





import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig




def  A_(      A					,   A					,   A ):
	UpperCAmelCase_		    =  TaConfig.from_pretrained(A )
	UpperCAmelCase_		    =  FlaxTaForConditionalGeneration(config=A )
	UpperCAmelCase_		    =  checkpoints.load_tax_checkpoint(A )

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

	# Encoder
	for layer_index in range(config.num_layers ):
		UpperCAmelCase_		    =  f"""layers_{str(A )}"""

		# 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(A )}"""

		# 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(A )
	print("""T5X Model was sucessfully converted!""" )


if __name__ == "__main__":
				UpperCamelCase__ :			Optional[int]       	=			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."""
				)
				UpperCamelCase__ :			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_tokenizers_available, is_torch_available


UpperCamelCase__ :			Any       	=			{
    """configuration_splinter""": ["""SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SplinterConfig"""],
    """tokenization_splinter""": ["""SplinterTokenizer"""],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase__ :			Any       	=			["""SplinterTokenizerFast"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase__ :			Union[str, Any]       	=			[
				    """SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """SplinterForQuestionAnswering""",
				    """SplinterForPreTraining""",
				    """SplinterLayer""",
				    """SplinterModel""",
				    """SplinterPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
				from .tokenization_splinter import SplinterTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_splinter_fast import SplinterTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_splinter import (
								    SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SplinterForPreTraining,
								    SplinterForQuestionAnswering,
								    SplinterLayer,
								    SplinterModel,
								    SplinterPreTrainedModel,
								)


else:
				import sys

				UpperCamelCase__ :			List[str]       	=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


