import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModel,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()


class 	lowerCamelCase_     (   UpperCAmelCase_						,	UpperCAmelCase_						,	UpperCAmelCase_						,	unittest.TestCase ):






    '''simple docstring'''






    a__	:						int						=      StableUnCLIPImgaImgPipeline
    a__	:						Optional[int]						=      TEXT_GUIDED_IMAGE_VARIATION_PARAMS
    a__	:						Union[str, Any]						=      TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    a__	:						Optional[Any]						=      frozenset(
        [] )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
    a__	:						int						=      frozenset([] )







    def 			UpperCamelCase__   (	self)       ->	Tuple:
        __UpperCamelCase     :Tuple			  =		32
        __UpperCamelCase     :Optional[int]			  =		embedder_hidden_size

        # image encoding components

        __UpperCamelCase     :Union[str, Any]			  =		CLIPImageProcessor(crop_size=32 , size=32)

        torch.manual_seed(0)
        __UpperCamelCase     :Union[str, Any]			  =		CLIPVisionModelWithProjection(
            CLIPVisionConfig(
                hidden_size=__lowercase , projection_dim=__lowercase , num_hidden_layers=5 , num_attention_heads=4 , image_size=32 , intermediate_size=37 , patch_size=1 , ))

        # regular denoising components

        torch.manual_seed(0)
        __UpperCamelCase     :str			  =		StableUnCLIPImageNormalizer(embedding_dim=__lowercase)
        __UpperCamelCase     :Optional[int]			  =		DDPMScheduler(beta_schedule='''squaredcos_cap_v2''')

        torch.manual_seed(0)
        __UpperCamelCase     :Union[str, Any]			  =		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

        torch.manual_seed(0)
        __UpperCamelCase     :Dict			  =		CLIPTextModel(
            CLIPTextConfig(
                bos_token_id=0 , eos_token_id=2 , hidden_size=__lowercase , projection_dim=32 , intermediate_size=37 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , ))

        torch.manual_seed(0)
        __UpperCamelCase     :List[Any]			  =		UNetaDConditionModel(
            sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''') , up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''') , block_out_channels=(32, 64) , attention_head_dim=(2, 4) , class_embed_type='''projection''' , projection_class_embeddings_input_dim=embedder_projection_dim * 2 , cross_attention_dim=__lowercase , layers_per_block=1 , upcast_attention=__lowercase , use_linear_projection=__lowercase , )

        torch.manual_seed(0)
        __UpperCamelCase     :Tuple			  =		DDIMScheduler(
            beta_schedule='''scaled_linear''' , beta_start=0.0_00_85 , beta_end=0.0_12 , prediction_type='''v_prediction''' , set_alpha_to_one=__lowercase , steps_offset=1 , )

        torch.manual_seed(0)
        __UpperCamelCase     :List[str]			  =		AutoencoderKL()

        __UpperCamelCase     :Tuple			  =		{
            # image encoding components
            '''feature_extractor''': feature_extractor,
            '''image_encoder''': image_encoder.eval(),
            # image noising components
            '''image_normalizer''': image_normalizer.eval(),
            '''image_noising_scheduler''': image_noising_scheduler,
            # regular denoising components
            '''tokenizer''': tokenizer,
            '''text_encoder''': text_encoder.eval(),
            '''unet''': unet.eval(),
            '''scheduler''': scheduler,
            '''vae''': vae.eval(),
        }

        return components







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase=0 , __lowercase=True)       ->	str:
        if str(__lowercase).startswith('''mps'''):
            __UpperCamelCase     :Union[str, Any]			  =		torch.manual_seed(__lowercase)
        else:
            __UpperCamelCase     :int			  =		torch.Generator(device=__lowercase).manual_seed(__lowercase)

        __UpperCamelCase     :int			  =		floats_tensor((1, 3, 32, 32) , rng=random.Random(__lowercase)).to(__lowercase)

        if pil_image:
            __UpperCamelCase     :List[Any]			  =		input_image * 0.5 + 0.5
            __UpperCamelCase     :Optional[Any]			  =		input_image.clamp(0 , 1)
            __UpperCamelCase     :int			  =		input_image.cpu().permute(0 , 2 , 3 , 1).float().numpy()
            __UpperCamelCase     :Optional[Any]			  =		DiffusionPipeline.numpy_to_pil(__lowercase)[0]

        return {
            "prompt": "An anime racoon running a marathon",
            "image": input_image,
            "generator": generator,
            "num_inference_steps": 2,
            "output_type": "np",
        }







    @skip_mps
    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        __UpperCamelCase     :Dict			  =		'''cpu'''  # ensure determinism for the device-dependent torch.Generator
        __UpperCamelCase     :Tuple			  =		self.get_dummy_components()
        __UpperCamelCase     :Any			  =		StableUnCLIPImgaImgPipeline(**__lowercase)
        __UpperCamelCase     :Optional[Any]			  =		sd_pipe.to(__lowercase)
        sd_pipe.set_progress_bar_config(disable=__lowercase)

        __UpperCamelCase     :List[Any]			  =		self.get_dummy_inputs(__lowercase)
        inputs.update({'''image_embeds''': None})
        __UpperCamelCase     :Any			  =		sd_pipe(**__lowercase).images
        __UpperCamelCase     :List[str]			  =		image[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        __UpperCamelCase     :List[Any]			  =		np.array([0.38_72, 0.72_24, 0.56_01, 0.47_41, 0.68_72, 0.58_14, 0.46_36, 0.38_67, 0.50_78])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-3







    def 			UpperCamelCase__   (	self)       ->	str:
        __UpperCamelCase     :Optional[Any]			  =		torch_device in ['''cpu''', '''mps''']

        self._test_attention_slicing_forward_pass(test_max_difference=__lowercase)







    def 			UpperCamelCase__   (	self)       ->	List[Any]:
        __UpperCamelCase     :Optional[Any]			  =		torch_device in ['''cpu''', '''mps''']

        self._test_inference_batch_single_identical(test_max_difference=__lowercase)







    @unittest.skipIf(
        torch_device != '''cuda''' or not is_xformers_available() , reason='''XFormers attention is only available with CUDA and `xformers` installed''' , )
    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        self._test_xformers_attention_forwardGenerator_pass(test_max_difference=__lowercase)


@slow
@require_torch_gpu
class 	lowerCamelCase_     (   unittest.TestCase ):






    '''simple docstring'''






    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        __UpperCamelCase     :int			  =		load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png''')

        __UpperCamelCase     :Any			  =		load_numpy(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy''')

        __UpperCamelCase     :List[Any]			  =		StableUnCLIPImgaImgPipeline.from_pretrained(
            '''fusing/stable-unclip-2-1-l-img2img''' , torch_dtype=torch.floataa)
        pipe.to(__lowercase)
        pipe.set_progress_bar_config(disable=__lowercase)
        # stable unclip will oom when integration tests are run on a V100,
        # so turn on memory savings
        pipe.enable_attention_slicing()
        pipe.enable_sequential_cpu_offload()

        __UpperCamelCase     :int			  =		torch.Generator(device='''cpu''').manual_seed(0)
        __UpperCamelCase     :Dict			  =		pipe(__lowercase , '''anime turle''' , generator=__lowercase , output_type='''np''')

        __UpperCamelCase     :Dict			  =		output.images[0]

        assert image.shape == (768, 768, 3)

        assert_mean_pixel_difference(__lowercase , __lowercase)







    def 			UpperCamelCase__   (	self)       ->	List[str]:
        __UpperCamelCase     :Optional[Any]			  =		load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png''')

        __UpperCamelCase     :Any			  =		load_numpy(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy''')

        __UpperCamelCase     :Any			  =		StableUnCLIPImgaImgPipeline.from_pretrained(
            '''fusing/stable-unclip-2-1-h-img2img''' , torch_dtype=torch.floataa)
        pipe.to(__lowercase)
        pipe.set_progress_bar_config(disable=__lowercase)
        # stable unclip will oom when integration tests are run on a V100,
        # so turn on memory savings
        pipe.enable_attention_slicing()
        pipe.enable_sequential_cpu_offload()

        __UpperCamelCase     :int			  =		torch.Generator(device='''cpu''').manual_seed(0)
        __UpperCamelCase     :Optional[int]			  =		pipe(__lowercase , '''anime turle''' , generator=__lowercase , output_type='''np''')

        __UpperCamelCase     :List[Any]			  =		output.images[0]

        assert image.shape == (768, 768, 3)

        assert_mean_pixel_difference(__lowercase , __lowercase)







    def 			UpperCamelCase__   (	self)       ->	List[str]:
        __UpperCamelCase     :Dict			  =		load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png''')

        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        __UpperCamelCase     :List[Any]			  =		StableUnCLIPImgaImgPipeline.from_pretrained(
            '''fusing/stable-unclip-2-1-h-img2img''' , torch_dtype=torch.floataa)
        __UpperCamelCase     :Union[str, Any]			  =		pipe.to(__lowercase)
        pipe.set_progress_bar_config(disable=__lowercase)
        pipe.enable_attention_slicing()
        pipe.enable_sequential_cpu_offload()

        __UpperCamelCase     :Optional[Any]			  =		pipe(
            __lowercase , '''anime turtle''' , num_inference_steps=2 , output_type='''np''' , )

        __UpperCamelCase     :int			  =		torch.cuda.max_memory_allocated()
        # make sure that less than 7 GB is allocated
        assert mem_bytes < 7 * 10**9






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
__lowercase							 =					logging.get_logger(__name__)

__lowercase							 =					{
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}



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







    for attribute in key.split('''.'''	):
        __UpperCamelCase     :str			  =		getattr(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

    if weight_type is not None:
        __UpperCamelCase     :Any			  =		getattr(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	).shape
    else:
        __UpperCamelCase     :Union[str, Any]			  =		hf_pointer.shape

    assert hf_shape == value.shape, (
        f"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
        f""" {value.shape} for {full_name}"""
    )

    if weight_type == "weight":
        __UpperCamelCase     :str			  =		value
    elif weight_type == "weight_g":
        __UpperCamelCase     :List[str]			  =		value
    elif weight_type == "weight_v":
        __UpperCamelCase     :str			  =		value
    elif weight_type == "bias":
        __UpperCamelCase     :Union[str, Any]			  =		value
    else:
        __UpperCamelCase     :str			  =		value

    logger.info(f"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."""	)



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







    __UpperCamelCase     :List[Any]			  =		[]
    __UpperCamelCase     :int			  =		fairseq_model.state_dict()

    __UpperCamelCase     :List[Any]			  =		hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

    for name, value in fairseq_dict.items():
        __UpperCamelCase     :List[Any]			  =		False
        if "conv_layers" in name:
            load_conv_layer(
                SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			hf_model.config.feat_extract_norm == '''group'''  ,			)
            __UpperCamelCase     :List[str]			  =		True
        else:
            for key, mapped_key in MAPPING.items():
                __UpperCamelCase     :Dict			  =		'''hubert.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

                if key in name or (key.split('''w2v_model.'''	)[-1] == name.split('''.'''	)[0] and not is_finetuned):
                    __UpperCamelCase     :Optional[Any]			  =		True
                    if "*" in mapped_key:
                        __UpperCamelCase     :List[str]			  =		name.split(SCREAMING_SNAKE_CASE	)[0].split('''.'''	)[-2]
                        __UpperCamelCase     :Optional[int]			  =		mapped_key.replace('''*'''  ,			SCREAMING_SNAKE_CASE	)
                    if "weight_g" in name:
                        __UpperCamelCase     :int			  =		'''weight_g'''
                    elif "weight_v" in name:
                        __UpperCamelCase     :List[Any]			  =		'''weight_v'''
                    elif "weight" in name:
                        __UpperCamelCase     :Dict			  =		'''weight'''
                    elif "bias" in name:
                        __UpperCamelCase     :Dict			  =		'''bias'''
                    else:
                        __UpperCamelCase     :Dict			  =		None
                    set_recursively(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
                continue
        if not is_used:
            unused_weights.append(SCREAMING_SNAKE_CASE	)

    logger.warning(f"""Unused weights: {unused_weights}"""	)



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







    __UpperCamelCase     :Tuple			  =		full_name.split('''conv_layers.'''	)[-1]
    __UpperCamelCase     :Optional[int]			  =		name.split('''.'''	)
    __UpperCamelCase     :str			  =		int(items[0]	)
    __UpperCamelCase     :List[Any]			  =		int(items[1]	)

    if type_id == 0:
        if "bias" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                f"""{full_name} has size {value.shape}, but"""
                f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
            )
            __UpperCamelCase     :Dict			  =		value
            logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""	)
        elif "weight" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                f"""{full_name} has size {value.shape}, but"""
                f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
            )
            __UpperCamelCase     :Any			  =		value
            logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""	)
    elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
        if "bias" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
                " found."
            )
            __UpperCamelCase     :int			  =		value
            logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""	)
        elif "weight" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                f"""{full_name} has size {value.shape}, but"""
                f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
            )
            __UpperCamelCase     :Union[str, Any]			  =		value
            logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""	)
    else:
        unused_weights.append(SCREAMING_SNAKE_CASE	)



@torch.no_grad()
def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE=None  ,			SCREAMING_SNAKE_CASE=None  ,			SCREAMING_SNAKE_CASE=True	):
    '''simple docstring'''







    if config_path is not None:
        __UpperCamelCase     :Tuple			  =		HubertConfig.from_pretrained(SCREAMING_SNAKE_CASE	)
    else:
        __UpperCamelCase     :Optional[int]			  =		HubertConfig()

    if is_finetuned:
        if dict_path:
            __UpperCamelCase     :Optional[int]			  =		Dictionary.load(SCREAMING_SNAKE_CASE	)

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            __UpperCamelCase     :Optional[int]			  =		target_dict.pad_index
            __UpperCamelCase     :Dict			  =		target_dict.bos_index
            __UpperCamelCase     :str			  =		target_dict.eos_index
            __UpperCamelCase     :Dict			  =		len(target_dict.symbols	)
            __UpperCamelCase     :List[Any]			  =		os.path.join(SCREAMING_SNAKE_CASE  ,			'''vocab.json'''	)
            if not os.path.isdir(SCREAMING_SNAKE_CASE	):
                logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(SCREAMING_SNAKE_CASE	)	)
                return
            os.makedirs(SCREAMING_SNAKE_CASE  ,			exist_ok=SCREAMING_SNAKE_CASE	)
            with open(SCREAMING_SNAKE_CASE  ,			'''w'''  ,			encoding='''utf-8'''	) as vocab_handle:
                json.dump(target_dict.indices  ,			SCREAMING_SNAKE_CASE	)
            __UpperCamelCase     :Optional[int]			  =		WavaVecaCTCTokenizer(
                SCREAMING_SNAKE_CASE  ,			unk_token=target_dict.unk_word  ,			pad_token=target_dict.pad_word  ,			bos_token=target_dict.bos_word  ,			eos_token=target_dict.eos_word  ,			word_delimiter_token='''|'''  ,			do_lower_case=SCREAMING_SNAKE_CASE  ,			)
            __UpperCamelCase     :Union[str, Any]			  =		True if config.feat_extract_norm == '''layer''' else False
            __UpperCamelCase     :Any			  =		WavaVecaFeatureExtractor(
                feature_size=1  ,			sampling_rate=16_000  ,			padding_value=0  ,			do_normalize=SCREAMING_SNAKE_CASE  ,			return_attention_mask=SCREAMING_SNAKE_CASE  ,			)
            __UpperCamelCase     :Any			  =		WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE  ,			tokenizer=SCREAMING_SNAKE_CASE	)
            processor.save_pretrained(SCREAMING_SNAKE_CASE	)

        __UpperCamelCase     :List[str]			  =		HubertForCTC(SCREAMING_SNAKE_CASE	)
    else:
        __UpperCamelCase     :str			  =		HubertModel(SCREAMING_SNAKE_CASE	)

    if is_finetuned:
        __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase     :int			  =		fairseq.checkpoint_utils.load_model_ensemble_and_task(
            [checkpoint_path]  ,			arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''	)[:-1]	)}	)
    else:
        __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase     :Any			  =		fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]	)

    __UpperCamelCase     :Dict			  =		model[0].eval()

    recursively_load_weights(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

    hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
    __lowercase							 =					argparse.ArgumentParser()
    parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
    parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
    parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
    parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
    parser.add_argument(
        '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
    )
    __lowercase							 =					parser.parse_args()
    convert_hubert_checkpoint(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
    )





