





"""simple docstring"""




import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig





def 			a_							(				_lowercase			,		_lowercase			,		_lowercase  ):
						_UpperCamelCase     :   Optional[Any]      =       TaConfig.from_pretrained(_lowercase  )
						_UpperCamelCase     :   Dict      =       FlaxTaForConditionalGeneration(config=_lowercase  )
						_UpperCamelCase     :   List[str]      =       checkpoints.load_tax_checkpoint(_lowercase  )

						_UpperCamelCase     :   List[str]      =       '''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

						# Encoder
						for layer_index in range(config.num_layers  ):
												_UpperCamelCase     :   Dict      =       F"""layers_{str(_lowercase  )}"""

												# Self-Attention
												_UpperCamelCase     :   Tuple      =       tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
												_UpperCamelCase     :   Any      =       tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
												_UpperCamelCase     :   List[str]      =       tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
												_UpperCamelCase     :   Tuple      =       tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

												# Layer Normalization
												_UpperCamelCase     :   Optional[int]      =       tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

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

												_UpperCamelCase     :   Optional[int]      =       tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

												# Layer Normalization
												_UpperCamelCase     :   Optional[int]      =       tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

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

												_UpperCamelCase     :   Dict      =       tax_attention_layer_norm

												if split_mlp_wi:
																		_UpperCamelCase     :   str      =       tax_mlp_wi_a
																		_UpperCamelCase     :   Union[str, Any]      =       tax_mlp_wi_a
												else:
																		_UpperCamelCase     :   int      =       tax_mlp_wi

												_UpperCamelCase     :   str      =       tax_mlp_wo
												_UpperCamelCase     :   int      =       tax_mlp_layer_norm

						# Only for layer 0:
						_UpperCamelCase     :   Any      =       tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
						_UpperCamelCase     :   Optional[Any]      =       tax_encoder_rel_embedding

						# Assigning
						_UpperCamelCase     :   int      =       tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
						_UpperCamelCase     :   Tuple      =       tax_encoder_norm

						# Decoder
						for layer_index in range(config.num_decoder_layers  ):
												_UpperCamelCase     :   int      =       F"""layers_{str(_lowercase  )}"""

												# Self-Attention
												_UpperCamelCase     :   Optional[int]      =       tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
												_UpperCamelCase     :   List[Any]      =       tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
												_UpperCamelCase     :   Optional[Any]      =       tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
												_UpperCamelCase     :   List[Any]      =       tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

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

												# Encoder-Decoder-Attention
												_UpperCamelCase     :   Optional[int]      =       tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''key'''][
												    '''kernel'''
												]
												_UpperCamelCase     :   List[Any]      =       tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''out'''][
												    '''kernel'''
												]
												_UpperCamelCase     :   List[Any]      =       tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''query'''][
												    '''kernel'''
												]
												_UpperCamelCase     :   Tuple      =       tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''value'''][
												    '''kernel'''
												]

												# Layer Normalization
												_UpperCamelCase     :   int      =       tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

												# MLP
												if split_mlp_wi:
																		_UpperCamelCase     :   str      =       tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
																		_UpperCamelCase     :   Union[str, Any]      =       tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
												else:
																		_UpperCamelCase     :   Optional[int]      =       tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

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

												# Layer Normalization
												_UpperCamelCase     :   Dict      =       tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

												# Assigning
												_UpperCamelCase     :   Any      =       tax_attention_key
												_UpperCamelCase     :   str      =       tax_attention_out
												_UpperCamelCase     :   int      =       tax_attention_query
												_UpperCamelCase     :   Tuple      =       tax_attention_value

												_UpperCamelCase     :   Optional[int]      =       tax_pre_attention_layer_norm

												_UpperCamelCase     :   List[Any]      =       tax_enc_dec_attention_key
												_UpperCamelCase     :   Optional[Any]      =       tax_enc_dec_attention_out
												_UpperCamelCase     :   Tuple      =       tax_enc_dec_attention_query
												_UpperCamelCase     :   Tuple      =       tax_enc_dec_attention_value

												_UpperCamelCase     :   str      =       tax_cross_layer_norm

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

												_UpperCamelCase     :   str      =       tax_mlp_wo

												_UpperCamelCase     :   Dict      =       txa_mlp_layer_norm

						# Decoder Normalization
						_UpperCamelCase     :   int      =       tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
						_UpperCamelCase     :   Any      =       txa_decoder_norm

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

						# Token Embeddings
						_UpperCamelCase     :   List[str]      =       tax_model['''target''']['''token_embedder''']['''embedding''']
						_UpperCamelCase     :   Tuple      =       txa_token_embeddings

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

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


if __name__ == "__main__":
						UpperCamelCase_	 =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_	 =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_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase_	 ={
    """configuration_camembert""": ["""CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CamembertConfig""", """CamembertOnnxConfig"""],
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase_	 =["""CamembertTokenizer"""]

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase_	 =["""CamembertTokenizerFast"""]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase_	 =[
						    """CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """CamembertForCausalLM""",
						    """CamembertForMaskedLM""",
						    """CamembertForMultipleChoice""",
						    """CamembertForQuestionAnswering""",
						    """CamembertForSequenceClassification""",
						    """CamembertForTokenClassification""",
						    """CamembertModel""",
						    """CamembertPreTrainedModel""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase_	 =[
						    """TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFCamembertForCausalLM""",
						    """TFCamembertForMaskedLM""",
						    """TFCamembertForMultipleChoice""",
						    """TFCamembertForQuestionAnswering""",
						    """TFCamembertForSequenceClassification""",
						    """TFCamembertForTokenClassification""",
						    """TFCamembertModel""",
						    """TFCamembertPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_camembert import CamembertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_camembert_fast import CamembertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_camembert import (
												    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    CamembertForCausalLM,
												    CamembertForMaskedLM,
												    CamembertForMultipleChoice,
												    CamembertForQuestionAnswering,
												    CamembertForSequenceClassification,
												    CamembertForTokenClassification,
												    CamembertModel,
												    CamembertPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_camembert import (
												    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFCamembertForCausalLM,
												    TFCamembertForMaskedLM,
												    TFCamembertForMultipleChoice,
												    TFCamembertForQuestionAnswering,
												    TFCamembertForSequenceClassification,
												    TFCamembertForTokenClassification,
												    TFCamembertModel,
												    TFCamembertPreTrainedModel,
												)

else:
						import sys

						UpperCamelCase_	 =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



