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







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase = WEIGHTS_NAME    )->     str:
 __UpperCAmelCase					=   convert_file_size_to_int(_lowerCAmelCase    )
 __UpperCAmelCase					=   []
 __UpperCAmelCase					=   {}
 __UpperCAmelCase					=   0
 __UpperCAmelCase					=   0

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

 __UpperCAmelCase					=   {}
 for layer in checkpoint_info.keys():
  __UpperCAmelCase					,    __UpperCAmelCase					,    __UpperCAmelCase					=   get_key_and_tensorstore_dict(
      _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase    )
  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(_lowerCAmelCase    )
  __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('/'    )    )       ,    _lowerCAmelCase    )
  __UpperCAmelCase					=   '/'.join(_lowerCAmelCase    )

  # 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(
       _lowerCAmelCase       ,    weights_name.replace('.bin'       ,    F'-{len(_lowerCAmelCase    )+1:05d}-of-???.bin'    )    )
   rename_and_save_block(_lowerCAmelCase       ,    _lowerCAmelCase    )
   sharded_state_dicts.append(current_block.keys()    )
   del current_block
   __UpperCAmelCase					=   {}
   __UpperCAmelCase					=   0

  __UpperCAmelCase					=   raw_weights.to(getattr(_lowerCAmelCase       ,    _lowerCAmelCase    )    )
  current_block_size += weight_size
  total_size += weight_size

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

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

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

 return metadata, index


if __name__ == "__main__":
    _A:					Tuple							=				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:					Union[str, 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     (      )->     List[str]:
 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(_lowerCAmelCase       ,    return_tensors='pt'    ).input_ids
 __UpperCAmelCase					=   model.generate(_lowerCAmelCase       ,    decoder_start_token_id=0    )
 print(tokenizer.decode(out[0]    )    )












'''simple docstring'''




import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


_A:					Union[str, Any]							=				get_tests_dir() + """/test_data/fsmt/fsmt_val_data.json"""
with io.open(filename, """r""", encoding="""utf-8""") as f:
    _A:					Optional[Any]							=				json.load(f)






@require_torch
class UpperCAmelCase							(		unittest.TestCase							):



    def 					__lowerCamelCase						(  self     ,			__A					):
     return FSMTTokenizer.from_pretrained(__A					)



    def 					__lowerCamelCase						(  self     ,			__A					):
     __UpperCAmelCase					=   FSMTForConditionalGeneration.from_pretrained(__A					).to(__A					)
     if torch_device == "cuda":
      model.half()
     return model




    @parameterized.expand(
        [
            ['en-ru', 2_6.0],
            ['ru-en', 2_2.0],
            ['en-de', 2_2.0],
            ['de-en', 2_9.0],
        ]					)
    @slow
    def 					__lowerCamelCase						(  self     ,			__A     ,			__A					):
     # note: this test is not testing the best performance since it only evals a small batch
     # but it should be enough to detect a regression in the output quality
     __UpperCAmelCase					=   f'facebook/wmt19-{pair}'
     __UpperCAmelCase					=   self.get_tokenizer(__A					)
     __UpperCAmelCase					=   self.get_model(__A					)

     __UpperCAmelCase					=   bleu_data[pair]['src']
     __UpperCAmelCase					=   bleu_data[pair]['tgt']

     __UpperCAmelCase					=   tokenizer(__A     ,			return_tensors='pt'     ,			truncation=__A     ,			padding='longest'					).to(__A					)
     __UpperCAmelCase					=   model.generate(
         input_ids=batch.input_ids     ,			num_beams=8     ,			)
     __UpperCAmelCase					=   tokenizer.batch_decode(
         __A     ,			skip_special_tokens=__A     ,			clean_up_tokenization_spaces=__A					)
     __UpperCAmelCase					=   calculate_bleu(__A     ,			__A					)
     print(__A					)
     self.assertGreaterEqual(scores['bleu']     ,			__A					)





