

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 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						int:  # picklable for multiprocessing



    """simple docstring"""






    return x.sum()




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						str:  # picklable for multiprocessing



    """simple docstring"""






    return i + 1






@dataclass
class 						__magic_name__					:
     UpperCamelCase_	:int
     UpperCamelCase_	:str






class 						__magic_name__					(							snake_case				):






     def 	UpperCAmelCase_				(				self	)->     Optional[int]:
         UpperCamelCase_			  =						{}
         UpperCamelCase_			  =						[]
         UpperCamelCase_			  =						1
         UpperCamelCase_			  =						[1, 2]
         UpperCamelCase_			  =						{"a": 1, "b": 2}
         UpperCamelCase_			  =						{"a": [1, 2], "b": [3, 4]}
         UpperCamelCase_			  =						{"a": {"1": 1}, "b": 2}
         UpperCamelCase_			  =						{"a": 1, "b": 2, "c": 3, "d": 4}
         UpperCamelCase_			  =						{}
         UpperCamelCase_			  =						[]
         UpperCamelCase_			  =						2
         UpperCamelCase_			  =						[2, 3]
         UpperCamelCase_			  =						{"a": 2, "b": 3}
         UpperCamelCase_			  =						{"a": [2, 3], "b": [4, 5]}
         UpperCamelCase_			  =						{"a": {"1": 2}, "b": 3}
         UpperCamelCase_			  =						{"a": 2, "b": 3, "c": 4, "d": 5}
         self.assertEqual(map_nested(_lowercase		,						_lowercase	)		,						_lowercase	)
         self.assertEqual(map_nested(_lowercase		,						_lowercase	)		,						_lowercase	)
         self.assertEqual(map_nested(_lowercase		,						_lowercase	)		,						_lowercase	)
         self.assertEqual(map_nested(_lowercase		,						_lowercase	)		,						_lowercase	)
         self.assertEqual(map_nested(_lowercase		,						_lowercase	)		,						_lowercase	)
         self.assertEqual(map_nested(_lowercase		,						_lowercase	)		,						_lowercase	)
         self.assertEqual(map_nested(_lowercase		,						_lowercase	)		,						_lowercase	)
         self.assertEqual(map_nested(_lowercase		,						_lowercase	)		,						_lowercase	)

         UpperCamelCase_			  =						2
         self.assertEqual(map_nested(_lowercase		,						_lowercase		,						num_proc=_lowercase	)		,						_lowercase	)
         self.assertEqual(map_nested(_lowercase		,						_lowercase		,						num_proc=_lowercase	)		,						_lowercase	)
         self.assertEqual(map_nested(_lowercase		,						_lowercase		,						num_proc=_lowercase	)		,						_lowercase	)
         self.assertEqual(map_nested(_lowercase		,						_lowercase		,						num_proc=_lowercase	)		,						_lowercase	)
         self.assertEqual(map_nested(_lowercase		,						_lowercase		,						num_proc=_lowercase	)		,						_lowercase	)
         self.assertEqual(map_nested(_lowercase		,						_lowercase		,						num_proc=_lowercase	)		,						_lowercase	)
         self.assertEqual(map_nested(_lowercase		,						_lowercase		,						num_proc=_lowercase	)		,						_lowercase	)
         self.assertEqual(map_nested(_lowercase		,						_lowercase		,						num_proc=_lowercase	)		,						_lowercase	)

         UpperCamelCase_			  =						{"a": np.eye(2	), "b": np.zeros(3	), "c": np.ones(2	)}
         UpperCamelCase_			  =						{"a": 2, "b": 0, "c": 2}
         UpperCamelCase_			  =						{
             "a": np.eye(2	).astype(_lowercase	),
             "b": np.zeros(3	).astype(_lowercase	),
             "c": np.ones(2	).astype(_lowercase	),
         }
         self.assertEqual(map_nested(_lowercase		,						_lowercase		,						map_numpy=_lowercase	)		,						_lowercase	)
         self.assertEqual(
             {k: v.tolist() for k, v in map_nested(_lowercase		,						_lowercase		,						map_numpy=_lowercase	).items()}		,						{k: v.tolist() for k, v in expected_map_nested_sna_int.items()}		,						)
         self.assertEqual(map_nested(_lowercase		,						_lowercase		,						map_numpy=_lowercase		,						num_proc=_lowercase	)		,						_lowercase	)
         self.assertEqual(
             {k: v.tolist() for k, v in map_nested(_lowercase		,						_lowercase		,						map_numpy=_lowercase		,						num_proc=_lowercase	).items()}		,						{k: v.tolist() for k, v in expected_map_nested_sna_int.items()}		,						)
         with self.assertRaises(_lowercase	):  # can't pickle a local lambda
             map_nested(lambda _lowercase	: x + 1		,						_lowercase		,						num_proc=_lowercase	)






     def 	UpperCAmelCase_				(				self	)->     List[str]:
         UpperCamelCase_			  =						{"a": 1, "b": 2}
         UpperCamelCase_			  =						{"a": 3, "b": 4}
         UpperCamelCase_			  =						{"a": 5, "b": 6}
         UpperCamelCase_			  =						sorted([("a", (1, 3, 5)), ("b", (2, 4, 6))]	)
         self.assertEqual(sorted(zip_dict(_lowercase		,						_lowercase		,						_lowercase	)	)		,						_lowercase	)






     def 	UpperCAmelCase_				(				self	)->     int:






         class 						__magic_name__					:
              UpperCamelCase_	:List[str]										=      """bar"""
         UpperCamelCase_			  =						Foo()
         self.assertEqual(foo.my_attr		,						"bar"	)
         with temporary_assignment(_lowercase		,						"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),
        (1_6, 1_6, 1_6),
        (1_6, 1_7, 1_6),
        (1_7, 1_6, 1_6),
    ]			,						)
def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						Tuple:



    """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:
        UpperCamelCase_			  =						{f"{i}": i for i in range(SCREAMING_SNAKE_CASE_			)}
        UpperCamelCase_			  =						map_nested(lambda SCREAMING_SNAKE_CASE_			: x + 1_0			,						SCREAMING_SNAKE_CASE_			,						num_proc=SCREAMING_SNAKE_CASE_			,						parallel_min_length=1_6			)
        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 						__magic_name__					(							snake_case				):






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

         UpperCamelCase_			  =						layers.Dense(2	)

         def gen_random_output():
             UpperCamelCase_			  =						tf.random.uniform((1, 3)	)
             return model(_lowercase	).numpy()

         with temp_seed(42		,						set_tensorflow=_lowercase	):
             UpperCamelCase_			  =						gen_random_output()
         with temp_seed(42		,						set_tensorflow=_lowercase	):
             UpperCamelCase_			  =						gen_random_output()
         UpperCamelCase_			  =						gen_random_output()

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






     @require_torch
     def 	UpperCAmelCase_				(				self	)->     int:
         import torch

         def gen_random_output():
             UpperCamelCase_			  =						torch.nn.Linear(3		,						2	)
             UpperCamelCase_			  =						torch.rand(1		,						3	)
             return model(_lowercase	).detach().numpy()

         with temp_seed(42		,						set_pytorch=_lowercase	):
             UpperCamelCase_			  =						gen_random_output()
         with temp_seed(42		,						set_pytorch=_lowercase	):
             UpperCamelCase_			  =						gen_random_output()
         UpperCamelCase_			  =						gen_random_output()

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






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

         with temp_seed(42	):
             UpperCamelCase_			  =						gen_random_output()
         with temp_seed(42	):
             UpperCamelCase_			  =						gen_random_output()
         UpperCamelCase_			  =						gen_random_output()

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




@pytest.mark.parametrize("input_data"			,						[{}]			)
def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						Union[str, Any]:



    """simple docstring"""






    UpperCamelCase_			  =						NestedDataStructure(SCREAMING_SNAKE_CASE_			).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 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						int:



    """simple docstring"""






    UpperCamelCase_			  =						NestedDataStructure(SCREAMING_SNAKE_CASE_			).flatten()
    assert output == expected_output




def 			lowerCAmelCase(      )->						Optional[Any]:



    """simple docstring"""






    UpperCamelCase_			  =						A(x=1			,						y="foobar"			)
    UpperCamelCase_			  =						{"x": 1, "y": "foobar"}
    assert asdict(SCREAMING_SNAKE_CASE_			) == expected_output

    UpperCamelCase_			  =						{"a": {"b": A(x=1_0			,						y="foo"			)}, "c": [A(x=2_0			,						y="bar"			)]}
    UpperCamelCase_			  =						{"a": {"b": {"x": 1_0, "y": "foo"}}, "c": [{"x": 2_0, "y": "bar"}]}
    assert asdict(SCREAMING_SNAKE_CASE_			) == expected_output

    with pytest.raises(SCREAMING_SNAKE_CASE_			):
        asdict([1, A(x=1_0			,						y="foo"			)]			)




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						Optional[Any]:



    """simple docstring"""






    return text.split()




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						Optional[int]:



    """simple docstring"""






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




def 			lowerCAmelCase(      )->						str:



    """simple docstring"""






    with Pool(2			) as pool:
        UpperCamelCase_			  =						list(iflatmap_unordered(SCREAMING_SNAKE_CASE_			,						_split_text			,						kwargs_iterable=[{"text": "hello there"}] * 1_0			)			)
        assert out.count("hello"			) == 1_0
        assert out.count("there"			) == 1_0
        assert len(SCREAMING_SNAKE_CASE_			) == 2_0

    # check multiprocess from pathos (uses dill for pickling)
    with multiprocess.Pool(2			) as pool:
        UpperCamelCase_			  =						list(iflatmap_unordered(SCREAMING_SNAKE_CASE_			,						_split_text			,						kwargs_iterable=[{"text": "hello there"}] * 1_0			)			)
        assert out.count("hello"			) == 1_0
        assert out.count("there"			) == 1_0
        assert len(SCREAMING_SNAKE_CASE_			) == 2_0

    # check that we get items as fast as possible
    with Pool(2			) as pool:
        UpperCamelCase_			  =						[]
        for yield_time, content in iflatmap_unordered(
            SCREAMING_SNAKE_CASE_			,						_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(SCREAMING_SNAKE_CASE_			)
        assert out.count("a"			) == 2
        assert out.count("b"			) == 2
        assert len(SCREAMING_SNAKE_CASE_			) == 4



import argparse

import torch

from transformers import (
    EncodecConfig,
    EncodecFeatureExtractor,
    EncodecModel,
    logging,
)


# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE		:List[str]						=     logging.get_logger("""transformers.models.encodec""")

SCREAMING_SNAKE_CASE		:Tuple						=     {
    """quantizer.vq.layers.*._codebook.inited""": """quantizer.layers.*.codebook.inited""",
    """quantizer.vq.layers.*._codebook.cluster_size""": """quantizer.layers.*.codebook.cluster_size""",
    """quantizer.vq.layers.*._codebook.embed""": """quantizer.layers.*.codebook.embed""",
    """quantizer.vq.layers.*._codebook.embed_avg""": """quantizer.layers.*.codebook.embed_avg""",
}
SCREAMING_SNAKE_CASE		:List[str]						=     {
    """encoder.model.0.conv.conv""": """encoder.layers.0.conv""",
    """encoder.model.1.block.1.conv.conv""": """encoder.layers.1.block.1.conv""",
    """encoder.model.1.block.3.conv.conv""": """encoder.layers.1.block.3.conv""",
    """encoder.model.1.shortcut.conv.conv""": """encoder.layers.1.shortcut.conv""",
    """encoder.model.3.conv.conv""": """encoder.layers.3.conv""",
    """encoder.model.4.block.1.conv.conv""": """encoder.layers.4.block.1.conv""",
    """encoder.model.4.block.3.conv.conv""": """encoder.layers.4.block.3.conv""",
    """encoder.model.4.shortcut.conv.conv""": """encoder.layers.4.shortcut.conv""",
    """encoder.model.6.conv.conv""": """encoder.layers.6.conv""",
    """encoder.model.7.block.1.conv.conv""": """encoder.layers.7.block.1.conv""",
    """encoder.model.7.block.3.conv.conv""": """encoder.layers.7.block.3.conv""",
    """encoder.model.7.shortcut.conv.conv""": """encoder.layers.7.shortcut.conv""",
    """encoder.model.9.conv.conv""": """encoder.layers.9.conv""",
    """encoder.model.10.block.1.conv.conv""": """encoder.layers.10.block.1.conv""",
    """encoder.model.10.block.3.conv.conv""": """encoder.layers.10.block.3.conv""",
    """encoder.model.10.shortcut.conv.conv""": """encoder.layers.10.shortcut.conv""",
    """encoder.model.12.conv.conv""": """encoder.layers.12.conv""",
    """encoder.model.13.lstm""": """encoder.layers.13.lstm""",
    """encoder.model.15.conv.conv""": """encoder.layers.15.conv""",
}
SCREAMING_SNAKE_CASE		:Union[str, Any]						=     {
    """encoder.model.0.conv.norm""": """encoder.layers.0.norm""",
    """encoder.model.1.block.1.conv.norm""": """encoder.layers.1.block.1.norm""",
    """encoder.model.1.block.3.conv.norm""": """encoder.layers.1.block.3.norm""",
    """encoder.model.1.shortcut.conv.norm""": """encoder.layers.1.shortcut.norm""",
    """encoder.model.3.conv.norm""": """encoder.layers.3.norm""",
    """encoder.model.4.block.1.conv.norm""": """encoder.layers.4.block.1.norm""",
    """encoder.model.4.block.3.conv.norm""": """encoder.layers.4.block.3.norm""",
    """encoder.model.4.shortcut.conv.norm""": """encoder.layers.4.shortcut.norm""",
    """encoder.model.6.conv.norm""": """encoder.layers.6.norm""",
    """encoder.model.7.block.1.conv.norm""": """encoder.layers.7.block.1.norm""",
    """encoder.model.7.block.3.conv.norm""": """encoder.layers.7.block.3.norm""",
    """encoder.model.7.shortcut.conv.norm""": """encoder.layers.7.shortcut.norm""",
    """encoder.model.9.conv.norm""": """encoder.layers.9.norm""",
    """encoder.model.10.block.1.conv.norm""": """encoder.layers.10.block.1.norm""",
    """encoder.model.10.block.3.conv.norm""": """encoder.layers.10.block.3.norm""",
    """encoder.model.10.shortcut.conv.norm""": """encoder.layers.10.shortcut.norm""",
    """encoder.model.12.conv.norm""": """encoder.layers.12.norm""",
    """encoder.model.15.conv.norm""": """encoder.layers.15.norm""",
}
SCREAMING_SNAKE_CASE		:Optional[Any]						=     {
    """decoder.model.0.conv.conv""": """decoder.layers.0.conv""",
    """decoder.model.1.lstm""": """decoder.layers.1.lstm""",
    """decoder.model.3.convtr.convtr""": """decoder.layers.3.conv""",
    """decoder.model.4.block.1.conv.conv""": """decoder.layers.4.block.1.conv""",
    """decoder.model.4.block.3.conv.conv""": """decoder.layers.4.block.3.conv""",
    """decoder.model.4.shortcut.conv.conv""": """decoder.layers.4.shortcut.conv""",
    """decoder.model.6.convtr.convtr""": """decoder.layers.6.conv""",
    """decoder.model.7.block.1.conv.conv""": """decoder.layers.7.block.1.conv""",
    """decoder.model.7.block.3.conv.conv""": """decoder.layers.7.block.3.conv""",
    """decoder.model.7.shortcut.conv.conv""": """decoder.layers.7.shortcut.conv""",
    """decoder.model.9.convtr.convtr""": """decoder.layers.9.conv""",
    """decoder.model.10.block.1.conv.conv""": """decoder.layers.10.block.1.conv""",
    """decoder.model.10.block.3.conv.conv""": """decoder.layers.10.block.3.conv""",
    """decoder.model.10.shortcut.conv.conv""": """decoder.layers.10.shortcut.conv""",
    """decoder.model.12.convtr.convtr""": """decoder.layers.12.conv""",
    """decoder.model.13.block.1.conv.conv""": """decoder.layers.13.block.1.conv""",
    """decoder.model.13.block.3.conv.conv""": """decoder.layers.13.block.3.conv""",
    """decoder.model.13.shortcut.conv.conv""": """decoder.layers.13.shortcut.conv""",
    """decoder.model.15.conv.conv""": """decoder.layers.15.conv""",
}
SCREAMING_SNAKE_CASE		:Optional[int]						=     {
    """decoder.model.0.conv.norm""": """decoder.layers.0.norm""",
    """decoder.model.3.convtr.norm""": """decoder.layers.3.norm""",
    """decoder.model.4.block.1.conv.norm""": """decoder.layers.4.block.1.norm""",
    """decoder.model.4.block.3.conv.norm""": """decoder.layers.4.block.3.norm""",
    """decoder.model.4.shortcut.conv.norm""": """decoder.layers.4.shortcut.norm""",
    """decoder.model.6.convtr.norm""": """decoder.layers.6.norm""",
    """decoder.model.7.block.1.conv.norm""": """decoder.layers.7.block.1.norm""",
    """decoder.model.7.block.3.conv.norm""": """decoder.layers.7.block.3.norm""",
    """decoder.model.7.shortcut.conv.norm""": """decoder.layers.7.shortcut.norm""",
    """decoder.model.9.convtr.norm""": """decoder.layers.9.norm""",
    """decoder.model.10.block.1.conv.norm""": """decoder.layers.10.block.1.norm""",
    """decoder.model.10.block.3.conv.norm""": """decoder.layers.10.block.3.norm""",
    """decoder.model.10.shortcut.conv.norm""": """decoder.layers.10.shortcut.norm""",
    """decoder.model.12.convtr.norm""": """decoder.layers.12.norm""",
    """decoder.model.13.block.1.conv.norm""": """decoder.layers.13.block.1.norm""",
    """decoder.model.13.block.3.conv.norm""": """decoder.layers.13.block.3.norm""",
    """decoder.model.13.shortcut.conv.norm""": """decoder.layers.13.shortcut.norm""",
    """decoder.model.15.conv.norm""": """decoder.layers.15.norm""",
}
SCREAMING_SNAKE_CASE		:List[Any]						=     {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
SCREAMING_SNAKE_CASE		:int						=     {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
SCREAMING_SNAKE_CASE		:Dict						=     []
SCREAMING_SNAKE_CASE		:int						=     []




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						List[Any]:



    """simple docstring"""






    for attribute in key.split("."			):
        UpperCamelCase_			  =						getattr(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)

    if weight_type is not None:
        UpperCamelCase_			  =						getattr(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			).shape
    else:
        UpperCamelCase_			  =						hf_pointer.shape

    if hf_shape != value.shape:
        raise ValueError(
            f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
            f" {value.shape} for {full_name}"			)

    if weight_type == "weight":
        UpperCamelCase_			  =						value
    elif weight_type == "weight_g":
        UpperCamelCase_			  =						value
    elif weight_type == "weight_v":
        UpperCamelCase_			  =						value
    elif weight_type == "bias":
        UpperCamelCase_			  =						value
    elif weight_type == "running_mean":
        UpperCamelCase_			  =						value
    elif weight_type == "running_var":
        UpperCamelCase_			  =						value
    elif weight_type == "num_batches_tracked":
        UpperCamelCase_			  =						value
    elif weight_type == "weight_ih_l0":
        UpperCamelCase_			  =						value
    elif weight_type == "weight_hh_l0":
        UpperCamelCase_			  =						value
    elif weight_type == "bias_ih_l0":
        UpperCamelCase_			  =						value
    elif weight_type == "bias_hh_l0":
        UpperCamelCase_			  =						value
    elif weight_type == "weight_ih_l1":
        UpperCamelCase_			  =						value
    elif weight_type == "weight_hh_l1":
        UpperCamelCase_			  =						value
    elif weight_type == "bias_ih_l1":
        UpperCamelCase_			  =						value
    elif weight_type == "bias_hh_l1":
        UpperCamelCase_			  =						value
    else:
        UpperCamelCase_			  =						value

    logger.info(f"{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}."			)




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						int:



    """simple docstring"""






    for key in ignore_keys:
        if key.endswith(".*"			):
            if name.startswith(key[:-1]			):
                return True
        elif ".*." in key:
            UpperCamelCase_  ,       UpperCamelCase_			  =						key.split(".*."			)
            if prefix in name and suffix in name:
                return True
        elif key in name:
            return True
    return False




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						int:



    """simple docstring"""






    UpperCamelCase_			  =						[]

    if model_name == "encodec_24khz" or "encodec_32khz":
        UpperCamelCase_			  =						MAPPING_24K
    elif model_name == "encodec_48khz":
        UpperCamelCase_			  =						MAPPING_48K
    else:
        raise ValueError(f"Unsupported model: {model_name}"			)

    for name, value in orig_dict.items():
        if should_ignore(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			):
            logger.info(f"{name} was ignored"			)
            continue

        UpperCamelCase_			  =						False
        for key, mapped_key in MAPPING.items():
            if "*" in key:
                UpperCamelCase_  ,       UpperCamelCase_			  =						key.split(".*."			)
                if prefix in name and suffix in name:
                    UpperCamelCase_			  =						suffix

            if key in name:
                # HACK otherwise .embed gets initialized with .embed_avg too
                if key.endswith("embed"			) and name.endswith("embed_avg"			):
                    continue

                UpperCamelCase_			  =						True
                if "*" in mapped_key:
                    UpperCamelCase_			  =						name.split(SCREAMING_SNAKE_CASE_			)[0].split("."			)[-2]
                    UpperCamelCase_			  =						mapped_key.replace("*"			,						SCREAMING_SNAKE_CASE_			)
                if "weight_g" in name:
                    UpperCamelCase_			  =						"weight_g"
                elif "weight_v" in name:
                    UpperCamelCase_			  =						"weight_v"
                elif "weight_ih_l0" in name:
                    UpperCamelCase_			  =						"weight_ih_l0"
                elif "weight_hh_l0" in name:
                    UpperCamelCase_			  =						"weight_hh_l0"
                elif "bias_ih_l0" in name:
                    UpperCamelCase_			  =						"bias_ih_l0"
                elif "bias_hh_l0" in name:
                    UpperCamelCase_			  =						"bias_hh_l0"
                elif "weight_ih_l1" in name:
                    UpperCamelCase_			  =						"weight_ih_l1"
                elif "weight_hh_l1" in name:
                    UpperCamelCase_			  =						"weight_hh_l1"
                elif "bias_ih_l1" in name:
                    UpperCamelCase_			  =						"bias_ih_l1"
                elif "bias_hh_l1" in name:
                    UpperCamelCase_			  =						"bias_hh_l1"
                elif "bias" in name:
                    UpperCamelCase_			  =						"bias"
                elif "weight" in name:
                    UpperCamelCase_			  =						"weight"
                elif "running_mean" in name:
                    UpperCamelCase_			  =						"running_mean"
                elif "running_var" in name:
                    UpperCamelCase_			  =						"running_var"
                elif "num_batches_tracked" in name:
                    UpperCamelCase_			  =						"num_batches_tracked"
                else:
                    UpperCamelCase_			  =						None
                set_recursively(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)
            continue
        if not is_used:
            unused_weights.append(SCREAMING_SNAKE_CASE_			)

    logger.warning(f"Unused weights: {unused_weights}"			)




@torch.no_grad()
def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_=None			,						SCREAMING_SNAKE_CASE_=None			,						)->						Union[str, Any]:



    """simple docstring"""






    if config_path is not None:
        UpperCamelCase_			  =						EncodecConfig.from_pretrained(SCREAMING_SNAKE_CASE_			)
    else:
        UpperCamelCase_			  =						EncodecConfig()

    if model_name == "encodec_24khz":
        pass  # config is already correct
    elif model_name == "encodec_32khz":
        UpperCamelCase_			  =						[8, 5, 4, 4]
        UpperCamelCase_			  =						[2.2]
        UpperCamelCase_			  =						6_4
        UpperCamelCase_			  =						3_2_0_0_0
        UpperCamelCase_			  =						2_0_4_8
        UpperCamelCase_			  =						False
        UpperCamelCase_			  =						False
        UpperCamelCase_			  =						False
    elif model_name == "encodec_48khz":
        UpperCamelCase_			  =						[8, 5, 4, 2]
        UpperCamelCase_			  =						[3.0, 6.0, 12.0, 24.0]
        UpperCamelCase_			  =						4_8_0_0_0
        UpperCamelCase_			  =						2
        UpperCamelCase_			  =						False
        UpperCamelCase_			  =						"time_group_norm"
        UpperCamelCase_			  =						True
        UpperCamelCase_			  =						1.0
        UpperCamelCase_			  =						0.01
    else:
        raise ValueError(f"Unknown model name: {model_name}"			)

    UpperCamelCase_			  =						EncodecModel(SCREAMING_SNAKE_CASE_			)

    UpperCamelCase_			  =						EncodecFeatureExtractor(
        feature_size=config.audio_channels			,						sampling_rate=config.sampling_rate			,						chunk_length_s=config.chunk_length_s			,						overlap=config.overlap			,						)
    feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_			)

    UpperCamelCase_			  =						torch.load(SCREAMING_SNAKE_CASE_			)
    if "best_state" in original_checkpoint:
        # we might have a training state saved, in which case discard the yaml results and just retain the weights
        UpperCamelCase_			  =						original_checkpoint["best_state"]
    recursively_load_weights(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)
    model.save_pretrained(SCREAMING_SNAKE_CASE_			)

    if repo_id:
        print("Pushing to the hub..."			)
        feature_extractor.push_to_hub(SCREAMING_SNAKE_CASE_			)
        model.push_to_hub(SCREAMING_SNAKE_CASE_			)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE		:Tuple						=     argparse.ArgumentParser()
 parser.add_argument(
     """--model""",
     default="""encodec_24khz""",
     type=str,
     help="""The model to convert. Should be one of 'encodec_24khz', 'encodec_32khz', 'encodec_48khz'.""",
 )
 parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to original checkpoint""")
 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."""
 )

 SCREAMING_SNAKE_CASE		:Dict						=     parser.parse_args()
 convert_checkpoint(
     args.model,
     args.checkpoint_path,
     args.pytorch_dump_folder_path,
     args.config_path,
     args.push_to_hub,
 )
