



'''simple docstring'''

import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch
							from torch import nn

							from transformers import RegNetForImageClassification, RegNetModel
							from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor

class      __magic_name__						:







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







							def 							__lowercase      (							self :	Optional[int]		):
									_a					:				List[Any]				=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

									_a					:				Optional[Any]				=							None
									if self.use_labels:
											_a					:				int				=							ids_tensor([self.batch_size]     ,self.num_labels		)

									_a					:				str				=							self.get_config()

									return config, pixel_values, labels







							def 							__lowercase      (							self :	List[str]		):
									return RegNetConfig(
									    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     ,)







							def 							__lowercase      (							self :	List[Any]     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	Optional[int]     ,_UpperCAmelCase :	Any		):
									_a					:				List[str]				=							RegNetModel(config=_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()
									_a					:				List[str]				=							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 							__lowercase      (							self :	Optional[int]     ,_UpperCAmelCase :	Any     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	List[Any]		):
									_a					:				Any				=							self.num_labels
									_a					:				int				=							RegNetForImageClassification(_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()
									_a					:				Union[str, Any]				=							model(_UpperCAmelCase     ,labels=_UpperCAmelCase		)
									self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.num_labels)		)







							def 							__lowercase      (							self :	List[str]		):
									_a					:				Dict				=							self.prepare_config_and_inputs()
									_a					:				Union[str, Any]				=							config_and_inputs
									_a					:				Tuple				=							{'pixel_values': pixel_values}
									return config, inputs_dict

@require_torch
class      __magic_name__						(   _UpperCamelCase      ,						_UpperCamelCase      ,						unittest.TestCase       ):
							lowerCAmelCase	:   Optional[int]           = (RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
							lowerCAmelCase	:   Optional[int]           = (
							    {'feature-extraction': RegNetModel, 'image-classification': RegNetForImageClassification}
							    if is_torch_available()
							    else {}
							)

							lowerCAmelCase	:   Tuple           = False
							lowerCAmelCase	:   int           = False
							lowerCAmelCase	:   Optional[Any]           = False
							lowerCAmelCase	:   List[Any]           = False







							def 							__lowercase      (							self :	str		):
									_a					:				Tuple				=							RegNetModelTester(self		)
									_a					:				Any				=							ConfigTester(self     ,config_class=_UpperCAmelCase     ,has_text_modality=_UpperCAmelCase		)







							def 							__lowercase      (							self :	Optional[Any]		):
									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 							__lowercase      (							self :	Optional[Any]		):
									return







							@unittest.skip(reason='RegNet does not use inputs_embeds'		)
							def 							__lowercase      (							self :	Dict		):
									pass







							@unittest.skip(reason='RegNet does not support input and output embeddings'		)
							def 							__lowercase      (							self :	Optional[Any]		):
									pass







							def 							__lowercase      (							self :	Dict		):
									_a					:				Tuple				=							self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											_a					:				Dict				=							model_class(_UpperCAmelCase		)
											_a					:				List[str]				=							inspect.signature(model.forward		)
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											_a					:				List[str]				=							[*signature.parameters.keys()]

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







							def 							__lowercase      (							self :	Union[str, Any]		):
									_a					:				List[Any]				=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_UpperCAmelCase		)







							def 							__lowercase      (							self :	Optional[Any]		):
									_a					:				Union[str, Any]				=							self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											_a					:				int				=							model_class(config=_UpperCAmelCase		)
											for name, module in model.named_modules():
													if isinstance(_UpperCAmelCase     ,(nn.BatchNormad, nn.GroupNorm)		):
															self.assertTrue(
															    torch.all(module.weight == 1		)     ,msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""     ,)
															self.assertTrue(
															    torch.all(module.bias == 0		)     ,msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""     ,)







							def 							__lowercase      (							self :	Union[str, Any]		):
									def check_hidden_states_output(_UpperCAmelCase :	Optional[int]     ,_UpperCAmelCase :	Optional[int]     ,_UpperCAmelCase :	str		):
											_a					:				List[Any]				=							model_class(_UpperCAmelCase		)
											model.to(_UpperCAmelCase		)
											model.eval()

											with torch.no_grad():
													_a					:				List[Any]				=							model(**self._prepare_for_class(_UpperCAmelCase     ,_UpperCAmelCase		)		)

											_a					:				Optional[Any]				=							outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

											_a					:				Union[str, Any]				=							self.model_tester.num_stages
											self.assertEqual(len(_UpperCAmelCase		)     ,expected_num_stages + 1		)

											# RegNet'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 // 2, self.model_tester.image_size // 2]     ,)

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

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

													check_hidden_states_output(_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase		)







							def 							__lowercase      (							self :	Union[str, Any]		):
									_a					:				Optional[int]				=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase		)







							@slow
							def 							__lowercase      (							self :	Optional[int]		):
									for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											_a					:				int				=							RegNetModel.from_pretrained(_UpperCAmelCase		)
											self.assertIsNotNone(_UpperCAmelCase		)



def 					__lowerCamelCase					(				)      ->				str:
		_a					:				str				=							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
		return image

@require_torch
@require_vision
class      __magic_name__						(   unittest.TestCase       ):







							@cached_property
							def 							__lowercase      (							self :	List[Any]		):
									return (
									    AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)
									    if is_vision_available()
									    else None
									)







							@slow
							def 							__lowercase      (							self :	Any		):
									_a					:				Union[str, Any]				=							RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(_UpperCAmelCase		)

									_a					:				Optional[Any]				=							self.default_image_processor
									_a					:				Optional[int]				=							prepare_img()
									_a					:				List[str]				=							image_processor(images=_UpperCAmelCase     ,return_tensors='pt'		).to(_UpperCAmelCase		)

									# forward pass
									with torch.no_grad():
											_a					:				Any				=							model(**_UpperCAmelCase		)

									# verify the logits
									_a					:				Dict				=							torch.Size((1, 1000)		)
									self.assertEqual(outputs.logits.shape     ,_UpperCAmelCase		)

									_a					:				List[str]				=							torch.tensor([-0.41_80, -1.50_51, -3.48_36]		).to(_UpperCAmelCase		)

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









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

set_seed(770)


__lowerCAmelCase						      =       {
    '''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''',
}


__lowerCAmelCase						      =       {
    '''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''',
    },
}

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



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_=False  )      ->				Optional[int]:
		_a					:				int				=							model_type
		if use_small:
				key += "_small"
		return os.path.join(lowerCAmelCase_						,      REMOTE_MODEL_PATHS[key]['file_name']  )



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				int:
		os.makedirs(lowerCAmelCase_						,      exist_ok=lowerCAmelCase_  )
		hf_hub_download(repo_id=lowerCAmelCase_						,      filename=lowerCAmelCase_						,      local_dir=lowerCAmelCase_  )



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

		# convert Bark model arguments to HF Bark model arguments
		_a					:				List[Any]				=							model_args.pop('n_head'  )
		_a					:				Any				=							model_args.pop('n_embd'  )
		_a					:				List[Any]				=							model_args.pop('n_layer'  )

		_a					:				Optional[int]				=							ConfigClass(**checkpoint['model_args']  )
		_a					:				List[str]				=							ModelClass(config=lowerCAmelCase_  )
		_a					:				Tuple				=							GenerationConfigClass()

		_a					:				Optional[Any]				=							model_generation_config
		_a					:				Optional[Any]				=							checkpoint['model']
		# fixup checkpoint
		_a					:				int				=							'_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
						_a					:				str				=							k[len(lowerCAmelCase_  ) :]
						for old_layer_name in new_layer_name_dict:
								_a					:				List[Any]				=							new_k.replace(lowerCAmelCase_						,      new_layer_name_dict[old_layer_name]  )

						_a					:				List[Any]				=							state_dict.pop(lowerCAmelCase_  )

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

		_a					:				Optional[int]				=							'cpu'  # do conversion on cpu

		_a					:				Tuple				=							_get_ckpt_path(lowerCAmelCase_						,      use_small=lowerCAmelCase_  )
		_a					:				List[Any]				=							_load_model(lowerCAmelCase_						,      lowerCAmelCase_						,      model_type=lowerCAmelCase_						,      use_small=lowerCAmelCase_  )

		# load bark initial model
		_a					:				Any				=							_bark_load_model(lowerCAmelCase_						,      'cpu'						,      model_type=lowerCAmelCase_						,      use_small=lowerCAmelCase_  )

		if model_type == "text":
				_a					:				int				=							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
		_a					:				Any				=							5
		_a					:				List[str]				=							10

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

				_a					:				Tuple				=							model(lowerCAmelCase_  )

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

		else:
				_a					:				List[str]				=							3
				_a					:				List[Any]				=							8
				_a					:				Tuple				=							torch.randint(256						,      (batch_size, sequence_length, n_codes_total)						,      dtype=torch.int  )

				_a					:				Union[str, Any]				=							model(lowerCAmelCase_						,      lowerCAmelCase_  )
				_a					:				int				=							bark_model(lowerCAmelCase_						,      lowerCAmelCase_  )

				_a					:				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(lowerCAmelCase_  ).mkdir(exist_ok=lowerCAmelCase_  )
		model.save_pretrained(lowerCAmelCase_  )



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      )      ->				Any:
		_a					:				Any				=							os.path.join(lowerCAmelCase_						,      lowerCAmelCase_  )

		_a					:				int				=							BarkSemanticConfig.from_pretrained(os.path.join(lowerCAmelCase_						,      'config.json'  )  )
		_a					:				Any				=							BarkCoarseConfig.from_pretrained(os.path.join(lowerCAmelCase_						,      'config.json'  )  )
		_a					:				List[Any]				=							BarkFineConfig.from_pretrained(os.path.join(lowerCAmelCase_						,      'config.json'  )  )
		_a					:				List[str]				=							EncodecConfig.from_pretrained('facebook/encodec_24khz'  )

		_a					:				str				=							BarkSemanticModel.from_pretrained(lowerCAmelCase_  )
		_a					:				Dict				=							BarkCoarseModel.from_pretrained(lowerCAmelCase_  )
		_a					:				int				=							BarkFineModel.from_pretrained(lowerCAmelCase_  )
		_a					:				List[Any]				=							EncodecModel.from_pretrained('facebook/encodec_24khz'  )

		_a					:				Any				=							BarkConfig.from_sub_model_configs(
		    lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )

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

		_a					:				Optional[Any]				=							BarkModel(lowerCAmelCase_  )

		_a					:				List[str]				=							semantic
		_a					:				Union[str, Any]				=							coarseAcoustic
		_a					:				Optional[int]				=							fineAcoustic
		_a					:				Optional[Any]				=							codec

		_a					:				List[Any]				=							bark_generation_config

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


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

							__lowerCAmelCase						      =       parser.parse_args()

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




