




import os
from functools import partial
from glob import glob

import faiss
from datasets import Features, Sequence, Value, concatenate_datasets, load_dataset, load_from_disk

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast

def      lowerCAmelCase_     (					lowerCamelCase:    List[Any]       ,			lowerCamelCase:    Tuple=1_00       ,			lowerCamelCase:    str=" "  )   ->     Union[str, Any]:


      '''simple docstring'''




      lowerCamelCase__ 					=			text.split(lowerCamelCase  )
      return [character.join(text[i : i + n]  ).strip() for i in range(0       ,			len(lowerCamelCase  )       ,			lowerCamelCase  )]

def      lowerCAmelCase_     (					lowerCamelCase:    Any  )   ->     str:


      '''simple docstring'''




      lowerCamelCase__					,						lowerCamelCase__ 					=			[], []
      for title, text in zip(documents['title']       ,			documents['text']  ):
            if text is not None:
                  for passage in split_text(lowerCamelCase  ):
                        titles.append(title if title is not None else ''  )
                        texts.append(lowerCamelCase  )
      return {"title": titles, "text": texts}

def      lowerCAmelCase_     (					lowerCamelCase:    Tuple       ,			lowerCamelCase:    List[str]       ,			lowerCamelCase:    Optional[int]       ,			lowerCamelCase:    int       ,			lowerCamelCase:    Optional[int]       ,			lowerCamelCase:    Tuple  )   ->     List[Any]:


      '''simple docstring'''




      lowerCamelCase__ 					=			load_dataset(
          'csv'       ,			data_files=[csv_path]       ,			split='train'       ,			delimiter='\t'       ,			column_names=['title', 'text']  )
      lowerCamelCase__ 					=			kb_dataset.map(
          lowerCamelCase       ,			batched=lowerCamelCase       ,			num_proc=1  )  # if you want you can load already splitted csv.
      lowerCamelCase__ 					=			[kb_dataset.shard(lowerCamelCase       ,			lowerCamelCase       ,			contiguous=lowerCamelCase  ) for i in range(lowerCamelCase  )]
      lowerCamelCase__ 					=			kb_list[process_num]

      lowerCamelCase__ 					=			'data_' + str(lowerCamelCase  )
      lowerCamelCase__ 					=			os.path.join(lowerCamelCase       ,			lowerCamelCase  )

      lowerCamelCase__ 					=			DPRContextEncoderTokenizerFast.from_pretrained('facebook/dpr-ctx_encoder-multiset-base'  )
      lowerCamelCase__ 					=			ctx_encoder.to(device=lowerCamelCase  )

      def embed(
          lowerCamelCase:    dict       ,			lowerCamelCase:    DPRContextEncoder       ,			lowerCamelCase:    DPRContextEncoderTokenizerFast       ,			lowerCamelCase:    Optional[Any]  ) -> dict:
            lowerCamelCase__ 					=			ctx_tokenizer(
                documents['title']       ,			documents['text']       ,			truncation=lowerCamelCase       ,			padding='longest'       ,			return_tensors='pt'  )['input_ids']
            lowerCamelCase__ 					=			ctx_encoder(input_ids.to(device=lowerCamelCase  )       ,			return_dict=lowerCamelCase  ).pooler_output
            return {"embeddings": embeddings.detach().cpu().numpy()}

      lowerCamelCase__ 					=			Features(
          {'text': Value('string'  ), 'title': Value('string'  ), 'embeddings': Sequence(Value('float32'  )  )}  )  # optional, save as float32 instead of float64 to save space

      lowerCamelCase__ 					=			data_shrad.map(
          partial(lowerCamelCase       ,			ctx_encoder=lowerCamelCase       ,			ctx_tokenizer=lowerCamelCase       ,			device=lowerCamelCase  )       ,			batched=lowerCamelCase       ,			batch_size=16       ,			features=lowerCamelCase       ,			)
      dataset.save_to_disk(lowerCamelCase  )


def      lowerCAmelCase_     (					lowerCamelCase:    List[Any]       ,			lowerCamelCase:    Dict  )   ->     Optional[int]:


      '''simple docstring'''




      lowerCamelCase__ 					=			[]

      for shard_address in glob(str(lowerCamelCase  ) + '/*/'  ):
            data_shard_list.append(load_from_disk(lowerCamelCase  )  )

      lowerCamelCase__ 					=			concatenate_datasets(lowerCamelCase  )
      faiss.omp_set_num_threads(96  )

      lowerCamelCase__ 					=			faiss.IndexHNSWFlat(7_68       ,			1_28       ,			faiss.METRIC_INNER_PRODUCT  )
      concat.add_faiss_index('embeddings'       ,			custom_index=lowerCamelCase  )
      concat.get_index('embeddings'  ).save(
          lowerCamelCase  )  # since we load the index in to memory,we can directly update the index in the disk





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[Any]				      =get_tests_dir("""fixtures""")
__SCREAMING_SNAKE_CASE      :				int				      =os.path.join(dirname(get_tests_dir()))
__SCREAMING_SNAKE_CASE      :				List[str]				      =dirname(os.path.abspath(__file__))

# default torch.distributed port
__SCREAMING_SNAKE_CASE      :				Tuple				      ="""10999"""

__SCREAMING_SNAKE_CASE      :				List[Any]				      ="""t5-small"""

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

# missing tokenizer files
__SCREAMING_SNAKE_CASE      :				List[str]				      ="""hf-internal-testing/tiny-random-convbert"""
__SCREAMING_SNAKE_CASE      :				Optional[Any]				      ="""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      :				int				      ="""hf-internal-testing/tiny-random-transfo-xl"""  # same as ctrl

# other issues with tiny models
__SCREAMING_SNAKE_CASE      :				Any				      ="""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      :				Union[str, Any]				      ="""hf-internal-testing/tiny-random-dpr"""
# - "dpr"  examples/research_projects/rag-end2end-retriever/
__SCREAMING_SNAKE_CASE      :				Dict				      ="""hf-internal-testing/tiny-random-rag"""
# - "rag" research_projects
__SCREAMING_SNAKE_CASE      :				Any				      =""""""
# - "luke" Entities classes - no plan to make such example
__SCREAMING_SNAKE_CASE      :				Optional[int]				      ="""hf-internal-testing/tiny-random-lxmert"""
# - "lxmert" doesn't work with run_qa.py
__SCREAMING_SNAKE_CASE      :				List[str]				      ="""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      :				str				      ="""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      :				int				      ="""hf-internal-testing/tiny-random-tapas"""

def      lowerCAmelCase_     (					lowerCamelCase:    str=False  )   ->     str:


      '''simple docstring'''




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

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

      lowerCamelCase__ 					=			get_launcher(distributed=lowerCamelCase  )

      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      :				str				      =make_task_cmds()

__SCREAMING_SNAKE_CASE      :				Optional[Any]				      ="""zero2"""
__SCREAMING_SNAKE_CASE      :				List[Any]				      ="""zero3"""

__SCREAMING_SNAKE_CASE      :				str				      =[ZEROa, ZEROa]

def      lowerCAmelCase_     (					lowerCamelCase:    Optional[Any]       ,			lowerCamelCase:    List[str]       ,			lowerCamelCase:    Any  )   ->     List[Any]:


      '''simple docstring'''




      lowerCamelCase__ 					=			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
__SCREAMING_SNAKE_CASE      :				List[str]				      =list(itertools.product(stages, task_cmds.keys()))






@slow
@require_deepspeed
@require_torch_gpu
class      A     (						_lowerCAmelCase						):
     """simple docstring"""

     def 	__snake_case				(      self					:				int		,					A__					:				List[str]		,					A__					:				List[Any]     )			->    Any:
           # 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(A__		,					name_func=A__     )
     def 	__snake_case				(      self					:				Union[str, Any]		,					A__					:				List[Any]		,					A__					:				Union[str, Any]     )			->    Any:
           # testing the ability to do a run followed by recovery of full fp32 weights

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

           # 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(A__		,					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(A__		,					shell=A__     )
           assert os.path.exists(A__     ), 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