



"""simple docstring"""






import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()






def 					snake_case__       (		__lowerCamelCase    :   Tuple							,		__lowerCamelCase    :   Any							,		__lowerCamelCase    :   Optional[int]							,		__lowerCamelCase    :   Optional[int]="attention"     ):
			"""simple docstring"""
			lowerCamelCase__		:		Dict		     =params[f'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
			lowerCamelCase__		:		List[Any]		     =params[f'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
			lowerCamelCase__		:		List[Any]		     =params[f'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
			lowerCamelCase__		:		str		     =params[f'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
			return k, o, q, v






def 					snake_case__       (		__lowerCamelCase    :   List[str]							,		__lowerCamelCase    :   Dict							,		__lowerCamelCase    :   Optional[Any]							,		__lowerCamelCase    :   Tuple=False     ):
			"""simple docstring"""
			if split_mlp_wi:
						lowerCamelCase__		:		Union[str, Any]		     =params[f'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
						lowerCamelCase__		:		List[str]		     =params[f'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
						lowerCamelCase__		:		Tuple		     =(wi_a, wi_a)
			else:
						lowerCamelCase__		:		Any		     =params[f'''{prefix}/layers_{i}/mlp/wi/kernel''']

			lowerCamelCase__		:		Optional[int]		     =params[f'''{prefix}/layers_{i}/mlp/wo/kernel''']
			return wi, wo






def 					snake_case__       (		__lowerCamelCase    :   Tuple							,		__lowerCamelCase    :   List[str]							,		__lowerCamelCase    :   Optional[int]							,		__lowerCamelCase    :   str     ):
			"""simple docstring"""
			return params[f'''{prefix}/layers_{i}/{layer_name}/scale''']






def 					snake_case__       (		__lowerCamelCase    :   dict							,		*, __lowerCamelCase    :   int							,		__lowerCamelCase    :   bool     ):
			"""simple docstring"""
			lowerCamelCase__		:		Union[str, Any]		     =traverse_util.flatten_dict(variables['''target''']     )
			lowerCamelCase__		:		Tuple		     ={'''/'''.join(__lowerCamelCase     ): v for k, v in old.items()}

			# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
			lowerCamelCase__		:		Union[str, Any]		     ='''encoder/layers_0/mlp/wi_0/kernel''' in old
			print('''Split MLP:'''							,		__lowerCamelCase     )

			lowerCamelCase__		:		List[str]		     =collections.OrderedDict()

			# Shared embeddings.
			lowerCamelCase__		:		Dict		     =old['''token_embedder/embedding''']

			# Encoder.
			for i in range(__lowerCamelCase     ):
						# Block i, layer 0 (Self Attention).
						lowerCamelCase__		:		str		     =tax_layer_norm_lookup(__lowerCamelCase							,		__lowerCamelCase							,		'''encoder'''							,		'''pre_attention_layer_norm'''     )
						lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__		:		int		     =tax_attention_lookup(__lowerCamelCase							,		__lowerCamelCase							,		'''encoder'''							,		'''attention'''     )
						lowerCamelCase__		:		Optional[int]		     =layer_norm
						lowerCamelCase__		:		Tuple		     =k.T
						lowerCamelCase__		:		Any		     =o.T
						lowerCamelCase__		:		List[str]		     =q.T
						lowerCamelCase__		:		List[Any]		     =v.T

						# Block i, layer 1 (MLP).
						lowerCamelCase__		:		Any		     =tax_layer_norm_lookup(__lowerCamelCase							,		__lowerCamelCase							,		'''encoder'''							,		'''pre_mlp_layer_norm'''     )
						lowerCamelCase__     ,       lowerCamelCase__		:		Optional[int]		     =tax_mlp_lookup(__lowerCamelCase							,		__lowerCamelCase							,		'''encoder'''							,		__lowerCamelCase     )
						lowerCamelCase__		:		Any		     =layer_norm
						if split_mlp_wi:
									lowerCamelCase__		:		Any		     =wi[0].T
									lowerCamelCase__		:		int		     =wi[1].T
						else:
									lowerCamelCase__		:		Optional[Any]		     =wi.T
						lowerCamelCase__		:		Union[str, Any]		     =wo.T

			lowerCamelCase__		:		List[str]		     =old[
			    '''encoder/relpos_bias/rel_embedding'''
			].T
			lowerCamelCase__		:		Union[str, Any]		     =old['''encoder/encoder_norm/scale''']

			if not is_encoder_only:
						# Decoder.
						for i in range(__lowerCamelCase     ):
									# Block i, layer 0 (Self Attention).
									lowerCamelCase__		:		str		     =tax_layer_norm_lookup(__lowerCamelCase							,		__lowerCamelCase							,		'''decoder'''							,		'''pre_self_attention_layer_norm'''     )
									lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__		:		List[str]		     =tax_attention_lookup(__lowerCamelCase							,		__lowerCamelCase							,		'''decoder'''							,		'''self_attention'''     )
									lowerCamelCase__		:		Optional[int]		     =layer_norm
									lowerCamelCase__		:		str		     =k.T
									lowerCamelCase__		:		Optional[Any]		     =o.T
									lowerCamelCase__		:		List[str]		     =q.T
									lowerCamelCase__		:		Any		     =v.T

									# Block i, layer 1 (Cross Attention).
									lowerCamelCase__		:		List[Any]		     =tax_layer_norm_lookup(__lowerCamelCase							,		__lowerCamelCase							,		'''decoder'''							,		'''pre_cross_attention_layer_norm'''     )
									lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__		:		Optional[int]		     =tax_attention_lookup(__lowerCamelCase							,		__lowerCamelCase							,		'''decoder'''							,		'''encoder_decoder_attention'''     )
									lowerCamelCase__		:		List[str]		     =layer_norm
									lowerCamelCase__		:		str		     =k.T
									lowerCamelCase__		:		List[str]		     =o.T
									lowerCamelCase__		:		Dict		     =q.T
									lowerCamelCase__		:		str		     =v.T

									# Block i, layer 2 (MLP).
									lowerCamelCase__		:		int		     =tax_layer_norm_lookup(__lowerCamelCase							,		__lowerCamelCase							,		'''decoder'''							,		'''pre_mlp_layer_norm'''     )
									lowerCamelCase__     ,       lowerCamelCase__		:		Any		     =tax_mlp_lookup(__lowerCamelCase							,		__lowerCamelCase							,		'''decoder'''							,		__lowerCamelCase     )
									lowerCamelCase__		:		Tuple		     =layer_norm
									if split_mlp_wi:
												lowerCamelCase__		:		str		     =wi[0].T
												lowerCamelCase__		:		Tuple		     =wi[1].T
									else:
												lowerCamelCase__		:		Dict		     =wi.T
									lowerCamelCase__		:		Tuple		     =wo.T

						lowerCamelCase__		:		Optional[Any]		     =old['''decoder/decoder_norm/scale''']
						lowerCamelCase__		:		List[str]		     =old[
						    '''decoder/relpos_bias/rel_embedding'''
						].T

						# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
						if "decoder/logits_dense/kernel" in old:
									lowerCamelCase__		:		str		     =old['''decoder/logits_dense/kernel'''].T

			return new






def 					snake_case__       (		__lowerCamelCase    :   Union[str, Any]							,		__lowerCamelCase    :   bool     ):
			"""simple docstring"""
			lowerCamelCase__		:		str		     =collections.OrderedDict([(k, torch.from_numpy(v.copy()     )) for (k, v) in converted_params.items()]     )

			# Add what is missing.
			if "encoder.embed_tokens.weight" not in state_dict:
						lowerCamelCase__		:		Union[str, Any]		     =state_dict['''shared.weight''']

			if not is_encoder_only:
						if "decoder.embed_tokens.weight" not in state_dict:
									lowerCamelCase__		:		int		     =state_dict['''shared.weight''']

						if "lm_head.weight" not in state_dict:  # For old 1.0 models.
									print('''Using shared word embeddings as lm_head.'''     )
									lowerCamelCase__		:		Optional[Any]		     =state_dict['''shared.weight''']

			return state_dict






def 					snake_case__       (		__lowerCamelCase    :   Dict							,		__lowerCamelCase    :   Optional[Any]							,		__lowerCamelCase    :   Any							,		__lowerCamelCase    :   str     ):
			"""simple docstring"""
			lowerCamelCase__		:		List[Any]		     =checkpoints.load_tax_checkpoint(__lowerCamelCase     )
			lowerCamelCase__		:		Union[str, Any]		     =convert_tax_to_pytorch(__lowerCamelCase							,		num_layers=config.num_layers							,		is_encoder_only=__lowerCamelCase     )
			lowerCamelCase__		:		int		     =make_state_dict(__lowerCamelCase							,		__lowerCamelCase     )
			model.load_state_dict(__lowerCamelCase							,		strict=__lowerCamelCase     )






def 					snake_case__       (		__lowerCamelCase    :   Any							,		__lowerCamelCase    :   Optional[int]							,		__lowerCamelCase    :   int							,		__lowerCamelCase    :   bool = False     ):
			"""simple docstring"""
			lowerCamelCase__		:		int		     =TaConfig.from_json_file(__lowerCamelCase     )
			print(f'''Building PyTorch model from configuration: {config}'''     )
			# Non-v1.1 checkpoints could also use T5Model, but this works for all.
			# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
			if is_encoder_only:
						lowerCamelCase__		:		int		     =TaEncoderModel(__lowerCamelCase     )
			else:
						lowerCamelCase__		:		Dict		     =TaForConditionalGeneration(__lowerCamelCase     )

			# Load weights from tf checkpoint
			load_tax_weights_in_ta(__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase     )

			# Save pytorch-model
			print(f'''Save PyTorch model to {pytorch_dump_path}'''     )
			model.save_pretrained(__lowerCamelCase     )

			# Verify that we can load the checkpoint.
			model.from_pretrained(__lowerCamelCase     )
			print('''Done'''     )


if __name__ == "__main__":
				_lowercase			:			Union[str, Any]										=						argparse.ArgumentParser(description="Converts a native T5X checkpoint into a PyTorch checkpoint.")
				# Required parameters
				parser.add_argument(
				    "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path to the T5X checkpoint."
				)
				parser.add_argument(
				    "--config_file",
				    default=None,
				    type=str,
				    required=True,
				    help="The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.",
				)
				parser.add_argument(
				    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
				)
				parser.add_argument(
				    "--is_encoder_only", action="store_true", help="Check if the model is encoder-decoder model", default=False
				)
				_lowercase			:			List[Any]										=						parser.parse_args()
				convert_tax_checkpoint_to_pytorch(
				    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
				)









"""simple docstring"""






from ..utils import DummyObject, requires_backends

class 	__SCREAMING_SNAKE_CASE     (				metaclass=lowerCAmelCase_       ):


					'''simple docstring'''





					_a             =							['torch', 'torchsde']



					def __init__(	self							:  Union[str, Any],  *lowerCamelCase							:  str,  **lowerCamelCase							:  int					)-> Tuple:
								requires_backends(self,  ['''torch''', '''torchsde''']					)



					@classmethod
					def 		snake_case	(	cls							:  List[str],  *lowerCamelCase							:  Optional[Any],  **lowerCamelCase							:  Dict					)-> str:
								requires_backends(cls,  ['''torch''', '''torchsde''']					)




					@classmethod
					def 		snake_case	(	cls							:  Tuple,  *lowerCamelCase							:  Dict,  **lowerCamelCase							:  Tuple					)-> List[str]:
								requires_backends(cls,  ['''torch''', '''torchsde''']					)




