






'''simple docstring'''

import json
import os
from typing import Dict, List, Optional, Tuple

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


UpperCamelCase							=logging.get_logger(__name__)


UpperCamelCase							={
    "vocab_file": "vocab.json",
    "tokenizer_config_file": "tokenizer_config.json",
    "merges_file": "merges.txt",
}

UpperCamelCase							={
    "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							="</w>"
UpperCamelCase							="@@ "



def 							snake_case					(     a_							: Tuple	)       ->				List[Any]:





      """simple docstring"""

      UpperCamelCase_				:   int					    =							set()
      UpperCamelCase_				:   Union[str, Any]					    =							word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)	)
            UpperCamelCase_				:   List[Any]					    =							char
      return pairs


# Speech2Text2 has no max input length
UpperCamelCase							={"facebook/s2t-wav2vec2-large-en-de": 1024}




class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 __a			:						Optional[int]      =						VOCAB_FILES_NAMES
 __a			:						int      =						PRETRAINED_VOCAB_FILES_MAP
 __a			:						List[str]      =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __a			:						Tuple      =						['''input_ids''', '''attention_mask''']
 def __init__(							self			,     __lowerCAmelCase			,     __lowerCAmelCase="<s>"			,     __lowerCAmelCase="<pad>"			,     __lowerCAmelCase="</s>"			,     __lowerCAmelCase="<unk>"			,     __lowerCAmelCase=False			,     __lowerCAmelCase=None			,     **__lowerCAmelCase			,     ):
       super().__init__(
           unk_token=__lowerCAmelCase			,     bos_token=__lowerCAmelCase			,     eos_token=__lowerCAmelCase			,     pad_token=__lowerCAmelCase			,     do_lower_case=__lowerCAmelCase			,     **__lowerCAmelCase			,     )

       UpperCamelCase_				:   List[Any]					    =							do_lower_case

       with open(__lowerCAmelCase			,     encoding="""utf-8""" ) as vocab_handle:
             UpperCamelCase_				:   Any					    =							json.load(__lowerCAmelCase )
       UpperCamelCase_				:   List[Any]					    =							{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." )

             UpperCamelCase_				:   Union[str, Any]					    =							None
             UpperCamelCase_				:   Any					    =							None
       else:
             with open(__lowerCAmelCase			,     encoding="""utf-8""" ) as merges_handle:
                   UpperCamelCase_				:   Any					    =							merges_handle.read().split("""\n""" )[:-1]

             UpperCamelCase_				:   str					    =							[tuple(merge.split()[:2] ) for merge in merges]
             UpperCamelCase_				:   Tuple					    =							dict(zip(__lowerCAmelCase			,     range(len(__lowerCAmelCase ) ) ) )
             UpperCamelCase_				:   List[str]					    =							{}
 @property
 def   _UpperCAmelCase       (							self ):
       return len(self.decoder )
 def   _UpperCAmelCase       (							self ):
       return dict(self.encoder			,     **self.added_tokens_encoder )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Optional[Any]					    =							tuple(token[:-1] ) + (token[-1] + BPE_TOKEN_MERGES,)
       if token in self.cache:
             return self.cache[token]
       UpperCamelCase_				:   List[str]					    =							get_pairs(__lowerCAmelCase )

       if not pairs:
             return token

       while True:
             UpperCamelCase_				:   str					    =							min(__lowerCAmelCase			,     key=lambda __lowerCAmelCase : self.bpe_ranks.get(__lowerCAmelCase			,     float("""inf""" ) ) )
             if bigram not in self.bpe_ranks:
                   break
             UpperCamelCase_				:   List[Any]					    =							bigram
             UpperCamelCase_				:   List[str]					    =							[]
             UpperCamelCase_				:   List[str]					    =							0
             while i < len(__lowerCAmelCase ):
                   try:
                         UpperCamelCase_				:   Tuple					    =							word.index(__lowerCAmelCase			,     __lowerCAmelCase )
                   except ValueError:
                         new_word.extend(word[i:] )
                         break
                   else:
                         new_word.extend(word[i:j] )
                         UpperCamelCase_				:   Optional[int]					    =							j

                   if word[i] == first and i < len(__lowerCAmelCase ) - 1 and word[i + 1] == second:
                         new_word.append(first + second )
                         i += 2
                   else:
                         new_word.append(word[i] )
                         i += 1
             UpperCamelCase_				:   int					    =							tuple(__lowerCAmelCase )
             UpperCamelCase_				:   int					    =							new_word
             if len(__lowerCAmelCase ) == 1:
                   break
             else:
                   UpperCamelCase_				:   Optional[Any]					    =							get_pairs(__lowerCAmelCase )
       UpperCamelCase_				:   Union[str, Any]					    =							""" """.join(__lowerCAmelCase )
       if word == "\n  " + BPE_TOKEN_MERGES:
             UpperCamelCase_				:   List[str]					    =							"""\n""" + BPE_TOKEN_MERGES

       if word.endswith(__lowerCAmelCase ):
             UpperCamelCase_				:   Any					    =							word.replace(__lowerCAmelCase			,     """""" )

       UpperCamelCase_				:   Union[str, Any]					    =							word.replace(""" """			,     __lowerCAmelCase )
       UpperCamelCase_				:   Tuple					    =							word
       return word
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):

       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:
             UpperCamelCase_				:   Tuple					    =							text.lower()

       UpperCamelCase_				:   Any					    =							text.split()

       UpperCamelCase_				:   Optional[int]					    =							[]
       for token in text:
             if token:
                   split_tokens.extend(list(self.bpe(__lowerCAmelCase ).split(""" """ ) ) )

       return split_tokens
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       return self.encoder.get(__lowerCAmelCase			,     self.encoder.get(self.unk_token ) )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   int					    =							self.decoder.get(__lowerCAmelCase			,     self.unk_token )
       return result
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Union[str, Any]					    =							""" """.join(__lowerCAmelCase )

       # make sure @@ tokens are concatenated
       UpperCamelCase_				:   Tuple					    =							"""""".join(string.split(__lowerCAmelCase ) )

       return string





 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase = None ):
       if not os.path.isdir(__lowerCAmelCase ):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
             return
       UpperCamelCase_				:   Tuple					    =							os.path.join(
           __lowerCAmelCase			,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
       UpperCamelCase_				:   List[Any]					    =							os.path.join(
           __lowerCAmelCase			,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""] )

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

       UpperCamelCase_				:   List[Any]					    =							0
       if self.bpe_ranks is None:
             return (vocab_file,)

       with open(__lowerCAmelCase			,     """w"""			,     encoding="""utf-8""" ) as writer:
             for bpe_tokens, token_index in sorted(self.bpe_ranks.items()			,     key=lambda __lowerCAmelCase : 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!""" )
                         UpperCamelCase_				:   List[Any]					    =							token_index
                   writer.write(""" """.join(__lowerCAmelCase ) + """\n""" )
                   index += 1

       return (vocab_file, merges_file)














'''simple docstring'''

import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler




@dataclass
class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 __a			:						int      =						0
 __a			:						bool      =						False
 __a			:						float      =						3.0






class     A    (      unittest.TestCase						):






 """simple docstring"""





 def   _UpperCAmelCase       (							self ):
       # If no defaults are changed, `to_kwargs` returns an empty dict.
       self.assertDictEqual(MockClass().to_kwargs()			,     {} )
       self.assertDictEqual(MockClass(a=2 ).to_kwargs()			,     {"""a""": 2} )
       self.assertDictEqual(MockClass(a=2			,     b=__lowerCAmelCase ).to_kwargs()			,     {"""a""": 2, """b""": True} )
       self.assertDictEqual(MockClass(a=2			,     c=2.25 ).to_kwargs()			,     {"""a""": 2, """c""": 2.25} )
 @require_cuda
 def   _UpperCAmelCase       (							self ):
       # If no defaults are changed, `to_kwargs` returns an empty dict.
       UpperCamelCase_				:   int					    =							GradScalerKwargs(init_scale=10_24			,     growth_factor=2 )
       AcceleratorState._reset_state()
       UpperCamelCase_				:   Dict					    =							Accelerator(mixed_precision="""fp16"""			,     kwargs_handlers=[scaler_handler] )
       print(accelerator.use_fpaa )
       UpperCamelCase_				:   Any					    =							accelerator.scaler

       # Check the kwargs have been applied
       self.assertEqual(scaler._init_scale			,     10_24.0 )
       self.assertEqual(scaler._growth_factor			,     2.0 )

       # Check the other values are at the default
       self.assertEqual(scaler._backoff_factor			,     0.5 )
       self.assertEqual(scaler._growth_interval			,     20_00 )
       self.assertEqual(scaler._enabled			,     __lowerCAmelCase )





 @require_multi_gpu
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Optional[int]					    =							["""torchrun""", F"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__ )]
       execute_subprocess_async(__lowerCAmelCase			,     env=os.environ.copy() )


if __name__ == "__main__":
      UpperCamelCase							=DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
      UpperCamelCase							=Accelerator(kwargs_handlers=[ddp_scaler])
      UpperCamelCase							=torch.nn.Linear(100, 200)
      UpperCamelCase							=accelerator.prepare(model)

      # Check the values changed in kwargs
      UpperCamelCase							=""
      UpperCamelCase							=model.bucket_bytes_cap // (1024 * 1024)
      if observed_bucket_cap_map != 15:
            error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
      if model.find_unused_parameters is not True:
            error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

      # Check the values of the defaults
      if model.dim != 0:
            error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
      if model.broadcast_buffers is not True:
            error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
      if model.gradient_as_bucket_view is not False:
            error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

      # Raise error at the end to make sure we don't stop at the first failure.
      if len(error_msg) > 0:
            raise ValueError(error_msg)






