"""simple docstring"""


import inspect
import unittest

from transformers import ResNetConfig
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_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import ResNetBackbone, ResNetForImageClassification, ResNetModel
 from transformers.models.resnet.modeling_resnet import RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor






class  a__     :


   def __init__(  self,					_UpperCAmelCase,					_UpperCAmelCase=3,					_UpperCAmelCase=32,					_UpperCAmelCase=3,					_UpperCAmelCase=10,					_UpperCAmelCase=[10, 20, 30, 40],					_UpperCAmelCase=[1, 1, 2, 1],					_UpperCAmelCase=True,					_UpperCAmelCase=True,					_UpperCAmelCase="relu",					_UpperCAmelCase=3,					_UpperCAmelCase=None,					_UpperCAmelCase=["stage2", "stage3", "stage4"],					_UpperCAmelCase=[2, 3, 4],					):
        '''simple docstring'''



        lowercase__								=				parent
        lowercase__								=				batch_size
        lowercase__								=				image_size
        lowercase__								=				num_channels
        lowercase__								=				embeddings_size
        lowercase__								=				hidden_sizes
        lowercase__								=				depths
        lowercase__								=				is_training
        lowercase__								=				use_labels
        lowercase__								=				hidden_act
        lowercase__								=				num_labels
        lowercase__								=				scope
        lowercase__								=				len(__SCREAMING_SNAKE_CASE	)
        lowercase__								=				out_features
        lowercase__								=				out_indices


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

        lowercase__								=				None
        if self.use_labels:
             lowercase__								=				ids_tensor([self.batch_size],					self.num_labels	)

        lowercase__								=				self.get_config()

        return config, pixel_values, labels


   def 						snake_case__						(  self	):
        '''simple docstring'''



        return ResNetConfig(
            num_channels=self.num_channels,					embeddings_size=self.embeddings_size,					hidden_sizes=self.hidden_sizes,					depths=self.depths,					hidden_act=self.hidden_act,					num_labels=self.num_labels,					out_features=self.out_features,					out_indices=self.out_indices,					)


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase	):
        '''simple docstring'''



        lowercase__								=				ResNetModel(config=__SCREAMING_SNAKE_CASE	)
        model.to(__SCREAMING_SNAKE_CASE	)
        model.eval()
        lowercase__								=				model(__SCREAMING_SNAKE_CASE	)
        # expected last hidden states: B, C, H // 32, W // 32
        self.parent.assertEqual(
            result.last_hidden_state.shape,					(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32),					)


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase	):
        '''simple docstring'''



        lowercase__								=				self.num_labels
        lowercase__								=				ResNetForImageClassification(__SCREAMING_SNAKE_CASE	)
        model.to(__SCREAMING_SNAKE_CASE	)
        model.eval()
        lowercase__								=				model(__SCREAMING_SNAKE_CASE,					labels=__SCREAMING_SNAKE_CASE	)
        self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.num_labels)	)


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase	):
        '''simple docstring'''



        lowercase__								=				ResNetBackbone(config=__SCREAMING_SNAKE_CASE	)
        model.to(__SCREAMING_SNAKE_CASE	)
        model.eval()
        lowercase__								=				model(__SCREAMING_SNAKE_CASE	)

        # verify feature maps
        self.parent.assertEqual(len(result.feature_maps	),					len(config.out_features	)	)
        self.parent.assertListEqual(list(result.feature_maps[0].shape	),					[self.batch_size, self.hidden_sizes[1], 4, 4]	)

        # verify channels
        self.parent.assertEqual(len(model.channels	),					len(config.out_features	)	)
        self.parent.assertListEqual(model.channels,					config.hidden_sizes[1:]	)

        # verify backbone works with out_features=None
        lowercase__								=				None
        lowercase__								=				ResNetBackbone(config=__SCREAMING_SNAKE_CASE	)
        model.to(__SCREAMING_SNAKE_CASE	)
        model.eval()
        lowercase__								=				model(__SCREAMING_SNAKE_CASE	)

        # verify feature maps
        self.parent.assertEqual(len(result.feature_maps	),					1	)
        self.parent.assertListEqual(list(result.feature_maps[0].shape	),					[self.batch_size, self.hidden_sizes[-1], 1, 1]	)

        # verify channels
        self.parent.assertEqual(len(model.channels	),					1	)
        self.parent.assertListEqual(model.channels,					[config.hidden_sizes[-1]]	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.prepare_config_and_inputs()
        lowercase__								=				config_and_inputs
        lowercase__								=				{'''pixel_values''': pixel_values}
        return config, inputs_dict






@require_torch
class  a__     ( lowerCamelCase_       ,       lowerCamelCase_       ,       unittest.TestCase						):
   snake_case_				=   (
       (
           ResNetModel,
           ResNetForImageClassification,
           ResNetBackbone,
       )
       if is_torch_available()
       else ()
   )
   snake_case_				=   (
       {"feature-extraction": ResNetModel, "image-classification": ResNetForImageClassification}
       if is_torch_available()
       else {}
   )

   snake_case_				=   True
   snake_case_				=   False
   snake_case_				=   False
   snake_case_				=   False
   snake_case_				=   False


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				ResNetModelTester(self	)
        lowercase__								=				ConfigTester(self,					config_class=__SCREAMING_SNAKE_CASE,					has_text_modality=__SCREAMING_SNAKE_CASE	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        self.create_and_test_config_common_properties()
        self.config_tester.create_and_test_config_to_json_string()
        self.config_tester.create_and_test_config_to_json_file()
        self.config_tester.create_and_test_config_from_and_save_pretrained()
        self.config_tester.create_and_test_config_with_num_labels()
        self.config_tester.check_config_can_be_init_without_params()
        self.config_tester.check_config_arguments_init()


   def 						snake_case__						(  self	):
        '''simple docstring'''



        return


   @unittest.skip(reason="ResNet does not use inputs_embeds"	)
   def 						snake_case__						(  self	):
        '''simple docstring'''



        pass


   @unittest.skip(reason="ResNet does not support input and output embeddings"	)
   def 						snake_case__						(  self	):
        '''simple docstring'''



        pass


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.model_tester.prepare_config_and_inputs_for_common()

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

             lowercase__								=				['''pixel_values''']
             self.assertListEqual(arg_names[:1],					__SCREAMING_SNAKE_CASE	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_backbone(*__SCREAMING_SNAKE_CASE	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.model_tester.prepare_config_and_inputs_for_common()

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


   def 						snake_case__						(  self	):
        '''simple docstring'''



        def check_hidden_states_output(_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase	):
             lowercase__								=				model_class(__SCREAMING_SNAKE_CASE	)
             model.to(__SCREAMING_SNAKE_CASE	)
             model.eval()

             with torch.no_grad():
                  lowercase__								=				model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE,					__SCREAMING_SNAKE_CASE	)	)

             lowercase__								=				outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

             lowercase__								=				self.model_tester.num_stages
             self.assertEqual(len(__SCREAMING_SNAKE_CASE	),					expected_num_stages + 1	)

             # ResNet's feature maps are of shape (batch_size, num_channels, height, width)
             self.assertListEqual(
                 list(hidden_states[0].shape[-2:]	),					[self.model_tester.image_size // 4, self.model_tester.image_size // 4],					)

        lowercase__								=				self.model_tester.prepare_config_and_inputs_for_common()
        lowercase__								=				['''basic''', '''bottleneck''']
        for model_class in self.all_model_classes:
             for layer_type in layers_type:
                  lowercase__								=				layer_type
                  lowercase__								=				True
                  check_hidden_states_output(__SCREAMING_SNAKE_CASE,					__SCREAMING_SNAKE_CASE,					__SCREAMING_SNAKE_CASE	)

                  # check that output_hidden_states also work using config
                  del inputs_dict["output_hidden_states"]
                  lowercase__								=				True

                  check_hidden_states_output(__SCREAMING_SNAKE_CASE,					__SCREAMING_SNAKE_CASE,					__SCREAMING_SNAKE_CASE	)


   @unittest.skip(reason="ResNet does not use feedforward chunking"	)
   def 						snake_case__						(  self	):
        '''simple docstring'''



        pass


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE	)


   @slow
   def 						snake_case__						(  self	):
        '''simple docstring'''



        for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowercase__								=				ResNetModel.from_pretrained(__SCREAMING_SNAKE_CASE	)
             self.assertIsNotNone(__SCREAMING_SNAKE_CASE	)







def        __a (		):






     '''simple docstring'''



     lowercase__								=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
     return image






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


   @cached_property
   def 						snake_case__						(  self	):
        '''simple docstring'''



        return (
            AutoImageProcessor.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]	)
            if is_vision_available()
            else None
        )


   @slow
   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]	).to(__SCREAMING_SNAKE_CASE	)

        lowercase__								=				self.default_image_processor
        lowercase__								=				prepare_img()
        lowercase__								=				image_processor(images=__SCREAMING_SNAKE_CASE,					return_tensors="pt"	).to(__SCREAMING_SNAKE_CASE	)

        # forward pass
        with torch.no_grad():
             lowercase__								=				model(**__SCREAMING_SNAKE_CASE	)

        # verify the logits
        lowercase__								=				torch.Size((1, 1000)	)
        self.assertEqual(outputs.logits.shape,					__SCREAMING_SNAKE_CASE	)

        lowercase__								=				torch.tensor([-11.1_069, -9.7_877, -8.3_777]	).to(__SCREAMING_SNAKE_CASE	)

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






@require_torch
class  a__     ( lowerCamelCase_       ,       unittest.TestCase						):
   snake_case_				=   (ResNetBackbone,) if is_torch_available() else ()
   snake_case_				=   False
   snake_case_				=   ResNetConfig


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				ResNetModelTester(self	)






'''simple docstring'''







import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_lowercase					:    Optional[int]       				=			logging.get_logger(__name__)

_lowercase					:    List[Any]       				=			{
    "post_extract_proj": "feature_projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "attention.self.query_proj": "encoder.encoder.layer.*.attention.self.query_proj",
    "attention.self.key_proj": "encoder.encoder.layer.*.attention.self.key_proj",
    "attention.self.value_proj": "encoder.encoder.layer.*.attention.self.value_proj",
    "attention.output.dense": "encoder.encoder.layer.*.attention.output.dense",
    "attention.output.LayerNorm": "encoder.encoder.layer.*.attention.output.LayerNorm",
    "intermediate.dense": "encoder.encoder.layer.*.intermediate.dense",
    "output.dense": "encoder.encoder.layer.*.output.dense",
    "output.LayerNorm": "encoder.encoder.layer.*.output.LayerNorm",
    "encoder.encoder.rel_embeddings": "encoder.encoder.rel_embeddings",
    "encoder.encoder.LayerNorm": "encoder.encoder.LayerNorm",
    "encoder.upsample.0": "encoder.upsample.projection",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					List[str] , __SCREAMING_SNAKE_CASE   :					Dict , __SCREAMING_SNAKE_CASE   :					List[Any] , __SCREAMING_SNAKE_CASE   :					int , __SCREAMING_SNAKE_CASE   :					Any				):



						"""simple docstring"""





						for attribute in key.split('''.'''				):
												lowercase_      :	Optional[int]						=			getattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE				)

						if weight_type is not None:
												lowercase_      :	Optional[int]						=			getattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE				).shape
						else:
												lowercase_      :	Optional[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":
												lowercase_      :	int						=			value
						elif weight_type == "weight_g":
												lowercase_      :	Dict						=			value
						elif weight_type == "weight_v":
												lowercase_      :	Dict						=			value
						elif weight_type == "bias":
												lowercase_      :	Union[str, Any]						=			value
						else:
												lowercase_      :	Optional[Any]						=			value

						logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''				)
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					Union[str, Any] , __SCREAMING_SNAKE_CASE   :					Union[str, Any] , __SCREAMING_SNAKE_CASE   :					Optional[Any]				):



						"""simple docstring"""





						lowercase_      :	Tuple						=			[]
						lowercase_      :	Union[str, Any]						=			fairseq_model.state_dict()

						lowercase_      :	Dict						=			hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

						for name, value in fairseq_dict.items():
												lowercase_      :	str						=			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''' , )
																		lowercase_      :	int						=			True
												else:
																		for key, mapped_key in MAPPING.items():
																								lowercase_      :	Union[str, Any]						=			'''sew_d.''' + 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]:
																														lowercase_      :	Dict						=			True
																														if "*" in mapped_key:
																																				lowercase_      :	str						=			name.split(__SCREAMING_SNAKE_CASE				)[0].split('''.'''				)[-2]
																																				if not layer_index.isnumeric():
																																										continue
																																				lowercase_      :	Tuple						=			mapped_key.replace('''*''' , __SCREAMING_SNAKE_CASE				)
																														if "weight_g" in name:
																																				lowercase_      :	Dict						=			'''weight_g'''
																														elif "weight_v" in name:
																																				lowercase_      :	Dict						=			'''weight_v'''
																														elif "weight" in name:
																																				lowercase_      :	int						=			'''weight'''
																														elif "bias" in name:
																																				lowercase_      :	List[Any]						=			'''bias'''
																														else:
																																				lowercase_      :	Optional[Any]						=			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    snake_case_						(							__SCREAMING_SNAKE_CASE   :					List[Any] , __SCREAMING_SNAKE_CASE   :					int , __SCREAMING_SNAKE_CASE   :					int , __SCREAMING_SNAKE_CASE   :					Tuple , __SCREAMING_SNAKE_CASE   :					Optional[int]				):



						"""simple docstring"""





						lowercase_      :	int						=			full_name.split('''conv_layers.'''				)[-1]
						lowercase_      :	int						=			name.split('''.'''				)
						lowercase_      :	int						=			int(items[0]				)
						lowercase_      :	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.'''
																		)
																		lowercase_      :	int						=			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.'''
																		)
																		lowercase_      :	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."
																		)
																		lowercase_      :	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.'''
																		)
																		lowercase_      :	List[str]						=			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				)
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					Any , __SCREAMING_SNAKE_CASE   :					Tuple				):



						"""simple docstring"""





						lowercase_      :	str						=			SEWDConfig()
						if is_finetuned:
												lowercase_      :	Optional[int]						=			model.wav_encoder.wav_model.cfg
						else:
												lowercase_      :	Dict						=			model.cfg

						lowercase_      :	Tuple						=			fs_config.conv_bias
						lowercase_      :	Dict						=			eval(fs_config.conv_feature_layers				)
						lowercase_      :	Union[str, Any]						=			[x[0] for x in conv_layers]
						lowercase_      :	Dict						=			[x[1] for x in conv_layers]
						lowercase_      :	List[str]						=			[x[2] for x in conv_layers]
						lowercase_      :	Any						=			'''gelu'''
						lowercase_      :	Union[str, Any]						=			'''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
						lowercase_      :	Union[str, Any]						=			0.0
						lowercase_      :	str						=			fs_config.activation_fn.name
						lowercase_      :	Union[str, Any]						=			fs_config.encoder_embed_dim
						lowercase_      :	Any						=			0.02
						lowercase_      :	Any						=			fs_config.encoder_ffn_embed_dim
						lowercase_      :	List[str]						=			1E-5
						lowercase_      :	Union[str, Any]						=			fs_config.encoder_layerdrop
						lowercase_      :	Union[str, Any]						=			fs_config.encoder_attention_heads
						lowercase_      :	Dict						=			fs_config.conv_pos_groups
						lowercase_      :	int						=			fs_config.conv_pos
						lowercase_      :	Union[str, Any]						=			len(__SCREAMING_SNAKE_CASE				)
						lowercase_      :	Optional[Any]						=			fs_config.encoder_layers
						lowercase_      :	List[Any]						=			fs_config.squeeze_factor
						# DeBERTa-specific parameters:
						lowercase_      :	Union[str, Any]						=			fs_config.max_position_embeddings
						lowercase_      :	Tuple						=			fs_config.position_buckets
						lowercase_      :	str						=			fs_config.share_att_key
						lowercase_      :	List[Any]						=			fs_config.relative_attention
						lowercase_      :	Any						=			fs_config.position_biased_input
						lowercase_      :	Union[str, Any]						=			tuple(fs_config.pos_att_type.split('''|'''				)				)
						lowercase_      :	List[str]						=			fs_config.norm_rel_ebd

						# take care of any params that are overridden by the Wav2VecCtc model
						if is_finetuned:
												lowercase_      :	List[Any]						=			model.cfg
												lowercase_      :	List[str]						=			fs_config.final_dropout
												lowercase_      :	Union[str, Any]						=			fs_config.layerdrop
						lowercase_      :	Optional[int]						=			fs_config.activation_dropout
						lowercase_      :	Dict						=			fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
						lowercase_      :	int						=			fs_config.attention_dropout
						lowercase_      :	Union[str, Any]						=			fs_config.dropout_input
						lowercase_      :	Any						=			fs_config.dropout
						lowercase_      :	List[str]						=			fs_config.mask_channel_length
						lowercase_      :	Union[str, Any]						=			fs_config.mask_channel_prob
						lowercase_      :	str						=			fs_config.mask_length
						lowercase_      :	List[Any]						=			fs_config.mask_prob

						lowercase_      :	Optional[int]						=			'''Wav2Vec2FeatureExtractor'''
						lowercase_      :	Dict						=			'''Wav2Vec2CTCTokenizer'''

						return config
@torch.no_grad()
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					Optional[Any] , __SCREAMING_SNAKE_CASE   :					Any , __SCREAMING_SNAKE_CASE   :					int=None , __SCREAMING_SNAKE_CASE   :					List[Any]=None , __SCREAMING_SNAKE_CASE   :					Tuple=True				):



						"""simple docstring"""





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

						if config_path is not None:
												lowercase_      :	Union[str, Any]						=			SEWDConfig.from_pretrained(__SCREAMING_SNAKE_CASE				)
						else:
												lowercase_      :	List[str]						=			convert_config(model[0] , __SCREAMING_SNAKE_CASE				)
						lowercase_      :	Tuple						=			model[0].eval()

						lowercase_      :	Any						=			True if config.feat_extract_norm == '''layer''' else False
						lowercase_      :	List[Any]						=			WavaVecaFeatureExtractor(
						    feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=__SCREAMING_SNAKE_CASE , return_attention_mask=__SCREAMING_SNAKE_CASE , )

						if is_finetuned:
												if dict_path:
																		lowercase_      :	Optional[int]						=			Dictionary.load(__SCREAMING_SNAKE_CASE				)

																		# important change bos & pad token id since CTC symbol is <pad> and
																		# not <s> as in fairseq
																		lowercase_      :	int						=			target_dict.pad_index
																		lowercase_      :	List[Any]						=			target_dict.bos_index
																		lowercase_      :	Dict						=			target_dict.pad_index
																		lowercase_      :	Dict						=			target_dict.bos_index
																		lowercase_      :	List[str]						=			target_dict.eos_index
																		lowercase_      :	Dict						=			len(target_dict.symbols				)
																		lowercase_      :	Optional[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				)
																		lowercase_      :	List[str]						=			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 , )
																		lowercase_      :	Optional[int]						=			WavaVecaProcessor(feature_extractor=__SCREAMING_SNAKE_CASE , tokenizer=__SCREAMING_SNAKE_CASE				)
																		processor.save_pretrained(__SCREAMING_SNAKE_CASE				)

												lowercase_      :	int						=			SEWDForCTC(__SCREAMING_SNAKE_CASE				)
						else:
												lowercase_      :	Union[str, Any]						=			SEWDModel(__SCREAMING_SNAKE_CASE				)
												feature_extractor.save_pretrained(__SCREAMING_SNAKE_CASE				)

						recursively_load_weights(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE				)

						hf_model.save_pretrained(__SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
							_lowercase					:    List[str]       				=			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(
							    "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
							)
							_lowercase					:    str       				=			parser.parse_args()
							convert_sew_checkpoint(
							    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
							)



