






'''simple docstring'''


import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig







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





						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__":
					lowerCamelCase_					:			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.'''
					)
					lowerCamelCase_					:			Optional[int]     						=				parser.parse_args()
					convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)








'''simple docstring'''


from typing import TYPE_CHECKING

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


lowerCamelCase_					:			str     						=				{'''configuration_hubert''': ['''HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''HubertConfig''']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase_					:			Union[str, Any]     						=				[
					    '''HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''HubertForCTC''',
					    '''HubertForSequenceClassification''',
					    '''HubertModel''',
					    '''HubertPreTrainedModel''',
					]


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase_					:			Optional[Any]     						=				[
					    '''TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFHubertForCTC''',
					    '''TFHubertModel''',
					    '''TFHubertPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_hubert import (
										    HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    HubertForCTC,
										    HubertForSequenceClassification,
										    HubertModel,
										    HubertPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_hubert import (
										    TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFHubertForCTC,
										    TFHubertModel,
										    TFHubertPreTrainedModel,
										)


else:
					import sys

					lowerCamelCase_					:			Dict     						=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
