


"""simple docstring"""






import argparse
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import (
    RobertaTokenizer,
    TrOCRConfig,
    TrOCRForCausalLM,
    TrOCRProcessor,
    VisionEncoderDecoderModel,
    ViTConfig,
    ViTImageProcessor,
    ViTModel,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase__				:Any			   =	logging.get_logger(__name__)





def 				A_       ( snake_case__       ,					snake_case__		)					->  List[Any]:
			_UpperCamelCase					:Any					     =	[]
			for i in range(encoder_config.num_hidden_layers		):
						# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
						rename_keys.append(
						    (f"encoder.deit.blocks.{i}.norm1.weight", f"encoder.encoder.layer.{i}.layernorm_before.weight")		)
						rename_keys.append((f"encoder.deit.blocks.{i}.norm1.bias", f"encoder.encoder.layer.{i}.layernorm_before.bias")		)
						rename_keys.append(
						    (f"encoder.deit.blocks.{i}.attn.proj.weight", f"encoder.encoder.layer.{i}.attention.output.dense.weight")		)
						rename_keys.append(
						    (f"encoder.deit.blocks.{i}.attn.proj.bias", f"encoder.encoder.layer.{i}.attention.output.dense.bias")		)
						rename_keys.append(
						    (f"encoder.deit.blocks.{i}.norm2.weight", f"encoder.encoder.layer.{i}.layernorm_after.weight")		)
						rename_keys.append((f"encoder.deit.blocks.{i}.norm2.bias", f"encoder.encoder.layer.{i}.layernorm_after.bias")		)
						rename_keys.append(
						    (f"encoder.deit.blocks.{i}.mlp.fc1.weight", f"encoder.encoder.layer.{i}.intermediate.dense.weight")		)
						rename_keys.append(
						    (f"encoder.deit.blocks.{i}.mlp.fc1.bias", f"encoder.encoder.layer.{i}.intermediate.dense.bias")		)
						rename_keys.append(
						    (f"encoder.deit.blocks.{i}.mlp.fc2.weight", f"encoder.encoder.layer.{i}.output.dense.weight")		)
						rename_keys.append((f"encoder.deit.blocks.{i}.mlp.fc2.bias", f"encoder.encoder.layer.{i}.output.dense.bias")		)

			# cls token, position embeddings and patch embeddings of encoder
			rename_keys.extend(
			    [
			        ('''encoder.deit.cls_token''', '''encoder.embeddings.cls_token'''),
			        ('''encoder.deit.pos_embed''', '''encoder.embeddings.position_embeddings'''),
			        ('''encoder.deit.patch_embed.proj.weight''', '''encoder.embeddings.patch_embeddings.projection.weight'''),
			        ('''encoder.deit.patch_embed.proj.bias''', '''encoder.embeddings.patch_embeddings.projection.bias'''),
			        ('''encoder.deit.norm.weight''', '''encoder.layernorm.weight'''),
			        ('''encoder.deit.norm.bias''', '''encoder.layernorm.bias'''),
			    ]		)

			return rename_keys





def 				A_       ( snake_case__       ,					snake_case__		)					->  Any:
			for i in range(encoder_config.num_hidden_layers		):
						# queries, keys and values (only weights, no biases)
						_UpperCamelCase					:List[Any]					     =	state_dict.pop(f"encoder.deit.blocks.{i}.attn.qkv.weight"		)

						_UpperCamelCase					:Union[str, Any]					     =	in_proj_weight[
						    : encoder_config.hidden_size, :
						]
						_UpperCamelCase					:Union[str, Any]					     =	in_proj_weight[
						    encoder_config.hidden_size : encoder_config.hidden_size * 2, :
						]
						_UpperCamelCase					:str					     =	in_proj_weight[
						    -encoder_config.hidden_size :, :
						]





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__		)					->  List[str]:
			_UpperCamelCase					:List[str]					     =	dct.pop(snake_case__		)
			_UpperCamelCase					:List[str]					     =	val





def 				A_       ( snake_case__		)					->  List[str]:
			if "handwritten" in checkpoint_url:
						_UpperCamelCase					:Any					     =	'''https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg'''  # industry
						# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-12.jpg" # have
						# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-10.jpg" # let
						# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg"  #
						# url = "https://fki.tic.heia-fr.ch/static/img/a01-122.jpg"
			elif "printed" in checkpoint_url or "stage1" in checkpoint_url:
						_UpperCamelCase					:int					     =	'''https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg'''
			_UpperCamelCase					:List[Any]					     =	Image.open(requests.get(snake_case__       ,					stream=snake_case__		).raw		).convert('''RGB'''		)
			return im





@torch.no_grad()
def 				A_       ( snake_case__       ,					snake_case__		)					->  Dict:
			_UpperCamelCase					:Optional[int]					     =	ViTConfig(image_size=3_84       ,					qkv_bias=snake_case__		)
			_UpperCamelCase					:Optional[int]					     =	TrOCRConfig()

			# size of the architecture
			if "base" in checkpoint_url:
						_UpperCamelCase					:Union[str, Any]					     =	7_68
			elif "large" in checkpoint_url:
						# use ViT-large encoder
						_UpperCamelCase					:Tuple					     =	10_24
						_UpperCamelCase					:int					     =	40_96
						_UpperCamelCase					:Any					     =	24
						_UpperCamelCase					:int					     =	16
						_UpperCamelCase					:Optional[Any]					     =	10_24
			else:
						raise ValueError('''Should either find \'base\' or \'large\' in checkpoint URL'''		)

			# the large-printed + stage1 checkpoints uses sinusoidal position embeddings, no layernorm afterwards
			if "large-printed" in checkpoint_url or "stage1" in checkpoint_url:
						_UpperCamelCase					:List[str]					     =	False
						_UpperCamelCase					:int					     =	'''relu'''
						_UpperCamelCase					:Tuple					     =	10_24
						_UpperCamelCase					:Any					     =	True
						_UpperCamelCase					:Optional[int]					     =	False
						_UpperCamelCase					:Optional[int]					     =	False

			# load HuggingFace model
			_UpperCamelCase					:Tuple					     =	ViTModel(snake_case__       ,					add_pooling_layer=snake_case__		)
			_UpperCamelCase					:int					     =	TrOCRForCausalLM(snake_case__		)
			_UpperCamelCase					:Dict					     =	VisionEncoderDecoderModel(encoder=snake_case__       ,					decoder=snake_case__		)
			model.eval()

			# load state_dict of original model, rename some keys
			_UpperCamelCase					:List[str]					     =	torch.hub.load_state_dict_from_url(snake_case__       ,					map_location='''cpu'''       ,					check_hash=snake_case__		)['''model''']

			_UpperCamelCase					:Tuple					     =	create_rename_keys(snake_case__       ,					snake_case__		)
			for src, dest in rename_keys:
						rename_key(snake_case__       ,					snake_case__       ,					snake_case__		)
			read_in_q_k_v(snake_case__       ,					snake_case__		)

			# remove parameters we don't need
			del state_dict["encoder.deit.head.weight"]
			del state_dict["encoder.deit.head.bias"]
			del state_dict["decoder.version"]

			# add prefix to decoder keys
			for key, val in state_dict.copy().items():
						_UpperCamelCase					:Tuple					     =	state_dict.pop(snake_case__		)
						if key.startswith('''decoder'''		) and "output_projection" not in key:
									_UpperCamelCase					:Any					     =	val
						else:
									_UpperCamelCase					:List[Any]					     =	val

    # load state dict
			model.load_state_dict(snake_case__		)

			# Check outputs on an image
			_UpperCamelCase					:List[str]					     =	ViTImageProcessor(size=encoder_config.image_size		)
			_UpperCamelCase					:Tuple					     =	RobertaTokenizer.from_pretrained('''roberta-large'''		)
			_UpperCamelCase					:Any					     =	TrOCRProcessor(snake_case__       ,					snake_case__		)

			_UpperCamelCase					:Any					     =	processor(images=prepare_img(snake_case__		)       ,					return_tensors='''pt'''		).pixel_values

			# verify logits
			_UpperCamelCase					:str					     =	torch.tensor([[model.config.decoder.decoder_start_token_id]]		)
			_UpperCamelCase					:Union[str, Any]					     =	model(pixel_values=snake_case__       ,					decoder_input_ids=snake_case__		)
			_UpperCamelCase					:Union[str, Any]					     =	outputs.logits

			_UpperCamelCase					:List[str]					     =	torch.Size([1, 1, 5_02_65]		)
			if "trocr-base-handwritten" in checkpoint_url:
						_UpperCamelCase					:Union[str, Any]					     =	torch.tensor(
						    [-1.45_02, -4.66_83, -0.53_47, -2.92_91, 9.14_35, -3.05_71, 8.97_64, 1.75_60, 8.73_58, -1.53_11]		)
			elif "trocr-large-handwritten" in checkpoint_url:
						_UpperCamelCase					:Tuple					     =	torch.tensor(
						    [-2.64_37, -1.31_29, -2.25_96, -5.34_55, 6.35_39, 1.76_04, 5.49_91, 1.47_02, 5.61_13, 2.01_70]		)
			elif "trocr-base-printed" in checkpoint_url:
						_UpperCamelCase					:Tuple					     =	torch.tensor(
						    [-5.68_16, -5.83_88, 1.13_98, -6.90_34, 6.85_05, -2.43_93, 1.22_84, -1.02_32, -1.96_61, -3.92_10]		)
			elif "trocr-large-printed" in checkpoint_url:
						_UpperCamelCase					:Optional[Any]					     =	torch.tensor(
						    [-6.01_62, -7.09_59, 4.41_55, -5.10_63, 7.04_68, -3.16_31, 2.64_66, -0.30_81, -0.81_06, -1.75_35]		)

			if "stage1" not in checkpoint_url:
						assert logits.shape == expected_shape, "Shape of logits not as expected"
						assert torch.allclose(logits[0, 0, :10]       ,					snake_case__       ,					atol=1E-3		), "First elements of logits not as expected"

			Path(snake_case__		).mkdir(exist_ok=snake_case__		)
			print(f"Saving model to {pytorch_dump_folder_path}"		)
			model.save_pretrained(snake_case__		)
			print(f"Saving processor to {pytorch_dump_folder_path}"		)
			processor.save_pretrained(snake_case__		)


if __name__ == "__main__":
					UpperCamelCase__				:Optional[Any]			   =	argparse.ArgumentParser()

					parser.add_argument(
					    """--checkpoint_url""",
					    default="""https://layoutlm.blob.core.windows.net/trocr/model_zoo/fairseq/trocr-base-handwritten.pt""",
					    type=str,
					    help="""URL to the original PyTorch checkpoint (.pth file).""",
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
					)
					UpperCamelCase__				:Dict			   =	parser.parse_args()
					convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)





"""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 (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

UpperCamelCase__				:int			   =	{
    """post_extract_proj""": """feature_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.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 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__		)					->  Tuple:
			for attribute in key.split('''.'''		):
						_UpperCamelCase					:Any					     =	getattr(snake_case__       ,					snake_case__		)

			if weight_type is not None:
						_UpperCamelCase					:Any					     =	getattr(snake_case__       ,					snake_case__		).shape
			else:
						_UpperCamelCase					:Optional[int]					     =	hf_pointer.shape

			assert hf_shape == value.shape, (
			    f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
			    f" {value.shape} for {full_name}"
			)

			if weight_type == "weight":
						_UpperCamelCase					:str					     =	value
			elif weight_type == "weight_g":
						_UpperCamelCase					:Dict					     =	value
			elif weight_type == "weight_v":
						_UpperCamelCase					:Optional[Any]					     =	value
			elif weight_type == "bias":
						_UpperCamelCase					:str					     =	value
			else:
						_UpperCamelCase					:int					     =	value

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





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__		)					->  Tuple:
			_UpperCamelCase					:Optional[int]					     =	[]
			_UpperCamelCase					:List[str]					     =	fairseq_model.state_dict()

			_UpperCamelCase					:str					     =	hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

			for name, value in fairseq_dict.items():
						_UpperCamelCase					:Optional[Any]					     =	False
						if "conv_layers" in name:
									load_conv_layer(
									    snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__       ,					hf_model.config.feat_extract_norm == '''group'''       ,					)
									_UpperCamelCase					:Dict					     =	True
						else:
									for key, mapped_key in MAPPING.items():
												_UpperCamelCase					:Optional[int]					     =	'''sew.''' + 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]:
															_UpperCamelCase					:List[str]					     =	True
															if "*" in mapped_key:
																		_UpperCamelCase					:List[str]					     =	name.split(snake_case__		)[0].split('''.'''		)[-2]
																		_UpperCamelCase					:Tuple					     =	mapped_key.replace('''*'''       ,					snake_case__		)
															if "weight_g" in name:
																		_UpperCamelCase					:List[Any]					     =	'''weight_g'''
															elif "weight_v" in name:
																		_UpperCamelCase					:Union[str, Any]					     =	'''weight_v'''
															elif "weight" in name:
																		_UpperCamelCase					:List[Any]					     =	'''weight'''
															elif "bias" in name:
																		_UpperCamelCase					:List[Any]					     =	'''bias'''
															else:
																		_UpperCamelCase					:List[Any]					     =	None
															set_recursively(snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__		)
												continue
						if not is_used:
									unused_weights.append(snake_case__		)

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





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__		)					->  Optional[Any]:
			_UpperCamelCase					:Optional[int]					     =	full_name.split('''conv_layers.'''		)[-1]
			_UpperCamelCase					:Optional[int]					     =	name.split('''.'''		)
			_UpperCamelCase					:Optional[Any]					     =	int(items[0]		)
			_UpperCamelCase					:List[str]					     =	int(items[1]		)

			if type_id == 0:
						if "bias" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
									    f"{full_name} has size {value.shape}, but"
									    f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
									)
									_UpperCamelCase					:Optional[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."
									)
									_UpperCamelCase					:Optional[Any]					     =	value
									logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."		)
			elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
						if "bias" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
									    f"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"
									    " found."
									)
									_UpperCamelCase					:int					     =	value
									logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."		)
						elif "weight" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
									    f"{full_name} has size {value.shape}, but"
									    f" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
									)
									_UpperCamelCase					:Dict					     =	value
									logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."		)
			else:
						unused_weights.append(snake_case__		)





def 				A_       ( snake_case__       ,					snake_case__		)					->  List[str]:
			_UpperCamelCase					:str					     =	SEWConfig()
			if is_finetuned:
						_UpperCamelCase					:Optional[int]					     =	model.wav_encoder.wav_model.cfg
			else:
						_UpperCamelCase					:Dict					     =	model.cfg

			_UpperCamelCase					:Dict					     =	fs_config.conv_bias
			_UpperCamelCase					:int					     =	eval(fs_config.conv_feature_layers		)
			_UpperCamelCase					:List[Any]					     =	[x[0] for x in conv_layers]
			_UpperCamelCase					:Optional[int]					     =	[x[1] for x in conv_layers]
			_UpperCamelCase					:Optional[int]					     =	[x[2] for x in conv_layers]
			_UpperCamelCase					:str					     =	'''gelu'''
			_UpperCamelCase					:Optional[int]					     =	'''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
			_UpperCamelCase					:List[Any]					     =	0.0
			_UpperCamelCase					:Optional[int]					     =	fs_config.activation_fn.name
			_UpperCamelCase					:str					     =	fs_config.encoder_embed_dim
			_UpperCamelCase					:Dict					     =	0.02
			_UpperCamelCase					:Optional[int]					     =	fs_config.encoder_ffn_embed_dim
			_UpperCamelCase					:str					     =	1E-5
			_UpperCamelCase					:int					     =	fs_config.encoder_layerdrop
			_UpperCamelCase					:Union[str, Any]					     =	fs_config.encoder_attention_heads
			_UpperCamelCase					:List[str]					     =	fs_config.conv_pos_groups
			_UpperCamelCase					:List[Any]					     =	fs_config.conv_pos
			_UpperCamelCase					:List[str]					     =	len(snake_case__		)
			_UpperCamelCase					:Optional[int]					     =	fs_config.encoder_layers
			_UpperCamelCase					:Optional[int]					     =	fs_config.squeeze_factor

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

			_UpperCamelCase					:List[Any]					     =	'''Wav2Vec2FeatureExtractor'''
			_UpperCamelCase					:Optional[Any]					     =	'''Wav2Vec2CTCTokenizer'''

			return config





@torch.no_grad()
def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__=None       ,					snake_case__=None       ,					snake_case__=True		)					->  int:

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

			if config_path is not None:
						_UpperCamelCase					:Any					     =	SEWConfig.from_pretrained(snake_case__		)
			else:
						_UpperCamelCase					:List[str]					     =	convert_config(model[0]       ,					snake_case__		)
			_UpperCamelCase					:List[str]					     =	model[0].eval()

			_UpperCamelCase					:Optional[Any]					     =	True if config.feat_extract_norm == '''layer''' else False
			_UpperCamelCase					:str					     =	WavaVecaFeatureExtractor(
			    feature_size=1       ,					sampling_rate=1_60_00       ,					padding_value=0       ,					do_normalize=snake_case__       ,					return_attention_mask=snake_case__       ,					)

			if is_finetuned:
						if dict_path:
									_UpperCamelCase					:int					     =	Dictionary.load(snake_case__		)

									# important change bos & pad token id since CTC symbol is <pad> and
									# not <s> as in fairseq
									_UpperCamelCase					:List[Any]					     =	target_dict.pad_index
									_UpperCamelCase					:str					     =	target_dict.bos_index
									_UpperCamelCase					:int					     =	target_dict.pad_index
									_UpperCamelCase					:Dict					     =	target_dict.bos_index
									_UpperCamelCase					:int					     =	target_dict.eos_index
									_UpperCamelCase					:str					     =	len(target_dict.symbols		)
									_UpperCamelCase					:Optional[int]					     =	os.path.join(snake_case__       ,					'''vocab.json'''		)
									if not os.path.isdir(snake_case__		):
												logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(snake_case__		)		)
												return
									os.makedirs(snake_case__       ,					exist_ok=snake_case__		)
									with open(snake_case__       ,					'''w'''       ,					encoding='''utf-8'''		) as vocab_handle:
												json.dump(target_dict.indices       ,					snake_case__		)
									_UpperCamelCase					:int					     =	WavaVecaCTCTokenizer(
									    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=snake_case__       ,					)
									_UpperCamelCase					:Dict					     =	WavaVecaProcessor(feature_extractor=snake_case__       ,					tokenizer=snake_case__		)
									processor.save_pretrained(snake_case__		)

						_UpperCamelCase					:Tuple					     =	SEWForCTC(snake_case__		)
			else:
						_UpperCamelCase					:str					     =	SEWModel(snake_case__		)
						feature_extractor.save_pretrained(snake_case__		)

			recursively_load_weights(snake_case__       ,					snake_case__       ,					snake_case__		)

			hf_model.save_pretrained(snake_case__		)


if __name__ == "__main__":
					UpperCamelCase__				: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"""
					)
					UpperCamelCase__				:Tuple			   =	parser.parse_args()
					convert_sew_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
					)

