
'''simple docstring'''




import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase    )->     Any:
 __UpperCAmelCase					=   AutoConfig.from_pretrained(_lowerCAmelCase    )
 __UpperCAmelCase					=   FlaxAutoModelForSeqaSeqLM.from_config(config=_lowerCAmelCase    )
 __UpperCAmelCase					=   checkpoints.load_tax_checkpoint(_lowerCAmelCase    )

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

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

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

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

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

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

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

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

  __UpperCAmelCase					=   tax_attention_layer_norm

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

  if split_mlp_wi:
   __UpperCAmelCase					=   tax_mlp_wi_a
   __UpperCAmelCase					=   tax_mlp_wi_a
  else:
   __UpperCAmelCase					=   tax_mlp_wi

  __UpperCAmelCase					=   tax_mlp_wo
  __UpperCAmelCase					=   tax_mlp_layer_norm

  __UpperCAmelCase					=   flax_model_encoder_layer_block

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

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

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

 # Decoder
 for layer_index in range(config.num_layers    ):
  __UpperCAmelCase					=   F'layers_{str(_lowerCAmelCase    )}'

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

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

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

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

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

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

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

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

  __UpperCAmelCase					=   tax_pre_attention_layer_norm

  __UpperCAmelCase					=   tax_enc_dec_attention_key
  __UpperCAmelCase					=   tax_enc_dec_attention_out
  __UpperCAmelCase					=   tax_enc_dec_attention_query
  __UpperCAmelCase					=   tax_enc_dec_attention_value

  __UpperCAmelCase					=   tax_cross_layer_norm

  if split_mlp_wi:
   __UpperCAmelCase					=   tax_mlp_wi_a
   __UpperCAmelCase					=   tax_mlp_wi_a
  else:
   __UpperCAmelCase					=   tax_mlp_wi

  __UpperCAmelCase					=   tax_mlp_wo

  __UpperCAmelCase					=   txa_mlp_layer_norm

  __UpperCAmelCase					=   flax_model_decoder_layer_block

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

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

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

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

 flax_model.save_pretrained(_lowerCAmelCase    )
 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:					List[str]							=				parser.parse_args()
    convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)












'''simple docstring'''




import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


_A:					Union[str, Any]							=				get_tests_dir() + """/test_data/fsmt/fsmt_val_data.json"""
with io.open(filename, """r""", encoding="""utf-8""") as f:
    _A:					Optional[Any]							=				json.load(f)






@require_torch
class UpperCAmelCase							(		unittest.TestCase							):



    def 					__lowerCamelCase						(  self     ,			__A					):
     return FSMTTokenizer.from_pretrained(__A					)



    def 					__lowerCamelCase						(  self     ,			__A					):
     __UpperCAmelCase					=   FSMTForConditionalGeneration.from_pretrained(__A					).to(__A					)
     if torch_device == "cuda":
      model.half()
     return model




    @parameterized.expand(
        [
            ['en-ru', 2_6.0],
            ['ru-en', 2_2.0],
            ['en-de', 2_2.0],
            ['de-en', 2_9.0],
        ]					)
    @slow
    def 					__lowerCamelCase						(  self     ,			__A     ,			__A					):
     # note: this test is not testing the best performance since it only evals a small batch
     # but it should be enough to detect a regression in the output quality
     __UpperCAmelCase					=   f'facebook/wmt19-{pair}'
     __UpperCAmelCase					=   self.get_tokenizer(__A					)
     __UpperCAmelCase					=   self.get_model(__A					)

     __UpperCAmelCase					=   bleu_data[pair]['src']
     __UpperCAmelCase					=   bleu_data[pair]['tgt']

     __UpperCAmelCase					=   tokenizer(__A     ,			return_tensors='pt'     ,			truncation=__A     ,			padding='longest'					).to(__A					)
     __UpperCAmelCase					=   model.generate(
         input_ids=batch.input_ids     ,			num_beams=8     ,			)
     __UpperCAmelCase					=   tokenizer.batch_decode(
         __A     ,			skip_special_tokens=__A     ,			clean_up_tokenization_spaces=__A					)
     __UpperCAmelCase					=   calculate_bleu(__A     ,			__A					)
     print(__A					)
     self.assertGreaterEqual(scores['bleu']     ,			__A					)





