"""simple docstring"""






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()
__A			   = logging.get_logger(__name__)

set_seed(7_7_0)


__A			   = {
    "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",
}


__A			   = {
    "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",
    },
}

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



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase=False )  ->  Optional[Any]:
   lowercase__:   Dict					=      model_type
   if use_small:
      key += "_small"
   return os.path.join(__UpperCAmelCase       ,    REMOTE_MODEL_PATHS[key]['''file_name'''] )



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase )  ->  Any:
   os.makedirs(__UpperCAmelCase       ,    exist_ok=__UpperCAmelCase )
   hf_hub_download(repo_id=__UpperCAmelCase       ,    filename=__UpperCAmelCase       ,    local_dir=__UpperCAmelCase )



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase=False       ,    __UpperCAmelCase="text" )  ->  int:
   if model_type == "text":
      lowercase__:   Tuple					=      BarkSemanticModel
      lowercase__:   List[Any]					=      BarkSemanticConfig
      lowercase__:   List[Any]					=      BarkSemanticGenerationConfig
   elif model_type == "coarse":
      lowercase__:   Optional[int]					=      BarkCoarseModel
      lowercase__:   List[str]					=      BarkCoarseConfig
      lowercase__:   Any					=      BarkCoarseGenerationConfig
   elif model_type == "fine":
      lowercase__:   Any					=      BarkFineModel
      lowercase__:   Tuple					=      BarkFineConfig
      lowercase__:   List[Any]					=      BarkFineGenerationConfig
   else:
      raise NotImplementedError()
   lowercase__:   List[str]					=      F"""{model_type}_small""" if use_small else model_type
   lowercase__:   Tuple					=      REMOTE_MODEL_PATHS[model_key]
   if not os.path.exists(__UpperCAmelCase ):
      logger.info(F"""{model_type} model not found, downloading into `{CACHE_DIR}`.""" )
      _download(model_info['''repo_id''']       ,    model_info['''file_name'''] )
   lowercase__:   int					=      torch.load(__UpperCAmelCase       ,    map_location=__UpperCAmelCase )
   # this is a hack
   lowercase__:   Optional[Any]					=      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__:   Optional[Any]					=      model_args.pop('''n_head''' )
   lowercase__:   List[Any]					=      model_args.pop('''n_embd''' )
   lowercase__:   Optional[int]					=      model_args.pop('''n_layer''' )

   lowercase__:   Tuple					=      ConfigClass(**checkpoint['''model_args'''] )
   lowercase__:   Optional[int]					=      ModelClass(config=__UpperCAmelCase )
   lowercase__:   Optional[Any]					=      GenerationConfigClass()

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

         lowercase__:   Optional[int]					=      state_dict.pop(__UpperCAmelCase )

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

   return model



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

   lowercase__:   List[str]					=      '''cpu'''  # do conversion on cpu

   lowercase__:   Union[str, Any]					=      _get_ckpt_path(__UpperCAmelCase       ,    use_small=__UpperCAmelCase )
   lowercase__:   Tuple					=      _load_model(__UpperCAmelCase       ,    __UpperCAmelCase       ,    model_type=__UpperCAmelCase       ,    use_small=__UpperCAmelCase )

   # load bark initial model
   lowercase__:   int					=      _bark_load_model(__UpperCAmelCase       ,    '''cpu'''       ,    model_type=__UpperCAmelCase       ,    use_small=__UpperCAmelCase )

   if model_type == "text":
      lowercase__:   List[str]					=      bark_model['''model''']

   if model.num_parameters(exclude_embeddings=__UpperCAmelCase ) != 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__:   List[Any]					=      5
   lowercase__:   Any					=      1_0

   if model_type in ["text", "coarse"]:
      lowercase__:   Union[str, Any]					=      torch.randint(2_5_6       ,    (batch_size, sequence_length)       ,    dtype=torch.int )
      lowercase__:   str					=      bark_model(__UpperCAmelCase )[0]

      lowercase__:   List[str]					=      model(__UpperCAmelCase )

      # take last logits
      lowercase__:   Optional[int]					=      output_new_model_total.logits[:, [-1], :]

   else:
      lowercase__:   Dict					=      3
      lowercase__:   Dict					=      8
      lowercase__:   List[Any]					=      torch.randint(2_5_6       ,    (batch_size, sequence_length, n_codes_total)       ,    dtype=torch.int )

      lowercase__:   List[str]					=      model(__UpperCAmelCase       ,    __UpperCAmelCase )
      lowercase__:   int					=      bark_model(__UpperCAmelCase       ,    __UpperCAmelCase )

      lowercase__:   List[str]					=      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(__UpperCAmelCase ).mkdir(exist_ok=__UpperCAmelCase )
   model.save_pretrained(__UpperCAmelCase )



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    )  ->  Union[str, Any]:
   lowercase__:   Union[str, Any]					=      os.path.join(__UpperCAmelCase       ,    __UpperCAmelCase )

   lowercase__:   Optional[int]					=      BarkSemanticConfig.from_pretrained(os.path.join(__UpperCAmelCase       ,    '''config.json''' ) )
   lowercase__:   Any					=      BarkCoarseConfig.from_pretrained(os.path.join(__UpperCAmelCase       ,    '''config.json''' ) )
   lowercase__:   str					=      BarkFineConfig.from_pretrained(os.path.join(__UpperCAmelCase       ,    '''config.json''' ) )
   lowercase__:   Tuple					=      EncodecConfig.from_pretrained('''facebook/encodec_24khz''' )

   lowercase__:   Any					=      BarkSemanticModel.from_pretrained(__UpperCAmelCase )
   lowercase__:   Any					=      BarkCoarseModel.from_pretrained(__UpperCAmelCase )
   lowercase__:   int					=      BarkFineModel.from_pretrained(__UpperCAmelCase )
   lowercase__:   List[Any]					=      EncodecModel.from_pretrained('''facebook/encodec_24khz''' )

   lowercase__:   Dict					=      BarkConfig.from_sub_model_configs(
       __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase )

   lowercase__:   Any					=      BarkGenerationConfig.from_sub_model_configs(
       semantic.generation_config       ,    coarseAcoustic.generation_config       ,    fineAcoustic.generation_config )

   lowercase__:   int					=      BarkModel(__UpperCAmelCase )

   lowercase__:   str					=      semantic
   lowercase__:   int					=      coarseAcoustic
   lowercase__:   str					=      fineAcoustic
   lowercase__:   List[Any]					=      codec

   lowercase__:   int					=      bark_generation_config

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


if __name__ == "__main__":
       __A			   = 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.")

       __A			   = parser.parse_args()

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

"""simple docstring"""






from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import tensorflow as tf

       from transformers import TFResNetForImageClassification, TFResNetModel
       from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import AutoImageProcessor


class     UpperCAmelCase						:



    """simple docstring"""

    def __init__(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase=3    ,			_UpperCAmelCase=32    ,			_UpperCAmelCase=3    ,			_UpperCAmelCase=10    ,			_UpperCAmelCase=[10, 20, 30, 40]    ,			_UpperCAmelCase=[1, 1, 2, 1]    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase="relu"    ,			_UpperCAmelCase=3    ,			_UpperCAmelCase=None    ,			):
       lowercase__:   Optional[Any]					=      parent
       lowercase__:   Union[str, Any]					=      batch_size
       lowercase__:   int					=      image_size
       lowercase__:   Optional[Any]					=      num_channels
       lowercase__:   Optional[int]					=      embeddings_size
       lowercase__:   Dict					=      hidden_sizes
       lowercase__:   Union[str, Any]					=      depths
       lowercase__:   str					=      is_training
       lowercase__:   Optional[int]					=      use_labels
       lowercase__:   List[str]					=      hidden_act
       lowercase__:   Dict					=      num_labels
       lowercase__:   Any					=      scope
       lowercase__:   Optional[Any]					=      len(_UpperCAmelCase							)






    def _snake_case			(   self							):
       lowercase__:   Union[str, Any]					=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

       lowercase__:   List[Any]					=      None
       if self.use_labels:
          lowercase__:   Any					=      ids_tensor([self.batch_size]    ,			self.num_labels							)

       lowercase__:   Optional[int]					=      self.get_config()

       return config, pixel_values, labels






    def _snake_case			(   self							):
       return ResNetConfig(
           num_channels=self.num_channels    ,			embeddings_size=self.embeddings_size    ,			hidden_sizes=self.hidden_sizes    ,			depths=self.depths    ,			hidden_act=self.hidden_act    ,			num_labels=self.num_labels    ,			image_size=self.image_size    ,			)






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):
       lowercase__:   Optional[Any]					=      TFResNetModel(config=_UpperCAmelCase							)
       lowercase__:   Dict					=      model(_UpperCAmelCase							)
       # expected last hidden states: B, C, H // 32, W // 32
       self.parent.assertEqual(
           result.last_hidden_state.shape    ,			(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)    ,			)






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):
       lowercase__:   str					=      self.num_labels
       lowercase__:   int					=      TFResNetForImageClassification(_UpperCAmelCase							)
       lowercase__:   Optional[Any]					=      model(_UpperCAmelCase    ,			labels=_UpperCAmelCase							)
       self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.num_labels)							)






    def _snake_case			(   self							):
       lowercase__:   int					=      self.prepare_config_and_inputs()
       lowercase__, lowercase__, lowercase__:   Optional[Any]					=      config_and_inputs
       lowercase__:   Union[str, Any]					=      {'''pixel_values''': pixel_values}
       return config, inputs_dict



@require_tf
class     UpperCAmelCase						(_UpperCAmelCase    ,_UpperCAmelCase    ,unittest.TestCase  ):



    """simple docstring"""

    _UpperCAmelCase	:Any						    = (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
    _UpperCAmelCase	:List[str]						    = (
        {"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
        if is_tf_available()
        else {}
    )

    _UpperCAmelCase	:Any						    = False
    _UpperCAmelCase	:List[str]						    = False
    _UpperCAmelCase	:Optional[Any]						    = False
    _UpperCAmelCase	:Tuple						    = False
    _UpperCAmelCase	:List[Any]						    = False






    def _snake_case			(   self							):
       lowercase__:   Union[str, Any]					=      TFResNetModelTester(self							)
       lowercase__:   Optional[int]					=      ConfigTester(self    ,			config_class=_UpperCAmelCase    ,			has_text_modality=_UpperCAmelCase							)






    def _snake_case			(   self							):
       self.create_and_test_config_common_properties()
       self.config_tester.create_and_test_config_to_json_string()
       self.config_tester.create_and_test_config_to_json_file()
       self.config_tester.create_and_test_config_from_and_save_pretrained()
       self.config_tester.create_and_test_config_with_num_labels()
       self.config_tester.check_config_can_be_init_without_params()
       self.config_tester.check_config_arguments_init()






    def _snake_case			(   self							):
       return






    @unittest.skip(reason='''ResNet does not use inputs_embeds'''							)
    def _snake_case			(   self							):
       pass






    @unittest.skip(reason='''ResNet does not support input and output embeddings'''							)
    def _snake_case			(   self							):
       pass






    def _snake_case			(   self							):
       lowercase__, lowercase__:   int					=      self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
          lowercase__:   Optional[Any]					=      model_class(_UpperCAmelCase							)
          lowercase__:   str					=      inspect.signature(model.call							)
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          lowercase__:   int					=      [*signature.parameters.keys()]

          lowercase__:   Optional[Any]					=      ['''pixel_values''']
          self.assertListEqual(arg_names[:1]    ,			_UpperCAmelCase							)






    def _snake_case			(   self							):
       lowercase__:   Any					=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_UpperCAmelCase							)






    def _snake_case			(   self							):
       def check_hidden_states_output(_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):
          lowercase__:   Union[str, Any]					=      model_class(_UpperCAmelCase							)
          lowercase__:   List[Any]					=      model(**self._prepare_for_class(_UpperCAmelCase    ,			_UpperCAmelCase							)							)

          lowercase__:   Union[str, Any]					=      outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

          lowercase__:   Tuple					=      self.model_tester.num_stages
          self.assertEqual(len(_UpperCAmelCase							)    ,			expected_num_stages + 1							)

          # ResNet's feature maps are of shape (batch_size, num_channels, height, width)
          self.assertListEqual(
              list(hidden_states[0].shape[-2:]							)    ,			[self.model_tester.image_size // 4, self.model_tester.image_size // 4]    ,			)

       lowercase__, lowercase__:   Optional[Any]					=      self.model_tester.prepare_config_and_inputs_for_common()
       lowercase__:   Union[str, Any]					=      ['''basic''', '''bottleneck''']
       for model_class in self.all_model_classes:
          for layer_type in layers_type:
             lowercase__:   Tuple					=      layer_type
             lowercase__:   Any					=      True
             check_hidden_states_output(_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase							)

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             lowercase__:   List[str]					=      True

             check_hidden_states_output(_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase							)






    def _snake_case			(   self							):
       lowercase__:   Optional[int]					=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase							)






    @slow
    def _snake_case			(   self							):
       for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowercase__:   Dict					=      TFResNetModel.from_pretrained(_UpperCAmelCase							)
          self.assertIsNotNone(_UpperCAmelCase							)



def SCREAMING_SNAKE_CASE__			(   )  ->  List[Any]:
   lowercase__:   List[Any]					=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
   return image




@require_tf
@require_vision
class     UpperCAmelCase						(unittest.TestCase  ):



    """simple docstring"""

    @cached_property
    def _snake_case			(   self							):
       return (
           AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)
           if is_vision_available()
           else None
       )






    @slow
    def _snake_case			(   self							):
       lowercase__:   Tuple					=      TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)

       lowercase__:   Any					=      self.default_image_processor
       lowercase__:   List[Any]					=      prepare_img()
       lowercase__:   List[Any]					=      image_processor(images=_UpperCAmelCase    ,			return_tensors='''tf'''							)

       # forward pass
       lowercase__:   Dict					=      model(**_UpperCAmelCase							)

       # verify the logits
       lowercase__:   int					=      tf.TensorShape((1, 1000)							)
       self.assertEqual(outputs.logits.shape    ,			_UpperCAmelCase							)

       lowercase__:   Dict					=      tf.constant([-11.1_069, -9.7_877, -8.3_777]							)

       self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()    ,			_UpperCAmelCase    ,			atol=1e-4							)							)
