




'''simple docstring'''





import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)
from transformers.utils import is_sentencepiece_available


if is_sentencepiece_available():
   from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


if is_sentencepiece_available():
   UpperCamelCase_						:      List[Any]       =						get_tests_dir('''fixtures/test_sentencepiece.model''')


if is_torch_available():
   from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

UpperCamelCase_						:      Optional[Any]       =						128022
UpperCamelCase_						:      Optional[int]       =						128028

@require_sentencepiece
class        _a    (						__lowerCAmelCase , unittest.TestCase							):
     SCREAMING_SNAKE_CASE_							:   str       =						MaMaaaTokenizer
     SCREAMING_SNAKE_CASE_							:   List[Any]       =						False
     SCREAMING_SNAKE_CASE_							:   Tuple       =						False
     SCREAMING_SNAKE_CASE_							:   int       =						True






     def     _lowercase     (			self   )							->      Dict:
          super().setUp()

          _snake_case							=				["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
          _snake_case							=				dict(zip(_SCREAMING_SNAKE_CASE ,range(len(_SCREAMING_SNAKE_CASE   )   )   )   )
          _snake_case							=				Path(self.tmpdirname   )
          save_json(_SCREAMING_SNAKE_CASE ,save_dir / VOCAB_FILES_NAMES["vocab_file"]   )
          if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
               copyfile(_SCREAMING_SNAKE_CASE ,save_dir / VOCAB_FILES_NAMES["spm_file"]   )

          _snake_case							=				MaMaaaTokenizer.from_pretrained(self.tmpdirname   )
          tokenizer.save_pretrained(self.tmpdirname   )






     def     _lowercase     (			self ,**_SCREAMING_SNAKE_CASE   )							->      Optional[Any]:
          return MaMaaaTokenizer.from_pretrained(self.tmpdirname ,**_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      str:
          return (
              "This is a test",
              "This is a test",
          )






     def     _lowercase     (			self   )							->      Optional[int]:
          _snake_case							=				"</s>"
          _snake_case							=				0

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






     def     _lowercase     (			self   )							->      Union[str, Any]:
          _snake_case							=				self.get_tokenizer()
          _snake_case							=				list(tokenizer.get_vocab().keys()   )

          self.assertEqual(vocab_keys[0] ,"</s>"   )
          self.assertEqual(vocab_keys[1] ,"<unk>"   )
          self.assertEqual(vocab_keys[-1] ,"<s>"   )
          self.assertEqual(len(_SCREAMING_SNAKE_CASE   ) ,tokenizer.vocab_size + len(tokenizer.get_added_vocab()   )   )






     @unittest.skip("Skip this test while all models are still to be uploaded."   )
     def     _lowercase     (			self   )							->      List[str]:
          pass






     def     _lowercase     (			self   )							->      Optional[int]:
          _snake_case							=				self.get_tokenizer()

          _snake_case							=				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   ) ,[2, 3, 4, 5, 6] ,)

          _snake_case							=				tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]   )
          self.assertListEqual(_SCREAMING_SNAKE_CASE ,["▁This", "▁is", "▁a", "▁t", "est"]   )

          _snake_case							=				tokenizer.convert_tokens_to_string(_SCREAMING_SNAKE_CASE   )
          self.assertEqual(_SCREAMING_SNAKE_CASE ,"This is a test"   )






     @slow
     def     _lowercase     (			self   )							->      Dict:
          # fmt: off
          _snake_case							=				{"input_ids": [[128_022, 110_108, 397, 11, 38_272, 2_247, 124_811, 285, 18_105, 1_586, 207, 7, 39_534, 4_428, 397, 1_019, 18_105, 1_586, 207, 7, 41_337, 16_786, 241, 7, 20_214, 17, 125_690, 10_398, 7, 44_378, 58_069, 68_342, 7_798, 7_343, 11, 299, 33_310, 4, 158, 37_350, 94_077, 4_569, 299, 33_310, 90, 4, 52_840, 290, 4, 31_270, 112, 299, 682, 4, 52_840, 39_953, 14_079, 193, 52_519, 90_894, 17_894, 120_697, 11, 40_445, 551, 17, 1_019, 52_519, 90_894, 17_756, 963, 11, 40_445, 480, 17, 9_792, 1_120, 5_173, 1_393, 6_240, 16_786, 241, 120_996, 28, 1_245, 1_393, 118_240, 11_123, 1_019, 93_612, 2_691, 10_618, 98_058, 120_409, 1_928, 279, 4, 40_683, 367, 178, 207, 1_019, 103, 103_121, 506, 65_296, 5, 2], [128_022, 21_217, 367, 117, 125_450, 128, 719, 7, 7_308, 40, 93_612, 12_669, 1_116, 16_704, 71, 17_785, 3_699, 15_592, 35, 144, 9_584, 241, 11_943, 713, 950, 799, 2_247, 88_427, 150, 149, 118_813, 120_706, 1_019, 106_906, 81_518, 28, 1_224, 22_799, 397, 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], [128_022, 1_658, 123_311, 5_155, 5_578, 4_722, 279, 14_947, 2_366, 1_120, 1_197, 14, 1_348, 9_232, 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, 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, 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], [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, 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=_SCREAMING_SNAKE_CASE ,model_name="facebook/m2m100_418M" ,revision="c168bae485c864188cf9aa0e4108b0b6934dc91e" ,)







@require_torch
@require_sentencepiece
@require_tokenizers
class        _a    (						unittest.TestCase							):
     SCREAMING_SNAKE_CASE_							:   Optional[int]       =						"""facebook/m2m100_418M"""
     SCREAMING_SNAKE_CASE_							:   List[str]       =						[
         """In my opinion, there are two levels of response from the French government.""",
         """NSA Affair Emphasizes Complete Lack of Debate on Intelligence""",
     ]
     SCREAMING_SNAKE_CASE_							:   Optional[int]       =						[
         """Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.""",
         """L'affaire NSA souligne l'absence totale de débat sur le renseignement""",
     ]

     # fmt: off
     SCREAMING_SNAKE_CASE_							:   int       =						[EN_CODE, 5_93, 19_49, 11_57_81, 4, 7_15_86, 42_34, 6_06_33, 12_62_33, 4_32, 12_38_08, 1_55_92, 11_97, 11_71_32, 12_06_18, 5, 2]






     @classmethod
     def     _lowercase     (			cls   )							->      Tuple:
          _snake_case							=				MaMaaaTokenizer.from_pretrained(
              cls.checkpoint_name ,src_lang="en" ,tgt_lang="fr"   )
          _snake_case							=				1
          return cls






     def     _lowercase     (			self   )							->      List[str]:
          self.assertEqual(self.tokenizer.get_lang_id("ar"   ) ,128_006   )
          self.assertEqual(self.tokenizer.get_lang_id("en"   ) ,128_022   )
          self.assertEqual(self.tokenizer.get_lang_id("ro"   ) ,128_076   )
          self.assertEqual(self.tokenizer.get_lang_id("mr"   ) ,128_063   )






     def     _lowercase     (			self   )							->      str:
          _snake_case							=				self.tokenizer.get_vocab()
          self.assertEqual(len(_SCREAMING_SNAKE_CASE   ) ,self.tokenizer.vocab_size   )
          self.assertEqual(vocab["<unk>"] ,3   )
          self.assertIn(self.tokenizer.get_lang_token("en"   ) ,_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self   )							->      Optional[Any]:
          _snake_case							=				"en"
          _snake_case							=				self.tokenizer.batch_encode_plus(self.src_text   ).input_ids[0]
          self.assertListEqual(self.expected_src_tokens ,_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self   )							->      Union[str, Any]:
          self.assertIn(_SCREAMING_SNAKE_CASE ,self.tokenizer.all_special_ids   )
          # fmt: off
          _snake_case							=				[FR_CODE, 5_364, 82, 8_642, 4, 294, 47, 8, 14_028, 136, 3_286, 9_706, 6, 90_797, 6, 144_012, 162, 88_128, 30_061, 5, 2]
          # fmt: on
          _snake_case							=				self.tokenizer.decode(_SCREAMING_SNAKE_CASE ,skip_special_tokens=_SCREAMING_SNAKE_CASE   )
          _snake_case							=				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     _lowercase     (			self   )							->      Union[str, Any]:
          _snake_case							=				tempfile.mkdtemp()
          _snake_case							=				self.tokenizer.lang_token_to_id
          self.tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				MaMaaaTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE   )
          self.assertDictEqual(new_tok.lang_token_to_id ,_SCREAMING_SNAKE_CASE   )






     @require_torch
     def     _lowercase     (			self   )							->      List[Any]:
          _snake_case							=				"en"
          _snake_case							=				"fr"

          _snake_case							=				self.tokenizer(self.src_text ,text_target=self.tgt_text ,padding=_SCREAMING_SNAKE_CASE ,return_tensors="pt"   )

          _snake_case							=				shift_tokens_right(
              batch["labels"] ,self.tokenizer.pad_token_id ,self.tokenizer.eos_token_id   )

          for k in batch:
               _snake_case							=				batch[k].tolist()
          # batch = {k: v.tolist() for k,v in batch.items()}
          # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
          # batch.decoder_inputs_ids[0][0] ==
          assert batch.input_ids[1][0] == EN_CODE
          assert batch.input_ids[1][-1] == 2
          assert batch.labels[1][0] == FR_CODE
          assert batch.labels[1][-1] == 2
          assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]






     @require_torch
     def     _lowercase     (			self   )							->      str:
          _snake_case							=				"mr"
          self.assertListEqual(self.tokenizer.prefix_tokens ,[self.tokenizer.get_lang_id("mr"   )]   )
          self.assertListEqual(self.tokenizer.suffix_tokens ,[self.tokenizer.eos_token_id]   )

          _snake_case							=				"zh"
          self.assertListEqual(self.tokenizer.prefix_tokens ,[self.tokenizer.get_lang_id("zh"   )]   )
          self.assertListEqual(self.tokenizer.suffix_tokens ,[self.tokenizer.eos_token_id]   )






     @require_torch
     def     _lowercase     (			self   )							->      Optional[int]:
          _snake_case							=				"mr"
          self.tokenizer._switch_to_target_mode()
          self.assertListEqual(self.tokenizer.prefix_tokens ,[self.tokenizer.get_lang_id("mr"   )]   )
          self.assertListEqual(self.tokenizer.suffix_tokens ,[self.tokenizer.eos_token_id]   )
          self.tokenizer._switch_to_input_mode()
          self.assertListEqual(self.tokenizer.prefix_tokens ,[self.tokenizer.get_lang_id(self.tokenizer.src_lang   )]   )

          _snake_case							=				"zh"
          self.tokenizer._switch_to_target_mode()
          self.assertListEqual(self.tokenizer.prefix_tokens ,[self.tokenizer.get_lang_id("zh"   )]   )
          self.assertListEqual(self.tokenizer.suffix_tokens ,[self.tokenizer.eos_token_id]   )
          self.tokenizer._switch_to_input_mode()
          self.assertListEqual(self.tokenizer.prefix_tokens ,[self.tokenizer.get_lang_id(self.tokenizer.src_lang   )]   )






     @require_torch
     def     _lowercase     (			self   )							->      List[str]:
          _snake_case							=				self.tokenizer._build_translation_inputs("A test" ,return_tensors="pt" ,src_lang="en" ,tgt_lang="ar"   )

          self.assertEqual(
              nested_simplify(_SCREAMING_SNAKE_CASE   ) ,{
                  # en_XX, A, test, EOS
                  "input_ids": [[128_022, 58, 4_183, 2]],
                  "attention_mask": [[1, 1, 1, 1]],
                  # ar_AR
                  "forced_bos_token_id": 128_006,
              } ,)








'''simple docstring'''





import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()





def      __a     (       _UpperCamelCase:   Dict ,						_UpperCamelCase:   Optional[int] ,						_UpperCamelCase:   List[str]   )						->			Optional[Any]:

     """simple docstring"""




     return params[F"""{prefix}/{prefix}/relpos_bias/rel_embedding"""][:, i, :]





def      __a     (       _UpperCamelCase:   List[Any] ,						_UpperCamelCase:   Optional[Any] ,						_UpperCamelCase:   Dict ,						_UpperCamelCase:   Optional[Any]="attention"   )						->			Any:

     """simple docstring"""




     _snake_case							=				_snake_case							=				np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/key/kernel"""][:, i, :, :]   )
     _snake_case							=				k_tmp.reshape(k_tmp.shape[0] ,						k_tmp.shape[1] * k_tmp.shape[2]   )
     _snake_case							=				np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/out/kernel"""][:, i, :, :]   )
     _snake_case							=				o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1] ,						o_tmp.shape[2]   )
     _snake_case							=				np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/query/kernel"""][:, i, :, :]   )
     _snake_case							=				q_tmp.reshape(q_tmp.shape[0] ,						q_tmp.shape[1] * q_tmp.shape[2]   )
     _snake_case							=				np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/value/kernel"""][:, i, :, :]   )
     _snake_case							=				v_tmp.reshape(v_tmp.shape[0] ,						v_tmp.shape[1] * v_tmp.shape[2]   )
     return k, o, q, v





def      __a     (       _UpperCamelCase:   Tuple ,						_UpperCamelCase:   Optional[int] ,						_UpperCamelCase:   Optional[int] ,						_UpperCamelCase:   Optional[int]=False   )						->			List[Any]:

     """simple docstring"""




     if split_mlp_wi:
          _snake_case							=				params[F"""{prefix}/{prefix}/mlp/wi_0/kernel"""][:, i, :]
          _snake_case							=				params[F"""{prefix}/{prefix}/mlp/wi_1/kernel"""][:, i, :]
          _snake_case							=				(wi_a, wi_a)
     else:
          _snake_case							=				params[F"""{prefix}/{prefix}/mlp/wi/kernel"""][:, i, :]

     _snake_case							=				params[F"""{prefix}/{prefix}/mlp/wo/kernel"""][:, i, :]
     return wi, wo





def      __a     (       _UpperCamelCase:   Optional[int] ,						_UpperCamelCase:   Dict ,						_UpperCamelCase:   Union[str, Any] ,						_UpperCamelCase:   Union[str, Any]   )						->			List[Any]:

     """simple docstring"""




     return params[F"""{prefix}/{prefix}/{layer_name}/scale"""][:, i]





def      __a     (       _UpperCamelCase:   dict ,						*, _UpperCamelCase:   int ,						_UpperCamelCase:   bool ,						_UpperCamelCase:   bool = False   )						->			str:

     """simple docstring"""




     _snake_case							=				traverse_util.flatten_dict(variables["target"]   )
     _snake_case							=				{"/".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
     _snake_case							=				"encoder/encoder/mlp/wi_0/kernel" in old
     print("Split MLP:" ,						_UpperCamelCase   )

     _snake_case							=				collections.OrderedDict()

     # Shared embeddings.
     _snake_case							=				old["token_embedder/embedding"]

     # Encoder.
     for i in range(_UpperCamelCase   ):
          # Block i, layer 0 (Self Attention).
          _snake_case							=				tax_layer_norm_lookup(_UpperCamelCase ,						_UpperCamelCase ,						"encoder" ,						"pre_attention_layer_norm"   )
          _snake_case							, _snake_case							, _snake_case							, _snake_case							=				tax_attention_lookup(_UpperCamelCase ,						_UpperCamelCase ,						"encoder" ,						"attention"   )
          _snake_case							=				layer_norm
          _snake_case							=				k.T
          _snake_case							=				o.T
          _snake_case							=				q.T
          _snake_case							=				v.T

          # Block i, layer 1 (MLP).
          _snake_case							=				tax_layer_norm_lookup(_UpperCamelCase ,						_UpperCamelCase ,						"encoder" ,						"pre_mlp_layer_norm"   )
          _snake_case							, _snake_case							=				tax_mlp_lookup(_UpperCamelCase ,						_UpperCamelCase ,						"encoder" ,						_UpperCamelCase   )
          _snake_case							=				layer_norm
          if split_mlp_wi:
               _snake_case							=				wi[0].T
               _snake_case							=				wi[1].T
          else:
               _snake_case							=				wi.T
          _snake_case							=				wo.T
          if scalable_attention:
               # convert the rel_embedding of each layer
               _snake_case							=				tax_relpos_bias_lookup(
                   _UpperCamelCase ,						_UpperCamelCase ,						"encoder"   ).T

     _snake_case							=				old["encoder/encoder_norm/scale"]

     if not scalable_attention:
          _snake_case							=				tax_relpos_bias_lookup(
              _UpperCamelCase ,						0 ,						"encoder"   ).T
          _snake_case							=				tax_relpos_bias_lookup(
              _UpperCamelCase ,						0 ,						"decoder"   ).T

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

               # Block i, layer 1 (Cross Attention).
               _snake_case							=				tax_layer_norm_lookup(_UpperCamelCase ,						_UpperCamelCase ,						"decoder" ,						"pre_cross_attention_layer_norm"   )
               _snake_case							, _snake_case							, _snake_case							, _snake_case							=				tax_attention_lookup(_UpperCamelCase ,						_UpperCamelCase ,						"decoder" ,						"encoder_decoder_attention"   )
               _snake_case							=				layer_norm
               _snake_case							=				k.T
               _snake_case							=				o.T
               _snake_case							=				q.T
               _snake_case							=				v.T

               # Block i, layer 2 (MLP).
               _snake_case							=				tax_layer_norm_lookup(_UpperCamelCase ,						_UpperCamelCase ,						"decoder" ,						"pre_mlp_layer_norm"   )
               _snake_case							, _snake_case							=				tax_mlp_lookup(_UpperCamelCase ,						_UpperCamelCase ,						"decoder" ,						_UpperCamelCase   )
               _snake_case							=				layer_norm
               if split_mlp_wi:
                    _snake_case							=				wi[0].T
                    _snake_case							=				wi[1].T
               else:
                    _snake_case							=				wi.T
               _snake_case							=				wo.T

               if scalable_attention:
                    # convert the rel_embedding of each layer
                    _snake_case							=				tax_relpos_bias_lookup(_UpperCamelCase ,						_UpperCamelCase ,						"decoder"   ).T

          _snake_case							=				old["decoder/decoder_norm/scale"]

          # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
          if "decoder/logits_dense/kernel" in old:
               _snake_case							=				old["decoder/logits_dense/kernel"].T

     return new





def      __a     (       _UpperCamelCase:   Any ,						_UpperCamelCase:   bool   )						->			Dict:

     """simple docstring"""




     _snake_case							=				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:
          _snake_case							=				state_dict["shared.weight"]

     if not is_encoder_only:
          if "decoder.embed_tokens.weight" not in state_dict:
               _snake_case							=				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."   )
               _snake_case							=				state_dict["shared.weight"]

     return state_dict





def      __a     (       _UpperCamelCase:   str ,						_UpperCamelCase:   List[str] ,						_UpperCamelCase:   Any ,						_UpperCamelCase:   str ,						_UpperCamelCase:   List[Any]   )						->			Dict:

     """simple docstring"""




     _snake_case							=				checkpoints.load_tax_checkpoint(_UpperCamelCase   )
     _snake_case							=				convert_tax_to_pytorch(
         _UpperCamelCase ,						num_layers=config.num_layers ,						is_encoder_only=_UpperCamelCase ,						scalable_attention=_UpperCamelCase   )
     _snake_case							=				make_state_dict(_UpperCamelCase ,						_UpperCamelCase   )
     model.load_state_dict(_UpperCamelCase ,						strict=_UpperCamelCase   )





def      __a     (       _UpperCamelCase:   Union[str, Any] ,						_UpperCamelCase:   Union[str, Any] ,						_UpperCamelCase:   Optional[Any] ,						_UpperCamelCase:   bool = False ,						_UpperCamelCase:   bool = False ,						)						->			Dict:

     """simple docstring"""




     _snake_case							=				MTaConfig.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:
          _snake_case							=				UMTaEncoderModel(_UpperCamelCase   )
     else:
          _snake_case							=				UMTaForConditionalGeneration(_UpperCamelCase   )

     # Load weights from tf checkpoint
     load_tax_weights_in_ta(_UpperCamelCase ,						_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__":
   UpperCamelCase_						:      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
   )
   parser.add_argument(
       '''--scalable_attention''',
       action='''store_true''',
       help='''Whether the model uses scaled attention (umt5 model)''',
       default=False,
   )
   UpperCamelCase_						:      Union[str, Any]       =						parser.parse_args()
   convert_tax_checkpoint_to_pytorch(
       args.tax_checkpoint_path,
       args.config_file,
       args.pytorch_dump_path,
       args.is_encoder_only,
       args.scalable_attention,
   )


