import inspect
import unittest

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

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


if is_torch_available():
    import torch
    from torch import nn

    from transformers import YolosForObjectDetection, YolosModel
    from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor


class 	lowerCamelCase_     :






    '''simple docstring'''






    def __init__(	self , __lowercase , __lowercase=13 , __lowercase=[30, 30] , __lowercase=2 , __lowercase=3 , __lowercase=True , __lowercase=True , __lowercase=32 , __lowercase=5 , __lowercase=4 , __lowercase=37 , __lowercase="gelu" , __lowercase=0.1 , __lowercase=0.1 , __lowercase=10 , __lowercase=0.02 , __lowercase=3 , __lowercase=None , __lowercase=8 , __lowercase=10 , )       ->	List[str]:
        __UpperCamelCase     :Optional[int]			  =		parent
        __UpperCamelCase     :List[str]			  =		batch_size
        __UpperCamelCase     :Tuple			  =		image_size
        __UpperCamelCase     :int			  =		patch_size
        __UpperCamelCase     :Union[str, Any]			  =		num_channels
        __UpperCamelCase     :Any			  =		is_training
        __UpperCamelCase     :Union[str, Any]			  =		use_labels
        __UpperCamelCase     :List[Any]			  =		hidden_size
        __UpperCamelCase     :Tuple			  =		num_hidden_layers
        __UpperCamelCase     :Tuple			  =		num_attention_heads
        __UpperCamelCase     :Union[str, Any]			  =		intermediate_size
        __UpperCamelCase     :Union[str, Any]			  =		hidden_act
        __UpperCamelCase     :int			  =		hidden_dropout_prob
        __UpperCamelCase     :Dict			  =		attention_probs_dropout_prob
        __UpperCamelCase     :Optional[int]			  =		type_sequence_label_size
        __UpperCamelCase     :List[str]			  =		initializer_range
        __UpperCamelCase     :Dict			  =		num_labels
        __UpperCamelCase     :Tuple			  =		scope
        __UpperCamelCase     :Optional[int]			  =		n_targets
        __UpperCamelCase     :Any			  =		num_detection_tokens
        # we set the expected sequence length (which is used in several tests)
        # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
        __UpperCamelCase     :str			  =		(image_size[1] // patch_size) * (image_size[0] // patch_size)
        __UpperCamelCase     :Dict			  =		num_patches + 1 + self.num_detection_tokens







    def 			UpperCamelCase__   (	self)       ->	List[str]:
        __UpperCamelCase     :List[Any]			  =		floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]])

        __UpperCamelCase     :Tuple			  =		None
        if self.use_labels:
            # labels is a list of Dict (each Dict being the labels for a given example in the batch)
            __UpperCamelCase     :List[Any]			  =		[]
            for i in range(self.batch_size):
                __UpperCamelCase     :Optional[Any]			  =		{}
                __UpperCamelCase     :str			  =		torch.randint(
                    high=self.num_labels , size=(self.n_targets,) , device=__lowercase)
                __UpperCamelCase     :Optional[Any]			  =		torch.rand(self.n_targets , 4 , device=__lowercase)
                labels.append(__lowercase)

        __UpperCamelCase     :Union[str, Any]			  =		self.get_config()

        return config, pixel_values, labels







    def 			UpperCamelCase__   (	self)       ->	List[Any]:
        return YolosConfig(
            image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=__lowercase , initializer_range=self.initializer_range , num_detection_tokens=self.num_detection_tokens , num_labels=self.num_labels , )







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase)       ->	Optional[Any]:
        __UpperCamelCase     :Optional[Any]			  =		YolosModel(config=__lowercase)
        model.to(__lowercase)
        model.eval()
        __UpperCamelCase     :Union[str, Any]			  =		model(__lowercase)
        self.parent.assertEqual(
            result.last_hidden_state.shape , (self.batch_size, self.expected_seq_len, self.hidden_size))







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase)       ->	int:
        __UpperCamelCase     :str			  =		YolosForObjectDetection(__lowercase)
        model.to(__lowercase)
        model.eval()

        __UpperCamelCase     :str			  =		model(pixel_values=__lowercase)
        __UpperCamelCase     :Optional[int]			  =		model(__lowercase)

        self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_detection_tokens, self.num_labels + 1))
        self.parent.assertEqual(result.pred_boxes.shape , (self.batch_size, self.num_detection_tokens, 4))

        __UpperCamelCase     :Dict			  =		model(pixel_values=__lowercase , labels=__lowercase)

        self.parent.assertEqual(result.loss.shape , ())
        self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_detection_tokens, self.num_labels + 1))
        self.parent.assertEqual(result.pred_boxes.shape , (self.batch_size, self.num_detection_tokens, 4))







    def 			UpperCamelCase__   (	self)       ->	Any:
        __UpperCamelCase     :int			  =		self.prepare_config_and_inputs()
        __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase     :Union[str, Any]			  =		config_and_inputs
        __UpperCamelCase     :Union[str, Any]			  =		{'''pixel_values''': pixel_values}
        return config, inputs_dict


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






    '''simple docstring'''






    a__	:						Optional[int]						=      (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
    a__	:						Tuple						=      (
        {"""feature-extraction""": YolosModel, """object-detection""": YolosForObjectDetection} if is_torch_available() else {}
    )

    a__	:						Optional[Any]						=      False
    a__	:						Optional[Any]						=      False
    a__	:						Union[str, Any]						=      False
    a__	:						List[str]						=      False







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase=False)       ->	Tuple:
        __UpperCamelCase     :Optional[Any]			  =		super()._prepare_for_class(__lowercase , __lowercase , return_labels=__lowercase)

        if return_labels:
            if model_class.__name__ == "YolosForObjectDetection":
                __UpperCamelCase     :Dict			  =		[]
                for i in range(self.model_tester.batch_size):
                    __UpperCamelCase     :Union[str, Any]			  =		{}
                    __UpperCamelCase     :Tuple			  =		torch.ones(
                        size=(self.model_tester.n_targets,) , device=__lowercase , dtype=torch.long)
                    __UpperCamelCase     :str			  =		torch.ones(
                        self.model_tester.n_targets , 4 , device=__lowercase , dtype=torch.float)
                    labels.append(__lowercase)
                __UpperCamelCase     :Union[str, Any]			  =		labels

        return inputs_dict







    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        __UpperCamelCase     :Any			  =		YolosModelTester(self)
        __UpperCamelCase     :List[Any]			  =		ConfigTester(self , config_class=__lowercase , has_text_modality=__lowercase , hidden_size=37)







    def 			UpperCamelCase__   (	self)       ->	Tuple:
        self.config_tester.run_common_tests()







    def 			UpperCamelCase__   (	self)       ->	List[str]:
        # YOLOS does not use inputs_embeds
        pass







    def 			UpperCamelCase__   (	self)       ->	List[Any]:
        __UpperCamelCase  ,       __UpperCamelCase     :Dict			  =		self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            __UpperCamelCase     :Tuple			  =		model_class(__lowercase)
            self.assertIsInstance(model.get_input_embeddings() , (nn.Module))
            __UpperCamelCase     :Tuple			  =		model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(__lowercase , nn.Linear))







    def 			UpperCamelCase__   (	self)       ->	Optional[int]:
        __UpperCamelCase  ,       __UpperCamelCase     :List[Any]			  =		self.model_tester.prepare_config_and_inputs_for_common()

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

            __UpperCamelCase     :Tuple			  =		['''pixel_values''']
            self.assertListEqual(arg_names[:1] , __lowercase)







    def 			UpperCamelCase__   (	self)       ->	Any:
        __UpperCamelCase     :List[Any]			  =		self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__lowercase)







    def 			UpperCamelCase__   (	self)       ->	str:
        __UpperCamelCase  ,       __UpperCamelCase     :int			  =		self.model_tester.prepare_config_and_inputs_for_common()
        __UpperCamelCase     :str			  =		True

        # in YOLOS, the seq_len is different
        __UpperCamelCase     :List[str]			  =		self.model_tester.expected_seq_len
        for model_class in self.all_model_classes:
            __UpperCamelCase     :Dict			  =		True
            __UpperCamelCase     :List[Any]			  =		False
            __UpperCamelCase     :Optional[Any]			  =		True
            __UpperCamelCase     :List[Any]			  =		model_class(__lowercase)
            model.to(__lowercase)
            model.eval()
            with torch.no_grad():
                __UpperCamelCase     :Optional[Any]			  =		model(**self._prepare_for_class(__lowercase , __lowercase))
            __UpperCamelCase     :List[str]			  =		outputs.attentions
            self.assertEqual(len(__lowercase) , self.model_tester.num_hidden_layers)

            # check that output_attentions also work using config
            del inputs_dict["output_attentions"]
            __UpperCamelCase     :Optional[Any]			  =		True
            __UpperCamelCase     :List[str]			  =		model_class(__lowercase)
            model.to(__lowercase)
            model.eval()
            with torch.no_grad():
                __UpperCamelCase     :Optional[Any]			  =		model(**self._prepare_for_class(__lowercase , __lowercase))
            __UpperCamelCase     :List[str]			  =		outputs.attentions
            self.assertEqual(len(__lowercase) , self.model_tester.num_hidden_layers)

            self.assertListEqual(
                list(attentions[0].shape[-3:]) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
            __UpperCamelCase     :int			  =		len(__lowercase)

            # Check attention is always last and order is fine
            __UpperCamelCase     :Any			  =		True
            __UpperCamelCase     :List[Any]			  =		True
            __UpperCamelCase     :Dict			  =		model_class(__lowercase)
            model.to(__lowercase)
            model.eval()
            with torch.no_grad():
                __UpperCamelCase     :int			  =		model(**self._prepare_for_class(__lowercase , __lowercase))

            __UpperCamelCase     :List[Any]			  =		1
            self.assertEqual(out_len + added_hidden_states , len(__lowercase))

            __UpperCamelCase     :Union[str, Any]			  =		outputs.attentions

            self.assertEqual(len(__lowercase) , self.model_tester.num_hidden_layers)
            self.assertListEqual(
                list(self_attentions[0].shape[-3:]) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )







    def 			UpperCamelCase__   (	self)       ->	Optional[Any]:
        def check_hidden_states_output(__lowercase , __lowercase , __lowercase):
            __UpperCamelCase     :List[Any]			  =		model_class(__lowercase)
            model.to(__lowercase)
            model.eval()

            with torch.no_grad():
                __UpperCamelCase     :Optional[Any]			  =		model(**self._prepare_for_class(__lowercase , __lowercase))

            __UpperCamelCase     :Any			  =		outputs.hidden_states

            __UpperCamelCase     :Any			  =		getattr(
                self.model_tester , '''expected_num_hidden_layers''' , self.model_tester.num_hidden_layers + 1)
            self.assertEqual(len(__lowercase) , __lowercase)

            # YOLOS has a different seq_length
            __UpperCamelCase     :List[str]			  =		self.model_tester.expected_seq_len

            self.assertListEqual(
                list(hidden_states[0].shape[-2:]) , [seq_length, self.model_tester.hidden_size] , )

        __UpperCamelCase  ,       __UpperCamelCase     :Dict			  =		self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            __UpperCamelCase     :Tuple			  =		True
            check_hidden_states_output(__lowercase , __lowercase , __lowercase)

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            __UpperCamelCase     :Any			  =		True

            check_hidden_states_output(__lowercase , __lowercase , __lowercase)







    def 			UpperCamelCase__   (	self)       ->	Any:
        __UpperCamelCase     :Tuple			  =		self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_object_detection(*__lowercase)







    @slow
    def 			UpperCamelCase__   (	self)       ->	Any:
        for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            __UpperCamelCase     :Any			  =		YolosModel.from_pretrained(__lowercase)
            self.assertIsNotNone(__lowercase)



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







    __UpperCamelCase     :Union[str, Any]			  =		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
    return image


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






    '''simple docstring'''






    @cached_property
    def 			UpperCamelCase__   (	self)       ->	List[str]:
        return AutoImageProcessor.from_pretrained('''hustvl/yolos-small''') if is_vision_available() else None







    @slow
    def 			UpperCamelCase__   (	self)       ->	Any:
        __UpperCamelCase     :List[str]			  =		YolosForObjectDetection.from_pretrained('''hustvl/yolos-small''').to(__lowercase)

        __UpperCamelCase     :Dict			  =		self.default_image_processor
        __UpperCamelCase     :Optional[int]			  =		prepare_img()
        __UpperCamelCase     :Any			  =		image_processor(images=__lowercase , return_tensors='''pt''').to(__lowercase)

        # forward pass
        with torch.no_grad():
            __UpperCamelCase     :Optional[int]			  =		model(inputs.pixel_values)

        # verify outputs
        __UpperCamelCase     :str			  =		torch.Size((1, 100, 92))
        self.assertEqual(outputs.logits.shape , __lowercase)

        __UpperCamelCase     :int			  =		torch.tensor(
            [[-24.02_48, -10.30_24, -14.82_90], [-42.03_92, -16.82_00, -27.43_34], [-27.27_43, -11.81_54, -18.71_48]] , device=__lowercase , )
        __UpperCamelCase     :Any			  =		torch.tensor(
            [[0.25_59, 0.54_55, 0.47_06], [0.29_89, 0.72_79, 0.18_75], [0.77_32, 0.40_17, 0.44_62]] , device=__lowercase)
        self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , __lowercase , atol=1E-4))
        self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3] , __lowercase , atol=1E-4))

        # verify postprocessing
        __UpperCamelCase     :Tuple			  =		image_processor.post_process_object_detection(
            __lowercase , threshold=0.3 , target_sizes=[image.size[::-1]])[0]
        __UpperCamelCase     :Optional[int]			  =		torch.tensor([0.99_94, 0.97_90, 0.99_64, 0.99_72, 0.98_61]).to(__lowercase)
        __UpperCamelCase     :Optional[int]			  =		[75, 75, 17, 63, 17]
        __UpperCamelCase     :int			  =		torch.tensor([3_35.06_09, 79.38_48, 3_75.42_16, 1_87.24_95]).to(__lowercase)

        self.assertEqual(len(results['''scores''']) , 5)
        self.assertTrue(torch.allclose(results['''scores'''] , __lowercase , atol=1E-4))
        self.assertSequenceEqual(results['''labels'''].tolist() , __lowercase)
        self.assertTrue(torch.allclose(results['''boxes'''][0, :] , __lowercase))






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
    )





