


import inspect
import unittest

from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
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, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
		from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor




class 	a__       :





							'''simple docstring'''






							def __init__(	self      ,					lowerCamelCase_      ,					lowerCamelCase_=13      ,					lowerCamelCase_=32      ,					lowerCamelCase_=3      ,					lowerCamelCase_=4      ,					lowerCamelCase_=[10, 20, 30, 40]      ,					lowerCamelCase_=[2, 2, 3, 2]      ,					lowerCamelCase_=True      ,					lowerCamelCase_=True      ,					lowerCamelCase_=37      ,					lowerCamelCase_="gelu"      ,					lowerCamelCase_=10      ,					lowerCamelCase_=0.02      ,					lowerCamelCase_=["stage2", "stage3", "stage4"]      ,					lowerCamelCase_=[2, 3, 4]      ,					lowerCamelCase_=None      ,					) ->							Any:
														lowerCAmelCase__      =			parent
														lowerCAmelCase__      =			batch_size
														lowerCAmelCase__      =			image_size
														lowerCAmelCase__      =			num_channels
														lowerCAmelCase__      =			num_stages
														lowerCAmelCase__      =			hidden_sizes
														lowerCAmelCase__      =			depths
														lowerCAmelCase__      =			is_training
														lowerCAmelCase__      =			use_labels
														lowerCAmelCase__      =			intermediate_size
														lowerCAmelCase__      =			hidden_act
														lowerCAmelCase__      =			num_labels
														lowerCAmelCase__      =			initializer_range
														lowerCAmelCase__      =			out_features
														lowerCAmelCase__      =			out_indices
														lowerCAmelCase__      =			scope







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Any:
														lowerCAmelCase__      =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

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

														lowerCAmelCase__      =			self.get_config()

														return config, pixel_values, labels







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Optional[int]:
														return ConvNextVaConfig(
														    num_channels=self.num_channels      ,					hidden_sizes=self.hidden_sizes      ,					depths=self.depths      ,					num_stages=self.num_stages      ,					hidden_act=self.hidden_act      ,					is_decoder=lowercase_      ,					initializer_range=self.initializer_range      ,					out_features=self.out_features      ,					out_indices=self.out_indices      ,					num_labels=self.num_labels      ,					)







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							Union[str, Any]:
														lowerCAmelCase__      =			ConvNextVaModel(config=lowercase_     )
														model.to(lowercase_     )
														model.eval()
														lowerCAmelCase__      =			model(lowercase_     )
														# 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 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							Tuple:
														lowerCAmelCase__      =			ConvNextVaForImageClassification(lowercase_     )
														model.to(lowercase_     )
														model.eval()
														lowerCAmelCase__      =			model(lowercase_      ,					labels=lowercase_     )
														self.parent.assertEqual(result.logits.shape      ,					(self.batch_size, self.num_labels)     )







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							Union[str, Any]:
														lowerCAmelCase__      =			ConvNextVaBackbone(config=lowercase_     )
														model.to(lowercase_     )
														model.eval()
														lowerCAmelCase__      =			model(lowercase_     )

														# verify hidden states
														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
														lowerCAmelCase__      =			None
														lowerCAmelCase__      =			ConvNextVaBackbone(config=lowercase_     )
														model.to(lowercase_     )
														model.eval()
														lowerCAmelCase__      =			model(lowercase_     )

														# 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 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Dict:
														lowerCAmelCase__      =			self.prepare_config_and_inputs()
														lowerCAmelCase__      =			config_and_inputs
														lowerCAmelCase__      =			{"pixel_values": pixel_values}
														return config, inputs_dict







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							int:
														lowerCAmelCase__      =			self.prepare_config_and_inputs()
														lowerCAmelCase__      =			config_and_inputs
														lowerCAmelCase__      =			{"pixel_values": pixel_values, "labels": labels}
														return config, inputs_dict




@require_torch
class 	a__       (							snake_case__       ,       snake_case__       ,       unittest.TestCase						):





							'''simple docstring'''






							lowercase__    :     Optional[Any]					=   (
							    (
							        ConvNextVaModel,
							        ConvNextVaForImageClassification,
							        ConvNextVaBackbone,
							    )
							    if is_torch_available()
							    else ()
							)
							lowercase__    :     Dict					=   (
							    {"feature-extraction": ConvNextVaModel, "image-classification": ConvNextVaForImageClassification}
							    if is_torch_available()
							    else {}
							)

							lowercase__    :     Union[str, Any]					=   False
							lowercase__    :     Tuple					=   False
							lowercase__    :     str					=   False
							lowercase__    :     List[str]					=   False
							lowercase__    :     List[str]					=   False







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							int:
														lowerCAmelCase__      =			ConvNextVaModelTester(self     )
														lowerCAmelCase__      =			ConfigTester(self      ,					config_class=lowercase_      ,					has_text_modality=lowercase_      ,					hidden_size=37     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Tuple:
														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 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Union[str, Any]:
														return







							@unittest.skip(reason='''ConvNextV2 does not use inputs_embeds'''     )
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Union[str, Any]:
														pass







							@unittest.skip(reason='''ConvNextV2 does not support input and output embeddings'''     )
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Tuple:
														pass







							@unittest.skip(reason='''ConvNextV2 does not use feedforward chunking'''     )
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							List[str]:
														pass







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Optional[int]:
														if not self.model_tester.is_training:
																					return

														for model_class in self.all_model_classes:
																					lowerCAmelCase__      =			self.model_tester.prepare_config_and_inputs_with_labels()
																					lowerCAmelCase__      =			True

																					if model_class.__name__ in [
																					    *get_values(lowercase_     ),
																					    *get_values(lowercase_     ),
																					]:
																												continue

																					lowerCAmelCase__      =			model_class(lowercase_     )
																					model.to(lowercase_     )
																					model.train()
																					lowerCAmelCase__      =			self._prepare_for_class(lowercase_      ,					lowercase_      ,					return_labels=lowercase_     )
																					lowerCAmelCase__      =			model(**lowercase_     ).loss
																					loss.backward()







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Optional[int]:
														if not self.model_tester.is_training:
																					return

														for model_class in self.all_model_classes:
																					lowerCAmelCase__      =			self.model_tester.prepare_config_and_inputs_with_labels()
																					lowerCAmelCase__      =			False
																					lowerCAmelCase__      =			True

																					if (
																					    model_class.__name__
																					    in [*get_values(lowercase_     ), *get_values(lowercase_     )]
																					    or not model_class.supports_gradient_checkpointing
																					):
																												continue

																					lowerCAmelCase__      =			model_class(lowercase_     )
																					model.to(lowercase_     )
																					model.gradient_checkpointing_enable()
																					model.train()
																					lowerCAmelCase__      =			self._prepare_for_class(lowercase_      ,					lowercase_      ,					return_labels=lowercase_     )
																					lowerCAmelCase__      =			model(**lowercase_     ).loss
																					loss.backward()







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							int:
														lowerCAmelCase__      =			self.model_tester.prepare_config_and_inputs_for_common()

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

																					lowerCAmelCase__      =			["pixel_values"]
																					self.assertListEqual(arg_names[:1]      ,					lowercase_     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							str:
														lowerCAmelCase__      =			self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*lowercase_     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							List[Any]:
														def check_hidden_states_output(lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_     ):
																					lowerCAmelCase__      =			model_class(lowercase_     )
																					model.to(lowercase_     )
																					model.eval()

																					with torch.no_grad():
																												lowerCAmelCase__      =			model(**self._prepare_for_class(lowercase_      ,					lowercase_     )     )

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

																					lowerCAmelCase__      =			self.model_tester.num_stages
																					self.assertEqual(len(lowercase_     )      ,					expected_num_stages + 1     )

																					# ConvNextV2'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]      ,					)

														lowerCAmelCase__      =			self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCAmelCase__      =			True
																					check_hidden_states_output(lowercase_      ,					lowercase_      ,					lowercase_     )

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

																					check_hidden_states_output(lowercase_      ,					lowercase_      ,					lowercase_     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Any:
														lowerCAmelCase__      =			self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*lowercase_     )







							@slow
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Optional[Any]:
														for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					lowerCAmelCase__      =			ConvNextVaModel.from_pretrained(lowercase_     )
																					self.assertIsNotNone(lowercase_     )


def       _snake_case     (  )		->					Tuple:
							lowerCAmelCase__      =			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
							return image






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





							'''simple docstring'''






							@cached_property
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Any:
														return AutoImageProcessor.from_pretrained('''facebook/convnextv2-tiny-1k-224'''     ) if is_vision_available() else None







							@slow
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Optional[Any]:
														lowerCAmelCase__      =			ConvNextVaForImageClassification.from_pretrained('''facebook/convnextv2-tiny-1k-224'''     ).to(lowercase_     )

														lowerCAmelCase__      =			self.default_image_processor
														lowerCAmelCase__      =			prepare_img()
														lowerCAmelCase__      =			preprocessor(images=lowercase_      ,					return_tensors='''pt'''     ).to(lowercase_     )

														# forward pass
														with torch.no_grad():
																					lowerCAmelCase__      =			model(**lowercase_     )

														# verify the logits
														lowerCAmelCase__      =			torch.Size((1, 10_00)     )
														self.assertEqual(outputs.logits.shape      ,					lowercase_     )

														lowerCAmelCase__      =			torch.tensor([0.9_996, 0.1_966, -0.4_386]     ).to(lowercase_     )
														self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,					lowercase_      ,					atol=1e-4     )     )



'''simple docstring'''






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

__UpperCAmelCase					       =							{
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.linear_k''': '''encoder.layers.*.self_attn.linear_k''',
    '''self_attn.linear_v''': '''encoder.layers.*.self_attn.linear_v''',
    '''self_attn.linear_q''': '''encoder.layers.*.self_attn.linear_q''',
    '''self_attn.pos_bias_u''': '''encoder.layers.*.self_attn.pos_bias_u''',
    '''self_attn.pos_bias_v''': '''encoder.layers.*.self_attn.pos_bias_v''',
    '''self_attn.linear_out''': '''encoder.layers.*.self_attn.linear_out''',
    '''self_attn.linear_pos''': '''encoder.layers.*.self_attn.linear_pos''',
    '''self_attn.rotary_emb''': '''encoder.embed_positions''',
    '''self_attn_layer_norm''': '''encoder.layers.*.self_attn_layer_norm''',
    '''conv_module.pointwise_conv1''': '''encoder.layers.*.conv_module.pointwise_conv1''',
    '''conv_module.pointwise_conv2''': '''encoder.layers.*.conv_module.pointwise_conv2''',
    '''conv_module.depthwise_conv''': '''encoder.layers.*.conv_module.depthwise_conv''',
    '''conv_module.batch_norm''': '''encoder.layers.*.conv_module.batch_norm''',
    '''conv_module.layer_norm''': '''encoder.layers.*.conv_module.layer_norm''',
    '''ffn1.w_1''': '''encoder.layers.*.ffn1.intermediate_dense''',
    '''ffn1.w_2''': '''encoder.layers.*.ffn1.output_dense''',
    '''ffn1.layer_norm''': '''encoder.layers.*.ffn1_layer_norm''',
    '''ffn2.w_1''': '''encoder.layers.*.ffn2.intermediate_dense''',
    '''ffn2.w_2''': '''encoder.layers.*.ffn2.output_dense''',
    '''ffn2.layer_norm''': '''encoder.layers.*.ffn2_layer_norm''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''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''',
}
__UpperCAmelCase					       =							[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]


def       _snake_case     (  A				,					A				,					A				,					A				,					A )		->					Optional[Any]:
							for attribute in key.split('''.''' ):
														lowerCAmelCase__      =			getattr(A				,					A )

							if weight_type is not None:
														lowerCAmelCase__      =			getattr(A				,					A ).shape
							else:
														lowerCAmelCase__      =			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":
														lowerCAmelCase__      =			value
							elif weight_type == "weight_g":
														lowerCAmelCase__      =			value
							elif weight_type == "weight_v":
														lowerCAmelCase__      =			value
							elif weight_type == "bias":
														lowerCAmelCase__      =			value
							elif weight_type == "running_mean":
														lowerCAmelCase__      =			value
							elif weight_type == "running_var":
														lowerCAmelCase__      =			value
							elif weight_type == "num_batches_tracked":
														lowerCAmelCase__      =			value
							elif weight_type == "inv_freq":
														lowerCAmelCase__      =			value
							else:
														lowerCAmelCase__      =			value

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


def       _snake_case     (  A				,					A				,					A )		->					Any:
							lowerCAmelCase__      =			[]
							lowerCAmelCase__      =			fairseq_model.state_dict()

							lowerCAmelCase__      =			hf_model.wavaveca_conformer.feature_extractor

							for name, value in fairseq_dict.items():
														lowerCAmelCase__      =			False
														if "conv_layers" in name:
																					load_conv_layer(
																					    A				,					A				,					A				,					A				,					hf_model.config.feat_extract_norm == '''group'''				,					)
																					lowerCAmelCase__      =			True
														else:
																					for key, mapped_key in MAPPING.items():
																												lowerCAmelCase__      =			'''wav2vec2_conformer.''' + 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]:
																																			lowerCAmelCase__      =			True
																																			if "*" in mapped_key:
																																										lowerCAmelCase__      =			name.split(A )[0].split('''.''' )[-2]
																																										lowerCAmelCase__      =			mapped_key.replace('''*'''				,					A )
																																			if "pos_bias_u" in name:
																																										lowerCAmelCase__      =			None
																																			elif "pos_bias_v" in name:
																																										lowerCAmelCase__      =			None
																																			elif "weight_g" in name:
																																										lowerCAmelCase__      =			'''weight_g'''
																																			elif "weight_v" in name:
																																										lowerCAmelCase__      =			'''weight_v'''
																																			elif "bias" in name:
																																										lowerCAmelCase__      =			'''bias'''
																																			elif "weight" in name:
																																										# TODO: don't match quantizer.weight_proj
																																										lowerCAmelCase__      =			'''weight'''
																																			elif "running_mean" in name:
																																										lowerCAmelCase__      =			'''running_mean'''
																																			elif "inv_freq" in name:
																																										lowerCAmelCase__      =			'''inv_freq'''
																																			elif "running_var" in name:
																																										lowerCAmelCase__      =			'''running_var'''
																																			elif "num_batches_tracked" in name:
																																										lowerCAmelCase__      =			'''num_batches_tracked'''
																																			else:
																																										lowerCAmelCase__      =			None
																																			set_recursively(A				,					A				,					A				,					A				,					A )
																												continue
														if not is_used:
																					unused_weights.append(A )

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


def       _snake_case     (  A				,					A				,					A				,					A				,					A )		->					Tuple:
							lowerCAmelCase__      =			full_name.split('''conv_layers.''' )[-1]
							lowerCAmelCase__      =			name.split('''.''' )
							lowerCAmelCase__      =			int(items[0] )
							lowerCAmelCase__      =			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.""" )
																					lowerCAmelCase__      =			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.""" )
																					lowerCAmelCase__      =			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.""" )
																					lowerCAmelCase__      =			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.""" )
																					lowerCAmelCase__      =			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       _snake_case     (  A				,					A				,					A=None				,					A=None				,					A=True )		->					Optional[int]:
							if config_path is not None:
														lowerCAmelCase__      =			WavaVecaConformerConfig.from_pretrained(A				,					hidden_act='''swish''' )
							else:
														lowerCAmelCase__      =			WavaVecaConformerConfig()

							if "rope" in checkpoint_path:
														lowerCAmelCase__      =			'''rotary'''

							if is_finetuned:
														if dict_path:
																					lowerCAmelCase__      =			Dictionary.load(A )

																					# important change bos & pad token id since CTC symbol is <pad> and
																					# not <s> as in fairseq
																					lowerCAmelCase__      =			target_dict.pad_index
																					lowerCAmelCase__      =			target_dict.bos_index
																					lowerCAmelCase__      =			target_dict.eos_index
																					lowerCAmelCase__      =			len(target_dict.symbols )
																					lowerCAmelCase__      =			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 )
																					lowerCAmelCase__      =			target_dict.indices

																					# fairseq has the <pad> and <s> switched
																					lowerCAmelCase__      =			0
																					lowerCAmelCase__      =			1
																					with open(A				,					'''w'''				,					encoding='''utf-8''' ) as vocab_handle:
																												json.dump(A				,					A )
																					lowerCAmelCase__      =			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				,					)
																					lowerCAmelCase__      =			True if config.feat_extract_norm == '''layer''' else False
																					lowerCAmelCase__      =			WavaVecaFeatureExtractor(
																					    feature_size=1				,					sampling_rate=16000				,					padding_value=0				,					do_normalize=A				,					return_attention_mask=A				,					)
																					lowerCAmelCase__      =			WavaVecaProcessor(feature_extractor=A				,					tokenizer=A )
																					processor.save_pretrained(A )

														lowerCAmelCase__      =			WavaVecaConformerForCTC(A )
							else:
														lowerCAmelCase__      =			WavaVecaConformerForPreTraining(A )

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

														lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__      =			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]				,					task=A )

							lowerCAmelCase__      =			model[0].eval()

							recursively_load_weights(A				,					A				,					not is_finetuned )

							hf_wavavec.save_pretrained(A )


if __name__ == "__main__":
		__UpperCAmelCase					       =							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'''
		)
		__UpperCAmelCase					       =							parser.parse_args()
		convert_wavaveca_conformer_checkpoint(
		    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
		)