




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()
_A     =			logging.get_logger(__name__)

_A     =			{
    '''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 __UpperCamelCase			(       _A ,  _A ,  _A ,  _A ,  _A  ):
						for attribute in key.split('''.'''  ):
												lowerCAmelCase_      			=  getattr(_A ,  _A  )

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

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





def __UpperCamelCase			(       _A ,  _A ,  _A  ):
						lowerCAmelCase_      			=  []
						lowerCAmelCase_      			=  fairseq_model.state_dict()

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

						for name, value in fairseq_dict.items():
												lowerCAmelCase_      			=  False
												if "conv_layers" in name:
																		load_conv_layer(
																		    _A ,  _A ,  _A ,  _A ,  hf_model.config.feat_extract_norm == '''group''' ,  )
																		lowerCAmelCase_      			=  True
												else:
																		for key, mapped_key in MAPPING.items():
																								lowerCAmelCase_      			=  '''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]:
																														lowerCAmelCase_      			=  True
																														if "*" in mapped_key:
																																				lowerCAmelCase_      			=  name.split(_A  )[0].split('''.'''  )[-2]
																																				lowerCAmelCase_      			=  mapped_key.replace('''*''' ,  _A  )
																														if "weight_g" in name:
																																				lowerCAmelCase_      			=  '''weight_g'''
																														elif "weight_v" in name:
																																				lowerCAmelCase_      			=  '''weight_v'''
																														elif "weight" in name:
																																				lowerCAmelCase_      			=  '''weight'''
																														elif "bias" in name:
																																				lowerCAmelCase_      			=  '''bias'''
																														else:
																																				lowerCAmelCase_      			=  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 __UpperCamelCase			(       _A ,  _A ,  _A ,  _A ,  _A  ):
						lowerCAmelCase_      			=  full_name.split('''conv_layers.'''  )[-1]
						lowerCAmelCase_      			=  name.split('''.'''  )
						lowerCAmelCase_      			=  int(items[0]  )
						lowerCAmelCase_      			=  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."
																		)
																		lowerCAmelCase_      			=  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."
																		)
																		lowerCAmelCase_      			=  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."
																		)
																		lowerCAmelCase_      			=  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."
																		)
																		lowerCAmelCase_      			=  value
																		logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."  )
						else:
												unused_weights.append(_A  )





def __UpperCamelCase			(       _A ,  _A  ):
						lowerCAmelCase_      			=  SEWConfig()
						if is_finetuned:
												lowerCAmelCase_      			=  model.wav_encoder.wav_model.cfg
						else:
												lowerCAmelCase_      			=  model.cfg

						lowerCAmelCase_      			=  fs_config.conv_bias
						lowerCAmelCase_      			=  eval(fs_config.conv_feature_layers  )
						lowerCAmelCase_      			=  [x[0] for x in conv_layers]
						lowerCAmelCase_      			=  [x[1] for x in conv_layers]
						lowerCAmelCase_      			=  [x[2] for x in conv_layers]
						lowerCAmelCase_      			=  '''gelu'''
						lowerCAmelCase_      			=  '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
						lowerCAmelCase_      			=  0.0
						lowerCAmelCase_      			=  fs_config.activation_fn.name
						lowerCAmelCase_      			=  fs_config.encoder_embed_dim
						lowerCAmelCase_      			=  0.0_2
						lowerCAmelCase_      			=  fs_config.encoder_ffn_embed_dim
						lowerCAmelCase_      			=  1E-5
						lowerCAmelCase_      			=  fs_config.encoder_layerdrop
						lowerCAmelCase_      			=  fs_config.encoder_attention_heads
						lowerCAmelCase_      			=  fs_config.conv_pos_groups
						lowerCAmelCase_      			=  fs_config.conv_pos
						lowerCAmelCase_      			=  len(_A  )
						lowerCAmelCase_      			=  fs_config.encoder_layers
						lowerCAmelCase_      			=  fs_config.squeeze_factor

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

						lowerCAmelCase_      			=  '''Wav2Vec2FeatureExtractor'''
						lowerCAmelCase_      			=  '''Wav2Vec2CTCTokenizer'''

						return config





@torch.no_grad()
def __UpperCamelCase			(       _A ,  _A ,  _A=None ,  _A=None ,  _A=True  ):

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

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

						lowerCAmelCase_      			=  True if config.feat_extract_norm == '''layer''' else False
						lowerCAmelCase_      			=  WavaVecaFeatureExtractor(
						    feature_size=1 ,  sampling_rate=16000 ,  padding_value=0 ,  do_normalize=_A ,  return_attention_mask=_A ,  )

						if is_finetuned:
												if dict_path:
																		lowerCAmelCase_      			=  Dictionary.load(_A  )

																		# important change bos & pad token id since CTC symbol is <pad> and
																		# not <s> as in fairseq
																		lowerCAmelCase_      			=  target_dict.pad_index
																		lowerCAmelCase_      			=  target_dict.bos_index
																		lowerCAmelCase_      			=  target_dict.pad_index
																		lowerCAmelCase_      			=  target_dict.bos_index
																		lowerCAmelCase_      			=  target_dict.eos_index
																		lowerCAmelCase_      			=  len(target_dict.symbols  )
																		lowerCAmelCase_      			=  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  )
																		lowerCAmelCase_      			=  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 ,  )
																		lowerCAmelCase_      			=  WavaVecaProcessor(feature_extractor=_A ,  tokenizer=_A  )
																		processor.save_pretrained(_A  )

												lowerCAmelCase_      			=  SEWForCTC(_A  )
						else:
												lowerCAmelCase_      			=  SEWModel(_A  )
												feature_extractor.save_pretrained(_A  )

						recursively_load_weights(_A ,  _A ,  _A  )

						hf_model.save_pretrained(_A  )


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('''--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'''
						)
						_A     =			parser.parse_args()
						convert_sew_checkpoint(
						    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
						)










from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
						if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
						from .pipeline_unclip import UnCLIPPipeline
						from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
						from .text_proj import UnCLIPTextProjModel




