
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple

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


__lowerCamelCase							=       logging.get_logger(__name__)

__lowerCamelCase							=       {
    'vocab_file': 'vocab.txt',
    'merges_file': 'bpe.codes',
}

__lowerCamelCase							=       {
    'vocab_file': {
        'vinai/phobert-base': 'https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt',
        'vinai/phobert-large': 'https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt',
    },
    'merges_file': {
        'vinai/phobert-base': 'https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes',
        'vinai/phobert-large': 'https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes',
    },
}

__lowerCamelCase							=       {
    'vinai/phobert-base': 2_56,
    'vinai/phobert-large': 2_56,
}


def 			UpperCamelCase					(    __lowerCamelCase     : Union[str, Any]    ):
       snake_case       :   Dict							=      set()
       snake_case       :   List[str]							=      word[0]
       for char in word[1:]:
              pairs.add((prev_char, char)    )
              snake_case       :   Tuple							=      char

       snake_case       :   Tuple							=      set(_lowerCamelCase    )
       return pairs






class   UpperCAmelCase		(						__lowercase ):
   A__					:		Optional[Any]						     =     VOCAB_FILES_NAMES
   A__					:		List[Any]						     =     PRETRAINED_VOCAB_FILES_MAP
   A__					:		List[str]						     =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



   def __init__(self		:					Optional[Any]    ,						snake_case__		:					Union[str, Any]    ,						snake_case__		:					Dict    ,						snake_case__		:					Any="<s>"    ,						snake_case__		:					Optional[int]="</s>"    ,						snake_case__		:					List[Any]="</s>"    ,						snake_case__		:					Dict="<s>"    ,						snake_case__		:					Tuple="<unk>"    ,						snake_case__		:					Union[str, Any]="<pad>"    ,						snake_case__		:					Tuple="<mask>"    ,						**snake_case__		:					Any    ,						)		->	str:


          '''simple docstring'''


          super().__init__(
              bos_token=snake_case__    ,						eos_token=snake_case__    ,						unk_token=snake_case__    ,						sep_token=snake_case__    ,						cls_token=snake_case__    ,						pad_token=snake_case__    ,						mask_token=snake_case__    ,						**snake_case__    ,						)

          snake_case       :   Union[str, Any]							=      vocab_file
          snake_case       :   List[Any]							=      merges_file

          snake_case       :   str							=      {}
          snake_case       :   Optional[Any]							=      0
          snake_case       :   List[str]							=      1
          snake_case       :   str							=      2
          snake_case       :   Optional[int]							=      3

          self.add_from_file(snake_case__ )

          snake_case       :   Optional[int]							=      {v: k for k, v in self.encoder.items()}

          with open(snake_case__    ,						encoding="utf-8" ) as merges_handle:
                 snake_case       :   List[str]							=      merges_handle.read().split("\n" )[:-1]
          snake_case       :   Tuple							=      [tuple(merge.split()[:-1] ) for merge in merges]
          snake_case       :   Tuple							=      dict(zip(snake_case__    ,						range(len(snake_case__ ) ) ) )
          snake_case       :   Union[str, Any]							=      {}



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any    ,						snake_case__		:					List[int]    ,						snake_case__		:					Optional[List[int]] = None )		->	List[int]:


          '''simple docstring'''


          if token_ids_a is None:
                 return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          snake_case       :   Union[str, Any]							=      [self.cls_token_id]
          snake_case       :   List[str]							=      [self.sep_token_id]
          return cls + token_ids_a + sep + sep + token_ids_a + sep



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str]    ,						snake_case__		:					List[int]    ,						snake_case__		:					Optional[List[int]] = None    ,						snake_case__		:					bool = False )		->	List[int]:


          '''simple docstring'''


          if already_has_special_tokens:
                 return super().get_special_tokens_mask(
                     token_ids_a=snake_case__    ,						token_ids_a=snake_case__    ,						already_has_special_tokens=snake_case__ )

          if token_ids_a is None:
                 return [1] + ([0] * len(snake_case__ )) + [1]
          return [1] + ([0] * len(snake_case__ )) + [1, 1] + ([0] * len(snake_case__ )) + [1]



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any]    ,						snake_case__		:					List[int]    ,						snake_case__		:					Optional[List[int]] = None )		->	List[int]:


          '''simple docstring'''


          snake_case       :   Optional[int]							=      [self.sep_token_id]
          snake_case       :   Dict							=      [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]



   @property
   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any] )		->	Any:


          '''simple docstring'''


          return len(self.encoder )



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str] )		->	int:


          '''simple docstring'''


          return dict(self.encoder    ,						**self.added_tokens_encoder )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any]    ,						snake_case__		:					Optional[int] )		->	Union[str, Any]:


          '''simple docstring'''


          if token in self.cache:
                 return self.cache[token]
          snake_case       :   List[Any]							=      tuple(snake_case__ )
          snake_case       :   List[Any]							=      tuple(list(word[:-1] ) + [word[-1] + "</w>"] )
          snake_case       :   List[str]							=      get_pairs(snake_case__ )

          if not pairs:
                 return token

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



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str]    ,						snake_case__		:					Tuple )		->	Union[str, Any]:


          '''simple docstring'''


          snake_case       :   Any							=      []

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

          for token in words:
                 split_tokens.extend(list(self.bpe(snake_case__ ).split(" " ) ) )
          return split_tokens



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any    ,						snake_case__		:					str )		->	int:


          '''simple docstring'''


          return self.encoder.get(snake_case__    ,						self.encoder.get(self.unk_token ) )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					Optional[int] )		->	Optional[int]:


          '''simple docstring'''


          return self.decoder.get(snake_case__    ,						self.unk_token )



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str]    ,						snake_case__		:					Dict )		->	Dict:


          '''simple docstring'''


          snake_case       :   Dict							=      " ".join(snake_case__ ).replace("@@ "    ,						"" ).strip()
          return out_string



   def 				_SCREAMING_SNAKE_CASE   (self		:					int    ,						snake_case__		:					str    ,						snake_case__		:					Optional[str] = None )		->	Tuple[str]:


          '''simple docstring'''


          if not os.path.isdir(snake_case__ ):
                 logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
                 return
          snake_case       :   Optional[int]							=      os.path.join(
              snake_case__    ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
          snake_case       :   Union[str, Any]							=      os.path.join(
              snake_case__    ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )

          if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case__ ):
                 copyfile(self.vocab_file    ,						snake_case__ )

          if os.path.abspath(self.merges_file ) != os.path.abspath(snake_case__ ):
                 copyfile(self.merges_file    ,						snake_case__ )

          return out_vocab_file, out_merge_file



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str]    ,						snake_case__		:					str )		->	Dict:


          '''simple docstring'''


          if isinstance(snake_case__    ,						snake_case__ ):
                 try:
                        with open(snake_case__    ,						"r"    ,						encoding="utf-8" ) as fd:
                               self.add_from_file(snake_case__ )
                 except FileNotFoundError as fnfe:
                        raise fnfe
                 except UnicodeError:
                        raise Exception(f"""Incorrect encoding detected in {f}, please rebuild the dataset""" )
                 return

          snake_case       :   Any							=      f.readlines()
          for lineTmp in lines:
                 snake_case       :   Tuple							=      lineTmp.strip()
                 snake_case       :   List[str]							=      line.rfind(" " )
                 if idx == -1:
                        raise ValueError("Incorrect dictionary format, expected '<token> <cnt>'" )
                 snake_case       :   Any							=      line[:idx]
                 snake_case       :   List[Any]							=      len(self.encoder )







# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


lowercase  :					Optional[int]													=       Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

lowercase  :					Optional[Any]													=       {'base': 'patrickvonplaten/wav2vec2_tiny_random', 'robust': 'patrickvonplaten/wav2vec2_tiny_random_robust'}

lowercase  :					str													=       'zero2'
lowercase  :					Optional[int]													=       'zero3'
lowercase  :					Optional[Any]													=       [ZEROa, ZEROa]




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			str						,  _lowerCamelCase :			Union[str, Any]						,  _lowerCamelCase :			List[str])					->    int:






							'''simple docstring'''





							__UpperCamelCase	:   Union[str, Any]			   =					parameterized.to_safe_name("_".join(str(_lowerCamelCase) for x in param.args))
							return F'{func.__name__}_{param_based_name}'


# Cartesian-product of zero stages with models to test
lowercase  :					List[str]													=       list(itertools.product(stages, models.keys()))




@slow
@require_deepspeed
@require_torch_gpu
class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	@parameterized.expand(a     ,   name_func=a						)
	def    _lowerCamelCase				(					self		:Dict     ,   a		:Optional[Any]     ,   a		:str						)		-> Optional[int]:
								self.run_and_check(
								    stage=a     ,   model=a     ,   distributed=a     ,   fpaa=a     ,   )







	@require_torch_multi_gpu
	@parameterized.expand(a     ,   name_func=a						)
	def    _lowerCamelCase				(					self		:List[str]     ,   a		:str     ,   a		:str						)		-> List[Any]:
								self.run_and_check(
								    stage=a     ,   model=a     ,   distributed=a     ,   fpaa=a     ,   )







	@parameterized.expand(a     ,   name_func=a						)
	def    _lowerCamelCase				(					self		:List[Any]     ,   a		:List[str]     ,   a		:int						)		-> Optional[int]:
								self.run_and_check(
								    stage=a     ,   model=a     ,   distributed=a     ,   fpaa=a     ,   )







	@require_torch_multi_gpu
	@parameterized.expand(a     ,   name_func=a						)
	def    _lowerCamelCase				(					self		:List[str]     ,   a		:List[Any]     ,   a		:Dict						)		-> int:
								self.run_and_check(
								    stage=a     ,   model=a     ,   distributed=a     ,   fpaa=a     ,   )







	def    _lowerCamelCase				(					self		:Any     ,   a		:List[str]						)		-> Optional[Any]:
								# XXX: run_asr is premature and doesn't save any results
								# so all we check for now is that the process didn't fail
								pass







	def    _lowerCamelCase				(					self		:Optional[Any]     ,   a		:str     ,   a		:str     ,   a		:int = 1_0     ,   a		:bool = True     ,   a		:bool = True     ,   a		:bool = True     ,   )		-> Any:
								__UpperCamelCase	:   Optional[Any]			   =					models[model]

								__UpperCamelCase	:   List[Any]			   =					self.run_trainer(
								    stage=a     ,   model_name=a     ,   eval_steps=a     ,   num_train_epochs=1     ,   distributed=a     ,   fpaa=a     ,   )

								self.do_checks(a						)

								return output_dir







	def    _lowerCamelCase				(					self		:List[str]     ,   a		:str     ,   a		:str     ,   a		:int = 1_0     ,   a		:int = 1     ,   a		:bool = True     ,   a		:bool = True     ,   )		-> Dict:
								__UpperCamelCase	:   int			   =					self.get_auto_remove_tmp_dir("./xxx"     ,   after=a						)
								__UpperCamelCase	:   int			   =					f'\n            --model_name_or_path {model_name}\n            --dataset_name hf-internal-testing/librispeech_asr_dummy\n            --dataset_config_name clean\n            --train_split_name validation\n            --validation_split_name validation\n            --output_dir {output_dir}\n            --num_train_epochs {str(a						)}\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 2\n            --evaluation_strategy steps\n            --learning_rate 5e-4\n            --warmup_steps 8\n            --orthography timit\n            --preprocessing_num_workers 1\n            --group_by_length\n            --freeze_feature_extractor\n            --report_to none\n            --save_steps 0\n            --eval_steps {eval_steps}\n            --report_to none\n        '.split()

								if fpaa:
															args.extend(["--fp16"]						)

								# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
								# hence the separate config files
								__UpperCamelCase	:   Dict			   =					f'--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'.split()
								__UpperCamelCase	:   int			   =					[f'{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py']
								__UpperCamelCase	:   Optional[Any]			   =					self.get_launcher(a						)

								__UpperCamelCase	:   Optional[int]			   =					launcher + script + args + ds_args
								# keep for quick debug
								# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
								execute_subprocess_async(a     ,   env=self.get_env()						)

								return output_dir







	def    _lowerCamelCase				(					self		:Any     ,   a		:List[Any]=False						)		-> List[Any]:
								# 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup
								# - it won't be able to handle that
								# 2. for now testing with just 2 gpus max (since some quality tests may give different
								# results with mode gpus because we use very little data)
								__UpperCamelCase	:   List[Any]			   =					min(2     ,   get_gpu_count()						) if distributed else 1
								return f'deepspeed --num_nodes 1 --num_gpus {num_gpus}'.split()