



'''simple docstring'''







import numpy as np  # noqa: E402

from ...configuration_utils import ConfigMixin, register_to_config
from ...schedulers.scheduling_utils import SchedulerMixin


try:
      import librosa  # noqa: E402

      lowercase__     :  Optional[Any]    					=			True
      lowercase__     :  int    					=			''
except Exception as e:
      lowercase__     :  Union[str, Any]    					=			False
      lowercase__     :  List[Any]    					=			(
          F'''Cannot import librosa because {e}. Make sure to correctly install librosa to be able to install it.'''
      )


from PIL import Image  # noqa: E402

class       lowerCamelCase_							(	lowerCAmelCase_					,    lowerCAmelCase_  ):






   '''simple docstring'''

   A :     int				=	"mel_config.json"


   @register_to_config
   def __init__(  self     ,						__UpperCamelCase = 2_56     ,						__UpperCamelCase = 2_56     ,						__UpperCamelCase = 2_20_50     ,						__UpperCamelCase = 20_48     ,						__UpperCamelCase = 5_12     ,						__UpperCamelCase = 80     ,						__UpperCamelCase = 32     ,						):
       UpperCamelCase__      =			hop_length
       UpperCamelCase__      =			sample_rate
       UpperCamelCase__      =			n_fft
       UpperCamelCase__      =			top_db
       UpperCamelCase__      =			n_iter
       self.set_resolution(__UpperCamelCase     ,						__UpperCamelCase )
       UpperCamelCase__      =			None

       if not _librosa_can_be_imported:
           raise ValueError(_import_error )


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase     ,						__UpperCamelCase ):
       UpperCamelCase__      =			x_res
       UpperCamelCase__      =			y_res
       UpperCamelCase__      =			self.y_res
       UpperCamelCase__      =			self.x_res * self.hop_length - 1


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase = None     ,						__UpperCamelCase = None ):
       if audio_file is not None:
           UpperCamelCase__    ,  UpperCamelCase__      =			librosa.load(__UpperCamelCase     ,						mono=__UpperCamelCase     ,						sr=self.sr )
       else:
           UpperCamelCase__      =			raw_audio

       # Pad with silence if necessary.
       if len(self.audio ) < self.x_res * self.hop_length:
           UpperCamelCase__      =			np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio ),) )] )


   def 	_UpperCamelCase       (  self ):
       return len(self.audio ) // self.slice_size


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase = 0 ):
       return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]


   def 	_UpperCamelCase       (  self ):
       return self.sr


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase ):
       UpperCamelCase__      =			librosa.feature.melspectrogram(
           y=self.get_audio_slice(__UpperCamelCase )     ,						sr=self.sr     ,						n_fft=self.n_fft     ,						hop_length=self.hop_length     ,						n_mels=self.n_mels )
       UpperCamelCase__      =			librosa.power_to_db(__UpperCamelCase     ,						ref=np.max     ,						top_db=self.top_db )
       UpperCamelCase__      =			(((log_S + self.top_db) * 2_55 / self.top_db).clip(0     ,						2_55 ) + 0.5).astype(np.uinta )
       UpperCamelCase__      =			Image.fromarray(__UpperCamelCase )
       return image







   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase ):
       UpperCamelCase__      =			np.frombuffer(image.tobytes()     ,						dtype="uint8" ).reshape((image.height, image.width) )
       UpperCamelCase__      =			bytedata.astype("float" ) * self.top_db / 2_55 - self.top_db
       UpperCamelCase__      =			librosa.db_to_power(__UpperCamelCase )
       UpperCamelCase__      =			librosa.feature.inverse.mel_to_audio(
           __UpperCamelCase     ,						sr=self.sr     ,						n_fft=self.n_fft     ,						hop_length=self.hop_length     ,						n_iter=self.n_iter )
       return audio









'''simple docstring'''







import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig





def 						snake_case__ (lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_	):
    UpperCamelCase__      =			TaConfig.from_pretrained(lowerCAmelCase_	)
    UpperCamelCase__      =			FlaxTaForConditionalGeneration(config=lowerCAmelCase_	)
    UpperCamelCase__      =			checkpoints.load_tax_checkpoint(lowerCAmelCase_	)

    UpperCamelCase__      =			"wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

    # Encoder
    for layer_index in range(config.num_layers	):
        UpperCamelCase__      =			F'layers_{str(lowerCAmelCase_	)}'

        # Self-Attention
        UpperCamelCase__      =			tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
        UpperCamelCase__      =			tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
        UpperCamelCase__      =			tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
        UpperCamelCase__      =			tax_model["target"]["encoder"][layer_name]["attention"]["value"]["kernel"]

        # Layer Normalization
        UpperCamelCase__      =			tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

        if split_mlp_wi:
            UpperCamelCase__      =			tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
            UpperCamelCase__      =			tax_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
        else:
            UpperCamelCase__      =			tax_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]

        UpperCamelCase__      =			tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

        # Layer Normalization
        UpperCamelCase__      =			tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

        # Assigning
        UpperCamelCase__      =			tax_attention_key
        UpperCamelCase__      =			tax_attention_out
        UpperCamelCase__      =			tax_attention_query
        UpperCamelCase__      =			tax_attention_value

        UpperCamelCase__      =			tax_attention_layer_norm

        if split_mlp_wi:
            UpperCamelCase__      =			tax_mlp_wi_a
            UpperCamelCase__      =			tax_mlp_wi_a
        else:
            UpperCamelCase__      =			tax_mlp_wi

        UpperCamelCase__      =			tax_mlp_wo
        UpperCamelCase__      =			tax_mlp_layer_norm

    # Only for layer 0:
    UpperCamelCase__      =			tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
    UpperCamelCase__      =			tax_encoder_rel_embedding

    # Assigning
    UpperCamelCase__      =			tax_model["target"]["encoder"]["encoder_norm"]["scale"]
    UpperCamelCase__      =			tax_encoder_norm

    # Decoder
    for layer_index in range(config.num_decoder_layers	):
        UpperCamelCase__      =			F'layers_{str(lowerCAmelCase_	)}'

        # Self-Attention
        UpperCamelCase__      =			tax_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
        UpperCamelCase__      =			tax_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
        UpperCamelCase__      =			tax_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"]
        UpperCamelCase__      =			tax_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"]

        # Layer Normalization
        UpperCamelCase__      =			tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
            "scale"
        ]

        # Encoder-Decoder-Attention
        UpperCamelCase__      =			tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["key"][
            "kernel"
        ]
        UpperCamelCase__      =			tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["out"][
            "kernel"
        ]
        UpperCamelCase__      =			tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["query"][
            "kernel"
        ]
        UpperCamelCase__      =			tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["value"][
            "kernel"
        ]

        # Layer Normalization
        UpperCamelCase__      =			tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

        # MLP
        if split_mlp_wi:
            UpperCamelCase__      =			tax_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"]
            UpperCamelCase__      =			tax_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"]
        else:
            UpperCamelCase__      =			tax_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"]

        UpperCamelCase__      =			tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

        # Layer Normalization
        UpperCamelCase__      =			tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

        # Assigning
        UpperCamelCase__      =			tax_attention_key
        UpperCamelCase__      =			tax_attention_out
        UpperCamelCase__      =			tax_attention_query
        UpperCamelCase__      =			tax_attention_value

        UpperCamelCase__      =			tax_pre_attention_layer_norm

        UpperCamelCase__      =			tax_enc_dec_attention_key
        UpperCamelCase__      =			tax_enc_dec_attention_out
        UpperCamelCase__      =			tax_enc_dec_attention_query
        UpperCamelCase__      =			tax_enc_dec_attention_value

        UpperCamelCase__      =			tax_cross_layer_norm

        if split_mlp_wi:
            UpperCamelCase__      =			tax_mlp_wi_a
            UpperCamelCase__      =			tax_mlp_wi_a
        else:
            UpperCamelCase__      =			tax_mlp_wi

        UpperCamelCase__      =			tax_mlp_wo

        UpperCamelCase__      =			txa_mlp_layer_norm

    # Decoder Normalization
    UpperCamelCase__      =			tax_model["target"]["decoder"]["decoder_norm"]["scale"]
    UpperCamelCase__      =			txa_decoder_norm

    # Only for layer 0:
    UpperCamelCase__      =			tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
    UpperCamelCase__      =			tax_decoder_rel_embedding

    # Token Embeddings
    UpperCamelCase__      =			tax_model["target"]["token_embedder"]["embedding"]
    UpperCamelCase__      =			txa_token_embeddings

    # LM Head (only in v1.1 checkpoints)
    if "logits_dense" in tax_model["target"]["decoder"]:
        UpperCamelCase__      =			tax_model["target"]["decoder"]["logits_dense"]["kernel"]

    flax_model.save_pretrained(lowerCAmelCase_	)
    print("T5X Model was sucessfully converted!"	)


if __name__ == "__main__":
      lowercase__     :  Any    					=			argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path the TX5 checkpoint.'
      )
      parser.add_argument('--config_name', default=None, type=str, required=True, help='Config name of T5 model.')
      parser.add_argument(
          '--flax_dump_folder_path', default=None, type=str, required=True, help='Path to the output FLAX model.'
      )
      lowercase__     :  int    					=			parser.parse_args()
      convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)




