






'''simple docstring'''

import json
import os
from typing import Optional, Tuple

import regex as re

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


UpperCamelCase							=logging.get_logger(__name__)

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

UpperCamelCase							={
    "vocab_file": {"ctrl": "https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json"},
    "merges_file": {"ctrl": "https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt"},
}

UpperCamelCase							={
    "ctrl": 256,
}

UpperCamelCase							={
    "Pregnancy": 16_8629,
    "Christianity": 7675,
    "Explain": 10_6423,
    "Fitness": 6_3440,
    "Saving": 6_3163,
    "Ask": 2_7171,
    "Ass": 9_5985,
    "Joke": 16_3509,
    "Questions": 4_5622,
    "Thoughts": 4_9605,
    "Retail": 5_2342,
    "Feminism": 16_4338,
    "Writing": 1_1992,
    "Atheism": 19_2263,
    "Netflix": 4_8616,
    "Computing": 3_9639,
    "Opinion": 4_3213,
    "Alone": 4_4967,
    "Funny": 5_8917,
    "Gaming": 4_0358,
    "Human": 4088,
    "India": 1331,
    "Joker": 7_7138,
    "Diet": 3_6206,
    "Legal": 1_1859,
    "Norman": 4939,
    "Tip": 7_2689,
    "Weight": 5_2343,
    "Movies": 4_6273,
    "Running": 2_3425,
    "Science": 2090,
    "Horror": 3_7793,
    "Confession": 6_0572,
    "Finance": 1_2250,
    "Politics": 1_6360,
    "Scary": 19_1985,
    "Support": 1_2654,
    "Technologies": 3_2516,
    "Teenage": 6_6160,
    "Event": 3_2769,
    "Learned": 6_7460,
    "Notion": 18_2770,
    "Wikipedia": 3_7583,
    "Books": 6665,
    "Extract": 7_6050,
    "Confessions": 10_2701,
    "Conspiracy": 7_5932,
    "Links": 6_3674,
    "Narcissus": 15_0425,
    "Relationship": 5_4766,
    "Relationships": 13_4796,
    "Reviews": 4_1671,
    "News": 4256,
    "Translation": 2_6820,
    "multilingual": 12_8406,
}



def 							snake_case					(     a_							: str	)       ->				Any:





      """simple docstring"""

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

      UpperCamelCase_				:   List[Any]					    =							set(a_	)
      return pairs




class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 __a			:						Dict      =						VOCAB_FILES_NAMES
 __a			:						List[str]      =						PRETRAINED_VOCAB_FILES_MAP
 __a			:						Optional[Any]      =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __a			:						Tuple      =						CONTROL_CODES
 def __init__(							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase="<unk>"			,     **__lowerCAmelCase ):
       super().__init__(unk_token=__lowerCAmelCase			,     **__lowerCAmelCase )

       with open(__lowerCAmelCase			,     encoding="""utf-8""" ) as vocab_handle:
             UpperCamelCase_				:   int					    =							json.load(__lowerCAmelCase )
       UpperCamelCase_				:   Tuple					    =							{v: k for k, v in self.encoder.items()}
       with open(__lowerCAmelCase			,     encoding="""utf-8""" ) as merges_handle:
             UpperCamelCase_				:   Tuple					    =							merges_handle.read().split("""\n""" )[1:-1]
       UpperCamelCase_				:   Union[str, Any]					    =							[tuple(merge.split() ) for merge in merges]
       UpperCamelCase_				:   Optional[int]					    =							dict(zip(__lowerCAmelCase			,     range(len(__lowerCAmelCase ) ) ) )
       UpperCamelCase_				:   Union[str, Any]					    =							{}
 @property
 def   _UpperCAmelCase       (							self ):
       return len(self.encoder )
 def   _UpperCAmelCase       (							self ):
       return dict(self.encoder			,     **self.added_tokens_encoder )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       if token in self.cache:
             return self.cache[token]
       UpperCamelCase_				:   Union[str, Any]					    =							tuple(__lowerCAmelCase )
       UpperCamelCase_				:   str					    =							tuple(list(word[:-1] ) + [word[-1] + """</w>"""] )
       UpperCamelCase_				:   List[Any]					    =							get_pairs(__lowerCAmelCase )

       if not pairs:
             return token

       while True:
             UpperCamelCase_				:   Any					    =							min(__lowerCAmelCase			,     key=lambda __lowerCAmelCase : self.bpe_ranks.get(__lowerCAmelCase			,     float("""inf""" ) ) )
             if bigram not in self.bpe_ranks:
                   break
             UpperCamelCase_    ,			UpperCamelCase_				:   Tuple					    =							bigram
             UpperCamelCase_				:   Dict					    =							[]
             UpperCamelCase_				:   Tuple					    =							0
             while i < len(__lowerCAmelCase ):
                   try:
                         UpperCamelCase_				:   Any					    =							word.index(__lowerCAmelCase			,     __lowerCAmelCase )
                   except ValueError:
                         new_word.extend(word[i:] )
                         break
                   else:
                         new_word.extend(word[i:j] )
                         UpperCamelCase_				:   Any					    =							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_				:   str					    =							tuple(__lowerCAmelCase )
             UpperCamelCase_				:   List[Any]					    =							new_word
             if len(__lowerCAmelCase ) == 1:
                   break
             else:
                   UpperCamelCase_				:   Any					    =							get_pairs(__lowerCAmelCase )
       UpperCamelCase_				:   Union[str, Any]					    =							"""@@ """.join(__lowerCAmelCase )
       UpperCamelCase_				:   str					    =							word[:-4]
       UpperCamelCase_				:   Dict					    =							word
       return word
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   str					    =							[]

       UpperCamelCase_				:   Optional[Any]					    =							re.findall(R"""\S+\n?"""			,     __lowerCAmelCase )

       for token in words:
             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 ):
       return self.decoder.get(__lowerCAmelCase			,     self.unk_token )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   str					    =							""" """.join(__lowerCAmelCase ).replace("""@@ """			,     """""" ).strip()
       return out_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_				:   List[str]					    =							os.path.join(
           __lowerCAmelCase			,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
       UpperCamelCase_				:   Dict					    =							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_				:   int					    =							0
       with open(__lowerCAmelCase			,     """w"""			,     encoding="""utf-8""" ) as writer:
             writer.write("""#version: 0.2\n""" )
             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 {merge_file}: BPE merge indices are not consecutive."
                             """ Please check that the tokenizer is not corrupted!""" )
                         UpperCamelCase_				:   int					    =							token_index
                   writer.write(""" """.join(__lowerCAmelCase ) + """\n""" )
                   index += 1

       return vocab_file, merge_file

 # def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
 #     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
 #     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
 #     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
 #     return ''.join(tokens_generated_so_far)














'''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)






