





# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


_lowerCamelCase							=     {
    'configuration_tvlt': ['TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TvltConfig'],
    'feature_extraction_tvlt': ['TvltFeatureExtractor'],
    'processing_tvlt': ['TvltProcessor'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase							=     [
	    'TVLT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TvltModel',
	    'TvltForPreTraining',
	    'TvltForAudioVisualClassification',
	    'TvltPreTrainedModel',
	]

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase							=     ['TvltImageProcessor']


if TYPE_CHECKING:
	from .configuration_tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig
	from .processing_tvlt import TvltProcessor
	from .feature_extraction_tvlt import TvltFeatureExtractor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tvlt import (
		    TVLT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TvltForAudioVisualClassification,
		    TvltForPreTraining,
		    TvltModel,
		    TvltPreTrainedModel,
		)

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .image_processing_tvlt import TvltImageProcessor


else:
	import sys

	_lowerCamelCase							=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






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 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							Optional[Any]=False      ):
		# 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)
		SCREAMING_SNAKE_CASE__								= min(2   ,				get_gpu_count()      ) if distributed else 1
		SCREAMING_SNAKE_CASE__								= os.environ.get("""DS_TEST_PORT"""   ,				UpperCamelCase__      )
		return f'''deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'''.split()






def 			SCREAMING_SNAKE_CASE__							(    ):
		SCREAMING_SNAKE_CASE__								= f'''{FIXTURE_DIRECTORY}/tests_samples'''
		SCREAMING_SNAKE_CASE__								= f'''{data_dir_samples}/wmt_en_ro'''
		SCREAMING_SNAKE_CASE__								= f'''{data_dir_samples}/xsum'''
		SCREAMING_SNAKE_CASE__								= """
        --do_train
        --max_train_samples 4
        --per_device_train_batch_size 2
        --num_train_epochs 1
        --fp16
        --report_to none
        --overwrite_output_dir
        """.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.
		SCREAMING_SNAKE_CASE__								= {
		    """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""",
		    ],
		}

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

		SCREAMING_SNAKE_CASE__								= {
		    """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
        ''',
		}

		SCREAMING_SNAKE_CASE__								= get_launcher(distributed=UpperCamelCase__      )

		SCREAMING_SNAKE_CASE__								= {}
		for task, args in tasks.items():
				SCREAMING_SNAKE_CASE__								= args.split()
				for model in tasksamodels[task]:
						SCREAMING_SNAKE_CASE__								= globals()[f'''{model.upper().replace('-'   ,				'_'      )}_TINY''']
						SCREAMING_SNAKE_CASE__								= f'''--model_name_or_path {model_name}'''.split()
						SCREAMING_SNAKE_CASE__								= 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 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							List[str]   ,				UpperCamelCase__:							str   ,				UpperCamelCase__:							Tuple      ):
		# 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
		SCREAMING_SNAKE_CASE__								= parameterized.to_safe_name("""_""".join(str(UpperCamelCase__      ) 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 					UpperCamelCase_    (       UpperCamelCase__      ):


							def        _snake_case					(							self      :Tuple							,			__A      :int							,			__A      :str						)				->				List[Any]:



									"""simple docstring"""



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

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

									SCREAMING_SNAKE_CASE__								= self.get_auto_remove_tmp_dir()
									SCREAMING_SNAKE_CASE__								= 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      :List[Any]							,			__A      :Tuple							,			__A      :int						)				->				Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								= 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
									SCREAMING_SNAKE_CASE__								= f'''{output_dir}/checkpoint-1'''
									SCREAMING_SNAKE_CASE__								= f'''{chkpt_dir}/out.bin'''
									SCREAMING_SNAKE_CASE__								= 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