






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A       :					str     					=     {
    "configuration_bart": ["BART_PRETRAINED_CONFIG_ARCHIVE_MAP", "BartConfig", "BartOnnxConfig"],
    "tokenization_bart": ["BartTokenizer"],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A       :					Tuple     					=     ["BartTokenizerFast"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A       :					Optional[Any]     					=     [
				    "BART_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "BartForCausalLM",
				    "BartForConditionalGeneration",
				    "BartForQuestionAnswering",
				    "BartForSequenceClassification",
				    "BartModel",
				    "BartPretrainedModel",
				    "PretrainedBartModel",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A       :					Union[str, Any]     					=     [
				    "TFBartForConditionalGeneration",
				    "TFBartForSequenceClassification",
				    "TFBartModel",
				    "TFBartPretrainedModel",
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A       :					Union[str, Any]     					=     [
				    "FlaxBartDecoderPreTrainedModel",
				    "FlaxBartForCausalLM",
				    "FlaxBartForConditionalGeneration",
				    "FlaxBartForQuestionAnswering",
				    "FlaxBartForSequenceClassification",
				    "FlaxBartModel",
				    "FlaxBartPreTrainedModel",
				]

if TYPE_CHECKING:
				from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
				from .tokenization_bart import BartTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_bart_fast import BartTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_bart import (
								    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BartForCausalLM,
								    BartForConditionalGeneration,
								    BartForQuestionAnswering,
								    BartForSequenceClassification,
								    BartModel,
								    BartPretrainedModel,
								    PretrainedBartModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_bart import (
								    TFBartForConditionalGeneration,
								    TFBartForSequenceClassification,
								    TFBartModel,
								    TFBartPretrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_bart import (
								    FlaxBartDecoderPreTrainedModel,
								    FlaxBartForCausalLM,
								    FlaxBartForConditionalGeneration,
								    FlaxBartForQuestionAnswering,
								    FlaxBartForSequenceClassification,
								    FlaxBartModel,
								    FlaxBartPreTrainedModel,
								)

else:
				import sys

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










import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig





def 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__    ,    UpperCamelCase__    ,    UpperCamelCase__				)						->       Union[str, Any]:







			'''simple docstring'''

			UpperCAmelCase     	=       TaConfig.from_pretrained(UpperCamelCase__				)
			UpperCAmelCase     	=       FlaxTaForConditionalGeneration(config=UpperCamelCase__				)
			UpperCAmelCase     	=       checkpoints.load_tax_checkpoint(UpperCamelCase__				)

			UpperCAmelCase     	=       '''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

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

						# 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(UpperCamelCase__				)}"""

						# 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(UpperCamelCase__				)
			print('''T5X Model was sucessfully converted!'''				)


if __name__ == "__main__":
				__A       :					Union[str, 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       :					Any     					=     parser.parse_args()
				convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)



