


import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


snake_case				:    Dict       				=     get_tests_dir('''fixtures/test_sentencepiece.model''')







@require_sentencepiece
@require_tokenizers
class 		_snake_case  (      _snake_case						,    unittest.TestCase							):
 SCREAMING_SNAKE_CASE__      =       XLMRobertaTokenizer
 SCREAMING_SNAKE_CASE__      =       XLMRobertaTokenizerFast
 SCREAMING_SNAKE_CASE__      =       True
 SCREAMING_SNAKE_CASE__      =       True




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       super().setUp()

       # We have a SentencePiece fixture for testing
       a						:Dict 							=       XLMRobertaTokenizer(_lowerCamelCase					,  keep_accents=_lowerCamelCase    )
       tokenizer.save_pretrained(self.tmpdirname    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Union[str, Any] 							=       '''<pad>'''
       a						:Optional[int] 							=       1

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowerCamelCase    )					,  _lowerCamelCase    )
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowerCamelCase    )					,  _lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:int 							=       list(self.get_tokenizer().get_vocab().keys()    )

       self.assertEqual(vocab_keys[0]					,  '''<s>'''    )
       self.assertEqual(vocab_keys[1]					,  '''<pad>'''    )
       self.assertEqual(vocab_keys[-1]					,  '''<mask>'''    )
       self.assertEqual(len(_lowerCamelCase    )					,  1002    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       self.assertEqual(self.get_tokenizer().vocab_size					,  1002    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Dict 							=       XLMRobertaTokenizer(_lowerCamelCase					,  keep_accents=_lowerCamelCase    )

       a						:str 							=       tokenizer.tokenize('''This is a test'''    )
       self.assertListEqual(_lowerCamelCase					,  ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']    )

       self.assertListEqual(
           tokenizer.convert_tokens_to_ids(_lowerCamelCase    )					,  [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]					,  )

       a						:Any 							=       tokenizer.tokenize('''I was born in 92000, and this is falsé.'''    )
       self.assertListEqual(
           _lowerCamelCase					,  [
               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						:List[str] 							=       tokenizer.convert_tokens_to_ids(_lowerCamelCase    )
       self.assertListEqual(
           _lowerCamelCase					,  [
               value + tokenizer.fairseq_offset
               for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
               #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
           ]					,  )

       a						:int 							=       tokenizer.convert_ids_to_tokens(_lowerCamelCase    )
       self.assertListEqual(
           _lowerCamelCase					,  [
               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        SCREAMING_SNAKE_CASE__				(		self    ):
       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						:Dict 							=       (self.rust_tokenizer_class, '''hf-internal-testing/tiny-xlm-roberta''', {})
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''    ):
                   a						:List[str] 							=       self.rust_tokenizer_class.from_pretrained(_lowerCamelCase					,  **_lowerCamelCase    )
                   a						:Optional[Any] 							=       self.tokenizer_class.from_pretrained(_lowerCamelCase					,  **_lowerCamelCase    )

                   a						:Any 							=       tempfile.mkdtemp()

                   a						:int 							=       tokenizer_r.save_pretrained(_lowerCamelCase    )
                   a						:List[Any] 							=       tokenizer_p.save_pretrained(_lowerCamelCase    )

                   # 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						:Union[str, Any] 							=       tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f    )
                   self.assertSequenceEqual(_lowerCamelCase					,  _lowerCamelCase    )

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

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

                   shutil.rmtree(_lowerCamelCase    )

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

                   a						:Any 							=       tokenizer_r.save_pretrained(_lowerCamelCase					,  legacy_format=_lowerCamelCase    )
                   a						:List[str] 							=       tokenizer_p.save_pretrained(_lowerCamelCase    )

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

                   # Checks everything loads correctly in the same way
                   a						:str 							=       tokenizer_r.from_pretrained(_lowerCamelCase    )
                   a						:Tuple 							=       tokenizer_p.from_pretrained(_lowerCamelCase    )

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

                   shutil.rmtree(_lowerCamelCase    )

                   # Save tokenizer rust, legacy_format=False
                   a						:str 							=       tempfile.mkdtemp()

                   a						:int 							=       tokenizer_r.save_pretrained(_lowerCamelCase					,  legacy_format=_lowerCamelCase    )
                   a						:Any 							=       tokenizer_p.save_pretrained(_lowerCamelCase    )

                   # 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(_lowerCamelCase    )
                   a						:List[str] 							=       tokenizer_p.from_pretrained(_lowerCamelCase    )

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

                   shutil.rmtree(_lowerCamelCase    )




 @cached_property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       return XLMRobertaTokenizer.from_pretrained('''xlm-roberta-base'''    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       with tempfile.NamedTemporaryFile() as f:
             shutil.copyfile(_lowerCamelCase					,  f.name    )
             a						:Union[str, Any] 							=       XLMRobertaTokenizer(f.name					,  keep_accents=_lowerCamelCase    )
             a						:Optional[Any] 							=       pickle.dumps(_lowerCamelCase    )
       pickle.loads(_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       if not self.test_rust_tokenizer:
             return

       a						:Union[str, Any] 							=       self.get_tokenizer()
       a						:Dict 							=       self.get_rust_tokenizer()

       a						:Any 							=       '''I was born in 92000, and this is falsé.'''

       a						:int 							=       tokenizer.tokenize(_lowerCamelCase    )
       a						:Dict 							=       rust_tokenizer.tokenize(_lowerCamelCase    )
       self.assertListEqual(_lowerCamelCase					,  _lowerCamelCase    )

       a						:Dict 							=       tokenizer.encode(_lowerCamelCase					,  add_special_tokens=_lowerCamelCase    )
       a						:Union[str, Any] 							=       rust_tokenizer.encode(_lowerCamelCase					,  add_special_tokens=_lowerCamelCase    )
       self.assertListEqual(_lowerCamelCase					,  _lowerCamelCase    )

       a						:Union[str, Any] 							=       self.get_rust_tokenizer()
       a						:Tuple 							=       tokenizer.encode(_lowerCamelCase    )
       a						:Union[str, Any] 							=       rust_tokenizer.encode(_lowerCamelCase    )
       self.assertListEqual(_lowerCamelCase					,  _lowerCamelCase    )




 @slow
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Optional[int] 							=       '''Hello World!'''
       a						:Optional[Any] 							=       [0, 3_5378, 6661, 38, 2]
       # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
       # xlmr.eval()
       # xlmr.encode(symbols)

       self.assertListEqual(_lowerCamelCase					,  self.big_tokenizer.encode(_lowerCamelCase    )    )




 @slow
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:int 							=       (
           '''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
           ''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
       )
       a						:List[Any] 							=       [
           0,
           3293,
           83,
           10,
           4552,
           4989,
           7986,
           678,
           10,
           5915,
           111,
           17_9459,
           12_4850,
           4,
           6044,
           237,
           12,
           6,
           5,
           6,
           4,
           6780,
           705,
           15,
           1388,
           44,
           378,
           1_0114,
           711,
           152,
           20,
           6,
           5,
           2_2376,
           642,
           1221,
           1_5190,
           3_4153,
           450,
           5608,
           959,
           1119,
           5_7702,
           136,
           186,
           47,
           1098,
           2_9367,
           47,
           # 4426, # What fairseq tokenizes from "<unk>": "_<"
           # 3678, # What fairseq tokenizes from "<unk>": "unk"
           # 2740, # What fairseq tokenizes from "<unk>": ">"
           3,  # What we tokenize from "<unk>": "<unk>"
           6,  # Residue from the tokenization: an extra sentencepiece underline
           4,
           6044,
           237,
           6284,
           5_0901,
           528,
           31,
           90,
           34,
           927,
           2,
       ]
       # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
       # xlmr.eval()
       # xlmr.encode(symbols)

       self.assertListEqual(_lowerCamelCase					,  self.big_tokenizer.encode(_lowerCamelCase    )    )




 @slow
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       # fmt: off
       a						:Optional[Any] 							=       {'''input_ids''': [[0, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 5428, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 1_2399, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 12_2009, 11_5774, 23, 805, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [0, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=_lowerCamelCase					,  model_name='''xlm-roberta-base'''					,  revision='''d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3'''					,  )









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 				__lowerCamelCase						( UpperCAmelCase_						:		Union[str, Any] ,  UpperCAmelCase_						:		int ,  UpperCAmelCase_						:		Optional[Any] ,  UpperCAmelCase_						:		Union[str, Any]="attention"						):




      """simple docstring"""





      a						:Optional[int] 							=       params[F'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
      a						:Optional[Any] 							=       params[F'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
      a						:int 							=       params[F'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
      a						:Optional[Any] 							=       params[F'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
      return k, o, q, v
def 				__lowerCamelCase						( UpperCAmelCase_						:		Optional[int] ,  UpperCAmelCase_						:		str ,  UpperCAmelCase_						:		List[str] ,  UpperCAmelCase_						:		int=False						):




      """simple docstring"""





      if split_mlp_wi:
            a						:int 							=       params[F'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
            a						:Optional[Any] 							=       params[F'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
            a						:Dict 							=       (wi_a, wi_a)
      else:
            a						:Optional[Any] 							=       params[F'''{prefix}/layers_{i}/mlp/wi/kernel''']

      a						:Dict 							=       params[F'''{prefix}/layers_{i}/mlp/wo/kernel''']
      return wi, wo
def 				__lowerCamelCase						( UpperCAmelCase_						:		str ,  UpperCAmelCase_						:		int ,  UpperCAmelCase_						:		List[str] ,  UpperCAmelCase_						:		Optional[int]						):




      """simple docstring"""





      return params[F'''{prefix}/layers_{i}/{layer_name}/scale''']
def 				__lowerCamelCase						( UpperCAmelCase_						:		dict ,  *, UpperCAmelCase_						:		int ,  UpperCAmelCase_						:		bool						):




      """simple docstring"""





      a						:str 							=       traverse_util.flatten_dict(variables['''target''']						)
      a						:Any 							=       {'''/'''.join(UpperCAmelCase_						): v for k, v in old.items()}

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

      a						:Optional[Any] 							=       collections.OrderedDict()

      # Shared embeddings.
      a						:Union[str, Any] 							=       old['''token_embedder/embedding''']

      # Encoder.
      for i in range(UpperCAmelCase_						):
            # Block i, layer 0 (Self Attention).
            a						:Optional[Any] 							=       tax_layer_norm_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''encoder''' ,  '''pre_attention_layer_norm'''						)
            a							,				a							,				a							,				a						:Optional[int] 							=       tax_attention_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''encoder''' ,  '''attention'''						)
            a						:List[Any] 							=       layer_norm
            a						:str 							=       k.T
            a						:Dict 							=       o.T
            a						:int 							=       q.T
            a						:Optional[Any] 							=       v.T

            # Block i, layer 1 (MLP).
            a						:Tuple 							=       tax_layer_norm_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''encoder''' ,  '''pre_mlp_layer_norm'''						)
            a							,				a						:List[Any] 							=       tax_mlp_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''encoder''' ,  UpperCAmelCase_						)
            a						:Any 							=       layer_norm
            if split_mlp_wi:
                  a						:Any 							=       wi[0].T
                  a						:Tuple 							=       wi[1].T
            else:
                  a						:List[str] 							=       wi.T
            a						:List[Any] 							=       wo.T

      a						:Union[str, Any] 							=       old[
          '''encoder/relpos_bias/rel_embedding'''
      ].T
      a						:Optional[Any] 							=       old['''encoder/encoder_norm/scale''']

      if not is_encoder_only:
            # Decoder.
            for i in range(UpperCAmelCase_						):
                  # Block i, layer 0 (Self Attention).
                  a						:List[str] 							=       tax_layer_norm_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''decoder''' ,  '''pre_self_attention_layer_norm'''						)
                  a							,				a							,				a							,				a						:List[Any] 							=       tax_attention_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''decoder''' ,  '''self_attention'''						)
                  a						:List[Any] 							=       layer_norm
                  a						:Tuple 							=       k.T
                  a						:int 							=       o.T
                  a						:Any 							=       q.T
                  a						:Optional[int] 							=       v.T

                  # Block i, layer 1 (Cross Attention).
                  a						:str 							=       tax_layer_norm_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''decoder''' ,  '''pre_cross_attention_layer_norm'''						)
                  a							,				a							,				a							,				a						:Any 							=       tax_attention_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''decoder''' ,  '''encoder_decoder_attention'''						)
                  a						:str 							=       layer_norm
                  a						:Optional[Any] 							=       k.T
                  a						:Any 							=       o.T
                  a						:Dict 							=       q.T
                  a						:Optional[Any] 							=       v.T

                  # Block i, layer 2 (MLP).
                  a						:Optional[int] 							=       tax_layer_norm_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''decoder''' ,  '''pre_mlp_layer_norm'''						)
                  a							,				a						:List[Any] 							=       tax_mlp_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''decoder''' ,  UpperCAmelCase_						)
                  a						:Optional[int] 							=       layer_norm
                  if split_mlp_wi:
                        a						:int 							=       wi[0].T
                        a						:Tuple 							=       wi[1].T
                  else:
                        a						:str 							=       wi.T
                  a						:Dict 							=       wo.T

            a						:Any 							=       old['''decoder/decoder_norm/scale''']
            a						:Optional[Any] 							=       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						:Union[str, Any] 							=       old['''decoder/logits_dense/kernel'''].T

      return new
def 				__lowerCamelCase						( UpperCAmelCase_						:		Any ,  UpperCAmelCase_						:		bool						):




      """simple docstring"""





      a						:List[Any] 							=       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						:Optional[Any] 							=       state_dict['''shared.weight''']

      if not is_encoder_only:
            if "decoder.embed_tokens.weight" not in state_dict:
                  a						:Tuple 							=       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						:Optional[Any] 							=       state_dict['''shared.weight''']

      return state_dict
def 				__lowerCamelCase						( UpperCAmelCase_						:		Any ,  UpperCAmelCase_						:		Union[str, Any] ,  UpperCAmelCase_						:		Optional[int] ,  UpperCAmelCase_						:		Optional[int]						):




      """simple docstring"""





      a						:Tuple 							=       checkpoints.load_tax_checkpoint(UpperCAmelCase_						)
      a						:Optional[int] 							=       convert_tax_to_pytorch(UpperCAmelCase_ ,  num_layers=config.num_layers ,  is_encoder_only=UpperCAmelCase_						)
      a						:Tuple 							=       make_state_dict(UpperCAmelCase_ ,  UpperCAmelCase_						)
      model.load_state_dict(UpperCAmelCase_ ,  strict=UpperCAmelCase_						)
def 				__lowerCamelCase						( UpperCAmelCase_						:		int ,  UpperCAmelCase_						:		Optional[Any] ,  UpperCAmelCase_						:		str ,  UpperCAmelCase_						:		bool = False						):




      """simple docstring"""





      a						:List[Any] 							=       TaConfig.from_json_file(UpperCAmelCase_						)
      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						:Any 							=       TaEncoderModel(UpperCAmelCase_						)
      else:
            a						:List[str] 							=       TaForConditionalGeneration(UpperCAmelCase_						)

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

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

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


if __name__ == "__main__":
     snake_case				:    Any       				=     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
     )
     snake_case				:    Optional[Any]       				=     parser.parse_args()
     convert_tax_checkpoint_to_pytorch(
         args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
     )





