

'''simple docstring'''






import copy
from typing import Dict

from ...generation.configuration_utils import GenerationConfig
from ...utils import logging


snake_case_       =       logging.get_logger(__name__)
class 	SCREAMING_SNAKE_CASE__			(				__A      ):
    _A			    =							"""semantic"""




    def __init__(					self				,     lowercase__=1_0000				,     lowercase__=True				,     lowercase__=768				,     lowercase__=False				,     lowercase__=False				,     lowercase__=False				,     lowercase__=False				,     lowercase__=0.7				,     lowercase__=True				,     lowercase__=1_0048				,     lowercase__=12_9595				,     lowercase__=12_9599				,     lowercase__=1_0000				,     lowercase__=256				,     lowercase__=49.9				,     **lowercase__				,     ):






          """simple docstring"""






          super().__init__(
              temperature=lowerCamelCase__				,     do_sample=lowerCamelCase__				,     eos_token_id=lowerCamelCase__				,     renormalize_logits=lowerCamelCase__				,     max_new_tokens=lowerCamelCase__				,     output_scores=lowerCamelCase__				,     return_dict_in_generate=lowerCamelCase__				,     output_hidden_states=lowerCamelCase__				,     output_attentions=lowerCamelCase__				,     **lowerCamelCase__				,     )

          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       text_encoding_offset
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       text_pad_token
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       eos_token_id
          SCREAMING_SNAKE_CASE_      :			Tuple     =       semantic_infer_token
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       semantic_vocab_size
          SCREAMING_SNAKE_CASE_      :			List[str]     =       max_input_semantic_length
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       semantic_rate_hz






class 	SCREAMING_SNAKE_CASE__			(				__A      ):
    _A			    =							"""coarse_acoustics"""




    def __init__(					self				,     lowercase__=True				,     lowercase__=False				,     lowercase__=False				,     lowercase__=False				,     lowercase__=False				,     lowercase__=0.7				,     lowercase__=True				,     lowercase__=1_2048				,     lowercase__=75				,     lowercase__=2				,     lowercase__=1_2050				,     lowercase__=256				,     lowercase__ = 630				,     lowercase__ = 60				,     **lowercase__				,     ):






          """simple docstring"""






          super().__init__(
              temperature=lowerCamelCase__				,     do_sample=lowerCamelCase__				,     renormalize_logits=lowerCamelCase__				,     output_scores=lowerCamelCase__				,     return_dict_in_generate=lowerCamelCase__				,     output_hidden_states=lowerCamelCase__				,     output_attentions=lowerCamelCase__				,     **lowerCamelCase__				,     )

          SCREAMING_SNAKE_CASE_      :			Dict     =       coarse_semantic_pad_token
          SCREAMING_SNAKE_CASE_      :			Tuple     =       coarse_rate_hz
          SCREAMING_SNAKE_CASE_      :			str     =       n_coarse_codebooks
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       coarse_infer_token
          SCREAMING_SNAKE_CASE_      :			List[str]     =       max_coarse_input_length
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       max_coarse_history
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       sliding_window_len






class 	SCREAMING_SNAKE_CASE__			(				__A      ):
    _A			    =							"""fine_acoustics"""




    def __init__(					self				,     lowercase__=0.5				,     lowercase__=512				,     lowercase__=1024				,     lowercase__=8				,     **lowercase__				,     ):






          """simple docstring"""






          super().__init__(temperature=lowerCamelCase__			)

          SCREAMING_SNAKE_CASE_      :			List[str]     =       max_fine_history_length
          SCREAMING_SNAKE_CASE_      :			str     =       max_fine_input_length
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       n_fine_codebooks






class 	SCREAMING_SNAKE_CASE__			(				__A      ):
    _A			    =							"""bark"""
    _A			    =							True




    def __init__(					self				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__=2_4000				,     lowercase__=1024				,     **lowercase__				,     ):






          """simple docstring"""






          if semantic_config is None:
                SCREAMING_SNAKE_CASE_      :			Dict     =       {}
                logger.info("semantic_config is None. initializing the semantic model with default values."			)

          if coarse_acoustics_config is None:
                SCREAMING_SNAKE_CASE_      :			int     =       {}
                logger.info("coarse_acoustics_config is None. initializing the coarse model with default values."			)

          if fine_acoustics_config is None:
                SCREAMING_SNAKE_CASE_      :			Dict     =       {}
                logger.info("fine_acoustics_config is None. initializing the fine model with default values."			)

          SCREAMING_SNAKE_CASE_      :			Dict     =       BarkSemanticGenerationConfig(**lowerCamelCase__			)
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       BarkCoarseGenerationConfig(**lowerCamelCase__			)
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       BarkFineGenerationConfig(**lowerCamelCase__			)

          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       sample_rate
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       codebook_size




    @classmethod
    def   __lowerCamelCase      (					cls				,     lowercase__				,     lowercase__				,     lowercase__				,     **lowercase__				,     ):






          """simple docstring"""






          return cls(
              semantic_config=semantic_config.to_dict()				,     coarse_acoustics_config=coarse_acoustics_config.to_dict()				,     fine_acoustics_config=fine_acoustics_config.to_dict()				,     **lowerCamelCase__				,     )







    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Any     =       copy.deepcopy(self.__dict__			)

          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       self.semantic_config.to_dict()
          SCREAMING_SNAKE_CASE_      :			Any     =       self.coarse_acoustics_config.to_dict()
          SCREAMING_SNAKE_CASE_      :			List[str]     =       self.fine_acoustics_config.to_dict()

          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       self.__class__.model_type
          return output







'''simple docstring'''
import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig
def 				snake_case_  (   a__		:	Optional[int]    ,a__		:	str    ,a__		:	int			):



    """simple docstring"""




    __lowercase							=						TaConfig.from_pretrained(a__			)
    __lowercase							=						FlaxTaForConditionalGeneration(config=a__			)
    __lowercase							=						checkpoints.load_tax_checkpoint(a__			)

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

    # Encoder
    for layer_index in range(config.num_layers			):
        __lowercase							=						f'layers_{str(a__			)}'

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

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

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

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

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

        # Assigning
        __lowercase							=						tax_attention_key
        __lowercase							=						tax_attention_out
        __lowercase							=						tax_attention_query
        __lowercase							=						tax_attention_value

        __lowercase							=						tax_attention_layer_norm

        if split_mlp_wi:
            __lowercase							=						tax_mlp_wi_a
            __lowercase							=						tax_mlp_wi_a
        else:
            __lowercase							=						tax_mlp_wi

        __lowercase							=						tax_mlp_wo
        __lowercase							=						tax_mlp_layer_norm

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

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

    # Decoder
    for layer_index in range(config.num_decoder_layers			):
        __lowercase							=						f'layers_{str(a__			)}'

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

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

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

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

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

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

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

        # Assigning
        __lowercase							=						tax_attention_key
        __lowercase							=						tax_attention_out
        __lowercase							=						tax_attention_query
        __lowercase							=						tax_attention_value

        __lowercase							=						tax_pre_attention_layer_norm

        __lowercase							=						tax_enc_dec_attention_key
        __lowercase							=						tax_enc_dec_attention_out
        __lowercase							=						tax_enc_dec_attention_query
        __lowercase							=						tax_enc_dec_attention_value

        __lowercase							=						tax_cross_layer_norm

        if split_mlp_wi:
            __lowercase							=						tax_mlp_wi_a
            __lowercase							=						tax_mlp_wi_a
        else:
            __lowercase							=						tax_mlp_wi

        __lowercase							=						tax_mlp_wo

        __lowercase							=						txa_mlp_layer_norm

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

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

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

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

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


if __name__ == "__main__":
  A							:      Optional[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."""
  )
  A							:      Dict 	=       parser.parse_args()
  convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)




