





import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig


def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Dict  ,				__lowercase:		Union[str, Any]  ,				__lowercase:		List[str]):



  '''simple docstring'''






  __lowerCAmelCase								=					TaConfig.from_pretrained(__lowercase)
  __lowerCAmelCase								=					FlaxTaForConditionalGeneration(config=__lowercase)
  __lowerCAmelCase								=					checkpoints.load_tax_checkpoint(__lowercase)

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

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

    # Self-Attention
    __lowerCAmelCase								=					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
    __lowerCAmelCase								=					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
    __lowerCAmelCase								=					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
    __lowerCAmelCase								=					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

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

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

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

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

    # Assigning
    __lowerCAmelCase								=					tax_attention_key
    __lowerCAmelCase								=					tax_attention_out
    __lowerCAmelCase								=					tax_attention_query
    __lowerCAmelCase								=					tax_attention_value

    __lowerCAmelCase								=					tax_attention_layer_norm

    if split_mlp_wi:
      __lowerCAmelCase								=					tax_mlp_wi_a
      __lowerCAmelCase								=					tax_mlp_wi_a
    else:
      __lowerCAmelCase								=					tax_mlp_wi

    __lowerCAmelCase								=					tax_mlp_wo
    __lowerCAmelCase								=					tax_mlp_layer_norm

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

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

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

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

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

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

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

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

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

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

    # Assigning
    __lowerCAmelCase								=					tax_attention_key
    __lowerCAmelCase								=					tax_attention_out
    __lowerCAmelCase								=					tax_attention_query
    __lowerCAmelCase								=					tax_attention_value

    __lowerCAmelCase								=					tax_pre_attention_layer_norm

    __lowerCAmelCase								=					tax_enc_dec_attention_key
    __lowerCAmelCase								=					tax_enc_dec_attention_out
    __lowerCAmelCase								=					tax_enc_dec_attention_query
    __lowerCAmelCase								=					tax_enc_dec_attention_value

    __lowerCAmelCase								=					tax_cross_layer_norm

    if split_mlp_wi:
      __lowerCAmelCase								=					tax_mlp_wi_a
      __lowerCAmelCase								=					tax_mlp_wi_a
    else:
      __lowerCAmelCase								=					tax_mlp_wi

    __lowerCAmelCase								=					tax_mlp_wo

    __lowerCAmelCase								=					txa_mlp_layer_norm

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

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

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

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

  flax_model.save_pretrained(__lowercase)
  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 TX5 checkpoint."""
      )
      parser.add_argument("""--config_name""", default=None, type=str, required=True, help="""Config name of 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 os
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
      import torch

      from transformers import GPTaLMHeadModel

@require_torch
@require_sentencepiece
@require_tokenizers
class 	__snake_case					(      unittest.TestCase ):






    '''simple docstring'''





    @slow
    @unittest.skip('''Model is not available.'''				)
    def 						A		( self    :		str				):

      """simple docstring"""





      __lowerCAmelCase								=					'''nvidia/megatron-gpt2-345m/'''
      if "MYDIR" in os.environ:
        __lowerCAmelCase								=					os.path.join(os.environ['''MYDIR''']      ,    snake_case				)
      __lowerCAmelCase								=					GPTaLMHeadModel.from_pretrained(snake_case				)
      model.to(snake_case				)
      model.half()

      __lowerCAmelCase								=					torch.tensor(
          [[101, 7_110, 1_005, 1_056, 2_023, 11_333, 17_413, 1_029, 102]]      ,    device=snake_case      ,    dtype=torch.long      ,    )

      with torch.no_grad():
        __lowerCAmelCase								=					model(snake_case				).logits

      __lowerCAmelCase								=					torch.Size((1, 9, 50_257)				)
      self.assertEqual(output.shape      ,    snake_case				)

      __lowerCAmelCase								=					torch.tensor(
          [
              4.9414,
              -0.2920,
              -1.2148,
              -4.0273,
              -0.5161,
              -5.2109,
              -1.2412,
              -1.8301,
              -1.7734,
              -4.7148,
              -0.2317,
              -1.0811,
              -2.1777,
              0.4141,
              -3.7969,
              -4.0586,
              -2.5332,
              -3.3809,
              4.3867,
          ]      ,    device=snake_case      ,    dtype=torch.half      ,    )

      for i in range(19				):
        __lowerCAmelCase						,    __lowerCAmelCase								=					8 * i // 17, 2_792 * i  # along the diagonal
        __lowerCAmelCase						,    __lowerCAmelCase								=					output[0, r, c], expected_diag[i]
        __lowerCAmelCase								=					f'''row={r} col={c} computed={computed} expected={expected}'''
        self.assertAlmostEqual(snake_case      ,    snake_case      ,    delta=1e-4      ,    msg=snake_case				)
