



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()




class 					snake_case__						(       lowerCAmelCase_				,     unittest.TestCase     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	KandinskyImgaImgPipeline
     _SCREAMING_SNAKE_CASE						=	["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
     _SCREAMING_SNAKE_CASE						=	[
         """prompt""",
         """negative_prompt""",
         """image_embeds""",
         """negative_image_embeds""",
         """image""",
     ]
     _SCREAMING_SNAKE_CASE						=	[
         """generator""",
         """height""",
         """width""",
         """strength""",
         """guidance_scale""",
         """negative_prompt""",
         """num_inference_steps""",
         """return_dict""",
         """guidance_scale""",
         """num_images_per_prompt""",
         """output_type""",
         """return_dict""",
     ]
     _SCREAMING_SNAKE_CASE						=	False






     @property
     def    lowercase_   (       self  :  Dict					)   ->Optional[Any]:
            return 3_2






     @property
     def    lowercase_   (       self  :  Dict					)   ->int:
            return 3_2






     @property
     def    lowercase_   (       self  :  List[str]					)   ->Tuple:
            return self.time_input_dim






     @property
     def    lowercase_   (       self  :  str					)   ->Optional[Any]:
            return self.time_input_dim * 4






     @property
     def    lowercase_   (       self  :  str					)   ->Optional[Any]:
            return 1_0_0






     @property
     def    lowercase_   (       self  :  Optional[Any]					)   ->List[Any]:
            snake_case__							:	List[Any]   	=	XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'					)
            return tokenizer






     @property
     def    lowercase_   (       self  :  Optional[int]					)   ->Tuple:
            torch.manual_seed(0					)
            snake_case__							:	Union[str, Any]   	=	MCLIPConfig(
                numDims=self.cross_attention_dim, transformerDimensions=self.text_embedder_hidden_size, hidden_size=self.text_embedder_hidden_size, intermediate_size=3_7, num_attention_heads=4, num_hidden_layers=5, vocab_size=1_0_0_5, )

            snake_case__							:	str   	=	MultilingualCLIP(_snake_case					)
            snake_case__							:	Tuple   	=	text_encoder.eval()

            return text_encoder






     @property
     def    lowercase_   (       self  :  Optional[int]					)   ->Dict:
            torch.manual_seed(0					)

            snake_case__							:	Tuple   	=	{
                'in_channels': 4,
                # Out channels is double in channels because predicts mean and variance
                'out_channels': 8,
                'addition_embed_type': 'text_image',
                'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
                'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
                'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
                'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
                'layers_per_block': 1,
                'encoder_hid_dim': self.text_embedder_hidden_size,
                'encoder_hid_dim_type': 'text_image_proj',
                'cross_attention_dim': self.cross_attention_dim,
                'attention_head_dim': 4,
                'resnet_time_scale_shift': 'scale_shift',
                'class_embed_type': None,
            }

            snake_case__							:	Tuple   	=	UNetaDConditionModel(**_snake_case					)
            return model






     @property
     def    lowercase_   (       self  :  Optional[Any]					)   ->int:
            return {
                "block_out_channels": [3_2, 6_4],
                "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
                "in_channels": 3,
                "latent_channels": 4,
                "layers_per_block": 1,
                "norm_num_groups": 8,
                "norm_type": "spatial",
                "num_vq_embeddings": 1_2,
                "out_channels": 3,
                "up_block_types": [
                    "AttnUpDecoderBlock2D",
                    "UpDecoderBlock2D",
                ],
                "vq_embed_dim": 4,
            }






     @property
     def    lowercase_   (       self  :  Tuple					)   ->List[str]:
            torch.manual_seed(0					)
            snake_case__							:	Optional[int]   	=	VQModel(**self.dummy_movq_kwargs					)
            return model






     def    lowercase_   (       self  :  Union[str, Any]					)   ->Optional[int]:
            snake_case__							:	List[Any]   	=	self.dummy_text_encoder
            snake_case__							:	Tuple   	=	self.dummy_tokenizer
            snake_case__							:	Dict   	=	self.dummy_unet
            snake_case__							:	List[Any]   	=	self.dummy_movq

            snake_case__							:	Tuple   	=	{
                'num_train_timesteps': 1_0_0_0,
                'beta_schedule': 'linear',
                'beta_start': 0.0_0_0_8_5,
                'beta_end': 0.0_1_2,
                'clip_sample': False,
                'set_alpha_to_one': False,
                'steps_offset': 0,
                'prediction_type': 'epsilon',
                'thresholding': False,
            }

            snake_case__							:	Any   	=	DDIMScheduler(**_snake_case					)

            snake_case__							:	Any   	=	{
                'text_encoder': text_encoder,
                'tokenizer': tokenizer,
                'unet': unet,
                'scheduler': scheduler,
                'movq': movq,
            }

            return components






     def    lowercase_   (       self  :  int, _snake_case  :  Any, _snake_case  :  Dict=0					)   ->Dict:
            snake_case__							:	Dict   	=	floats_tensor((1, self.cross_attention_dim), rng=random.Random(_snake_case					)					).to(_snake_case					)
            snake_case__							:	str   	=	floats_tensor((1, self.cross_attention_dim), rng=random.Random(seed + 1					)					).to(_snake_case					)
            # create init_image
            snake_case__							:	Optional[Any]   	=	floats_tensor((1, 3, 6_4, 6_4), rng=random.Random(_snake_case					)					).to(_snake_case					)
            snake_case__							:	Dict   	=	image.cpu().permute(0, 2, 3, 1					)[0]
            snake_case__							:	Optional[Any]   	=	Image.fromarray(np.uinta(_snake_case					)					).convert('RGB'					).resize((2_5_6, 2_5_6)					)

            if str(_snake_case					).startswith('mps'					):
                   snake_case__							:	List[str]   	=	torch.manual_seed(_snake_case					)
            else:
                   snake_case__							:	Optional[int]   	=	torch.Generator(device=_snake_case					).manual_seed(_snake_case					)
            snake_case__							:	List[str]   	=	{
                'prompt': 'horse',
                'image': init_image,
                'image_embeds': image_embeds,
                'negative_image_embeds': negative_image_embeds,
                'generator': generator,
                'height': 6_4,
                'width': 6_4,
                'num_inference_steps': 1_0,
                'guidance_scale': 7.0,
                'strength': 0.2,
                'output_type': 'np',
            }
            return inputs






     def    lowercase_   (       self  :  Optional[int]					)   ->Dict:
            snake_case__							:	int   	=	'cpu'

            snake_case__							:	Union[str, Any]   	=	self.get_dummy_components()

            snake_case__							:	Tuple   	=	self.pipeline_class(**_snake_case					)
            snake_case__							:	Tuple   	=	pipe.to(_snake_case					)

            pipe.set_progress_bar_config(disable=_snake_case					)

            snake_case__							:	Any   	=	pipe(**self.get_dummy_inputs(_snake_case					)					)
            snake_case__							:	Optional[int]   	=	output.images

            snake_case__							:	Union[str, Any]   	=	pipe(
                **self.get_dummy_inputs(_snake_case					), return_dict=_snake_case, )[0]

            snake_case__							:	List[str]   	=	image[0, -3:, -3:, -1]
            snake_case__							:	List[Any]   	=	image_from_tuple[0, -3:, -3:, -1]

            assert image.shape == (1, 6_4, 6_4, 3)

            snake_case__							:	Any   	=	np.array(
                [0.6_1_4_7_4_9_4_3, 0.6_0_7_3_5_3_9, 0.4_3_3_0_8_5_4_4, 0.5_9_2_8_2_6_9, 0.4_7_4_9_3_5_9_5, 0.4_6_7_5_5_9_7_3, 0.4_6_1_3_8_3_8, 0.4_5_3_6_8_7_9_7, 0.5_0_1_1_9_2_3_3]					)
            assert (
                np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2
            ), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
            assert (
                np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1e-2
            ), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''




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






     """simple docstring"""


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






     def    lowercase_   (       self  :  Dict					)   ->List[str]:
            snake_case__							:	Dict   	=	load_numpy(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                '/kandinsky/kandinsky_img2img_frog.npy'					)

            snake_case__							:	Optional[Any]   	=	load_image(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'					)
            snake_case__							:	Tuple   	=	'A red cartoon frog, 4k'

            snake_case__							:	Optional[Any]   	=	KandinskyPriorPipeline.from_pretrained(
                'kandinsky-community/kandinsky-2-1-prior', torch_dtype=torch.floataa					)
            pipe_prior.to(_snake_case					)

            snake_case__							:	List[Any]   	=	KandinskyImgaImgPipeline.from_pretrained(
                'kandinsky-community/kandinsky-2-1', torch_dtype=torch.floataa					)
            snake_case__							:	str   	=	pipeline.to(_snake_case					)

            pipeline.set_progress_bar_config(disable=_snake_case					)

            snake_case__							:	Any   	=	torch.Generator(device='cpu'					).manual_seed(0					)
            snake_case__	,				snake_case__							:	Optional[int]   	=	pipe_prior(
                _snake_case, generator=_snake_case, num_inference_steps=5, negative_prompt='', ).to_tuple()

            snake_case__							:	Optional[Any]   	=	pipeline(
                _snake_case, image=_snake_case, image_embeds=_snake_case, negative_image_embeds=_snake_case, generator=_snake_case, num_inference_steps=1_0_0, height=7_6_8, width=7_6_8, strength=0.2, output_type='np', )

            snake_case__							:	Tuple   	=	output.images[0]

            assert image.shape == (7_6_8, 7_6_8, 3)

            assert_mean_pixel_difference(_snake_case, _snake_case					)











import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
a_   :List[Any]        =      logging.get_logger(__name__)

a_   :List[Any]        =      {
    "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",
    "adapter_layer": "encoder.layers.*.adapter_layer",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
    "pooling_layer.linear": "projector",
    "pooling_layer.projection": "classifier",
}
a_   :List[Any]        =      [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "projector",
    "classifier",
]
def        lowercase_							(A : Dict      ):
       snake_case__							:	Optional[Any]   	=	{}
       with open(A		,					'r'      ) as file:
              for line_number, line in enumerate(A      ):
                     snake_case__							:	Dict   	=	line.strip()
                     if line:
                            snake_case__							:	int   	=	line.split()
                            snake_case__							:	List[str]   	=	line_number
                            snake_case__							:	Dict   	=	words[0]
                            snake_case__							:	Optional[Any]   	=	value
       return result
def        lowercase_							(A : int		,					A : int		,					A : Optional[int]		,					A : Optional[Any]		,					A : Tuple      ):
       for attribute in key.split('.'      ):
              snake_case__							:	Optional[int]   	=	getattr(A		,					A      )

       snake_case__							:	Union[str, Any]   	=	None
       for param_key in PARAM_MAPPING.keys():
              if full_name.endswith(A      ):
                     snake_case__							:	List[str]   	=	PARAM_MAPPING[full_name.split('.'      )[-1]]
                     snake_case__							:	Dict   	=	'param'

       if weight_type is not None and weight_type != "param":
              snake_case__							:	Union[str, Any]   	=	getattr(A		,					A      ).shape
       elif weight_type is not None and weight_type == "param":
              snake_case__							:	Optional[int]   	=	hf_pointer
              for attribute in hf_param_name.split('.'      ):
                     snake_case__							:	Optional[Any]   	=	getattr(A		,					A      )
              snake_case__							:	Dict   	=	shape_pointer.shape

              # let's reduce dimension
              snake_case__							:	List[Any]   	=	value[0]
       else:
              snake_case__							:	Union[str, Any]   	=	hf_pointer.shape

       if hf_shape != value.shape:
              raise ValueError(
                  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":
              snake_case__							:	Any   	=	value
       elif weight_type == "weight_g":
              snake_case__							:	List[Any]   	=	value
       elif weight_type == "weight_v":
              snake_case__							:	Any   	=	value
       elif weight_type == "bias":
              snake_case__							:	List[Any]   	=	value
       elif weight_type == "param":
              for attribute in hf_param_name.split('.'      ):
                     snake_case__							:	int   	=	getattr(A		,					A      )
              snake_case__							:	Optional[int]   	=	value
       else:
              snake_case__							:	Optional[Any]   	=	value

       logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''      )
def        lowercase_							(A : Tuple		,					A : List[Any]		,					A : int		,					A : str		,					A : Tuple      ):
       snake_case__							:	Optional[int]   	=	None
       for param_key in PARAM_MAPPING.keys():
              if full_name.endswith(A      ):
                     snake_case__							:	List[str]   	=	PARAM_MAPPING[full_name.split('.'      )[-1]]
                     snake_case__							:	str   	=	'param'

       if weight_type is not None and weight_type != "param":
              snake_case__							:	int   	=	'.'.join([key, weight_type]      )
       elif weight_type is not None and weight_type == "param":
              snake_case__							:	Any   	=	'.'.join([key, hf_param_name]      )
       else:
              snake_case__							:	Dict   	=	key

       snake_case__							:	List[str]   	=	value if 'lm_head' in full_key else value[0]


a_   :List[str]        =      {
    "W_a": "linear_1.weight",
    "W_b": "linear_2.weight",
    "b_a": "linear_1.bias",
    "b_b": "linear_2.bias",
    "ln_W": "norm.weight",
    "ln_b": "norm.bias",
}
def        lowercase_							(A : str		,					A : Optional[Any]		,					A : Optional[Any]=None		,					A : List[str]=None      ):
       snake_case__							:	Optional[int]   	=	False
       for key, mapped_key in MAPPING.items():
              snake_case__							:	Tuple   	=	'wav2vec2.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
              if key in name or key.split('w2v_model.'      )[-1] == name.split('.'      )[0]:
                     snake_case__							:	Optional[int]   	=	True
                     if "*" in mapped_key:
                            snake_case__							:	List[Any]   	=	name.split(A      )[0].split('.'      )[-2]
                            snake_case__							:	Union[str, Any]   	=	mapped_key.replace('*'		,					A      )
                     if "weight_g" in name:
                            snake_case__							:	Tuple   	=	'weight_g'
                     elif "weight_v" in name:
                            snake_case__							:	List[str]   	=	'weight_v'
                     elif "bias" in name:
                            snake_case__							:	Dict   	=	'bias'
                     elif "weight" in name:
                            # TODO: don't match quantizer.weight_proj
                            snake_case__							:	Optional[int]   	=	'weight'
                     else:
                            snake_case__							:	str   	=	None
                     if hf_dict is not None:
                            rename_dict(A		,					A		,					A		,					A		,					A      )
                     else:
                            set_recursively(A		,					A		,					A		,					A		,					A      )
                     return is_used
       return is_used
def        lowercase_							(A : Optional[Any]		,					A : Dict		,					A : Optional[int]      ):
       snake_case__							:	Dict   	=	[]
       snake_case__							:	Tuple   	=	fairseq_model.state_dict()

       snake_case__							:	str   	=	hf_model.wavaveca.feature_extractor

       for name, value in fairseq_dict.items():
              snake_case__							:	str   	=	False
              if "conv_layers" in name:
                     load_conv_layer(
                         A		,					A		,					A		,					A		,					hf_model.config.feat_extract_norm == 'group'		,					)
                     snake_case__							:	Any   	=	True
              else:
                     snake_case__							:	Dict   	=	load_wavaveca_layer(A		,					A		,					A      )
              if not is_used:
                     unused_weights.append(A      )

       logger.warning(F'''Unused weights: {unused_weights}'''      )
def        lowercase_							(A : Dict		,					A : Optional[Any]		,					A : Tuple		,					A : str		,					A : List[str]      ):
       snake_case__							:	List[Any]   	=	full_name.split('conv_layers.'      )[-1]
       snake_case__							:	List[str]   	=	name.split('.'      )
       snake_case__							:	List[Any]   	=	int(items[0]      )
       snake_case__							:	str   	=	int(items[1]      )

       if type_id == 0:
              if "bias" in name:
                     if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                            raise ValueError(
                                F'''{full_name} has size {value.shape}, but'''
                                F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''      )
                     snake_case__							:	Any   	=	value
                     logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''      )
              elif "weight" in name:
                     if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                            raise ValueError(
                                F'''{full_name} has size {value.shape}, but'''
                                F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''      )
                     snake_case__							:	str   	=	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:
                     if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                            raise ValueError(
                                F'''{full_name} has size {value.shape}, but'''
                                F''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''      )
                     snake_case__							:	str   	=	value
                     logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''      )
              elif "weight" in name:
                     if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                            raise ValueError(
                                F'''{full_name} has size {value.shape}, but'''
                                F''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''      )
                     snake_case__							:	int   	=	value
                     logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''      )
       else:
              unused_weights.append(A      )
@torch.no_grad()
def        lowercase_							(A : Union[str, Any]		,					A : str		,					A : Tuple=None		,					A : List[str]=None		,					A : Any=True		,					A : Optional[int]=False      ):
       if config_path is not None:
              snake_case__							:	List[Any]   	=	WavaVecaConfig.from_pretrained(A      )
       else:
              snake_case__							:	List[Any]   	=	WavaVecaConfig()

       if is_seq_class:
              snake_case__							:	Dict   	=	read_txt_into_dict(A      )
              snake_case__							:	Any   	=	idalabel
              snake_case__							:	Union[str, Any]   	=	WavaVecaForSequenceClassification(A      )
              snake_case__							:	Any   	=	WavaVecaFeatureExtractor(
                  feature_size=1		,					sampling_rate=1_6_0_0_0		,					padding_value=0		,					do_normalize=A		,					return_attention_mask=A		,					)
              feature_extractor.save_pretrained(A      )

       elif is_finetuned:
              if dict_path:
                     snake_case__							:	str   	=	Dictionary.load(A      )

                     # important change bos & pad token id since CTC symbol is <pad> and
                     # not <s> as in fairseq
                     snake_case__							:	List[str]   	=	target_dict.pad_index
                     snake_case__							:	Optional[int]   	=	target_dict.bos_index
                     snake_case__							:	Optional[int]   	=	target_dict.eos_index
                     snake_case__							:	List[Any]   	=	len(target_dict.symbols      )
                     snake_case__							:	str   	=	os.path.join(A		,					'vocab.json'      )
                     if not os.path.isdir(A      ):
                            logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(A      )      )
                            return
                     os.makedirs(A		,					exist_ok=A      )
                     snake_case__							:	Optional[Any]   	=	target_dict.indices

                     # fairseq has the <pad> and <s> switched
                     snake_case__							:	Optional[Any]   	=	0
                     snake_case__							:	Union[str, Any]   	=	1
                     with open(A		,					'w'		,					encoding='utf-8'      ) as vocab_handle:
                            json.dump(A		,					A      )
                     snake_case__							:	List[Any]   	=	WavaVecaCTCTokenizer(
                         A		,					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=A		,					)
                     snake_case__							:	str   	=	True if config.feat_extract_norm == 'layer' else False
                     snake_case__							:	Optional[Any]   	=	WavaVecaFeatureExtractor(
                         feature_size=1		,					sampling_rate=1_6_0_0_0		,					padding_value=0		,					do_normalize=A		,					return_attention_mask=A		,					)
                     snake_case__							:	Union[str, Any]   	=	WavaVecaProcessor(feature_extractor=A		,					tokenizer=A      )
                     processor.save_pretrained(A      )

              snake_case__							:	str   	=	WavaVecaForCTC(A      )
       else:
              snake_case__							:	int   	=	WavaVecaForPreTraining(A      )

       if is_finetuned or is_seq_class:
              snake_case__	,				snake_case__	,				snake_case__							:	str   	=	fairseq.checkpoint_utils.load_model_ensemble_and_task(
                  [checkpoint_path]		,					arg_overrides={'data': '/'.join(dict_path.split('/'      )[:-1]      )}      )
       else:
              snake_case__							:	Tuple   	=	argparse.Namespace(task='audio_pretraining'      )
              snake_case__							:	str   	=	fairseq.tasks.setup_task(A      )

              snake_case__	,				snake_case__	,				snake_case__							:	Any   	=	fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]		,					task=A      )

       snake_case__							:	List[Any]   	=	model[0].eval()

       recursively_load_weights(A		,					A		,					not is_finetuned      )

       hf_wavavec.save_pretrained(A      )


if __name__ == "__main__":
 a_   :List[Any]        =      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"
 )
 parser.add_argument(
     "--is_seq_class",
     action="store_true",
     help="Whether the model to convert is a fine-tuned sequence classification model or not",
 )
 a_   :str        =      parser.parse_args()

 a_   :Tuple        =      not args.not_finetuned and not args.is_seq_class
 convert_wavaveca_checkpoint(
     args.checkpoint_path,
     args.pytorch_dump_folder_path,
     args.config_path,
     args.dict_path,
     is_finetuned,
     args.is_seq_class,
 )






