






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       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						Tuple:
       if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
              # expert layer
              _UpperCAmelCase							=    flax_key_tuple[:-1] + ("""weight""",)
              _UpperCAmelCase							=    torch.permute(__snake_case   ,      (0, 2, 1) )
       elif flax_key_tuple[-1] == "kernel" and ".".join(__snake_case ):
              # 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       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case )				->						List[str]:
       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       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						Optional[Any]:
       _UpperCAmelCase							=    rename_keys(__snake_case )
       _UpperCAmelCase							=    {}
       for k, v in current_block.items():
              _UpperCAmelCase							=    v
       _UpperCAmelCase							=    new_current_block
       torch.save(__snake_case   ,      __snake_case )




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case = WEIGHTS_NAME )				->						Any:
       _UpperCAmelCase							=    convert_file_size_to_int(__snake_case )
       _UpperCAmelCase							=    []
       _UpperCAmelCase							=    {}
       _UpperCAmelCase							=    0
       _UpperCAmelCase							=    0

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

       _UpperCAmelCase							=    {}
       for layer in checkpoint_info.keys():
              _UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase							=    get_key_and_tensorstore_dict(
                  __snake_case   ,      __snake_case   ,      __snake_case )
              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(__snake_case )
              _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("""/""" ) )   ,      __snake_case )
              _UpperCAmelCase							=    """/""".join(__snake_case )

              # 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(
                         __snake_case   ,      weights_name.replace(""".bin"""   ,      f"""-{len(__snake_case )+1:05d}-of-???.bin""" ) )
                     rename_and_save_block(__snake_case   ,      __snake_case )
                     sharded_state_dicts.append(current_block.keys() )
                     del current_block
                     _UpperCAmelCase							=    {}
                     _UpperCAmelCase							=    0

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

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

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

       # Otherwise, let's build the index
       _UpperCAmelCase							=    {}
       _UpperCAmelCase							=    {}
       for idx, shard in enumerate(__snake_case ):
              _UpperCAmelCase							=    weights_name.replace(
                  """.bin"""   ,      f"""-{idx+1:05d}-of-{len(__snake_case ):05d}.bin""" )  # len(sharded_state_dicts):05d}
              _UpperCAmelCase							=    os.path.join(__snake_case   ,      weights_name.replace(""".bin"""   ,      f"""-{idx+1:05d}-of-???.bin""" ) )
              os.rename(__snake_case   ,      os.path.join(__snake_case   ,      __snake_case ) )
              _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(__snake_case   ,      __snake_case )   ,      """w"""   ,      encoding="""utf-8""" ) as f:
              _UpperCAmelCase							=    json.dumps(__snake_case   ,      indent=2   ,      sort_keys=__snake_case ) + """\n"""
              f.write(__snake_case )

       return metadata, index


if __name__ == "__main__":
  __a:     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.''',
  )
  __a:     int   					=					parser.parse_args()
  shard_on_the_fly(
      args.switch_tax_checkpoint_path,
      args.pytorch_dump_folder_path,
      args.max_shard_size,
      args.dtype,
  )





def       _SCREAMING_SNAKE_CASE (							)				->						Optional[int]:
       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(__snake_case   ,      return_tensors="""pt""" ).input_ids
       _UpperCAmelCase							=    model.generate(__snake_case   ,      decoder_start_token_id=0 )
       print(tokenizer.decode(out[0] ) )







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
  import torch

  from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
class 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase     ):

  '''simple docstring'''







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


         """simple docstring"""
         _UpperCAmelCase							=    XLMRobertaModel.from_pretrained("""xlm-roberta-base"""							)
         _UpperCAmelCase							=    torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]]							)
         # The dog is cute and lives in the garden house

         _UpperCAmelCase							=    torch.Size((1, 12, 768)							)  # batch_size, sequence_length, embedding_vector_dim
         _UpperCAmelCase							=    torch.tensor(
             [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]							)
         #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
         #  xlmr.eval()
         #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
         with torch.no_grad():
                _UpperCAmelCase							=    model(lowerCamelCase							)["""last_hidden_state"""].detach()
         self.assertEqual(output.shape       ,				lowerCamelCase							)
         # compare the actual values for a slice of last dim
         self.assertTrue(torch.allclose(output[:, :, -1]       ,				lowerCamelCase       ,				atol=1E-3							)							)





  @slow
  def  lowerCamelCase       (      self		:			Optional[int]							)						->	Union[str, Any]:


         """simple docstring"""
         _UpperCAmelCase							=    XLMRobertaModel.from_pretrained("""xlm-roberta-large"""							)
         _UpperCAmelCase							=    torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]]							)
         # The dog is cute and lives in the garden house

         _UpperCAmelCase							=    torch.Size((1, 12, 1024)							)  # batch_size, sequence_length, embedding_vector_dim
         _UpperCAmelCase							=    torch.tensor(
             [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]]							)
         #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
         #  xlmr.eval()
         #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
         with torch.no_grad():
                _UpperCAmelCase							=    model(lowerCamelCase							)["""last_hidden_state"""].detach()
         self.assertEqual(output.shape       ,				lowerCamelCase							)
         # compare the actual values for a slice of last dim
         self.assertTrue(torch.allclose(output[:, :, -1]       ,				lowerCamelCase       ,				atol=1E-3							)							)