






"""simple docstring"""







from binascii import hexlify
from hashlib import shaaaa
from os import urandom

# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526

_UpperCamelCase					      =  {
    # 1536-bit
    5: {
        """prime""": int(
            """FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"""
            + """29024E088A67CC74020BBEA63B139B22514A08798E3404DD"""
            + """EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"""
            + """E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"""
            + """EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"""
            + """C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"""
            + """83655D23DCA3AD961C62F356208552BB9ED529077096966D"""
            + """670C354E4ABC9804F1746C08CA237327FFFFFFFFFFFFFFFF""",
            base=16,
        ),
        """generator""": 2,
    },
    # 2048-bit
    14: {
        """prime""": int(
            """FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"""
            + """29024E088A67CC74020BBEA63B139B22514A08798E3404DD"""
            + """EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"""
            + """E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"""
            + """EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"""
            + """C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"""
            + """83655D23DCA3AD961C62F356208552BB9ED529077096966D"""
            + """670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"""
            + """E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"""
            + """DE2BCBF6955817183995497CEA956AE515D2261898FA0510"""
            + """15728E5A8AACAA68FFFFFFFFFFFFFFFF""",
            base=16,
        ),
        """generator""": 2,
    },
    # 3072-bit
    15: {
        """prime""": int(
            """FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"""
            + """29024E088A67CC74020BBEA63B139B22514A08798E3404DD"""
            + """EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"""
            + """E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"""
            + """EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"""
            + """C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"""
            + """83655D23DCA3AD961C62F356208552BB9ED529077096966D"""
            + """670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"""
            + """E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"""
            + """DE2BCBF6955817183995497CEA956AE515D2261898FA0510"""
            + """15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"""
            + """ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"""
            + """ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"""
            + """F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"""
            + """BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"""
            + """43DB5BFCE0FD108E4B82D120A93AD2CAFFFFFFFFFFFFFFFF""",
            base=16,
        ),
        """generator""": 2,
    },
    # 4096-bit
    16: {
        """prime""": int(
            """FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"""
            + """29024E088A67CC74020BBEA63B139B22514A08798E3404DD"""
            + """EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"""
            + """E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"""
            + """EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"""
            + """C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"""
            + """83655D23DCA3AD961C62F356208552BB9ED529077096966D"""
            + """670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"""
            + """E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"""
            + """DE2BCBF6955817183995497CEA956AE515D2261898FA0510"""
            + """15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"""
            + """ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"""
            + """ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"""
            + """F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"""
            + """BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"""
            + """43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"""
            + """88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"""
            + """2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"""
            + """287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"""
            + """1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"""
            + """93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934063199"""
            + """FFFFFFFFFFFFFFFF""",
            base=16,
        ),
        """generator""": 2,
    },
    # 6144-bit
    17: {
        """prime""": int(
            """FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD129024E08"""
            + """8A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B"""
            + """302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9"""
            + """A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE6"""
            + """49286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8"""
            + """FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D"""
            + """670C354E4ABC9804F1746C08CA18217C32905E462E36CE3BE39E772C"""
            + """180E86039B2783A2EC07A28FB5C55DF06F4C52C9DE2BCBF695581718"""
            + """3995497CEA956AE515D2261898FA051015728E5A8AAAC42DAD33170D"""
            + """04507A33A85521ABDF1CBA64ECFB850458DBEF0A8AEA71575D060C7D"""
            + """B3970F85A6E1E4C7ABF5AE8CDB0933D71E8C94E04A25619DCEE3D226"""
            + """1AD2EE6BF12FFA06D98A0864D87602733EC86A64521F2B18177B200C"""
            + """BBE117577A615D6C770988C0BAD946E208E24FA074E5AB3143DB5BFC"""
            + """E0FD108E4B82D120A92108011A723C12A787E6D788719A10BDBA5B26"""
            + """99C327186AF4E23C1A946834B6150BDA2583E9CA2AD44CE8DBBBC2DB"""
            + """04DE8EF92E8EFC141FBECAA6287C59474E6BC05D99B2964FA090C3A2"""
            + """233BA186515BE7ED1F612970CEE2D7AFB81BDD762170481CD0069127"""
            + """D5B05AA993B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"""
            + """36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BDF8FF9406"""
            + """AD9E530EE5DB382F413001AEB06A53ED9027D831179727B0865A8918"""
            + """DA3EDBEBCF9B14ED44CE6CBACED4BB1BDB7F1447E6CC254B33205151"""
            + """2BD7AF426FB8F401378CD2BF5983CA01C64B92ECF032EA15D1721D03"""
            + """F482D7CE6E74FEF6D55E702F46980C82B5A84031900B1C9E59E7C97F"""
            + """BEC7E8F323A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"""
            + """CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE32806A1D58B"""
            + """B7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55CDA56C9EC2EF29632"""
            + """387FE8D76E3C0468043E8F663F4860EE12BF2D5B0B7474D6E694F91E"""
            + """6DCC4024FFFFFFFFFFFFFFFF""",
            base=16,
        ),
        """generator""": 2,
    },
    # 8192-bit
    18: {
        """prime""": int(
            """FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"""
            + """29024E088A67CC74020BBEA63B139B22514A08798E3404DD"""
            + """EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"""
            + """E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"""
            + """EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"""
            + """C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"""
            + """83655D23DCA3AD961C62F356208552BB9ED529077096966D"""
            + """670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"""
            + """E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"""
            + """DE2BCBF6955817183995497CEA956AE515D2261898FA0510"""
            + """15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"""
            + """ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"""
            + """ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"""
            + """F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"""
            + """BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"""
            + """43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"""
            + """88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"""
            + """2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"""
            + """287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"""
            + """1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"""
            + """93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"""
            + """36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BD"""
            + """F8FF9406AD9E530EE5DB382F413001AEB06A53ED9027D831"""
            + """179727B0865A8918DA3EDBEBCF9B14ED44CE6CBACED4BB1B"""
            + """DB7F1447E6CC254B332051512BD7AF426FB8F401378CD2BF"""
            + """5983CA01C64B92ECF032EA15D1721D03F482D7CE6E74FEF6"""
            + """D55E702F46980C82B5A84031900B1C9E59E7C97FBEC7E8F3"""
            + """23A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"""
            + """CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE328"""
            + """06A1D58BB7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55C"""
            + """DA56C9EC2EF29632387FE8D76E3C0468043E8F663F4860EE"""
            + """12BF2D5B0B7474D6E694F91E6DBE115974A3926F12FEE5E4"""
            + """38777CB6A932DF8CD8BEC4D073B931BA3BC832B68D9DD300"""
            + """741FA7BF8AFC47ED2576F6936BA424663AAB639C5AE4F568"""
            + """3423B4742BF1C978238F16CBE39D652DE3FDB8BEFC848AD9"""
            + """22222E04A4037C0713EB57A81A23F0C73473FC646CEA306B"""
            + """4BCBC8862F8385DDFA9D4B7FA2C087E879683303ED5BDD3A"""
            + """062B3CF5B3A278A66D2A13F83F44F82DDF310EE074AB6A36"""
            + """4597E899A0255DC164F31CC50846851DF9AB48195DED7EA1"""
            + """B1D510BD7EE74D73FAF36BC31ECFA268359046F4EB879F92"""
            + """4009438B481C6CD7889A002ED5EE382BC9190DA6FC026E47"""
            + """9558E4475677E9AA9E3050E2765694DFC81F56E880B96E71"""
            + """60C980DD98EDD3DFFFFFFFFFFFFFFFFF""",
            base=16,
        ),
        """generator""": 2,
    },
}







class  lowerCamelCase__ :






    def __init__(			self     ,A = 14 ):
          if group not in primes:
                raise ValueError("""Unsupported Group""" )
          UpperCAmelCase          =	primes[group]["""prime"""]
          UpperCAmelCase          =	primes[group]["""generator"""]

          UpperCAmelCase          =	int(hexlify(urandom(32 ) )     ,base=16 )






    def     _UpperCamelCase							(			self ):
          return hex(self.__private_key )[2:]






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	pow(self.generator     ,self.__private_key     ,self.prime )
          return hex(A )[2:]






    def     _UpperCamelCase							(			self     ,A ):
          # check if the other public key is valid based on NIST SP800-56
          return (
              2 <= key <= self.prime - 2
              and pow(A     ,(self.prime - 1) // 2     ,self.prime ) == 1
          )






    def     _UpperCamelCase							(			self     ,A ):
          UpperCAmelCase          =	int(A     ,base=16 )
          if not self.is_valid_public_key(A ):
                raise ValueError("""Invalid public key""" )
          UpperCAmelCase          =	pow(A     ,self.__private_key     ,self.prime )
          return shaaaa(str(A ).encode() ).hexdigest()






    @staticmethod
    def     _UpperCamelCase							(			A     ,A ):
          # check if the other public key is valid based on NIST SP800-56
          return (
              2 <= remote_public_key_str <= prime - 2
              and pow(A     ,(prime - 1) // 2     ,A ) == 1
          )






    @staticmethod
    def     _UpperCamelCase							(			A     ,A     ,A = 14 ):
          UpperCAmelCase          =	int(A     ,base=16 )
          UpperCAmelCase          =	int(A     ,base=16 )
          UpperCAmelCase          =	primes[group]["""prime"""]
          if not DiffieHellman.is_valid_public_key_static(A     ,A ):
                raise ValueError("""Invalid public key""" )
          UpperCAmelCase          =	pow(A     ,A     ,A )
          return shaaaa(str(A ).encode() ).hexdigest()


if __name__ == "__main__":
  import doctest

  doctest.testmod()














"""simple docstring"""







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()
_UpperCamelCase					      =  logging.get_logger("""transformers.models.speecht5""")

_UpperCamelCase					      =  {
    """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""",
}
_UpperCamelCase					      =  {
    """text_encoder_prenet.encoder_prenet.0""": """speecht5.encoder.prenet.embed_tokens""",
    """text_encoder_prenet.encoder_prenet.1.alpha""": """speecht5.encoder.prenet.encode_positions.alpha""",
}
_UpperCamelCase					      =  {
    """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""",
}
_UpperCamelCase					      =  {
    """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""",
}
_UpperCamelCase					      =  {
    """text_decoder_prenet.embed_tokens""": """speecht5.decoder.prenet.embed_tokens""",
}
_UpperCamelCase					      =  {
    """text_decoder_postnet.output_projection""": """text_decoder_postnet.lm_head""",
}
_UpperCamelCase					      =  {
    """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""",
}
_UpperCamelCase					      =  {
    """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""",
}
_UpperCamelCase					      =  {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
_UpperCamelCase					      =  {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
_UpperCamelCase					      =  {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
_UpperCamelCase					      =  []
_UpperCamelCase					      =  [
    """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""",
]
_UpperCamelCase					      =  IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """speech_decoder_prenet.*""",
    """speech_decoder_postnet.*""",
]
_UpperCamelCase					      =  IGNORE_KEYS + [
    """encoder.proj""",
    """speech_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]
_UpperCamelCase					      =  IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]

def _a			(		_snake_case				,	_snake_case				,	_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      for attribute in key.split("""."""							):
            UpperCAmelCase          =	getattr(_snake_case				,	_snake_case							)

      if weight_type is not None:
            UpperCAmelCase          =	getattr(_snake_case				,	_snake_case							).shape
      else:
            UpperCAmelCase          =	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":
            UpperCAmelCase          =	value
      elif weight_type == "weight_g":
            UpperCAmelCase          =	value
      elif weight_type == "weight_v":
            UpperCAmelCase          =	value
      elif weight_type == "bias":
            UpperCAmelCase          =	value
      elif weight_type == "running_mean":
            UpperCAmelCase          =	value
      elif weight_type == "running_var":
            UpperCAmelCase          =	value
      elif weight_type == "num_batches_tracked":
            UpperCAmelCase          =	value
      else:
            UpperCAmelCase          =	value

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

def _a			(		_snake_case				,	_snake_case							):







      """simple docstring"""






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

def _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	[]

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

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

            UpperCAmelCase          =	False
            if "conv_layers" in name:
                  load_conv_layer(
                      _snake_case				,	_snake_case				,	_snake_case				,	_snake_case				,	hf_model.config.feat_extract_norm == """group"""				,	)
                  UpperCAmelCase          =	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:
                              UpperCAmelCase      , UpperCAmelCase          =	key.split(""".*."""							)
                              if prefix in name and suffix in name:
                                    UpperCAmelCase          =	suffix

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

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

def _a			(		_snake_case				,	_snake_case				,	_snake_case				,	_snake_case				,	_snake_case							):







      """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:
                  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.'''							)
                  UpperCAmelCase          =	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.'''							)
                  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:
                  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.'''							)
                  UpperCAmelCase          =	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.'''							)
                  UpperCAmelCase          =	value
                  logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''							)
      else:
            unused_weights.append(_snake_case							)

@torch.no_grad()
def _a			(		_snake_case				,	_snake_case				,	_snake_case				,	_snake_case=None				,	_snake_case=None				,	_snake_case=None				,	):







      """simple docstring"""






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

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

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

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

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

      UpperCAmelCase          =	torch.load(_snake_case							)
      recursively_load_weights(fairseq_checkpoint["""model"""]				,	_snake_case				,	_snake_case							)

      model.save_pretrained(_snake_case							)

      if repo_id:
            print("""Pushing to the hub..."""							)
            processor.push_to_hub(_snake_case							)
            model.push_to_hub(_snake_case							)


if __name__ == "__main__":
  _UpperCamelCase					      =  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."""
  )

  _UpperCamelCase					      =  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,
  )



