



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:					str      =						logging.get_logger(__name__)

_lowercase:					str      =						{
    "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        a( A			:       str					,	A			:       str					,	A			:       Optional[Any]					,	A			:       Optional[int]					,	A			:       Union[str, Any]			)						->			Union[str, Any]:
					"""simple docstring"""





					for attribute in key.split("."			):
										a   	=  getattr(lowerCAmelCase_					,	lowerCAmelCase_			)

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

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






def        a( A			:       Tuple					,	A			:       Any					,	A			:       Union[str, Any]			)						->			List[str]:
					"""simple docstring"""





					a   	=  []
					a   	=  fairseq_model.state_dict()

					a   	=  hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

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

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






def        a( A			:       str					,	A			:       Optional[int]					,	A			:       List[str]					,	A			:       Union[str, Any]					,	A			:       Tuple			)						->			str:
					"""simple docstring"""





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






def        a( A			:       Dict					,	A			:       str			)						->			int:
					"""simple docstring"""





					a   	=  SEWDConfig()
					if is_finetuned:
										a   	=  model.wav_encoder.wav_model.cfg
					else:
										a   	=  model.cfg

					a   	=  fs_config.conv_bias
					a   	=  eval(fs_config.conv_feature_layers			)
					a   	=  [x[0] for x in conv_layers]
					a   	=  [x[1] for x in conv_layers]
					a   	=  [x[2] for x in conv_layers]
					a   	=  '''gelu'''
					a   	=  '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
					a   	=  0.0
					a   	=  fs_config.activation_fn.name
					a   	=  fs_config.encoder_embed_dim
					a   	=  0.02
					a   	=  fs_config.encoder_ffn_embed_dim
					a   	=  1e-5
					a   	=  fs_config.encoder_layerdrop
					a   	=  fs_config.encoder_attention_heads
					a   	=  fs_config.conv_pos_groups
					a   	=  fs_config.conv_pos
					a   	=  len(lowerCAmelCase_			)
					a   	=  fs_config.encoder_layers
					a   	=  fs_config.squeeze_factor
					# DeBERTa-specific parameters:
					a   	=  fs_config.max_position_embeddings
					a   	=  fs_config.position_buckets
					a   	=  fs_config.share_att_key
					a   	=  fs_config.relative_attention
					a   	=  fs_config.position_biased_input
					a   	=  tuple(fs_config.pos_att_type.split("|"			)			)
					a   	=  fs_config.norm_rel_ebd

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

					a   	=  '''Wav2Vec2FeatureExtractor'''
					a   	=  '''Wav2Vec2CTCTokenizer'''

					return config






@torch.no_grad()
def        a( A			:       Optional[Any]					,	A			:       Optional[Any]					,	A			:       str=None					,	A			:       Any=None					,	A			:       Any=True			)						->			str:
					"""simple docstring"""





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

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

					a   	=  True if config.feat_extract_norm == '''layer''' else False
					a   	=  WavaVecaFeatureExtractor(
					    feature_size=1					,	sampling_rate=1_6000					,	padding_value=0					,	do_normalize=lowerCAmelCase_					,	return_attention_mask=lowerCAmelCase_					,	)

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

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

										a   	=  SEWDForCTC(lowerCAmelCase_			)
					else:
										a   	=  SEWDModel(lowerCAmelCase_			)
										feature_extractor.save_pretrained(lowerCAmelCase_			)

					recursively_load_weights(lowerCAmelCase_					,	lowerCAmelCase_					,	lowerCAmelCase_			)

					hf_model.save_pretrained(lowerCAmelCase_			)


if __name__ == "__main__":
				_lowercase:					List[Any]      =						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:					int      =						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 TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase				 =   {"""configuration_pegasus""": ["""PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusConfig"""]}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase				 =   ["""PegasusTokenizer"""]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase				 =   ["""PegasusTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase				 =   [
      """PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """PegasusForCausalLM""",
      """PegasusForConditionalGeneration""",
      """PegasusModel""",
      """PegasusPreTrainedModel""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase				 =   [
      """TFPegasusForConditionalGeneration""",
      """TFPegasusModel""",
      """TFPegasusPreTrainedModel""",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase				 =   [
      """FlaxPegasusForConditionalGeneration""",
      """FlaxPegasusModel""",
      """FlaxPegasusPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_pegasus import PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_pegasus import PegasusTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_pegasus_fast import PegasusTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_pegasus import (
        PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
        PegasusForCausalLM,
        PegasusForConditionalGeneration,
        PegasusModel,
        PegasusPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_pegasus import TFPegasusForConditionalGeneration, TFPegasusModel, TFPegasusPreTrainedModel

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_pegasus import (
        FlaxPegasusForConditionalGeneration,
        FlaxPegasusModel,
        FlaxPegasusPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase				 =   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)