




'''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 	snake_case_					(UpperCamelCase :						Dict				,		UpperCamelCase :						Tuple   ):





       '''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 	snake_case_					(UpperCamelCase :						int				,		UpperCamelCase :						int				,		UpperCamelCase :						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 	snake_case_					(UpperCamelCase :						List[Any]				,		UpperCamelCase :						List[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 	snake_case_					(UpperCamelCase :						Union[str, Any]				,		UpperCamelCase :						Optional[int]				,		UpperCamelCase :						Tuple				,		UpperCamelCase :						List[str]				,		UpperCamelCase :						str = WEIGHTS_NAME   ):





       '''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__":
    _snake_case   :   Any     							=	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.',
    )
    _snake_case   :   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 	snake_case_					():





       '''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]   )   )







'''simple docstring'''


import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_snake_case   :   Tuple     							=	get_tests_dir('fixtures/test_sentencepiece_no_bos.model')

@require_sentencepiece
@require_tokenizers
class     A					(	_a  ,unittest.TestCase					):
							lowercase_           =       PegasusTokenizer
							lowercase_           =       PegasusTokenizerFast
							lowercase_           =       True
							lowercase_           =       True







							def      __lowerCAmelCase    (						self :						str      )    ->							Any:


														"""simple docstring"""


														super().setUp()

														# We have a SentencePiece fixture for testing
														_a			  =       PegasusTokenizer(lowerCAmelCase_      )
														tokenizer.save_pretrained(self.tmpdirname      )







							@cached_property
							def      __lowerCAmelCase    (						self :						Any      )    ->							Optional[int]:


														"""simple docstring"""


														return PegasusTokenizer.from_pretrained('''google/pegasus-large'''      )







							def      __lowerCAmelCase    (						self :						Optional[int]							,    **lowerCAmelCase_ :						List[str]      )    ->							PegasusTokenizer:


														"""simple docstring"""


														return PegasusTokenizer.from_pretrained(self.tmpdirname							,    **lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						Optional[Any]							,    lowerCAmelCase_ :						List[str]      )    ->							Any:


														"""simple docstring"""


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







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


														"""simple docstring"""


														_a			  =       '''</s>'''
														_a			  =       1

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







							def      __lowerCAmelCase    (						self :						Tuple      )    ->							List[str]:


														"""simple docstring"""


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

														self.assertEqual(vocab_keys[0]							,    '''<pad>'''      )
														self.assertEqual(vocab_keys[1]							,    '''</s>'''      )
														self.assertEqual(vocab_keys[-1]							,    '''v'''      )
														self.assertEqual(len(lowerCAmelCase_      )							,    11_03      )







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


														"""simple docstring"""


														self.assertEqual(self.get_tokenizer().vocab_size							,    11_03      )







							def      __lowerCAmelCase    (						self :						Tuple      )    ->							str:


														"""simple docstring"""


														_a			  =       self.rust_tokenizer_class.from_pretrained(self.tmpdirname      )
														_a			  =       self.tokenizer_class.from_pretrained(self.tmpdirname      )
														_a			  =       (
														    '''Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'''
														    ''' </s> <pad> <pad> <pad>'''
														)
														_a			  =       rust_tokenizer([raw_input_str]							,    return_tensors=lowerCAmelCase_							,    add_special_tokens=lowerCAmelCase_      ).input_ids[0]
														_a			  =       py_tokenizer([raw_input_str]							,    return_tensors=lowerCAmelCase_							,    add_special_tokens=lowerCAmelCase_      ).input_ids[0]
														self.assertListEqual(lowerCAmelCase_							,    lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						Optional[Any]      )    ->							List[str]:


														"""simple docstring"""


														_a			  =       self._large_tokenizer
														# <mask_1> masks whole sentence while <mask_2> masks single word
														_a			  =       '''<mask_1> To ensure a <mask_2> flow of bank resolutions.'''
														_a			  =       [2, 4_13, 6_15, 1_14, 3, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
														_a			  =       tokenizer([raw_input_str]							,    return_tensors=lowerCAmelCase_      ).input_ids[0]
														self.assertListEqual(lowerCAmelCase_							,    lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						Dict      )    ->							Dict:


														"""simple docstring"""


														_a			  =       self._large_tokenizer
														# The tracebacks for the following asserts are **better** without messages or self.assertEqual
														assert tokenizer.vocab_size == 9_61_03
														assert tokenizer.pad_token_id == 0
														assert tokenizer.eos_token_id == 1
														assert tokenizer.offset == 1_03
														assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_05
														assert tokenizer.unk_token == "<unk>"
														assert tokenizer.model_max_length == 10_24
														_a			  =       '''To ensure a smooth flow of bank resolutions.'''
														_a			  =       [4_13, 6_15, 1_14, 22_91, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
														_a			  =       tokenizer([raw_input_str]							,    return_tensors=lowerCAmelCase_      ).input_ids[0]
														self.assertListEqual(lowerCAmelCase_							,    lowerCAmelCase_      )
														assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]      ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]







							@require_torch
							def      __lowerCAmelCase    (						self :						Tuple      )    ->							Union[str, Any]:


														"""simple docstring"""


														_a			  =       ['''This is going to be way too long.''' * 1_50, '''short example''']
														_a			  =       ['''not super long but more than 5 tokens''', '''tiny''']
														_a			  =       self._large_tokenizer(lowerCAmelCase_							,    padding=lowerCAmelCase_							,    truncation=lowerCAmelCase_							,    return_tensors='''pt'''      )
														_a			  =       self._large_tokenizer(
														    text_target=lowerCAmelCase_							,    max_length=5							,    padding=lowerCAmelCase_							,    truncation=lowerCAmelCase_							,    return_tensors='''pt'''      )

														assert batch.input_ids.shape == (2, 10_24)
														assert batch.attention_mask.shape == (2, 10_24)
														assert targets["input_ids"].shape == (2, 5)
														assert len(lowerCAmelCase_      ) == 2  # input_ids, attention_mask.







							@slow
							def      __lowerCAmelCase    (						self :						List[Any]      )    ->							int:


														"""simple docstring"""


														_a			  =       {'''input_ids''': [[3_89_79, 1_43, 1_84_85, 6_06, 1_30, 2_66_69, 8_76_86, 1_21, 5_41_89, 11_29, 1_11, 2_66_69, 8_76_86, 1_21, 91_14, 1_47_87, 1_21, 1_32_49, 1_58, 5_92, 9_56, 1_21, 1_46_21, 3_15_76, 1_43, 6_26_13, 1_08, 96_88, 9_30, 4_34_30, 1_15_62, 6_26_13, 3_04, 1_08, 1_14_43, 8_97, 1_08, 93_14, 1_74_15, 6_33_99, 1_08, 1_14_43, 76_14, 1_83_16, 1_18, 42_84, 71_48, 1_24_30, 1_43, 14_00, 2_57_03, 1_58, 1_11, 42_84, 71_48, 1_17_72, 1_43, 2_12_97, 10_64, 1_58, 1_22, 2_04, 35_06, 17_54, 11_33, 1_47_87, 15_81, 1_15, 3_32_24, 44_82, 1_11, 13_55, 1_10, 2_91_73, 3_17, 5_08_33, 1_08, 2_01_47, 9_46_65, 1_11, 7_71_98, 1_07, 1], [1_10, 6_26_13, 1_17, 6_38, 1_12, 11_33, 1_21, 2_00_98, 13_55, 7_90_50, 1_38_72, 1_35, 15_96, 5_35_41, 13_52, 1_41, 1_30_39, 55_42, 1_24, 3_02, 5_18, 1_11, 2_68, 29_56, 1_15, 1_49, 44_27, 1_07, 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], [1_39, 12_35, 27_99, 1_82_89, 1_77_80, 2_04, 1_09, 94_74, 12_96, 1_07, 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]], '''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, 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, 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=lowerCAmelCase_							,    model_name='''google/bigbird-pegasus-large-arxiv'''							,    revision='''ba85d0851d708441f91440d509690f1ab6353415'''							,    )



@require_sentencepiece
@require_tokenizers
class     A					(	_a  ,unittest.TestCase					):
							lowercase_           =       PegasusTokenizer
							lowercase_           =       PegasusTokenizerFast
							lowercase_           =       True
							lowercase_           =       True







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


														"""simple docstring"""


														super().setUp()

														# We have a SentencePiece fixture for testing
														_a			  =       PegasusTokenizer(lowerCAmelCase_							,    offset=0							,    mask_token_sent=lowerCAmelCase_							,    mask_token='''[MASK]'''      )
														tokenizer.save_pretrained(self.tmpdirname      )







							@cached_property
							def      __lowerCAmelCase    (						self :						Dict      )    ->							int:


														"""simple docstring"""


														return PegasusTokenizer.from_pretrained('''google/bigbird-pegasus-large-arxiv'''      )







							def      __lowerCAmelCase    (						self :						Optional[int]							,    **lowerCAmelCase_ :						Tuple      )    ->							PegasusTokenizer:


														"""simple docstring"""


														return PegasusTokenizer.from_pretrained(self.tmpdirname							,    **lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						str							,    lowerCAmelCase_ :						Optional[int]      )    ->							Optional[Any]:


														"""simple docstring"""


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







							def      __lowerCAmelCase    (						self :						List[Any]      )    ->							str:


														"""simple docstring"""


														_a			  =       self.rust_tokenizer_class.from_pretrained(self.tmpdirname      )
														_a			  =       self.tokenizer_class.from_pretrained(self.tmpdirname      )
														_a			  =       (
														    '''Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'''
														    ''' <pad> <pad> <pad>'''
														)
														_a			  =       rust_tokenizer([raw_input_str]							,    return_tensors=lowerCAmelCase_							,    add_special_tokens=lowerCAmelCase_      ).input_ids[0]
														_a			  =       py_tokenizer([raw_input_str]							,    return_tensors=lowerCAmelCase_							,    add_special_tokens=lowerCAmelCase_      ).input_ids[0]
														self.assertListEqual(lowerCAmelCase_							,    lowerCAmelCase_      )







							@require_torch
							def      __lowerCAmelCase    (						self :						Optional[int]      )    ->							Tuple:


														"""simple docstring"""


														_a			  =       ['''This is going to be way too long.''' * 10_00, '''short example''']
														_a			  =       ['''not super long but more than 5 tokens''', '''tiny''']
														_a			  =       self._large_tokenizer(lowerCAmelCase_							,    padding=lowerCAmelCase_							,    truncation=lowerCAmelCase_							,    return_tensors='''pt'''      )
														_a			  =       self._large_tokenizer(
														    text_target=lowerCAmelCase_							,    max_length=5							,    padding=lowerCAmelCase_							,    truncation=lowerCAmelCase_							,    return_tensors='''pt'''      )

														assert batch.input_ids.shape == (2, 40_96)
														assert batch.attention_mask.shape == (2, 40_96)
														assert targets["input_ids"].shape == (2, 5)
														assert len(lowerCAmelCase_      ) == 2  # input_ids, attention_mask.







							def      __lowerCAmelCase    (						self :						List[str]      )    ->							Any:


														"""simple docstring"""


														_a			  =       (
														    '''This is an example string that is used to test the original TF implementation against the HF'''
														    ''' implementation'''
														)

														_a			  =       self._large_tokenizer(lowerCAmelCase_      ).input_ids

														self.assertListEqual(
														    lowerCAmelCase_							,    [1_82, 1_17, 1_42, 5_87, 42_11, 1_20, 1_17, 2_63, 1_12, 8_04, 1_09, 8_56, 2_50_16, 31_37, 4_64, 1_09, 2_69_55, 31_37, 1]							,    )

