



"""simple docstring"""





import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


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

__lowerCAmelCase       :					Union[str, 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',
    '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',
}
__lowerCAmelCase       :					Tuple      					=					[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Any					, __UpperCamelCase      :			Union[str, Any]					, __UpperCamelCase      :			int					, __UpperCamelCase      :			Dict					, __UpperCamelCase      :			Dict    ):


						'''simple docstring'''






						for attribute in key.split("""."""    ):
												snake_case_   : int						=			getattr(__UpperCamelCase					, __UpperCamelCase    )

						if weight_type is not None:
												snake_case_   : Optional[int]						=			getattr(__UpperCamelCase					, __UpperCamelCase    ).shape
						else:
												snake_case_   : List[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":
												snake_case_   : str						=			value
						elif weight_type == "weight_g":
												snake_case_   : List[Any]						=			value
						elif weight_type == "weight_v":
												snake_case_   : Tuple						=			value
						elif weight_type == "bias":
												snake_case_   : List[Any]						=			value
						else:
												snake_case_   : Union[str, Any]						=			value

						logger.info(F'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			List[Any]					, __UpperCamelCase      :			str    ):


						'''simple docstring'''






						snake_case_   : Dict						=			[]
						snake_case_   : Optional[Any]						=			fairseq_model.state_dict()

						snake_case_   : Optional[Any]						=			hf_model.feature_extractor
						snake_case_   : Tuple						=			hf_model.adapter

						for name, value in fairseq_dict.items():
												snake_case_   : Tuple						=			False
												if "conv_layers" in name:
																		load_conv_layer(
																		    __UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase					, hf_model.config.feat_extract_norm == """group"""					, )
																		snake_case_   : Optional[Any]						=			True
												elif any(x in name for x in ["""adaptor""", """w2v_encoder.proj.""", """w2v_proj_ln."""]    ):
																		load_adapter(__UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase    )
																		snake_case_   : Any						=			True
												else:
																		for key, mapped_key in MAPPING.items():
																								if key in name or key.split("""w2v_model."""    )[-1] == name.split("""."""    )[0]:
																														snake_case_   : Tuple						=			True
																														if "*" in mapped_key:
																																				snake_case_   : Optional[int]						=			name.split(__UpperCamelCase    )[0].split("""."""    )[-2]
																																				snake_case_   : Dict						=			mapped_key.replace("""*"""					, __UpperCamelCase    )
																														if "weight_g" in name:
																																				snake_case_   : Tuple						=			'''weight_g'''
																														elif "weight_v" in name:
																																				snake_case_   : Tuple						=			'''weight_v'''
																														elif "bias" in name:
																																				snake_case_   : Union[str, Any]						=			'''bias'''
																														elif "weight" in name:
																																				snake_case_   : Union[str, Any]						=			'''weight'''
																														else:
																																				snake_case_   : List[Any]						=			None
																														set_recursively(__UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase    )
																								continue
												if not is_used:
																		unused_weights.append(__UpperCamelCase    )

						logger.warning(F'Unused weights: {unused_weights}'    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Optional[int]					, __UpperCamelCase      :			Union[str, Any]					, __UpperCamelCase      :			Dict					, __UpperCamelCase      :			Any					, __UpperCamelCase      :			Optional[int]    ):


						'''simple docstring'''






						snake_case_   : Optional[Any]						=			full_name.split("""conv_layers."""    )[-1]
						snake_case_   : Tuple						=			name.split("""."""    )
						snake_case_   : List[Any]						=			int(items[0]    )
						snake_case_   : int						=			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.'
																		)
																		snake_case_   : 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.'
																		)
																		snake_case_   : List[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."
																		)
																		snake_case_   : List[str]						=			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.'
																		)
																		snake_case_   : Any						=			value
																		logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'    )
						else:
												unused_weights.append(__UpperCamelCase    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Dict					, __UpperCamelCase      :			List[Any]					, __UpperCamelCase      :			str					, __UpperCamelCase      :			Union[str, Any]    ):


						'''simple docstring'''






						snake_case_   : Any						=			full_name.split("""adaptor."""    )[-1]
						snake_case_   : Union[str, Any]						=			name.split("""."""    )

						if items[1].isdigit():
												snake_case_   : Tuple						=			int(items[1]    )
						else:
												snake_case_   : int						=			None

						if "adaptor" not in full_name:
												if "proj_ln" in full_name:
																		# has to be layer norm
																		if "bias" in name:
																								assert (
																								    value.shape == adapter.proj_layer_norm.bias.data.shape
																								), F'{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found.'
																								snake_case_   : Dict						=			value
																								logger.info(F'Adapter proj layer norm bias was initialized from {full_name}.'    )
																		if "weight" in name:
																								assert (
																								    value.shape == adapter.proj_layer_norm.weight.data.shape
																								), F'{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found.'
																								snake_case_   : int						=			value
												else:
																		# has to be projection layer
																		if "bias" in name:
																								assert (
																								    value.shape == adapter.proj.bias.data.shape
																								), F'{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found.'
																								snake_case_   : List[str]						=			value
																								logger.info(F'Adapter proj layer bias was initialized from {full_name}.'    )
																		if "weight" in name:
																								assert (
																								    value.shape == adapter.proj.weight.data.shape
																								), F'{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found.'
																								snake_case_   : Optional[Any]						=			value
																								logger.info(F'Adapter proj layer weight was initialized from {full_name}.'    )
						elif isinstance(__UpperCamelCase					, __UpperCamelCase    ):
												if "bias" in name:
																		assert (
																		    value.shape == adapter.layers[layer_id].conv.bias.data.shape
																		), F'{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found.'
																		snake_case_   : List[Any]						=			value
																		logger.info(F'Adapter layer {layer_id} bias was initialized from {full_name}.'    )
												elif "weight" in name:
																		assert (
																		    value.shape == adapter.layers[layer_id].conv.weight.data.shape
																		), F'{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found.'
																		snake_case_   : int						=			value
																		logger.info(F'Adapter layer {layer_id} bias was initialized from {full_name}.'    )
						else:
												unused_weights.append(__UpperCamelCase    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Tuple    ):


						'''simple docstring'''






						snake_case_   : Optional[Any]						=			emb.weight.shape
						snake_case_   : List[str]						=			nn.Linear(__UpperCamelCase					, __UpperCamelCase					, bias=__UpperCamelCase    )
						snake_case_   : Tuple						=			emb.weight.data
						return lin_layer
@torch.no_grad()
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Any					, __UpperCamelCase      :			List[str]					, __UpperCamelCase      :			str					, __UpperCamelCase      :			Union[str, Any]					, __UpperCamelCase      :			Tuple					, __UpperCamelCase      :			int					, __UpperCamelCase      :			Any					, __UpperCamelCase      :			str					, __UpperCamelCase      :			List[str]					, __UpperCamelCase      :			Tuple					, __UpperCamelCase      :			List[str]					, ):


						'''simple docstring'''






						snake_case_   : int						=			WavaVecaConfig.from_pretrained(
						    __UpperCamelCase					, add_adapter=__UpperCamelCase					, adapter_stride=__UpperCamelCase					, adapter_kernel_size=__UpperCamelCase					, use_auth_token=__UpperCamelCase					, output_hidden_size=__UpperCamelCase					, )
						snake_case_   : Union[str, Any]						=			MBartConfig.from_pretrained(__UpperCamelCase    )

						# load model
						snake_case_   : List[Any]						=			fairseq.checkpoint_utils.load_model_ensemble_and_task(
						    [checkpoint_path]					, arg_overrides={
						        """config_yaml""": config_yaml_path,
						        """data""": """/""".join(dict_path.split("""/"""    )[:-1]    ),
						        """w2v_path""": checkpoint_path,
						        """load_pretrained_decoder_from""": None,
						    }					, )
						snake_case_   : List[Any]						=			model[0].eval()

						# load feature extractor
						snake_case_   : str						=			WavaVecaFeatureExtractor.from_pretrained(__UpperCamelCase					, use_auth_token=__UpperCamelCase    )

						# set weights for wav2vec2 encoder
						snake_case_   : Dict						=			WavaVecaModel(__UpperCamelCase    )

						recursively_load_weights_wavaveca(model.encoder					, __UpperCamelCase    )

						# load decoder weights
						snake_case_   : Optional[int]						=			MBartForCausalLM(__UpperCamelCase    )
						snake_case_   : Tuple						=			hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()					, strict=__UpperCamelCase    )
						logger.warning(F'The following keys are missing when loading the decoder weights: {missing_keys}'    )
						logger.warning(F'The following keys are unexpected when loading the decoder weights: {unexpected_keys}'    )

						snake_case_   : Optional[Any]						=			SpeechEncoderDecoderModel(encoder=__UpperCamelCase					, decoder=__UpperCamelCase    )
						snake_case_   : Union[str, Any]						=			False

						snake_case_   : Union[str, Any]						=			MBartaaTokenizer(__UpperCamelCase    )
						tokenizer.save_pretrained(__UpperCamelCase    )

						snake_case_   : List[Any]						=			hf_wavavec.config.to_dict()
						snake_case_   : List[Any]						=			tokenizer.pad_token_id
						snake_case_   : Dict						=			tokenizer.bos_token_id
						snake_case_   : List[str]						=			tokenizer.eos_token_id
						snake_case_   : str						=			'''mbart50'''
						snake_case_   : Optional[int]						=			'''wav2vec2'''

						snake_case_   : str						=			tokenizer.eos_token_id
						snake_case_   : Optional[int]						=			2_5_0_0_0_4
						snake_case_   : Union[str, Any]						=			tokenizer.eos_token_id

						snake_case_   : List[Any]						=			SpeechEncoderDecoderConfig.from_dict(__UpperCamelCase    )

						hf_wavavec.save_pretrained(__UpperCamelCase    )
						feature_extractor.save_pretrained(__UpperCamelCase    )


if __name__ == "__main__":
					__lowerCAmelCase       :					Tuple      					=					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_yaml_path''', default=None, type=str, help='''Path to yaml file of fine-tuned model''')
					parser.add_argument(
					    '''--encoder_config_path''',
					    default='''facebook/wav2vec2-xls-r-1b''',
					    type=str,
					    help='''Path to hf encoder wav2vec2 checkpoint config''',
					)
					parser.add_argument(
					    '''--decoder_config_path''',
					    default='''facebook/mbart-large-50-one-to-many-mmt''',
					    type=str,
					    help='''Path to hf decoder checkpoint config''',
					)
					parser.add_argument('''--add_adapter''', default=True, type=bool, help='''whethere to add model adapter layers''')
					parser.add_argument('''--adapter_stride''', default=2, type=int, help='''stride of adapter layers''')
					parser.add_argument('''--adapter_kernel_size''', default=3, type=int, help='''kernel size of adapter layers''')
					parser.add_argument('''--encoder_output_dim''', default=1024, type=int, help='''encoder output dim''')
					parser.add_argument('''--start_token_id''', default=25_0004, type=int, help='''`decoder_start_token_id` of model config''')

					__lowerCAmelCase       :					List[Any]      					=					parser.parse_args()
					convert_wavaveca_checkpoint(
					    args.checkpoint_path,
					    args.pytorch_dump_folder_path,
					    args.dict_path,
					    args.config_yaml_path,
					    encoder_config_path=args.encoder_config_path,
					    decoder_config_path=args.decoder_config_path,
					    add_adapter=args.add_adapter,
					    adapter_kernel_size=args.adapter_kernel_size,
					    adapter_stride=args.adapter_stride,
					    decoder_start_token_id=args.start_token_id,
					    encoder_output_dim=args.encoder_output_dim,
					)










"""simple docstring"""





import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def 	__lowerCAmelCase						(		__UpperCamelCase      :			List[Any]    ):


						'''simple docstring'''






						snake_case_   : List[str]						=			[]
						embed.append(
						    (
						        F'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight',
						        F'stage{idx}.patch_embed.proj.weight',
						    )    )
						embed.append(
						    (
						        F'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias',
						        F'stage{idx}.patch_embed.proj.bias',
						    )    )
						embed.append(
						    (
						        F'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight',
						        F'stage{idx}.patch_embed.norm.weight',
						    )    )
						embed.append(
						    (
						        F'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias',
						        F'stage{idx}.patch_embed.norm.bias',
						    )    )
						return embed
def 	__lowerCAmelCase						(		__UpperCamelCase      :			str					, __UpperCamelCase      :			int    ):


						'''simple docstring'''






						snake_case_   : str						=			[]
						attention_weights.append(
						    (
						        F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight',
						        F'stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight',
						    )    )
						attention_weights.append(
						    (
						        F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight',
						        F'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight',
						    )    )
						attention_weights.append(
						    (
						        F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias',
						        F'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias',
						    )    )
						attention_weights.append(
						    (
						        F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean',
						        F'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean',
						    )    )
						attention_weights.append(
						    (
						        F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var',
						        F'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var',
						    )    )
						attention_weights.append(
						    (
						        F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked',
						        F'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked',
						    )    )
						attention_weights.append(
						    (
						        F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight',
						        F'stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight',
						    )    )
						attention_weights.append(
						    (
						        F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight',
						        F'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight',
						    )    )
						attention_weights.append(
						    (
						        F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias',
						        F'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias',
						    )    )
						attention_weights.append(
						    (
						        F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean',
						        F'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean',
						    )    )
						attention_weights.append(
						    (
						        F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var',
						        F'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var',
						    )    )
						attention_weights.append(
						    (
						        F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked',
						        F'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked',
						    )    )
						attention_weights.append(
						    (
						        F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight',
						        F'stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight',
						    )    )
						attention_weights.append(
						    (
						        F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight',
						        F'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight',
						    )    )
						attention_weights.append(
						    (
						        F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias',
						        F'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias',
						    )    )
						attention_weights.append(
						    (
						        F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean',
						        F'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean',
						    )    )
						attention_weights.append(
						    (
						        F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var',
						        F'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var',
						    )    )
						attention_weights.append(
						    (
						        F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked',
						        F'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked',
						    )    )
						attention_weights.append(
						    (
						        F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight',
						        F'stage{idx}.blocks.{cnt}.attn.proj_q.weight',
						    )    )
						attention_weights.append(
						    (
						        F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias',
						        F'stage{idx}.blocks.{cnt}.attn.proj_q.bias',
						    )    )
						attention_weights.append(
						    (
						        F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight',
						        F'stage{idx}.blocks.{cnt}.attn.proj_k.weight',
						    )    )
						attention_weights.append(
						    (
						        F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias',
						        F'stage{idx}.blocks.{cnt}.attn.proj_k.bias',
						    )    )
						attention_weights.append(
						    (
						        F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight',
						        F'stage{idx}.blocks.{cnt}.attn.proj_v.weight',
						    )    )
						attention_weights.append(
						    (
						        F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias',
						        F'stage{idx}.blocks.{cnt}.attn.proj_v.bias',
						    )    )
						attention_weights.append(
						    (
						        F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight',
						        F'stage{idx}.blocks.{cnt}.attn.proj.weight',
						    )    )
						attention_weights.append(
						    (
						        F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias',
						        F'stage{idx}.blocks.{cnt}.attn.proj.bias',
						    )    )
						attention_weights.append(
						    (F'cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight', F'stage{idx}.blocks.{cnt}.mlp.fc1.weight')    )
						attention_weights.append(
						    (F'cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias', F'stage{idx}.blocks.{cnt}.mlp.fc1.bias')    )
						attention_weights.append(
						    (F'cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight', F'stage{idx}.blocks.{cnt}.mlp.fc2.weight')    )
						attention_weights.append(
						    (F'cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias', F'stage{idx}.blocks.{cnt}.mlp.fc2.bias')    )
						attention_weights.append(
						    (F'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight', F'stage{idx}.blocks.{cnt}.norm1.weight')    )
						attention_weights.append(
						    (F'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias', F'stage{idx}.blocks.{cnt}.norm1.bias')    )
						attention_weights.append(
						    (F'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight', F'stage{idx}.blocks.{cnt}.norm2.weight')    )
						attention_weights.append(
						    (F'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias', F'stage{idx}.blocks.{cnt}.norm2.bias')    )
						return attention_weights
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Tuple    ):


						'''simple docstring'''






						snake_case_   : int						=			[]
						token.append((F'cvt.encoder.stages.{idx}.cls_token', """stage2.cls_token""")    )
						return token
def 	__lowerCAmelCase						(		):


						'''simple docstring'''






						snake_case_   : Union[str, Any]						=			[]
						head.append(("""layernorm.weight""", """norm.weight""")    )
						head.append(("""layernorm.bias""", """norm.bias""")    )
						head.append(("""classifier.weight""", """head.weight""")    )
						head.append(("""classifier.bias""", """head.bias""")    )
						return head
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Union[str, Any]					, __UpperCamelCase      :			Union[str, Any]					, __UpperCamelCase      :			Optional[Any]					, __UpperCamelCase      :			List[Any]    ):


						'''simple docstring'''






						snake_case_   : Union[str, Any]						=			"""imagenet-1k-id2label.json"""
						snake_case_   : Optional[Any]						=			1_0_0_0

						snake_case_   : Any						=			"""huggingface/label-files"""
						snake_case_   : Tuple						=			num_labels
						snake_case_   : Dict						=			json.load(open(cached_download(hf_hub_url(__UpperCamelCase					, __UpperCamelCase					, repo_type="""dataset"""    )    )					, """r"""    )    )
						snake_case_   : str						=			{int(__UpperCamelCase    ): v for k, v in idalabel.items()}

						snake_case_   : List[str]						=			idalabel
						snake_case_   : Any						=			{v: k for k, v in idalabel.items()}

						snake_case_   : Dict						=			CvtConfig(num_labels=__UpperCamelCase					, idalabel=__UpperCamelCase					, labelaid=__UpperCamelCase    )

						# For depth size 13 (13 = 1+2+10)
						if cvt_model.rsplit("""/"""					, 1    )[-1][4:6] == "13":
												snake_case_   : Any						=			[1, 2, 1_0]

						# For depth size 21 (21 = 1+4+16)
						elif cvt_model.rsplit("""/"""					, 1    )[-1][4:6] == "21":
												snake_case_   : Any						=			[1, 4, 1_6]

						# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
						else:
												snake_case_   : Optional[int]						=			[2, 2, 2_0]
												snake_case_   : str						=			[3, 1_2, 1_6]
												snake_case_   : Any						=			[1_9_2, 7_6_8, 1_0_2_4]

						snake_case_   : Union[str, Any]						=			CvtForImageClassification(__UpperCamelCase    )
						snake_case_   : str						=			AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""    )
						snake_case_   : List[Any]						=			image_size
						snake_case_   : str						=			torch.load(__UpperCamelCase					, map_location=torch.device("""cpu"""    )    )

						snake_case_   : Any						=			OrderedDict()
						snake_case_   : Tuple						=			[]

						for idx in range(len(config.depth    )    ):
												if config.cls_token[idx]:
																		snake_case_   : Optional[Any]						=			list_of_state_dict + cls_token(__UpperCamelCase    )
												snake_case_   : str						=			list_of_state_dict + embeddings(__UpperCamelCase    )
												for cnt in range(config.depth[idx]    ):
																		snake_case_   : List[str]						=			list_of_state_dict + attention(__UpperCamelCase					, __UpperCamelCase    )

						snake_case_   : str						=			list_of_state_dict + final()
						for gg in list_of_state_dict:
												print(__UpperCamelCase    )
						for i in range(len(__UpperCamelCase    )    ):
												snake_case_   : Union[str, Any]						=			original_weights[list_of_state_dict[i][1]]

						model.load_state_dict(__UpperCamelCase    )
						model.save_pretrained(__UpperCamelCase    )
						image_processor.save_pretrained(__UpperCamelCase    )


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
					__lowerCAmelCase       :					Optional[int]      					=					argparse.ArgumentParser()
					parser.add_argument(
					    '''--cvt_model''',
					    default='''cvt-w24''',
					    type=str,
					    help='''Name of the cvt model you\'d like to convert.''',
					)
					parser.add_argument(
					    '''--image_size''',
					    default=384,
					    type=int,
					    help='''Input Image Size''',
					)
					parser.add_argument(
					    '''--cvt_file_name''',
					    default=R'''cvtmodels\CvT-w24-384x384-IN-22k.pth''',
					    type=str,
					    help='''Input Image Size''',
					)
					parser.add_argument(
					    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
					)

					__lowerCAmelCase       :					Dict      					=					parser.parse_args()
					convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)

