






"""simple docstring"""

from math import cos, sin, sqrt, tau

from audio_filters.iir_filter import IIRFilter




def __lowercase    (							_a	,    _a	,    _a = 1 / sqrt(2					)					):
    snake_case_				:  Optional[int]       =			tau * frequency / samplerate
    snake_case_				:  Any       =			sin(_a					)
    snake_case_				:  List[str]       =			cos(_a					)
    snake_case_				:  Tuple       =			_sin / (2 * q_factor)

    snake_case_				:  str       =			(1 - _cos) / 2
    snake_case_				:  Dict       =			1 - _cos

    snake_case_				:  Dict       =			1 + alpha
    snake_case_				:  Dict       =			-2 * _cos
    snake_case_				:  Union[str, Any]       =			1 - alpha

    snake_case_				:  List[str]       =			IIRFilter(2					)
    filt.set_coefficients([aa, aa, aa]	,    [ba, ba, ba]					)
    return filt




def __lowercase    (							_a	,    _a	,    _a = 1 / sqrt(2					)					):
    snake_case_				:  int       =			tau * frequency / samplerate
    snake_case_				:  int       =			sin(_a					)
    snake_case_				:  str       =			cos(_a					)
    snake_case_				:  Tuple       =			_sin / (2 * q_factor)

    snake_case_				:  str       =			(1 + _cos) / 2
    snake_case_				:  Optional[int]       =			-1 - _cos

    snake_case_				:  Any       =			1 + alpha
    snake_case_				:  Optional[int]       =			-2 * _cos
    snake_case_				:  str       =			1 - alpha

    snake_case_				:  List[str]       =			IIRFilter(2					)
    filt.set_coefficients([aa, aa, aa]	,    [ba, ba, ba]					)
    return filt




def __lowercase    (							_a	,    _a	,    _a = 1 / sqrt(2					)					):
    snake_case_				:  str       =			tau * frequency / samplerate
    snake_case_				:  Any       =			sin(_a					)
    snake_case_				:  Union[str, Any]       =			cos(_a					)
    snake_case_				:  Tuple       =			_sin / (2 * q_factor)

    snake_case_				:  List[str]       =			_sin / 2
    snake_case_				:  List[str]       =			0
    snake_case_				:  List[str]       =			-ba

    snake_case_				:  Optional[Any]       =			1 + alpha
    snake_case_				:  Optional[int]       =			-2 * _cos
    snake_case_				:  Optional[Any]       =			1 - alpha

    snake_case_				:  Optional[int]       =			IIRFilter(2					)
    filt.set_coefficients([aa, aa, aa]	,    [ba, ba, ba]					)
    return filt




def __lowercase    (							_a	,    _a	,    _a = 1 / sqrt(2					)					):
    snake_case_				:  Optional[int]       =			tau * frequency / samplerate
    snake_case_				:  int       =			sin(_a					)
    snake_case_				:  List[Any]       =			cos(_a					)
    snake_case_				:  Optional[Any]       =			_sin / (2 * q_factor)

    snake_case_				:  int       =			1 - alpha
    snake_case_				:  Optional[int]       =			-2 * _cos
    snake_case_				:  Optional[int]       =			1 + alpha

    snake_case_				:  str       =			IIRFilter(2					)
    filt.set_coefficients([ba, ba, ba]	,    [ba, ba, ba]					)
    return filt




def __lowercase    (							_a	,    _a	,    _a	,    _a = 1 / sqrt(2					)	,    ):
    snake_case_				:  Tuple       =			tau * frequency / samplerate
    snake_case_				:  Optional[int]       =			sin(_a					)
    snake_case_				:  Union[str, Any]       =			cos(_a					)
    snake_case_				:  Tuple       =			_sin / (2 * q_factor)
    snake_case_				:  Dict       =			10 ** (gain_db / 40)

    snake_case_				:  List[Any]       =			1 + alpha * big_a
    snake_case_				:  Union[str, Any]       =			-2 * _cos
    snake_case_				:  Optional[Any]       =			1 - alpha * big_a
    snake_case_				:  Tuple       =			1 + alpha / big_a
    snake_case_				:  List[Any]       =			-2 * _cos
    snake_case_				:  Dict       =			1 - alpha / big_a

    snake_case_				:  int       =			IIRFilter(2					)
    filt.set_coefficients([aa, aa, aa]	,    [ba, ba, ba]					)
    return filt




def __lowercase    (							_a	,    _a	,    _a	,    _a = 1 / sqrt(2					)	,    ):
    snake_case_				:  Dict       =			tau * frequency / samplerate
    snake_case_				:  Optional[int]       =			sin(_a					)
    snake_case_				:  Optional[Any]       =			cos(_a					)
    snake_case_				:  str       =			_sin / (2 * q_factor)
    snake_case_				:  str       =			10 ** (gain_db / 40)
    snake_case_				:  List[str]       =			(big_a + 1) - (big_a - 1) * _cos
    snake_case_				:  Dict       =			(big_a + 1) + (big_a - 1) * _cos
    snake_case_				:  Union[str, Any]       =			(big_a - 1) - (big_a + 1) * _cos
    snake_case_				:  List[str]       =			(big_a - 1) + (big_a + 1) * _cos
    snake_case_				:  int       =			2 * sqrt(_a					) * alpha

    snake_case_				:  Dict       =			big_a * (pmc + aaa)
    snake_case_				:  int       =			2 * big_a * mpc
    snake_case_				:  int       =			big_a * (pmc - aaa)
    snake_case_				:  Union[str, Any]       =			ppmc + aaa
    snake_case_				:  Optional[int]       =			-2 * pmpc
    snake_case_				:  Optional[Any]       =			ppmc - aaa

    snake_case_				:  Dict       =			IIRFilter(2					)
    filt.set_coefficients([aa, aa, aa]	,    [ba, ba, ba]					)
    return filt






def __lowercase    (							_a	,    _a	,    _a	,    _a = 1 / sqrt(2					)	,    ):
    snake_case_				:  int       =			tau * frequency / samplerate
    snake_case_				:  Any       =			sin(_a					)
    snake_case_				:  Union[str, Any]       =			cos(_a					)
    snake_case_				:  int       =			_sin / (2 * q_factor)
    snake_case_				:  Union[str, Any]       =			10 ** (gain_db / 40)
    snake_case_				:  int       =			(big_a + 1) - (big_a - 1) * _cos
    snake_case_				:  Optional[int]       =			(big_a + 1) + (big_a - 1) * _cos
    snake_case_				:  Dict       =			(big_a - 1) - (big_a + 1) * _cos
    snake_case_				:  Optional[int]       =			(big_a - 1) + (big_a + 1) * _cos
    snake_case_				:  List[Any]       =			2 * sqrt(_a					) * alpha

    snake_case_				:  str       =			big_a * (ppmc + aaa)
    snake_case_				:  Optional[int]       =			-2 * big_a * pmpc
    snake_case_				:  str       =			big_a * (ppmc - aaa)
    snake_case_				:  List[str]       =			pmc + aaa
    snake_case_				:  List[str]       =			2 * mpc
    snake_case_				:  str       =			pmc - aaa

    snake_case_				:  Any       =			IIRFilter(2					)
    filt.set_coefficients([aa, aa, aa]	,    [ba, ba, ba]					)
    return filt










"""simple docstring"""

import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM




def __lowercase    (							_a	,    _a	,    _a					):
    snake_case_				:  Tuple       =			AutoConfig.from_pretrained(_a					)
    snake_case_				:  Tuple       =			FlaxAutoModelForSeqaSeqLM.from_config(config=_a					)
    snake_case_				:  Union[str, Any]       =			checkpoints.load_tax_checkpoint(_a					)

    snake_case_				:  Optional[int]       =			'''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

    if config.model_type == "t5":
        snake_case_				:  str       =			'''SelfAttention'''
    if config.model_type == "longt5" and config.encoder_attention_type == "local":
        snake_case_				:  List[str]       =			'''LocalSelfAttention'''
    elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
        snake_case_				:  Dict       =			'''TransientGlobalSelfAttention'''
    else:
        raise ValueError(
            '''Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'''
            ''' attribute with a value from [\'local\', \'transient-global].'''					)

    # Encoder
    for layer_index in range(config.num_layers					):
        snake_case_				:  Any       =			f"layers_{str(_a					)}"

        # Self-Attention
        snake_case_				:  Dict       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
        snake_case_				:  Tuple       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
        snake_case_				:  Tuple       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
        snake_case_				:  Optional[int]       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

        # Global input layer norm
        if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            snake_case_				:  Tuple       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

        # Layer Normalization
        snake_case_				:  List[str]       =			tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

        if split_mlp_wi:
            snake_case_				:  int       =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
            snake_case_				:  List[str]       =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
        else:
            snake_case_				:  int       =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

        snake_case_				:  str       =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

        # Layer Normalization
        snake_case_				:  Optional[Any]       =			tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

        # Assigning
        snake_case_				:  Optional[Any]       =			flax_model.params['''encoder''']['''block'''][str(_a					)]['''layer''']
        snake_case_				:  List[str]       =			tax_attention_key
        snake_case_				:  Optional[Any]       =			tax_attention_out
        snake_case_				:  Any       =			tax_attention_query
        snake_case_				:  str       =			tax_attention_value

        snake_case_				:  Dict       =			tax_attention_layer_norm

        # Global input layer norm
        if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            snake_case_				:  Union[str, Any]       =			tax_global_layer_norm

        if split_mlp_wi:
            snake_case_				:  Any       =			tax_mlp_wi_a
            snake_case_				:  List[Any]       =			tax_mlp_wi_a
        else:
            snake_case_				:  Union[str, Any]       =			tax_mlp_wi

        snake_case_				:  List[Any]       =			tax_mlp_wo
        snake_case_				:  int       =			tax_mlp_layer_norm

        snake_case_				:  Any       =			flax_model_encoder_layer_block

    # Only for layer 0:
    snake_case_				:  Optional[int]       =			tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
    snake_case_				:  Any       =			tax_encoder_rel_embedding

    # Side/global relative position_bias + layer norm
    if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
        snake_case_				:  List[str]       =			tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
        snake_case_				:  Tuple       =			tax_encoder_global_rel_embedding

    # Assigning
    snake_case_				:  Dict       =			tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
    snake_case_				:  Any       =			tax_encoder_norm

    # Decoder
    for layer_index in range(config.num_layers					):
        snake_case_				:  Tuple       =			f"layers_{str(_a					)}"

        # Self-Attention
        snake_case_				:  List[str]       =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
        snake_case_				:  Optional[int]       =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
        snake_case_				:  List[Any]       =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
        snake_case_				:  Optional[int]       =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

        # Layer Normalization
        snake_case_				:  str       =			tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
            '''scale'''
        ]

        # Encoder-Decoder-Attention
        snake_case_				:  Any       =			tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']
        snake_case_				:  Optional[Any]       =			tax_enc_dec_attention_module['''key''']['''kernel''']
        snake_case_				:  str       =			tax_enc_dec_attention_module['''out''']['''kernel''']
        snake_case_				:  Union[str, Any]       =			tax_enc_dec_attention_module['''query''']['''kernel''']
        snake_case_				:  List[str]       =			tax_enc_dec_attention_module['''value''']['''kernel''']

        # Layer Normalization
        snake_case_				:  Optional[int]       =			tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

        # MLP
        if split_mlp_wi:
            snake_case_				:  List[Any]       =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
            snake_case_				:  List[str]       =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
        else:
            snake_case_				:  Dict       =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

        snake_case_				:  List[Any]       =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

        # Layer Normalization
        snake_case_				:  List[Any]       =			tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

        # Assigning
        snake_case_				:  Dict       =			flax_model.params['''decoder''']['''block'''][str(_a					)]['''layer''']
        snake_case_				:  int       =			tax_attention_key
        snake_case_				:  List[Any]       =			tax_attention_out
        snake_case_				:  Any       =			tax_attention_query
        snake_case_				:  Dict       =			tax_attention_value

        snake_case_				:  str       =			tax_pre_attention_layer_norm

        snake_case_				:  Any       =			tax_enc_dec_attention_key
        snake_case_				:  str       =			tax_enc_dec_attention_out
        snake_case_				:  int       =			tax_enc_dec_attention_query
        snake_case_				:  Any       =			tax_enc_dec_attention_value

        snake_case_				:  Optional[Any]       =			tax_cross_layer_norm

        if split_mlp_wi:
            snake_case_				:  Tuple       =			tax_mlp_wi_a
            snake_case_				:  List[Any]       =			tax_mlp_wi_a
        else:
            snake_case_				:  List[Any]       =			tax_mlp_wi

        snake_case_				:  Dict       =			tax_mlp_wo

        snake_case_				:  List[Any]       =			txa_mlp_layer_norm

        snake_case_				:  Optional[int]       =			flax_model_decoder_layer_block

    # Decoder Normalization
    snake_case_				:  str       =			tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
    snake_case_				:  Tuple       =			txa_decoder_norm

    # Only for layer 0:
    snake_case_				:  str       =			tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
    snake_case_				:  Optional[Any]       =			tax_decoder_rel_embedding

    # Token Embeddings
    snake_case_				:  Union[str, Any]       =			tax_model['''target''']['''token_embedder''']['''embedding''']
    snake_case_				:  Optional[int]       =			txa_token_embeddings

    # LM Head (only in v1.1 and LongT5 checkpoints)
    if "logits_dense" in tax_model["target"]["decoder"]:
        snake_case_				:  Union[str, Any]       =			tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

    flax_model.save_pretrained(_a					)
    print('''T5X Model was sucessfully converted!'''					)


if __name__ == "__main__":
     lowercase__					:						Tuple												=			argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path the T5X checkpoint.'''
     )
     parser.add_argument('''--config_name''', default=None, type=str, required=True, help='''Config name of LongT5/T5 model.''')
     parser.add_argument(
         '''--flax_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output FLAX model.'''
     )
     lowercase__					:						Dict												=			parser.parse_args()
     convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)





