





import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class UpperCAmelCase_				( _SCREAMING_SNAKE_CASE     ,		unittest.TestCase ):
  __lowerCAmelCase   :						Optional[Any]							=   BarthezTokenizer
  __lowerCAmelCase   :						Tuple							=   BarthezTokenizerFast
  __lowerCAmelCase   :						Optional[Any]							=   True
  __lowerCAmelCase   :						Any							=   True


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       super().setUp()

       A_          =       BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez''' )
       tokenizer.save_pretrained(self.tmpdirname )
       tokenizer.save_pretrained(self.tmpdirname ,legacy_format=__snake_case )
       A_          =       tokenizer


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       '''<pad>'''
       A_          =       1

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


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       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(__snake_case ) ,1_0_1_1_2_2 )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       self.assertEqual(self.get_tokenizer().vocab_size ,1_0_1_1_2_2 )


  @require_torch
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
       A_          =       [0, 5_7, 3_0_1_8, 7_0_3_0_7, 9_1, 2]

       A_          =       self.tokenizer(
           __snake_case ,max_length=len(__snake_case ) ,padding=__snake_case ,truncation=__snake_case ,return_tensors='''pt''' )
       self.assertIsInstance(__snake_case ,__snake_case )

       self.assertEqual((2, 6) ,batch.input_ids.shape )
       self.assertEqual((2, 6) ,batch.attention_mask.shape )
       A_          =       batch.input_ids.tolist()[0]
       self.assertListEqual(__snake_case ,__snake_case )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       if not self.test_rust_tokenizer:
            return

       A_          =       self.get_tokenizer()
       A_          =       self.get_rust_tokenizer()

       A_          =       '''I was born in 92000, and this is falsé.'''

       A_          =       tokenizer.tokenize(__snake_case )
       A_          =       rust_tokenizer.tokenize(__snake_case )
       self.assertListEqual(__snake_case ,__snake_case )

       A_          =       tokenizer.encode(__snake_case ,add_special_tokens=__snake_case )
       A_          =       rust_tokenizer.encode(__snake_case ,add_special_tokens=__snake_case )
       self.assertListEqual(__snake_case ,__snake_case )

       A_          =       self.get_rust_tokenizer()
       A_          =       tokenizer.encode(__snake_case )
       A_          =       rust_tokenizer.encode(__snake_case )
       self.assertListEqual(__snake_case ,__snake_case )




  @slow
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       {'''input_ids''': [[0, 4_9_0, 1_4_3_2_8, 4_5_0_7, 3_5_4, 4_7, 4_3_6_6_9, 9_5, 2_5, 7_8_1_1_7, 2_0_2_1_5, 1_9_7_7_9, 1_9_0, 2_2, 4_0_0, 4, 3_5_3_4_3, 8_0_3_1_0, 6_0_3, 8_6, 2_4_9_3_7, 1_0_5, 3_3_4_3_8, 9_4_7_6_2, 1_9_6, 3_9_6_4_2, 7, 1_5, 1_5_9_3_3, 1_7_3, 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], [0, 1_0_5_3_4, 8_7, 2_5, 6_6, 3_3_5_8, 1_9_6, 5_5_2_8_9, 8, 8_2_9_6_1, 8_1, 2_2_0_4, 7_5_2_0_3, 7, 1_5, 7_6_3, 1_2_9_5_6, 2_1_6, 1_7_8, 1_4_3_2_8, 9_5_9_5, 1_3_7_7, 6_9_6_9_3, 7, 4_4_8, 7_1_0_2_1, 1_9_6, 1_8_1_0_6, 1_4_3_7, 1_3_9_7_4, 1_0_8, 9_0_8_3, 4, 4_9_3_1_5, 7, 3_9, 8_6, 1_3_2_6, 2_7_9_3, 4_6_3_3_3, 4, 4_4_8, 1_9_6, 7_4_5_8_8, 7, 4_9_3_1_5, 7, 3_9, 2_1, 8_2_2, 3_8_4_7_0, 7_4, 2_1, 6_6_7_2_3, 6_2_4_8_0, 8, 2_2_0_5_0, 5, 2]], '''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, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
       # fmt: on

       # moussaKam/mbarthez is a french model. So we also use french texts.
       A_          =       [
           '''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
           '''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
           '''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
           '''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
           '''telles que la traduction et la synthèse de texte.''',
       ]

       self.tokenizer_integration_test_util(
           expected_encoding=__snake_case ,model_name='''moussaKam/mbarthez''' ,revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6''' ,sequences=__snake_case ,)










import argparse
import json
import os

import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile

from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
    rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int

def      UpperCAmelCase_    (    _UpperCAmelCase							:str  ,						_UpperCAmelCase							:Optional[Any]				)	->				Optional[int]:
     '''simple docstring'''


     if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
          # expert layer
          A_          =       flax_key_tuple[:-1] + ('''weight''',)
          A_          =       torch.permute(_UpperCAmelCase  ,						(0, 2, 1)				)
     elif flax_key_tuple[-1] == "kernel" and ".".join(_UpperCAmelCase				):
          # linear layer
          A_          =       flax_key_tuple[:-1] + ('''weight''',)
          A_          =       flax_tensor.T
     elif flax_key_tuple[-1] in ["scale", "embedding"]:
          A_          =       flax_key_tuple[:-1] + ('''weight''',)

     return flax_key_tuple, flax_tensor

def      UpperCAmelCase_    (    _UpperCAmelCase							:Optional[int]  ,						_UpperCAmelCase							:str  ,						_UpperCAmelCase							:Any				)	->				Dict:
     '''simple docstring'''


     if "metadata" in layer:
          A_          =       layer.split('''metadata'''				)
          A_          =       ''''''.join(split_layer[0]				)[:-1]
          A_          =       [tuple(('''metadata''' + split_layer[1]).split('''/'''				)				)]
     elif "kvstore" in layer:
          A_          =       layer.split('''kvstore'''				)
          A_          =       ''''''.join(split_layer[0]				)[:-1]
          A_          =       [tuple(('''kvstore''' + split_layer[1]).split('''/'''				)				)]

     else:
          A_          =       layer.split('''/'''				)
          A_          =       '''/'''.join(split_layer[:-1]				)
          A_          =       (split_layer[-1],)

     if "kvstore/path" in layer:
          A_          =       f'{switch_checkpoint_path}/{checkpoint_info[layer]}'
     elif "kvstore/driver" in layer:
          A_          =       '''file'''
     else:
          A_          =       checkpoint_info[layer]

     return curr_real_layer_name, split_layer, content

def      UpperCAmelCase_    (    _UpperCAmelCase							:int  ,						_UpperCAmelCase							:Union[str, Any]				)	->				Any:
     '''simple docstring'''


     A_          =       rename_keys(_UpperCAmelCase				)
     A_          =       {}
     for k, v in current_block.items():
          A_          =       v
     A_          =       new_current_block
     torch.save(_UpperCAmelCase  ,						_UpperCAmelCase				)

def      UpperCAmelCase_    (    _UpperCAmelCase							:Union[str, Any]  ,						_UpperCAmelCase							:str  ,						_UpperCAmelCase							:List[Any]  ,						_UpperCAmelCase							:str  ,						_UpperCAmelCase							:str = WEIGHTS_NAME				)	->				Optional[int]:
     '''simple docstring'''


     A_          =       convert_file_size_to_int(_UpperCAmelCase				)
     A_          =       []
     A_          =       {}
     A_          =       0
     A_          =       0

     os.makedirs(_UpperCAmelCase  ,						exist_ok=_UpperCAmelCase				)
     with gfile.GFile(switch_checkpoint_path + '''/checkpoint'''  ,						'''rb'''				) as fp:
          A_          =       serialization.msgpack_restore(fp.read()				)['''optimizer''']['''target''']
          A_          =       flatten_dict(_UpperCAmelCase  ,						sep='''/'''				)

     A_          =       {}
     for layer in checkpoint_info.keys():
          A_   ,    A_   ,    A_          =       get_key_and_tensorstore_dict(
              _UpperCAmelCase  ,						_UpperCAmelCase  ,						_UpperCAmelCase				)
          if curr_real_layer_name in all_layers:
               A_          =       content
          else:
               A_          =       {split_layer[-1]: content}

     for key in all_layers.keys():
          # open tensorstore file
          A_          =       ts.open(unflatten_dict(all_layers[key]				)				).result().read().result()
          A_          =       torch.tensor(_UpperCAmelCase				)
          A_          =       raw_weights.numel() * dtype_byte_size(raw_weights.dtype				)

          # use the renaming pattern from the small conversion scripts
          A_   ,    A_          =       rename_base_flax_keys(tuple(key.split('''/'''				)				)  ,						_UpperCAmelCase				)
          A_          =       '''/'''.join(_UpperCAmelCase				)

          # If this weight is going to tip up over the maximal size, we split.
          if current_block_size + weight_size > max_shard_size:
               A_          =       os.path.join(
                   _UpperCAmelCase  ,						weights_name.replace('''.bin'''  ,						f'-{len(_UpperCAmelCase				)+1:05d}-of-???.bin'				)				)
               rename_and_save_block(_UpperCAmelCase  ,						_UpperCAmelCase				)
               sharded_state_dicts.append(current_block.keys()				)
               del current_block
               A_          =       {}
               A_          =       0

          A_          =       raw_weights.to(getattr(_UpperCAmelCase  ,						_UpperCAmelCase				)				)
          current_block_size += weight_size
          total_size += weight_size

     # Add the last block
     A_          =       os.path.join(_UpperCAmelCase  ,						weights_name.replace('''.bin'''  ,						f'-{len(_UpperCAmelCase				)+1:05d}-of-???.bin'				)				)
     rename_and_save_block(_UpperCAmelCase  ,						_UpperCAmelCase				)
     sharded_state_dicts.append(current_block.keys()				)

     # If we only have one shard, we return it
     if len(_UpperCAmelCase				) == 1:
          return {weights_name: sharded_state_dicts[0]}, None

     # Otherwise, let's build the index
     A_          =       {}
     A_          =       {}
     for idx, shard in enumerate(_UpperCAmelCase				):
          A_          =       weights_name.replace(
              '''.bin'''  ,						f'-{idx+1:05d}-of-{len(_UpperCAmelCase				):05d}.bin'				)  # len(sharded_state_dicts):05d}
          A_          =       os.path.join(_UpperCAmelCase  ,						weights_name.replace('''.bin'''  ,						f'-{idx+1:05d}-of-???.bin'				)				)
          os.rename(_UpperCAmelCase  ,						os.path.join(_UpperCAmelCase  ,						_UpperCAmelCase				)				)
          A_          =       shard
          for key in shard:
               A_          =       shard_file

    # Add the metadata
     A_          =       {'''total_size''': total_size}
     A_          =       {'''metadata''': metadata, '''weight_map''': weight_map}

     with open(os.path.join(_UpperCAmelCase  ,						_UpperCAmelCase				)  ,						'''w'''  ,						encoding='''utf-8'''				) as f:
          A_          =       json.dumps(_UpperCAmelCase  ,						indent=2  ,						sort_keys=_UpperCAmelCase				) + '''\n'''
          f.write(_UpperCAmelCase				)

     return metadata, index


if __name__ == "__main__":
      a__      :     Dict         =		argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--switch_t5x_checkpoint_path',
          default='/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600',
          type=str,
          required=False,
          help='Path to a directory containing a folder per layer. Follows the original Google format.',
      )
      parser.add_argument('--max_shard_size', default='10GB', required=False, help='Max shard size')
      parser.add_argument('--dtype', default='bfloat16', type=str, required=False, help='dtype of the saved model')
      parser.add_argument(
          '--pytorch_dump_folder_path',
          default='/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted',
          type=str,
          required=False,
          help='Path to the output pytorch model.',
      )
      a__      :     Tuple         =		parser.parse_args()
      shard_on_the_fly(
          args.switch_tax_checkpoint_path,
          args.pytorch_dump_folder_path,
          args.max_shard_size,
          args.dtype,
      )


def      UpperCAmelCase_    (    )	->				Union[str, Any]:
     '''simple docstring'''


     from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

     A_          =       SwitchTransformersConfig.from_pretrained('''google/switch-base-8'''				)
     config.save_pretrained('''/home/arthur_huggingface_co/transformers/switch_converted'''				)
     A_          =       SwitchTransformersForConditionalGeneration.from_pretrained(
         '''/home/arthur_huggingface_co/transformers/switch_converted'''  ,						device_map='''auto'''				)

     A_          =       TaTokenizer.from_pretrained('''t5-small'''				)
     A_          =       '''A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.'''

     A_          =       tokenizer(_UpperCAmelCase  ,						return_tensors='''pt'''				).input_ids
     A_          =       model.generate(_UpperCAmelCase  ,						decoder_start_token_id=0				)
     print(tokenizer.decode(out[0]				)				)



