

import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase_							:					List[Any]		 =   get_tests_dir('''fixtures/test_sentencepiece_with_bytefallback.model''')






@require_sentencepiece
@require_tokenizers
class      lowerCamelCase_      (	_lowercase						,      unittest.TestCase	):
	_lowercase		:   Union[str, Any]     					=				GPTSwaTokenizer
	_lowercase		:   Dict     					=				False
	_lowercase		:   Tuple     					=				True
	_lowercase		:   Union[str, Any]     					=				False




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

		# We have a SentencePiece fixture for testing
		__A :			Tuple		    =			GPTSwaTokenizer(__A					,      eos_token="""<unk>"""					,      bos_token="""<unk>"""					,      pad_token="""<unk>"""							)

		tokenizer.save_pretrained(self.tmpdirname							)




	def        lowerCAmelCase_       (     self						:     Tuple					,      __A						:     int							):
		__A :			Optional[Any]		    =			"""This is a test"""
		__A :			str		    =			"""This is a test"""
		return input_text, output_text




	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		__A :			List[str]		    =			"""<s>"""
		__A :			List[Any]		    =			1

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




	def        lowerCAmelCase_       (     self						:     str							):
		__A :			Any		    =			list(self.get_tokenizer().get_vocab().keys()							)

		self.assertEqual(vocab_keys[0]					,      """<unk>"""							)
		self.assertEqual(vocab_keys[1]					,      """<s>"""							)
		self.assertEqual(vocab_keys[-1]					,      """j"""							)
		self.assertEqual(len(__A							)					,      2000							)




	def        lowerCAmelCase_       (     self						:     Dict							):
		self.assertEqual(self.get_tokenizer().vocab_size					,      2000							)




	def        lowerCAmelCase_       (     self						:     List[Any]							):
		__A :			Optional[int]		    =			GPTSwaTokenizer(__A							)

		__A :			int		    =			tokenizer.tokenize("""This is a test"""							)
		self.assertListEqual(__A					,      ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]							)

		self.assertListEqual(tokenizer.convert_tokens_to_ids(__A							)					,      [465, 287, 265, 631, 842]							)

		__A :			List[Any]		    =			tokenizer.tokenize("""I was born in 92000, and this is falsé."""							)
		# fmt: off
		self.assertListEqual(
		    __A					,      ["""▁I""", """▁was""", """▁bor""", """n""", """▁in""", """▁""", """<0x39>""", """2""", """0""", """0""", """0""", """,""", """▁and""", """▁this""", """▁is""", """▁f""", """al""", """s""", """<0xC3>""", """<0xA9>""", """."""]					,      )
		# fmt: on

		__A :			Optional[int]		    =			tokenizer.convert_tokens_to_ids(__A							)
		self.assertListEqual(
		    __A					,      [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260]					,      )

		__A :			Union[str, Any]		    =			tokenizer.convert_ids_to_tokens(__A							)
		# fmt: off
		self.assertListEqual(
		    __A					,      ["""▁I""", """▁was""", """▁bor""", """n""", """▁in""", """▁""", """<0x39>""", """2""", """0""", """0""", """0""", """,""", """▁and""", """▁this""", """▁is""", """▁f""", """al""", """s""", """<0xC3>""", """<0xA9>""", """."""]							)
		# fmt: on




	def        lowerCAmelCase_       (     self						:     List[Any]							):
		__A :			Union[str, Any]		    =			GPTSwaTokenizer(__A							)
		__A :			Optional[int]		    =			["""This is a test""", """I was born in 92000, and this is falsé."""]
		__A :			Tuple		    =			[
		    [465, 287, 265, 631, 842],
		    [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
		]

		# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
		for text, expected_ids in zip(__A					,      __A							):
			self.assertListEqual(tokenizer.encode_fast(__A							)					,      __A							)

		# Test that decode_fast returns the input text
		for text, token_ids in zip(__A					,      __A							):
			self.assertEqual(tokenizer.decode_fast(__A							)					,      __A							)




	@slow
	def        lowerCAmelCase_       (     self						:     Tuple							):
		__A :			Any		    =			[
		    """<|python|>def fibonacci(n)\n    if n < 0:\n        print('Incorrect input')""",
		    """Hey there, how are you doing this fine day?""",
		    """This is a text with a trailing spaces followed by a dot     .""",
		    """Häj sväjs lillebrör! =)""",
		    """Det är inget fel på Mr. Cool""",
		]

		# fmt: off
		__A :			Any		    =			{"""input_ids""": [[6_3423, 5, 6811, 1_4954, 282, 816, 3821, 6_3466, 6_3425, 6_3462, 18, 6_3978, 678, 301, 1320, 6_3423, 6_3455, 6_3458, 18, 6_3982, 4246, 3940, 1901, 4_7789, 5547, 1_8994], [1_9630, 1100, 6_3446, 1342, 633, 544, 4488, 593, 5102, 2416, 6_3495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1652, 428, 268, 1936, 515, 268, 5_8593, 2_2413, 9106, 546, 268, 3_3213, 6_3979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_5130, 6_3450, 924, 6_3449, 2249, 4062, 1558, 318, 6_3504, 2_1498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2827, 2559, 332, 6575, 6_3443, 2_6801, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """token_type_ids""": [[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, 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]], """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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [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], [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]]}
		# fmt: on
		self.tokenizer_integration_test_util(
		    expected_encoding=__A					,      model_name="""AI-Sweden/gpt-sw3-126m"""					,      sequences=__A					,      )













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()
UpperCamelCase    							=      logging.get_logger(__name__)

set_seed(770)


UpperCamelCase    							=      {
    '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',
}


UpperCamelCase    							=      {
    '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',
    },
}

UpperCamelCase    							=      os.path.dirname(os.path.abspath(__file__))
UpperCamelCase    							=      os.path.join(os.path.expanduser('~'), '.cache')
UpperCamelCase    							=      os.path.join(os.getenv('XDG_CACHE_HOME', default_cache_dir), 'suno', 'bark_v0')


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Optional[int]					,							__lowerCAmelCase							:					List[str]=False							)     ->  int:
   __UpperCamelCase	:			Optional[Any]					=					model_type
   if use_small:
      key += "_small"
   return os.path.join(__lowerCAmelCase					,							REMOTE_MODEL_PATHS[key]["""file_name"""]							)


def 				__lowerCamelCase     (							__lowerCAmelCase							:					int					,							__lowerCAmelCase							:					Optional[Any]							)     ->  List[Any]:
   os.makedirs(__lowerCAmelCase					,							exist_ok=__lowerCAmelCase							)
   hf_hub_download(repo_id=__lowerCAmelCase					,							filename=__lowerCAmelCase					,							local_dir=__lowerCAmelCase							)


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

   # convert Bark model arguments to HF Bark model arguments
   __UpperCamelCase	:			Tuple					=					model_args.pop("""n_head"""							)
   __UpperCamelCase	:			Tuple					=					model_args.pop("""n_embd"""							)
   __UpperCamelCase	:			Union[str, Any]					=					model_args.pop("""n_layer"""							)

   __UpperCamelCase	:			List[Any]					=					ConfigClass(**checkpoint["""model_args"""]							)
   __UpperCamelCase	:			Optional[int]					=					ModelClass(config=__lowerCAmelCase							)
   __UpperCamelCase	:			Dict					=					GenerationConfigClass()

   __UpperCamelCase	:			Any					=					model_generation_config
   __UpperCamelCase	:			str					=					checkpoint["""model"""]
   # fixup checkpoint
   __UpperCamelCase	:			Dict					=					"""_orig_mod."""
   for k, v in list(state_dict.items()							):
      if k.startswith(__lowerCAmelCase							):
         # replace part of the key with corresponding layer name in HF implementation
         __UpperCamelCase	:			List[str]					=					k[len(__lowerCAmelCase							) :]
         for old_layer_name in new_layer_name_dict:
            __UpperCamelCase	:			List[Any]					=					new_k.replace(__lowerCAmelCase					,							new_layer_name_dict[old_layer_name]							)

         __UpperCamelCase	:			Optional[Any]					=					state_dict.pop(__lowerCAmelCase							)

   __UpperCamelCase	:			int					=					set(state_dict.keys()							) - set(model.state_dict().keys()							)
   __UpperCamelCase	:			List[Any]					=					{k for k in extra_keys if not k.endswith(""".attn.bias"""							)}
   __UpperCamelCase	:			Dict					=					set(model.state_dict().keys()							) - set(state_dict.keys()							)
   __UpperCamelCase	:			Optional[int]					=					{k for k in missing_keys if not k.endswith(""".attn.bias"""							)}
   if len(__lowerCAmelCase							) != 0:
      raise ValueError(f'extra keys found: {extra_keys}'							)
   if len(__lowerCAmelCase							) != 0:
      raise ValueError(f'missing keys: {missing_keys}'							)
   model.load_state_dict(__lowerCAmelCase					,							strict=__lowerCAmelCase							)
   __UpperCamelCase	:			Tuple					=					model.num_parameters(exclude_embeddings=__lowerCAmelCase							)
   __UpperCamelCase	:			Optional[int]					=					checkpoint["""best_val_loss"""].item()
   logger.info(f'model loaded: {round(n_params/1e6					,							1							)}M params, {round(__lowerCAmelCase					,							3							)} loss'							)
   model.eval()
   model.to(__lowerCAmelCase							)
   del checkpoint, state_dict

   return model


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Union[str, Any]					,							__lowerCAmelCase							:					int=False					,							__lowerCAmelCase							:					Tuple="text"							)     ->  Tuple:
   if model_type not in ("text", "coarse", "fine"):
      raise NotImplementedError()

   __UpperCamelCase	:			str					=					"""cpu"""  # do conversion on cpu

   __UpperCamelCase	:			str					=					_get_ckpt_path(__lowerCAmelCase					,							use_small=__lowerCAmelCase							)
   __UpperCamelCase	:			Optional[int]					=					_load_model(__lowerCAmelCase					,							__lowerCAmelCase					,							model_type=__lowerCAmelCase					,							use_small=__lowerCAmelCase							)

   # load bark initial model
   __UpperCamelCase	:			Dict					=					_bark_load_model(__lowerCAmelCase					,							"""cpu"""					,							model_type=__lowerCAmelCase					,							use_small=__lowerCAmelCase							)

   if model_type == "text":
      __UpperCamelCase	:			Dict					=					bark_model["""model"""]

   if model.num_parameters(exclude_embeddings=__lowerCAmelCase							) != 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
   __UpperCamelCase	:			List[str]					=					5
   __UpperCamelCase	:			List[Any]					=					10

   if model_type in ["text", "coarse"]:
      __UpperCamelCase	:			List[Any]					=					torch.randint(256					,							(batch_size, sequence_length)					,							dtype=torch.int							)
      __UpperCamelCase	:			List[str]					=					bark_model(__lowerCAmelCase							)[0]

      __UpperCamelCase	:			Dict					=					model(__lowerCAmelCase							)

      # take last logits
      __UpperCamelCase	:			Union[str, Any]					=					output_new_model_total.logits[:, [-1], :]

   else:
      __UpperCamelCase	:			Optional[int]					=					3
      __UpperCamelCase	:			Tuple					=					8
      __UpperCamelCase	:			int					=					torch.randint(256					,							(batch_size, sequence_length, n_codes_total)					,							dtype=torch.int							)

      __UpperCamelCase	:			Union[str, Any]					=					model(__lowerCAmelCase					,							__lowerCAmelCase							)
      __UpperCamelCase	:			Tuple					=					bark_model(__lowerCAmelCase					,							__lowerCAmelCase							)

      __UpperCamelCase	:			Dict					=					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(__lowerCAmelCase							).mkdir(exist_ok=__lowerCAmelCase							)
   model.save_pretrained(__lowerCAmelCase							)


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Optional[int]					,							__lowerCAmelCase							:					Optional[int]					,							__lowerCAmelCase							:					List[str]					,							__lowerCAmelCase							:					List[str]					,							__lowerCAmelCase							:					int					,							__lowerCAmelCase							:					Optional[Any]					,							)     ->  int:
   __UpperCamelCase	:			Optional[int]					=					os.path.join(__lowerCAmelCase					,							__lowerCAmelCase							)

   __UpperCamelCase	:			str					=					BarkSemanticConfig.from_pretrained(os.path.join(__lowerCAmelCase					,							"""config.json"""							)							)
   __UpperCamelCase	:			List[str]					=					BarkCoarseConfig.from_pretrained(os.path.join(__lowerCAmelCase					,							"""config.json"""							)							)
   __UpperCamelCase	:			List[Any]					=					BarkFineConfig.from_pretrained(os.path.join(__lowerCAmelCase					,							"""config.json"""							)							)
   __UpperCamelCase	:			List[str]					=					EncodecConfig.from_pretrained("""facebook/encodec_24khz"""							)

   __UpperCamelCase	:			Optional[int]					=					BarkSemanticModel.from_pretrained(__lowerCAmelCase							)
   __UpperCamelCase	:			List[Any]					=					BarkCoarseModel.from_pretrained(__lowerCAmelCase							)
   __UpperCamelCase	:			Union[str, Any]					=					BarkFineModel.from_pretrained(__lowerCAmelCase							)
   __UpperCamelCase	:			Union[str, Any]					=					EncodecModel.from_pretrained("""facebook/encodec_24khz"""							)

   __UpperCamelCase	:			Dict					=					BarkConfig.from_sub_model_configs(
       __lowerCAmelCase					,							__lowerCAmelCase					,							__lowerCAmelCase					,							__lowerCAmelCase							)

   __UpperCamelCase	:			List[Any]					=					BarkGenerationConfig.from_sub_model_configs(
       semantic.generation_config					,							coarseAcoustic.generation_config					,							fineAcoustic.generation_config							)

   __UpperCamelCase	:			Optional[int]					=					BarkModel(__lowerCAmelCase							)

   __UpperCamelCase	:			List[Any]					=					semantic
   __UpperCamelCase	:			Any					=					coarseAcoustic
   __UpperCamelCase	:			Dict					=					fineAcoustic
   __UpperCamelCase	:			List[str]					=					codec

   __UpperCamelCase	:			Optional[Any]					=					bark_generation_config

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


if __name__ == "__main__":
       UpperCamelCase    							=      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.')

       UpperCamelCase    							=      parser.parse_args()

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



