


import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig







def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


	lowerCamelCase_			:							str      =       TaConfig.from_pretrained(lowerCAmelCase_)
	lowerCamelCase_			:							Optional[Any]      =       FlaxTaForConditionalGeneration(config=lowerCAmelCase_)
	lowerCamelCase_			:							Tuple      =       checkpoints.load_tax_checkpoint(lowerCAmelCase_)

	lowerCamelCase_			:							List[Any]      =       "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(lowerCAmelCase_)}"""

		# Self-Attention
		lowerCamelCase_			:							Tuple      =       tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
		lowerCamelCase_			:							Dict      =       tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
		lowerCamelCase_			:							Optional[Any]      =       tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
		lowerCamelCase_			:							Tuple      =       tax_model["target"]["encoder"][layer_name]["attention"]["value"]["kernel"]

		# Layer Normalization
		lowerCamelCase_			:							Optional[int]      =       tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

		if split_mlp_wi:
			lowerCamelCase_			:							str      =       tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
			lowerCamelCase_			:							Tuple      =       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_			:							Optional[Any]      =       tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

		# Assigning
		lowerCamelCase_			:							str      =       tax_attention_key
		lowerCamelCase_			:							Tuple      =       tax_attention_out
		lowerCamelCase_			:							str      =       tax_attention_query
		lowerCamelCase_			:							List[Any]      =       tax_attention_value

		lowerCamelCase_			:							List[str]      =       tax_attention_layer_norm

		if split_mlp_wi:
			lowerCamelCase_			:							List[str]      =       tax_mlp_wi_a
			lowerCamelCase_			:							Tuple      =       tax_mlp_wi_a
		else:
			lowerCamelCase_			:							int      =       tax_mlp_wi

		lowerCamelCase_			:							List[str]      =       tax_mlp_wo
		lowerCamelCase_			:							List[Any]      =       tax_mlp_layer_norm

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

	# Assigning
	lowerCamelCase_			:							Optional[int]      =       tax_model["target"]["encoder"]["encoder_norm"]["scale"]
	lowerCamelCase_			:							Union[str, Any]      =       tax_encoder_norm

	# Decoder
	for layer_index in range(config.num_decoder_layers):
		lowerCamelCase_			:							str      =       F"""layers_{str(lowerCAmelCase_)}"""

		# Self-Attention
		lowerCamelCase_			:							Optional[int]      =       tax_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
		lowerCamelCase_			:							Any      =       tax_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
		lowerCamelCase_			:							List[Any]      =       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_			:							Any      =       tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
		    "scale"
		]

		# Encoder-Decoder-Attention
		lowerCamelCase_			:							str      =       tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["key"][
		    "kernel"
		]
		lowerCamelCase_			:							Union[str, Any]      =       tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["out"][
		    "kernel"
		]
		lowerCamelCase_			:							str      =       tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["query"][
		    "kernel"
		]
		lowerCamelCase_			:							Optional[int]      =       tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["value"][
		    "kernel"
		]

		# Layer Normalization
		lowerCamelCase_			:							str      =       tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

		# MLP
		if split_mlp_wi:
			lowerCamelCase_			:							Any      =       tax_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"]
			lowerCamelCase_			:							List[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_			:							Optional[Any]      =       tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

		# Layer Normalization
		lowerCamelCase_			:							Dict      =       tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

		# Assigning
		lowerCamelCase_			:							List[Any]      =       tax_attention_key
		lowerCamelCase_			:							List[str]      =       tax_attention_out
		lowerCamelCase_			:							List[Any]      =       tax_attention_query
		lowerCamelCase_			:							Optional[int]      =       tax_attention_value

		lowerCamelCase_			:							Dict      =       tax_pre_attention_layer_norm

		lowerCamelCase_			:							Dict      =       tax_enc_dec_attention_key
		lowerCamelCase_			:							Any      =       tax_enc_dec_attention_out
		lowerCamelCase_			:							Optional[Any]      =       tax_enc_dec_attention_query
		lowerCamelCase_			:							Any      =       tax_enc_dec_attention_value

		lowerCamelCase_			:							List[str]      =       tax_cross_layer_norm

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

		lowerCamelCase_			:							Union[str, Any]      =       tax_mlp_wo

		lowerCamelCase_			:							Optional[Any]      =       txa_mlp_layer_norm

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

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

	# Token Embeddings
	lowerCamelCase_			:							Optional[Any]      =       tax_model["target"]["token_embedder"]["embedding"]
	lowerCamelCase_			:							List[Any]      =       txa_token_embeddings

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

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


if __name__ == "__main__":
							__magic_name__       					=      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.'''
							)
							__magic_name__       					=      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_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__magic_name__       					=      {
    '''configuration_big_bird''': ['''BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BigBirdConfig''', '''BigBirdOnnxConfig'''],
}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__magic_name__       					=      ['''BigBirdTokenizer''']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__magic_name__       					=      ['''BigBirdTokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__magic_name__       					=      [
							    '''BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''BigBirdForCausalLM''',
							    '''BigBirdForMaskedLM''',
							    '''BigBirdForMultipleChoice''',
							    '''BigBirdForPreTraining''',
							    '''BigBirdForQuestionAnswering''',
							    '''BigBirdForSequenceClassification''',
							    '''BigBirdForTokenClassification''',
							    '''BigBirdLayer''',
							    '''BigBirdModel''',
							    '''BigBirdPreTrainedModel''',
							    '''load_tf_weights_in_big_bird''',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__magic_name__       					=      [
							    '''FlaxBigBirdForCausalLM''',
							    '''FlaxBigBirdForMaskedLM''',
							    '''FlaxBigBirdForMultipleChoice''',
							    '''FlaxBigBirdForPreTraining''',
							    '''FlaxBigBirdForQuestionAnswering''',
							    '''FlaxBigBirdForSequenceClassification''',
							    '''FlaxBigBirdForTokenClassification''',
							    '''FlaxBigBirdModel''',
							    '''FlaxBigBirdPreTrainedModel''',
							]

if TYPE_CHECKING:
							from .configuration_big_bird import BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP, BigBirdConfig, BigBirdOnnxConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_big_bird import BigBirdTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_big_bird_fast import BigBirdTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_big_bird import (
														    BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BigBirdForCausalLM,
														    BigBirdForMaskedLM,
														    BigBirdForMultipleChoice,
														    BigBirdForPreTraining,
														    BigBirdForQuestionAnswering,
														    BigBirdForSequenceClassification,
														    BigBirdForTokenClassification,
														    BigBirdLayer,
														    BigBirdModel,
														    BigBirdPreTrainedModel,
														    load_tf_weights_in_big_bird,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_big_bird import (
														    FlaxBigBirdForCausalLM,
														    FlaxBigBirdForMaskedLM,
														    FlaxBigBirdForMultipleChoice,
														    FlaxBigBirdForPreTraining,
														    FlaxBigBirdForQuestionAnswering,
														    FlaxBigBirdForSequenceClassification,
														    FlaxBigBirdForTokenClassification,
														    FlaxBigBirdModel,
														    FlaxBigBirdPreTrainedModel,
														)

else:
							import sys

							__magic_name__       					=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

