

'''simple docstring'''
import json
import os
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_UpperCAmelCase  :		int  =		logging.get_logger(__name__)


_UpperCAmelCase  :		int  =		{
    '''vocab_file''': '''vocab.json''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
    '''merges_file''': '''merges.txt''',
}

_UpperCAmelCase  :		int  =		{
    '''vocab_file''': {
        '''facebook/s2t-wav2vec2-large-en-de''': (
            '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json'''
        ),
    },
    '''tokenizer_config_file''': {
        '''facebook/s2t-wav2vec2-large-en-de''': (
            '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json'''
        ),
    },
    '''merges_file''': {
        '''facebook/s2t-wav2vec2-large-en-de''': (
            '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt'''
        ),
    },
}

_UpperCAmelCase  :		str  =		'''</w>'''
_UpperCAmelCase  :		Dict  =		'''@@ '''




def 	UpperCamelCase   (       lowercase_						:    Union[str, Any]	)		->	Dict:




  '''simple docstring'''



  lowercase									=set()
  lowercase									=word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)	)
    lowercase									=char
  return pairs


# Speech2Text2 has no max input length
_UpperCAmelCase  :		str  =		{'''facebook/s2t-wav2vec2-large-en-de''': 10_24}




class 						__magic_name__   (							__SCREAMING_SNAKE_CASE ):
 UpperCamelCase__     =       VOCAB_FILES_NAMES
 UpperCamelCase__     =       PRETRAINED_VOCAB_FILES_MAP
 UpperCamelCase__     =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCamelCase__     =       ['input_ids', 'attention_mask']







 def __init__(       self  , snake_case_  , snake_case_="<s>"  , snake_case_="<pad>"  , snake_case_="</s>"  , snake_case_="<unk>"  , snake_case_=False  , snake_case_=None  , **snake_case_  , ):
   super().__init__(
       unk_token=snake_case_  , bos_token=snake_case_  , eos_token=snake_case_  , pad_token=snake_case_  , do_lower_case=snake_case_  , **snake_case_  , )

   lowercase									=do_lower_case

   with open(snake_case_  , encoding='''utf-8'''   ) as vocab_handle:
     lowercase									=json.load(snake_case_   )
   lowercase									={v: k for k, v in self.encoder.items()}

   if merges_file is None:
     logger.info(f'No merges files provided. {self.__class__.__name__} can only be used for decoding.'   )

     lowercase									=None
     lowercase									=None
   else:
     with open(snake_case_  , encoding='''utf-8'''   ) as merges_handle:
       lowercase									=merges_handle.read().split('''\n'''   )[:-1]

     lowercase									=[tuple(merge.split()[:2]   ) for merge in merges]
     lowercase									=dict(zip(snake_case_  , range(len(snake_case_   )   )   )   )
     lowercase									={}







 @property
 def 						_A(       self   ):
   return len(self.decoder   )







 def 						_A(       self   ):
   return dict(self.encoder  , **self.added_tokens_encoder   )







 def 						_A(       self  , snake_case_   ):
   lowercase									=tuple(token[:-1]   ) + (token[-1] + BPE_TOKEN_MERGES,)
   if token in self.cache:
     return self.cache[token]
   lowercase									=get_pairs(snake_case_   )

   if not pairs:
     return token

   while True:
     lowercase									=min(snake_case_  , key=lambda snake_case_   : self.bpe_ranks.get(snake_case_  , float('''inf'''   )   )   )
     if bigram not in self.bpe_ranks:
       break
     lowercase							, lowercase									=bigram
     lowercase									=[]
     lowercase									=0
     while i < len(snake_case_   ):
       try:
         lowercase									=word.index(snake_case_  , snake_case_   )
       except ValueError:
         new_word.extend(word[i:]   )
         break
       else:
         new_word.extend(word[i:j]   )
         lowercase									=j

       if word[i] == first and i < len(snake_case_   ) - 1 and word[i + 1] == second:
         new_word.append(first + second   )
         i += 2
       else:
         new_word.append(word[i]   )
         i += 1
     lowercase									=tuple(snake_case_   )
     lowercase									=new_word
     if len(snake_case_   ) == 1:
       break
     else:
       lowercase									=get_pairs(snake_case_   )
   lowercase									=''' '''.join(snake_case_   )
   if word == "\n  " + BPE_TOKEN_MERGES:
     lowercase									='''\n''' + BPE_TOKEN_MERGES

   if word.endswith(snake_case_   ):
     lowercase									=word.replace(snake_case_  , ''''''   )

   lowercase									=word.replace(''' '''  , snake_case_   )
   lowercase									=word
   return word







 def 						_A(       self  , snake_case_   ):

   if self.bpe_ranks is None:
     raise ValueError(
         '''This tokenizer was instantiated without a `merges.txt` file, so'''
         ''' that it can only be used for decoding, not for encoding.'''
         '''Make sure to provide `merges.txt` file at instantiation to enable '''
         '''encoding.'''   )

   if self.do_lower_case:
     lowercase									=text.lower()

   lowercase									=text.split()

   lowercase									=[]
   for token in text:
     if token:
       split_tokens.extend(list(self.bpe(snake_case_   ).split(''' '''   )   )   )

   return split_tokens







 def 						_A(       self  , snake_case_   ):
   return self.encoder.get(snake_case_  , self.encoder.get(self.unk_token   )   )







 def 						_A(       self  , snake_case_   ):
   lowercase									=self.decoder.get(snake_case_  , self.unk_token   )
   return result







 def 						_A(       self  , snake_case_   ):
   lowercase									=''' '''.join(snake_case_   )

   # make sure @@ tokens are concatenated
   lowercase									=''''''.join(string.split(snake_case_   )   )

   return string







 def 						_A(       self  , snake_case_  , snake_case_ = None   ):
   if not os.path.isdir(snake_case_   ):
     logger.error(f'Vocabulary path ({save_directory}) should be a directory'   )
     return
   lowercase									=os.path.join(
       snake_case_  , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )
   lowercase									=os.path.join(
       snake_case_  , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']   )

   with open(snake_case_  , '''w'''  , encoding='''utf-8'''   ) as f:
     f.write(json.dumps(self.encoder  , indent=2  , sort_keys=snake_case_  , ensure_ascii=snake_case_   ) + '''\n'''   )

   lowercase									=0
   if self.bpe_ranks is None:
     return (vocab_file,)

   with open(snake_case_  , '''w'''  , encoding='''utf-8'''   ) as writer:
     for bpe_tokens, token_index in sorted(self.bpe_ranks.items()  , key=lambda snake_case_   : kv[1]   ):
       if index != token_index:
         logger.warning(
             f'Saving vocabulary to {merges_file}: BPE merge indices are not consecutive.'
             ''' Please check that the tokenizer is not corrupted!'''   )
         lowercase									=token_index
       writer.write(''' '''.join(snake_case_   ) + '''\n'''   )
       index += 1

   return (vocab_file, merges_file)





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(      __UpperCamelCase	,				__UpperCamelCase )	->						Optional[Any]:
				if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
								# expert layer
								a__	:							List[str]      						= flax_key_tuple[:-1] + ("weight",)
								a__	:							Optional[int]      						= torch.permute(__UpperCamelCase	,				(0, 2, 1) )
				elif flax_key_tuple[-1] == "kernel" and ".".join(__UpperCamelCase ):
								# linear layer
								a__	:							Tuple      						= flax_key_tuple[:-1] + ("weight",)
								a__	:							Tuple      						= flax_tensor.T
				elif flax_key_tuple[-1] in ["scale", "embedding"]:
								a__	:							str      						= flax_key_tuple[:-1] + ("weight",)

				return flax_key_tuple, flax_tensor






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )	->						List[Any]:
				if "metadata" in layer:
								a__	:							Union[str, Any]      						= layer.split("metadata" )
								a__	:							Any      						= "".join(split_layer[0] )[:-1]
								a__	:							Optional[int]      						= [tuple(("metadata" + split_layer[1]).split("/" ) )]
				elif "kvstore" in layer:
								a__	:							Optional[int]      						= layer.split("kvstore" )
								a__	:							Union[str, Any]      						= "".join(split_layer[0] )[:-1]
								a__	:							Optional[Any]      						= [tuple(("kvstore" + split_layer[1]).split("/" ) )]

				else:
								a__	:							Any      						= layer.split("/" )
								a__	:							Union[str, Any]      						= "/".join(split_layer[:-1] )
								a__	:							Tuple      						= (split_layer[-1],)

				if "kvstore/path" in layer:
								a__	:							Tuple      						= F'{switch_checkpoint_path}/{checkpoint_info[layer]}'
				elif "kvstore/driver" in layer:
								a__	:							int      						= "file"
				else:
								a__	:							List[str]      						= checkpoint_info[layer]

				return curr_real_layer_name, split_layer, content






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase )	->						Optional[int]:
				a__	:							List[Any]      						= rename_keys(__UpperCamelCase )
				a__	:							Tuple      						= {}
				for k, v in current_block.items():
								a__	:							str      						= v
				a__	:							Optional[int]      						= new_current_block
				torch.save(__UpperCamelCase	,				__UpperCamelCase )






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase = WEIGHTS_NAME )	->						Any:
				a__	:							Optional[Any]      						= convert_file_size_to_int(__UpperCamelCase )
				a__	:							Tuple      						= []
				a__	:							Dict      						= {}
				a__	:							str      						= 0
				a__	:							Tuple      						= 0

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

				a__	:							Any      						= {}
				for layer in checkpoint_info.keys():
								a__						,   a__						,   a__	:							Optional[Any]      						= get_key_and_tensorstore_dict(
								    __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )
								if curr_real_layer_name in all_layers:
												a__	:							int      						= content
								else:
												a__	:							List[Any]      						= {split_layer[-1]: content}

				for key in all_layers.keys():
								# open tensorstore file
								a__	:							Tuple      						= ts.open(unflatten_dict(all_layers[key] ) ).result().read().result()
								a__	:							Tuple      						= torch.tensor(__UpperCamelCase )
								a__	:							str      						= raw_weights.numel() * dtype_byte_size(raw_weights.dtype )

								# use the renaming pattern from the small conversion scripts
								a__						,   a__	:							int      						= rename_base_flax_keys(tuple(key.split("/" ) )	,				__UpperCamelCase )
								a__	:							Union[str, Any]      						= "/".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__	:							int      						= 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__	:							int      						= {}
												a__	:							Optional[int]      						= 0

								a__	:							List[str]      						= raw_weights.to(getattr(__UpperCamelCase	,				__UpperCamelCase ) )
								current_block_size += weight_size
								total_size += weight_size

				# Add the last block
				a__	:							Optional[int]      						= 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__	:							int      						= {}
				a__	:							Tuple      						= {}
				for idx, shard in enumerate(__UpperCamelCase ):
								a__	:							Tuple      						= weights_name.replace(
								    ".bin"	,				F'-{idx+1:05d}-of-{len(__UpperCamelCase ):05d}.bin' )  # len(sharded_state_dicts):05d}
								a__	:							Optional[int]      						= os.path.join(__UpperCamelCase	,				weights_name.replace(".bin"	,				F'-{idx+1:05d}-of-???.bin' ) )
								os.rename(__UpperCamelCase	,				os.path.join(__UpperCamelCase	,				__UpperCamelCase ) )
								a__	:							List[str]      						= shard
								for key in shard:
												a__	:							Tuple      						= shard_file

    # Add the metadata
				a__	:							int      						= {"total_size": total_size}
				a__	:							List[str]      						= {"metadata": metadata, "weight_map": weight_map}

				with open(os.path.join(__UpperCamelCase	,				__UpperCamelCase )	,				"w"	,				encoding="utf-8" ) as f:
								a__	:							List[str]      						= json.dumps(__UpperCamelCase	,				indent=2	,				sort_keys=__UpperCamelCase ) + "\n"
								f.write(__UpperCamelCase )

				return metadata, index


if __name__ == "__main__":
						lowerCamelCase		       =      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.""",
						)
						lowerCamelCase		       =      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(      )	->						int:
				from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

				a__	:							Any      						= SwitchTransformersConfig.from_pretrained("google/switch-base-8" )
				config.save_pretrained("/home/arthur_huggingface_co/transformers/switch_converted" )
				a__	:							List[str]      						= SwitchTransformersForConditionalGeneration.from_pretrained(
				    "/home/arthur_huggingface_co/transformers/switch_converted"	,				device_map="auto" )

				a__	:							str      						= TaTokenizer.from_pretrained("t5-small" )
				a__	:							Tuple      						= "A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."

				a__	:							Optional[int]      						= tokenizer(__UpperCamelCase	,				return_tensors="pt" ).input_ids
				a__	:							Optional[Any]      						= model.generate(__UpperCamelCase	,				decoder_start_token_id=0 )
				print(tokenizer.decode(out[0] ) )


