"""simple docstring"""






import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase )  ->  Any:
   lowercase__:   Union[str, Any]					=      AutoConfig.from_pretrained(__UpperCAmelCase )
   lowercase__:   Any					=      FlaxAutoModelForSeqaSeqLM.from_config(config=__UpperCAmelCase )
   lowercase__:   List[str]					=      checkpoints.load_tax_checkpoint(__UpperCAmelCase )

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

   if config.model_type == "t5":
      lowercase__:   Any					=      '''SelfAttention'''
   if config.model_type == "longt5" and config.encoder_attention_type == "local":
      lowercase__:   Tuple					=      '''LocalSelfAttention'''
   elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
      lowercase__:   Any					=      '''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__:   str					=      F"""layers_{str(__UpperCAmelCase )}"""

      # Self-Attention
      lowercase__:   List[Any]					=      tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
      lowercase__:   List[Any]					=      tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
      lowercase__:   Optional[int]					=      tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
      lowercase__:   Dict					=      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__:   Any					=      tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

      # Layer Normalization
      lowercase__:   Union[str, Any]					=      tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

      if split_mlp_wi:
         lowercase__:   List[str]					=      tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
         lowercase__:   List[Any]					=      tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
      else:
         lowercase__:   List[str]					=      tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

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

      # Layer Normalization
      lowercase__:   Any					=      tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

      # Assigning
      lowercase__:   Optional[int]					=      flax_model.params['''encoder''']['''block'''][str(__UpperCAmelCase )]['''layer''']
      lowercase__:   Dict					=      tax_attention_key
      lowercase__:   List[Any]					=      tax_attention_out
      lowercase__:   Optional[int]					=      tax_attention_query
      lowercase__:   Optional[Any]					=      tax_attention_value

      lowercase__:   List[Any]					=      tax_attention_layer_norm

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

      if split_mlp_wi:
         lowercase__:   int					=      tax_mlp_wi_a
         lowercase__:   Optional[int]					=      tax_mlp_wi_a
      else:
         lowercase__:   int					=      tax_mlp_wi

      lowercase__:   int					=      tax_mlp_wo
      lowercase__:   Tuple					=      tax_mlp_layer_norm

      lowercase__:   int					=      flax_model_encoder_layer_block

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

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

   # Assigning
   lowercase__:   List[Any]					=      tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
   lowercase__:   Optional[Any]					=      tax_encoder_norm

   # Decoder
   for layer_index in range(config.num_layers ):
      lowercase__:   Tuple					=      F"""layers_{str(__UpperCAmelCase )}"""

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

      # Layer Normalization
      lowercase__:   Dict					=      tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
          '''scale'''
      ]

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

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

      # MLP
      if split_mlp_wi:
         lowercase__:   int					=      tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
         lowercase__:   Any					=      tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
      else:
         lowercase__:   Tuple					=      tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

      lowercase__:   List[str]					=      tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

      # Layer Normalization
      lowercase__:   Any					=      tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

      # Assigning
      lowercase__:   Tuple					=      flax_model.params['''decoder''']['''block'''][str(__UpperCAmelCase )]['''layer''']
      lowercase__:   Tuple					=      tax_attention_key
      lowercase__:   Any					=      tax_attention_out
      lowercase__:   Dict					=      tax_attention_query
      lowercase__:   Dict					=      tax_attention_value

      lowercase__:   Any					=      tax_pre_attention_layer_norm

      lowercase__:   Union[str, Any]					=      tax_enc_dec_attention_key
      lowercase__:   Optional[int]					=      tax_enc_dec_attention_out
      lowercase__:   Tuple					=      tax_enc_dec_attention_query
      lowercase__:   Optional[Any]					=      tax_enc_dec_attention_value

      lowercase__:   List[str]					=      tax_cross_layer_norm

      if split_mlp_wi:
         lowercase__:   Optional[Any]					=      tax_mlp_wi_a
         lowercase__:   Any					=      tax_mlp_wi_a
      else:
         lowercase__:   List[Any]					=      tax_mlp_wi

      lowercase__:   str					=      tax_mlp_wo

      lowercase__:   List[Any]					=      txa_mlp_layer_norm

      lowercase__:   str					=      flax_model_decoder_layer_block

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

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

   # Token Embeddings
   lowercase__:   List[str]					=      tax_model['''target''']['''token_embedder''']['''embedding''']
   lowercase__:   Union[str, Any]					=      txa_token_embeddings

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

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


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

"""simple docstring"""






import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
       import jax.numpy as jnp

       from transformers.models.roformer.modeling_flax_roformer import (
           FlaxRoFormerForMaskedLM,
           FlaxRoFormerForMultipleChoice,
           FlaxRoFormerForQuestionAnswering,
           FlaxRoFormerForSequenceClassification,
           FlaxRoFormerForTokenClassification,
           FlaxRoFormerModel,
       )


class     UpperCAmelCase						(unittest.TestCase  ):



    """simple docstring"""

    def __init__(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase=13    ,			_UpperCAmelCase=7    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=99    ,			_UpperCAmelCase=32    ,			_UpperCAmelCase=5    ,			_UpperCAmelCase=4    ,			_UpperCAmelCase=37    ,			_UpperCAmelCase="gelu"    ,			_UpperCAmelCase=0.1    ,			_UpperCAmelCase=0.1    ,			_UpperCAmelCase=512    ,			_UpperCAmelCase=16    ,			_UpperCAmelCase=2    ,			_UpperCAmelCase=0.02    ,			_UpperCAmelCase=4    ,			):
       lowercase__:   Dict					=      parent
       lowercase__:   List[str]					=      batch_size
       lowercase__:   Optional[Any]					=      seq_length
       lowercase__:   List[Any]					=      is_training
       lowercase__:   int					=      use_attention_mask
       lowercase__:   Tuple					=      use_token_type_ids
       lowercase__:   Union[str, Any]					=      use_labels
       lowercase__:   str					=      vocab_size
       lowercase__:   str					=      hidden_size
       lowercase__:   str					=      num_hidden_layers
       lowercase__:   Optional[int]					=      num_attention_heads
       lowercase__:   List[str]					=      intermediate_size
       lowercase__:   List[str]					=      hidden_act
       lowercase__:   Tuple					=      hidden_dropout_prob
       lowercase__:   int					=      attention_probs_dropout_prob
       lowercase__:   int					=      max_position_embeddings
       lowercase__:   Union[str, Any]					=      type_vocab_size
       lowercase__:   List[Any]					=      type_sequence_label_size
       lowercase__:   Any					=      initializer_range
       lowercase__:   str					=      num_choices






    def _snake_case			(   self							):
       lowercase__:   int					=      ids_tensor([self.batch_size, self.seq_length]    ,			self.vocab_size							)

       lowercase__:   List[Any]					=      None
       if self.use_attention_mask:
          lowercase__:   Tuple					=      random_attention_mask([self.batch_size, self.seq_length]							)

       lowercase__:   List[Any]					=      None
       if self.use_token_type_ids:
          lowercase__:   str					=      ids_tensor([self.batch_size, self.seq_length]    ,			self.type_vocab_size							)

       lowercase__:   Optional[int]					=      RoFormerConfig(
           vocab_size=self.vocab_size    ,			hidden_size=self.hidden_size    ,			num_hidden_layers=self.num_hidden_layers    ,			num_attention_heads=self.num_attention_heads    ,			intermediate_size=self.intermediate_size    ,			hidden_act=self.hidden_act    ,			hidden_dropout_prob=self.hidden_dropout_prob    ,			attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,			max_position_embeddings=self.max_position_embeddings    ,			type_vocab_size=self.type_vocab_size    ,			is_decoder=_UpperCAmelCase    ,			initializer_range=self.initializer_range    ,			)

       return config, input_ids, token_type_ids, attention_mask






    def _snake_case			(   self							):
       lowercase__:   str					=      self.prepare_config_and_inputs()
       lowercase__, lowercase__, lowercase__, lowercase__:   Optional[Any]					=      config_and_inputs
       lowercase__:   Dict					=      {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
       return config, inputs_dict



@require_flax
class     UpperCAmelCase						(_UpperCAmelCase    ,unittest.TestCase  ):



    """simple docstring"""

    _UpperCAmelCase	:List[str]						    = True

    _UpperCAmelCase	:Union[str, Any]						    = (
        (
            FlaxRoFormerModel,
            FlaxRoFormerForMaskedLM,
            FlaxRoFormerForSequenceClassification,
            FlaxRoFormerForTokenClassification,
            FlaxRoFormerForMultipleChoice,
            FlaxRoFormerForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )






    def _snake_case			(   self							):
       lowercase__:   str					=      FlaxRoFormerModelTester(self							)






    @slow
    def _snake_case			(   self							):
       for model_class_name in self.all_model_classes:
          lowercase__:   Dict					=      model_class_name.from_pretrained('''junnyu/roformer_chinese_small'''    ,			from_pt=_UpperCAmelCase							)
          lowercase__:   int					=      model(np.ones((1, 1)							)							)
          self.assertIsNotNone(_UpperCAmelCase							)



@require_flax
class     UpperCAmelCase						(unittest.TestCase  ):



    """simple docstring"""

    @slow
    def _snake_case			(   self							):
       lowercase__:   Any					=      FlaxRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''							)
       lowercase__:   Optional[int]					=      jnp.array([[0, 1, 2, 3, 4, 5]]							)
       lowercase__:   List[Any]					=      model(_UpperCAmelCase							)[0]

       lowercase__:   str					=      50000

       lowercase__:   Tuple					=      (1, 6, vocab_size)
       self.assertEqual(output.shape    ,			_UpperCAmelCase							)

       lowercase__:   List[Any]					=      jnp.array(
           [[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]]							)

       self.assertTrue(jnp.allclose(output[:, :3, :3]    ,			_UpperCAmelCase    ,			atol=1e-4							)							)
