'''simple docstring'''
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  __lowerCamelCase       (      _UpperCamelCase  :  Optional[int]							,    _UpperCamelCase  :  Optional[Any]					):


      '''simple docstring'''


      if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
            # expert layer
            UpperCAmelCase_            =  flax_key_tuple[:-1] + ('''weight''',)
            UpperCAmelCase_            =  torch.permute(_UpperCamelCase							,    (0, 2, 1)					)
      elif flax_key_tuple[-1] == "kernel" and ".".join(_UpperCamelCase					):
            # 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  __lowerCamelCase       (      _UpperCamelCase  :  List[Any]							,    _UpperCamelCase  :  int							,    _UpperCamelCase  :  List[Any]					):


      '''simple docstring'''


      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  __lowerCamelCase       (      _UpperCamelCase  :  Union[str, Any]							,    _UpperCamelCase  :  Optional[int]					):


      '''simple docstring'''


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



def  __lowerCamelCase       (      _UpperCamelCase  :  List[str]							,    _UpperCamelCase  :  List[str]							,    _UpperCamelCase  :  str							,    _UpperCamelCase  :  Any							,    _UpperCamelCase  :  str = WEIGHTS_NAME					):


      '''simple docstring'''


      UpperCAmelCase_            =  convert_file_size_to_int(_UpperCamelCase					)
      UpperCAmelCase_            =  []
      UpperCAmelCase_            =  {}
      UpperCAmelCase_            =  0
      UpperCAmelCase_            =  0

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

      UpperCAmelCase_            =  {}
      for layer in checkpoint_info.keys():
            UpperCAmelCase_      ,		UpperCAmelCase_      ,		UpperCAmelCase_            =  get_key_and_tensorstore_dict(
                _UpperCamelCase							,    _UpperCamelCase							,    _UpperCamelCase					)
            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(_UpperCamelCase					)
            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('''/'''					)					)							,    _UpperCamelCase					)
            UpperCAmelCase_            =  '''/'''.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:
                  UpperCAmelCase_            =  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
                  UpperCAmelCase_            =  {}
                  UpperCAmelCase_            =  0

            UpperCAmelCase_            =  raw_weights.to(getattr(_UpperCamelCase							,    _UpperCamelCase					)					)
            current_block_size += weight_size
            total_size += weight_size

      # Add the last block
      UpperCAmelCase_            =  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
      UpperCAmelCase_            =  {}
      UpperCAmelCase_            =  {}
      for idx, shard in enumerate(_UpperCamelCase					):
            UpperCAmelCase_            =  weights_name.replace(
                '''.bin'''							,    F"""-{idx+1:05d}-of-{len(_UpperCamelCase					):05d}.bin"""					)  # len(sharded_state_dicts):05d}
            UpperCAmelCase_            =  os.path.join(_UpperCamelCase							,    weights_name.replace('''.bin'''							,    F"""-{idx+1:05d}-of-???.bin"""					)					)
            os.rename(_UpperCamelCase							,    os.path.join(_UpperCamelCase							,    _UpperCamelCase					)					)
            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(_UpperCamelCase							,    _UpperCamelCase					)							,    '''w'''							,    encoding='''utf-8'''					) as f:
            UpperCAmelCase_            =  json.dumps(_UpperCamelCase							,    indent=2							,    sort_keys=_UpperCamelCase					) + '''\n'''
            f.write(_UpperCamelCase					)

      return metadata, index


if __name__ == "__main__":
      lowercase__  :      Optional[int]        =					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.",
      )
      lowercase__  :      Optional[Any]        =					parser.parse_args()
      shard_on_the_fly(
          args.switch_tax_checkpoint_path,
          args.pytorch_dump_folder_path,
          args.max_shard_size,
          args.dtype,
      )





def  __lowerCamelCase       (      ):


      '''simple docstring'''


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






'''simple docstring'''
import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors







@require_tokenizers
class lowerCamelCase							(	lowerCamelCase     ,		unittest.TestCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	MvpTokenizer
  lowerCAmelCase__										=	MvpTokenizerFast
  lowerCAmelCase__										=	True
  lowerCAmelCase__										=	filter_roberta_detectors






  def  lowerCAmelCase__ (      self			:       Union[str, Any]  )				->Tuple:
        super().setUp()
        UpperCAmelCase_            =  [
            '''l''',
            '''o''',
            '''w''',
            '''e''',
            '''r''',
            '''s''',
            '''t''',
            '''i''',
            '''d''',
            '''n''',
            '''\u0120''',
            '''\u0120l''',
            '''\u0120n''',
            '''\u0120lo''',
            '''\u0120low''',
            '''er''',
            '''\u0120lowest''',
            '''\u0120newer''',
            '''\u0120wider''',
            '''<unk>''',
        ]
        UpperCAmelCase_            =  dict(zip(UpperCAmelCase__  ,							range(len(UpperCAmelCase__  )  )  )  )
        UpperCAmelCase_            =  ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
        UpperCAmelCase_            =  {'''unk_token''': '''<unk>'''}

        UpperCAmelCase_            =  os.path.join(self.tmpdirname  ,							VOCAB_FILES_NAMES['''vocab_file''']  )
        UpperCAmelCase_            =  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(UpperCAmelCase__  ) + '''\n'''  )
        with open(self.merges_file  ,							'''w'''  ,							encoding='''utf-8'''  ) as fp:
              fp.write('''\n'''.join(UpperCAmelCase__  )  )






  def  lowerCAmelCase__ (      self			:       Tuple  ,							**UpperCAmelCase__			:       List[str]  )				->Dict:
        kwargs.update(self.special_tokens_map  )
        return self.tokenizer_class.from_pretrained(self.tmpdirname  ,							**UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       Optional[int]  ,							**UpperCAmelCase__			:       int  )				->Tuple:
        kwargs.update(self.special_tokens_map  )
        return self.rust_tokenizer_class.from_pretrained(self.tmpdirname  ,							**UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       Union[str, Any]  ,							UpperCAmelCase__			:       Optional[Any]  )				->Union[str, Any]:
        return "lower newer", "lower newer"






  @cached_property
  def  lowerCAmelCase__ (      self			:       Union[str, Any]  )				->Optional[int]:
        return MvpTokenizer.from_pretrained('''RUCAIBox/mvp'''  )






  @cached_property
  def  lowerCAmelCase__ (      self			:       Tuple  )				->Tuple:
        return MvpTokenizerFast.from_pretrained('''RUCAIBox/mvp'''  )






  @require_torch
  def  lowerCAmelCase__ (      self			:       Any  )				->Dict:
        UpperCAmelCase_            =  ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
        UpperCAmelCase_            =  [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase_            =  tokenizer(UpperCAmelCase__  ,							max_length=len(UpperCAmelCase__  )  ,							padding=UpperCAmelCase__  ,							return_tensors='''pt'''  )
              self.assertIsInstance(UpperCAmelCase__  ,							UpperCAmelCase__  )

              self.assertEqual((2, 9)  ,							batch.input_ids.shape  )
              self.assertEqual((2, 9)  ,							batch.attention_mask.shape  )
              UpperCAmelCase_            =  batch.input_ids.tolist()[0]
              self.assertListEqual(UpperCAmelCase__  ,							UpperCAmelCase__  )
              # Test that special tokens are reset






  @require_torch
  def  lowerCAmelCase__ (      self			:       str  )				->int:
        UpperCAmelCase_            =  ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase_            =  tokenizer(UpperCAmelCase__  ,							padding=UpperCAmelCase__  ,							return_tensors='''pt'''  )
              # check if input_ids are returned and no labels
              self.assertIn('''input_ids'''  ,							UpperCAmelCase__  )
              self.assertIn('''attention_mask'''  ,							UpperCAmelCase__  )
              self.assertNotIn('''labels'''  ,							UpperCAmelCase__  )
              self.assertNotIn('''decoder_attention_mask'''  ,							UpperCAmelCase__  )






  @require_torch
  def  lowerCAmelCase__ (      self			:       Tuple  )				->Optional[Any]:
        UpperCAmelCase_            =  [
            '''Summary of the text.''',
            '''Another summary.''',
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase_            =  tokenizer(text_target=UpperCAmelCase__  ,							max_length=32  ,							padding='''max_length'''  ,							return_tensors='''pt'''  )
              self.assertEqual(32  ,							targets['''input_ids'''].shape[1]  )






  @require_torch
  def  lowerCAmelCase__ (      self			:       List[str]  )				->int:
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase_            =  tokenizer(
                  ['''I am a small frog''' * 1024, '''I am a small frog''']  ,							padding=UpperCAmelCase__  ,							truncation=UpperCAmelCase__  ,							return_tensors='''pt'''  )
              self.assertIsInstance(UpperCAmelCase__  ,							UpperCAmelCase__  )
              self.assertEqual(batch.input_ids.shape  ,							(2, 1024)  )






  @require_torch
  def  lowerCAmelCase__ (      self			:       Dict  )				->Optional[int]:
        UpperCAmelCase_            =  ['''A long paragraph for summarization.''']
        UpperCAmelCase_            =  [
            '''Summary of the text.''',
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase_            =  tokenizer(UpperCAmelCase__  ,							text_target=UpperCAmelCase__  ,							return_tensors='''pt'''  )
              UpperCAmelCase_            =  inputs['''input_ids''']
              UpperCAmelCase_            =  inputs['''labels''']
              self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()  )
              self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()  )
              self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()  )
              self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()  )






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






  def  lowerCAmelCase__ (      self			:       Union[str, Any]  )				->Tuple:
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""  ):
                    UpperCAmelCase_            =  self.rust_tokenizer_class.from_pretrained(UpperCAmelCase__  ,							**UpperCAmelCase__  )
                    UpperCAmelCase_            =  self.tokenizer_class.from_pretrained(UpperCAmelCase__  ,							**UpperCAmelCase__  )
                    UpperCAmelCase_            =  '''A, <mask> AllenNLP sentence.'''
                    UpperCAmelCase_            =  tokenizer_r.encode_plus(UpperCAmelCase__  ,							add_special_tokens=UpperCAmelCase__  ,							return_token_type_ids=UpperCAmelCase__  )
                    UpperCAmelCase_            =  tokenizer_p.encode_plus(UpperCAmelCase__  ,							add_special_tokens=UpperCAmelCase__  ,							return_token_type_ids=UpperCAmelCase__  )

                    # 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''']  )  ,							)

                    UpperCAmelCase_            =  tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']  )
                    UpperCAmelCase_            =  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, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]  )
                    self.assertSequenceEqual(tokens_r['''input_ids''']  ,							[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]  )

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





