
import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig



def      _a     (   __UpperCamelCase       :  int	,__UpperCamelCase       :  Optional[Any]	,__UpperCamelCase       :  List[Any]    ):
			lowerCAmelCase__ :    Tuple				      =			TaConfig.from_pretrained(__UpperCamelCase    )
			lowerCAmelCase__ :    Dict				      =			FlaxTaForConditionalGeneration(config=__UpperCamelCase    )
			lowerCAmelCase__ :    List[str]				      =			checkpoints.load_tax_checkpoint(__UpperCamelCase    )

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

			# Encoder
			for layer_index in range(config.num_layers    ):
						lowerCAmelCase__ :    List[str]				      =			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__ :    List[str]				      =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
						lowerCAmelCase__ :    Optional[int]				      =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

						# Layer Normalization
						lowerCAmelCase__ :    List[Any]				      =			tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

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

						lowerCAmelCase__ :    Any				      =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

						# Layer Normalization
						lowerCAmelCase__ :    str				      =			tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

						# Assigning
						lowerCAmelCase__ :    List[str]				      =			tax_attention_key
						lowerCAmelCase__ :    List[str]				      =			tax_attention_out
						lowerCAmelCase__ :    int				      =			tax_attention_query
						lowerCAmelCase__ :    Dict				      =			tax_attention_value

						lowerCAmelCase__ :    Any				      =			tax_attention_layer_norm

						if split_mlp_wi:
									lowerCAmelCase__ :    Optional[Any]				      =			tax_mlp_wi_a
									lowerCAmelCase__ :    Tuple				      =			tax_mlp_wi_a
						else:
									lowerCAmelCase__ :    Tuple				      =			tax_mlp_wi

						lowerCAmelCase__ :    Optional[Any]				      =			tax_mlp_wo
						lowerCAmelCase__ :    Optional[int]				      =			tax_mlp_layer_norm

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

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

			# Decoder
			for layer_index in range(config.num_decoder_layers    ):
						lowerCAmelCase__ :    List[str]				      =			f'''layers_{str(__UpperCamelCase    )}'''

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

						# Layer Normalization
						lowerCAmelCase__ :    Optional[int]				      =			tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
						    '''scale'''
						]

						# Encoder-Decoder-Attention
						lowerCAmelCase__ :    Tuple				      =			tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''key'''][
						    '''kernel'''
						]
						lowerCAmelCase__ :    str				      =			tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''out'''][
						    '''kernel'''
						]
						lowerCAmelCase__ :    int				      =			tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''query'''][
						    '''kernel'''
						]
						lowerCAmelCase__ :    Tuple				      =			tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''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__ :    Any				      =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
						else:
									lowerCAmelCase__ :    List[str]				      =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

						lowerCAmelCase__ :    Dict				      =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

						# Layer Normalization
						lowerCAmelCase__ :    Optional[Any]				      =			tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

						# Assigning
						lowerCAmelCase__ :    str				      =			tax_attention_key
						lowerCAmelCase__ :    List[str]				      =			tax_attention_out
						lowerCAmelCase__ :    Union[str, Any]				      =			tax_attention_query
						lowerCAmelCase__ :    Optional[int]				      =			tax_attention_value

						lowerCAmelCase__ :    Optional[int]				      =			tax_pre_attention_layer_norm

						lowerCAmelCase__ :    Dict				      =			tax_enc_dec_attention_key
						lowerCAmelCase__ :    Any				      =			tax_enc_dec_attention_out
						lowerCAmelCase__ :    str				      =			tax_enc_dec_attention_query
						lowerCAmelCase__ :    List[str]				      =			tax_enc_dec_attention_value

						lowerCAmelCase__ :    Optional[Any]				      =			tax_cross_layer_norm

						if split_mlp_wi:
									lowerCAmelCase__ :    Optional[int]				      =			tax_mlp_wi_a
									lowerCAmelCase__ :    str				      =			tax_mlp_wi_a
						else:
									lowerCAmelCase__ :    Optional[Any]				      =			tax_mlp_wi

						lowerCAmelCase__ :    Dict				      =			tax_mlp_wo

						lowerCAmelCase__ :    Optional[int]				      =			txa_mlp_layer_norm

			# Decoder Normalization
			lowerCAmelCase__ :    int				      =			tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
			lowerCAmelCase__ :    Union[str, Any]				      =			txa_decoder_norm

			# Only for layer 0:
			lowerCAmelCase__ :    int				      =			tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
			lowerCAmelCase__ :    Optional[int]				      =			tax_decoder_rel_embedding

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

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

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


if __name__ == "__main__":
							A__       :   str   						=					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__       :   str   						=					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_tf_available, is_torch_available


A__       :   Union[str, Any]   						=					{"""configuration_cvt""": ["""CVT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CvtConfig"""]}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__       :   Union[str, Any]   						=					[
							    """CVT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """CvtForImageClassification""",
							    """CvtModel""",
							    """CvtPreTrainedModel""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__       :   str   						=					[
							    """TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFCvtForImageClassification""",
							    """TFCvtModel""",
							    """TFCvtPreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_cvt import CVT_PRETRAINED_CONFIG_ARCHIVE_MAP, CvtConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_cvt import (
														    CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    CvtForImageClassification,
														    CvtModel,
														    CvtPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_cvt import (
														    TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFCvtForImageClassification,
														    TFCvtModel,
														    TFCvtPreTrainedModel,
														)


else:
							import sys

							A__       :   Optional[Any]   						=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



