
import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	) ->						str:
    __lowercase		:			Optional[Any]	    =       AutoConfig.from_pretrained(__lowerCAmelCase	)
    __lowercase		:			Tuple	    =       FlaxAutoModelForSeqaSeqLM.from_config(config=__lowerCAmelCase	)
    __lowercase		:			Any	    =       checkpoints.load_tax_checkpoint(__lowerCAmelCase	)

    __lowercase		:			List[Any]	    =       '''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

    if config.model_type == "t5":
        __lowercase		:			Tuple	    =       '''SelfAttention'''
    if config.model_type == "longt5" and config.encoder_attention_type == "local":
        __lowercase		:			Optional[int]	    =       '''LocalSelfAttention'''
    elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
        __lowercase		:			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	):
        __lowercase		:			Dict	    =       F'layers_{str(__lowerCAmelCase	)}'

        # Self-Attention
        __lowercase		:			Union[str, Any]	    =       tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
        __lowercase		:			Union[str, Any]	    =       tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
        __lowercase		:			Tuple	    =       tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
        __lowercase		:			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":
            __lowercase		:			List[Any]	    =       tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

        # Layer Normalization
        __lowercase		:			List[str]	    =       tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

        if split_mlp_wi:
            __lowercase		:			Union[str, Any]	    =       tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
            __lowercase		:			str	    =       tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
        else:
            __lowercase		:			Union[str, Any]	    =       tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

        __lowercase		:			List[str]	    =       tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

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

        # Assigning
        __lowercase		:			Dict	    =       flax_model.params['''encoder''']['''block'''][str(__lowerCAmelCase	)]['''layer''']
        __lowercase		:			Dict	    =       tax_attention_key
        __lowercase		:			Any	    =       tax_attention_out
        __lowercase		:			str	    =       tax_attention_query
        __lowercase		:			str	    =       tax_attention_value

        __lowercase		:			Optional[Any]	    =       tax_attention_layer_norm

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

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

        __lowercase		:			str	    =       tax_mlp_wo
        __lowercase		:			Tuple	    =       tax_mlp_layer_norm

        __lowercase		:			Dict	    =       flax_model_encoder_layer_block

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

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

    # Assigning
    __lowercase		:			List[Any]	    =       tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
    __lowercase		:			Union[str, Any]	    =       tax_encoder_norm

    # Decoder
    for layer_index in range(config.num_layers	):
        __lowercase		:			int	    =       F'layers_{str(__lowerCAmelCase	)}'

        # Self-Attention
        __lowercase		:			str	    =       tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
        __lowercase		:			Dict	    =       tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
        __lowercase		:			Optional[Any]	    =       tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
        __lowercase		:			Any	    =       tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

        # Layer Normalization
        __lowercase		:			List[str]	    =       tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
            '''scale'''
        ]

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

        # Layer Normalization
        __lowercase		:			List[Any]	    =       tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

        # MLP
        if split_mlp_wi:
            __lowercase		:			List[str]	    =       tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
            __lowercase		:			List[str]	    =       tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
        else:
            __lowercase		:			Optional[int]	    =       tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

        __lowercase		:			List[Any]	    =       tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

        # Layer Normalization
        __lowercase		:			int	    =       tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

        # Assigning
        __lowercase		:			str	    =       flax_model.params['''decoder''']['''block'''][str(__lowerCAmelCase	)]['''layer''']
        __lowercase		:			Optional[Any]	    =       tax_attention_key
        __lowercase		:			Tuple	    =       tax_attention_out
        __lowercase		:			Tuple	    =       tax_attention_query
        __lowercase		:			int	    =       tax_attention_value

        __lowercase		:			Union[str, Any]	    =       tax_pre_attention_layer_norm

        __lowercase		:			List[str]	    =       tax_enc_dec_attention_key
        __lowercase		:			Tuple	    =       tax_enc_dec_attention_out
        __lowercase		:			Tuple	    =       tax_enc_dec_attention_query
        __lowercase		:			Tuple	    =       tax_enc_dec_attention_value

        __lowercase		:			Any	    =       tax_cross_layer_norm

        if split_mlp_wi:
            __lowercase		:			Any	    =       tax_mlp_wi_a
            __lowercase		:			Dict	    =       tax_mlp_wi_a
        else:
            __lowercase		:			Any	    =       tax_mlp_wi

        __lowercase		:			Tuple	    =       tax_mlp_wo

        __lowercase		:			Optional[int]	    =       txa_mlp_layer_norm

        __lowercase		:			Optional[Any]	    =       flax_model_decoder_layer_block

    # Decoder Normalization
    __lowercase		:			str	    =       tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
    __lowercase		:			str	    =       txa_decoder_norm

    # Only for layer 0:
    __lowercase		:			Optional[Any]	    =       tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
    __lowercase		:			Tuple	    =       tax_decoder_rel_embedding

    # Token Embeddings
    __lowercase		:			Optional[int]	    =       tax_model['''target''']['''token_embedder''']['''embedding''']
    __lowercase		:			Any	    =       txa_token_embeddings

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

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


if __name__ == "__main__":
 __lowerCAmelCase					:			int			      =		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."
 )
 __lowerCAmelCase					:			str			      =		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 is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
 import torch

 from transformers.activations import gelu_new, gelu_python, get_activation


@require_torch
class 						__lowerCAmelCase	(	unittest.TestCase      ):



      """simple docstring"""







      def 		snake_case_			(      self		:       Optional[Any]				):
          __lowercase		:			Union[str, Any]	    =       torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100]				)
          __lowercase		:			Any	    =       get_activation('''gelu'''				)
          self.assertTrue(torch.allclose(gelu_python(_snake_case				)				,       torch_builtin(_snake_case				)				)				)
          self.assertFalse(torch.allclose(gelu_python(_snake_case				)				,       gelu_new(_snake_case				)				)				)




      def 		snake_case_			(      self		:       Dict				):
          __lowercase		:			Any	    =       torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100]				)
          __lowercase		:			List[str]	    =       get_activation('''gelu'''				)
          __lowercase		:			Optional[Any]	    =       get_activation('''gelu_10'''				)

          __lowercase		:			Tuple	    =       torch_builtin(_snake_case				)
          __lowercase		:			List[Any]	    =       geluaa(_snake_case				)

          __lowercase		:			Tuple	    =       torch.where(y_gelu_aa < 10.0				,       1				,       0				)

          self.assertTrue(torch.max(_snake_case				).item() == 10.0				)
          self.assertTrue(torch.allclose(y_gelu * clipped_mask				,       y_gelu_aa * clipped_mask				)				)




      def 		snake_case_			(      self		:       Any				):
          get_activation('''gelu'''				)
          get_activation('''gelu_10'''				)
          get_activation('''gelu_fast'''				)
          get_activation('''gelu_new'''				)
          get_activation('''gelu_python'''				)
          get_activation('''gelu_pytorch_tanh'''				)
          get_activation('''linear'''				)
          get_activation('''mish'''				)
          get_activation('''quick_gelu'''				)
          get_activation('''relu'''				)
          get_activation('''sigmoid'''				)
          get_activation('''silu'''				)
          get_activation('''swish'''				)
          get_activation('''tanh'''				)
          with self.assertRaises(_snake_case				):
              get_activation('''bogus'''				)
          with self.assertRaises(_snake_case				):
              get_activation(_snake_case				)




      def 		snake_case_			(      self		:       Dict				):
          __lowercase		:			Union[str, Any]	    =       get_activation('''gelu'''				)
          __lowercase		:			List[str]	    =       1
          __lowercase		:			Union[str, Any]	    =       get_activation('''gelu'''				)
          self.assertEqual(acta.a				,       1				)
          with self.assertRaises(_snake_case				):
              __lowercase		:			Tuple	    =       acta.a


