


"""simple docstring"""




import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM

def        lowercase							(		lowerCAmelCase__ :     List[str]    ,					lowerCAmelCase__ :     List[str]    ,					lowerCAmelCase__ :     Any			) ->      Union[str, Any]:
		__a           =			AutoConfig.from_pretrained(lowerCAmelCase__			)
		__a           =			FlaxAutoModelForSeqaSeqLM.from_config(config=lowerCAmelCase__			)
		__a           =			checkpoints.load_tax_checkpoint(lowerCAmelCase__			)

		__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(lowerCAmelCase__			)}'''

				# 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(lowerCAmelCase__			)]['''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(lowerCAmelCase__			)}'''

				# 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(lowerCAmelCase__			)]['''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(lowerCAmelCase__			)
		print('''T5X Model was sucessfully converted!'''			)


if __name__ == "__main__":
				lowercase_			     =  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."
				)
				lowercase_			     =  parser.parse_args()
				convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)













import unittest

from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow


if is_flax_available():
       import jax

       from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
       from transformers.models.bert.modeling_flax_bert import FlaxBertModel
       from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel




@require_flax
class     SCREAMING_SNAKE_CASE__ (						unittest.TestCase      ):



 @slow
 def 				UpperCamelCase			(							self ):
        for model_name in ["bert-base-cased", "bert-large-uncased"]:
               with self.subTest(__lowerCamelCase ):
                      A__											=						AutoConfig.from_pretrained(__lowerCamelCase )
                      self.assertIsNotNone(__lowerCamelCase )
                      self.assertIsInstance(__lowerCamelCase,__lowerCamelCase )

                      A__											=						FlaxAutoModel.from_pretrained(__lowerCamelCase )
                      self.assertIsNotNone(__lowerCamelCase )
                      self.assertIsInstance(__lowerCamelCase,__lowerCamelCase )



 @slow
 def 				UpperCamelCase			(							self ):
        for model_name in ["roberta-base", "roberta-large"]:
               with self.subTest(__lowerCamelCase ):
                      A__											=						AutoConfig.from_pretrained(__lowerCamelCase )
                      self.assertIsNotNone(__lowerCamelCase )
                      self.assertIsInstance(__lowerCamelCase,__lowerCamelCase )

                      A__											=						FlaxAutoModel.from_pretrained(__lowerCamelCase )
                      self.assertIsNotNone(__lowerCamelCase )
                      self.assertIsInstance(__lowerCamelCase,__lowerCamelCase )



 @slow
 def 				UpperCamelCase			(							self ):
        for model_name in ["bert-base-cased", "bert-large-uncased"]:
               A__											=						AutoTokenizer.from_pretrained(__lowerCamelCase )
               A__											=						FlaxBertModel.from_pretrained(__lowerCamelCase )
               A__											=						tokenizer('''Do you support jax jitted function?''',return_tensors=TensorType.JAX )

               @jax.jit
               def eval(**__lowerCamelCase ):
                      return model(**__lowerCamelCase )

               eval(**__lowerCamelCase ).block_until_ready()



 @slow
 def 				UpperCamelCase			(							self ):
        for model_name in ["roberta-base", "roberta-large"]:
               A__											=						AutoTokenizer.from_pretrained(__lowerCamelCase )
               A__											=						FlaxRobertaModel.from_pretrained(__lowerCamelCase )
               A__											=						tokenizer('''Do you support jax jitted function?''',return_tensors=TensorType.JAX )

               @jax.jit
               def eval(**__lowerCamelCase ):
                      return model(**__lowerCamelCase )

               eval(**__lowerCamelCase ).block_until_ready()



 def 				UpperCamelCase			(							self ):
        with self.assertRaisesRegex(
            __lowerCamelCase,'''bert-base is not a local folder and is not a valid model identifier''' ):
               A__											=						FlaxAutoModel.from_pretrained('''bert-base''' )



 def 				UpperCamelCase			(							self ):
        with self.assertRaisesRegex(
            __lowerCamelCase,r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
               A__											=						FlaxAutoModel.from_pretrained(__lowerCamelCase,revision='''aaaaaa''' )



 def 				UpperCamelCase			(							self ):
        with self.assertRaisesRegex(
            __lowerCamelCase,'''hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack''',):
               A__											=						FlaxAutoModel.from_pretrained('''hf-internal-testing/config-no-model''' )




 def 				UpperCamelCase			(							self ):
        with self.assertRaisesRegex(__lowerCamelCase,'''Use `from_pt=True` to load this model''' ):
               A__											=						FlaxAutoModel.from_pretrained('''hf-internal-testing/tiny-bert-pt-only''' )





