






import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE__				     =			get_tests_dir('''fixtures/test_sentencepiece.model''')


if is_torch_available():
							from transformers.models.mbart.modeling_mbart import shift_tokens_right

SCREAMING_SNAKE_CASE__				     =			25_0004
SCREAMING_SNAKE_CASE__				     =			25_0020






@require_sentencepiece
@require_tokenizers
class 			_UpperCamelCase(       __lowerCamelCase		,				unittest.TestCase		):
		__SCREAMING_SNAKE_CASE  :       List[str]							=							MBartTokenizer
		__SCREAMING_SNAKE_CASE  :       Tuple							=							MBartTokenizerFast
		__SCREAMING_SNAKE_CASE  :       Optional[int]							=							True
		__SCREAMING_SNAKE_CASE  :       Tuple							=							True

		def    __lowerCAmelCase  (					self		:    Optional[Any]    ):



					'''simple docstring'''



					super().setUp()

					# We have a SentencePiece fixture for testing
					__a						:  Any									=     MBartTokenizer(SCREAMING_SNAKE_CASE__    ,       keep_accents=SCREAMING_SNAKE_CASE__    )
					tokenizer.save_pretrained(self.tmpdirname    )

		def    __lowerCAmelCase  (					self		:    Dict    ):



					'''simple docstring'''



					__a						:  List[Any]									=     MBartTokenizer(SCREAMING_SNAKE_CASE__    ,       keep_accents=SCREAMING_SNAKE_CASE__    )

					__a						:  List[Any]									=     tokenizer.tokenize('This is a test'    )
					self.assertListEqual(SCREAMING_SNAKE_CASE__    ,       ['▁This', '▁is', '▁a', '▁t', 'est']    )

					self.assertListEqual(
					    tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__    )    ,       [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]    ,       )

					__a						:  Optional[int]									=     tokenizer.tokenize('I was born in 92000, and this is falsé.'    )
					self.assertListEqual(
					    SCREAMING_SNAKE_CASE__    ,       [
					        SPIECE_UNDERLINE + 'I',
					        SPIECE_UNDERLINE + 'was',
					        SPIECE_UNDERLINE + 'b',
					        'or',
					        'n',
					        SPIECE_UNDERLINE + 'in',
					        SPIECE_UNDERLINE + '',
					        '9',
					        '2',
					        '0',
					        '0',
					        '0',
					        ',',
					        SPIECE_UNDERLINE + 'and',
					        SPIECE_UNDERLINE + 'this',
					        SPIECE_UNDERLINE + 'is',
					        SPIECE_UNDERLINE + 'f',
					        'al',
					        's',
					        'é',
					        '.',
					    ]    ,       )
					__a						:  Optional[Any]									=     tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__    )
					self.assertListEqual(
					    SCREAMING_SNAKE_CASE__    ,       [
					        value + tokenizer.fairseq_offset
					        for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
					        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
					    ]    ,       )

					__a						:  List[str]									=     tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__    )
					self.assertListEqual(
					    SCREAMING_SNAKE_CASE__    ,       [
					        SPIECE_UNDERLINE + 'I',
					        SPIECE_UNDERLINE + 'was',
					        SPIECE_UNDERLINE + 'b',
					        'or',
					        'n',
					        SPIECE_UNDERLINE + 'in',
					        SPIECE_UNDERLINE + '',
					        '<unk>',
					        '2',
					        '0',
					        '0',
					        '0',
					        ',',
					        SPIECE_UNDERLINE + 'and',
					        SPIECE_UNDERLINE + 'this',
					        SPIECE_UNDERLINE + 'is',
					        SPIECE_UNDERLINE + 'f',
					        'al',
					        's',
					        '<unk>',
					        '.',
					    ]    ,       )



		def    __lowerCAmelCase  (					self		:    str    ):



					'''simple docstring'''



					if not self.test_slow_tokenizer:
								# as we don't have a slow version, we can't compare the outputs between slow and fast versions
								return

					__a						:  Tuple									=     (self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-mbart', {})
					for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
								with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''    ):
											__a						:  Tuple									=     self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__    ,       **SCREAMING_SNAKE_CASE__    )
											__a						:  Dict									=     self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__    ,       **SCREAMING_SNAKE_CASE__    )

											__a						:  str									=     tempfile.mkdtemp()

											__a						:  Any									=     tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE__    )
											__a						:  Dict									=     tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE__    )

											# Checks it save with the same files + the tokenizer.json file for the fast one
											self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files    )    )
											__a						:  List[str]									=     tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f    )
											self.assertSequenceEqual(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    )

											# Checks everything loads correctly in the same way
											__a						:  Dict									=     tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE__    )
											__a						:  List[Any]									=     tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE__    )

											# Check special tokens are set accordingly on Rust and Python
											for key in tokenizer_pp.special_tokens_map:
														self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    )    )
														# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
														# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

											shutil.rmtree(SCREAMING_SNAKE_CASE__    )

											# Save tokenizer rust, legacy_format=True
											__a						:  Optional[Any]									=     tempfile.mkdtemp()

											__a						:  Optional[Any]									=     tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE__    ,       legacy_format=SCREAMING_SNAKE_CASE__    )
											__a						:  int									=     tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE__    )

											# Checks it save with the same files
											self.assertSequenceEqual(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    )

											# Checks everything loads correctly in the same way
											__a						:  Optional[Any]									=     tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE__    )
											__a						:  Optional[int]									=     tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE__    )

											# Check special tokens are set accordingly on Rust and Python
											for key in tokenizer_pp.special_tokens_map:
														self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    )    )

											shutil.rmtree(SCREAMING_SNAKE_CASE__    )

											# Save tokenizer rust, legacy_format=False
											__a						:  Tuple									=     tempfile.mkdtemp()

											__a						:  List[Any]									=     tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE__    ,       legacy_format=SCREAMING_SNAKE_CASE__    )
											__a						:  Tuple									=     tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE__    )

											# Checks it saved the tokenizer.json file
											self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files    )    )

											# Checks everything loads correctly in the same way
											__a						:  Union[str, Any]									=     tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE__    )
											__a						:  Union[str, Any]									=     tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE__    )

											# Check special tokens are set accordingly on Rust and Python
											for key in tokenizer_pp.special_tokens_map:
														self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    )    )

											shutil.rmtree(SCREAMING_SNAKE_CASE__    )






@require_torch
@require_sentencepiece
@require_tokenizers
class 			_UpperCamelCase(       unittest.TestCase		):
		__SCREAMING_SNAKE_CASE  :       List[Any]							=							'''facebook/mbart-large-en-ro'''
		__SCREAMING_SNAKE_CASE  :       Optional[Any]							=							[
		    ''' UN Chief Says There Is No Military Solution in Syria''',
		    ''' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.''',
		]
		__SCREAMING_SNAKE_CASE  :       Dict							=							[
		    '''Şeful ONU declară că nu există o soluţie militară în Siria''',
		    '''Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'''
		    ''' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'''
		    ''' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.''',
		]
		__SCREAMING_SNAKE_CASE  :       Optional[int]							=							[8274, 12_7873, 2_5916, 7, 8622, 2071, 438, 6_7485, 53, 18_7895, 23, 5_1712, 2, EN_CODE]

		@classmethod
		def    __lowerCAmelCase  (					cls		:    List[str]    ):



					'''simple docstring'''



					__a						:  MBartTokenizer									=     MBartTokenizer.from_pretrained(
					    cls.checkpoint_name    ,       src_lang='en_XX'    ,       tgt_lang='ro_RO'    )
					__a						:  Dict									=     1
					return cls

		def    __lowerCAmelCase  (					self		:    Union[str, Any]    ):



					'''simple docstring'''



					self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ar_AR']    ,       2_5_0_0_0_1    )
					self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['en_EN']    ,       2_5_0_0_0_4    )
					self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ro_RO']    ,       2_5_0_0_2_0    )

		def    __lowerCAmelCase  (					self		:    Optional[Any]    ):



					'''simple docstring'''



					__a						:  Dict									=     self.tokenizer.batch_encode_plus(self.src_text    ).input_ids[0]
					self.assertListEqual(self.expected_src_tokens    ,       SCREAMING_SNAKE_CASE__    )

		def    __lowerCAmelCase  (					self		:    Any    ):



					'''simple docstring'''



					self.assertIn(SCREAMING_SNAKE_CASE__    ,       self.tokenizer.all_special_ids    )
					__a						:  List[str]									=     [RO_CODE, 8_8_4, 9_0_1_9, 9_6, 9, 9_1_6, 8_6_7_9_2, 3_6, 1_8_7_4_3, 1_5_5_9_6, 5, 2]
					__a						:  Union[str, Any]									=     self.tokenizer.decode(SCREAMING_SNAKE_CASE__    ,       skip_special_tokens=SCREAMING_SNAKE_CASE__    )
					__a						:  Tuple									=     self.tokenizer.decode(generated_ids[1:]    ,       skip_special_tokens=SCREAMING_SNAKE_CASE__    )
					self.assertEqual(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    )
					self.assertNotIn(self.tokenizer.eos_token    ,       SCREAMING_SNAKE_CASE__    )

		def    __lowerCAmelCase  (					self		:    Optional[int]    ):



					'''simple docstring'''



					__a						:  Any									=     ['this is gunna be a long sentence ' * 2_0]
					assert isinstance(src_text[0]    ,       SCREAMING_SNAKE_CASE__    )
					__a						:  int									=     1_0
					__a						:  Dict									=     self.tokenizer(SCREAMING_SNAKE_CASE__    ,       max_length=SCREAMING_SNAKE_CASE__    ,       truncation=SCREAMING_SNAKE_CASE__    ).input_ids[0]
					self.assertEqual(ids[-2]    ,       2    )
					self.assertEqual(ids[-1]    ,       SCREAMING_SNAKE_CASE__    )
					self.assertEqual(len(SCREAMING_SNAKE_CASE__    )    ,       SCREAMING_SNAKE_CASE__    )

		def    __lowerCAmelCase  (					self		:    Optional[Any]    ):



					'''simple docstring'''



					self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR']    )    ,       [2_5_0_0_2_6, 2_5_0_0_0_1]    )

		def    __lowerCAmelCase  (					self		:    str    ):



					'''simple docstring'''



					__a						:  Tuple									=     tempfile.mkdtemp()
					__a						:  Optional[Any]									=     self.tokenizer.fairseq_tokens_to_ids
					self.tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__    )
					__a						:  List[Any]									=     MBartTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__    )
					self.assertDictEqual(new_tok.fairseq_tokens_to_ids    ,       SCREAMING_SNAKE_CASE__    )

		@require_torch
		def    __lowerCAmelCase  (					self		:    List[str]    ):



					'''simple docstring'''



					__a						:  str									=     self.tokenizer(self.src_text    ,       text_target=self.tgt_text    ,       padding=SCREAMING_SNAKE_CASE__    ,       return_tensors='pt'    )
					__a						:  Any									=     shift_tokens_right(batch['labels']    ,       self.tokenizer.pad_token_id    )

					# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
					assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
					assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
					assert batch.decoder_input_ids[1][-1] == 2
					assert batch.labels[1][-2:].tolist() == [2, RO_CODE]

		@require_torch
		def    __lowerCAmelCase  (					self		:    Optional[int]    ):



					'''simple docstring'''



					__a						:  str									=     self.tokenizer(
					    self.src_text    ,       text_target=self.tgt_text    ,       padding=SCREAMING_SNAKE_CASE__    ,       truncation=SCREAMING_SNAKE_CASE__    ,       max_length=len(self.expected_src_tokens    )    ,       return_tensors='pt'    ,       )

					__a						:  Optional[int]									=     shift_tokens_right(batch['labels']    ,       self.tokenizer.pad_token_id    )

					self.assertIsInstance(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    )

					self.assertEqual((2, 1_4)    ,       batch.input_ids.shape    )
					self.assertEqual((2, 1_4)    ,       batch.attention_mask.shape    )
					__a						:  List[Any]									=     batch.input_ids.tolist()[0]
					self.assertListEqual(self.expected_src_tokens    ,       SCREAMING_SNAKE_CASE__    )
					self.assertEqual(2    ,       batch.decoder_input_ids[0, -1]    )  # EOS
					# Test that special tokens are reset
					self.assertEqual(self.tokenizer.prefix_tokens    ,       []    )
					self.assertEqual(self.tokenizer.suffix_tokens    ,       [self.tokenizer.eos_token_id, EN_CODE]    )

		def    __lowerCAmelCase  (					self		:    List[Any]    ):



					'''simple docstring'''



					__a						:  str									=     self.tokenizer(self.src_text    ,       padding=SCREAMING_SNAKE_CASE__    ,       truncation=SCREAMING_SNAKE_CASE__    ,       max_length=3    ,       return_tensors='pt'    )
					__a						:  Optional[int]									=     self.tokenizer(
					    text_target=self.tgt_text    ,       padding=SCREAMING_SNAKE_CASE__    ,       truncation=SCREAMING_SNAKE_CASE__    ,       max_length=1_0    ,       return_tensors='pt'    )
					__a						:  Optional[Any]									=     targets['input_ids']
					__a						:  Dict									=     shift_tokens_right(SCREAMING_SNAKE_CASE__    ,       self.tokenizer.pad_token_id    )

					self.assertEqual(batch.input_ids.shape[1]    ,       3    )
					self.assertEqual(batch.decoder_input_ids.shape[1]    ,       1_0    )



		@require_torch
		def    __lowerCAmelCase  (					self		:    int    ):



					'''simple docstring'''



					__a						:  Dict									=     self.tokenizer._build_translation_inputs(
					    'A test'    ,       return_tensors='pt'    ,       src_lang='en_XX'    ,       tgt_lang='ar_AR'    )

					self.assertEqual(
					    nested_simplify(SCREAMING_SNAKE_CASE__    )    ,       {
					        # A, test, EOS, en_XX
					        'input_ids': [[6_2, 3_0_3_4, 2, 2_5_0_0_0_4]],
					        'attention_mask': [[1, 1, 1, 1]],
					        # ar_AR
					        'forced_bos_token_id': 2_5_0_0_0_1,
					    }    ,       )








import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()





def      A__					(			__A  :						List[str]     ,				__A  :						Optional[Any]     ,				__A  :						Union[str, Any]     ,				__A  :						int="attention"			)					->str:
      __A    =params[F'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
      __A    =params[F'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
      __A    =params[F'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
      __A    =params[F'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
      return k, o, q, v





def      A__					(			__A  :						Optional[int]     ,				__A  :						List[str]     ,				__A  :						Any     ,				__A  :						Tuple=False			)					->Any:
      if split_mlp_wi:
            __A    =params[F'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
            __A    =params[F'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
            __A    =(wi_a, wi_a)
      else:
            __A    =params[F'''{prefix}/layers_{i}/mlp/wi/kernel''']

      __A    =params[F'''{prefix}/layers_{i}/mlp/wo/kernel''']
      return wi, wo





def      A__					(			__A  :						int     ,				__A  :						Any     ,				__A  :						Any     ,				__A  :						Optional[Any]			)					->str:
      return params[F'''{prefix}/layers_{i}/{layer_name}/scale''']





def      A__					(			__A  :						dict     ,				*, __A  :						int     ,				__A  :						bool			)					->Optional[Any]:
      __A    =traverse_util.flatten_dict(variables['''target''']			)
      __A    ={'''/'''.join(__A			): v for k, v in old.items()}

      # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
      __A    ='''encoder/layers_0/mlp/wi_0/kernel''' in old
      print('''Split MLP:'''     ,				__A			)

      __A    =collections.OrderedDict()

      # Shared embeddings.
      __A    =old['''token_embedder/embedding''']

      # Encoder.
      for i in range(__A			):
            # Block i, layer 0 (Self Attention).
            __A    =tax_layer_norm_lookup(__A     ,				__A     ,				'''encoder'''     ,				'''pre_attention_layer_norm'''			)
            __A    ,		__A    ,		__A    ,		__A    =tax_attention_lookup(__A     ,				__A     ,				'''encoder'''     ,				'''attention'''			)
            __A    =layer_norm
            __A    =k.T
            __A    =o.T
            __A    =q.T
            __A    =v.T

            # Block i, layer 1 (MLP).
            __A    =tax_layer_norm_lookup(__A     ,				__A     ,				'''encoder'''     ,				'''pre_mlp_layer_norm'''			)
            __A    ,		__A    =tax_mlp_lookup(__A     ,				__A     ,				'''encoder'''     ,				__A			)
            __A    =layer_norm
            if split_mlp_wi:
                  __A    =wi[0].T
                  __A    =wi[1].T
            else:
                  __A    =wi.T
            __A    =wo.T

      __A    =old[
          '''encoder/relpos_bias/rel_embedding'''
      ].T
      __A    =old['''encoder/encoder_norm/scale''']

      if not is_encoder_only:
            # Decoder.
            for i in range(__A			):
                  # Block i, layer 0 (Self Attention).
                  __A    =tax_layer_norm_lookup(__A     ,				__A     ,				'''decoder'''     ,				'''pre_self_attention_layer_norm'''			)
                  __A    ,		__A    ,		__A    ,		__A    =tax_attention_lookup(__A     ,				__A     ,				'''decoder'''     ,				'''self_attention'''			)
                  __A    =layer_norm
                  __A    =k.T
                  __A    =o.T
                  __A    =q.T
                  __A    =v.T

                  # Block i, layer 1 (Cross Attention).
                  __A    =tax_layer_norm_lookup(__A     ,				__A     ,				'''decoder'''     ,				'''pre_cross_attention_layer_norm'''			)
                  __A    ,		__A    ,		__A    ,		__A    =tax_attention_lookup(__A     ,				__A     ,				'''decoder'''     ,				'''encoder_decoder_attention'''			)
                  __A    =layer_norm
                  __A    =k.T
                  __A    =o.T
                  __A    =q.T
                  __A    =v.T

                  # Block i, layer 2 (MLP).
                  __A    =tax_layer_norm_lookup(__A     ,				__A     ,				'''decoder'''     ,				'''pre_mlp_layer_norm'''			)
                  __A    ,		__A    =tax_mlp_lookup(__A     ,				__A     ,				'''decoder'''     ,				__A			)
                  __A    =layer_norm
                  if split_mlp_wi:
                        __A    =wi[0].T
                        __A    =wi[1].T
                  else:
                        __A    =wi.T
                  __A    =wo.T

            __A    =old['''decoder/decoder_norm/scale''']
            __A    =old[
                '''decoder/relpos_bias/rel_embedding'''
            ].T

            # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
            if "decoder/logits_dense/kernel" in old:
                  __A    =old['''decoder/logits_dense/kernel'''].T

      return new





def      A__					(			__A  :						Union[str, Any]     ,				__A  :						bool			)					->Any:
      __A    =collections.OrderedDict([(k, torch.from_numpy(v.copy()			)) for (k, v) in converted_params.items()]			)

      # Add what is missing.
      if "encoder.embed_tokens.weight" not in state_dict:
            __A    =state_dict['''shared.weight''']

      if not is_encoder_only:
            if "decoder.embed_tokens.weight" not in state_dict:
                  __A    =state_dict['''shared.weight''']

            if "lm_head.weight" not in state_dict:  # For old 1.0 models.
                  print('''Using shared word embeddings as lm_head.'''			)
                  __A    =state_dict['''shared.weight''']

      return state_dict





def      A__					(			__A  :						str     ,				__A  :						Optional[int]     ,				__A  :						int     ,				__A  :						Optional[Any]			)					->Tuple:
      __A    =checkpoints.load_tax_checkpoint(__A			)
      __A    =convert_tax_to_pytorch(__A     ,				num_layers=config.num_layers     ,				is_encoder_only=__A			)
      __A    =make_state_dict(__A     ,				__A			)
      model.load_state_dict(__A     ,				strict=__A			)





def      A__					(			__A  :						List[str]     ,				__A  :						str     ,				__A  :						str     ,				__A  :						bool = False			)					->List[str]:
      __A    =TaConfig.from_json_file(__A			)
      print(F'''Building PyTorch model from configuration: {config}'''			)
      # Non-v1.1 checkpoints could also use T5Model, but this works for all.
      # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
      if is_encoder_only:
            __A    =TaEncoderModel(__A			)
      else:
            __A    =TaForConditionalGeneration(__A			)

      # Load weights from tf checkpoint
      load_tax_weights_in_ta(__A     ,				__A     ,				__A     ,				__A			)

      # Save pytorch-model
      print(F'''Save PyTorch model to {pytorch_dump_path}'''			)
      model.save_pretrained(__A			)

      # Verify that we can load the checkpoint.
      model.from_pretrained(__A			)
      print('''Done'''			)


if __name__ == "__main__":
   _lowerCamelCase      :					List[str]					    =      argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
   # Required parameters
   parser.add_argument(
       '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
   )
   parser.add_argument(
       '''--config_file''',
       default=None,
       type=str,
       required=True,
       help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
   )
   parser.add_argument(
       '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
   )
   parser.add_argument(
       '''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
   )
   _lowerCamelCase      :					int					    =      parser.parse_args()
   convert_tax_checkpoint_to_pytorch(
       args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
   )


