


'''simple docstring'''





import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: Union[str, Any],_SCREAMING_SNAKE_CASE					: int,_SCREAMING_SNAKE_CASE					: List[str] ):






			"""simple docstring"""




			__A=	AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE )
			__A=	FlaxAutoModelForSeqaSeqLM.from_config(config=_SCREAMING_SNAKE_CASE )
			__A=	checkpoints.load_tax_checkpoint(_SCREAMING_SNAKE_CASE )

			__A=	'wi_0' in tax_model['target']['encoder']['layers_0']['mlp']

			if config.model_type == "t5":
						__A=	'SelfAttention'
			if config.model_type == "longt5" and config.encoder_attention_type == "local":
						__A=	'LocalSelfAttention'
			elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
						__A=	'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=	f"""layers_{str(_SCREAMING_SNAKE_CASE )}"""

						# Self-Attention
						__A=	tax_model['target']['encoder'][layer_name]['attention']['key']['kernel']
						__A=	tax_model['target']['encoder'][layer_name]['attention']['out']['kernel']
						__A=	tax_model['target']['encoder'][layer_name]['attention']['query']['kernel']
						__A=	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=	tax_model['target']['encoder'][layer_name]['attention']['T5LayerNorm_0']['scale']

						# Layer Normalization
						__A=	tax_model['target']['encoder'][layer_name]['pre_attention_layer_norm']['scale']

						if split_mlp_wi:
									__A=	tax_model['target']['encoder'][layer_name]['mlp']['wi_0']['kernel']
									__A=	tax_model['target']['encoder'][layer_name]['mlp']['wi_1']['kernel']
						else:
									__A=	tax_model['target']['encoder'][layer_name]['mlp']['wi']['kernel']

						__A=	tax_model['target']['encoder'][layer_name]['mlp']['wo']['kernel']

						# Layer Normalization
						__A=	tax_model['target']['encoder'][layer_name]['pre_mlp_layer_norm']['scale']

						# Assigning
						__A=	flax_model.params['encoder']['block'][str(_SCREAMING_SNAKE_CASE )]['layer']
						__A=	tax_attention_key
						__A=	tax_attention_out
						__A=	tax_attention_query
						__A=	tax_attention_value

						__A=	tax_attention_layer_norm

						# Global input layer norm
						if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
									__A=	tax_global_layer_norm

						if split_mlp_wi:
									__A=	tax_mlp_wi_a
									__A=	tax_mlp_wi_a
						else:
									__A=	tax_mlp_wi

						__A=	tax_mlp_wo
						__A=	tax_mlp_layer_norm

						__A=	flax_model_encoder_layer_block

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

			# Side/global relative position_bias + layer norm
			if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
						__A=	tax_model['target']['encoder']['side_relpos_bias']['rel_embedding'].T
						__A=	tax_encoder_global_rel_embedding

			# Assigning
			__A=	tax_model['target']['encoder']['encoder_norm']['scale']
			__A=	tax_encoder_norm

			# Decoder
			for layer_index in range(config.num_layers ):
						__A=	f"""layers_{str(_SCREAMING_SNAKE_CASE )}"""

						# Self-Attention
						__A=	tax_model['target']['decoder'][layer_name]['self_attention']['key']['kernel']
						__A=	tax_model['target']['decoder'][layer_name]['self_attention']['out']['kernel']
						__A=	tax_model['target']['decoder'][layer_name]['self_attention']['query']['kernel']
						__A=	tax_model['target']['decoder'][layer_name]['self_attention']['value']['kernel']

						# Layer Normalization
						__A=	tax_model['target']['decoder'][layer_name]['pre_self_attention_layer_norm'][
						    'scale'
						]

						# Encoder-Decoder-Attention
						__A=	tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']
						__A=	tax_enc_dec_attention_module['key']['kernel']
						__A=	tax_enc_dec_attention_module['out']['kernel']
						__A=	tax_enc_dec_attention_module['query']['kernel']
						__A=	tax_enc_dec_attention_module['value']['kernel']

						# Layer Normalization
						__A=	tax_model['target']['decoder'][layer_name]['pre_cross_attention_layer_norm']['scale']

						# MLP
						if split_mlp_wi:
									__A=	tax_model['target']['decoder'][layer_name]['mlp']['wi_0']['kernel']
									__A=	tax_model['target']['decoder'][layer_name]['mlp']['wi_1']['kernel']
						else:
									__A=	tax_model['target']['decoder'][layer_name]['mlp']['wi']['kernel']

						__A=	tax_model['target']['decoder'][layer_name]['mlp']['wo']['kernel']

						# Layer Normalization
						__A=	tax_model['target']['decoder'][layer_name]['pre_mlp_layer_norm']['scale']

						# Assigning
						__A=	flax_model.params['decoder']['block'][str(_SCREAMING_SNAKE_CASE )]['layer']
						__A=	tax_attention_key
						__A=	tax_attention_out
						__A=	tax_attention_query
						__A=	tax_attention_value

						__A=	tax_pre_attention_layer_norm

						__A=	tax_enc_dec_attention_key
						__A=	tax_enc_dec_attention_out
						__A=	tax_enc_dec_attention_query
						__A=	tax_enc_dec_attention_value

						__A=	tax_cross_layer_norm

						if split_mlp_wi:
									__A=	tax_mlp_wi_a
									__A=	tax_mlp_wi_a
						else:
									__A=	tax_mlp_wi

						__A=	tax_mlp_wo

						__A=	txa_mlp_layer_norm

						__A=	flax_model_decoder_layer_block

			# Decoder Normalization
			__A=	tax_model['target']['decoder']['decoder_norm']['scale']
			__A=	txa_decoder_norm

			# Only for layer 0:
			__A=	tax_model['target']['decoder']['relpos_bias']['rel_embedding'].T
			__A=	tax_decoder_rel_embedding

			# Token Embeddings
			__A=	tax_model['target']['token_embedder']['embedding']
			__A=	txa_token_embeddings

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

			flax_model.save_pretrained(_SCREAMING_SNAKE_CASE )
			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 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.'''
					)
					UpperCAmelCase__  			=				parser.parse_args()
					convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)







'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase__  			=				logging.get_logger(__name__)

UpperCAmelCase__  			=				{
    '''facebook/xlm-roberta-xl''': '''https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json''',
    '''facebook/xlm-roberta-xxl''': '''https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json''',
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}
class 						a__					(					a_   ):





			'''simple docstring'''

			A						:				Tuple					      =			'''xlm-roberta-xl'''
			def __init__(  self							:			Union[str, Any]			,    lowerCAmelCase_							:			Tuple=250_880			,    lowerCAmelCase_							:			Optional[int]=2_560			,    lowerCAmelCase_							:			Optional[Any]=36			,    lowerCAmelCase_							:			List[str]=32			,    lowerCAmelCase_							:			Optional[Any]=10_240			,    lowerCAmelCase_							:			Union[str, Any]="gelu"			,    lowerCAmelCase_							:			Optional[Any]=0.1			,    lowerCAmelCase_							:			Optional[Any]=0.1			,    lowerCAmelCase_							:			Optional[int]=514			,    lowerCAmelCase_							:			List[str]=1			,    lowerCAmelCase_							:			Optional[Any]=0.02			,    lowerCAmelCase_							:			List[str]=1E-05			,    lowerCAmelCase_							:			int=1			,    lowerCAmelCase_							:			List[str]=0			,    lowerCAmelCase_							:			List[Any]=2			,    lowerCAmelCase_							:			Optional[int]="absolute"			,    lowerCAmelCase_							:			List[Any]=True			,    lowerCAmelCase_							:			Tuple=None			,    **lowerCAmelCase_							:			List[str]			,    )      ->		str:
						super().__init__(pad_token_id=lowerCAmelCase_			,    bos_token_id=lowerCAmelCase_			,    eos_token_id=lowerCAmelCase_			,    **lowerCAmelCase_	)
						__A=	vocab_size
						__A=	hidden_size
						__A=	num_hidden_layers
						__A=	num_attention_heads
						__A=	hidden_act
						__A=	intermediate_size
						__A=	hidden_dropout_prob
						__A=	attention_probs_dropout_prob
						__A=	max_position_embeddings
						__A=	type_vocab_size
						__A=	initializer_range
						__A=	layer_norm_eps
						__A=	position_embedding_type
						__A=	use_cache
						__A=	classifier_dropout


class 						a__					(					a_   ):





			'''simple docstring'''

			@property
			def 					lowerCAmelCase     (  self							:			Optional[int]	)      ->		Mapping[str, Mapping[int, str]]:
						if self.task == "multiple-choice":
									__A=	{0: 'batch', 1: 'choice', 2: 'sequence'}
						else:
									__A=	{0: 'batch', 1: 'sequence'}
						return OrderedDict(
						    [
						        ('input_ids', dynamic_axis),
						        ('attention_mask', dynamic_axis),
						    ]	)



