






'''simple docstring'''







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

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


__lowerCAmelCase					   =       logging.get_logger(__name__)

__lowerCAmelCase					   =       {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

__lowerCAmelCase					   =       {
    'vocab_file': {
        'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/vocab.json',
    },
    'merges_file': {'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/merges.txt'},
}

__lowerCAmelCase					   =       {
    'microsoft/biogpt': 10_24,
}


def 			__UpperCamelCase				(			lowercase_					:     List[str]      ):
  """simple docstring"""




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




class      __SCREAMING_SNAKE_CASE					(UpperCAmelCase_					):


  """simple docstring"""


  _a     :   Tuple			 =    VOCAB_FILES_NAMES
  _a     :   Dict			 =    PRETRAINED_VOCAB_FILES_MAP
  _a     :   Union[str, Any]			 =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _a     :   List[str]			 =    ['''input_ids''', '''attention_mask''']







  def __init__( self      ,  UpperCamelCase__      ,  UpperCamelCase__      ,  UpperCamelCase__="<unk>"      ,  UpperCamelCase__="<s>"      ,  UpperCamelCase__="</s>"      ,  UpperCamelCase__="</s>"      ,  UpperCamelCase__="<pad>"      ,  **UpperCamelCase__      ,  ):






    """simple docstring"""


    super().__init__(
        bos_token=__UpperCamelCase      ,  eos_token=__UpperCamelCase      ,  sep_token=__UpperCamelCase      ,  unk_token=__UpperCamelCase      ,  pad_token=__UpperCamelCase      ,  **__UpperCamelCase      ,  )

    try:
      import sacremoses
    except ImportError:
      raise ImportError(
          'You need to install sacremoses to use BioGptTokenizer. '
          'See https://pypi.org/project/sacremoses/ for installation.'  )

    a_	    = 'en'
    a_	    = sacremoses
    # cache of sm.MosesTokenizer instance
    a_	    = {}
    a_	    = {}
    with open(__UpperCamelCase      ,  encoding='utf-8'  ) as vocab_handle:
      a_	    = json.load(__UpperCamelCase  )
    a_	    = {v: k for k, v in self.encoder.items()}
    with open(__UpperCamelCase      ,  encoding='utf-8'  ) as merges_handle:
      a_	    = merges_handle.read().split('\n'  )[:-1]
    a_	    = [tuple(merge.split()[:2]  ) for merge in merges]
    a_	    = dict(zip(__UpperCamelCase      ,  range(len(__UpperCamelCase  )  )  )  )
    a_	    = {}







  @property
  def       _a					( self  ):






    """simple docstring"""


    return len(self.encoder  )







  def       _a					( self  ):






    """simple docstring"""


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







  def       _a					( self      ,  UpperCamelCase__      ,  UpperCamelCase__  ):






    """simple docstring"""


    if lang not in self.cache_moses_tokenizer:
      a_	    = self.sm.MosesTokenizer(lang=__UpperCamelCase  )
      a_	    = moses_tokenizer
    return self.cache_moses_tokenizer[lang].tokenize(
        __UpperCamelCase      ,  aggressive_dash_splits=__UpperCamelCase      ,  return_str=__UpperCamelCase      ,  escape=__UpperCamelCase  )







  def       _a					( self      ,  UpperCamelCase__      ,  UpperCamelCase__  ):






    """simple docstring"""


    if lang not in self.cache_moses_detokenizer:
      a_	    = self.sm.MosesDetokenizer(lang=__UpperCamelCase  )
      a_	    = moses_detokenizer
    return self.cache_moses_detokenizer[lang].detokenize(__UpperCamelCase  )







  def       _a					( self      ,  UpperCamelCase__  ):






    """simple docstring"""


    a_	    = tuple(token[:-1]  ) + (token[-1] + '</w>',)
    if token in self.cache:
      return self.cache[token]
    a_	    = get_pairs(__UpperCamelCase  )

    if not pairs:
      return token + "</w>"

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

        if word[i] == first and i < len(__UpperCamelCase  ) - 1 and word[i + 1] == second:
          new_word.append(first + second  )
          i += 2
        else:
          new_word.append(word[i]  )
          i += 1
      a_	    = tuple(__UpperCamelCase  )
      a_	    = new_word
      if len(__UpperCamelCase  ) == 1:
        break
      else:
        a_	    = get_pairs(__UpperCamelCase  )
    a_	    = ' '.join(__UpperCamelCase  )
    if word == "\n  </w>":
      a_	    = '\n</w>'
    a_	    = word
    return word







  def       _a					( self      ,  UpperCamelCase__      ,  UpperCamelCase__=False  ):






    """simple docstring"""


    if bypass_tokenizer:
      a_	    = text.split()
    else:
      a_	    = self.moses_tokenize(__UpperCamelCase      ,  self.lang  )

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

    return split_tokens







  def       _a					( self      ,  UpperCamelCase__  ):






    """simple docstring"""


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







  def       _a					( self      ,  UpperCamelCase__  ):






    """simple docstring"""


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







  def       _a					( self      ,  UpperCamelCase__  ):






    """simple docstring"""


    a_	    = [t.replace(' '      ,  ''  ).replace('</w>'      ,  ' '  ) for t in tokens]
    a_	    = ''.join(__UpperCamelCase  ).split()
    # detokenize
    a_	    = self.moses_detokenize(__UpperCamelCase      ,  self.lang  )
    return text







  def       _a					( self      ,  UpperCamelCase__      ,  UpperCamelCase__ = None  ):






    """simple docstring"""


    if token_ids_a is None:
      return [self.sep_token_id] + token_ids_a
    a_	    = [self.sep_token_id]
    return sep + token_ids_a + sep + token_ids_a







  def       _a					( self      ,  UpperCamelCase__      ,  UpperCamelCase__ = None      ,  UpperCamelCase__ = False  ):






    """simple docstring"""


    if already_has_special_tokens:
      return super().get_special_tokens_mask(
          token_ids_a=__UpperCamelCase      ,  token_ids_a=__UpperCamelCase      ,  already_has_special_tokens=__UpperCamelCase  )
    # no bos used in fairseq
    if token_ids_a is not None:
      return [1] + ([0] * len(__UpperCamelCase  )) + [1] + ([0] * len(__UpperCamelCase  ))
    return [1] + ([0] * len(__UpperCamelCase  ))







  def       _a					( self      ,  UpperCamelCase__      ,  UpperCamelCase__ = None  ):






    """simple docstring"""


    a_	    = [self.sep_token_id]

    # no bos used in fairseq
    if token_ids_a is None:
      return len(token_ids_a + sep  ) * [0]
    return len(token_ids_a + sep  ) * [0] + len(token_ids_a + sep  ) * [1]







  def       _a					( self      ,  UpperCamelCase__      ,  UpperCamelCase__ = None  ):






    """simple docstring"""


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

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

    a_	    = 0
    with open(__UpperCamelCase      ,  'w'      ,  encoding='utf-8'  ) as writer:
      for bpe_tokens, token_index in sorted(self.bpe_ranks.items()      ,  key=lambda UpperCamelCase__  : 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!'  )
          a_	    = token_index
        writer.write(' '.join(__UpperCamelCase  ) + '\n'  )
        index += 1

    return vocab_file, merge_file







  def __getstate__( self  ):






    """simple docstring"""


    a_	    = self.__dict__.copy()
    a_	    = None
    return state







  def __setstate__( self      ,  UpperCamelCase__  ):






    """simple docstring"""


    a_	    = d

    try:
      import sacremoses
    except ImportError:
      raise ImportError(
          'You need to install sacremoses to use XLMTokenizer. '
          'See https://pypi.org/project/sacremoses/ for installation.'  )

    a_	    = sacremoses












'''simple docstring'''







import itertools
import os
import subprocess
from os.path import dirname

from parameterized import parameterized

from tests.trainer.test_trainer import TrainerIntegrationCommon  # noqa
from transformers import is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_tests_dir,
    require_deepspeed,
    require_torch_gpu,
    slow,
)
from transformers.trainer_utils import set_seed


if is_torch_available():
  from tests.trainer.test_trainer import (  # noqa
      RegressionModelConfig,
      RegressionPreTrainedModel,
      get_regression_trainer,
  )


set_seed(42)

__lowerCAmelCase					   =       get_tests_dir("fixtures")
__lowerCAmelCase					   =       os.path.join(dirname(get_tests_dir()))
__lowerCAmelCase					   =       dirname(os.path.abspath(__file__))

# default torch.distributed port
__lowerCAmelCase					   =       "10999"

__lowerCAmelCase					   =       "t5-small"

# *** Working Models ***
__lowerCAmelCase					   =       "hf-internal-testing/tiny-albert"
__lowerCAmelCase					   =       "sshleifer/bart-tiny-random"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-bert"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-bigbird_pegasus"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-big_bird"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-blenderbot"
__lowerCAmelCase					   =       "bigscience/bigscience-small-testing"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-deberta"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-deberta-v2"
__lowerCAmelCase					   =       "sshleifer/tiny-distilbert-base-cased"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-electra"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-flaubert"
__lowerCAmelCase					   =       "stas/tiny-wmt19-en-de"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-funnel"
__lowerCAmelCase					   =       "sshleifer/tiny-gpt2"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-gptj"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-gpt_neo"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-layoutlm"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-led"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-longformer"
__lowerCAmelCase					   =       "stas/tiny-m2m_100"  # hf tiny model is unsuitable
__lowerCAmelCase					   =       "sshleifer/tiny-marian-en-de"
__lowerCAmelCase					   =       "sshleifer/tiny-mbart"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-mobilebert"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-mpnet"
__lowerCAmelCase					   =       "stas/pegasus-cnn_dailymail-tiny-random"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-prophetnet"
__lowerCAmelCase					   =       "sshleifer/tiny-distilroberta-base"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-squeezebert"
__lowerCAmelCase					   =       "patrickvonplaten/t5-tiny-random"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-t5-v1.1"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-vit"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-xlm-roberta"
__lowerCAmelCase					   =       "sshleifer/tiny-xlnet-base-cased"


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-mt5"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-camembert"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-openai-gpt"

# missing tokenizer files
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-convbert"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-layoutlmv2"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-hubert"

# issues with tokenizer
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-ctrl"
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-transfo-xl"  # same as ctrl

# other issues with tiny models
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-ibert"  # multiple issues with either mlm/qa/clas
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-reformer"  # multiple issues with either mlm/qa/clas

# *** Lacking official examples to test with ***
# or not working with examples
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-dpr"
# - "dpr"  examples/research_projects/rag-end2end-retriever/
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-rag"
# - "rag" research_projects
__lowerCAmelCase					   =       ""
# - "luke" Entities classes - no plan to make such example
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-lxmert"
# - "lxmert" doesn't work with run_qa.py
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-clip"
# - "clip" nothing under pytorch examples - XXX: Suraj is working on adding some - check by end of Sep
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-speech_to_text"
# - "speech_to_text", nothing under pytorch examples


# *** Reactive mode ***
# models with low usage, unstable API, things about to change - do nothing about the following until someone runs into a problem
__lowerCAmelCase					   =       "hf-internal-testing/tiny-random-tapas"


def 			__UpperCamelCase				(			lowercase_					:     Optional[int]=False      ):
       """simple docstring"""




       a_	    = min(2      ,					get_gpu_count()      ) if distributed else 1
       a_	    = os.environ.get('DS_TEST_PORT'      ,					lowercase_      )
       return F'deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'.split()


def 			__UpperCamelCase				(			):
       """simple docstring"""




       a_	    = F'{FIXTURE_DIRECTORY}/tests_samples'
       a_	    = F'{data_dir_samples}/wmt_en_ro'
       a_	    = F'{data_dir_samples}/xsum'
       a_	    = '\n        --do_train\n        --max_train_samples 4\n        --per_device_train_batch_size 2\n        --num_train_epochs 1\n        --fp16\n        --report_to none\n        --overwrite_output_dir\n        '.split()

       # try to cover as many models as possible once (it's enough to run on one task per model)
       # but need a tiny model for each
       #
       # should have "{model_type.upper()}_TINY" corresponding vars defined, e.g., T5_TINY, etc.
       a_	    = {
           'trans': [
               'bart',
               'fsmt',
               'm2m_100',
               'marian',
               'mbart',
               't5',
               't5_v1',
               # "mt5", missing model files
           ],
           'sum': [
               'pegasus',
           ],
           'clm': [
               'big_bird',
               'bigbird_pegasus',
               'blenderbot',
               'bloom',
               'gpt2',
               'gpt_neo',
               'gptj',
               'xlm-roberta',
               'prophetnet',
               # "camembert", missing model files
           ],
           'mlm': [
               'albert',
               'deberta',
               'deberta-v2',
               'distilbert',
               'electra',
               'flaubert',
               'funnel',
               'layoutlm',
               # "reformer", # multiple issues with either mlm/qa/clas
           ],
           'qa': [
               'led',
               'longformer',
               'mobilebert',
               'mpnet',
               'roberta',
               'squeezebert',
               # "convbert", # missing tokenizer files
               # "layoutlmv2", missing model files
           ],
           'clas': [
               'bert',
               'xlnet',
               # "hubert", # missing tokenizer files
               # "ibert", # multiple issues with either mlm/qa/clas
               # "transfo-xl", # tokenizer issues as ctrl
               # "ctrl", # tokenizer issues
               # "openai-gpt", missing model files
               # "tapas", multiple issues
           ],
           'img_clas': [
               'vit',
           ],
       }

       a_	    = F'{ROOT_DIRECTORY}/examples/pytorch'

       a_	    = {
           'trans': F'\n        {scripts_dir}/translation/run_translation.py\n        --train_file {data_dir_wmt}/train.json\n        --source_lang en\n        --target_lang ro\n        ',
           'sum': F'\n        {scripts_dir}/summarization/run_summarization.py\n        --train_file {data_dir_xsum}/sample.json\n        --max_source_length 12\n        --max_target_length 12\n        --lang en\n        ',
           'clm': F'\n        {scripts_dir}/language-modeling/run_clm.py\n        --train_file {FIXTURE_DIRECTORY}/sample_text.txt\n        --block_size 8\n        ',
           'mlm': F'\n        {scripts_dir}/language-modeling/run_mlm.py\n        --train_file {FIXTURE_DIRECTORY}/sample_text.txt\n        ',
           'qa': F'\n        {scripts_dir}/question-answering/run_qa.py\n        --train_file {data_dir_samples}/SQUAD/sample.json\n        ',
           'clas': F'\n        {scripts_dir}/text-classification/run_glue.py\n        --train_file {data_dir_samples}/MRPC/train.csv\n        --max_seq_length 12\n        --task_name MRPC\n        ',
           'img_clas': F'\n        {scripts_dir}/image-classification/run_image_classification.py\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --remove_unused_columns False\n            --max_steps 10\n            --image_processor_name {DS_TESTS_DIRECTORY}/vit_feature_extractor.json\n        ',
       }

       a_	    = get_launcher(distributed=lowercase_      )

       a_	    = {}
       for task, args in tasks.items():
              a_	    = args.split()
              for model in tasksamodels[task]:
                     a_	    = globals()[F'{model.upper().replace("-"      ,					"_"      )}_TINY']
                     a_	    = F'--model_name_or_path {model_name}'.split()
                     a_	    = launcher + args + args_model + args_main

                     # # generation special case
                     # if task == "gen":
                     #     launcher = f"deepspeed --num_nodes 1 --num_gpus 1".split()
                     #     args_model += f"--model_type {model}".split()
                     #     cmds[f"{task}_{model}"] = launcher + args + args_model
                     # else:

       return cmds


__lowerCAmelCase					   =       make_task_cmds()

__lowerCAmelCase					   =       "zero2"
__lowerCAmelCase					   =       "zero3"

__lowerCAmelCase					   =       [ZEROa, ZEROa]


def 			__UpperCamelCase				(			lowercase_					:     str      ,					lowercase_					:     Optional[int]      ,					lowercase_					:     Dict      ):
       """simple docstring"""




       a_	    = parameterized.to_safe_name('_'.join(str(lowercase_      ) for x in param.args      )      )
       return F'{func.__name__}_{param_based_name}'


# Cartesian-product of zero stages with models to test
__lowerCAmelCase					   =       list(itertools.product(stages, task_cmds.keys()))




@slow
@require_deepspeed
@require_torch_gpu
class      __SCREAMING_SNAKE_CASE					(__A					):


  """simple docstring"""


  def       _a					( self      ,  UpperCamelCase__      ,  UpperCamelCase__  ):






         """simple docstring"""


         if task not in task_cmds:
                raise ValueError(f'don\'t know of task {task}, have {task_cmds.keys()}'  )

         a_	    = task_cmds[task]
         a_	    = f'--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json'.split()

         a_	    = self.get_auto_remove_tmp_dir()
         a_	    = f'--output_dir {output_dir}'.split()

         cmd += args_ds + args_out

         return cmd, output_dir







  @parameterized.expand(UpperCamelCase__      ,  name_func=UpperCamelCase__  )
  def       _a					( self      ,  UpperCamelCase__      ,  UpperCamelCase__  ):






         """simple docstring"""


         a_    ,  a_	    = self.get_task_cmd(UpperCamelCase__      ,  UpperCamelCase__  )

         # 1. generate the checkpoint
         cmd += "--save_steps 1".split()
         # keep for quick debug
         # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] + cmd)); die
         execute_subprocess_async(UpperCamelCase__      ,  env=self.get_env()  )

         # 2. test that the fp32 weights get reconsolidated
         a_	    = f'{output_dir}/checkpoint-1'
         a_	    = f'{chkpt_dir}/out.bin'
         a_	    = f'{chkpt_dir}/zero_to_fp32.py {chkpt_dir} {recovered_model_path}'
         # keep for quick debug
         # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
         subprocess.check_call(UpperCamelCase__      ,  shell=UpperCamelCase__  )
         assert os.path.exists(UpperCamelCase__  ), f'{recovered_model_path} was not found'

         # possibly could also test that the resulting saved model is usable but given that we use
         # random models we won't know if it's any good




