



'''simple docstring'''

import unittest

import numpy as np

from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_pytesseract_available():
							from PIL import Image

							from transformers import LayoutLMvaImageProcessor

class 	__magic_name__						(    unittest.TestCase   ):







							def __init__(							self					:       Tuple	,_UpperCAmelCase					:       Tuple	,_UpperCAmelCase					:       Tuple=7	,_UpperCAmelCase					:       Optional[int]=3	,_UpperCAmelCase					:       List[Any]=18	,_UpperCAmelCase					:       Dict=30	,_UpperCAmelCase					:       Dict=400	,_UpperCAmelCase					:       Any=True	,_UpperCAmelCase					:       List[Any]=None	,_UpperCAmelCase					:       Tuple=True	,):
									_a     :      str        =							size if size is not None else {'height': 18, 'width': 18}
									_a     :      List[str]        =							parent
									_a     :      Any        =							batch_size
									_a     :      int        =							num_channels
									_a     :      Optional[Any]        =							image_size
									_a     :      str        =							min_resolution
									_a     :      List[str]        =							max_resolution
									_a     :      Dict        =							do_resize
									_a     :      Union[str, Any]        =							size
									_a     :      Optional[int]        =							apply_ocr







							def 							__lowercase			(							self					:       Dict		):
									return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}

@require_torch
@require_pytesseract
class 	__magic_name__						(    _UpperCamelCase			,						unittest.TestCase   ):
							lowerCAmelCase       :    Dict   				=					LayoutLMvaImageProcessor if is_pytesseract_available() else None







							def 							__lowercase			(							self					:       Tuple		):
									_a     :      List[str]        =							LayoutLMvaImageProcessingTester(self		)







							@property
							def 							__lowercase			(							self					:       int		):
									return self.image_processor_tester.prepare_image_processor_dict()







							def 							__lowercase			(							self					:       Union[str, Any]		):
									_a     :      int        =							self.image_processing_class(**self.image_processor_dict		)
									self.assertTrue(hasattr(_UpperCAmelCase	,'do_resize'		)		)
									self.assertTrue(hasattr(_UpperCAmelCase	,'size'		)		)
									self.assertTrue(hasattr(_UpperCAmelCase	,'apply_ocr'		)		)







							def 							__lowercase			(							self					:       Union[str, Any]		):
									_a     :      Optional[int]        =							self.image_processing_class.from_dict(self.image_processor_dict		)
									self.assertEqual(image_processor.size	,{'height': 18, 'width': 18}		)

									_a     :      Any        =							self.image_processing_class.from_dict(self.image_processor_dict	,size=42		)
									self.assertEqual(image_processor.size	,{'height': 42, 'width': 42}		)







							def 							__lowercase			(							self					:       Any		):
									pass







							def 							__lowercase			(							self					:       str		):
									# Initialize image_processing
									_a     :      Optional[int]        =							self.image_processing_class(**self.image_processor_dict		)
									# create random PIL images
									_a     :      Any        =							prepare_image_inputs(self.image_processor_tester	,equal_resolution=_UpperCAmelCase		)
									for image in image_inputs:
											self.assertIsInstance(_UpperCAmelCase	,Image.Image		)

									# Test not batched input
									_a     :      Optional[Any]        =							image_processing(image_inputs[0]	,return_tensors='pt'		)
									self.assertEqual(
									    encoding.pixel_values.shape	,(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size['height'],
									        self.image_processor_tester.size['width'],
									    )	,)

									self.assertIsInstance(encoding.words	,_UpperCAmelCase		)
									self.assertIsInstance(encoding.boxes	,_UpperCAmelCase		)

									# Test batched
									_a     :      Union[str, Any]        =							image_processing(_UpperCAmelCase	,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.size['height'],
									        self.image_processor_tester.size['width'],
									    )	,)







							def 							__lowercase			(							self					:       Dict		):
									# Initialize image_processing
									_a     :      Tuple        =							self.image_processing_class(**self.image_processor_dict		)
									# create random numpy tensors
									_a     :      List[Any]        =							prepare_image_inputs(self.image_processor_tester	,equal_resolution=_UpperCAmelCase	,numpify=_UpperCAmelCase		)
									for image in image_inputs:
											self.assertIsInstance(_UpperCAmelCase	,np.ndarray		)

									# Test not batched input
									_a     :      List[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.size['height'],
									        self.image_processor_tester.size['width'],
									    )	,)

									# Test batched
									_a     :      Optional[Any]        =							image_processing(_UpperCAmelCase	,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.size['height'],
									        self.image_processor_tester.size['width'],
									    )	,)







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

									# Test not batched input
									_a     :      Optional[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.size['height'],
									        self.image_processor_tester.size['width'],
									    )	,)

									# Test batched
									_a     :      Dict        =							image_processing(_UpperCAmelCase	,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.size['height'],
									        self.image_processor_tester.size['width'],
									    )	,)







							def 							__lowercase			(							self					:       int		):
									# with apply_OCR = True
									_a     :      Any        =							LayoutLMvaImageProcessor()

									from datasets import load_dataset

									_a     :      Optional[Any]        =							load_dataset('hf-internal-testing/fixtures_docvqa'	,split='test'		)

									_a     :      int        =							Image.open(ds[0]['file']		).convert('RGB'		)

									_a     :      Dict        =							image_processing(_UpperCAmelCase	,return_tensors='pt'		)

									self.assertEqual(encoding.pixel_values.shape	,(1, 3, 224, 224)		)
									self.assertEqual(len(encoding.words		)	,len(encoding.boxes		)		)

									# fmt: off
									# the words and boxes were obtained with Tesseract 4.1.1
									_a     :      Dict        =							[['11:14', 'to', '11:39', 'a.m', '11:39', 'to', '11:44', 'a.m.', '11:44', 'a.m.', 'to', '12:25', 'p.m.', '12:25', 'to', '12:58', 'p.m.', '12:58', 'to', '4:00', 'p.m.', '2:00', 'to', '5:00', 'p.m.', 'Coffee', 'Break', 'Coffee', 'will', 'be', 'served', 'for', 'men', 'and', 'women', 'in', 'the', 'lobby', 'adjacent', 'to', 'exhibit', 'area.', 'Please', 'move', 'into', 'exhibit', 'area.', '(Exhibits', 'Open)', 'TRRF', 'GENERAL', 'SESSION', '(PART', '|)', 'Presiding:', 'Lee', 'A.', 'Waller', 'TRRF', 'Vice', 'President', '“Introductory', 'Remarks”', 'Lee', 'A.', 'Waller,', 'TRRF', 'Vice', 'Presi-', 'dent', 'Individual', 'Interviews', 'with', 'TRRF', 'Public', 'Board', 'Members', 'and', 'Sci-', 'entific', 'Advisory', 'Council', 'Mem-', 'bers', 'Conducted', 'by', 'TRRF', 'Treasurer', 'Philip', 'G.', 'Kuehn', 'to', 'get', 'answers', 'which', 'the', 'public', 'refrigerated', 'warehousing', 'industry', 'is', 'looking', 'for.', 'Plus', 'questions', 'from', 'the', 'floor.', 'Dr.', 'Emil', 'M.', 'Mrak,', 'University', 'of', 'Cal-', 'ifornia,', 'Chairman,', 'TRRF', 'Board;', 'Sam', 'R.', 'Cecil,', 'University', 'of', 'Georgia', 'College', 'of', 'Agriculture;', 'Dr.', 'Stanley', 'Charm,', 'Tufts', 'University', 'School', 'of', 'Medicine;', 'Dr.', 'Robert', 'H.', 'Cotton,', 'ITT', 'Continental', 'Baking', 'Company;', 'Dr.', 'Owen', 'Fennema,', 'University', 'of', 'Wis-', 'consin;', 'Dr.', 'Robert', 'E.', 'Hardenburg,', 'USDA.', 'Questions', 'and', 'Answers', 'Exhibits', 'Open', 'Capt.', 'Jack', 'Stoney', 'Room', 'TRRF', 'Scientific', 'Advisory', 'Council', 'Meeting', 'Ballroom', 'Foyer']]  # noqa: E231
									_a     :      Tuple        =							[[[141, 57, 214, 69], [228, 58, 252, 69], [141, 75, 216, 88], [230, 79, 280, 88], [142, 260, 218, 273], [230, 261, 255, 273], [143, 279, 218, 290], [231, 282, 290, 291], [143, 342, 218, 354], [231, 345, 289, 355], [202, 362, 227, 373], [143, 379, 220, 392], [231, 382, 291, 394], [144, 714, 220, 726], [231, 715, 256, 726], [144, 732, 220, 745], [232, 736, 291, 747], [144, 769, 218, 782], [231, 770, 256, 782], [141, 788, 202, 801], [215, 791, 274, 804], [143, 826, 204, 838], [215, 826, 240, 838], [142, 844, 202, 857], [215, 847, 274, 859], [334, 57, 427, 69], [440, 57, 522, 69], [369, 75, 461, 88], [469, 75, 516, 88], [528, 76, 562, 88], [570, 76, 667, 88], [675, 75, 711, 87], [721, 79, 778, 88], [789, 75, 840, 88], [369, 97, 470, 107], [484, 94, 507, 106], [518, 94, 562, 107], [576, 94, 655, 110], [668, 94, 792, 109], [804, 95, 829, 107], [369, 113, 465, 125], [477, 116, 547, 125], [562, 113, 658, 125], [671, 116, 748, 125], [761, 113, 811, 125], [369, 131, 465, 143], [477, 133, 548, 143], [563, 130, 698, 145], [710, 130, 802, 146], [336, 171, 412, 183], [423, 171, 572, 183], [582, 170, 716, 184], [728, 171, 817, 187], [829, 171, 844, 186], [338, 197, 482, 212], [507, 196, 557, 209], [569, 196, 595, 208], [610, 196, 702, 209], [505, 214, 583, 226], [595, 214, 656, 227], [670, 215, 807, 227], [335, 259, 543, 274], [556, 259, 708, 272], [372, 279, 422, 291], [435, 279, 460, 291], [474, 279, 574, 292], [587, 278, 664, 291], [676, 278, 738, 291], [751, 279, 834, 291], [372, 298, 434, 310], [335, 341, 483, 354], [497, 341, 655, 354], [667, 341, 728, 354], [740, 341, 825, 354], [335, 360, 430, 372], [442, 360, 534, 372], [545, 359, 687, 372], [697, 360, 754, 372], [765, 360, 823, 373], [334, 378, 428, 391], [440, 378, 577, 394], [590, 378, 705, 391], [720, 378, 801, 391], [334, 397, 400, 409], [370, 416, 529, 429], [544, 416, 576, 432], [587, 416, 665, 428], [677, 416, 814, 429], [372, 435, 452, 450], [465, 434, 495, 447], [511, 434, 600, 447], [611, 436, 637, 447], [649, 436, 694, 451], [705, 438, 824, 447], [369, 453, 452, 466], [464, 454, 509, 466], [522, 453, 611, 469], [625, 453, 792, 469], [370, 472, 556, 488], [570, 472, 684, 487], [697, 472, 718, 485], [732, 472, 835, 488], [369, 490, 411, 503], [425, 490, 484, 503], [496, 490, 635, 506], [645, 490, 707, 503], [718, 491, 761, 503], [771, 490, 840, 503], [336, 510, 374, 521], [388, 510, 447, 522], [460, 510, 489, 521], [503, 510, 580, 522], [592, 509, 736, 525], [745, 509, 770, 522], [781, 509, 840, 522], [338, 528, 434, 541], [448, 528, 596, 541], [609, 527, 687, 540], [700, 528, 792, 541], [336, 546, 397, 559], [407, 546, 431, 559], [443, 546, 525, 560], [537, 546, 680, 562], [688, 546, 714, 559], [722, 546, 837, 562], [336, 565, 449, 581], [461, 565, 485, 577], [497, 565, 665, 581], [681, 565, 718, 577], [732, 565, 837, 580], [337, 584, 438, 597], [452, 583, 521, 596], [535, 584, 677, 599], [690, 583, 787, 596], [801, 583, 825, 596], [338, 602, 478, 615], [492, 602, 530, 614], [543, 602, 638, 615], [650, 602, 676, 614], [688, 602, 788, 615], [802, 602, 843, 614], [337, 621, 502, 633], [516, 621, 615, 637], [629, 621, 774, 636], [789, 621, 827, 633], [337, 639, 418, 652], [432, 640, 571, 653], [587, 639, 731, 655], [743, 639, 769, 652], [780, 639, 841, 652], [338, 658, 440, 673], [455, 658, 491, 670], [508, 658, 602, 671], [616, 658, 638, 670], [654, 658, 835, 674], [337, 677, 429, 689], [337, 714, 482, 726], [495, 714, 548, 726], [561, 714, 683, 726], [338, 770, 461, 782], [474, 769, 554, 785], [489, 788, 562, 803], [576, 788, 643, 801], [656, 787, 751, 804], [764, 788, 844, 801], [334, 825, 421, 838], [430, 824, 574, 838], [584, 824, 723, 841], [335, 844, 450, 857], [464, 843, 583, 860], [628, 862, 755, 875], [769, 861, 848, 878]]]  # noqa: E231
									# fmt: on

									self.assertListEqual(encoding.words	,_UpperCAmelCase		)
									self.assertListEqual(encoding.boxes	,_UpperCAmelCase		)

									# with apply_OCR = False
									_a     :      Optional[int]        =							LayoutLMvaImageProcessor(apply_ocr=_UpperCAmelCase		)

									_a     :      Tuple        =							image_processing(_UpperCAmelCase	,return_tensors='pt'		)

									self.assertEqual(encoding.pixel_values.shape	,(1, 3, 224, 224)		)








"""simple docstring"""



import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock

import torch
from accelerate.utils import write_basic_config

from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available


logging.basicConfig(level=logging.DEBUG)

__snake_case   			=    logging.getLogger()







def   __lowerCAmelCase						(						)	->						str:


    """simple docstring"""





    snake_case    :      List[str]      					=    argparse.ArgumentParser()
    parser.add_argument("-f" )
    snake_case    :      Tuple      					=    parser.parse_args()
    return args.f







def   __lowerCAmelCase						(						lowercase  :  Optional[int] )	->						Dict:


    """simple docstring"""





    snake_case    :      Any      					=    {}
    snake_case    :      int      					=    os.path.join(lowercase						,							"all_results.json" )
    if os.path.exists(lowercase ):
        with open(lowercase						,							"r" ) as f:
            snake_case    :      Optional[int]      					=    json.load(lowercase )
    else:
        raise ValueError(F'can\'t find {path}' )
    return results







def   __lowerCAmelCase						(						)	->						Tuple:


    """simple docstring"""





    snake_case    :      Union[str, Any]      					=    torch.cuda.is_available() and torch_device == "cuda"
    return is_using_cuda and is_apex_available()


__snake_case   			=    logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class _lowerCAmelCase				(      snake_case_	):







   @classmethod
   def  lowerCamelCase		(	cls						)	->		Optional[Any]:

       '''simple docstring'''




       snake_case    :      Optional[int]      					=    tempfile.mkdtemp()
       snake_case    :      int      					=    os.path.join(cls.tmpdir						,	"default_config.yml"						)
       write_basic_config(save_location=cls.configPath						)
       snake_case    :      Union[str, Any]      					=    ["accelerate", "launch", "--config_file", cls.configPath]







   @classmethod
   def  lowerCamelCase		(	cls						)	->		Optional[Any]:

       '''simple docstring'''




       shutil.rmtree(cls.tmpdir						)







   @mock.patch.dict(os.environ						,	{"WANDB_MODE": "offline"}						)
   def  lowerCamelCase		(	self						)	->		int:

       '''simple docstring'''




       snake_case    :      str      					=    self.get_auto_remove_tmp_dir()
       snake_case    :      Dict      					=    F'\n            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --seed=42\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

       if is_cuda_and_apex_available():
           testargs.append("--fp16"						)

       run_command(self._launch_args + testargs						)
       snake_case    :      Union[str, Any]      					=    get_results(UpperCamelCase__						)
       self.assertGreaterEqual(result["eval_accuracy"]						,	0.75						)
       self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__						,	"epoch_0"						)						)						)
       self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__						,	"glue_no_trainer"						)						)						)







   @mock.patch.dict(os.environ						,	{"WANDB_MODE": "offline"}						)
   def  lowerCamelCase		(	self						)	->		int:

       '''simple docstring'''




       snake_case    :      Dict      					=    self.get_auto_remove_tmp_dir()
       snake_case    :      Dict      					=    F'\n            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --block_size 128\n            --per_device_train_batch_size 5\n            --per_device_eval_batch_size 5\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

       if torch.cuda.device_count() > 1:
           # Skipping because there are not enough batches to train the model + would need a drop_last to work.
           return

       run_command(self._launch_args + testargs						)
       snake_case    :      Any      					=    get_results(UpperCamelCase__						)
       self.assertLess(result["perplexity"]						,	100						)
       self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__						,	"epoch_0"						)						)						)
       self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__						,	"clm_no_trainer"						)						)						)







   @mock.patch.dict(os.environ						,	{"WANDB_MODE": "offline"}						)
   def  lowerCamelCase		(	self						)	->		Any:

       '''simple docstring'''




       snake_case    :      Union[str, Any]      					=    self.get_auto_remove_tmp_dir()
       snake_case    :      str      					=    F'\n            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --output_dir {tmp_dir}\n            --num_train_epochs=1\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

       run_command(self._launch_args + testargs						)
       snake_case    :      Dict      					=    get_results(UpperCamelCase__						)
       self.assertLess(result["perplexity"]						,	42						)
       self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__						,	"epoch_0"						)						)						)
       self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__						,	"mlm_no_trainer"						)						)						)







   @mock.patch.dict(os.environ						,	{"WANDB_MODE": "offline"}						)
   def  lowerCamelCase		(	self						)	->		Optional[Any]:

       '''simple docstring'''




       snake_case    :      Optional[Any]      					=    7 if get_gpu_count() > 1 else 2

       snake_case    :      Optional[int]      					=    self.get_auto_remove_tmp_dir()
       snake_case    :      List[str]      					=    F'\n            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

       run_command(self._launch_args + testargs						)
       snake_case    :      str      					=    get_results(UpperCamelCase__						)
       self.assertGreaterEqual(result["eval_accuracy"]						,	0.75						)
       self.assertLess(result["train_loss"]						,	0.5						)
       self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__						,	"epoch_0"						)						)						)
       self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__						,	"ner_no_trainer"						)						)						)







   @unittest.skip(reason="Fix me @muellerzr"						)
   @mock.patch.dict(os.environ						,	{"WANDB_MODE": "offline"}						)
   def  lowerCamelCase		(	self						)	->		str:

       '''simple docstring'''




       snake_case    :      List[Any]      					=    self.get_auto_remove_tmp_dir()
       snake_case    :      Dict      					=    F'\n            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --seed=42\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

       run_command(self._launch_args + testargs						)
       snake_case    :      Dict      					=    get_results(UpperCamelCase__						)
       # Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
       self.assertGreaterEqual(result["eval_f1"]						,	28						)
       self.assertGreaterEqual(result["eval_exact"]						,	28						)
       self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__						,	"epoch_0"						)						)						)
       self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__						,	"qa_no_trainer"						)						)						)







   @mock.patch.dict(os.environ						,	{"WANDB_MODE": "offline"}						)
   def  lowerCamelCase		(	self						)	->		int:

       '''simple docstring'''




       snake_case    :      Tuple      					=    self.get_auto_remove_tmp_dir()
       snake_case    :      List[str]      					=    F'\n            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/swag/sample.json\n            --validation_file tests/fixtures/tests_samples/swag/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=20\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --with_tracking\n        '.split()

       run_command(self._launch_args + testargs						)
       snake_case    :      Optional[Any]      					=    get_results(UpperCamelCase__						)
       self.assertGreaterEqual(result["eval_accuracy"]						,	0.8						)
       self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__						,	"swag_no_trainer"						)						)						)







   @slow
   @mock.patch.dict(os.environ						,	{"WANDB_MODE": "offline"}						)
   def  lowerCamelCase		(	self						)	->		str:

       '''simple docstring'''




       snake_case    :      Tuple      					=    self.get_auto_remove_tmp_dir()
       snake_case    :      Dict      					=    F'\n            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

       run_command(self._launch_args + testargs						)
       snake_case    :      Any      					=    get_results(UpperCamelCase__						)
       self.assertGreaterEqual(result["eval_rouge1"]						,	10						)
       self.assertGreaterEqual(result["eval_rouge2"]						,	2						)
       self.assertGreaterEqual(result["eval_rougeL"]						,	7						)
       self.assertGreaterEqual(result["eval_rougeLsum"]						,	7						)
       self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__						,	"epoch_0"						)						)						)
       self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__						,	"summarization_no_trainer"						)						)						)







   @slow
   @mock.patch.dict(os.environ						,	{"WANDB_MODE": "offline"}						)
   def  lowerCamelCase		(	self						)	->		List[Any]:

       '''simple docstring'''




       snake_case    :      Optional[Any]      					=    self.get_auto_remove_tmp_dir()
       snake_case    :      int      					=    F'\n            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py\n            --model_name_or_path sshleifer/student_marian_en_ro_6_1\n            --source_lang en\n            --target_lang ro\n            --train_file tests/fixtures/tests_samples/wmt16/sample.json\n            --validation_file tests/fixtures/tests_samples/wmt16/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --num_beams=6\n            --learning_rate=3e-3\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --source_lang en_XX\n            --target_lang ro_RO\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

       run_command(self._launch_args + testargs						)
       snake_case    :      Optional[int]      					=    get_results(UpperCamelCase__						)
       self.assertGreaterEqual(result["eval_bleu"]						,	30						)
       self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__						,	"epoch_0"						)						)						)
       self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__						,	"translation_no_trainer"						)						)						)







   @slow
   def  lowerCamelCase		(	self						)	->		Optional[int]:

       '''simple docstring'''




       snake_case    :      str      					=    logging.StreamHandler(sys.stdout						)
       logger.addHandler(UpperCamelCase__						)

       snake_case    :      Dict      					=    self.get_auto_remove_tmp_dir()
       snake_case    :      Dict      					=    F'\n            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py\n            --dataset_name huggingface/semantic-segmentation-test-sample\n            --output_dir {tmp_dir}\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n        '.split()

       run_command(self._launch_args + testargs						)
       snake_case    :      Optional[Any]      					=    get_results(UpperCamelCase__						)
       self.assertGreaterEqual(result["eval_overall_accuracy"]						,	0.10						)







   @mock.patch.dict(os.environ						,	{"WANDB_MODE": "offline"}						)
   def  lowerCamelCase		(	self						)	->		Union[str, Any]:

       '''simple docstring'''




       snake_case    :      Dict      					=    self.get_auto_remove_tmp_dir()
       snake_case    :      str      					=    F'\n            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py\n            --model_name_or_path google/vit-base-patch16-224-in21k\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --learning_rate 1e-4\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --max_train_steps 2\n            --train_val_split 0.1\n            --seed 42\n            --output_dir {tmp_dir}\n            --with_tracking\n            --checkpointing_steps 1\n        '.split()

       if is_cuda_and_apex_available():
           testargs.append("--fp16"						)

       run_command(self._launch_args + testargs						)
       snake_case    :      str      					=    get_results(UpperCamelCase__						)
       # The base model scores a 25%
       self.assertGreaterEqual(result["eval_accuracy"]						,	0.6						)
       self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__						,	"step_1"						)						)						)
       self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__						,	"image_classification_no_trainer"						)						)						)





