

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_(     snake_case__:    List[Any]    ,       snake_case__:    str  )			->					int:


 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 		UpperCamelCase_(     snake_case__:    Union[str, Any]    ,       snake_case__:    Optional[int]    ,       snake_case__:    List[Any]  )			->					Tuple:


 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_(     snake_case__:    Dict    ,       snake_case__:    Optional[Any]  )			->					int:
 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 		UpperCamelCase_(     snake_case__:    Dict    ,       snake_case__:    Any    ,       snake_case__:    List[str]    ,       snake_case__:    Tuple    ,       snake_case__:    str = WEIGHTS_NAME  )			->					Optional[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__":
 _UpperCamelCase     			=     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.''',
 )
 _UpperCamelCase     			=     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_(     )			->					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(snake_case__    ,       return_tensors='pt'  ).input_ids
 UpperCAmelCase__     							=							model.generate(snake_case__    ,       decoder_start_token_id=0  )
 print(tokenizer.decode(out[0]  )  )









'''simple docstring'''


import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led 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

@require_tokenizers
class      A       (						_a	,unittest.TestCase       ):
							lowercase_							     =							LEDTokenizer
							lowercase_							     =							LEDTokenizerFast
							lowercase_							     =							True







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


														"""simple docstring"""


														super().setUp()
														_a      	=							[
														    '''l''',
														    '''o''',
														    '''w''',
														    '''e''',
														    '''r''',
														    '''s''',
														    '''t''',
														    '''i''',
														    '''d''',
														    '''n''',
														    '''\u0120''',
														    '''\u0120l''',
														    '''\u0120n''',
														    '''\u0120lo''',
														    '''\u0120low''',
														    '''er''',
														    '''\u0120lowest''',
														    '''\u0120newer''',
														    '''\u0120wider''',
														    '''<unk>''',
														]
														_a      	=							dict(zip(lowerCAmelCase_				,     range(len(lowerCAmelCase_   )   )   )   )
														_a      	=							['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
														_a      	=							{'''unk_token''': '''<unk>'''}

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







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


														"""simple docstring"""


														kwargs.update(self.special_tokens_map   )
														return self.tokenizer_class.from_pretrained(self.tmpdirname				,     **lowerCAmelCase_   )







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


														"""simple docstring"""


														kwargs.update(self.special_tokens_map   )
														return self.rust_tokenizer_class.from_pretrained(self.tmpdirname				,     **lowerCAmelCase_   )







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


														"""simple docstring"""


														return "lower newer", "lower newer"







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


														"""simple docstring"""


														return LEDTokenizer.from_pretrained('''allenai/led-base-16384'''   )







							@cached_property
							def   __lowerCAmelCase     (    self			:    List[str]   )     ->				Union[str, Any]:


														"""simple docstring"""


														return LEDTokenizerFast.from_pretrained('''allenai/led-base-16384'''   )







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


														"""simple docstring"""


														_a      	=							['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
														_a      	=							[0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

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

																					self.assertEqual((2, 9)				,     batch.input_ids.shape   )
																					self.assertEqual((2, 9)				,     batch.attention_mask.shape   )
																					_a      	=							batch.input_ids.tolist()[0]
																					self.assertListEqual(lowerCAmelCase_				,     lowerCAmelCase_   )







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


														"""simple docstring"""


														_a      	=							['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
														for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																					_a      	=							tokenizer(lowerCAmelCase_				,     padding=lowerCAmelCase_				,     return_tensors='''pt'''   )
																					self.assertIn('''input_ids'''				,     lowerCAmelCase_   )
																					self.assertIn('''attention_mask'''				,     lowerCAmelCase_   )
																					self.assertNotIn('''labels'''				,     lowerCAmelCase_   )
																					self.assertNotIn('''decoder_attention_mask'''				,     lowerCAmelCase_   )







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


														"""simple docstring"""


														_a      	=							[
														    '''Summary of the text.''',
														    '''Another summary.''',
														]
														for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																					_a      	=							tokenizer(text_target=lowerCAmelCase_				,     max_length=32				,     padding='''max_length'''				,     return_tensors='''pt'''   )
																					self.assertEqual(32				,     targets['''input_ids'''].shape[1]   )







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


														"""simple docstring"""


														for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																					_a      	=							tokenizer(
																					    ['''I am a small frog''' * 10_24, '''I am a small frog''']				,     padding=lowerCAmelCase_				,     truncation=lowerCAmelCase_				,     return_tensors='''pt'''   )
																					self.assertIsInstance(lowerCAmelCase_				,     lowerCAmelCase_   )
																					self.assertEqual(batch.input_ids.shape				,     (2, 51_22)   )







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


														"""simple docstring"""


														_a      	=							['''A long paragraph for summarization.''']
														_a      	=							[
														    '''Summary of the text.''',
														]
														for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																					_a      	=							tokenizer(lowerCAmelCase_				,     return_tensors='''pt'''   )
																					_a      	=							tokenizer(text_target=lowerCAmelCase_				,     return_tensors='''pt'''   )
																					_a      	=							inputs['''input_ids''']
																					_a      	=							targets['''input_ids''']
																					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()   )







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


														"""simple docstring"""


														for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																					_a      	=							['''Summary of the text.''', '''Another summary.''']
																					_a      	=							[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

																					_a      	=							tokenizer(lowerCAmelCase_				,     padding=lowerCAmelCase_   )
																					_a      	=							[[0] * len(lowerCAmelCase_   ) for x in encoded_output['''input_ids''']]
																					_a      	=							tokenizer.pad(lowerCAmelCase_   )
																					self.assertSequenceEqual(outputs['''global_attention_mask''']				,     lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Any   )     ->				Dict:


														"""simple docstring"""


														pass







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


														"""simple docstring"""


														for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																					with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'   ):
																												_a      	=							self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_				,     **lowerCAmelCase_   )
																												_a      	=							self.tokenizer_class.from_pretrained(lowerCAmelCase_				,     **lowerCAmelCase_   )
																												_a      	=							'''A, <mask> AllenNLP sentence.'''
																												_a      	=							tokenizer_r.encode_plus(lowerCAmelCase_				,     add_special_tokens=lowerCAmelCase_				,     return_token_type_ids=lowerCAmelCase_   )
																												_a      	=							tokenizer_p.encode_plus(lowerCAmelCase_				,     add_special_tokens=lowerCAmelCase_				,     return_token_type_ids=lowerCAmelCase_   )
																												self.assertEqual(sum(tokens_r['''token_type_ids''']   )				,     sum(tokens_p['''token_type_ids''']   )   )
																												self.assertEqual(
																												    sum(tokens_r['''attention_mask''']   ) / len(tokens_r['''attention_mask''']   )				,     sum(tokens_p['''attention_mask''']   ) / len(tokens_p['''attention_mask''']   )				,     )

																												_a      	=							tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']   )
																												_a      	=							tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']   )
																												self.assertSequenceEqual(tokens_p['''input_ids''']				,     [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]   )
																												self.assertSequenceEqual(tokens_r['''input_ids''']				,     [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]   )

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

