

"""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)

SCREAMING_SNAKE_CASE_  :      Optional[int]							=      get_tests_dir('''fixtures''')
SCREAMING_SNAKE_CASE_  :      int							=      os.path.join(dirname(get_tests_dir()))
SCREAMING_SNAKE_CASE_  :      int							=      dirname(os.path.abspath(__file__))

# default torch.distributed port
SCREAMING_SNAKE_CASE_  :      str							=      '''10999'''

SCREAMING_SNAKE_CASE_  :      List[Any]							=      '''t5-small'''

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


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
SCREAMING_SNAKE_CASE_  :      str							=      '''hf-internal-testing/tiny-random-mt5'''
SCREAMING_SNAKE_CASE_  :      Optional[Any]							=      '''hf-internal-testing/tiny-random-camembert'''
SCREAMING_SNAKE_CASE_  :      int							=      '''hf-internal-testing/tiny-random-openai-gpt'''

# missing tokenizer files
SCREAMING_SNAKE_CASE_  :      Any							=      '''hf-internal-testing/tiny-random-convbert'''
SCREAMING_SNAKE_CASE_  :      List[str]							=      '''hf-internal-testing/tiny-random-layoutlmv2'''
SCREAMING_SNAKE_CASE_  :      List[str]							=      '''hf-internal-testing/tiny-random-hubert'''

# issues with tokenizer
SCREAMING_SNAKE_CASE_  :      List[Any]							=      '''hf-internal-testing/tiny-random-ctrl'''
SCREAMING_SNAKE_CASE_  :      Dict							=      '''hf-internal-testing/tiny-random-transfo-xl'''  # same as ctrl

# other issues with tiny models
SCREAMING_SNAKE_CASE_  :      Tuple							=      '''hf-internal-testing/tiny-random-ibert'''  # multiple issues with either mlm/qa/clas
SCREAMING_SNAKE_CASE_  :      int							=      '''hf-internal-testing/tiny-random-reformer'''  # multiple issues with either mlm/qa/clas

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



def 						UpperCAmelCase__ (					A__=False )   ->   Optional[int]:




   """simple docstring"""
   # 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)
   lowerCamelCase__						       =      min(2						,     get_gpu_count() ) if distributed else 1
   lowerCamelCase__						       =      os.environ.get("DS_TEST_PORT"						,     A__ )
   return f'deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'.split()



def 						UpperCAmelCase__ (					)   ->   Optional[int]:




   """simple docstring"""
   lowerCamelCase__						       =      f'{FIXTURE_DIRECTORY}/tests_samples'
   lowerCamelCase__						       =      f'{data_dir_samples}/wmt_en_ro'
   lowerCamelCase__						       =      f'{data_dir_samples}/xsum'
   lowerCamelCase__						       =      "\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.
   lowerCamelCase__						       =      {
       "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",
       ],
   }

   lowerCamelCase__						       =      f'{ROOT_DIRECTORY}/examples/pytorch'

   lowerCamelCase__						       =      {
       "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        ',
   }

   lowerCamelCase__						       =      get_launcher(distributed=A__ )

   lowerCamelCase__						       =      {}
   for task, args in tasks.items():
      lowerCamelCase__						       =      args.split()
      for model in tasksamodels[task]:
         lowerCamelCase__						       =      globals()[f'{model.upper().replace("-"						,     "_" )}_TINY']
         lowerCamelCase__						       =      f'--model_name_or_path {model_name}'.split()
         lowerCamelCase__						       =      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


SCREAMING_SNAKE_CASE_  :      Optional[Any]							=      make_task_cmds()

SCREAMING_SNAKE_CASE_  :      Tuple							=      '''zero2'''
SCREAMING_SNAKE_CASE_  :      Tuple							=      '''zero3'''

SCREAMING_SNAKE_CASE_  :      Optional[Any]							=      [ZEROa, ZEROa]



def 						UpperCAmelCase__ (					A__						,     A__						,     A__ )   ->   Tuple:




   """simple docstring"""
   # customize the test name generator function as we want both params to appear in the sub-test
   # name, as by default it shows only the first param
   lowerCamelCase__						       =      parameterized.to_safe_name("_".join(str(A__ ) for x in param.args ) )
   return f'{func.__name__}_{param_based_name}'


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


@slow
@require_deepspeed
@require_torch_gpu
class 		_A							(      __a       ):





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		) ->       Tuple:
         # return a ready to run train cmd
         if task not in task_cmds:
            raise ValueError(f'don\'t know of task {task}, have {task_cmds.keys()}'		)

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

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

         cmd += args_ds + args_out

         return cmd, output_dir





      @parameterized.expand(SCREAMING_SNAKE_CASE__     ,    name_func=SCREAMING_SNAKE_CASE__		)
      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		) ->       List[Any]:
         # testing the ability to do a run followed by recovery of full fp32 weights

         lowerCamelCase__       ,							lowerCamelCase__						       =      self.get_task_cmd(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		)

         # 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(SCREAMING_SNAKE_CASE__     ,    env=self.get_env()		)

         # 2. test that the fp32 weights get reconsolidated
         lowerCamelCase__						       =      f'{output_dir}/checkpoint-1'
         lowerCamelCase__						       =      f'{chkpt_dir}/out.bin'
         lowerCamelCase__						       =      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(SCREAMING_SNAKE_CASE__     ,    shell=SCREAMING_SNAKE_CASE__		)
         assert os.path.exists(SCREAMING_SNAKE_CASE__		), 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







"""simple docstring"""




import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_splinter import SplinterTokenizer


SCREAMING_SNAKE_CASE_  :      str							=      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_  :      List[Any]							=      {'''vocab_file''': '''vocab.txt'''}

SCREAMING_SNAKE_CASE_  :      int							=      {
    '''vocab_file''': {
        '''tau/splinter-base''': '''https://huggingface.co/tau/splinter-base/resolve/main/vocab.txt''',
        '''tau/splinter-base-qass''': '''https://huggingface.co/tau/splinter-base-qass/resolve/main/vocab.txt''',
        '''tau/splinter-large''': '''https://huggingface.co/tau/splinter-large/resolve/main/vocab.txt''',
        '''tau/splinter-large-qass''': '''https://huggingface.co/tau/splinter-large-qass/resolve/main/vocab.txt''',
    }
}

SCREAMING_SNAKE_CASE_  :      Union[str, Any]							=      {
    '''tau/splinter-base''': 512,
    '''tau/splinter-base-qass''': 512,
    '''tau/splinter-large''': 512,
    '''tau/splinter-large-qass''': 512,
}

SCREAMING_SNAKE_CASE_  :      List[Any]							=      {
    '''tau/splinter-base''': {'''do_lower_case''': False},
    '''tau/splinter-base-qass''': {'''do_lower_case''': False},
    '''tau/splinter-large''': {'''do_lower_case''': False},
    '''tau/splinter-large-qass''': {'''do_lower_case''': False},
}


class 		_A							(      __a       ):
      __a             =						VOCAB_FILES_NAMES
      __a             =						PRETRAINED_VOCAB_FILES_MAP
      __a             =						PRETRAINED_INIT_CONFIGURATION
      __a             =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __a             =						SplinterTokenizer





      def __init__(	self     ,    SCREAMING_SNAKE_CASE__=None     ,    SCREAMING_SNAKE_CASE__=None     ,    SCREAMING_SNAKE_CASE__=True     ,    SCREAMING_SNAKE_CASE__="[UNK]"     ,    SCREAMING_SNAKE_CASE__="[SEP]"     ,    SCREAMING_SNAKE_CASE__="[PAD]"     ,    SCREAMING_SNAKE_CASE__="[CLS]"     ,    SCREAMING_SNAKE_CASE__="[MASK]"     ,    SCREAMING_SNAKE_CASE__="[QUESTION]"     ,    SCREAMING_SNAKE_CASE__=True     ,    SCREAMING_SNAKE_CASE__=None     ,    **SCREAMING_SNAKE_CASE__     ,    ) ->       Optional[Any]:
         super().__init__(
             SCREAMING_SNAKE_CASE__     ,    tokenizer_file=SCREAMING_SNAKE_CASE__     ,    do_lower_case=SCREAMING_SNAKE_CASE__     ,    unk_token=SCREAMING_SNAKE_CASE__     ,    sep_token=SCREAMING_SNAKE_CASE__     ,    pad_token=SCREAMING_SNAKE_CASE__     ,    cls_token=SCREAMING_SNAKE_CASE__     ,    mask_token=SCREAMING_SNAKE_CASE__     ,    tokenize_chinese_chars=SCREAMING_SNAKE_CASE__     ,    strip_accents=SCREAMING_SNAKE_CASE__     ,    additional_special_tokens=(question_token,)     ,    **SCREAMING_SNAKE_CASE__     ,    )

         lowerCamelCase__						       =      json.loads(self.backend_tokenizer.normalizer.__getstate__()		)
         if (
             pre_tok_state.get("lowercase"     ,    SCREAMING_SNAKE_CASE__		) != do_lower_case
             or pre_tok_state.get("strip_accents"     ,    SCREAMING_SNAKE_CASE__		) != strip_accents
         ):
            lowerCamelCase__						       =      getattr(SCREAMING_SNAKE_CASE__     ,    pre_tok_state.pop("type"		)		)
            lowerCamelCase__						       =      do_lower_case
            lowerCamelCase__						       =      strip_accents
            lowerCamelCase__						       =      pre_tok_class(**SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						       =      do_lower_case





      @property
      def        _lowerCamelCase	(	self		) ->       Optional[Any]:
         return self.convert_tokens_to_ids(self.question_token		)





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__ = None		) ->       List[int]:
         if token_ids_a is None:
            return [self.cls_token_id] + token_ids_a + [self.sep_token_id]

         lowerCamelCase__						       =      [self.cls_token_id]
         lowerCamelCase__						       =      [self.sep_token_id]
         lowerCamelCase__						       =      [self.question_token_id] + [self.convert_tokens_to_ids("."		)]
         if self.padding_side == "right":
            # Input is question-then-context
            return cls + token_ids_a + question_suffix + sep + token_ids_a + sep
         else:
            # Input is context-then-question
            return cls + token_ids_a + sep + token_ids_a + question_suffix + sep





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__ = None		) ->       List[int]:
         lowerCamelCase__						       =      [self.sep_token_id]
         lowerCamelCase__						       =      [self.cls_token_id]
         lowerCamelCase__						       =      [self.question_token_id] + [self.convert_tokens_to_ids("."		)]
         if token_ids_a is None:
            return len(cls + token_ids_a + sep		) * [0]

         if self.padding_side == "right":
            # Input is question-then-context
            return len(cls + token_ids_a + question_suffix + sep		) * [0] + len(token_ids_a + sep		) * [1]
         else:
            # Input is context-then-question
            return len(cls + token_ids_a + sep		) * [0] + len(token_ids_a + question_suffix + sep		) * [1]





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__ = None		) ->       Tuple[str]:
         lowerCamelCase__						       =      self._tokenizer.model.save(SCREAMING_SNAKE_CASE__     ,    name=SCREAMING_SNAKE_CASE__		)
         return tuple(SCREAMING_SNAKE_CASE__		)




