





import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase )			->     Union[str, Any]:
    lowerCamelCase__    :      List[str]					 =		AutoConfig.from_pretrained(_UpperCAmelCase )
    lowerCamelCase__    :      Any					 =		FlaxAutoModelForSeqaSeqLM.from_config(config=_UpperCAmelCase )
    lowerCamelCase__    :      Optional[int]					 =		checkpoints.load_tax_checkpoint(_UpperCAmelCase )

    lowerCamelCase__    :      Dict					 =		'wi_0' in tax_model['target']['encoder']['layers_0']['mlp']

    if config.model_type == "t5":
        lowerCamelCase__    :      Optional[Any]					 =		'SelfAttention'
    if config.model_type == "longt5" and config.encoder_attention_type == "local":
        lowerCamelCase__    :      Union[str, Any]					 =		'LocalSelfAttention'
    elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
        lowerCamelCase__    :      Tuple					 =		'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 ):
        lowerCamelCase__    :      Union[str, Any]					 =		F"""layers_{str(_UpperCAmelCase )}"""

        # Self-Attention
        lowerCamelCase__    :      Any					 =		tax_model['target']['encoder'][layer_name]['attention']['key']['kernel']
        lowerCamelCase__    :      List[str]					 =		tax_model['target']['encoder'][layer_name]['attention']['out']['kernel']
        lowerCamelCase__    :      Union[str, Any]					 =		tax_model['target']['encoder'][layer_name]['attention']['query']['kernel']
        lowerCamelCase__    :      Tuple					 =		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":
            lowerCamelCase__    :      Optional[Any]					 =		tax_model['target']['encoder'][layer_name]['attention']['T5LayerNorm_0']['scale']

        # Layer Normalization
        lowerCamelCase__    :      int					 =		tax_model['target']['encoder'][layer_name]['pre_attention_layer_norm']['scale']

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

        lowerCamelCase__    :      int					 =		tax_model['target']['encoder'][layer_name]['mlp']['wo']['kernel']

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

        # Assigning
        lowerCamelCase__    :      Optional[int]					 =		flax_model.params['encoder']['block'][str(_UpperCAmelCase )]['layer']
        lowerCamelCase__    :      int					 =		tax_attention_key
        lowerCamelCase__    :      str					 =		tax_attention_out
        lowerCamelCase__    :      Optional[Any]					 =		tax_attention_query
        lowerCamelCase__    :      Dict					 =		tax_attention_value

        lowerCamelCase__    :      List[str]					 =		tax_attention_layer_norm

        # Global input layer norm
        if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            lowerCamelCase__    :      List[Any]					 =		tax_global_layer_norm

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

        lowerCamelCase__    :      List[str]					 =		tax_mlp_wo
        lowerCamelCase__    :      Optional[int]					 =		tax_mlp_layer_norm

        lowerCamelCase__    :      Any					 =		flax_model_encoder_layer_block

    # Only for layer 0:
    lowerCamelCase__    :      List[Any]					 =		tax_model['target']['encoder']['relpos_bias']['rel_embedding'].T
    lowerCamelCase__    :      List[str]					 =		tax_encoder_rel_embedding

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

    # Assigning
    lowerCamelCase__    :      Union[str, Any]					 =		tax_model['target']['encoder']['encoder_norm']['scale']
    lowerCamelCase__    :      str					 =		tax_encoder_norm

    # Decoder
    for layer_index in range(config.num_layers ):
        lowerCamelCase__    :      str					 =		F"""layers_{str(_UpperCAmelCase )}"""

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

        # Layer Normalization
        lowerCamelCase__    :      Union[str, Any]					 =		tax_model['target']['decoder'][layer_name]['pre_self_attention_layer_norm'][
            'scale'
        ]

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

        # Layer Normalization
        lowerCamelCase__    :      Dict					 =		tax_model['target']['decoder'][layer_name]['pre_cross_attention_layer_norm']['scale']

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

        lowerCamelCase__    :      Union[str, Any]					 =		tax_model['target']['decoder'][layer_name]['mlp']['wo']['kernel']

        # Layer Normalization
        lowerCamelCase__    :      List[str]					 =		tax_model['target']['decoder'][layer_name]['pre_mlp_layer_norm']['scale']

        # Assigning
        lowerCamelCase__    :      Tuple					 =		flax_model.params['decoder']['block'][str(_UpperCAmelCase )]['layer']
        lowerCamelCase__    :      Optional[Any]					 =		tax_attention_key
        lowerCamelCase__    :      str					 =		tax_attention_out
        lowerCamelCase__    :      int					 =		tax_attention_query
        lowerCamelCase__    :      Optional[Any]					 =		tax_attention_value

        lowerCamelCase__    :      Optional[Any]					 =		tax_pre_attention_layer_norm

        lowerCamelCase__    :      str					 =		tax_enc_dec_attention_key
        lowerCamelCase__    :      Dict					 =		tax_enc_dec_attention_out
        lowerCamelCase__    :      Optional[Any]					 =		tax_enc_dec_attention_query
        lowerCamelCase__    :      Optional[int]					 =		tax_enc_dec_attention_value

        lowerCamelCase__    :      Tuple					 =		tax_cross_layer_norm

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

        lowerCamelCase__    :      Optional[int]					 =		tax_mlp_wo

        lowerCamelCase__    :      Dict					 =		txa_mlp_layer_norm

        lowerCamelCase__    :      List[Any]					 =		flax_model_decoder_layer_block

    # Decoder Normalization
    lowerCamelCase__    :      str					 =		tax_model['target']['decoder']['decoder_norm']['scale']
    lowerCamelCase__    :      List[Any]					 =		txa_decoder_norm

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

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

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

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


if __name__ == "__main__":
    _UpperCAmelCase				:       int	   =				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."""
    )
    _UpperCAmelCase				:       Tuple	   =				parser.parse_args()
    convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available


__A        =    {
    "configuration_audio_spectrogram_transformer": [
        "AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ASTConfig",
    ]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__A        =    [
					    "AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "ASTForAudioClassification",
					    "ASTModel",
					    "ASTPreTrainedModel",
					]

try:
					if not is_speech_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__A        =    ["ASTFeatureExtractor"]

if TYPE_CHECKING:
					from .configuration_audio_spectrogram_transformer import (
					    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    ASTConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_audio_spectrogram_transformer import (
										    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ASTForAudioClassification,
										    ASTModel,
										    ASTPreTrainedModel,
										)

					try:
										if not is_speech_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
					import sys

					__A        =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


