCamelCase					:      Any          =   checkpoint['output_conv.1.weight_g']
		lowerCamelCase					:      Tuple          =   checkpoint['output_conv.1.weight_v']
		lowerCamelCase					:      int          =   checkpoint['output_conv.1.bias']

		hf_model.remove_weight_norm()
@torch.no_grad()
def 		UpperCAmelCase (		a_,						a_,						a_,						a_=None,						a_=None,						):





		'''simple docstring'''


		if config_path is not None:
				lowerCamelCase					:      str          =   SpeechTaHifiGanConfig.from_pretrained(a_      )
		else:
				lowerCamelCase					:      Dict          =   SpeechTaHifiGanConfig()

		lowerCamelCase					:      int          =   SpeechTaHifiGan(a_      )

		lowerCamelCase					:      Optional[Any]          =   torch.load(a_      )
		load_weights(orig_checkpoint['model']['generator'],						a_,						a_      )

		lowerCamelCase					:      Tuple          =   np.load(a_      )
		lowerCamelCase					:      str          =   stats[0].reshape(-1      )
		lowerCamelCase					:      Optional[int]          =   stats[1].reshape(-1      )
		lowerCamelCase					:      Dict          =   torch.from_numpy(a_      ).float()
		lowerCamelCase					:      Optional[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__":
					_A						=			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.'
					)

					_A						=			parser.parse_args()
					convert_hifigan_checkpoint(
					    args.checkpoint_path,
					    args.stats_path,
					    args.pytorch_dump_folder_path,
					    args.config_path,
					    args.push_to_hub,
					)






