'''simple docstring'''





import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE_:	Union[str, Any]    =get_tests_dir('fixtures/test_sentencepiece.model')

if is_torch_available():
    from transformers.models.mbart.modeling_mbart import shift_tokens_right

SCREAMING_SNAKE_CASE_:	Any    =25_00_04
SCREAMING_SNAKE_CASE_:	int    =25_00_20




@require_sentencepiece
@require_tokenizers
class 			__A (					UpperCamelCase__						,      unittest.TestCase      ):
    a__			:		Tuple			  =    MBartaaTokenizer
    a__			:		str			  =    MBartaaTokenizerFast
    a__			:		Union[str, Any]			  =    True
    a__			:		Dict			  =    True



    def    _lowercase		(self       :       int					):
         super().setUp()

         # We have a SentencePiece fixture for testing
         UpperCAmelCase_			       =  MBartaaTokenizer(__a  ,     src_lang="en_XX"  ,     tgt_lang="ro_RO"  ,     keep_accents=__a					)
         tokenizer.save_pretrained(self.tmpdirname					)



    def    _lowercase		(self       :       Tuple					):
         UpperCAmelCase_			       =  "<s>"
         UpperCAmelCase_			       =  0

         self.assertEqual(self.get_tokenizer()._convert_token_to_id(__a					)  ,     __a					)
         self.assertEqual(self.get_tokenizer()._convert_id_to_token(__a					)  ,     __a					)



    def    _lowercase		(self       :       Optional[Any]					):
         UpperCAmelCase_			       =  list(self.get_tokenizer().get_vocab().keys()					)

         self.assertEqual(vocab_keys[0]  ,     "<s>"					)
         self.assertEqual(vocab_keys[1]  ,     "<pad>"					)
         self.assertEqual(vocab_keys[-1]  ,     "<mask>"					)
         self.assertEqual(len(__a					)  ,     1054					)



    def    _lowercase		(self       :       Optional[int]					):
         self.assertEqual(self.get_tokenizer().vocab_size  ,     1054					)



    def    _lowercase		(self       :       int					):
         UpperCAmelCase_			       =  MBartaaTokenizer(__a  ,     src_lang="en_XX"  ,     tgt_lang="ro_RO"  ,     keep_accents=__a					)

         UpperCAmelCase_			       =  tokenizer.tokenize("This is a test"					)
         self.assertListEqual(__a  ,     ["▁This", "▁is", "▁a", "▁t", "est"]					)

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(__a					)  ,     [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]  ,     )

         UpperCAmelCase_			       =  tokenizer.tokenize("I was born in 92000, and this is falsé."					)
         self.assertListEqual(
             __a  ,     [SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "9", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "é", "."]  ,     )
         UpperCAmelCase_			       =  tokenizer.convert_tokens_to_ids(__a					)
         self.assertListEqual(
             __a  ,     [
                 value + tokenizer.fairseq_offset
                 for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
             ]  ,     )

         UpperCAmelCase_			       =  tokenizer.convert_ids_to_tokens(__a					)
         self.assertListEqual(
             __a  ,     [SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "<unk>", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "<unk>", "."]  ,     )



    @slow
    def    _lowercase		(self       :       Tuple					):
         # fmt: off
         UpperCAmelCase_			       =  {"input_ids": [[250004, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 5428, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 12399, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 122009, 115774, 23, 805, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [250004, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [250004, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
         # fmt: on

         self.tokenizer_integration_test_util(
             expected_encoding=__a  ,     model_name="facebook/mbart-large-50"  ,     revision="d3913889c59cd5c9e456b269c376325eabad57e2"  ,     )






    def    _lowercase		(self       :       List[str]					):
         if not self.test_slow_tokenizer:
              # as we don't have a slow version, we can't compare the outputs between slow and fast versions
              return

         UpperCAmelCase_			       =  (self.rust_tokenizer_class, "hf-internal-testing/tiny-random-mbart50", {})
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""					):
                   UpperCAmelCase_			       =  self.rust_tokenizer_class.from_pretrained(__a  ,     **__a					)
                   UpperCAmelCase_			       =  self.tokenizer_class.from_pretrained(__a  ,     **__a					)

                   UpperCAmelCase_			       =  tempfile.mkdtemp()

                   UpperCAmelCase_			       =  tokenizer_r.save_pretrained(__a					)
                   UpperCAmelCase_			       =  tokenizer_p.save_pretrained(__a					)

                   # Checks it save with the same files + the tokenizer.json file for the fast one
                   self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files					)					)
                   UpperCAmelCase_			       =  tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f					)
                   self.assertSequenceEqual(__a  ,     __a					)

                   # Checks everything loads correctly in the same way
                   UpperCAmelCase_			       =  tokenizer_r.from_pretrained(__a					)
                   UpperCAmelCase_			       =  tokenizer_p.from_pretrained(__a					)

                   # Check special tokens are set accordingly on Rust and Python
                   for key in tokenizer_pp.special_tokens_map:
                        self.assertTrue(hasattr(__a  ,     __a					)					)
                        # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
                        # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

                   shutil.rmtree(__a					)

                   # Save tokenizer rust, legacy_format=True
                   UpperCAmelCase_			       =  tempfile.mkdtemp()

                   UpperCAmelCase_			       =  tokenizer_r.save_pretrained(__a  ,     legacy_format=__a					)
                   UpperCAmelCase_			       =  tokenizer_p.save_pretrained(__a					)

                   # Checks it save with the same files
                   self.assertSequenceEqual(__a  ,     __a					)

                   # Checks everything loads correctly in the same way
                   UpperCAmelCase_			       =  tokenizer_r.from_pretrained(__a					)
                   UpperCAmelCase_			       =  tokenizer_p.from_pretrained(__a					)

                   # Check special tokens are set accordingly on Rust and Python
                   for key in tokenizer_pp.special_tokens_map:
                        self.assertTrue(hasattr(__a  ,     __a					)					)

                   shutil.rmtree(__a					)

                   # Save tokenizer rust, legacy_format=False
                   UpperCAmelCase_			       =  tempfile.mkdtemp()

                   UpperCAmelCase_			       =  tokenizer_r.save_pretrained(__a  ,     legacy_format=__a					)
                   UpperCAmelCase_			       =  tokenizer_p.save_pretrained(__a					)

                   # Checks it saved the tokenizer.json file
                   self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files					)					)

                   # Checks everything loads correctly in the same way
                   UpperCAmelCase_			       =  tokenizer_r.from_pretrained(__a					)
                   UpperCAmelCase_			       =  tokenizer_p.from_pretrained(__a					)

                   # Check special tokens are set accordingly on Rust and Python
                   for key in tokenizer_pp.special_tokens_map:
                        self.assertTrue(hasattr(__a  ,     __a					)					)

                   shutil.rmtree(__a					)




@require_torch
@require_sentencepiece
@require_tokenizers
class 			__A (					unittest.TestCase      ):
    a__			:		Optional[Any]			  =    """facebook/mbart-large-50-one-to-many-mmt"""
    a__			:		List[Any]			  =    [
        """ UN Chief Says There Is No Military Solution in Syria""",
        """ Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
    ]
    a__			:		Optional[int]			  =    [
        """Şeful ONU declară că nu există o soluţie militară în Siria""",
        """Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"""
        """ pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"""
        """ face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
    ]
    a__			:		Tuple			  =    [EN_CODE, 8_274, 127_873, 25_916, 7, 8_622, 2_071, 438, 67_485, 53, 187_895, 23, 51_712, 2]



    @classmethod
    def    _lowercase		(cls       :       str					):
         UpperCAmelCase_			       =  MBartaaTokenizer.from_pretrained(
             cls.checkpoint_name  ,     src_lang="en_XX"  ,     tgt_lang="ro_RO"					)
         UpperCAmelCase_			       =  1
         return cls



    def    _lowercase		(self       :       List[Any]					):
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"]  ,     250001					)
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"]  ,     250004					)
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"]  ,     250020					)
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["mr_IN"]  ,     250038					)



    def    _lowercase		(self       :       Any					):
         UpperCAmelCase_			       =  self.tokenizer.batch_encode_plus(self.src_text					).input_ids[0]
         self.assertListEqual(self.expected_src_tokens  ,     __a					)



    def    _lowercase		(self       :       List[Any]					):
         self.assertIn(__a  ,     self.tokenizer.all_special_ids					)
         UpperCAmelCase_			       =  [RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
         UpperCAmelCase_			       =  self.tokenizer.decode(__a  ,     skip_special_tokens=__a					)
         UpperCAmelCase_			       =  self.tokenizer.decode(generated_ids[1:]  ,     skip_special_tokens=__a					)
         self.assertEqual(__a  ,     __a					)
         self.assertNotIn(self.tokenizer.eos_token  ,     __a					)



    def    _lowercase		(self       :       Any					):
         UpperCAmelCase_			       =  ["this is gunna be a long sentence " * 20]
         assert isinstance(src_text[0]  ,     __a					)
         UpperCAmelCase_			       =  10
         UpperCAmelCase_			       =  self.tokenizer(__a  ,     max_length=__a  ,     truncation=__a					).input_ids[0]
         self.assertEqual(ids[0]  ,     __a					)
         self.assertEqual(ids[-1]  ,     2					)
         self.assertEqual(len(__a					)  ,     __a					)



    def    _lowercase		(self       :       Optional[Any]					):
         self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]					)  ,     [250053, 250001]					)



    def    _lowercase		(self       :       List[str]					):
         UpperCAmelCase_			       =  tempfile.mkdtemp()
         UpperCAmelCase_			       =  self.tokenizer.fairseq_tokens_to_ids
         self.tokenizer.save_pretrained(__a					)
         UpperCAmelCase_			       =  MBartaaTokenizer.from_pretrained(__a					)
         self.assertDictEqual(new_tok.fairseq_tokens_to_ids  ,     __a					)



    @require_torch
    def    _lowercase		(self       :       Dict					):
         UpperCAmelCase_			       =  self.tokenizer(self.src_text  ,     text_target=self.tgt_text  ,     padding=__a  ,     return_tensors="pt"					)
         UpperCAmelCase_			       =  shift_tokens_right(batch["labels"]  ,     self.tokenizer.pad_token_id					)

         # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
         assert batch.input_ids[1][0] == EN_CODE
         assert batch.input_ids[1][-1] == 2
         assert batch.labels[1][0] == RO_CODE
         assert batch.labels[1][-1] == 2
         assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]



    @require_torch
    def    _lowercase		(self       :       int					):
         UpperCAmelCase_			       =  self.tokenizer(
             self.src_text  ,     text_target=self.tgt_text  ,     padding=__a  ,     truncation=__a  ,     max_length=len(self.expected_src_tokens					)  ,     return_tensors="pt"  ,     )
         UpperCAmelCase_			       =  shift_tokens_right(batch["labels"]  ,     self.tokenizer.pad_token_id					)

         self.assertIsInstance(__a  ,     __a					)

         self.assertEqual((2, 14)  ,     batch.input_ids.shape					)
         self.assertEqual((2, 14)  ,     batch.attention_mask.shape					)
         UpperCAmelCase_			       =  batch.input_ids.tolist()[0]
         self.assertListEqual(self.expected_src_tokens  ,     __a					)
         self.assertEqual(2  ,     batch.decoder_input_ids[0, 0]					)  # decoder_start_token_id
         # Test that special tokens are reset
         self.assertEqual(self.tokenizer.prefix_tokens  ,     [EN_CODE]					)
         self.assertEqual(self.tokenizer.suffix_tokens  ,     [self.tokenizer.eos_token_id]					)



    def    _lowercase		(self       :       Union[str, Any]					):
         UpperCAmelCase_			       =  self.tokenizer(self.src_text  ,     padding=__a  ,     truncation=__a  ,     max_length=3  ,     return_tensors="pt"					)
         UpperCAmelCase_			       =  self.tokenizer(
             text_target=self.tgt_text  ,     padding=__a  ,     truncation=__a  ,     max_length=10  ,     return_tensors="pt"					)
         UpperCAmelCase_			       =  targets["input_ids"]
         UpperCAmelCase_			       =  shift_tokens_right(__a  ,     self.tokenizer.pad_token_id					)

         self.assertEqual(batch.input_ids.shape[1]  ,     3					)
         self.assertEqual(batch.decoder_input_ids.shape[1]  ,     10					)






    @require_torch
    def    _lowercase		(self       :       Dict					):
         UpperCAmelCase_			       =  self.tokenizer._build_translation_inputs(
             "A test"  ,     return_tensors="pt"  ,     src_lang="en_XX"  ,     tgt_lang="ar_AR"					)

         self.assertEqual(
             nested_simplify(__a					)  ,     {
                 # en_XX, A, test, EOS
                 "input_ids": [[250004, 62, 3034, 2]],
                 "attention_mask": [[1, 1, 1, 1]],
                 # ar_AR
                 "forced_bos_token_id": 250001,
             }  ,     )











import argparse
import os
from pathlib import Path

import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download

from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
    BarkCoarseConfig,
    BarkConfig,
    BarkFineConfig,
    BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
    BarkCoarseGenerationConfig,
    BarkFineGenerationConfig,
    BarkGenerationConfig,
    BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_   :      Dict			=   logging.get_logger(__name__)

set_seed(770)


SCREAMING_SNAKE_CASE_   :      List[Any]			=   {
    '''c_attn''': '''att_proj''',
    '''c_proj''': '''out_proj''',
    '''c_fc''': '''in_proj''',
    '''transformer.''': '''''',
    '''h.''': '''layers.''',
    '''ln_1''': '''layernorm_1''',
    '''ln_2''': '''layernorm_2''',
    '''ln_f''': '''layernorm_final''',
    '''wpe''': '''position_embeds_layer''',
    '''wte''': '''input_embeds_layer''',
}


SCREAMING_SNAKE_CASE_   :      Optional[Any]			=   {
    '''text_small''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''text.pt''',
    },
    '''coarse_small''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''coarse.pt''',
    },
    '''fine_small''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''fine.pt''',
    },
    '''text''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''text_2.pt''',
    },
    '''coarse''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''coarse_2.pt''',
    },
    '''fine''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''fine_2.pt''',
    },
}

SCREAMING_SNAKE_CASE_   :      List[str]			=   os.path.dirname(os.path.abspath(__file__))
SCREAMING_SNAKE_CASE_   :      List[Any]			=   os.path.join(os.path.expanduser('''~'''), '''.cache''')
SCREAMING_SNAKE_CASE_   :      Any			=   os.path.join(os.getenv('''XDG_CACHE_HOME''', default_cache_dir), '''suno''', '''bark_v0''')



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case=False							)		->    Union[str, Any]:
		__lowercase											=     model_type
		if use_small:
				key += "_small"
		return os.path.join(snake_case						,   REMOTE_MODEL_PATHS[key]['file_name']							)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    List[str]:
		os.makedirs(snake_case						,   exist_ok=snake_case							)
		hf_hub_download(repo_id=snake_case						,   filename=snake_case						,   local_dir=snake_case							)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case=False						,   snake_case="text"							)		->    List[Any]:
		if model_type == "text":
				__lowercase											=     BarkSemanticModel
				__lowercase											=     BarkSemanticConfig
				__lowercase											=     BarkSemanticGenerationConfig
		elif model_type == "coarse":
				__lowercase											=     BarkCoarseModel
				__lowercase											=     BarkCoarseConfig
				__lowercase											=     BarkCoarseGenerationConfig
		elif model_type == "fine":
				__lowercase											=     BarkFineModel
				__lowercase											=     BarkFineConfig
				__lowercase											=     BarkFineGenerationConfig
		else:
				raise NotImplementedError()
		__lowercase											=     F"{model_type}_small" if use_small else model_type
		__lowercase											=     REMOTE_MODEL_PATHS[model_key]
		if not os.path.exists(snake_case							):
				logger.info(F"{model_type} model not found, downloading into `{CACHE_DIR}`."							)
				_download(model_info['repo_id']						,   model_info['file_name']							)
		__lowercase											=     torch.load(snake_case						,   map_location=snake_case							)
		# this is a hack
		__lowercase											=     checkpoint['model_args']
		if "input_vocab_size" not in model_args:
				__lowercase											=     model_args['vocab_size']
				__lowercase											=     model_args['vocab_size']
				del model_args["vocab_size"]

		# convert Bark model arguments to HF Bark model arguments
		__lowercase											=     model_args.pop('n_head'							)
		__lowercase											=     model_args.pop('n_embd'							)
		__lowercase											=     model_args.pop('n_layer'							)

		__lowercase											=     ConfigClass(**checkpoint['model_args']							)
		__lowercase											=     ModelClass(config=snake_case							)
		__lowercase											=     GenerationConfigClass()

		__lowercase											=     model_generation_config
		__lowercase											=     checkpoint['model']
		# fixup checkpoint
		__lowercase											=     '_orig_mod.'
		for k, v in list(state_dict.items()							):
				if k.startswith(snake_case							):
						# replace part of the key with corresponding layer name in HF implementation
						__lowercase											=     k[len(snake_case							) :]
						for old_layer_name in new_layer_name_dict:
								__lowercase											=     new_k.replace(snake_case						,   new_layer_name_dict[old_layer_name]							)

						__lowercase											=     state_dict.pop(snake_case							)

		__lowercase											=     set(state_dict.keys()							) - set(model.state_dict().keys()							)
		__lowercase											=     {k for k in extra_keys if not k.endswith('.attn.bias'							)}
		__lowercase											=     set(model.state_dict().keys()							) - set(state_dict.keys()							)
		__lowercase											=     {k for k in missing_keys if not k.endswith('.attn.bias'							)}
		if len(snake_case							) != 0:
				raise ValueError(F"extra keys found: {extra_keys}"							)
		if len(snake_case							) != 0:
				raise ValueError(F"missing keys: {missing_keys}"							)
		model.load_state_dict(snake_case						,   strict=snake_case							)
		__lowercase											=     model.num_parameters(exclude_embeddings=snake_case							)
		__lowercase											=     checkpoint['best_val_loss'].item()
		logger.info(F"model loaded: {round(n_params/1E6						,   1							)}M params, {round(snake_case						,   3							)} loss"							)
		model.eval()
		model.to(snake_case							)
		del checkpoint, state_dict

		return model



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case=False						,   snake_case="text"							)		->    Tuple:
		if model_type not in ("text", "coarse", "fine"):
				raise NotImplementedError()

		__lowercase											=     'cpu'  # do conversion on cpu

		__lowercase											=     _get_ckpt_path(snake_case						,   use_small=snake_case							)
		__lowercase											=     _load_model(snake_case						,   snake_case						,   model_type=snake_case						,   use_small=snake_case							)

		# load bark initial model
		__lowercase											=     _bark_load_model(snake_case						,   'cpu'						,   model_type=snake_case						,   use_small=snake_case							)

		if model_type == "text":
				__lowercase											=     bark_model['model']

		if model.num_parameters(exclude_embeddings=snake_case							) != bark_model.get_num_params():
				raise ValueError('initial and new models don\'t have the same number of parameters'							)

		# check if same output as the bark model
		__lowercase											=     5
		__lowercase											=     10

		if model_type in ["text", "coarse"]:
				__lowercase											=     torch.randint(256						,   (batch_size, sequence_length)						,   dtype=torch.int							)
				__lowercase											=     bark_model(snake_case							)[0]

				__lowercase											=     model(snake_case							)

				# take last logits
				__lowercase											=     output_new_model_total.logits[:, [-1], :]

		else:
				__lowercase											=     3
				__lowercase											=     8
				__lowercase											=     torch.randint(256						,   (batch_size, sequence_length, n_codes_total)						,   dtype=torch.int							)

				__lowercase											=     model(snake_case						,   snake_case							)
				__lowercase											=     bark_model(snake_case						,   snake_case							)

				__lowercase											=     output_new_model_total.logits

		# output difference should come from the difference of self-attention implementation design
		if output_new_model.shape != output_old_model.shape:
				raise ValueError('initial and new outputs don\'t have the same shape'							)
		if (output_new_model - output_old_model).abs().max().item() > 1E-3:
				raise ValueError('initial and new outputs are not equal'							)

		Path(snake_case							).mkdir(exist_ok=snake_case							)
		model.save_pretrained(snake_case							)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case						,   snake_case						,   snake_case						,   snake_case						,   )		->    List[str]:
		__lowercase											=     os.path.join(snake_case						,   snake_case							)

		__lowercase											=     BarkSemanticConfig.from_pretrained(os.path.join(snake_case						,   'config.json'							)							)
		__lowercase											=     BarkCoarseConfig.from_pretrained(os.path.join(snake_case						,   'config.json'							)							)
		__lowercase											=     BarkFineConfig.from_pretrained(os.path.join(snake_case						,   'config.json'							)							)
		__lowercase											=     EncodecConfig.from_pretrained('facebook/encodec_24khz'							)

		__lowercase											=     BarkSemanticModel.from_pretrained(snake_case							)
		__lowercase											=     BarkCoarseModel.from_pretrained(snake_case							)
		__lowercase											=     BarkFineModel.from_pretrained(snake_case							)
		__lowercase											=     EncodecModel.from_pretrained('facebook/encodec_24khz'							)

		__lowercase											=     BarkConfig.from_sub_model_configs(
		    snake_case						,   snake_case						,   snake_case						,   snake_case							)

		__lowercase											=     BarkGenerationConfig.from_sub_model_configs(
		    semantic.generation_config						,   coarseAcoustic.generation_config						,   fineAcoustic.generation_config							)

		__lowercase											=     BarkModel(snake_case							)

		__lowercase											=     semantic
		__lowercase											=     coarseAcoustic
		__lowercase											=     fineAcoustic
		__lowercase											=     codec

		__lowercase											=     bark_generation_config

		Path(snake_case							).mkdir(exist_ok=snake_case							)
		bark.save_pretrained(snake_case						,   repo_id=snake_case						,   push_to_hub=snake_case							)


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE_   :      Optional[int]			=   argparse.ArgumentParser()
		# Required parameters

		parser.add_argument('''model_type''', type=str, help='''text, coarse or fine.''')
		parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
		parser.add_argument('''--is_small''', action='''store_true''', help='''convert the small version instead of the large.''')

		SCREAMING_SNAKE_CASE_   :      Optional[int]			=   parser.parse_args()

		load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)




