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 lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE=False	):
    '''simple docstring'''







    __UpperCamelCase     :str			  =		model_type
    if use_small:
        key += "_small"
    return os.path.join(SCREAMING_SNAKE_CASE  ,			REMOTE_MODEL_PATHS[key]['''file_name''']	)



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    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 lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE=False  ,			SCREAMING_SNAKE_CASE="text"	):
    '''simple docstring'''







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

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

    __UpperCamelCase     :Union[str, Any]			  =		ConfigClass(**checkpoint['''model_args''']	)
    __UpperCamelCase     :str			  =		ModelClass(config=SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :str			  =		GenerationConfigClass()

    __UpperCamelCase     :Tuple			  =		model_generation_config
    __UpperCamelCase     :str			  =		checkpoint['''model''']
    # fixup checkpoint
    __UpperCamelCase     :List[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
            __UpperCamelCase     :Optional[Any]			  =		k[len(SCREAMING_SNAKE_CASE	) :]
            for old_layer_name in new_layer_name_dict:
                __UpperCamelCase     :Union[str, Any]			  =		new_k.replace(SCREAMING_SNAKE_CASE  ,			new_layer_name_dict[old_layer_name]	)

            __UpperCamelCase     :List[str]			  =		state_dict.pop(SCREAMING_SNAKE_CASE	)

    __UpperCamelCase     :Tuple			  =		set(state_dict.keys()	) - set(model.state_dict().keys()	)
    __UpperCamelCase     :int			  =		{k for k in extra_keys if not k.endswith('''.attn.bias'''	)}
    __UpperCamelCase     :int			  =		set(model.state_dict().keys()	) - set(state_dict.keys()	)
    __UpperCamelCase     :int			  =		{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	)
    __UpperCamelCase     :List[Any]			  =		model.num_parameters(exclude_embeddings=SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :Tuple			  =		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 lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE=False  ,			SCREAMING_SNAKE_CASE="text"	):
    '''simple docstring'''







    if model_type not in ("text", "coarse", "fine"):
        raise NotImplementedError()

    __UpperCamelCase     :List[Any]			  =		'''cpu'''  # do conversion on cpu

    __UpperCamelCase     :List[Any]			  =		_get_ckpt_path(SCREAMING_SNAKE_CASE  ,			use_small=SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :int			  =		_load_model(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			model_type=SCREAMING_SNAKE_CASE  ,			use_small=SCREAMING_SNAKE_CASE	)

    # load bark initial model
    __UpperCamelCase     :Optional[Any]			  =		_bark_load_model(SCREAMING_SNAKE_CASE  ,			'''cpu'''  ,			model_type=SCREAMING_SNAKE_CASE  ,			use_small=SCREAMING_SNAKE_CASE	)

    if model_type == "text":
        __UpperCamelCase     :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
    __UpperCamelCase     :List[str]			  =		5
    __UpperCamelCase     :List[str]			  =		10

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

        __UpperCamelCase     :Optional[int]			  =		model(SCREAMING_SNAKE_CASE	)

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

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

        __UpperCamelCase     :Tuple			  =		model(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
        __UpperCamelCase     :str			  =		bark_model(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

        __UpperCamelCase     :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(SCREAMING_SNAKE_CASE	).mkdir(exist_ok=SCREAMING_SNAKE_CASE	)
    model.save_pretrained(SCREAMING_SNAKE_CASE	)



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			):
    '''simple docstring'''







    __UpperCamelCase     :List[str]			  =		os.path.join(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

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

    __UpperCamelCase     :Union[str, Any]			  =		BarkSemanticModel.from_pretrained(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :Dict			  =		BarkCoarseModel.from_pretrained(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :List[Any]			  =		BarkFineModel.from_pretrained(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :str			  =		EncodecModel.from_pretrained('''facebook/encodec_24khz'''	)

    __UpperCamelCase     :Tuple			  =		BarkConfig.from_sub_model_configs(
        SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

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

    __UpperCamelCase     :int			  =		BarkModel(SCREAMING_SNAKE_CASE	)

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

    __UpperCamelCase     :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							 =					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)












import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    TextToVideoSDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, load_numpy, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

@skip_mps
class 		UpperCamelCase__ (      lowerCAmelCase_							,      unittest.TestCase   ):



     '''simple docstring'''

     __snake_case    :	List[str]			=			TextToVideoSDPipeline
     __snake_case    :	int			=			TEXT_TO_IMAGE_PARAMS
     __snake_case    :	Tuple			=			TEXT_TO_IMAGE_BATCH_PARAMS
     # No `output_type`.
     __snake_case    :	Dict			=			frozenset(
         [
             "num_inference_steps",
             "generator",
             "latents",
             "return_dict",
             "callback",
             "callback_steps",
         ]   )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[int]       )     ->		Optional[int]:




           '''simple docstring'''


           torch.manual_seed(0       )
           SCREAMING_SNAKE_CASE		=						UNetaDConditionModel(
               block_out_channels=(32, 64, 64, 64)						,layers_per_block=2						,sample_size=32						,in_channels=4						,out_channels=4						,down_block_types=("""CrossAttnDownBlock3D""", """CrossAttnDownBlock3D""", """CrossAttnDownBlock3D""", """DownBlock3D""")						,up_block_types=("""UpBlock3D""", """CrossAttnUpBlock3D""", """CrossAttnUpBlock3D""", """CrossAttnUpBlock3D""")						,cross_attention_dim=32						,attention_head_dim=4						,)
           SCREAMING_SNAKE_CASE		=						DDIMScheduler(
               beta_start=0.00085						,beta_end=0.012						,beta_schedule="""scaled_linear"""						,clip_sample=lowerCamelCase__						,set_alpha_to_one=lowerCamelCase__						,)
           torch.manual_seed(0       )
           SCREAMING_SNAKE_CASE		=						AutoencoderKL(
               block_out_channels=[32, 64]						,in_channels=3						,out_channels=3						,down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]						,up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]						,latent_channels=4						,sample_size=128						,)
           torch.manual_seed(0       )
           SCREAMING_SNAKE_CASE		=						CLIPTextConfig(
               bos_token_id=0						,eos_token_id=2						,hidden_size=32						,intermediate_size=37						,layer_norm_eps=1e-0_5						,num_attention_heads=4						,num_hidden_layers=5						,pad_token_id=1						,vocab_size=1000						,hidden_act="""gelu"""						,projection_dim=512						,)
           SCREAMING_SNAKE_CASE		=						CLIPTextModel(lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""       )

           SCREAMING_SNAKE_CASE		=						{
               """unet""": unet,
               """scheduler""": scheduler,
               """vae""": vae,
               """text_encoder""": text_encoder,
               """tokenizer""": tokenizer,
           }
           return components






     def 				SCREAMING_SNAKE_CASE__  (							self						:    int						,lowerCamelCase__						:    Optional[Any]						,lowerCamelCase__						:    int=0       )     ->		List[Any]:




           '''simple docstring'''


           if str(lowerCamelCase__       ).startswith("""mps"""       ):
                 SCREAMING_SNAKE_CASE		=						torch.manual_seed(lowerCamelCase__       )
           else:
                 SCREAMING_SNAKE_CASE		=						torch.Generator(device=lowerCamelCase__       ).manual_seed(lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						{
               """prompt""": """A painting of a squirrel eating a burger""",
               """generator""": generator,
               """num_inference_steps""": 2,
               """guidance_scale""": 6.0,
               """output_type""": """pt""",
           }
           return inputs






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[Any]       )     ->		List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
           SCREAMING_SNAKE_CASE		=						self.get_dummy_components()
           SCREAMING_SNAKE_CASE		=						TextToVideoSDPipeline(**lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						sd_pipe.to(lowerCamelCase__       )
           sd_pipe.set_progress_bar_config(disable=lowerCamelCase__       )

           SCREAMING_SNAKE_CASE		=						self.get_dummy_inputs(lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						"""np"""
           SCREAMING_SNAKE_CASE		=						sd_pipe(**lowerCamelCase__       ).frames
           SCREAMING_SNAKE_CASE		=						frames[0][-3:, -3:, -1]

           assert frames[0].shape == (64, 64, 3)
           SCREAMING_SNAKE_CASE		=						np.array([158.0, 160.0, 153.0, 125.0, 100.0, 121.0, 111.0, 93.0, 113.0]       )

           assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Dict       )     ->		int:




           '''simple docstring'''


           self._test_attention_slicing_forward_pass(test_mean_pixel_difference=lowerCamelCase__						,expected_max_diff=3e-3       )






     @unittest.skipIf(
         torch_device != """cuda""" or not is_xformers_available()						,reason="""XFormers attention is only available with CUDA and `xformers` installed"""						,)
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Tuple       )     ->		Any:




           '''simple docstring'''


           self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=lowerCamelCase__						,expected_max_diff=1e-2       )






     @unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline."""       )
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[Any]       )     ->		List[Any]:




           '''simple docstring'''


           pass






     @unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline."""       )
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[Any]       )     ->		Any:




           '''simple docstring'''


           pass






     @unittest.skip(reason="""`num_images_per_prompt` argument is not supported for this pipeline."""       )
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[int]       )     ->		Tuple:




           '''simple docstring'''


           pass






     def 				SCREAMING_SNAKE_CASE__  (							self						:    List[str]       )     ->		Optional[Any]:




           '''simple docstring'''


           return super().test_progress_bar()

@slow
@skip_mps
class 		UpperCamelCase__ (      unittest.TestCase   ):



     '''simple docstring'''

     def 				SCREAMING_SNAKE_CASE__  (							self						:    Any       )     ->		Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy"""       )

           SCREAMING_SNAKE_CASE		=						TextToVideoSDPipeline.from_pretrained("""damo-vilab/text-to-video-ms-1.7b"""       )
           SCREAMING_SNAKE_CASE		=						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config       )
           SCREAMING_SNAKE_CASE		=						pipe.to("""cuda"""       )

           SCREAMING_SNAKE_CASE		=						"""Spiderman is surfing"""
           SCREAMING_SNAKE_CASE		=						torch.Generator(device="""cpu"""       ).manual_seed(0       )

           SCREAMING_SNAKE_CASE		=						pipe(lowerCamelCase__						,generator=lowerCamelCase__						,num_inference_steps=25						,output_type="""pt"""       ).frames
           SCREAMING_SNAKE_CASE		=						video_frames.cpu().numpy()

           assert np.abs(expected_video - video       ).mean() < 5e-2






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Any       )     ->		Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy"""       )

           SCREAMING_SNAKE_CASE		=						TextToVideoSDPipeline.from_pretrained("""damo-vilab/text-to-video-ms-1.7b"""       )
           SCREAMING_SNAKE_CASE		=						pipe.to("""cuda"""       )

           SCREAMING_SNAKE_CASE		=						"""Spiderman is surfing"""
           SCREAMING_SNAKE_CASE		=						torch.Generator(device="""cpu"""       ).manual_seed(0       )

           SCREAMING_SNAKE_CASE		=						pipe(lowerCamelCase__						,generator=lowerCamelCase__						,num_inference_steps=2						,output_type="""pt"""       ).frames
           SCREAMING_SNAKE_CASE		=						video_frames.cpu().numpy()

           assert np.abs(expected_video - video       ).mean() < 5e-2


