





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_							=				logging.get_logger(__name__)

UpperCAmelCase_							=				{
    """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  __magic_name__  (				lowercase				,       lowercase				,       lowercase				,       lowercase				,       lowercase				)				-> List[Any]:

							"""simple docstring"""



							for attribute in key.split("""."""				):
														lowercase_	:   str  				=     getattr(lowercase				,       lowercase				)

							if weight_type is not None:
														lowercase_	:   Dict  				=     getattr(lowercase				,       lowercase				).shape
							else:
														lowercase_	:   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":
														lowercase_	:   Dict  				=     value
							elif weight_type == "weight_g":
														lowercase_	:   Union[str, Any]  				=     value
							elif weight_type == "weight_v":
														lowercase_	:   Dict  				=     value
							elif weight_type == "bias":
														lowercase_	:   Optional[Any]  				=     value
							else:
														lowercase_	:   int  				=     value

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





def  __magic_name__  (				lowercase				,       lowercase				,       lowercase				)				-> int:

							"""simple docstring"""



							lowercase_	:   Any  				=     []
							lowercase_	:   Optional[Any]  				=     fairseq_model.state_dict()

							lowercase_	:   List[str]  				=     hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

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

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





def  __magic_name__  (				lowercase				,       lowercase				,       lowercase				,       lowercase				,       lowercase				)				-> List[Any]:

							"""simple docstring"""



							lowercase_	:   Any  				=     full_name.split("""conv_layers."""				)[-1]
							lowercase_	:   Union[str, Any]  				=     name.split("""."""				)
							lowercase_	:   List[str]  				=     int(items[0]				)
							lowercase_	:   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_	:   Union[str, Any]  				=     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_	:   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."
																					)
																					lowercase_	:   Optional[Any]  				=     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_	:   Dict  				=     value
																					logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""				)
							else:
														unused_weights.append(lowercase				)





def  __magic_name__  (				lowercase				,       lowercase				)				-> Optional[int]:

							"""simple docstring"""



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

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

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

							lowercase_	:   Tuple  				=     """Wav2Vec2FeatureExtractor"""
							lowercase_	:   str  				=     """Wav2Vec2CTCTokenizer"""

							return config





@torch.no_grad()
def  __magic_name__  (				lowercase				,       lowercase				,       lowercase=None				,       lowercase=None				,       lowercase=True				)				-> str:

							"""simple docstring"""



							if is_finetuned:
														lowercase_				,		lowercase_				,		lowercase_	:   List[str]  				=     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_	:   int  				=     SEWConfig.from_pretrained(lowercase				)
							else:
														lowercase_	:   Optional[int]  				=     convert_config(model[0]				,       lowercase				)
							lowercase_	:   Tuple  				=     model[0].eval()

							lowercase_	:   Optional[Any]  				=     True if config.feat_extract_norm == """layer""" else False
							lowercase_	:   Optional[int]  				=     WavaVecaFeatureExtractor(
							    feature_size=1				,       sampling_rate=16000				,       padding_value=0				,       do_normalize=lowercase				,       return_attention_mask=lowercase				,       )

							if is_finetuned:
														if dict_path:
																					lowercase_	:   Optional[Any]  				=     Dictionary.load(lowercase				)

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

														lowercase_	:   Any  				=     SEWForCTC(lowercase				)
							else:
														lowercase_	:   Any  				=     SEWModel(lowercase				)
														feature_extractor.save_pretrained(lowercase				)

							recursively_load_weights(lowercase				,       lowercase				,       lowercase				)

							hf_model.save_pretrained(lowercase				)


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






from string import ascii_uppercase

UpperCAmelCase_							=				{char: i for i, char in enumerate(ascii_uppercase)}
UpperCAmelCase_							=				dict(enumerate(ascii_uppercase))





def  __magic_name__  (				lowercase				,       lowercase				)				-> str:

							"""simple docstring"""



							lowercase_	:   Dict  				=     len(lowercase				)
							lowercase_	:   Tuple  				=     0
							while True:
														if x == i:
																					lowercase_	:   Dict  				=     0
														if len(lowercase				) == len(lowercase				):
																					break
														key += key[i]
														i += 1
							return key





def  __magic_name__  (				lowercase				,       lowercase				)				-> str:

							"""simple docstring"""



							lowercase_	:   Any  				=     """"""
							lowercase_	:   Optional[Any]  				=     0
							for letter in message:
														if letter == " ":
																					cipher_text += " "
														else:
																					lowercase_	:   Any  				=     (dicta[letter] - dicta[key_new[i]]) % 26
																					i += 1
																					cipher_text += dicta[x]
							return cipher_text





def  __magic_name__  (				lowercase				,       lowercase				)				-> str:

							"""simple docstring"""



							lowercase_	:   Optional[Any]  				=     """"""
							lowercase_	:   Optional[Any]  				=     0
							for letter in cipher_text:
														if letter == " ":
																					or_txt += " "
														else:
																					lowercase_	:   Optional[int]  				=     (dicta[letter] + dicta[key_new[i]] + 26) % 26
																					i += 1
																					or_txt += dicta[x]
							return or_txt





def  __magic_name__  (				)				-> None:

							"""simple docstring"""



							lowercase_	:   Union[str, Any]  				=     """THE GERMAN ATTACK"""
							lowercase_	:   str  				=     """SECRET"""
							lowercase_	:   int  				=     generate_key(lowercase				,       lowercase				)
							lowercase_	:   List[str]  				=     cipher_text(lowercase				,       lowercase				)
							print(f"""Encrypted Text = {s}"""				)
							print(f"""Original Text = {original_text(lowercase				,       lowercase				)}"""				)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()