
"""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)

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        __lowerCAmelCase							(_UpperCamelCase=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)
	__lowerCAmelCase     :		List[Any]		     = min(2			,    get_gpu_count()						) if distributed else 1
	__lowerCAmelCase     :		int		     = os.environ.get('DS_TEST_PORT'			,    _UpperCamelCase						)
	return F"deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}".split()
def        __lowerCAmelCase							():
	__lowerCAmelCase     :		Optional[Any]		     = F"{FIXTURE_DIRECTORY}/tests_samples"
	__lowerCAmelCase     :		Optional[Any]		     = F"{data_dir_samples}/wmt_en_ro"
	__lowerCAmelCase     :		Union[str, Any]		     = F"{data_dir_samples}/xsum"
	__lowerCAmelCase     :		Union[str, Any]		     = '\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[str]		     = {
	    '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     :		str		     = {
	    '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     :		Optional[int]		     = get_launcher(distributed=_UpperCamelCase						)

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

		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					):
			# 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     :		Tuple		     = f"--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json".split()

			__lowerCAmelCase     :		Any		     = self.get_auto_remove_tmp_dir()
			__lowerCAmelCase     :		Optional[Any]		     = 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					):
			# testing the ability to do a run followed by recovery of full fp32 weights

			__lowerCAmelCase     ,  __lowerCAmelCase     :		Dict		     = 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     :		int		     = f"{output_dir}/checkpoint-1"
			__lowerCAmelCase     :		Union[str, Any]		     = f"{chkpt_dir}/out.bin"
			__lowerCAmelCase     :		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(_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 unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import DeiTImageProcessor




class 					A__			(      unittest.TestCase):

		def __init__( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE=7			,						_SCREAMING_SNAKE_CASE=3			,						_SCREAMING_SNAKE_CASE=18			,						_SCREAMING_SNAKE_CASE=30			,						_SCREAMING_SNAKE_CASE=4_00			,						_SCREAMING_SNAKE_CASE=True			,						_SCREAMING_SNAKE_CASE=None			,						_SCREAMING_SNAKE_CASE=True			,						_SCREAMING_SNAKE_CASE=None			,						_SCREAMING_SNAKE_CASE=True			,						_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]			,						_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]			,						):
			__lowerCAmelCase     :		List[str]		     = size if size is not None else {'height': 20, 'width': 20}
			__lowerCAmelCase     :		List[str]		     = crop_size if crop_size is not None else {'height': 18, 'width': 18}

			__lowerCAmelCase     :		Any		     = parent
			__lowerCAmelCase     :		Optional[Any]		     = batch_size
			__lowerCAmelCase     :		Any		     = num_channels
			__lowerCAmelCase     :		Dict		     = image_size
			__lowerCAmelCase     :		Tuple		     = min_resolution
			__lowerCAmelCase     :		Union[str, Any]		     = max_resolution
			__lowerCAmelCase     :		Optional[int]		     = do_resize
			__lowerCAmelCase     :		Any		     = size
			__lowerCAmelCase     :		Union[str, Any]		     = do_center_crop
			__lowerCAmelCase     :		List[Any]		     = crop_size
			__lowerCAmelCase     :		Optional[int]		     = do_normalize
			__lowerCAmelCase     :		int		     = image_mean
			__lowerCAmelCase     :		Tuple		     = image_std







		def 				__lowerCamelCase				( self					):
			return {
			    "do_resize": self.do_resize,
			    "size": self.size,
			    "do_center_crop": self.do_center_crop,
			    "crop_size": self.crop_size,
			    "do_normalize": self.do_normalize,
			    "image_mean": self.image_mean,
			    "image_std": self.image_std,
			}




@require_torch
@require_vision
class 					A__			(      _lowerCamelCase			,	unittest.TestCase):
		A_		:			List[str]							  =      DeiTImageProcessor if is_vision_available() else None
		A_		:			int							  =      True

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		Optional[int]		     = DeiTImageProcessingTester(self					)

		@property
		def 				__lowerCamelCase				( self					):
			return self.image_processor_tester.prepare_image_processor_dict()

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		Any		     = self.image_processing_class(**self.image_processor_dict					)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,						'do_resize'					)					)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,						'size'					)					)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,						'do_center_crop'					)					)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,						'center_crop'					)					)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,						'do_normalize'					)					)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,						'image_mean'					)					)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,						'image_std'					)					)

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		Tuple		     = self.image_processing_class.from_dict(self.image_processor_dict					)
			self.assertEqual(image_processor.size			,						{'height': 20, 'width': 20}					)
			self.assertEqual(image_processor.crop_size			,						{'height': 18, 'width': 18}					)

			__lowerCAmelCase     :		str		     = self.image_processing_class.from_dict(self.image_processor_dict			,						size=42			,						crop_size=84					)
			self.assertEqual(image_processor.size			,						{'height': 42, 'width': 42}					)
			self.assertEqual(image_processor.crop_size			,						{'height': 84, 'width': 84}					)

		def 				__lowerCamelCase				( self					):
			pass

		def 				__lowerCamelCase				( self					):
			# Initialize image_processing
			__lowerCAmelCase     :		Dict		     = self.image_processing_class(**self.image_processor_dict					)
			# create random PIL images
			__lowerCAmelCase     :		List[str]		     = prepare_image_inputs(self.image_processor_tester			,						equal_resolution=_SCREAMING_SNAKE_CASE					)
			for image in image_inputs:
				self.assertIsInstance(_SCREAMING_SNAKE_CASE			,						Image.Image					)

			# Test not batched input
			__lowerCAmelCase     :		Dict		     = image_processing(image_inputs[0]			,						return_tensors='pt'					).pixel_values
			self.assertEqual(
			    encoded_images.shape			,						(
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    )			,						)

			# Test batched
			__lowerCAmelCase     :		int		     = image_processing(_SCREAMING_SNAKE_CASE			,						return_tensors='pt'					).pixel_values
			self.assertEqual(
			    encoded_images.shape			,						(
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    )			,						)

		def 				__lowerCamelCase				( self					):
			# Initialize image_processing
			__lowerCAmelCase     :		Optional[int]		     = self.image_processing_class(**self.image_processor_dict					)
			# create random numpy tensors
			__lowerCAmelCase     :		List[str]		     = prepare_image_inputs(self.image_processor_tester			,						equal_resolution=_SCREAMING_SNAKE_CASE			,						numpify=_SCREAMING_SNAKE_CASE					)
			for image in image_inputs:
				self.assertIsInstance(_SCREAMING_SNAKE_CASE			,						np.ndarray					)

			# Test not batched input
			__lowerCAmelCase     :		str		     = image_processing(image_inputs[0]			,						return_tensors='pt'					).pixel_values
			self.assertEqual(
			    encoded_images.shape			,						(
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    )			,						)

			# Test batched
			__lowerCAmelCase     :		Dict		     = image_processing(_SCREAMING_SNAKE_CASE			,						return_tensors='pt'					).pixel_values
			self.assertEqual(
			    encoded_images.shape			,						(
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    )			,						)







		def 				__lowerCamelCase				( self					):
			# Initialize image_processing
			__lowerCAmelCase     :		Union[str, Any]		     = self.image_processing_class(**self.image_processor_dict					)
			# create random PyTorch tensors
			__lowerCAmelCase     :		List[str]		     = prepare_image_inputs(self.image_processor_tester			,						equal_resolution=_SCREAMING_SNAKE_CASE			,						torchify=_SCREAMING_SNAKE_CASE					)
			for image in image_inputs:
				self.assertIsInstance(_SCREAMING_SNAKE_CASE			,						torch.Tensor					)

			# Test not batched input
			__lowerCAmelCase     :		Union[str, Any]		     = image_processing(image_inputs[0]			,						return_tensors='pt'					).pixel_values
			self.assertEqual(
			    encoded_images.shape			,						(
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    )			,						)

			# Test batched
			__lowerCAmelCase     :		Optional[int]		     = image_processing(_SCREAMING_SNAKE_CASE			,						return_tensors='pt'					).pixel_values
			self.assertEqual(
			    encoded_images.shape			,						(
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    )			,						)