

'''simple docstring'''

import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import YolosImageProcessor




class 	A_			(    unittest.TestCase							):






     '''simple docstring'''



     def __init__(		self			:					Union[str, Any]					,      lowercase_			:					Union[str, Any]					,      lowercase_			:					Optional[int]=7					,      lowercase_			:					Any=3					,      lowercase_			:					Optional[int]=30					,      lowercase_			:					str=400					,      lowercase_			:					List[Any]=True					,      lowercase_			:					Union[str, Any]=None					,      lowercase_			:					List[Any]=True					,      lowercase_			:					Optional[Any]=[0.5, 0.5, 0.5]					,      lowercase_			:					Union[str, Any]=[0.5, 0.5, 0.5]					,      lowercase_			:					Optional[int]=True					,      lowercase_			:					Union[str, Any]=1 / 255					,      lowercase_			:					str=True					,      )						->     int:
      # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
      UpperCAmelCase	:     Tuple						 =			size if size is not None else {'shortest_edge': 18, 'longest_edge': 1_333}
      UpperCAmelCase	:     Optional[Any]						 =			parent
      UpperCAmelCase	:     Optional[int]						 =			batch_size
      UpperCAmelCase	:     Any						 =			num_channels
      UpperCAmelCase	:     List[str]						 =			min_resolution
      UpperCAmelCase	:     Any						 =			max_resolution
      UpperCAmelCase	:     Dict						 =			do_resize
      UpperCAmelCase	:     Tuple						 =			size
      UpperCAmelCase	:     List[Any]						 =			do_normalize
      UpperCAmelCase	:     Tuple						 =			image_mean
      UpperCAmelCase	:     Union[str, Any]						 =			image_std
      UpperCAmelCase	:     Tuple						 =			do_rescale
      UpperCAmelCase	:     Optional[int]						 =			rescale_factor
      UpperCAmelCase	:     Optional[Any]						 =			do_pad


     def        UpperCAmelCase_	(		self			:					Any	)						->     List[Any]:
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_rescale": self.do_rescale,
          "rescale_factor": self.rescale_factor,
          "do_pad": self.do_pad,
      }




     def        UpperCAmelCase_	(		self			:					str					,      lowercase_			:					int					,      lowercase_			:					Optional[int]=False	)						->     List[str]:
      if not batched:
       UpperCAmelCase	:     Union[str, Any]						 =			image_inputs[0]
       if isinstance(lowercase_					,      Image.Image	):
        UpperCAmelCase	:     Any						 =			image.size
       else:
        UpperCAmelCase	:     int						 =			image.shape[1], image.shape[2]
       if w < h:
        UpperCAmelCase	:     Dict						 =			int(self.size['shortest_edge'] * h / w	)
        UpperCAmelCase	:     Tuple						 =			self.size['shortest_edge']
       elif w > h:
        UpperCAmelCase	:     Optional[Any]						 =			self.size['shortest_edge']
        UpperCAmelCase	:     str						 =			int(self.size['shortest_edge'] * w / h	)
       else:
        UpperCAmelCase	:     List[Any]						 =			self.size['shortest_edge']
        UpperCAmelCase	:     Union[str, Any]						 =			self.size['shortest_edge']

      else:
       UpperCAmelCase	:     Dict						 =			[]
       for image in image_inputs:
        UpperCAmelCase	:     str						 =			self.get_expected_values([image]	)
        expected_values.append((expected_height, expected_width)	)
       UpperCAmelCase	:     List[Any]						 =			max(lowercase_					,      key=lambda lowercase_	: item[0]	)[0]
       UpperCAmelCase	:     Any						 =			max(lowercase_					,      key=lambda lowercase_	: item[1]	)[1]

      return expected_height, expected_width




@require_torch
@require_vision
class 	A_			(    _snake_case	,       unittest.TestCase							):






     '''simple docstring'''



     UpperCAmelCase_						:     str  		=  YolosImageProcessor if is_vision_available() else None


     def        UpperCAmelCase_	(		self			:					Optional[int]	)						->     Tuple:
      UpperCAmelCase	:     Tuple						 =			YolosImageProcessingTester(self	)


     @property
     def        UpperCAmelCase_	(		self			:					str	)						->     str:
      return self.image_processor_tester.prepare_image_processor_dict()


     def        UpperCAmelCase_	(		self			:					int	)						->     List[str]:
      UpperCAmelCase	:     List[Any]						 =			self.image_processing_class(**self.image_processor_dict	)
      self.assertTrue(hasattr(lowercase_					,      'image_mean'	)	)
      self.assertTrue(hasattr(lowercase_					,      'image_std'	)	)
      self.assertTrue(hasattr(lowercase_					,      'do_normalize'	)	)
      self.assertTrue(hasattr(lowercase_					,      'do_resize'	)	)
      self.assertTrue(hasattr(lowercase_					,      'size'	)	)


     def        UpperCAmelCase_	(		self			:					Dict	)						->     Dict:
      UpperCAmelCase	:     List[Any]						 =			self.image_processing_class.from_dict(self.image_processor_dict	)
      self.assertEqual(image_processor.size					,      {'shortest_edge': 18, 'longest_edge': 1_333}	)
      self.assertEqual(image_processor.do_pad					,      lowercase_	)

      UpperCAmelCase	:     List[Any]						 =			self.image_processing_class.from_dict(
          self.image_processor_dict					,      size=42					,      max_size=84					,      pad_and_return_pixel_mask=lowercase_	)
      self.assertEqual(image_processor.size					,      {'shortest_edge': 42, 'longest_edge': 84}	)
      self.assertEqual(image_processor.do_pad					,      lowercase_	)


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


     def        UpperCAmelCase_	(		self			:					Tuple	)						->     List[str]:
      # Initialize image_processing
      UpperCAmelCase	:     str						 =			self.image_processing_class(**self.image_processor_dict	)
      # create random PIL images
      UpperCAmelCase	:     Union[str, Any]						 =			prepare_image_inputs(self.image_processor_tester					,      equal_resolution=lowercase_	)
      for image in image_inputs:
       self.assertIsInstance(lowercase_					,      Image.Image	)

      # Test not batched input
      UpperCAmelCase	:     List[str]						 =			image_processing(image_inputs[0]					,      return_tensors='pt'	).pixel_values

      UpperCAmelCase	:     Optional[Any]						 =			self.image_processor_tester.get_expected_values(lowercase_	)

      self.assertEqual(
          encoded_images.shape					,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)					,      )

      # Test batched
      UpperCAmelCase	:     Tuple						 =			self.image_processor_tester.get_expected_values(lowercase_					,      batched=lowercase_	)

      UpperCAmelCase	:     Dict						 =			image_processing(lowercase_					,      return_tensors='pt'	).pixel_values
      self.assertEqual(
          encoded_images.shape					,      (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )					,      )


     def        UpperCAmelCase_	(		self			:					str	)						->     Union[str, Any]:
      # Initialize image_processing
      UpperCAmelCase	:     List[Any]						 =			self.image_processing_class(**self.image_processor_dict	)
      # create random numpy tensors
      UpperCAmelCase	:     Tuple						 =			prepare_image_inputs(self.image_processor_tester					,      equal_resolution=lowercase_					,      numpify=lowercase_	)
      for image in image_inputs:
       self.assertIsInstance(lowercase_					,      np.ndarray	)

      # Test not batched input
      UpperCAmelCase	:     int						 =			image_processing(image_inputs[0]					,      return_tensors='pt'	).pixel_values

      UpperCAmelCase	:     List[str]						 =			self.image_processor_tester.get_expected_values(lowercase_	)

      self.assertEqual(
          encoded_images.shape					,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)					,      )

      # Test batched
      UpperCAmelCase	:     Optional[Any]						 =			image_processing(lowercase_					,      return_tensors='pt'	).pixel_values

      UpperCAmelCase	:     str						 =			self.image_processor_tester.get_expected_values(lowercase_					,      batched=lowercase_	)

      self.assertEqual(
          encoded_images.shape					,      (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )					,      )


     def        UpperCAmelCase_	(		self			:					List[str]	)						->     Optional[int]:
      # Initialize image_processing
      UpperCAmelCase	:     Union[str, Any]						 =			self.image_processing_class(**self.image_processor_dict	)
      # create random PyTorch tensors
      UpperCAmelCase	:     Tuple						 =			prepare_image_inputs(self.image_processor_tester					,      equal_resolution=lowercase_					,      torchify=lowercase_	)
      for image in image_inputs:
       self.assertIsInstance(lowercase_					,      torch.Tensor	)

      # Test not batched input
      UpperCAmelCase	:     List[Any]						 =			image_processing(image_inputs[0]					,      return_tensors='pt'	).pixel_values

      UpperCAmelCase	:     Dict						 =			self.image_processor_tester.get_expected_values(lowercase_	)

      self.assertEqual(
          encoded_images.shape					,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)					,      )

      # Test batched
      UpperCAmelCase	:     Optional[Any]						 =			image_processing(lowercase_					,      return_tensors='pt'	).pixel_values

      UpperCAmelCase	:     Tuple						 =			self.image_processor_tester.get_expected_values(lowercase_					,      batched=lowercase_	)

      self.assertEqual(
          encoded_images.shape					,      (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )					,      )


     def        UpperCAmelCase_	(		self			:					Optional[int]	)						->     int:
      # Initialize image_processings
      UpperCAmelCase	:     List[Any]						 =			self.image_processing_class(**self.image_processor_dict	)
      UpperCAmelCase	:     Optional[Any]						 =			self.image_processing_class(do_resize=lowercase_					,      do_normalize=lowercase_					,      do_rescale=lowercase_	)
      # create random PyTorch tensors
      UpperCAmelCase	:     Dict						 =			prepare_image_inputs(self.image_processor_tester					,      equal_resolution=lowercase_					,      torchify=lowercase_	)
      for image in image_inputs:
       self.assertIsInstance(lowercase_					,      torch.Tensor	)

      # Test whether the method "pad" and calling the image processor return the same tensors
      UpperCAmelCase	:     Any						 =			image_processing_a.pad(lowercase_					,      return_tensors='pt'	)
      UpperCAmelCase	:     Union[str, Any]						 =			image_processing_a(lowercase_					,      return_tensors='pt'	)

      self.assertTrue(
          torch.allclose(encoded_images_with_method['pixel_values']					,      encoded_images['pixel_values']					,      atol=1E-4	)	)


     @slow
     def        UpperCAmelCase_	(		self			:					Optional[Any]	)						->     Union[str, Any]:
      # prepare image and target
      UpperCAmelCase	:     Tuple						 =			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
      with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'					,      'r'	) as f:
       UpperCAmelCase	:     int						 =			json.loads(f.read()	)

      UpperCAmelCase	:     Dict						 =			{'image_id': 39_769, 'annotations': target}

      # encode them
      UpperCAmelCase	:     Any						 =			YolosImageProcessor.from_pretrained('hustvl/yolos-small'	)
      UpperCAmelCase	:     str						 =			image_processing(images=lowercase_					,      annotations=lowercase_					,      return_tensors='pt'	)

      # verify pixel values
      UpperCAmelCase	:     int						 =			torch.Size([1, 3, 800, 1_066]	)
      self.assertEqual(encoding['pixel_values'].shape					,      lowercase_	)

      UpperCAmelCase	:     Dict						 =			torch.tensor([0.2796, 0.3138, 0.3481]	)
      self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]					,      lowercase_					,      atol=1E-4	)	)

      # verify area
      UpperCAmelCase	:     List[Any]						 =			torch.tensor([5887.9600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]	)
      self.assertTrue(torch.allclose(encoding['labels'][0]['area']					,      lowercase_	)	)
      # verify boxes
      UpperCAmelCase	:     int						 =			torch.Size([6, 4]	)
      self.assertEqual(encoding['labels'][0]['boxes'].shape					,      lowercase_	)
      UpperCAmelCase	:     Any						 =			torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]	)
      self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]					,      lowercase_					,      atol=1E-3	)	)
      # verify image_id
      UpperCAmelCase	:     List[str]						 =			torch.tensor([39_769]	)
      self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']					,      lowercase_	)	)
      # verify is_crowd
      UpperCAmelCase	:     str						 =			torch.tensor([0, 0, 0, 0, 0, 0]	)
      self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']					,      lowercase_	)	)
      # verify class_labels
      UpperCAmelCase	:     str						 =			torch.tensor([75, 75, 63, 65, 17, 17]	)
      self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']					,      lowercase_	)	)
      # verify orig_size
      UpperCAmelCase	:     Optional[int]						 =			torch.tensor([480, 640]	)
      self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']					,      lowercase_	)	)
      # verify size
      UpperCAmelCase	:     str						 =			torch.tensor([800, 1_066]	)
      self.assertTrue(torch.allclose(encoding['labels'][0]['size']					,      lowercase_	)	)




     @slow
     def        UpperCAmelCase_	(		self			:					List[Any]	)						->     Optional[Any]:
      # prepare image, target and masks_path
      UpperCAmelCase	:     Optional[Any]						 =			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
      with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'					,      'r'	) as f:
       UpperCAmelCase	:     List[Any]						 =			json.loads(f.read()	)

      UpperCAmelCase	:     List[str]						 =			{'file_name': '000000039769.png', 'image_id': 39_769, 'segments_info': target}

      UpperCAmelCase	:     int						 =			pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'	)

      # encode them
      UpperCAmelCase	:     Optional[int]						 =			YolosImageProcessor(format='coco_panoptic'	)
      UpperCAmelCase	:     str						 =			image_processing(images=lowercase_					,      annotations=lowercase_					,      masks_path=lowercase_					,      return_tensors='pt'	)

      # verify pixel values
      UpperCAmelCase	:     Optional[int]						 =			torch.Size([1, 3, 800, 1_066]	)
      self.assertEqual(encoding['pixel_values'].shape					,      lowercase_	)

      UpperCAmelCase	:     Union[str, Any]						 =			torch.tensor([0.2796, 0.3138, 0.3481]	)
      self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]					,      lowercase_					,      atol=1E-4	)	)

      # verify area
      UpperCAmelCase	:     Union[str, Any]						 =			torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5879.6562, 7634.1147]	)
      self.assertTrue(torch.allclose(encoding['labels'][0]['area']					,      lowercase_	)	)
      # verify boxes
      UpperCAmelCase	:     str						 =			torch.Size([6, 4]	)
      self.assertEqual(encoding['labels'][0]['boxes'].shape					,      lowercase_	)
      UpperCAmelCase	:     List[Any]						 =			torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]	)
      self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]					,      lowercase_					,      atol=1E-3	)	)
      # verify image_id
      UpperCAmelCase	:     Dict						 =			torch.tensor([39_769]	)
      self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']					,      lowercase_	)	)
      # verify is_crowd
      UpperCAmelCase	:     int						 =			torch.tensor([0, 0, 0, 0, 0, 0]	)
      self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']					,      lowercase_	)	)
      # verify class_labels
      UpperCAmelCase	:     Union[str, Any]						 =			torch.tensor([17, 17, 63, 75, 75, 93]	)
      self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']					,      lowercase_	)	)
      # verify masks
      UpperCAmelCase	:     Union[str, Any]						 =			822_873
      self.assertEqual(encoding['labels'][0]['masks'].sum().item()					,      lowercase_	)
      # verify orig_size
      UpperCAmelCase	:     str						 =			torch.tensor([480, 640]	)
      self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']					,      lowercase_	)	)
      # verify size
      UpperCAmelCase	:     Any						 =			torch.tensor([800, 1_066]	)
      self.assertTrue(torch.allclose(encoding['labels'][0]['size']					,      lowercase_	)	)







'''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()
lowercase__         =      logging.get_logger(__name__)

set_seed(770)


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




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_=False     ):
 UpperCAmelCase	:     List[str]						 =			model_type
 if use_small:
  key += "_small"
 return os.path.join(UpperCAmelCase_  ,	REMOTE_MODEL_PATHS[key]['file_name']     )




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 os.makedirs(UpperCAmelCase_  ,	exist_ok=UpperCAmelCase_     )
 hf_hub_download(repo_id=UpperCAmelCase_  ,	filename=UpperCAmelCase_  ,	local_dir=UpperCAmelCase_     )




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_=False  ,	UpperCAmelCase_="text"     ):
 if model_type == "text":
  UpperCAmelCase	:     Dict						 =			BarkSemanticModel
  UpperCAmelCase	:     List[Any]						 =			BarkSemanticConfig
  UpperCAmelCase	:     Optional[int]						 =			BarkSemanticGenerationConfig
 elif model_type == "coarse":
  UpperCAmelCase	:     List[str]						 =			BarkCoarseModel
  UpperCAmelCase	:     Dict						 =			BarkCoarseConfig
  UpperCAmelCase	:     int						 =			BarkCoarseGenerationConfig
 elif model_type == "fine":
  UpperCAmelCase	:     List[Any]						 =			BarkFineModel
  UpperCAmelCase	:     Optional[Any]						 =			BarkFineConfig
  UpperCAmelCase	:     Dict						 =			BarkFineGenerationConfig
 else:
  raise NotImplementedError()
 UpperCAmelCase	:     Optional[int]						 =			F"""{model_type}_small""" if use_small else model_type
 UpperCAmelCase	:     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']     )
 UpperCAmelCase	:     Optional[Any]						 =			torch.load(UpperCAmelCase_  ,	map_location=UpperCAmelCase_     )
 # this is a hack
 UpperCAmelCase	:     str						 =			checkpoint['model_args']
 if "input_vocab_size" not in model_args:
  UpperCAmelCase	:     Union[str, Any]						 =			model_args['vocab_size']
  UpperCAmelCase	:     Union[str, Any]						 =			model_args['vocab_size']
  del model_args["vocab_size"]

 # convert Bark model arguments to HF Bark model arguments
 UpperCAmelCase	:     Any						 =			model_args.pop('n_head'     )
 UpperCAmelCase	:     Optional[Any]						 =			model_args.pop('n_embd'     )
 UpperCAmelCase	:     Union[str, Any]						 =			model_args.pop('n_layer'     )

 UpperCAmelCase	:     List[Any]						 =			ConfigClass(**checkpoint['model_args']     )
 UpperCAmelCase	:     List[str]						 =			ModelClass(config=UpperCAmelCase_     )
 UpperCAmelCase	:     List[str]						 =			GenerationConfigClass()

 UpperCAmelCase	:     Dict						 =			model_generation_config
 UpperCAmelCase	:     int						 =			checkpoint['model']
 # fixup checkpoint
 UpperCAmelCase	:     Tuple						 =			'_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
   UpperCAmelCase	:     str						 =			k[len(UpperCAmelCase_     ) :]
   for old_layer_name in new_layer_name_dict:
    UpperCAmelCase	:     List[Any]						 =			new_k.replace(UpperCAmelCase_  ,	new_layer_name_dict[old_layer_name]     )

   UpperCAmelCase	:     List[Any]						 =			state_dict.pop(UpperCAmelCase_     )

 UpperCAmelCase	:     Tuple						 =			set(state_dict.keys()     ) - set(model.state_dict().keys()     )
 UpperCAmelCase	:     Optional[int]						 =			{k for k in extra_keys if not k.endswith('.attn.bias'     )}
 UpperCAmelCase	:     str						 =			set(model.state_dict().keys()     ) - set(state_dict.keys()     )
 UpperCAmelCase	:     str						 =			{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_     )
 UpperCAmelCase	:     List[str]						 =			model.num_parameters(exclude_embeddings=UpperCAmelCase_     )
 UpperCAmelCase	:     Optional[Any]						 =			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 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_=False  ,	UpperCAmelCase_="text"     ):
 if model_type not in ("text", "coarse", "fine"):
  raise NotImplementedError()

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

 UpperCAmelCase	:     List[str]						 =			_get_ckpt_path(UpperCAmelCase_  ,	use_small=UpperCAmelCase_     )
 UpperCAmelCase	:     Optional[int]						 =			_load_model(UpperCAmelCase_  ,	UpperCAmelCase_  ,	model_type=UpperCAmelCase_  ,	use_small=UpperCAmelCase_     )

 # load bark initial model
 UpperCAmelCase	:     List[str]						 =			_bark_load_model(UpperCAmelCase_  ,	'cpu'  ,	model_type=UpperCAmelCase_  ,	use_small=UpperCAmelCase_     )

 if model_type == "text":
  UpperCAmelCase	:     Tuple						 =			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
 UpperCAmelCase	:     Optional[int]						 =			5
 UpperCAmelCase	:     Optional[int]						 =			10

 if model_type in ["text", "coarse"]:
  UpperCAmelCase	:     List[Any]						 =			torch.randint(2_56  ,	(batch_size, sequence_length)  ,	dtype=torch.int     )
  UpperCAmelCase	:     Optional[Any]						 =			bark_model(UpperCAmelCase_     )[0]

  UpperCAmelCase	:     List[str]						 =			model(UpperCAmelCase_     )

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

 else:
  UpperCAmelCase	:     Optional[int]						 =			3
  UpperCAmelCase	:     List[Any]						 =			8
  UpperCAmelCase	:     Dict						 =			torch.randint(2_56  ,	(batch_size, sequence_length, n_codes_total)  ,	dtype=torch.int     )

  UpperCAmelCase	:     str						 =			model(UpperCAmelCase_  ,	UpperCAmelCase_     )
  UpperCAmelCase	:     Dict						 =			bark_model(UpperCAmelCase_  ,	UpperCAmelCase_     )

  UpperCAmelCase	:     Any						 =			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 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	):
 UpperCAmelCase	:     int						 =			os.path.join(UpperCAmelCase_  ,	UpperCAmelCase_     )

 UpperCAmelCase	:     Dict						 =			BarkSemanticConfig.from_pretrained(os.path.join(UpperCAmelCase_  ,	'config.json'     )     )
 UpperCAmelCase	:     Any						 =			BarkCoarseConfig.from_pretrained(os.path.join(UpperCAmelCase_  ,	'config.json'     )     )
 UpperCAmelCase	:     Union[str, Any]						 =			BarkFineConfig.from_pretrained(os.path.join(UpperCAmelCase_  ,	'config.json'     )     )
 UpperCAmelCase	:     Any						 =			EncodecConfig.from_pretrained('facebook/encodec_24khz'     )

 UpperCAmelCase	:     Dict						 =			BarkSemanticModel.from_pretrained(UpperCAmelCase_     )
 UpperCAmelCase	:     Tuple						 =			BarkCoarseModel.from_pretrained(UpperCAmelCase_     )
 UpperCAmelCase	:     Optional[int]						 =			BarkFineModel.from_pretrained(UpperCAmelCase_     )
 UpperCAmelCase	:     str						 =			EncodecModel.from_pretrained('facebook/encodec_24khz'     )

 UpperCAmelCase	:     Optional[Any]						 =			BarkConfig.from_sub_model_configs(
     UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     )

 UpperCAmelCase	:     Optional[int]						 =			BarkGenerationConfig.from_sub_model_configs(
     semantic.generation_config  ,	coarseAcoustic.generation_config  ,	fineAcoustic.generation_config     )

 UpperCAmelCase	:     str						 =			BarkModel(UpperCAmelCase_     )

 UpperCAmelCase	:     int						 =			semantic
 UpperCAmelCase	:     Tuple						 =			coarseAcoustic
 UpperCAmelCase	:     Union[str, Any]						 =			fineAcoustic
 UpperCAmelCase	:     Union[str, Any]						 =			codec

 UpperCAmelCase	:     Optional[int]						 =			bark_generation_config

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


if __name__ == "__main__":
 lowercase__         =      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__         =      parser.parse_args()

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




