'''simple docstring'''

import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class       UpperCAmelCase  (						a__   ,						unittest.TestCase ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   RobertaTokenizer
       SCREAMING_SNAKE_CASE 		=   RobertaTokenizerFast
       SCREAMING_SNAKE_CASE 		=   True
       SCREAMING_SNAKE_CASE 		=   {"cls_token": "<s>"}

       def _lowerCAmelCase(						self			)  ->  Optional[int]:
        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        lowercase__    :   List[Any]						= [
            '''l''',
            '''o''',
            '''w''',
            '''e''',
            '''r''',
            '''s''',
            '''t''',
            '''i''',
            '''d''',
            '''n''',
            '''\u0120''',
            '''\u0120l''',
            '''\u0120n''',
            '''\u0120lo''',
            '''\u0120low''',
            '''er''',
            '''\u0120lowest''',
            '''\u0120newer''',
            '''\u0120wider''',
            '''<unk>''',
        ]
        lowercase__    :   Union[str, Any]						= dict(zip(__lowerCAmelCase     ,       range(len(__lowerCAmelCase			)			)			)			)
        lowercase__    :   Union[str, Any]						= ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
        lowercase__    :   Any						= {'''unk_token''': '''<unk>'''}

        lowercase__    :   Any						= os.path.join(self.tmpdirname     ,       VOCAB_FILES_NAMES['''vocab_file''']			)
        lowercase__    :   Optional[int]						= os.path.join(self.tmpdirname     ,       VOCAB_FILES_NAMES['''merges_file''']			)
        with open(self.vocab_file     ,       '''w'''     ,       encoding='''utf-8'''			) as fp:
         fp.write(json.dumps(__lowerCAmelCase			) + '''\n'''			)
        with open(self.merges_file     ,       '''w'''     ,       encoding='''utf-8'''			) as fp:
         fp.write('''\n'''.join(__lowerCAmelCase			)			)

       def _lowerCAmelCase(						self     ,       **__lowerCAmelCase			)  ->  Optional[Any]:
        kwargs.update(self.special_tokens_map			)
        return self.tokenizer_class.from_pretrained(self.tmpdirname     ,       **__lowerCAmelCase			)

       def _lowerCAmelCase(						self     ,       **__lowerCAmelCase			)  ->  Optional[int]:
        kwargs.update(self.special_tokens_map			)
        return RobertaTokenizerFast.from_pretrained(self.tmpdirname     ,       **__lowerCAmelCase			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  List[str]:
        lowercase__    :   int						= '''lower newer'''
        lowercase__    :   List[str]						= '''lower newer'''
        return input_text, output_text

       def _lowerCAmelCase(						self			)  ->  str:
        lowercase__    :   str						= self.tokenizer_class(self.vocab_file     ,       self.merges_file     ,       **self.special_tokens_map			)
        lowercase__    :   Optional[int]						= '''lower newer'''
        lowercase__    :   Optional[Any]						= ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
        lowercase__    :   int						= tokenizer.tokenize(__lowerCAmelCase			)  # , add_prefix_space=True)
        self.assertListEqual(__lowerCAmelCase     ,       __lowerCAmelCase			)

        lowercase__    :   Dict						= tokens + [tokenizer.unk_token]
        lowercase__    :   Any						= [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase			)     ,       __lowerCAmelCase			)

       def _lowerCAmelCase(						self			)  ->  str:
        lowercase__    :   List[str]						= self.get_tokenizer()

        self.assertListEqual(tokenizer.encode('''Hello world!'''     ,       add_special_tokens=__lowerCAmelCase			)     ,       [0, 31414, 232, 328, 2]			)
        self.assertListEqual(
            tokenizer.encode('''Hello world! cécé herlolip 418'''     ,       add_special_tokens=__lowerCAmelCase			)     ,       [0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]     ,       )

       @slow
       def _lowerCAmelCase(						self			)  ->  Optional[Any]:
        lowercase__    :   Union[str, Any]						= self.tokenizer_class.from_pretrained('''roberta-base'''			)

        lowercase__    :   Union[str, Any]						= tokenizer.encode('''sequence builders'''     ,       add_special_tokens=__lowerCAmelCase			)
        lowercase__    :   Optional[Any]						= tokenizer.encode('''multi-sequence build'''     ,       add_special_tokens=__lowerCAmelCase			)

        lowercase__    :   List[Any]						= tokenizer.encode(
            '''sequence builders'''     ,       add_special_tokens=__lowerCAmelCase     ,       add_prefix_space=__lowerCAmelCase			)
        lowercase__    :   int						= tokenizer.encode(
            '''sequence builders'''     ,       '''multi-sequence build'''     ,       add_special_tokens=__lowerCAmelCase     ,       add_prefix_space=__lowerCAmelCase			)

        lowercase__    :   Union[str, Any]						= tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase			)
        lowercase__    :   Any						= tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase     ,       __lowerCAmelCase			)

        assert encoded_sentence == encoded_text_from_decode
        assert encoded_pair == encoded_pair_from_decode

       def _lowerCAmelCase(						self			)  ->  Optional[int]:
        lowercase__    :   Union[str, Any]						= self.get_tokenizer()

        lowercase__    :   List[Any]						= '''Encode this sequence.'''
        lowercase__    :   int						= tokenizer.byte_encoder[''' '''.encode('''utf-8'''			)[0]]

        # Testing encoder arguments
        lowercase__    :   Any						= tokenizer.encode(__lowerCAmelCase     ,       add_special_tokens=__lowerCAmelCase     ,       add_prefix_space=__lowerCAmelCase			)
        lowercase__    :   List[Any]						= tokenizer.convert_ids_to_tokens(encoded[0]			)[0]
        self.assertNotEqual(__lowerCAmelCase     ,       __lowerCAmelCase			)

        lowercase__    :   List[Any]						= tokenizer.encode(__lowerCAmelCase     ,       add_special_tokens=__lowerCAmelCase     ,       add_prefix_space=__lowerCAmelCase			)
        lowercase__    :   Union[str, Any]						= tokenizer.convert_ids_to_tokens(encoded[0]			)[0]
        self.assertEqual(__lowerCAmelCase     ,       __lowerCAmelCase			)

        tokenizer.add_special_tokens({'''bos_token''': '''<s>'''}			)
        lowercase__    :   Union[str, Any]						= tokenizer.encode(__lowerCAmelCase     ,       add_special_tokens=__lowerCAmelCase			)
        lowercase__    :   int						= tokenizer.convert_ids_to_tokens(encoded[1]			)[0]
        self.assertNotEqual(__lowerCAmelCase     ,       __lowerCAmelCase			)

        # Testing spaces after special tokens
        lowercase__    :   List[Any]						= '''<mask>'''
        tokenizer.add_special_tokens(
            {'''mask_token''': AddedToken(__lowerCAmelCase     ,       lstrip=__lowerCAmelCase     ,       rstrip=__lowerCAmelCase			)}			)  # mask token has a left space
        lowercase__    :   List[str]						= tokenizer.convert_tokens_to_ids(__lowerCAmelCase			)

        lowercase__    :   List[str]						= '''Encode <mask> sequence'''
        lowercase__    :   Optional[Any]						= '''Encode <mask>sequence'''

        lowercase__    :   str						= tokenizer.encode(__lowerCAmelCase			)
        lowercase__    :   List[str]						= encoded.index(__lowerCAmelCase			)
        lowercase__    :   List[Any]						= tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]			)[0]
        self.assertEqual(__lowerCAmelCase     ,       __lowerCAmelCase			)

        lowercase__    :   Dict						= tokenizer.encode(__lowerCAmelCase			)
        lowercase__    :   Any						= encoded.index(__lowerCAmelCase			)
        lowercase__    :   Dict						= tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]			)[0]
        self.assertNotEqual(__lowerCAmelCase     ,       __lowerCAmelCase			)

       def _lowerCAmelCase(						self			)  ->  Optional[Any]:
        pass

       def _lowerCAmelCase(						self			)  ->  Tuple:
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""			):
          lowercase__    :   int						= self.rust_tokenizer_class.from_pretrained(__lowerCAmelCase     ,       **__lowerCAmelCase			)
          lowercase__    :   Optional[int]						= self.tokenizer_class.from_pretrained(__lowerCAmelCase     ,       **__lowerCAmelCase			)
          lowercase__    :   int						= '''A, <mask> AllenNLP sentence.'''
          lowercase__    :   List[Any]						= tokenizer_r.encode_plus(__lowerCAmelCase     ,       add_special_tokens=__lowerCAmelCase     ,       return_token_type_ids=__lowerCAmelCase			)
          lowercase__    :   str						= tokenizer_p.encode_plus(__lowerCAmelCase     ,       add_special_tokens=__lowerCAmelCase     ,       return_token_type_ids=__lowerCAmelCase			)

          # token_type_ids should put 0 everywhere
          self.assertEqual(sum(tokens_r['''token_type_ids''']			)     ,       sum(tokens_p['''token_type_ids''']			)			)

          # attention_mask should put 1 everywhere, so sum over length should be 1
          self.assertEqual(
              sum(tokens_r['''attention_mask''']			) / len(tokens_r['''attention_mask''']			)     ,       sum(tokens_p['''attention_mask''']			) / len(tokens_p['''attention_mask''']			)     ,       )

          lowercase__    :   int						= tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']			)
          lowercase__    :   Dict						= tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']			)

          # Rust correctly handles the space before the mask while python doesnt
          self.assertSequenceEqual(tokens_p['''input_ids''']     ,       [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]			)
          self.assertSequenceEqual(tokens_r['''input_ids''']     ,       [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]			)

          self.assertSequenceEqual(
              __lowerCAmelCase     ,       ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']			)
          self.assertSequenceEqual(
              __lowerCAmelCase     ,       ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']			)

       def _lowerCAmelCase(						self			)  ->  Union[str, Any]:
        for trim_offsets, add_prefix_space in itertools.product([True, False]     ,       repeat=2			):
         lowercase__    :   Any						= self.rust_tokenizer_class.from_pretrained(
             self.tmpdirname     ,       use_fast=__lowerCAmelCase     ,       add_prefix_space=__lowerCAmelCase     ,       trim_offsets=__lowerCAmelCase			)

         lowercase__    :   List[str]						= json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()			)
         lowercase__    :   Optional[Any]						= json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()			)

         self.assertEqual(pre_tokenizer_state['''add_prefix_space''']     ,       __lowerCAmelCase			)

         self.assertEqual(post_processor_state['''add_prefix_space''']     ,       __lowerCAmelCase			)
         self.assertEqual(post_processor_state['''trim_offsets''']     ,       __lowerCAmelCase			)


       def _lowerCAmelCase(						self			)  ->  Any:
        # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
        # `trim_offsets`
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""			):
          lowercase__    :   List[Any]						= '''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
          lowercase__    :   Union[str, Any]						= F"""{text_of_1_token} {text_of_1_token}"""

          lowercase__    :   Optional[int]						= self.rust_tokenizer_class.from_pretrained(
              __lowerCAmelCase     ,       use_fast=__lowerCAmelCase     ,       add_prefix_space=__lowerCAmelCase     ,       trim_offsets=__lowerCAmelCase			)
          lowercase__    :   str						= tokenizer_r(__lowerCAmelCase     ,       return_offsets_mapping=__lowerCAmelCase     ,       add_special_tokens=__lowerCAmelCase			)
          self.assertEqual(encoding.offset_mapping[0]     ,       (0, len(__lowerCAmelCase			))			)
          self.assertEqual(
              encoding.offset_mapping[1]     ,       (len(__lowerCAmelCase			) + 1, len(__lowerCAmelCase			) + 1 + len(__lowerCAmelCase			))     ,       )

          lowercase__    :   int						= self.rust_tokenizer_class.from_pretrained(
              __lowerCAmelCase     ,       use_fast=__lowerCAmelCase     ,       add_prefix_space=__lowerCAmelCase     ,       trim_offsets=__lowerCAmelCase			)
          lowercase__    :   str						= tokenizer_r(__lowerCAmelCase     ,       return_offsets_mapping=__lowerCAmelCase     ,       add_special_tokens=__lowerCAmelCase			)
          self.assertEqual(encoding.offset_mapping[0]     ,       (0, len(__lowerCAmelCase			))			)
          self.assertEqual(
              encoding.offset_mapping[1]     ,       (len(__lowerCAmelCase			) + 1, len(__lowerCAmelCase			) + 1 + len(__lowerCAmelCase			))     ,       )

          lowercase__    :   Optional[int]						= self.rust_tokenizer_class.from_pretrained(
              __lowerCAmelCase     ,       use_fast=__lowerCAmelCase     ,       add_prefix_space=__lowerCAmelCase     ,       trim_offsets=__lowerCAmelCase			)
          lowercase__    :   List[str]						= tokenizer_r(__lowerCAmelCase     ,       return_offsets_mapping=__lowerCAmelCase     ,       add_special_tokens=__lowerCAmelCase			)
          self.assertEqual(encoding.offset_mapping[0]     ,       (0, len(__lowerCAmelCase			))			)
          self.assertEqual(
              encoding.offset_mapping[1]     ,       (len(__lowerCAmelCase			), len(__lowerCAmelCase			) + 1 + len(__lowerCAmelCase			))     ,       )

          lowercase__    :   str						= self.rust_tokenizer_class.from_pretrained(
              __lowerCAmelCase     ,       use_fast=__lowerCAmelCase     ,       add_prefix_space=__lowerCAmelCase     ,       trim_offsets=__lowerCAmelCase			)
          lowercase__    :   Optional[Any]						= tokenizer_r(__lowerCAmelCase     ,       return_offsets_mapping=__lowerCAmelCase     ,       add_special_tokens=__lowerCAmelCase			)
          self.assertEqual(encoding.offset_mapping[0]     ,       (0, len(__lowerCAmelCase			))			)
          self.assertEqual(
              encoding.offset_mapping[1]     ,       (len(__lowerCAmelCase			), len(__lowerCAmelCase			) + 1 + len(__lowerCAmelCase			))     ,       )

          lowercase__    :   Any						= F""" {text}"""

          # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
          #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
          # )
          # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
          # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
          # self.assertEqual(
          #     encoding.offset_mapping[1],
          #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
          # )

          lowercase__    :   Tuple						= self.rust_tokenizer_class.from_pretrained(
              __lowerCAmelCase     ,       use_fast=__lowerCAmelCase     ,       add_prefix_space=__lowerCAmelCase     ,       trim_offsets=__lowerCAmelCase			)
          lowercase__    :   Optional[int]						= tokenizer_r(__lowerCAmelCase     ,       return_offsets_mapping=__lowerCAmelCase     ,       add_special_tokens=__lowerCAmelCase			)
          self.assertEqual(encoding.offset_mapping[0]     ,       (1, 1 + len(__lowerCAmelCase			))			)
          self.assertEqual(
              encoding.offset_mapping[1]     ,       (1 + len(__lowerCAmelCase			) + 1, 1 + len(__lowerCAmelCase			) + 1 + len(__lowerCAmelCase			))     ,       )

          lowercase__    :   List[Any]						= self.rust_tokenizer_class.from_pretrained(
              __lowerCAmelCase     ,       use_fast=__lowerCAmelCase     ,       add_prefix_space=__lowerCAmelCase     ,       trim_offsets=__lowerCAmelCase			)
          lowercase__    :   Dict						= tokenizer_r(__lowerCAmelCase     ,       return_offsets_mapping=__lowerCAmelCase     ,       add_special_tokens=__lowerCAmelCase			)
          self.assertEqual(encoding.offset_mapping[0]     ,       (0, 1 + len(__lowerCAmelCase			))			)
          self.assertEqual(
              encoding.offset_mapping[1]     ,       (1 + len(__lowerCAmelCase			), 1 + len(__lowerCAmelCase			) + 1 + len(__lowerCAmelCase			))     ,       )

          lowercase__    :   List[Any]						= self.rust_tokenizer_class.from_pretrained(
              __lowerCAmelCase     ,       use_fast=__lowerCAmelCase     ,       add_prefix_space=__lowerCAmelCase     ,       trim_offsets=__lowerCAmelCase			)
          lowercase__    :   Optional[int]						= tokenizer_r(__lowerCAmelCase     ,       return_offsets_mapping=__lowerCAmelCase     ,       add_special_tokens=__lowerCAmelCase			)
          self.assertEqual(encoding.offset_mapping[0]     ,       (0, 1 + len(__lowerCAmelCase			))			)
          self.assertEqual(
              encoding.offset_mapping[1]     ,       (1 + len(__lowerCAmelCase			), 1 + len(__lowerCAmelCase			) + 1 + len(__lowerCAmelCase			))     ,       )


'''simple docstring'''

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       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase="attention"					):
 lowercase__    :   Dict						= params[F"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
 lowercase__    :   Dict						= params[F"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
 lowercase__    :   int						= params[F"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
 lowercase__    :   Any						= params[F"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
 return k, o, q, v
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase=False					):
 if split_mlp_wi:
  lowercase__    :   Tuple						= params[F"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
  lowercase__    :   Tuple						= params[F"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
  lowercase__    :   Dict						= (wi_a, wi_a)
 else:
  lowercase__    :   List[Any]						= params[F"""{prefix}/layers_{i}/mlp/wi/kernel"""]

 lowercase__    :   int						= params[F"""{prefix}/layers_{i}/mlp/wo/kernel"""]
 return wi, wo
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase					):
 return params[F"""{prefix}/layers_{i}/{layer_name}/scale"""]
def       __UpperCamelCase	(       UpperCAmelCase		,					*, UpperCAmelCase		,					UpperCAmelCase					):
 lowercase__    :   Union[str, Any]						= traverse_util.flatten_dict(variables['''target''']					)
 lowercase__    :   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
 lowercase__    :   List[str]						= '''encoder/layers_0/mlp/wi_0/kernel''' in old
 print('''Split MLP:'''		,					UpperCAmelCase					)

 lowercase__    :   Union[str, Any]						= collections.OrderedDict()

 # Shared embeddings.
 lowercase__    :   str						= old['''token_embedder/embedding''']

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

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

 lowercase__    :   Dict						= old[
     '''encoder/relpos_bias/rel_embedding'''
 ].T
 lowercase__    :   str						= old['''encoder/encoder_norm/scale''']

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

   # Block i, layer 1 (Cross Attention).
   lowercase__    :   List[Any]						= tax_layer_norm_lookup(UpperCAmelCase		,					UpperCAmelCase		,					'''decoder'''		,					'''pre_cross_attention_layer_norm'''					)
   lowercase__			,							lowercase__			,							lowercase__			,							lowercase__    :   Any						= tax_attention_lookup(UpperCAmelCase		,					UpperCAmelCase		,					'''decoder'''		,					'''encoder_decoder_attention'''					)
   lowercase__    :   Any						= layer_norm
   lowercase__    :   Optional[int]						= k.T
   lowercase__    :   Optional[Any]						= o.T
   lowercase__    :   Tuple						= q.T
   lowercase__    :   List[Any]						= v.T

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

  lowercase__    :   Any						= old['''decoder/decoder_norm/scale''']
  lowercase__    :   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:
   lowercase__    :   Dict						= old['''decoder/logits_dense/kernel'''].T

 return new
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase					):
 lowercase__    :   int						= 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:
  lowercase__    :   Optional[int]						= state_dict['''shared.weight''']

 if not is_encoder_only:
  if "decoder.embed_tokens.weight" not in state_dict:
   lowercase__    :   List[Any]						= 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.'''					)
   lowercase__    :   Union[str, Any]						= state_dict['''shared.weight''']

 return state_dict
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase					):
 lowercase__    :   List[str]						= checkpoints.load_tax_checkpoint(UpperCAmelCase					)
 lowercase__    :   str						= convert_tax_to_pytorch(UpperCAmelCase		,					num_layers=config.num_layers		,					is_encoder_only=UpperCAmelCase					)
 lowercase__    :   List[Any]						= make_state_dict(UpperCAmelCase		,					UpperCAmelCase					)
 model.load_state_dict(UpperCAmelCase		,					strict=UpperCAmelCase					)
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase = False					):
 lowercase__    :   Dict						= 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:
  lowercase__    :   List[str]						= TaEncoderModel(UpperCAmelCase					)
 else:
  lowercase__    :   Tuple						= 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__":
  __a:			Optional[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
  )
  __a:			Tuple	       =       parser.parse_args()
  convert_tax_checkpoint_to_pytorch(
      args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
  )

