import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
A_      =      logging.get_logger("transformers.models.speecht5")

A_      =      {
    "speech_encoder_prenet.layer_norm": "speecht5.encoder.prenet.feature_projection.layer_norm",
    "speech_encoder_prenet.post_extract_proj": "speecht5.encoder.prenet.feature_projection.projection",
    "speech_encoder_prenet.pos_conv.0": "speecht5.encoder.prenet.pos_conv_embed.conv",
    "speech_encoder_prenet.mask_emb": "speecht5.encoder.prenet.masked_spec_embed",
}
A_      =      {
    "text_encoder_prenet.encoder_prenet.0": "speecht5.encoder.prenet.embed_tokens",
    "text_encoder_prenet.encoder_prenet.1.alpha": "speecht5.encoder.prenet.encode_positions.alpha",
}
A_      =      {
    "speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0": "speecht5.decoder.prenet.layers.0",
    "speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0": "speecht5.decoder.prenet.layers.1",
    "speech_decoder_prenet.decoder_prenet.0.1": "speecht5.decoder.prenet.final_layer",
    "speech_decoder_prenet.decoder_prenet.1.alpha": "speecht5.decoder.prenet.encode_positions.alpha",
    "speech_decoder_prenet.spkembs_layer.0": "speecht5.decoder.prenet.speaker_embeds_layer",
}
A_      =      {
    "speech_decoder_postnet.feat_out": "speech_decoder_postnet.feat_out",
    "speech_decoder_postnet.prob_out": "speech_decoder_postnet.prob_out",
    "speech_decoder_postnet.postnet.postnet.0.0": "speech_decoder_postnet.layers.0.conv",
    "speech_decoder_postnet.postnet.postnet.0.1": "speech_decoder_postnet.layers.0.batch_norm",
    "speech_decoder_postnet.postnet.postnet.1.0": "speech_decoder_postnet.layers.1.conv",
    "speech_decoder_postnet.postnet.postnet.1.1": "speech_decoder_postnet.layers.1.batch_norm",
    "speech_decoder_postnet.postnet.postnet.2.0": "speech_decoder_postnet.layers.2.conv",
    "speech_decoder_postnet.postnet.postnet.2.1": "speech_decoder_postnet.layers.2.batch_norm",
    "speech_decoder_postnet.postnet.postnet.3.0": "speech_decoder_postnet.layers.3.conv",
    "speech_decoder_postnet.postnet.postnet.3.1": "speech_decoder_postnet.layers.3.batch_norm",
    "speech_decoder_postnet.postnet.postnet.4.0": "speech_decoder_postnet.layers.4.conv",
    "speech_decoder_postnet.postnet.postnet.4.1": "speech_decoder_postnet.layers.4.batch_norm",
}
A_      =      {
    "text_decoder_prenet.embed_tokens": "speecht5.decoder.prenet.embed_tokens",
}
A_      =      {
    "text_decoder_postnet.output_projection": "text_decoder_postnet.lm_head",
}
A_      =      {
    "encoder.layers.*.self_attn.k_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj",
    "encoder.layers.*.self_attn.v_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj",
    "encoder.layers.*.self_attn.q_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj",
    "encoder.layers.*.self_attn.out_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj",
    "encoder.layers.*.self_attn_layer_norm": "speecht5.encoder.wrapped_encoder.layers.*.layer_norm",
    "encoder.layers.*.fc1": "speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense",
    "encoder.layers.*.fc2": "speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense",
    "encoder.layers.*.final_layer_norm": "speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "speecht5.encoder.wrapped_encoder.layer_norm",
    "encoder.pos_emb.pe_k": "speecht5.encoder.wrapped_encoder.embed_positions.pe_k",
}
A_      =      {
    "decoder.layers.*.self_attn.k_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj",
    "decoder.layers.*.self_attn.v_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj",
    "decoder.layers.*.self_attn.q_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj",
    "decoder.layers.*.self_attn.out_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj",
    "decoder.layers.*.self_attn_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm",
    "decoder.layers.*.encoder_attn.k_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj",
    "decoder.layers.*.encoder_attn.v_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj",
    "decoder.layers.*.encoder_attn.q_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj",
    "decoder.layers.*.encoder_attn.out_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj",
    "decoder.layers.*.encoder_attn_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm",
    "decoder.layers.*.fc1": "speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense",
    "decoder.layers.*.fc2": "speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense",
    "decoder.layers.*.final_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm",
}
A_      =      {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
A_      =      {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
A_      =      {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
A_      =      []
A_      =      [
    "encoder.version",
    "encoder.layers.*.norm_k.weight",
    "encoder.layers.*.norm_k.bias",
    "decoder.version",
    "decoder.layers.*.norm_k.weight",
    "decoder.layers.*.norm_k.bias",
    "decoder.pos_emb.pe_k",
    "speech_encoder_prenet.embed_positions._float_tensor",
    "text_decoder_prenet.embed_positions._float_tensor",
]
A_      =      IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "speech_decoder_prenet.*",
    "speech_decoder_postnet.*",
]
A_      =      IGNORE_KEYS + [
    "encoder.proj",
    "speech_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]
A_      =      IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )->       Optional[int]:



			"""simple docstring"""







			for attribute in key.split('''.'''       ):
						lowercase							=		getattr(UpperCAmelCase,		UpperCAmelCase       )

			if weight_type is not None:
						lowercase							=		getattr(UpperCAmelCase,		UpperCAmelCase       ).shape
			else:
						lowercase							=		hf_pointer.shape

			if hf_shape != value.shape:
						raise ValueError(
						    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							=		value
			elif weight_type == "weight_g":
						lowercase							=		value
			elif weight_type == "weight_v":
						lowercase							=		value
			elif weight_type == "bias":
						lowercase							=		value
			elif weight_type == "running_mean":
						lowercase							=		value
			elif weight_type == "running_var":
						lowercase							=		value
			elif weight_type == "num_batches_tracked":
						lowercase							=		value
			else:
						lowercase							=		value

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






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       List[Any]:



			"""simple docstring"""







			for key in ignore_keys:
						if key.endswith('''.*'''       ):
									if name.startswith(key[:-1]       ):
												return True
						elif ".*." in key:
									lowercase		,lowercase							=		key.split('''.*.'''       )
									if prefix in name and suffix in name:
												return True
						elif key in name:
									return True
			return False






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )->       Optional[int]:



			"""simple docstring"""







			lowercase							=		[]

			if task == "s2t":
						lowercase							=		hf_model.speechta.encoder.prenet.feature_encoder
						lowercase							=		MAPPING_S2T
						lowercase							=		IGNORE_KEYS_S2T
			elif task == "t2s":
						lowercase							=		None
						lowercase							=		MAPPING_T2S
						lowercase							=		IGNORE_KEYS_T2S
			elif task == "s2s":
						lowercase							=		hf_model.speechta.encoder.prenet.feature_encoder
						lowercase							=		MAPPING_S2S
						lowercase							=		IGNORE_KEYS_S2S
			else:
						raise ValueError(f'Unsupported task: {task}'       )

			for name, value in fairseq_dict.items():
						if should_ignore(UpperCAmelCase,		UpperCAmelCase       ):
									logger.info(f'{name} was ignored'       )
									continue

						lowercase							=		False
						if "conv_layers" in name:
									load_conv_layer(
									    UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		hf_model.config.feat_extract_norm == '''group''',		)
									lowercase							=		True
						else:
									for key, mapped_key in MAPPING.items():
												# mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

												if "*" in key:
															lowercase		,lowercase							=		key.split('''.*.'''       )
															if prefix in name and suffix in name:
																		lowercase							=		suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
												if key in name:
															lowercase							=		True
															if "*" in mapped_key:
																		lowercase							=		name.split(UpperCAmelCase       )[0].split('''.'''       )[-2]
																		lowercase							=		mapped_key.replace('''*''',		UpperCAmelCase       )
															if "weight_g" in name:
																		lowercase							=		'''weight_g'''
															elif "weight_v" in name:
																		lowercase							=		'''weight_v'''
															elif "bias" in name:
																		lowercase							=		'''bias'''
															elif "weight" in name:
																		lowercase							=		'''weight'''
															elif "running_mean" in name:
																		lowercase							=		'''running_mean'''
															elif "running_var" in name:
																		lowercase							=		'''running_var'''
															elif "num_batches_tracked" in name:
																		lowercase							=		'''num_batches_tracked'''
															else:
																		lowercase							=		None
															set_recursively(UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )
												continue
						if not is_used:
									unused_weights.append(UpperCAmelCase       )

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






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )->       Optional[Any]:



			"""simple docstring"""







			lowercase							=		full_name.split('''conv_layers.'''       )[-1]
			lowercase							=		name.split('''.'''       )
			lowercase							=		int(items[0]       )
			lowercase							=		int(items[1]       )

			if type_id == 0:
						if "bias" in name:
									if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
												raise ValueError(
												    f'{full_name} has size {value.shape}, but'
												    f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'       )
									lowercase							=		value
									logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'       )
						elif "weight" in name:
									if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
												raise ValueError(
												    f'{full_name} has size {value.shape}, but'
												    f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'       )
									lowercase							=		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:
									if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
												raise ValueError(
												    f'{full_name} has size {value.shape}, but'
												    f' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'       )
									lowercase							=		value
									logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'       )
						elif "weight" in name:
									if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
												raise ValueError(
												    f'{full_name} has size {value.shape}, but'
												    f' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'       )
									lowercase							=		value
									logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'       )
			else:
						unused_weights.append(UpperCAmelCase       )






@torch.no_grad()
def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase=None,		UpperCAmelCase=None,		UpperCAmelCase=None,		)->       Optional[int]:



			"""simple docstring"""







			if config_path is not None:
						lowercase							=		SpeechTaConfig.from_pretrained(UpperCAmelCase       )
			else:
						lowercase							=		SpeechTaConfig()

			if task == "s2t":
						lowercase							=		config.max_text_positions
						lowercase							=		SpeechTaForSpeechToText(UpperCAmelCase       )
			elif task == "t2s":
						lowercase							=		1876
						lowercase							=		600
						lowercase							=		config.max_speech_positions
						lowercase							=		SpeechTaForTextToSpeech(UpperCAmelCase       )
			elif task == "s2s":
						lowercase							=		1876
						lowercase							=		config.max_speech_positions
						lowercase							=		SpeechTaForSpeechToSpeech(UpperCAmelCase       )
			else:
						raise ValueError(f'Unknown task name: {task}'       )

			if vocab_path:
						lowercase							=		SpeechTaTokenizer(UpperCAmelCase,		model_max_length=config.max_text_positions       )

						# Mask token behaves like a normal word, i.e. include the space before it
						lowercase							=		AddedToken('''<mask>''',		lstrip=UpperCAmelCase,		rstrip=UpperCAmelCase       )
						lowercase							=		mask_token
						tokenizer.add_special_tokens({'''mask_token''': mask_token}       )
						tokenizer.add_tokens(['''<ctc_blank>''']       )

			lowercase							=		SpeechTaFeatureExtractor()
			lowercase							=		SpeechTaProcessor(tokenizer=UpperCAmelCase,		feature_extractor=UpperCAmelCase       )
			processor.save_pretrained(UpperCAmelCase       )

			lowercase							=		torch.load(UpperCAmelCase       )
			recursively_load_weights(fairseq_checkpoint['''model'''],		UpperCAmelCase,		UpperCAmelCase       )

			model.save_pretrained(UpperCAmelCase       )

			if repo_id:
						print('''Pushing to the hub...'''       )
						processor.push_to_hub(UpperCAmelCase       )
						model.push_to_hub(UpperCAmelCase       )


if __name__ == "__main__":
						A_      =      argparse.ArgumentParser()
						parser.add_argument(
						    "--task",
						    default="s2t",
						    type=str,
						    help="Type of the SpeechT5 model you'd like to convert. Should be one of 's2t', 't2s', 's2s'.",
						)
						parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to fairseq checkpoint")
						parser.add_argument("--vocab_path", default=None, type=str, help="Path to SentencePiece model")
						parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
						parser.add_argument(
						    "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
						)
						parser.add_argument(
						    "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
						)

						A_      =      parser.parse_args()
						convert_speechta_checkpoint(
						    args.task,
						    args.checkpoint_path,
						    args.pytorch_dump_folder_path,
						    args.config_path,
						    args.vocab_path,
						    args.push_to_hub,
						)






import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

A_      =      3






def        __UpperCAmelCase    (       UpperCAmelCase       )->       int:



			"""simple docstring"""







			print('''Generating primitive root of p'''       )
			while True:
						lowercase							=		random.randrange(3,		UpperCAmelCase       )
						if pow(UpperCAmelCase,		2,		UpperCAmelCase       ) == 1:
									continue
						if pow(UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       ) == 1:
									continue
						return g






def        __UpperCAmelCase    (       UpperCAmelCase       )->       tuple[tuple[int, int, int, int], tuple[int, int]]:



			"""simple docstring"""







			print('''Generating prime p...'''       )
			lowercase							=		rabin_miller.generate_large_prime(UpperCAmelCase       )  # select large prime number.
			lowercase							=		primitive_root(UpperCAmelCase       )  # one primitive root on modulo p.
			lowercase							=		random.randrange(3,		UpperCAmelCase       )  # private_key -> have to be greater than 2 for safety.
			lowercase							=		cryptomath.find_mod_inverse(pow(UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       ),		UpperCAmelCase       )

			lowercase							=		(key_size, e_a, e_a, p)
			lowercase							=		(key_size, d)

			return public_key, private_key






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       None:



			"""simple docstring"""







			if os.path.exists(f'{name}_pubkey.txt'       ) or os.path.exists(f'{name}_privkey.txt'       ):
						print('''\nWARNING:'''       )
						print(
						    f'"{name}_pubkey.txt" or "{name}_privkey.txt" already exists. \n'
						    '''Use a different name or delete these files and re-run this program.'''       )
						sys.exit()

			lowercase		,lowercase							=		generate_key(UpperCAmelCase       )
			print(f'\nWriting public key to file {name}_pubkey.txt...'       )
			with open(f'{name}_pubkey.txt',		'''w'''       ) as fo:
						fo.write(f'{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}'       )

			print(f'Writing private key to file {name}_privkey.txt...'       )
			with open(f'{name}_privkey.txt',		'''w'''       ) as fo:
						fo.write(f'{private_key[0]},{private_key[1]}'       )






def        __UpperCAmelCase    (       )->       None:



			"""simple docstring"""







			print('''Making key files...'''       )
			make_key_files('''elgamal''',		2048       )
			print('''Key files generation successful'''       )


if __name__ == "__main__":
						main()





