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

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
     from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


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

__magic_name__								={'''target_lang''': '''fi''', '''source_lang''': '''en'''}
__magic_name__								='''>>zh<<'''
__magic_name__								='''Helsinki-NLP/'''

if is_torch_available():
     __magic_name__								='''pt'''
elif is_tf_available():
     __magic_name__								='''tf'''
else:
     __magic_name__								='''jax'''



@require_sentencepiece
class     _A   (    __UpperCamelCase	, unittest.TestCase							):
  SCREAMING_SNAKE_CASE_	:	Optional[int]						=MarianTokenizer
  SCREAMING_SNAKE_CASE_	:	Optional[int]						=False
  SCREAMING_SNAKE_CASE_	:	Optional[int]						=True






  def 		_a					(self	)		->      List[Any]:





       '''simple docstring'''





       super().setUp()
       UpperCamelCase__										=      ['''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''', '''\u0120''', '''<pad>''']
       UpperCamelCase__										=      dict(zip(SCREAMING_SNAKE_CASE_   ,				range(len(SCREAMING_SNAKE_CASE_	)	)	)	)
       UpperCamelCase__										=      Path(self.tmpdirname	)
       save_json(SCREAMING_SNAKE_CASE_   ,				save_dir / VOCAB_FILES_NAMES['''vocab''']	)
       save_json(SCREAMING_SNAKE_CASE_   ,				save_dir / VOCAB_FILES_NAMES['''tokenizer_config_file''']	)
       if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
            copyfile(SCREAMING_SNAKE_CASE_   ,				save_dir / VOCAB_FILES_NAMES['''source_spm''']	)
            copyfile(SCREAMING_SNAKE_CASE_   ,				save_dir / VOCAB_FILES_NAMES['''target_spm''']	)

       UpperCamelCase__										=      MarianTokenizer.from_pretrained(self.tmpdirname	)
       tokenizer.save_pretrained(self.tmpdirname	)






  def 		_a					(self   ,				**SCREAMING_SNAKE_CASE_	)		->      MarianTokenizer:





       '''simple docstring'''





       return MarianTokenizer.from_pretrained(self.tmpdirname   ,				**SCREAMING_SNAKE_CASE_	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      Dict:





       '''simple docstring'''





       return (
           "This is a test",
           "This is a test",
       )






  def 		_a					(self	)		->      List[str]:





       '''simple docstring'''





       UpperCamelCase__										=      '''</s>'''
       UpperCamelCase__										=      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 		_a					(self	)		->      Union[str, Any]:





       '''simple docstring'''





       UpperCamelCase__										=      list(self.get_tokenizer().get_vocab().keys()	)

       self.assertEqual(vocab_keys[0]   ,				'''</s>'''	)
       self.assertEqual(vocab_keys[1]   ,				'''<unk>'''	)
       self.assertEqual(vocab_keys[-1]   ,				'''<pad>'''	)
       self.assertEqual(len(SCREAMING_SNAKE_CASE_	)   ,				9	)






  def 		_a					(self	)		->      Optional[Any]:





       '''simple docstring'''





       self.assertEqual(self.get_tokenizer().vocab_size   ,				9	)






  def 		_a					(self	)		->      List[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      MarianTokenizer.from_pretrained(F"{ORG_NAME}opus-mt-en-de"	)
       UpperCamelCase__										=      en_de_tokenizer(['''I am a small frog''']   ,				return_tensors=SCREAMING_SNAKE_CASE_	)
       self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      [38, 121, 14, 697, 3_8848, 0]
       self.assertListEqual(SCREAMING_SNAKE_CASE_   ,				batch.input_ids[0]	)

       UpperCamelCase__										=      tempfile.mkdtemp()
       en_de_tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      [x.name for x in Path(SCREAMING_SNAKE_CASE_	).glob('''*'''	)]
       self.assertIn('''source.spm'''   ,				SCREAMING_SNAKE_CASE_	)
       MarianTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      Dict:





       '''simple docstring'''





       UpperCamelCase__										=      self.get_tokenizer()

       UpperCamelCase__										=      tok(
           ['''I am a small frog''' * 1000, '''I am a small frog''']   ,				padding=SCREAMING_SNAKE_CASE_   ,				truncation=SCREAMING_SNAKE_CASE_   ,				return_tensors=SCREAMING_SNAKE_CASE_	)
       self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)
       self.assertEqual(batch.input_ids.shape   ,				(2, 512)	)






  def 		_a					(self	)		->      Optional[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      self.get_tokenizer()
       UpperCamelCase__										=      tok(['''I am a tiny frog''', '''I am a small frog''']   ,				padding=SCREAMING_SNAKE_CASE_   ,				return_tensors=SCREAMING_SNAKE_CASE_	)
       self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)
       self.assertEqual(batch_smaller.input_ids.shape   ,				(2, 10)	)






  @slow
  def 		_a					(self	)		->      List[str]:





       '''simple docstring'''





       UpperCamelCase__										=      {'''input_ids''': [[4_3495, 462, 20, 4_2164, 1369, 52, 464, 132, 1703, 492, 13, 7491, 3_8999, 6, 8, 464, 132, 1703, 492, 13, 4669, 3_7867, 13, 7525, 27, 1593, 988, 13, 3_3972, 7029, 6, 20, 8251, 383, 2, 270, 5866, 3788, 2, 2353, 8251, 1_2338, 2, 1_3958, 387, 2, 3629, 6953, 188, 2900, 2, 1_3958, 8011, 1_1501, 23, 8460, 4073, 3_4009, 20, 435, 1_1439, 27, 8, 8460, 4073, 6004, 20, 9988, 375, 27, 33, 266, 1945, 1076, 1350, 3_7867, 3288, 5, 577, 1076, 4374, 8, 5082, 5, 2_6453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 1_0767, 6, 316, 304, 4239, 3, 0], [148, 1_5722, 19, 1839, 12, 1350, 13, 2_2327, 5082, 5418, 4_7567, 3_5938, 59, 318, 1_9552, 108, 2183, 54, 1_4976, 4835, 32, 547, 1114, 8, 315, 2417, 5, 92, 1_9088, 3, 0, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100], [36, 6395, 1_2570, 3_9147, 1_1597, 6, 266, 4, 4_5405, 7296, 3, 0, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100]], '''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, 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], [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]]}  # noqa: E501
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=SCREAMING_SNAKE_CASE_   ,				model_name='''Helsinki-NLP/opus-mt-en-de'''   ,				revision='''1a8c2263da11e68e50938f97e10cd57820bd504c'''   ,				decode_kwargs={'''use_source_tokenizer''': True}   ,				)






  def 		_a					(self	)		->      List[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      MarianTokenizer.from_pretrained('''hf-internal-testing/test-marian-two-vocabs'''	)

       UpperCamelCase__										=      '''Tämä on testi'''
       UpperCamelCase__										=      '''This is a test'''

       UpperCamelCase__										=      [76, 7, 2047, 2]
       UpperCamelCase__										=      [69, 12, 11, 940, 2]

       UpperCamelCase__										=      tokenizer(SCREAMING_SNAKE_CASE_	).input_ids
       self.assertListEqual(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      tokenizer(text_target=SCREAMING_SNAKE_CASE_	).input_ids
       self.assertListEqual(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      tokenizer.decode(SCREAMING_SNAKE_CASE_   ,				skip_special_tokens=SCREAMING_SNAKE_CASE_	)
       self.assertEqual(SCREAMING_SNAKE_CASE_   ,				SCREAMING_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    (						A ,							A							):
     if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
          # expert layer
          UpperCamelCase__										=      flax_key_tuple[:-1] + ('''weight''',)
          UpperCamelCase__										=      torch.permute(A ,							(0, 2, 1)							)
     elif flax_key_tuple[-1] == "kernel" and ".".join(A							):
          # linear layer
          UpperCamelCase__										=      flax_key_tuple[:-1] + ('''weight''',)
          UpperCamelCase__										=      flax_tensor.T
     elif flax_key_tuple[-1] in ["scale", "embedding"]:
          UpperCamelCase__										=      flax_key_tuple[:-1] + ('''weight''',)

     return flax_key_tuple, flax_tensor







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

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

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

     return curr_real_layer_name, split_layer, content







def  __UpperCamelCase    (						A ,							A							):
     UpperCamelCase__										=      rename_keys(A							)
     UpperCamelCase__										=      {}
     for k, v in current_block.items():
          UpperCamelCase__										=      v
     UpperCamelCase__										=      new_current_block
     torch.save(A ,							A							)







def  __UpperCamelCase    (						A ,							A ,							A ,							A ,							A = WEIGHTS_NAME							):
     UpperCamelCase__										=      convert_file_size_to_int(A							)
     UpperCamelCase__										=      []
     UpperCamelCase__										=      {}
     UpperCamelCase__										=      0
     UpperCamelCase__										=      0

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

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

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

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

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

          UpperCamelCase__										=      raw_weights.to(getattr(A ,							A							)							)
          current_block_size += weight_size
          total_size += weight_size

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

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

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

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

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

     return metadata, index


if __name__ == "__main__":
     __magic_name__								=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.''',
     )
     __magic_name__								=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    (						):
     from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

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



