


"""simple docstring"""







import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
		a__       :							List[Any]        =  '''pt'''
elif is_tf_available():
		a__       :							int        =  '''tf'''
else:
		a__       :							Optional[Any]        =  '''jax'''




class 				UpperCamelCase_			(				UpperCamelCase    ,						unittest.TestCase):

	"""simple docstring"""





	snake_case__       :						Tuple					      =    PerceiverTokenizer
	snake_case__       :						List[Any]					      =    False




	def 						UpperCAmelCase_   (				self    :       Union[str, Any]     ) ->			int:
						super().setUp()
						__SCREAMING_SNAKE_CASE		     =       PerceiverTokenizer()
						tokenizer.save_pretrained(self.tmpdirname     )




	@cached_property
	def 						UpperCAmelCase_   (				self    :       str     ) ->			Optional[Any]:
						return PerceiverTokenizer.from_pretrained("deepmind/language-perceiver"     )




	def 						UpperCAmelCase_   (				self    :       Optional[Any]    ,    **UpperCAmelCase__    :       Dict     ) ->			PerceiverTokenizer:
						return self.tokenizer_class.from_pretrained(self.tmpdirname    ,    **UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       Any    ,    UpperCAmelCase__    :       Any    ,    UpperCAmelCase__    :       Optional[Any]=False    ,    UpperCAmelCase__    :       Optional[int]=2_0    ,    UpperCAmelCase__    :       List[Any]=5     ) ->			Tuple[str, list]:
						# XXX The default common tokenizer tests assume that every ID is decodable on its own.
						# This assumption is invalid for Perceiver because single bytes might not be
						# valid utf-8 (byte 128 for instance).
						# Here we're overriding the smallest possible method to provide
						# a clean sequence without making the same assumption.

						__SCREAMING_SNAKE_CASE		     =       []
						for i in range(len(UpperCAmelCase__     )     ):
											try:
																__SCREAMING_SNAKE_CASE		     =       tokenizer.decode([i]    ,    clean_up_tokenization_spaces=UpperCAmelCase__     )
											except UnicodeDecodeError:
																pass
											toks.append((i, tok)     )

						__SCREAMING_SNAKE_CASE		     =       list(filter(lambda UpperCAmelCase__     : re.match(R"^[ a-zA-Z]+$"    ,    t[1]     )    ,    UpperCAmelCase__     )     )
						__SCREAMING_SNAKE_CASE		     =       list(filter(lambda UpperCAmelCase__     : [t[0]] == tokenizer.encode(t[1]    ,    add_special_tokens=UpperCAmelCase__     )    ,    UpperCAmelCase__     )     )
						if max_length is not None and len(UpperCAmelCase__     ) > max_length:
											__SCREAMING_SNAKE_CASE		     =       toks[:max_length]
						if min_length is not None and len(UpperCAmelCase__     ) < min_length and len(UpperCAmelCase__     ) > 0:
											while len(UpperCAmelCase__     ) < min_length:
																__SCREAMING_SNAKE_CASE		     =       toks + toks
        # toks_str = [t[1] for t in toks]
						__SCREAMING_SNAKE_CASE		     =       [t[0] for t in toks]

						# Ensure consistency
						__SCREAMING_SNAKE_CASE		     =       tokenizer.decode(UpperCAmelCase__    ,    clean_up_tokenization_spaces=UpperCAmelCase__     )
						if " " not in output_txt and len(UpperCAmelCase__     ) > 1:
											__SCREAMING_SNAKE_CASE		     =       (
											    tokenizer.decode([toks_ids[0]]    ,    clean_up_tokenization_spaces=UpperCAmelCase__     )
											    + " "
											    + tokenizer.decode(toks_ids[1:]    ,    clean_up_tokenization_spaces=UpperCAmelCase__     )
											)
						if with_prefix_space:
											__SCREAMING_SNAKE_CASE		     =       " " + output_txt
						__SCREAMING_SNAKE_CASE		     =       tokenizer.encode(UpperCAmelCase__    ,    add_special_tokens=UpperCAmelCase__     )
						return output_txt, output_ids




	def 						UpperCAmelCase_   (				self    :       Any     ) ->			Optional[int]:
						__SCREAMING_SNAKE_CASE		     =       self.perceiver_tokenizer
						__SCREAMING_SNAKE_CASE		     =       "Unicode €."
						__SCREAMING_SNAKE_CASE		     =       tokenizer(UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       [4, 9_1, 1_1_6, 1_1_1, 1_0_5, 1_1_7, 1_0_6, 1_0_7, 3_8, 2_3_2, 1_3_6, 1_7_8, 5_2, 5]
						self.assertEqual(encoded["input_ids"]    ,    UpperCAmelCase__     )

						# decoding
						__SCREAMING_SNAKE_CASE		     =       tokenizer.decode(UpperCAmelCase__     )
						self.assertEqual(UpperCAmelCase__    ,    "[CLS]Unicode €.[SEP]"     )

						__SCREAMING_SNAKE_CASE		     =       tokenizer("e è é ê ë"     )
						__SCREAMING_SNAKE_CASE		     =       [4, 1_0_7, 3_8, 2_0_1, 1_7_4, 3_8, 2_0_1, 1_7_5, 3_8, 2_0_1, 1_7_6, 3_8, 2_0_1, 1_7_7, 5]
						self.assertEqual(encoded["input_ids"]    ,    UpperCAmelCase__     )
						# decoding
						__SCREAMING_SNAKE_CASE		     =       tokenizer.decode(UpperCAmelCase__     )
						self.assertEqual(UpperCAmelCase__    ,    "[CLS]e è é ê ë[SEP]"     )

						# encode/decode, but with `encode` instead of `__call__`
						self.assertEqual(tokenizer.decode(tokenizer.encode("e è é ê ë"     )     )    ,    "[CLS]e è é ê ë[SEP]"     )




	def 						UpperCAmelCase_   (				self    :       Any     ) ->			Any:
						__SCREAMING_SNAKE_CASE		     =       self.perceiver_tokenizer
						__SCREAMING_SNAKE_CASE		     =       ["A long paragraph for summarization.", "Another paragraph for summarization."]
						# fmt: off
						__SCREAMING_SNAKE_CASE		     =       [4, 7_1, 3_8, 1_1_4, 1_1_7, 1_1_6, 1_0_9, 3_8, 1_1_8, 1_0_3, 1_2_0, 1_0_3, 1_0_9, 1_2_0, 1_0_3, 1_1_8, 1_1_0, 3_8, 1_0_8, 1_1_7, 1_2_0, 3_8, 1_2_1, 1_2_3, 1_1_5, 1_1_5, 1_0_3, 1_2_0, 1_1_1, 1_2_8, 1_0_3, 1_2_2, 1_1_1, 1_1_7, 1_1_6, 5_2, 5, 0]
						# fmt: on
						__SCREAMING_SNAKE_CASE		     =       tokenizer(UpperCAmelCase__    ,    padding=UpperCAmelCase__    ,    return_tensors=UpperCAmelCase__     )
						self.assertIsInstance(UpperCAmelCase__    ,    UpperCAmelCase__     )

						if FRAMEWORK != "jax":
											__SCREAMING_SNAKE_CASE		     =       list(batch.input_ids.numpy()[0]     )
						else:
											__SCREAMING_SNAKE_CASE		     =       list(batch.input_ids.tolist()[0]     )

						self.assertListEqual(UpperCAmelCase__    ,    UpperCAmelCase__     )

						self.assertEqual((2, 3_8)    ,    batch.input_ids.shape     )
						self.assertEqual((2, 3_8)    ,    batch.attention_mask.shape     )




	def 						UpperCAmelCase_   (				self    :       Optional[int]     ) ->			List[Any]:
						__SCREAMING_SNAKE_CASE		     =       self.perceiver_tokenizer
						__SCREAMING_SNAKE_CASE		     =       ["A long paragraph for summarization.", "Another paragraph for summarization."]
						__SCREAMING_SNAKE_CASE		     =       tokenizer(UpperCAmelCase__    ,    padding=UpperCAmelCase__    ,    return_tensors=UpperCAmelCase__     )
						# check if input_ids are returned and no decoder_input_ids
						self.assertIn("input_ids"    ,    UpperCAmelCase__     )
						self.assertIn("attention_mask"    ,    UpperCAmelCase__     )
						self.assertNotIn("decoder_input_ids"    ,    UpperCAmelCase__     )
						self.assertNotIn("decoder_attention_mask"    ,    UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       List[Any]     ) ->			Union[str, Any]:
						__SCREAMING_SNAKE_CASE		     =       self.perceiver_tokenizer
						__SCREAMING_SNAKE_CASE		     =       [
						    "Summary of the text.",
						    "Another summary.",
						]
						__SCREAMING_SNAKE_CASE		     =       tokenizer(
						    text_target=UpperCAmelCase__    ,    max_length=3_2    ,    padding="max_length"    ,    truncation=UpperCAmelCase__    ,    return_tensors=UpperCAmelCase__     )
						self.assertEqual(3_2    ,    targets["input_ids"].shape[1]     )




	def 						UpperCAmelCase_   (				self    :       Any     ) ->			Any:
						# safety check on max_len default value so we are sure the test works
						__SCREAMING_SNAKE_CASE		     =       self.get_tokenizers()
						for tokenizer in tokenizers:
											with self.subTest(F"""{tokenizer.__class__.__name__}"""     ):
																self.assertNotEqual(tokenizer.model_max_length    ,    4_2     )

        # Now let's start the test
						__SCREAMING_SNAKE_CASE		     =       self.get_tokenizers()
						for tokenizer in tokenizers:
											with self.subTest(F"""{tokenizer.__class__.__name__}"""     ):
																# Isolate this from the other tests because we save additional tokens/etc
																__SCREAMING_SNAKE_CASE		     =       tempfile.mkdtemp()

																__SCREAMING_SNAKE_CASE		     =       " He is very happy, UNwant\u00E9d,running"
																__SCREAMING_SNAKE_CASE		     =       tokenizer.encode(UpperCAmelCase__    ,    add_special_tokens=UpperCAmelCase__     )
																tokenizer.save_pretrained(UpperCAmelCase__     )

																__SCREAMING_SNAKE_CASE		     =       tokenizer.__class__.from_pretrained(UpperCAmelCase__     )
																__SCREAMING_SNAKE_CASE		     =       after_tokenizer.encode(UpperCAmelCase__    ,    add_special_tokens=UpperCAmelCase__     )
																self.assertListEqual(UpperCAmelCase__    ,    UpperCAmelCase__     )

																shutil.rmtree(UpperCAmelCase__     )

						__SCREAMING_SNAKE_CASE		     =       self.get_tokenizers(model_max_length=4_2     )
						for tokenizer in tokenizers:
											with self.subTest(F"""{tokenizer.__class__.__name__}"""     ):
																# Isolate this from the other tests because we save additional tokens/etc
																__SCREAMING_SNAKE_CASE		     =       tempfile.mkdtemp()

																__SCREAMING_SNAKE_CASE		     =       " He is very happy, UNwant\u00E9d,running"
																tokenizer.add_tokens(["bim", "bambam"]     )
																__SCREAMING_SNAKE_CASE		     =       tokenizer.additional_special_tokens
																additional_special_tokens.append("new_additional_special_token"     )
																tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens}     )
																__SCREAMING_SNAKE_CASE		     =       tokenizer.encode(UpperCAmelCase__    ,    add_special_tokens=UpperCAmelCase__     )
																tokenizer.save_pretrained(UpperCAmelCase__     )

																__SCREAMING_SNAKE_CASE		     =       tokenizer.__class__.from_pretrained(UpperCAmelCase__     )
																__SCREAMING_SNAKE_CASE		     =       after_tokenizer.encode(UpperCAmelCase__    ,    add_special_tokens=UpperCAmelCase__     )
																self.assertListEqual(UpperCAmelCase__    ,    UpperCAmelCase__     )
																self.assertIn("new_additional_special_token"    ,    after_tokenizer.additional_special_tokens     )
																self.assertEqual(after_tokenizer.model_max_length    ,    4_2     )

																__SCREAMING_SNAKE_CASE		     =       tokenizer.__class__.from_pretrained(UpperCAmelCase__    ,    model_max_length=4_3     )
																self.assertEqual(tokenizer.model_max_length    ,    4_3     )

																shutil.rmtree(UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       Optional[int]     ) ->			Union[str, Any]:
						__SCREAMING_SNAKE_CASE		     =       []
						if self.test_slow_tokenizer:
											tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())     )

						if self.test_rust_tokenizer:
											tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())     )

						for tokenizer_class, tokenizer_utils in tokenizer_list:
											with tempfile.TemporaryDirectory() as tmp_dir:
																tokenizer_utils.save_pretrained(UpperCAmelCase__     )

																with open(os.path.join(UpperCAmelCase__    ,    "special_tokens_map.json"     )    ,    encoding="utf-8"     ) as json_file:
																					__SCREAMING_SNAKE_CASE		     =       json.load(UpperCAmelCase__     )

																with open(os.path.join(UpperCAmelCase__    ,    "tokenizer_config.json"     )    ,    encoding="utf-8"     ) as json_file:
																					__SCREAMING_SNAKE_CASE		     =       json.load(UpperCAmelCase__     )

																__SCREAMING_SNAKE_CASE		     =       [F"""<extra_id_{i}>""" for i in range(1_2_5     )]

																__SCREAMING_SNAKE_CASE		     =       added_tokens_extra_ids + [
																    "an_additional_special_token"
																]
																__SCREAMING_SNAKE_CASE		     =       added_tokens_extra_ids + [
																    "an_additional_special_token"
																]

																with open(os.path.join(UpperCAmelCase__    ,    "special_tokens_map.json"     )    ,    "w"    ,    encoding="utf-8"     ) as outfile:
																					json.dump(UpperCAmelCase__    ,    UpperCAmelCase__     )
																with open(os.path.join(UpperCAmelCase__    ,    "tokenizer_config.json"     )    ,    "w"    ,    encoding="utf-8"     ) as outfile:
																					json.dump(UpperCAmelCase__    ,    UpperCAmelCase__     )

																# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
																# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
																# "special_tokens_map.json" files
																__SCREAMING_SNAKE_CASE		     =       tokenizer_class.from_pretrained(
																    UpperCAmelCase__    ,    )
																self.assertIn(
																    "an_additional_special_token"    ,    tokenizer_without_change_in_init.additional_special_tokens     )
																self.assertEqual(
																    ["an_additional_special_token"]    ,    tokenizer_without_change_in_init.convert_ids_to_tokens(
																        tokenizer_without_change_in_init.convert_tokens_to_ids(["an_additional_special_token"]     )     )    ,    )

																# Now we test that we can change the value of additional_special_tokens in the from_pretrained
																__SCREAMING_SNAKE_CASE		     =       added_tokens_extra_ids + [AddedToken("a_new_additional_special_token"    ,    lstrip=UpperCAmelCase__     )]
																__SCREAMING_SNAKE_CASE		     =       tokenizer_class.from_pretrained(
																    UpperCAmelCase__    ,    additional_special_tokens=UpperCAmelCase__    ,    )

																self.assertIn("a_new_additional_special_token"    ,    tokenizer.additional_special_tokens     )
																self.assertEqual(
																    ["a_new_additional_special_token"]    ,    tokenizer.convert_ids_to_tokens(
																        tokenizer.convert_tokens_to_ids(["a_new_additional_special_token"]     )     )    ,    )




	def 						UpperCAmelCase_   (				self    :       int     ) ->			Optional[int]:
						__SCREAMING_SNAKE_CASE		     =       self.perceiver_tokenizer
						self.assertEqual(tokenizer.decode([1_7_8]     )    ,    "�"     )




	def 						UpperCAmelCase_   (				self    :       Tuple     ) ->			Any:
						pass




	def 						UpperCAmelCase_   (				self    :       Any     ) ->			List[Any]:
						pass




	def 						UpperCAmelCase_   (				self    :       int     ) ->			Tuple:
						pass




	def 						UpperCAmelCase_   (				self    :       Union[str, Any]     ) ->			int:
						pass




	def 						UpperCAmelCase_   (				self    :       Any     ) ->			str:
						# The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
						# strings and special added tokens as tokens
						__SCREAMING_SNAKE_CASE		     =       self.get_tokenizers(fast=UpperCAmelCase__    ,    do_lower_case=UpperCAmelCase__     )
						for tokenizer in tokenizers:
											with self.subTest(F"""{tokenizer.__class__.__name__}"""     ):
																__SCREAMING_SNAKE_CASE		     =       ["[CLS]", "t", "h", "i", "s", " ", "i", "s", " ", "a", " ", "t", "e", "s", "t", "[SEP]"]
																__SCREAMING_SNAKE_CASE		     =       tokenizer.convert_tokens_to_string(UpperCAmelCase__     )

																self.assertIsInstance(UpperCAmelCase__    ,    UpperCAmelCase__     )




"""simple docstring"""







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()
a__       :							Union[str, Any]        =  logging.get_logger('''transformers.models.encodec''')

a__       :							Optional[Any]        =  {
    '''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''',
}
a__       :							int        =  {
    '''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''',
}
a__       :							List[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''',
}
a__       :							List[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''',
}
a__       :							Union[str, Any]        =  {
    '''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''',
}
a__       :							List[Any]        =  {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
a__       :							Any        =  {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
a__       :							List[Any]        =  []
a__       :							str        =  []




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


					for attribute in key.split("."     ):
										__SCREAMING_SNAKE_CASE		     =       getattr(lowerCAmelCase_						,		lowerCAmelCase_     )

					if weight_type is not None:
										__SCREAMING_SNAKE_CASE		     =       getattr(lowerCAmelCase_						,		lowerCAmelCase_     ).shape
					else:
										__SCREAMING_SNAKE_CASE		     =       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":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "weight_g":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "weight_v":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "bias":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "running_mean":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "running_var":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "num_batches_tracked":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "weight_ih_l0":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "weight_hh_l0":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "bias_ih_l0":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "bias_hh_l0":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "weight_ih_l1":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "weight_hh_l1":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "bias_ih_l1":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "bias_hh_l1":
										__SCREAMING_SNAKE_CASE		     =       value
					else:
										__SCREAMING_SNAKE_CASE		     =       value

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




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


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




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       []

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

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

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

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

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

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




@torch.no_grad()
def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_=None						,		lowerCAmelCase_=None						,		):




					'''simple docstring'''


					if config_path is not None:
										__SCREAMING_SNAKE_CASE		     =       EncodecConfig.from_pretrained(lowerCAmelCase_     )
					else:
										__SCREAMING_SNAKE_CASE		     =       EncodecConfig()

					if model_name == "encodec_24khz":
										pass  # config is already correct
					elif model_name == "encodec_32khz":
										__SCREAMING_SNAKE_CASE		     =       [8, 5, 4, 4]
										__SCREAMING_SNAKE_CASE		     =       [2.2]
										__SCREAMING_SNAKE_CASE		     =       64
										__SCREAMING_SNAKE_CASE		     =       3_2000
										__SCREAMING_SNAKE_CASE		     =       2048
										__SCREAMING_SNAKE_CASE		     =       False
										__SCREAMING_SNAKE_CASE		     =       False
										__SCREAMING_SNAKE_CASE		     =       False
					elif model_name == "encodec_48khz":
										__SCREAMING_SNAKE_CASE		     =       [8, 5, 4, 2]
										__SCREAMING_SNAKE_CASE		     =       [3.0, 6.0, 12.0, 24.0]
										__SCREAMING_SNAKE_CASE		     =       4_8000
										__SCREAMING_SNAKE_CASE		     =       2
										__SCREAMING_SNAKE_CASE		     =       False
										__SCREAMING_SNAKE_CASE		     =       "time_group_norm"
										__SCREAMING_SNAKE_CASE		     =       True
										__SCREAMING_SNAKE_CASE		     =       1.0
										__SCREAMING_SNAKE_CASE		     =       0.01
					else:
										raise ValueError(f"""Unknown model name: {model_name}"""     )

					__SCREAMING_SNAKE_CASE		     =       EncodecModel(lowerCAmelCase_     )

					__SCREAMING_SNAKE_CASE		     =       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(lowerCAmelCase_     )

					__SCREAMING_SNAKE_CASE		     =       torch.load(lowerCAmelCase_     )
					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
										__SCREAMING_SNAKE_CASE		     =       original_checkpoint["best_state"]
					recursively_load_weights(lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     )
					model.save_pretrained(lowerCAmelCase_     )

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


if __name__ == "__main__":
		a__       :							List[str]        =  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.'''
		)

		a__       :							Optional[int]        =  parser.parse_args()
		convert_checkpoint(
		    args.model,
		    args.checkpoint_path,
		    args.pytorch_dump_folder_path,
		    args.config_path,
		    args.push_to_hub,
		)
