






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   snake_case		(				UpperCAmelCase						:							Dict=False ):
				A									=   min(2,    get_gpu_count() ) if distributed else 1
				A									=   os.environ.get('DS_TEST_PORT',    lowerCAmelCase__ )
				return f'deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'.split()







def   snake_case		(				):
				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=lowerCAmelCase__ )

				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   snake_case		(				UpperCAmelCase						:							Tuple,    UpperCAmelCase						:							Optional[Any],    UpperCAmelCase						:							Union[str, Any] ):
				A									=   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
lowerCAmelCase_     =			list(itertools.product(stages, task_cmds.keys()))



@slow
@require_deepspeed
@require_torch_gpu
class   UpperCamelCase			(					SCREAMING_SNAKE_CASE__			):







		"""simple docstring"""



		def    A(	self		:							Optional[Any]    ,_SCREAMING_SNAKE_CASE		:							int    ,_SCREAMING_SNAKE_CASE		:							str		)     -> List[str]:


						'''simple docstring'''
						# 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()}'		)

						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(A__    ,name_func=A__		)
		def    A(	self		:							Tuple    ,_SCREAMING_SNAKE_CASE		:							Dict    ,_SCREAMING_SNAKE_CASE		:							Dict		)     -> int:


						'''simple docstring'''
						# testing the ability to do a run followed by recovery of full fp32 weights

						A			,		A									=   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
						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(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 unittest

import torch

from diffusers import DDIMScheduler, TextToVideoZeroPipeline
from diffusers.utils import load_pt, require_torch_gpu, slow

from ..test_pipelines_common import assert_mean_pixel_difference







@slow
@require_torch_gpu
class 							lowercase						(   unittest.TestCase						):




							def 					A__ (							self):
												lowercase		      =		'''runwayml/stable-diffusion-v1-5'''
												lowercase		      =		TextToVideoZeroPipeline.from_pretrained(A__  ,torch_dtype=torch.floataa).to('''cuda''')
												lowercase		      =		DDIMScheduler.from_config(pipe.scheduler.config)
												lowercase		      =		torch.Generator(device='''cuda''').manual_seed(0)

												lowercase		      =		'''A bear is playing a guitar on Times Square'''
												lowercase		      =		pipe(prompt=A__  ,generator=A__).images

												lowercase		      =		load_pt(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text-to-video/A bear is playing a guitar on Times Square.pt''')

												assert_mean_pixel_difference(A__  ,A__)




