
'''simple docstring'''







import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
							import numpy as np
							import torch

if is_vision_available():
							from PIL import Image
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					Union[str, Any] , __SCREAMING_SNAKE_CASE   :					List[str]=False , __SCREAMING_SNAKE_CASE   :					Any=False , __SCREAMING_SNAKE_CASE   :					Union[str, Any]=False				):



						"""simple docstring"""





						assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

						lowercase_      :	int						=			[]
						for i in range(image_processor_tester.batch_size				):
												if equal_resolution:
																		lowercase_      :	List[Any]						=			image_processor_tester.max_resolution
												else:
																		# To avoid getting image width/height 0
																		lowercase_      :	Any						=			image_processor_tester.min_resolution
																		if getattr(__SCREAMING_SNAKE_CASE , '''size_divisor''' , __SCREAMING_SNAKE_CASE				):
																								# If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
																								lowercase_      :	List[str]						=			max(image_processor_tester.size_divisor , __SCREAMING_SNAKE_CASE				)
																		lowercase_		,					lowercase_      :	Optional[Any]						=			np.random.choice(np.arange(__SCREAMING_SNAKE_CASE , image_processor_tester.max_resolution				) , 2				)
												image_inputs.append(
												    np.random.randint(255 , size=(image_processor_tester.num_channels, width, height) , dtype=np.uinta				)				)

						if not numpify and not torchify:
												# PIL expects the channel dimension as last dimension
												lowercase_      :	Optional[int]						=			[Image.fromarray(np.moveaxis(__SCREAMING_SNAKE_CASE , 0 , -1				)				) for image in image_inputs]

						if torchify:
												lowercase_      :	int						=			[torch.from_numpy(__SCREAMING_SNAKE_CASE				) for image in image_inputs]

						return image_inputs
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					Dict , __SCREAMING_SNAKE_CASE   :					List[Any]=10 , __SCREAMING_SNAKE_CASE   :					List[str]=10 , __SCREAMING_SNAKE_CASE   :					Dict=False , __SCREAMING_SNAKE_CASE   :					Tuple=False				):



						"""simple docstring"""





						lowercase_      :	Any						=			[]
						for i in range(image_processor_tester.num_frames				):
												video.append(np.random.randint(255 , size=(image_processor_tester.num_channels, width, height) , dtype=np.uinta				)				)

						if not numpify and not torchify:
												# PIL expects the channel dimension as last dimension
												lowercase_      :	Union[str, Any]						=			[Image.fromarray(np.moveaxis(__SCREAMING_SNAKE_CASE , 0 , -1				)				) for frame in video]

						if torchify:
												lowercase_      :	int						=			[torch.from_numpy(__SCREAMING_SNAKE_CASE				) for frame in video]

						return video
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					Tuple , __SCREAMING_SNAKE_CASE   :					Union[str, Any]=False , __SCREAMING_SNAKE_CASE   :					Optional[int]=False , __SCREAMING_SNAKE_CASE   :					Tuple=False				):



						"""simple docstring"""





						assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

						lowercase_      :	Dict						=			[]
						for i in range(image_processor_tester.batch_size				):
												if equal_resolution:
																		lowercase_      :	Any						=			image_processor_tester.max_resolution
												else:
																		lowercase_		,					lowercase_      :	List[str]						=			np.random.choice(
																		    np.arange(image_processor_tester.min_resolution , image_processor_tester.max_resolution				) , 2				)
																		lowercase_      :	str						=			prepare_video(
																		    image_processor_tester=__SCREAMING_SNAKE_CASE , width=__SCREAMING_SNAKE_CASE , height=__SCREAMING_SNAKE_CASE , numpify=__SCREAMING_SNAKE_CASE , torchify=__SCREAMING_SNAKE_CASE , )
												video_inputs.append(__SCREAMING_SNAKE_CASE				)

						return video_inputs






class 					lowerCAmelCase__  :
					lowerCAmelCase_	  =	None






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	Optional[Any]						=			self.image_processing_class(**self.image_processor_dict						)
											lowercase_      :	Any						=			json.loads(image_processor.to_json_string()						)
											for key, value in self.image_processor_dict.items():
																	self.assertEqual(obj[key]							,				__SCREAMING_SNAKE_CASE						)






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	Tuple						=			self.image_processing_class(**self.image_processor_dict						)

											with tempfile.TemporaryDirectory() as tmpdirname:
																	lowercase_      :	List[str]						=			os.path.join(__SCREAMING_SNAKE_CASE							,				'''image_processor.json'''						)
																	image_processor_first.to_json_file(__SCREAMING_SNAKE_CASE						)
																	lowercase_      :	List[Any]						=			self.image_processing_class.from_json_file(__SCREAMING_SNAKE_CASE						)

											self.assertEqual(image_processor_second.to_dict()							,				image_processor_first.to_dict()						)






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	int						=			self.image_processing_class(**self.image_processor_dict						)

											with tempfile.TemporaryDirectory() as tmpdirname:
																	lowercase_      :	List[str]						=			image_processor_first.save_pretrained(__SCREAMING_SNAKE_CASE						)[0]
																	check_json_file_has_correct_format(__SCREAMING_SNAKE_CASE						)
																	lowercase_      :	List[Any]						=			self.image_processing_class.from_pretrained(__SCREAMING_SNAKE_CASE						)

											self.assertEqual(image_processor_second.to_dict()							,				image_processor_first.to_dict()						)






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	Optional[int]						=			self.image_processing_class()
											self.assertIsNotNone(__SCREAMING_SNAKE_CASE						)






					@require_torch
					@require_vision
					def       _snake_case       (  self						):


											"""simple docstring"""





											if self.test_cast_dtype is not None:
																	# Initialize image_processor
																	lowercase_      :	Optional[Any]						=			self.image_processing_class(**self.image_processor_dict						)

																	# create random PyTorch tensors
																	lowercase_      :	List[Any]						=			prepare_image_inputs(self.image_processor_tester							,				equal_resolution=__SCREAMING_SNAKE_CASE							,				torchify=__SCREAMING_SNAKE_CASE						)

																	lowercase_      :	Tuple						=			image_processor(__SCREAMING_SNAKE_CASE							,				return_tensors='''pt'''						)
																	# for layoutLM compatiblity
																	self.assertEqual(encoding.pixel_values.device							,				torch.device('''cpu'''						)						)
																	self.assertEqual(encoding.pixel_values.dtype							,				torch.floataa						)

																	lowercase_      :	Optional[int]						=			image_processor(__SCREAMING_SNAKE_CASE							,				return_tensors='''pt'''						).to(torch.floataa						)
																	self.assertEqual(encoding.pixel_values.device							,				torch.device('''cpu'''						)						)
																	self.assertEqual(encoding.pixel_values.dtype							,				torch.floataa						)

																	lowercase_      :	Union[str, Any]						=			image_processor(__SCREAMING_SNAKE_CASE							,				return_tensors='''pt'''						).to('''cpu'''							,				torch.bfloataa						)
																	self.assertEqual(encoding.pixel_values.device							,				torch.device('''cpu'''						)						)
																	self.assertEqual(encoding.pixel_values.dtype							,				torch.bfloataa						)

																	with self.assertRaises(__SCREAMING_SNAKE_CASE						):
																							lowercase_      :	List[str]						=			image_processor(__SCREAMING_SNAKE_CASE							,				return_tensors='''pt'''						).to(torch.bfloataa							,				'''cpu'''						)

																	# Try with text + image feature
																	lowercase_      :	List[str]						=			image_processor(__SCREAMING_SNAKE_CASE							,				return_tensors='''pt'''						)
																	encoding.update({'''input_ids''': torch.LongTensor([[1, 2, 3], [4, 5, 6]]						)}						)
																	lowercase_      :	Optional[Any]						=			encoding.to(torch.floataa						)

																	self.assertEqual(encoding.pixel_values.device							,				torch.device('''cpu'''						)						)
																	self.assertEqual(encoding.pixel_values.dtype							,				torch.floataa						)
																	self.assertEqual(encoding.input_ids.dtype							,				torch.long						)





'''simple docstring'''







import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

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

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


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

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



						"""simple docstring"""





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

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

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

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

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



						"""simple docstring"""





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

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

						for name, value in fairseq_dict.items():
												lowercase_      :	str						=			False
												if "conv_layers" in name:
																		load_conv_layer(
																		    __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , hf_model.config.feat_extract_norm == '''group''' , )
																		lowercase_      :	int						=			True
												else:
																		for key, mapped_key in MAPPING.items():
																								lowercase_      :	Union[str, Any]						=			'''sew_d.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

																								if key in name or key.split('''w2v_model.'''				)[-1] == name.split('''.'''				)[0]:
																														lowercase_      :	Dict						=			True
																														if "*" in mapped_key:
																																				lowercase_      :	str						=			name.split(__SCREAMING_SNAKE_CASE				)[0].split('''.'''				)[-2]
																																				if not layer_index.isnumeric():
																																										continue
																																				lowercase_      :	Tuple						=			mapped_key.replace('''*''' , __SCREAMING_SNAKE_CASE				)
																														if "weight_g" in name:
																																				lowercase_      :	Dict						=			'''weight_g'''
																														elif "weight_v" in name:
																																				lowercase_      :	Dict						=			'''weight_v'''
																														elif "weight" in name:
																																				lowercase_      :	int						=			'''weight'''
																														elif "bias" in name:
																																				lowercase_      :	List[Any]						=			'''bias'''
																														else:
																																				lowercase_      :	Optional[Any]						=			None
																														set_recursively(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE				)
																								continue
												if not is_used:
																		unused_weights.append(__SCREAMING_SNAKE_CASE				)

						logger.warning(F'''Unused weights: {unused_weights}'''				)
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					List[Any] , __SCREAMING_SNAKE_CASE   :					int , __SCREAMING_SNAKE_CASE   :					int , __SCREAMING_SNAKE_CASE   :					Tuple , __SCREAMING_SNAKE_CASE   :					Optional[int]				):



						"""simple docstring"""





						lowercase_      :	int						=			full_name.split('''conv_layers.'''				)[-1]
						lowercase_      :	int						=			name.split('''.'''				)
						lowercase_      :	int						=			int(items[0]				)
						lowercase_      :	List[Any]						=			int(items[1]				)

						if type_id == 0:
												if "bias" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
																		    F'''{full_name} has size {value.shape}, but'''
																		    F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
																		)
																		lowercase_      :	int						=			value
																		logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''				)
												elif "weight" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
																		    F'''{full_name} has size {value.shape}, but'''
																		    F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
																		)
																		lowercase_      :	Any						=			value
																		logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''				)
						elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
												if "bias" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
																		    F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
																		    " found."
																		)
																		lowercase_      :	int						=			value
																		logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''				)
												elif "weight" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
																		    F'''{full_name} has size {value.shape}, but'''
																		    F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
																		)
																		lowercase_      :	List[str]						=			value
																		logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''				)
						else:
												unused_weights.append(__SCREAMING_SNAKE_CASE				)
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					Any , __SCREAMING_SNAKE_CASE   :					Tuple				):



						"""simple docstring"""





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

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

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

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

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



						"""simple docstring"""





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

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

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

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

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

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

						recursively_load_weights(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE				)

						hf_model.save_pretrained(__SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
							_lowercase					:    List[str]       				=			argparse.ArgumentParser()
							parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
							parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
							parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
							parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
							parser.add_argument(
							    "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
							)
							_lowercase					:    str       				=			parser.parse_args()
							convert_sew_checkpoint(
							    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
							)



