





import inspect
import os

import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed

import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils.testing import (
    AccelerateTestCase,
    TempDirTestCase,
    execute_subprocess_async,
    require_cuda,
    require_fsdp,
    require_multi_gpu,
    slow,
)
from accelerate.utils.constants import (
    FSDP_AUTO_WRAP_POLICY,
    FSDP_BACKWARD_PREFETCH,
    FSDP_SHARDING_STRATEGY,
    FSDP_STATE_DICT_TYPE,
)
from accelerate.utils.dataclasses import FullyShardedDataParallelPlugin
from accelerate.utils.other import patch_environment


set_seed(4_2)

SCREAMING_SNAKE_CASE__							:      Dict         =    """bert-base-cased"""
SCREAMING_SNAKE_CASE__							:      Tuple         =    """fp16"""
SCREAMING_SNAKE_CASE__							:      Any         =    """bf16"""
SCREAMING_SNAKE_CASE__							:      str         =    [FPaa, BFaa]







@require_fsdp
@require_cuda
class 	__lowerCAmelCase      (       __a	):





   def 						_snake_case			(			self   )					->	Tuple:
      """simple docstring"""





      super().setUp()

      a__						:    Optional[Any]   		=	dict(
          ACCELERATE_USE_FSDP="true"       ,      MASTER_ADDR="localhost"       ,      MASTER_PORT="10999"       ,      RANK="0"       ,      LOCAL_RANK="0"       ,      WORLD_SIZE="1"       ,      )





   def 						_snake_case			(			self   )					->	List[Any]:
      """simple docstring"""





      from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy

      for i, strategy in enumerate(_lowerCamelCase   ):
         a__						:    int   		=	self.dist_env.copy()
         a__						:    Optional[Any]   		=	F"""{i + 1}"""
         a__						:    str   		=	strategy
         with mockenv_context(**_lowerCamelCase   ):
            a__						:    List[str]   		=	FullyShardedDataParallelPlugin()
            self.assertEqual(fsdp_plugin.sharding_strategy       ,      ShardingStrategy(i + 1   )   )





   def 						_snake_case			(			self   )					->	Dict:
      """simple docstring"""





      from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch

      for i, prefetch_policy in enumerate(_lowerCamelCase   ):
         a__						:    Tuple   		=	self.dist_env.copy()
         a__						:    Optional[Any]   		=	prefetch_policy
         with mockenv_context(**_lowerCamelCase   ):
            a__						:    List[str]   		=	FullyShardedDataParallelPlugin()
            if prefetch_policy == "NO_PREFETCH":
               self.assertIsNone(fsdp_plugin.backward_prefetch   )
            else:
               self.assertEqual(fsdp_plugin.backward_prefetch       ,      BackwardPrefetch(i + 1   )   )





   def 						_snake_case			(			self   )					->	Any:
      """simple docstring"""





      from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType

      for i, state_dict_type in enumerate(_lowerCamelCase   ):
         a__						:    Optional[Any]   		=	self.dist_env.copy()
         a__						:    Tuple   		=	state_dict_type
         with mockenv_context(**_lowerCamelCase   ):
            a__						:    Any   		=	FullyShardedDataParallelPlugin()
            self.assertEqual(fsdp_plugin.state_dict_type       ,      StateDictType(i + 1   )   )
            if state_dict_type == "FULL_STATE_DICT":
               self.assertTrue(fsdp_plugin.state_dict_config.offload_to_cpu   )
               self.assertTrue(fsdp_plugin.state_dict_config.ranka_only   )





   def 						_snake_case			(			self   )					->	List[Any]:
      """simple docstring"""





      a__						:    str   		=	AutoModel.from_pretrained(_lowerCamelCase   )
      for policy in FSDP_AUTO_WRAP_POLICY:
         a__						:    int   		=	self.dist_env.copy()
         a__						:    int   		=	policy
         if policy == "TRANSFORMER_BASED_WRAP":
            a__						:    List[Any]   		=	"BertLayer"
         elif policy == "SIZE_BASED_WRAP":
            a__						:    List[Any]   		=	"2000"
         with mockenv_context(**_lowerCamelCase   ):
            a__						:    int   		=	FullyShardedDataParallelPlugin()
            fsdp_plugin.set_auto_wrap_policy(_lowerCamelCase   )
            if policy == "NO_WRAP":
               self.assertIsNone(fsdp_plugin.auto_wrap_policy   )
            else:
               self.assertIsNotNone(fsdp_plugin.auto_wrap_policy   )

      a__						:    Optional[Any]   		=	self.dist_env.copy()
      a__						:    List[str]   		=	"TRANSFORMER_BASED_WRAP"
      a__						:    Optional[int]   		=	"T5Layer"
      with mockenv_context(**_lowerCamelCase   ):
         a__						:    List[str]   		=	FullyShardedDataParallelPlugin()
         with self.assertRaises(_lowerCamelCase   ) as cm:
            fsdp_plugin.set_auto_wrap_policy(_lowerCamelCase   )
         self.assertTrue("Could not find the transformer layer class to wrap in the model." in str(cm.exception   )   )

      a__						:    Union[str, Any]   		=	self.dist_env.copy()
      a__						:    str   		=	"SIZE_BASED_WRAP"
      a__						:    Optional[Any]   		=	"0"
      with mockenv_context(**_lowerCamelCase   ):
         a__						:    Dict   		=	FullyShardedDataParallelPlugin()
         fsdp_plugin.set_auto_wrap_policy(_lowerCamelCase   )
         self.assertIsNone(fsdp_plugin.auto_wrap_policy   )





   def 						_snake_case			(			self   )					->	Dict:
      """simple docstring"""





      from torch.distributed.fsdp.fully_sharded_data_parallel import MixedPrecision
      from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler

      for mp_dtype in dtypes:
         a__						:    Any   		=	self.dist_env.copy()
         a__						:    Tuple   		=	mp_dtype
         with mockenv_context(**_lowerCamelCase   ):
            a__						:    Any   		=	Accelerator()
            if mp_dtype == "fp16":
               a__						:    Dict   		=	torch.floataa
            elif mp_dtype == "bf16":
               a__						:    Any   		=	torch.bfloataa
            a__						:    int   		=	MixedPrecision(param_dtype=_lowerCamelCase       ,      reduce_dtype=_lowerCamelCase       ,      buffer_dtype=_lowerCamelCase   )
            self.assertEqual(accelerator.state.fsdp_plugin.mixed_precision_policy       ,      _lowerCamelCase   )
            if mp_dtype == FPaa:
               self.assertTrue(isinstance(accelerator.scaler       ,      _lowerCamelCase   )   )
            elif mp_dtype == BFaa:
               self.assertIsNone(accelerator.scaler   )
            AcceleratorState._reset_state(_lowerCamelCase   )





   def 						_snake_case			(			self   )					->	Optional[Any]:
      """simple docstring"""





      from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload

      for flag in [True, False]:
         a__						:    Union[str, Any]   		=	self.dist_env.copy()
         a__						:    Optional[int]   		=	str(_lowerCamelCase   ).lower()
         with mockenv_context(**_lowerCamelCase   ):
            a__						:    str   		=	FullyShardedDataParallelPlugin()
            self.assertEqual(fsdp_plugin.cpu_offload       ,      CPUOffload(offload_params=_lowerCamelCase   )   )







@require_fsdp
@require_multi_gpu
@slow
class 	__lowerCAmelCase      (       __a	):





   def 						_snake_case			(			self   )					->	str:
      """simple docstring"""





      super().setUp()
      a__						:    List[Any]   		=	0.82
      a__						:    List[str]   		=	[
          "fsdp_shard_grad_op_transformer_based_wrap",
          "fsdp_full_shard_transformer_based_wrap",
      ]
      a__						:    Tuple   		=	{
          "multi_gpu_fp16": 3_200,
          "fsdp_shard_grad_op_transformer_based_wrap_fp16": 2_000,
          "fsdp_full_shard_transformer_based_wrap_fp16": 1_900,
          # Disabling below test as it overwhelms the RAM memory usage
          # on CI self-hosted runner leading to tests getting killed.
          # "fsdp_full_shard_cpu_offload_transformer_based_wrap_fp32": 1500,  # fp16 was leading to indefinite hang
      }
      a__						:    Optional[Any]   		=	160
      a__						:    List[Any]   		=	160

      a__						:    Tuple   		=	inspect.getfile(accelerate.test_utils   )
      a__						:    Tuple   		=	os.path.sep.join(mod_file.split(os.path.sep   )[:-1] + ["scripts", "external_deps"]   )





   def 						_snake_case			(			self   )					->	List[Any]:
      """simple docstring"""





      a__						:    Dict   		=	os.path.join(self.test_scripts_folder       ,      "test_performance.py"   )
      a__						:    str   		=	["accelerate", "launch", "--num_processes=2", "--num_machines=1", "--machine_rank=0", "--use_fsdp"]
      for config in self.performance_configs:
         a__						:    Tuple   		=	cmd.copy()
         for i, strategy in enumerate(_lowerCamelCase   ):
            if strategy.lower() in config:
               cmd_config.append(F"""--fsdp_sharding_strategy={i+1}"""   )
               break

         if "fp32" in config:
            cmd_config.append("--mixed_precision=no"   )
         else:
            cmd_config.append("--mixed_precision=fp16"   )

         if "cpu_offload" in config:
            cmd_config.append("--fsdp_offload_params=True"   )

         for policy in FSDP_AUTO_WRAP_POLICY:
            if policy.lower() in config:
               cmd_config.append(F"""--fsdp_auto_wrap_policy={policy}"""   )
               break

         if policy == "TRANSFORMER_BASED_WRAP":
            cmd_config.append("--fsdp_transformer_layer_cls_to_wrap=BertLayer"   )
         elif policy == "SIZE_BASED_WRAP":
            cmd_config.append("--fsdp_min_num_params=2000"   )

         cmd_config.extend(
             [
                 self.test_file_path,
                 F"""--output_dir={self.tmpdir}""",
                 F"""--performance_lower_bound={self.performance_lower_bound}""",
             ]   )
         with patch_environment(omp_num_threads=1   ):
            execute_subprocess_async(_lowerCamelCase       ,      env=os.environ.copy()   )





   def 						_snake_case			(			self   )					->	Union[str, Any]:
      """simple docstring"""





      a__						:    int   		=	os.path.join(self.test_scripts_folder       ,      "test_checkpointing.py"   )
      a__						:    Dict   		=	[
          "accelerate",
          "launch",
          "--num_processes=2",
          "--num_machines=1",
          "--machine_rank=0",
          "--use_fsdp",
          "--mixed_precision=fp16",
          "--fsdp_transformer_layer_cls_to_wrap=BertLayer",
      ]

      for i, strategy in enumerate(_lowerCamelCase   ):
         a__						:    Union[str, Any]   		=	cmd.copy()
         cmd_config.append(F"""--fsdp_sharding_strategy={i+1}"""   )
         if strategy != "FULL_SHARD":
            continue
         a__						:    Optional[int]   		=	len(_lowerCamelCase   )
         for state_dict_type in FSDP_STATE_DICT_TYPE:
            a__						:    Union[str, Any]   		=	cmd_config[:state_dict_config_index]
            cmd_config.append(F"""--fsdp_state_dict_type={state_dict_type}"""   )
            cmd_config.extend(
                [
                    self.test_file_path,
                    F"""--output_dir={self.tmpdir}""",
                    "--partial_train_epoch=1",
                ]   )
            with patch_environment(omp_num_threads=1   ):
               execute_subprocess_async(_lowerCamelCase       ,      env=os.environ.copy()   )

            a__						:    str   		=	cmd_config[:-1]
            a__						:    str   		=	os.path.join(self.tmpdir       ,      "epoch_0"   )
            cmd_config.extend(
                [
                    F"""--resume_from_checkpoint={resume_from_checkpoint}""",
                ]   )
            with patch_environment(omp_num_threads=1   ):
               execute_subprocess_async(_lowerCamelCase       ,      env=os.environ.copy()   )





   def 						_snake_case			(			self   )					->	Tuple:
      """simple docstring"""





      a__						:    int   		=	os.path.join(self.test_scripts_folder       ,      "test_peak_memory_usage.py"   )
      a__						:    Dict   		=	[
          "accelerate",
          "launch",
          "--num_processes=2",
          "--num_machines=1",
          "--machine_rank=0",
      ]
      for spec, peak_mem_upper_bound in self.peak_memory_usage_upper_bound.items():
         a__						:    str   		=	cmd.copy()
         if "fp16" in spec:
            cmd_config.extend(["--mixed_precision=fp16"]   )
         else:
            cmd_config.extend(["--mixed_precision=no"]   )

         if "multi_gpu" in spec:
            continue
         else:
            cmd_config.extend(["--use_fsdp"]   )
            for i, strategy in enumerate(_lowerCamelCase   ):
               if strategy.lower() in spec:
                  cmd_config.append(F"""--fsdp_sharding_strategy={i+1}"""   )
                  break

            if "cpu_offload" in spec:
               cmd_config.append("--fsdp_offload_params=True"   )

            for policy in FSDP_AUTO_WRAP_POLICY:
               if policy.lower() in spec:
                  cmd_config.append(F"""--fsdp_auto_wrap_policy={policy}"""   )
                  break

            if policy == "TRANSFORMER_BASED_WRAP":
               cmd_config.append("--fsdp_transformer_layer_cls_to_wrap=BertLayer"   )
            elif policy == "SIZE_BASED_WRAP":
               cmd_config.append("--fsdp_min_num_params=2000"   )

         cmd_config.extend(
             [
                 self.test_file_path,
                 F"""--output_dir={self.tmpdir}""",
                 F"""--peak_memory_upper_bound={peak_mem_upper_bound}""",
                 F"""--n_train={self.n_train}""",
                 F"""--n_val={self.n_val}""",
             ]   )
         with patch_environment(omp_num_threads=1   ):
            execute_subprocess_async(_lowerCamelCase       ,      env=os.environ.copy()   )



"""simple docstring"""






import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation

_lowercase							=				logging.get_logger(__name__)


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

_lowercase							=				{
    '''vocab_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'''},
    '''merges_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'''},
    '''tokenizer_config_file''': {
        '''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'''
    },
}

_lowercase							=				{'''facebook/blenderbot-3B''': 128}



@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def      lowerCAmelCase__			(		)						->Any:
			__lowercase       =       (
			    list(range(ord("!"      )			,   ord("~"      ) + 1      )      ) + list(range(ord("¡"      )			,   ord("¬"      ) + 1      )      ) + list(range(ord("®"      )			,   ord("ÿ"      ) + 1      )      )
			)
			__lowercase       =       bs[:]
			__lowercase       =       0
			for b in range(2**8      ):
						if b not in bs:
									bs.append(__magic_name__      )
									cs.append(2**8 + n      )
									n += 1
			__lowercase       =       [chr(__magic_name__      ) for n in cs]
			return dict(zip(__magic_name__			,   __magic_name__      )      )



def      lowerCAmelCase__			(		__magic_name__      )						->Tuple:
			__lowercase       =       set()
			__lowercase       =       word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)      )
						__lowercase       =       char
			return pairs







class 					__a     ( __a       ):



							'''simple docstring'''







							_lowerCamelCase					:			List[str]          =   VOCAB_FILES_NAMES
							_lowerCamelCase					:			Tuple          =   PRETRAINED_VOCAB_FILES_MAP
							_lowerCamelCase					:			int          =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_lowerCamelCase					:			Dict          =   ["""input_ids""", """attention_mask"""]
							def __init__(     self  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase="replace"  ,    _lowerCamelCase="<s>"  ,    _lowerCamelCase="</s>"  ,    _lowerCamelCase="</s>"  ,    _lowerCamelCase="<s>"  ,    _lowerCamelCase="<unk>"  ,    _lowerCamelCase="<pad>"  ,    _lowerCamelCase="<mask>"  ,    _lowerCamelCase=False  ,    **_lowerCamelCase  ,    )			->							int:




										'''simple docstring'''
										__lowercase       =       AddedToken(_lowerCamelCase  ,    lstrip=_lowerCamelCase  ,    rstrip=_lowerCamelCase       ) if isinstance(_lowerCamelCase  ,    _lowerCamelCase       ) else bos_token
										__lowercase       =       AddedToken(_lowerCamelCase  ,    lstrip=_lowerCamelCase  ,    rstrip=_lowerCamelCase       ) if isinstance(_lowerCamelCase  ,    _lowerCamelCase       ) else eos_token
										__lowercase       =       AddedToken(_lowerCamelCase  ,    lstrip=_lowerCamelCase  ,    rstrip=_lowerCamelCase       ) if isinstance(_lowerCamelCase  ,    _lowerCamelCase       ) else sep_token
										__lowercase       =       AddedToken(_lowerCamelCase  ,    lstrip=_lowerCamelCase  ,    rstrip=_lowerCamelCase       ) if isinstance(_lowerCamelCase  ,    _lowerCamelCase       ) else cls_token
										__lowercase       =       AddedToken(_lowerCamelCase  ,    lstrip=_lowerCamelCase  ,    rstrip=_lowerCamelCase       ) if isinstance(_lowerCamelCase  ,    _lowerCamelCase       ) else unk_token
										__lowercase       =       AddedToken(_lowerCamelCase  ,    lstrip=_lowerCamelCase  ,    rstrip=_lowerCamelCase       ) if isinstance(_lowerCamelCase  ,    _lowerCamelCase       ) else pad_token

										# Mask token behave like a normal word, i.e. include the space before it
										__lowercase       =       AddedToken(_lowerCamelCase  ,    lstrip=_lowerCamelCase  ,    rstrip=_lowerCamelCase       ) if isinstance(_lowerCamelCase  ,    _lowerCamelCase       ) else mask_token

										super().__init__(
										    errors=_lowerCamelCase  ,    bos_token=_lowerCamelCase  ,    eos_token=_lowerCamelCase  ,    unk_token=_lowerCamelCase  ,    sep_token=_lowerCamelCase  ,    cls_token=_lowerCamelCase  ,    pad_token=_lowerCamelCase  ,    mask_token=_lowerCamelCase  ,    add_prefix_space=_lowerCamelCase  ,    **_lowerCamelCase  ,    )

										with open(_lowerCamelCase  ,    encoding="utf-8"       ) as vocab_handle:
													__lowercase       =       json.load(_lowerCamelCase       )
										__lowercase       =       {v: k for k, v in self.encoder.items()}
										__lowercase       =       errors  # how to handle errors in decoding
										__lowercase       =       bytes_to_unicode()
										__lowercase       =       {v: k for k, v in self.byte_encoder.items()}
										with open(_lowerCamelCase  ,    encoding="utf-8"       ) as merges_handle:
													__lowercase       =       merges_handle.read().split("\n"       )[1:-1]
										__lowercase       =       [tuple(merge.split()       ) for merge in bpe_merges]
										__lowercase       =       dict(zip(_lowerCamelCase  ,    range(len(_lowerCamelCase       )       )       )       )
										__lowercase       =       {}
										__lowercase       =       add_prefix_space

										# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
										__lowercase       =       re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"       )
							@property
							# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Tuple:




										'''simple docstring'''
										return len(self.encoder       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Dict:




										'''simple docstring'''
										return dict(self.encoder  ,    **self.added_tokens_encoder       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							int:




										'''simple docstring'''
										if token in self.cache:
													return self.cache[token]
										__lowercase       =       tuple(_lowerCamelCase       )
										__lowercase       =       get_pairs(_lowerCamelCase       )

										if not pairs:
													return token

										while True:
													__lowercase       =       min(_lowerCamelCase  ,    key=lambda _lowerCamelCase       : self.bpe_ranks.get(_lowerCamelCase  ,    float("inf"       )       )       )
													if bigram not in self.bpe_ranks:
																break
													__lowercase   ,					__lowercase       =       bigram
													__lowercase       =       []
													__lowercase       =       0
													while i < len(_lowerCamelCase       ):
																try:
																			__lowercase       =       word.index(_lowerCamelCase  ,    _lowerCamelCase       )
																except ValueError:
																			new_word.extend(word[i:]       )
																			break
																else:
																			new_word.extend(word[i:j]       )
																			__lowercase       =       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
													__lowercase       =       tuple(_lowerCamelCase       )
													__lowercase       =       new_word
													if len(_lowerCamelCase       ) == 1:
																break
													else:
																__lowercase       =       get_pairs(_lowerCamelCase       )
										__lowercase       =       " ".join(_lowerCamelCase       )
										__lowercase       =       word
										return word
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							Union[str, Any]:




										'''simple docstring'''
										__lowercase       =       []
										for token in re.findall(self.pat  ,    _lowerCamelCase       ):
													__lowercase       =       "".join(
													    self.byte_encoder[b] for b in token.encode("utf-8"       )       )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
													bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_lowerCamelCase       ).split(" "       )       )
										return bpe_tokens
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							Tuple:




										'''simple docstring'''
										return self.encoder.get(_lowerCamelCase  ,    self.encoder.get(self.unk_token       )       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							str:




										'''simple docstring'''
										return self.decoder.get(_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							List[str]:




										'''simple docstring'''
										__lowercase       =       "".join(_lowerCamelCase       )
										__lowercase       =       bytearray([self.byte_decoder[c] for c in text]       ).decode("utf-8"  ,    errors=self.errors       )
										return text
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = None       )			->							Tuple[str]:




										'''simple docstring'''
										if not os.path.isdir(_lowerCamelCase       ):
													logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''       )
													return
										__lowercase       =       os.path.join(
										    _lowerCamelCase  ,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]       )
										__lowercase       =       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"       )

										__lowercase       =       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!"       )
																			__lowercase       =       token_index
																writer.write(" ".join(_lowerCamelCase       ) + "\n"       )
																index += 1

										return vocab_file, merge_file
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = None  ,    _lowerCamelCase = False       )			->							List[int]:




										'''simple docstring'''
										if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=_lowerCamelCase  ,    token_ids_a=_lowerCamelCase  ,    already_has_special_tokens=_lowerCamelCase       )

										if token_ids_a is None:
													return [1] + ([0] * len(_lowerCamelCase       )) + [1]
										return [1] + ([0] * len(_lowerCamelCase       )) + [1, 1] + ([0] * len(_lowerCamelCase       )) + [1]
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = None       )			->							List[int]:




										'''simple docstring'''
										__lowercase       =       [self.sep_token_id]
										__lowercase       =       [self.cls_token_id]

										if token_ids_a is None:
													return len(cls + token_ids_a + sep       ) * [0]
										return len(cls + token_ids_a + sep + sep + token_ids_a + sep       ) * [0]
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase=False  ,    **_lowerCamelCase       )			->							int:




										'''simple docstring'''
										__lowercase       =       kwargs.pop("add_prefix_space"  ,    self.add_prefix_space       )
										if (is_split_into_words or add_prefix_space) and (len(_lowerCamelCase       ) > 0 and not text[0].isspace()):
													__lowercase       =       " " + text
										return (text, kwargs)
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = None       )			->							Tuple:




										'''simple docstring'''
										return token_ids_a + [self.eos_token_id]





							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							List[int]:




										'''simple docstring'''
										__lowercase       =       []
										for is_user, text in conversation.iter_texts():
													if is_user:
																# We need to space prefix as it's being done within blenderbot
																inputs.append(" " + text       )
													else:
																# Generated responses should contain them already.
																inputs.append(_lowerCamelCase       )

										__lowercase       =       "  ".join(_lowerCamelCase       )
										__lowercase       =       self.encode(_lowerCamelCase       )
										if len(_lowerCamelCase       ) > self.model_max_length:
													__lowercase       =       input_ids[-self.model_max_length :]
													logger.warning(f'''Trimmed input from conversation as it was longer than {self.model_max_length} tokens.'''       )
										return input_ids





