





"""simple docstring"""




import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Any       ,			_lowercase		:       Union[str, Any]       ,			_lowercase		:       List[str]   )					->Dict:






	'''simple docstring'''






	a :						List[Any]      =							AutoConfig.from_pretrained(_lowercase   )
	a :						Optional[int]      =							FlaxAutoModelForSeqaSeqLM.from_config(config=_lowercase   )
	a :						int      =							checkpoints.load_tax_checkpoint(_lowercase   )

	a :						Optional[Any]      =							"wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

	if config.model_type == "t5":
		a :						Union[str, Any]      =							"SelfAttention"
	if config.model_type == "longt5" and config.encoder_attention_type == "local":
		a :						Dict      =							"LocalSelfAttention"
	elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
		a :						str      =							"TransientGlobalSelfAttention"
	else:
		raise ValueError(
		    "Given config is expected to have `model_type='t5'`, or `model_type='longt5` with `encoder_attention_type`"
		    " attribute with a value from ['local', 'transient-global]."   )

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

		# Self-Attention
		a :						Union[str, Any]      =							tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
		a :						Tuple      =							tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
		a :						Optional[Any]      =							tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
		a :						List[Any]      =							tax_model["target"]["encoder"][layer_name]["attention"]["value"]["kernel"]

		# Global input layer norm
		if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
			a :						List[str]      =							tax_model["target"]["encoder"][layer_name]["attention"]["T5LayerNorm_0"]["scale"]

		# Layer Normalization
		a :						List[Any]      =							tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

		if split_mlp_wi:
			a :						List[Any]      =							tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
			a :						Any      =							tax_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
		else:
			a :						Optional[Any]      =							tax_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]

		a :						Dict      =							tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

		# Layer Normalization
		a :						Union[str, Any]      =							tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

		# Assigning
		a :						int      =							flax_model.params["encoder"]["block"][str(_lowercase   )]["layer"]
		a :						Union[str, Any]      =							tax_attention_key
		a :						Union[str, Any]      =							tax_attention_out
		a :						Optional[Any]      =							tax_attention_query
		a :						List[str]      =							tax_attention_value

		a :						Dict      =							tax_attention_layer_norm

		# Global input layer norm
		if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
			a :						List[str]      =							tax_global_layer_norm

		if split_mlp_wi:
			a :						Optional[Any]      =							tax_mlp_wi_a
			a :						int      =							tax_mlp_wi_a
		else:
			a :						Dict      =							tax_mlp_wi

		a :						int      =							tax_mlp_wo
		a :						Tuple      =							tax_mlp_layer_norm

		a :						Tuple      =							flax_model_encoder_layer_block

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

	# Side/global relative position_bias + layer norm
	if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
		a :						str      =							tax_model["target"]["encoder"]["side_relpos_bias"]["rel_embedding"].T
		a :						Union[str, Any]      =							tax_encoder_global_rel_embedding

	# Assigning
	a :						int      =							tax_model["target"]["encoder"]["encoder_norm"]["scale"]
	a :						Tuple      =							tax_encoder_norm

	# Decoder
	for layer_index in range(config.num_layers   ):
		a :						Any      =							F"""layers_{str(_lowercase   )}"""

		# Self-Attention
		a :						Optional[int]      =							tax_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
		a :						Union[str, Any]      =							tax_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
		a :						Any      =							tax_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"]
		a :						Any      =							tax_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"]

		# Layer Normalization
		a :						str      =							tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
		    "scale"
		]

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

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

		# MLP
		if split_mlp_wi:
			a :						Union[str, Any]      =							tax_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"]
			a :						List[Any]      =							tax_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"]
		else:
			a :						Dict      =							tax_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"]

		a :						Optional[int]      =							tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

		# Layer Normalization
		a :						Optional[int]      =							tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

		# Assigning
		a :						Union[str, Any]      =							flax_model.params["decoder"]["block"][str(_lowercase   )]["layer"]
		a :						int      =							tax_attention_key
		a :						Any      =							tax_attention_out
		a :						List[Any]      =							tax_attention_query
		a :						int      =							tax_attention_value

		a :						int      =							tax_pre_attention_layer_norm

		a :						Optional[Any]      =							tax_enc_dec_attention_key
		a :						Optional[int]      =							tax_enc_dec_attention_out
		a :						Dict      =							tax_enc_dec_attention_query
		a :						Any      =							tax_enc_dec_attention_value

		a :						List[Any]      =							tax_cross_layer_norm

		if split_mlp_wi:
			a :						Any      =							tax_mlp_wi_a
			a :						List[str]      =							tax_mlp_wi_a
		else:
			a :						List[Any]      =							tax_mlp_wi

		a :						List[str]      =							tax_mlp_wo

		a :						str      =							txa_mlp_layer_norm

		a :						List[str]      =							flax_model_decoder_layer_block

	# Decoder Normalization
	a :						Tuple      =							tax_model["target"]["decoder"]["decoder_norm"]["scale"]
	a :						Union[str, Any]      =							txa_decoder_norm

	# Only for layer 0:
	a :						Tuple      =							tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
	a :						int      =							tax_decoder_rel_embedding

	# Token Embeddings
	a :						Union[str, Any]      =							tax_model["target"]["token_embedder"]["embedding"]
	a :						Optional[int]      =							txa_token_embeddings

	# LM Head (only in v1.1 and LongT5 checkpoints)
	if "logits_dense" in tax_model["target"]["decoder"]:
		a :						Dict      =							tax_model["target"]["decoder"]["logits_dense"]["kernel"]

	flax_model.save_pretrained(_lowercase   )
	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 T5X checkpoint.'''
		)
		parser.add_argument('''--config_name''', default=None, type=str, required=True, help='''Config name of LongT5/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)









"""simple docstring"""




import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
a		:					Optional[int]	      =     logging.get_logger(__name__)






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       str       ,			_lowercase		:       str   )					->int:






	'''simple docstring'''






	a :						Optional[Any]      =							RobertaPreLayerNormConfig.from_pretrained(
	    _lowercase       ,			architectures=["RobertaPreLayerNormForMaskedLM"]   )

	# convert state_dict
	a :						Any      =							torch.load(hf_hub_download(repo_id=_lowercase       ,			filename="pytorch_model.bin"   )   )
	a :						int      =							{}
	for tensor_key, tensor_value in original_state_dict.items():
		# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
		if tensor_key.startswith("roberta."   ):
			a :						List[Any]      =							"roberta_prelayernorm." + tensor_key[len("roberta."   ) :]

		# The original implementation contains weights which are not used, remove them from the state_dict
		if tensor_key.endswith(".self.LayerNorm.weight"   ) or tensor_key.endswith(".self.LayerNorm.bias"   ):
			continue

		a :						Dict      =							tensor_value

	a :						Optional[Any]      =							RobertaPreLayerNormForMaskedLM.from_pretrained(
	    pretrained_model_name_or_path=_lowercase       ,			config=_lowercase       ,			state_dict=_lowercase   )
	model.save_pretrained(_lowercase   )

	# convert tokenizer
	a :						List[Any]      =							AutoTokenizer.from_pretrained(_lowercase   )
	tokenizer.save_pretrained(_lowercase   )


if __name__ == "__main__":
		a		:					Any	      =     argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--checkpoint-repo''',
		    default=None,
		    type=str,
		    required=True,
		    help='''Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		a		:					Tuple	      =     parser.parse_args()
		convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)


