
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()
lowercase  :					Any						    =							logging.get_logger(__name__)

set_seed(770)


lowercase  :					List[str]						    =							{
    """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""",
}


lowercase  :					str						    =							{
    """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""",
    },
}

lowercase  :					Dict						    =							os.path.dirname(os.path.abspath(__file__))
lowercase  :					Optional[int]						    =							os.path.join(os.path.expanduser("""~"""), """.cache""")
lowercase  :					Optional[Any]						    =							os.path.join(os.getenv("""XDG_CACHE_HOME""", default_cache_dir), """suno""", """bark_v0""")







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=False				) ->    Tuple:
       lowercase							:     int       	=      model_type
       if use_small:
              key += "_small"
       return os.path.join(SCREAMING_SNAKE_CASE__					,	REMOTE_MODEL_PATHS[key]["""file_name"""]				)







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







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

       # convert Bark model arguments to HF Bark model arguments
       lowercase							:     Tuple       	=      model_args.pop("""n_head"""				)
       lowercase							:     Optional[int]       	=      model_args.pop("""n_embd"""				)
       lowercase							:     Tuple       	=      model_args.pop("""n_layer"""				)

       lowercase							:     Dict       	=      ConfigClass(**checkpoint["""model_args"""]				)
       lowercase							:     int       	=      ModelClass(config=SCREAMING_SNAKE_CASE__				)
       lowercase							:     Dict       	=      GenerationConfigClass()

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

                     lowercase							:     Dict       	=      state_dict.pop(SCREAMING_SNAKE_CASE__				)

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

       return model







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=False					,	SCREAMING_SNAKE_CASE__="text"				) ->    Any:
       if model_type not in ("text", "coarse", "fine"):
              raise NotImplementedError()

       lowercase							:     Any       	=      """cpu"""  # do conversion on cpu

       lowercase							:     Optional[Any]       	=      _get_ckpt_path(SCREAMING_SNAKE_CASE__					,	use_small=SCREAMING_SNAKE_CASE__				)
       lowercase							:     Dict       	=      _load_model(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	model_type=SCREAMING_SNAKE_CASE__					,	use_small=SCREAMING_SNAKE_CASE__				)

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

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

       if model.num_parameters(exclude_embeddings=SCREAMING_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							:     Optional[int]       	=      5
       lowercase							:     Any       	=      10

       if model_type in ["text", "coarse"]:
              lowercase							:     Optional[int]       	=      torch.randint(256					,	(batch_size, sequence_length)					,	dtype=torch.int				)
              lowercase							:     Tuple       	=      bark_model(SCREAMING_SNAKE_CASE__				)[0]

              lowercase							:     Any       	=      model(SCREAMING_SNAKE_CASE__				)

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

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

              lowercase							:     int       	=      model(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
              lowercase							:     List[Any]       	=      bark_model(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)

              lowercase							:     Optional[int]       	=      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(SCREAMING_SNAKE_CASE__				).mkdir(exist_ok=SCREAMING_SNAKE_CASE__				)
       model.save_pretrained(SCREAMING_SNAKE_CASE__				)







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	) ->    Optional[Any]:
       lowercase							:     Any       	=      os.path.join(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)

       lowercase							:     List[str]       	=      BarkSemanticConfig.from_pretrained(os.path.join(SCREAMING_SNAKE_CASE__					,	"""config.json"""				)				)
       lowercase							:     Optional[Any]       	=      BarkCoarseConfig.from_pretrained(os.path.join(SCREAMING_SNAKE_CASE__					,	"""config.json"""				)				)
       lowercase							:     Optional[int]       	=      BarkFineConfig.from_pretrained(os.path.join(SCREAMING_SNAKE_CASE__					,	"""config.json"""				)				)
       lowercase							:     Tuple       	=      EncodecConfig.from_pretrained("""facebook/encodec_24khz"""				)

       lowercase							:     str       	=      BarkSemanticModel.from_pretrained(SCREAMING_SNAKE_CASE__				)
       lowercase							:     List[Any]       	=      BarkCoarseModel.from_pretrained(SCREAMING_SNAKE_CASE__				)
       lowercase							:     Optional[int]       	=      BarkFineModel.from_pretrained(SCREAMING_SNAKE_CASE__				)
       lowercase							:     int       	=      EncodecModel.from_pretrained("""facebook/encodec_24khz"""				)

       lowercase							:     Optional[Any]       	=      BarkConfig.from_sub_model_configs(
           SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)

       lowercase							:     List[str]       	=      BarkGenerationConfig.from_sub_model_configs(
           semantic.generation_config					,	coarseAcoustic.generation_config					,	fineAcoustic.generation_config				)

       lowercase							:     Optional[int]       	=      BarkModel(SCREAMING_SNAKE_CASE__				)

       lowercase							:     int       	=      semantic
       lowercase							:     Union[str, Any]       	=      coarseAcoustic
       lowercase							:     List[str]       	=      fineAcoustic
       lowercase							:     Dict       	=      codec

       lowercase							:     Optional[int]       	=      bark_generation_config

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


if __name__ == "__main__":
  lowercase  :					Dict						    =							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.""")

  lowercase  :					str						    =							parser.parse_args()

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


import json
import os
import unittest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class 	_lowerCAmelCase					(	__a					,      unittest.TestCase       ):
				_lowercase					=CLIPTokenizer
				_lowercase					=CLIPTokenizerFast
				_lowercase					=True
				_lowercase					={}
				_lowercase					=False




				def      __a						(					self     )				->      Dict:
							super().setUp()

							# fmt: off
							lowerCAmelCase_						=	["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
							# fmt: on
							lowerCAmelCase_						=	dict(zip(_UpperCamelCase				,				range(len(_UpperCamelCase     )     )     )     )
							lowerCAmelCase_						=	["#version: 0.2", "l o", "lo w</w>", "e r</w>"]
							lowerCAmelCase_						=	{"unk_token": "<unk>"}

							lowerCAmelCase_						=	os.path.join(self.tmpdirname				,				VOCAB_FILES_NAMES["vocab_file"]     )
							lowerCAmelCase_						=	os.path.join(self.tmpdirname				,				VOCAB_FILES_NAMES["merges_file"]     )
							with open(self.vocab_file				,				"w"				,				encoding="utf-8"     ) as fp:
										fp.write(json.dumps(_UpperCamelCase     ) + "\n"     )
							with open(self.merges_file				,				"w"				,				encoding="utf-8"     ) as fp:
										fp.write("\n".join(_UpperCamelCase     )     )




				def      __a						(					self				,				**_UpperCamelCase     )				->      Any:
							kwargs.update(self.special_tokens_map     )
							return CLIPTokenizer.from_pretrained(self.tmpdirname				,				**_UpperCamelCase     )




				def      __a						(					self				,				**_UpperCamelCase     )				->      int:
							kwargs.update(self.special_tokens_map     )
							return CLIPTokenizerFast.from_pretrained(self.tmpdirname				,				**_UpperCamelCase     )




				def      __a						(					self				,				_UpperCamelCase     )				->      List[str]:
							lowerCAmelCase_						=	"lower newer"
							lowerCAmelCase_						=	"lower newer"
							return input_text, output_text




				def      __a						(					self     )				->      List[Any]:
							lowerCAmelCase_						=	CLIPTokenizer(self.vocab_file				,				self.merges_file				,				**self.special_tokens_map     )
							lowerCAmelCase_						=	"lower newer"
							lowerCAmelCase_						=	["lo", "w", "er</w>", "n", "e", "w", "er</w>"]
							lowerCAmelCase_						=	tokenizer.tokenize(_UpperCamelCase     )
							self.assertListEqual(_UpperCamelCase				,				_UpperCamelCase     )

							lowerCAmelCase_						=	tokens + [tokenizer.unk_token]
							lowerCAmelCase_						=	[10, 2, 16, 9, 3, 2, 16, 20]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCamelCase     )				,				_UpperCamelCase     )




				@require_ftfy
				def      __a						(					self     )				->      Union[str, Any]:
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""     ):
													lowerCAmelCase_						=	self.tokenizer_class.from_pretrained(_UpperCamelCase				,				**_UpperCamelCase     )
													lowerCAmelCase_						=	self.rust_tokenizer_class.from_pretrained(_UpperCamelCase				,				**_UpperCamelCase     )

													lowerCAmelCase_						=	"A\n'll 11p223RF☆ho!!to?'d'd''d of a cat to-$''d."
													lowerCAmelCase_						=	tokenizer_s.tokenize(_UpperCamelCase     )
													lowerCAmelCase_						=	tokenizer_r.tokenize(_UpperCamelCase     )

													self.assertListEqual(_UpperCamelCase				,				_UpperCamelCase     )

													# Test that the tokenization is identical on an example containing a character (Latin Small Letter A
													# with Tilde) encoded in 2 different ways
													lowerCAmelCase_						=	"xa\u0303y" + " " + "x\xe3y"
													lowerCAmelCase_						=	tokenizer_s.tokenize(_UpperCamelCase     )
													lowerCAmelCase_						=	tokenizer_r.tokenize(_UpperCamelCase     )

													self.assertListEqual(_UpperCamelCase				,				_UpperCamelCase     )

													# Test that the tokenization is identical on unicode of space type
													lowerCAmelCase_						=	[
													    "\u0009",  # (horizontal tab, '\t')
													    "\u000B",  # (vertical tab)
													    "\u000C",  # (form feed)
													    "\u0020",  # (space, ' ')
													    "\u200E",  # (left-to-right mark):w
													    "\u200F",  # (right-to-left mark)
													]
													for unicode_seq in spaces_unicodes:
																lowerCAmelCase_						=	tokenizer_s.tokenize(_UpperCamelCase     )
																lowerCAmelCase_						=	tokenizer_r.tokenize(_UpperCamelCase     )

																self.assertListEqual(_UpperCamelCase				,				_UpperCamelCase     )

													# Test that the tokenization is identical on unicode of line break type
													lowerCAmelCase_						=	[
													    "\u000A",  # (line feed, '\n')
													    "\r\n",  # (carriage return and line feed, '\r\n')
													    "\u000D",  # (carriage return, '\r')
													    "\r",  # (carriage return, '\r')
													    "\u000D",  # (carriage return, '\r')
													    "\u2028",  # (line separator)
													    "\u2029",  # (paragraph separator)
													    # "\u0085", # (next line)
													]

													# The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
													# it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
													# space (and thus into an empty list).

													for unicode_seq in line_break_unicodes:
																lowerCAmelCase_						=	tokenizer_s.tokenize(_UpperCamelCase     )
																lowerCAmelCase_						=	tokenizer_r.tokenize(_UpperCamelCase     )

																self.assertListEqual(_UpperCamelCase				,				_UpperCamelCase     )




				def      __a						(					self     )				->      str:
							# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space`
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""     ):
													lowerCAmelCase_						=	"hello"  # `hello` is a token in the vocabulary of `pretrained_name`
													lowerCAmelCase_						=	f"""{text_of_1_token} {text_of_1_token}"""

													lowerCAmelCase_						=	self.rust_tokenizer_class.from_pretrained(
													    _UpperCamelCase				,				use_fast=_UpperCamelCase				,				)
													lowerCAmelCase_						=	tokenizer_r(_UpperCamelCase				,				return_offsets_mapping=_UpperCamelCase				,				add_special_tokens=_UpperCamelCase     )
													self.assertEqual(encoding.offset_mapping[0]				,				(0, len(_UpperCamelCase     ))     )
													self.assertEqual(
													    encoding.offset_mapping[1]				,				(len(_UpperCamelCase     ) + 1, len(_UpperCamelCase     ) + 1 + len(_UpperCamelCase     ))				,				)

													lowerCAmelCase_						=	f""" {text}"""

													lowerCAmelCase_						=	self.rust_tokenizer_class.from_pretrained(
													    _UpperCamelCase				,				use_fast=_UpperCamelCase				,				)
													lowerCAmelCase_						=	tokenizer_r(_UpperCamelCase				,				return_offsets_mapping=_UpperCamelCase				,				add_special_tokens=_UpperCamelCase     )
													self.assertEqual(encoding.offset_mapping[0]				,				(1, 1 + len(_UpperCamelCase     ))     )
													self.assertEqual(
													    encoding.offset_mapping[1]				,				(1 + len(_UpperCamelCase     ) + 1, 1 + len(_UpperCamelCase     ) + 1 + len(_UpperCamelCase     ))				,				)




				def      __a						(					self     )				->      Optional[Any]:
							# Test related to the breaking change introduced in transformers v4.17.0
							# We need to check that an error in raised when the user try to load a previous version of the tokenizer.
							with self.assertRaises(_UpperCamelCase     ) as context:
										self.rust_tokenizer_class.from_pretrained("robot-test/old-clip-tokenizer"     )

							self.assertTrue(
							    context.exception.args[0].startswith(
							        "The `backend_tokenizer` provided does not match the expected format."     )     )




				@require_ftfy
				def      __a						(					self     )				->      str:
							super().test_tokenization_python_rust_equals()




				def      __a						(					self     )				->      Any:
							# CLIP always lower cases letters
							pass



