


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(4_2)

__magic_name__       					=      get_tests_dir('''fixtures''')
__magic_name__       					=      os.path.join(dirname(get_tests_dir()))
__magic_name__       					=      dirname(os.path.abspath(__file__))

# default torch.distributed port
__magic_name__       					=      '''10999'''

__magic_name__       					=      '''t5-small'''

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


# *** To Fix ***


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

# missing tokenizer files
__magic_name__       					=      '''hf-internal-testing/tiny-random-convbert'''
__magic_name__       					=      '''hf-internal-testing/tiny-random-layoutlmv2'''
__magic_name__       					=      '''hf-internal-testing/tiny-random-hubert'''

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

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

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







def     __magic_name__  (      lowerCAmelCase_=False):
	'''simple docstring'''


	lowerCamelCase_			:							Dict      =       min(2      ,					get_gpu_count()) if distributed else 1
	lowerCamelCase_			:							Tuple      =       os.environ.get("DS_TEST_PORT"      ,					lowerCAmelCase_)
	return F"""deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}""".split()







def     __magic_name__  (      ):
	'''simple docstring'''


	lowerCamelCase_			:							Dict      =       F"""{FIXTURE_DIRECTORY}/tests_samples"""
	lowerCamelCase_			:							int      =       F"""{data_dir_samples}/wmt_en_ro"""
	lowerCamelCase_			:							Tuple      =       F"""{data_dir_samples}/xsum"""
	lowerCamelCase_			:							Dict      =       "\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_			:							List[Any]      =       {
	    "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_			:							Tuple      =       F"""{ROOT_DIRECTORY}/examples/pytorch"""

	lowerCamelCase_			:							int      =       {
	    "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_			:							Any      =       get_launcher(distributed=lowerCAmelCase_)

	lowerCamelCase_			:							List[Any]      =       {}
	for task, args in tasks.items():
		lowerCamelCase_			:							List[str]      =       args.split()
		for model in tasksamodels[task]:
			lowerCamelCase_			:							Optional[Any]      =       globals()[F"""{model.upper().replace('-'      ,					'_')}_TINY"""]
			lowerCamelCase_			:							Optional[int]      =       F"""--model_name_or_path {model_name}""".split()
			lowerCamelCase_			:							int      =       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


__magic_name__       					=      make_task_cmds()

__magic_name__       					=      '''zero2'''
__magic_name__       					=      '''zero3'''

__magic_name__       					=      [ZEROa, ZEROa]







def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


	lowerCamelCase_			:							Dict      =       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
__magic_name__       					=      list(itertools.product(stages, task_cmds.keys()))





@slow
@require_deepspeed
@require_torch_gpu
class    lowerCAmelCase__				(			__lowerCamelCase       ):






						"""simple docstring"""




						def 					_UpperCamelCase			(   self				,							a_				,							a_     ):
							# 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_			:							Optional[Any]      =       task_cmds[task]
							lowerCamelCase_			:							Any      =       F"""--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json""".split()

							lowerCamelCase_			:							Dict      =       self.get_auto_remove_tmp_dir()
							lowerCamelCase_			:							Dict      =       F"""--output_dir {output_dir}""".split()

							cmd += args_ds + args_out

							return cmd, output_dir




						@parameterized.expand(a_				,							name_func=a_     )
						def 					_UpperCamelCase			(   self				,							a_				,							a_     ):
							# testing the ability to do a run followed by recovery of full fp32 weights

							lowerCamelCase_   ,lowerCamelCase_			:							str      =       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_			:							List[Any]      =       F"""{output_dir}/checkpoint-1"""
							lowerCamelCase_			:							Optional[Any]      =       F"""{chkpt_dir}/out.bin"""
							lowerCamelCase_			:							List[str]      =       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





import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


__magic_name__       					=      logging.getLogger(__name__)





class    lowerCAmelCase__				:






						"""simple docstring"""




						def __init__(   self     ):
							lowerCamelCase_			:							List[Any]      =       False




						def 					_UpperCamelCase			(   self				,							a_				,							a_				,							a_				,							a_     ):
							if not self.initialized:
								lowerCamelCase_			:							Optional[int]      =       RagRetriever(
								    a_				,							question_encoder_tokenizer=a_				,							generator_tokenizer=a_				,							index=a_				,							init_retrieval=a_				,							)
								lowerCamelCase_			:							Any      =       True




						def 					_UpperCamelCase			(   self     ):
							self.retriever.index.init_index()




						def 					_UpperCamelCase			(   self     ):
							# delete the old self.retriever object before assigning the new index
							del self.retriever
							lowerCamelCase_			:							Optional[int]      =       False




						def 					_UpperCamelCase			(   self				,							a_				,							a_     ):
							lowerCamelCase_   ,lowerCamelCase_			:							List[Any]      =       self.retriever._main_retrieve(a_				,							a_     )
							lowerCamelCase_			:							str      =       self.retriever.index.get_doc_dicts(a_     )
							return doc_ids, retrieved_doc_embeds, doc_dicts





class    lowerCAmelCase__				(			__lowerCamelCase       ):






						"""simple docstring"""




						def __init__(   self				,							a_				,							a_				,							a_				,							a_				,							a_=None     ):
							if index is not None and index.is_initialized() and len(a_     ) > 0:
								raise ValueError(
								    "When using Ray for distributed fine-tuning, "
								    "you'll need to provide the paths instead, "
								    "as the dataset and the index are loaded "
								    "separately. More info in examples/rag/use_own_knowledge_dataset.py "     )

							super().__init__(
							    a_				,							question_encoder_tokenizer=a_				,							generator_tokenizer=a_				,							index=a_				,							init_retrieval=a_				,							)

							lowerCamelCase_			:							List[str]      =       retrieval_workers
							lowerCamelCase_			:							str      =       question_encoder_tokenizer
							lowerCamelCase_			:							Optional[int]      =       generator_tokenizer
							if len(self.retrieval_workers     ) > 0:
								ray.get(
								    [
								        worker.create_rag_retriever.remote(a_				,							a_				,							a_				,							a_     )
								        for worker in self.retrieval_workers
								    ]     )




						def 					_UpperCamelCase			(   self     ):
							logger.info("initializing retrieval"     )

							if len(self.retrieval_workers     ) > 0:
								ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]     )
							else:
								# Non-distributed training. Load index into this same process.
								self.index.init_index()




						def 					_UpperCamelCase			(   self				,							a_				,							a_     ):
							if len(self.retrieval_workers     ) > 0:
								# Select a random retrieval actor.
								lowerCamelCase_			:							List[Any]      =       self.retrieval_workers[random.randint(0				,							len(self.retrieval_workers     ) - 1     )]
								lowerCamelCase_   ,lowerCamelCase_   ,lowerCamelCase_			:							str      =       ray.get(
								    random_worker.retrieve.remote(a_				,							a_     )     )
							else:
								lowerCamelCase_   ,lowerCamelCase_			:							Optional[int]      =       self._main_retrieve(a_				,							a_     )
								lowerCamelCase_			:							Any      =       self.index.get_doc_dicts(a_     )
							return retrieved_doc_embeds, doc_ids, doc_dicts




						@classmethod
						def 					_UpperCamelCase			(   cls				,							a_				,							a_=None				,							**a_     ):
							return super(a_				,							cls     ).get_tokenizers(a_				,							a_				,							**a_     )




						@classmethod
						def 					_UpperCamelCase			(   cls				,							a_				,							a_				,							a_=None				,							**a_     ):
							lowerCamelCase_			:							str      =       kwargs.pop("config"				,							a_     ) or RagConfig.from_pretrained(a_				,							**a_     )
							lowerCamelCase_			:							Tuple      =       RagTokenizer.from_pretrained(a_				,							config=a_     )
							lowerCamelCase_			:							str      =       rag_tokenizer.question_encoder
							lowerCamelCase_			:							int      =       rag_tokenizer.generator

							if indexed_dataset is not None:
								lowerCamelCase_			:							int      =       "custom"
								lowerCamelCase_			:							List[str]      =       CustomHFIndex(config.retrieval_vector_size				,							a_     )
							else:
								lowerCamelCase_			:							str      =       cls._build_index(a_     )

							return cls(
							    a_				,							question_encoder_tokenizer=a_				,							generator_tokenizer=a_				,							retrieval_workers=a_				,							index=a_				,							)




						def 					_UpperCamelCase			(   self     ):
							logger.info("re-loading the new dataset with embeddings"     )
							# access from the training loop

							ray.get([worker.clear_object.remote() for worker in self.retrieval_workers]     )

							# build the index object again
							lowerCamelCase_			:							Optional[int]      =       self._build_index(self.config     )

							ray.get(
							    [
							        worker.create_rag_retriever.remote(
							            self.config				,							self.question_encoder_tokenizer				,							self.generator_tokenizer				,							a_     )
							        for worker in self.retrieval_workers
							    ]     )

