


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()
lowerCAmelCase__  :Optional[Any]           =							logging.get_logger(__name__)

lowerCAmelCase__  :Optional[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 	lowerCAmelCase__			(      a__:		Union[str, Any]      ,  a__:		List[Any]      ,  a__:		Optional[int]      ,  a__:		Any      ,  a__:		Dict	)  ->		Union[str, Any]:


							'''simple docstring'''





							for attribute in key.split('.'	):
														_UpperCAmelCase					=      getattr(a__      ,  a__	)

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

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

def 	lowerCAmelCase__			(      a__:		Tuple      ,  a__:		Dict      ,  a__:		List[Any]	)  ->		Tuple:


							'''simple docstring'''





							_UpperCAmelCase					=      []
							_UpperCAmelCase					=      fairseq_model.state_dict()

							_UpperCAmelCase					=      hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

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

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

def 	lowerCAmelCase__			(      a__:		List[str]      ,  a__:		Tuple      ,  a__:		List[Any]      ,  a__:		Tuple      ,  a__:		Tuple	)  ->		Optional[int]:


							'''simple docstring'''





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

def 	lowerCAmelCase__			(      a__:		str      ,  a__:		List[str]	)  ->		Tuple:


							'''simple docstring'''





							_UpperCAmelCase					=      SEWConfig()
							if is_finetuned:
														_UpperCAmelCase					=      model.wav_encoder.wav_model.cfg
							else:
														_UpperCAmelCase					=      model.cfg

							_UpperCAmelCase					=      fs_config.conv_bias
							_UpperCAmelCase					=      eval(fs_config.conv_feature_layers	)
							_UpperCAmelCase					=      [x[0] for x in conv_layers]
							_UpperCAmelCase					=      [x[1] for x in conv_layers]
							_UpperCAmelCase					=      [x[2] for x in conv_layers]
							_UpperCAmelCase					=      'gelu'
							_UpperCAmelCase					=      'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
							_UpperCAmelCase					=      0.0
							_UpperCAmelCase					=      fs_config.activation_fn.name
							_UpperCAmelCase					=      fs_config.encoder_embed_dim
							_UpperCAmelCase					=      0.02
							_UpperCAmelCase					=      fs_config.encoder_ffn_embed_dim
							_UpperCAmelCase					=      1e-5
							_UpperCAmelCase					=      fs_config.encoder_layerdrop
							_UpperCAmelCase					=      fs_config.encoder_attention_heads
							_UpperCAmelCase					=      fs_config.conv_pos_groups
							_UpperCAmelCase					=      fs_config.conv_pos
							_UpperCAmelCase					=      len(a__	)
							_UpperCAmelCase					=      fs_config.encoder_layers
							_UpperCAmelCase					=      fs_config.squeeze_factor

							# take care of any params that are overridden by the Wav2VecCtc model
							if is_finetuned:
														_UpperCAmelCase					=      model.cfg
														_UpperCAmelCase					=      fs_config.final_dropout
														_UpperCAmelCase					=      fs_config.layerdrop
							_UpperCAmelCase					=      fs_config.activation_dropout
							_UpperCAmelCase					=      fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
							_UpperCAmelCase					=      fs_config.attention_dropout
							_UpperCAmelCase					=      fs_config.dropout_input
							_UpperCAmelCase					=      fs_config.dropout
							_UpperCAmelCase					=      fs_config.mask_channel_length
							_UpperCAmelCase					=      fs_config.mask_channel_prob
							_UpperCAmelCase					=      fs_config.mask_length
							_UpperCAmelCase					=      fs_config.mask_prob

							_UpperCAmelCase					=      'Wav2Vec2FeatureExtractor'
							_UpperCAmelCase					=      'Wav2Vec2CTCTokenizer'

							return config

@torch.no_grad()
def 	lowerCAmelCase__			(      a__:		int      ,  a__:		List[str]      ,  a__:		Any=None      ,  a__:		Dict=None      ,  a__:		int=True	)  ->		List[str]:


							'''simple docstring'''





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

							if config_path is not None:
														_UpperCAmelCase					=      SEWConfig.from_pretrained(a__	)
							else:
														_UpperCAmelCase					=      convert_config(model[0]      ,  a__	)
							_UpperCAmelCase					=      model[0].eval()

							_UpperCAmelCase					=      True if config.feat_extract_norm == 'layer' else False
							_UpperCAmelCase					=      WavaVecaFeatureExtractor(
							    feature_size=1      ,  sampling_rate=1_6_0_0_0      ,  padding_value=0      ,  do_normalize=a__      ,  return_attention_mask=a__      ,  )

							if is_finetuned:
														if dict_path:
																					_UpperCAmelCase					=      Dictionary.load(a__	)

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

														_UpperCAmelCase					=      SEWForCTC(a__	)
							else:
														_UpperCAmelCase					=      SEWModel(a__	)
														feature_extractor.save_pretrained(a__	)

							recursively_load_weights(a__      ,  a__      ,  a__	)

							hf_model.save_pretrained(a__	)


if __name__ == "__main__":
		lowerCAmelCase__  :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'''
		)
		lowerCAmelCase__  :List[Any]           =							parser.parse_args()
		convert_sew_checkpoint(
		    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
		)




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


lowerCAmelCase__  :List[str]             =							logging.get_logger(__name__)

class 		__a				(    UpperCAmelCase      ):
		_a     :  Optional[int]           =     ['pixel_values']
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE = True      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = PIL.Image.BICUBIC      ,   _SCREAMING_SNAKE_CASE = True      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = 1 / 255      ,   _SCREAMING_SNAKE_CASE = True      ,   _SCREAMING_SNAKE_CASE = True      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	None:




									"""simple docstring"""





									super().__init__(**_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      size if size is not None else {'height': 256, 'width': 256}
									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      crop_size if crop_size is not None else {'height': 224, 'width': 224}
									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE      ,   param_name='crop_size'						)

									_UpperCAmelCase					=      do_resize
									_UpperCAmelCase					=      size
									_UpperCAmelCase					=      resample
									_UpperCAmelCase					=      do_center_crop
									_UpperCAmelCase					=      crop_size
									_UpperCAmelCase					=      do_rescale
									_UpperCAmelCase					=      rescale_factor
									_UpperCAmelCase					=      do_normalize
									_UpperCAmelCase					=      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									_UpperCAmelCase					=      image_std if image_std is not None else IMAGENET_STANDARD_STD
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = PIL.Image.BICUBIC      ,   _SCREAMING_SNAKE_CASE = None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	np.ndarray:




									"""simple docstring"""





									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE						)
									if "height" not in size or "width" not in size:
																raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''						)
									return resize(
									    _SCREAMING_SNAKE_CASE      ,   size=(size['height'], size['width'])      ,   resample=_SCREAMING_SNAKE_CASE      ,   data_format=_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	np.ndarray:




									"""simple docstring"""





									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE						)
									if "height" not in size or "width" not in size:
																raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''						)
									return center_crop(_SCREAMING_SNAKE_CASE      ,   size=(size['height'], size['width'])      ,   data_format=_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	str:




									"""simple docstring"""





									return rescale(_SCREAMING_SNAKE_CASE      ,   scale=_SCREAMING_SNAKE_CASE      ,   data_format=_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	np.ndarray:




									"""simple docstring"""





									return normalize(_SCREAMING_SNAKE_CASE      ,   mean=_SCREAMING_SNAKE_CASE      ,   std=_SCREAMING_SNAKE_CASE      ,   data_format=_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = ChannelDimension.FIRST      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	PIL.Image.Image:




									"""simple docstring"""





									_UpperCAmelCase					=      do_resize if do_resize is not None else self.do_resize
									_UpperCAmelCase					=      resample if resample is not None else self.resample
									_UpperCAmelCase					=      do_center_crop if do_center_crop is not None else self.do_center_crop
									_UpperCAmelCase					=      do_rescale if do_rescale is not None else self.do_rescale
									_UpperCAmelCase					=      rescale_factor if rescale_factor is not None else self.rescale_factor
									_UpperCAmelCase					=      do_normalize if do_normalize is not None else self.do_normalize
									_UpperCAmelCase					=      image_mean if image_mean is not None else self.image_mean
									_UpperCAmelCase					=      image_std if image_std is not None else self.image_std

									_UpperCAmelCase					=      size if size is not None else self.size
									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      crop_size if crop_size is not None else self.crop_size
									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE      ,   param_name='crop_size'						)

									_UpperCAmelCase					=      make_list_of_images(_SCREAMING_SNAKE_CASE						)

									if not valid_images(_SCREAMING_SNAKE_CASE						):
																raise ValueError(
																    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
																    'torch.Tensor, tf.Tensor or jax.ndarray.'						)

									if do_resize and size is None or resample is None:
																raise ValueError('Size and resample must be specified if do_resize is True.'						)

									if do_center_crop and crop_size is None:
																raise ValueError('Crop size must be specified if do_center_crop is True.'						)

									if do_rescale and rescale_factor is None:
																raise ValueError('Rescale factor must be specified if do_rescale is True.'						)

									if do_normalize and (image_mean is None or image_std is None):
																raise ValueError('Image mean and std must be specified if do_normalize is True.'						)

									# All transformations expect numpy arrays.
									_UpperCAmelCase					=      [to_numpy_array(_SCREAMING_SNAKE_CASE						) for image in images]

									if do_resize:
																_UpperCAmelCase					=      [self.resize(image=_SCREAMING_SNAKE_CASE      ,   size=_SCREAMING_SNAKE_CASE      ,   resample=_SCREAMING_SNAKE_CASE						) for image in images]

									if do_center_crop:
																_UpperCAmelCase					=      [self.center_crop(image=_SCREAMING_SNAKE_CASE      ,   size=_SCREAMING_SNAKE_CASE						) for image in images]

									if do_rescale:
																_UpperCAmelCase					=      [self.rescale(image=_SCREAMING_SNAKE_CASE      ,   scale=_SCREAMING_SNAKE_CASE						) for image in images]

									if do_normalize:
																_UpperCAmelCase					=      [self.normalize(image=_SCREAMING_SNAKE_CASE      ,   mean=_SCREAMING_SNAKE_CASE      ,   std=_SCREAMING_SNAKE_CASE						) for image in images]

									_UpperCAmelCase					=      [to_channel_dimension_format(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						) for image in images]

									_UpperCAmelCase					=      {'pixel_values': images}
									return BatchFeature(data=_SCREAMING_SNAKE_CASE      ,   tensor_type=_SCREAMING_SNAKE_CASE						)
