


import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


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


def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					lowercase_  							=			DPTConfig()

					if "large" in checkpoint_url:
										lowercase_  							=			1_0_2_4
										lowercase_  							=			4_0_9_6
										lowercase_  							=			2_4
										lowercase_  							=			1_6
										lowercase_  							=			[5, 1_1, 1_7, 2_3]
										lowercase_  							=			[2_5_6, 5_1_2, 1_0_2_4, 1_0_2_4]
										lowercase_  							=			(1, 3_8_4, 3_8_4)

					if "ade" in checkpoint_url:
										lowercase_  							=			True

										lowercase_  							=			1_5_0
										lowercase_  							=			"""huggingface/label-files"""
										lowercase_  							=			"""ade20k-id2label.json"""
										lowercase_  							=			json.load(open(cached_download(hf_hub_url(UpperCamelCase__						,							UpperCamelCase__						,							repo_type="""dataset"""  )  )						,							"""r"""  )  )
										lowercase_  							=			{int(UpperCamelCase__  ): v for k, v in idalabel.items()}
										lowercase_  							=			idalabel
										lowercase_  							=			{v: k for k, v in idalabel.items()}
										lowercase_  							=			[1, 1_5_0, 4_8_0, 4_8_0]

					return config, expected_shape


def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					lowercase_  							=			["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
					for k in ignore_keys:
										state_dict.pop(UpperCamelCase__						,							UpperCamelCase__  )


def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					if (
					    "pretrained.model" in name
					    and "cls_token" not in name
					    and "pos_embed" not in name
					    and "patch_embed" not in name
					):
										lowercase_  							=			name.replace("""pretrained.model"""						,							"""dpt.encoder"""  )
					if "pretrained.model" in name:
										lowercase_  							=			name.replace("""pretrained.model"""						,							"""dpt.embeddings"""  )
					if "patch_embed" in name:
										lowercase_  							=			name.replace("""patch_embed"""						,							"""patch_embeddings"""  )
					if "pos_embed" in name:
										lowercase_  							=			name.replace("""pos_embed"""						,							"""position_embeddings"""  )
					if "attn.proj" in name:
										lowercase_  							=			name.replace("""attn.proj"""						,							"""attention.output.dense"""  )
					if "proj" in name and "project" not in name:
										lowercase_  							=			name.replace("""proj"""						,							"""projection"""  )
					if "blocks" in name:
										lowercase_  							=			name.replace("""blocks"""						,							"""layer"""  )
					if "mlp.fc1" in name:
										lowercase_  							=			name.replace("""mlp.fc1"""						,							"""intermediate.dense"""  )
					if "mlp.fc2" in name:
										lowercase_  							=			name.replace("""mlp.fc2"""						,							"""output.dense"""  )
					if "norm1" in name:
										lowercase_  							=			name.replace("""norm1"""						,							"""layernorm_before"""  )
					if "norm2" in name:
										lowercase_  							=			name.replace("""norm2"""						,							"""layernorm_after"""  )
					if "scratch.output_conv" in name:
										lowercase_  							=			name.replace("""scratch.output_conv"""						,							"""head"""  )
					if "scratch" in name:
										lowercase_  							=			name.replace("""scratch"""						,							"""neck"""  )
					if "layer1_rn" in name:
										lowercase_  							=			name.replace("""layer1_rn"""						,							"""convs.0"""  )
					if "layer2_rn" in name:
										lowercase_  							=			name.replace("""layer2_rn"""						,							"""convs.1"""  )
					if "layer3_rn" in name:
										lowercase_  							=			name.replace("""layer3_rn"""						,							"""convs.2"""  )
					if "layer4_rn" in name:
										lowercase_  							=			name.replace("""layer4_rn"""						,							"""convs.3"""  )
					if "refinenet" in name:
										lowercase_  							=			int(name[len("""neck.refinenet"""  ) : len("""neck.refinenet"""  ) + 1]  )
										# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
										lowercase_  							=			name.replace(F'''refinenet{layer_idx}'''						,							F'''fusion_stage.layers.{abs(layer_idx-4  )}'''  )
					if "out_conv" in name:
										lowercase_  							=			name.replace("""out_conv"""						,							"""projection"""  )
					if "resConfUnit1" in name:
										lowercase_  							=			name.replace("""resConfUnit1"""						,							"""residual_layer1"""  )
					if "resConfUnit2" in name:
										lowercase_  							=			name.replace("""resConfUnit2"""						,							"""residual_layer2"""  )
					if "conv1" in name:
										lowercase_  							=			name.replace("""conv1"""						,							"""convolution1"""  )
					if "conv2" in name:
										lowercase_  							=			name.replace("""conv2"""						,							"""convolution2"""  )
					# readout blocks
					if "pretrained.act_postprocess1.0.project.0" in name:
										lowercase_  							=			name.replace("""pretrained.act_postprocess1.0.project.0"""						,							"""neck.reassemble_stage.readout_projects.0.0"""  )
					if "pretrained.act_postprocess2.0.project.0" in name:
										lowercase_  							=			name.replace("""pretrained.act_postprocess2.0.project.0"""						,							"""neck.reassemble_stage.readout_projects.1.0"""  )
					if "pretrained.act_postprocess3.0.project.0" in name:
										lowercase_  							=			name.replace("""pretrained.act_postprocess3.0.project.0"""						,							"""neck.reassemble_stage.readout_projects.2.0"""  )
					if "pretrained.act_postprocess4.0.project.0" in name:
										lowercase_  							=			name.replace("""pretrained.act_postprocess4.0.project.0"""						,							"""neck.reassemble_stage.readout_projects.3.0"""  )
					# resize blocks
					if "pretrained.act_postprocess1.3" in name:
										lowercase_  							=			name.replace("""pretrained.act_postprocess1.3"""						,							"""neck.reassemble_stage.layers.0.projection"""  )
					if "pretrained.act_postprocess1.4" in name:
										lowercase_  							=			name.replace("""pretrained.act_postprocess1.4"""						,							"""neck.reassemble_stage.layers.0.resize"""  )
					if "pretrained.act_postprocess2.3" in name:
										lowercase_  							=			name.replace("""pretrained.act_postprocess2.3"""						,							"""neck.reassemble_stage.layers.1.projection"""  )
					if "pretrained.act_postprocess2.4" in name:
										lowercase_  							=			name.replace("""pretrained.act_postprocess2.4"""						,							"""neck.reassemble_stage.layers.1.resize"""  )
					if "pretrained.act_postprocess3.3" in name:
										lowercase_  							=			name.replace("""pretrained.act_postprocess3.3"""						,							"""neck.reassemble_stage.layers.2.projection"""  )
					if "pretrained.act_postprocess4.3" in name:
										lowercase_  							=			name.replace("""pretrained.act_postprocess4.3"""						,							"""neck.reassemble_stage.layers.3.projection"""  )
					if "pretrained.act_postprocess4.4" in name:
										lowercase_  							=			name.replace("""pretrained.act_postprocess4.4"""						,							"""neck.reassemble_stage.layers.3.resize"""  )
					if "pretrained" in name:
										lowercase_  							=			name.replace("""pretrained"""						,							"""dpt"""  )
					if "bn" in name:
										lowercase_  							=			name.replace("""bn"""						,							"""batch_norm"""  )
					if "head" in name:
										lowercase_  							=			name.replace("""head"""						,							"""head.head"""  )
					if "encoder.norm" in name:
										lowercase_  							=			name.replace("""encoder.norm"""						,							"""layernorm"""  )
					if "auxlayer" in name:
										lowercase_  							=			name.replace("""auxlayer"""						,							"""auxiliary_head.head"""  )

					return name


def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__  ):
					for i in range(config.num_hidden_layers  ):
										# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
										lowercase_  							=			state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.weight'''  )
										lowercase_  							=			state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.bias'''  )
										# next, add query, keys and values (in that order) to the state dict
										lowercase_  							=			in_proj_weight[: config.hidden_size, :]
										lowercase_  							=			in_proj_bias[: config.hidden_size]
										lowercase_  							=			in_proj_weight[
										    config.hidden_size : config.hidden_size * 2, :
										]
										lowercase_  							=			in_proj_bias[
										    config.hidden_size : config.hidden_size * 2
										]
										lowercase_  							=			in_proj_weight[
										    -config.hidden_size :, :
										]
										lowercase_  							=			in_proj_bias[-config.hidden_size :]


def 			UpperCAmelCase_				(      ):
					lowercase_  							=			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
					lowercase_  							=			Image.open(requests.get(UpperCamelCase__						,							stream=UpperCamelCase__  ).raw  )
					return im


@torch.no_grad()
def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__  ):
					lowercase_							,					lowercase_  							=			get_dpt_config(UpperCamelCase__  )
					# load original state_dict from URL
					lowercase_  							=			torch.hub.load_state_dict_from_url(UpperCamelCase__						,							map_location="""cpu"""  )
					# remove certain keys
					remove_ignore_keys_(UpperCamelCase__  )
					# rename keys
					for key in state_dict.copy().keys():
										lowercase_  							=			state_dict.pop(UpperCamelCase__  )
										lowercase_  							=			val
					# read in qkv matrices
					read_in_q_k_v(UpperCamelCase__						,							UpperCamelCase__  )

					# load HuggingFace model
					lowercase_  							=			DPTForSemanticSegmentation(UpperCamelCase__  ) if """ade""" in checkpoint_url else DPTForDepthEstimation(UpperCamelCase__  )
					model.load_state_dict(UpperCamelCase__  )
					model.eval()

					# Check outputs on an image
					lowercase_  							=			4_8_0 if """ade""" in checkpoint_url else 3_8_4
					lowercase_  							=			DPTImageProcessor(size=UpperCamelCase__  )

					lowercase_  							=			prepare_img()
					lowercase_  							=			image_processor(UpperCamelCase__						,							return_tensors="""pt"""  )

					# forward pass
					lowercase_  							=			model(**UpperCamelCase__  ).logits if """ade""" in checkpoint_url else model(**UpperCamelCase__  ).predicted_depth

					# Assert logits
					lowercase_  							=			torch.tensor([[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]  )
					if "ade" in checkpoint_url:
										lowercase_  							=			torch.tensor([[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]  )
					assert outputs.shape == torch.Size(UpperCamelCase__  )
					assert (
					    torch.allclose(outputs[0, 0, :3, :3]						,							UpperCamelCase__						,							atol=1e-4  )
					    if "ade" in checkpoint_url
					    else torch.allclose(outputs[0, :3, :3]						,							UpperCamelCase__  )
					)

					Path(UpperCamelCase__  ).mkdir(exist_ok=UpperCamelCase__  )
					print(F'''Saving model to {pytorch_dump_folder_path}'''  )
					model.save_pretrained(UpperCamelCase__  )
					print(F'''Saving image processor to {pytorch_dump_folder_path}'''  )
					image_processor.save_pretrained(UpperCamelCase__  )

					if push_to_hub:
										print("""Pushing model to hub..."""  )
										model.push_to_hub(
										    repo_path_or_name=Path(UpperCamelCase__						,							UpperCamelCase__  )						,							organization="""nielsr"""						,							commit_message="""Add model"""						,							use_temp_dir=UpperCamelCase__						,							)
										image_processor.push_to_hub(
										    repo_path_or_name=Path(UpperCamelCase__						,							UpperCamelCase__  )						,							organization="""nielsr"""						,							commit_message="""Add image processor"""						,							use_temp_dir=UpperCamelCase__						,							)


if __name__ == "__main__":
							a					  =     argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--checkpoint_url',
							    default='https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt',
							    type=str,
							    help='URL of the original DPT checkpoint you\'d like to convert.',
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path',
							    default=None,
							    type=str,
							    required=True,
							    help='Path to the output PyTorch model directory.',
							)
							parser.add_argument(
							    '--push_to_hub',
							    action='store_true',
							)
							parser.add_argument(
							    '--model_name',
							    default='dpt-large',
							    type=str,
							    help='Name of the model, in case you\'re pushing to the hub.',
							)

							a					  =     parser.parse_args()
							convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)






import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


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

a					  =     {
    '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',
}
a					  =     [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]


def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__  ):
					for attribute in key.split("""."""  ):
										lowercase_  							=			getattr(UpperCAmelCase__						,							UpperCAmelCase__  )

					if weight_type is not None:
										lowercase_  							=			getattr(UpperCAmelCase__						,							UpperCAmelCase__  ).shape
					else:
										lowercase_  							=			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_  							=			value
					elif weight_type == "weight_g":
										lowercase_  							=			value
					elif weight_type == "weight_v":
										lowercase_  							=			value
					elif weight_type == "bias":
										lowercase_  							=			value
					else:
										lowercase_  							=			value

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


def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__  ):
					lowercase_  							=			[]
					lowercase_  							=			fairseq_model.state_dict()

					lowercase_  							=			hf_model.feature_extractor

					# if encoder has different dim to decoder -> use proj_weight
					lowercase_  							=			None

					for name, value in fairseq_dict.items():
										lowercase_  							=			False
										if "conv_layers" in name:
															load_conv_layer(
															    UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							hf_model.config.feat_extract_norm == """group"""						,							)
															lowercase_  							=			True
										elif name.split("""."""  )[0] == "proj":
															lowercase_  							=			fairseq_model.proj
															lowercase_  							=			True
										else:
															for key, mapped_key in MAPPING.items():
																				if key in name or key.split("""w2v_model."""  )[-1] == name.split("""."""  )[0]:
																									lowercase_  							=			True
																									if "*" in mapped_key:
																														lowercase_  							=			name.split(UpperCAmelCase__  )[0].split("""."""  )[-2]
																														lowercase_  							=			mapped_key.replace("""*"""						,							UpperCAmelCase__  )
																									if "weight_g" in name:
																														lowercase_  							=			"""weight_g"""
																									elif "weight_v" in name:
																														lowercase_  							=			"""weight_v"""
																									elif "bias" in name:
																														lowercase_  							=			"""bias"""
																									elif "weight" in name:
																														lowercase_  							=			"""weight"""
																									else:
																														lowercase_  							=			None
																									set_recursively(UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__  )
																				continue
										if not is_used:
															unused_weights.append(UpperCAmelCase__  )

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

					return proj_weight


def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__  ):
					lowercase_  							=			full_name.split("""conv_layers."""  )[-1]
					lowercase_  							=			name.split("""."""  )
					lowercase_  							=			int(items[0]  )
					lowercase_  							=			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_  							=			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_  							=			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_  							=			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_  							=			value
															logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''  )
					else:
										unused_weights.append(UpperCAmelCase__  )


def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					lowercase_							,					lowercase_  							=			emb.weight.shape
					lowercase_  							=			nn.Linear(UpperCAmelCase__						,							UpperCAmelCase__						,							bias=UpperCAmelCase__  )
					lowercase_  							=			emb.weight.data
					return lin_layer


def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					with open(UpperCAmelCase__						,							"""r"""						,							encoding="""utf-8"""  ) as f:
										lowercase_  							=			f.readlines()
										lowercase_  							=			[line.split(""" """  )[0] for line in lines]

					lowercase_  							=			len(UpperCAmelCase__  )

					lowercase_  							=			{
					    """<s>""": 0,
					    """<pad>""": 1,
					    """</s>""": 2,
					    """<unk>""": 3,
					}

					vocab_dict.update(dict(zip(UpperCAmelCase__						,							range(4						,							num_words + 4  )  )  )  )
					return vocab_dict


@torch.no_grad()
def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							):
					lowercase_  							=			WavaVecaConfig.from_pretrained(UpperCAmelCase__  )
					lowercase_  							=			SpeechaTextaConfig.from_pretrained(
					    UpperCAmelCase__						,							vocab_size=UpperCAmelCase__						,							decoder_layers=UpperCAmelCase__						,							do_stable_layer_norm=UpperCAmelCase__  )

					lowercase_  							=			WavaVecaFeatureExtractor(
					    feature_size=1						,							sampling_rate=1_6_0_0_0						,							padding_value=0						,							do_normalize=UpperCAmelCase__						,							return_attention_mask=UpperCAmelCase__						,							)

					lowercase_							,					lowercase_							,					lowercase_  							=			fairseq.checkpoint_utils.load_model_ensemble_and_task(
					    [checkpoint_path]						,							arg_overrides={"""data""": """/""".join(dict_path.split("""/"""  )[:-1]  )}  )
					lowercase_  							=			model[0].eval()

					# set weights for wav2vec2 encoder
					lowercase_  							=			WavaVecaModel(UpperCAmelCase__  )
					lowercase_  							=			recursively_load_weights_wavaveca(model.encoder						,							UpperCAmelCase__  )

					lowercase_  							=			SpeechaTextaForCausalLM(UpperCAmelCase__  )
					lowercase_							,					lowercase_  							=			hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()						,							strict=UpperCAmelCase__  )

					# set output linear layer
					unexpected_keys.remove("""embed_out"""  )
					lowercase_  							=			nn.Parameter(model.decoder.embed_out.detach()  )

					# layer norm is init to identity matrix so leaving it is fine
					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}'''  )

					lowercase_  							=			SpeechEncoderDecoderModel(encoder=UpperCAmelCase__						,							decoder=UpperCAmelCase__  )
					lowercase_  							=			False

					# add projection layer
					lowercase_  							=			nn.Parameter(projection_layer.weight  )
					lowercase_  							=			nn.Parameter(projection_layer.bias  )

					lowercase_  							=			create_vocab_dict(UpperCAmelCase__  )

					with open(os.path.join(UpperCAmelCase__						,							"""vocab.json"""  )						,							"""w"""  ) as fp:
										json.dump(UpperCAmelCase__						,							UpperCAmelCase__  )

					lowercase_  							=			SpeechaTextaTokenizer(os.path.join(UpperCAmelCase__						,							"""vocab.json"""  )  )
					tokenizer.save_pretrained(UpperCAmelCase__  )

					lowercase_  							=			hf_wavavec.config.to_dict()
					lowercase_  							=			tokenizer.pad_token_id
					lowercase_  							=			tokenizer.bos_token_id
					lowercase_  							=			tokenizer.eos_token_id
					lowercase_  							=			"""speech_to_text_2"""
					lowercase_  							=			"""wav2vec2"""

					lowercase_  							=			SpeechEncoderDecoderConfig.from_dict(UpperCAmelCase__  )

					hf_wavavec.save_pretrained(UpperCAmelCase__  )
					feature_extractor.save_pretrained(UpperCAmelCase__  )


if __name__ == "__main__":
							a					  =     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(
							    '--encoder_config_path',
							    default='facebook/wav2vec2-large-lv60',
							    type=str,
							    help='Path to hf encoder wav2vec2 checkpoint config',
							)
							parser.add_argument(
							    '--decoder_config_path',
							    default='facebook/s2t-small-mustc-en-fr-st',
							    type=str,
							    help='Path to hf decoder s2t checkpoint config',
							)
							parser.add_argument('--vocab_size', default=1_0_2_2_4, type=int, help='Vocab size of decoder')
							parser.add_argument('--num_decoder_layers', default=7, type=int, help='Number of decoder layers')

							a					  =     parser.parse_args()
							convert_wavaveca_checkpoint(
							    args.checkpoint_path,
							    args.pytorch_dump_folder_path,
							    args.dict_path,
							    encoder_config_path=args.encoder_config_path,
							    decoder_config_path=args.decoder_config_path,
							    vocab_size=args.vocab_size,
							    num_decoder_layers=args.num_decoder_layers,
							)


