

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 PoolFormerImageProcessor





class 			_SCREAMING_SNAKE_CASE (   unittest.TestCase		):







   '''simple docstring'''




   def __init__(       self					:			str     , __lowerCamelCase					:			Dict     , __lowerCamelCase					:			List[Any]=7     , __lowerCamelCase					:			Any=3     , __lowerCamelCase					:			Any=30     , __lowerCamelCase					:			Any=400     , __lowerCamelCase					:			Union[str, Any]=True     , __lowerCamelCase					:			List[Any]=None     , __lowerCamelCase					:			Optional[int]=0.9     , __lowerCamelCase					:			Dict=None     , __lowerCamelCase					:			Dict=True     , __lowerCamelCase					:			List[Any]=[0.5, 0.5, 0.5]     , __lowerCamelCase					:			Dict=[0.5, 0.5, 0.5]     , ):
       SCREAMING_SNAKE_CASE										=   size if size is not None else {"shortest_edge": 30}
       SCREAMING_SNAKE_CASE										=   crop_size if crop_size is not None else {"height": 30, "width": 30}
       SCREAMING_SNAKE_CASE										=   parent
       SCREAMING_SNAKE_CASE										=   batch_size
       SCREAMING_SNAKE_CASE										=   num_channels
       SCREAMING_SNAKE_CASE										=   min_resolution
       SCREAMING_SNAKE_CASE										=   max_resolution
       SCREAMING_SNAKE_CASE										=   do_resize_and_center_crop
       SCREAMING_SNAKE_CASE										=   size
       SCREAMING_SNAKE_CASE										=   crop_pct
       SCREAMING_SNAKE_CASE										=   crop_size
       SCREAMING_SNAKE_CASE										=   do_normalize
       SCREAMING_SNAKE_CASE										=   image_mean
       SCREAMING_SNAKE_CASE										=   image_std





   def      _snake_case		(       self					:			Dict			):
       return {
           "size": self.size,
           "do_resize_and_center_crop": self.do_resize_and_center_crop,
           "crop_pct": self.crop_pct,
           "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 			_SCREAMING_SNAKE_CASE (   __snake_case					,       unittest.TestCase		):







   '''simple docstring'''




   lowerCamelCase__				=      PoolFormerImageProcessor if is_vision_available() else None





   def      _snake_case		(       self					:			List[Any]			):
       SCREAMING_SNAKE_CASE										=   PoolFormerImageProcessingTester(self			)





   @property
   def      _snake_case		(       self					:			Optional[int]			):
       return self.image_processor_tester.prepare_image_processor_dict()





   def      _snake_case		(       self					:			Union[str, Any]			):
       SCREAMING_SNAKE_CASE										=   self.image_processing_class(**self.image_processor_dict			)
       self.assertTrue(hasattr(__lowerCamelCase     , "do_resize_and_center_crop"			)			)
       self.assertTrue(hasattr(__lowerCamelCase     , "size"			)			)
       self.assertTrue(hasattr(__lowerCamelCase     , "crop_pct"			)			)
       self.assertTrue(hasattr(__lowerCamelCase     , "do_normalize"			)			)
       self.assertTrue(hasattr(__lowerCamelCase     , "image_mean"			)			)
       self.assertTrue(hasattr(__lowerCamelCase     , "image_std"			)			)





   def      _snake_case		(       self					:			Union[str, Any]			):
       SCREAMING_SNAKE_CASE										=   self.image_processing_class.from_dict(self.image_processor_dict			)
       self.assertEqual(image_processor.size     , {"shortest_edge": 30}			)
       self.assertEqual(image_processor.crop_size     , {"height": 30, "width": 30}			)

       SCREAMING_SNAKE_CASE										=   self.image_processing_class.from_dict(self.image_processor_dict     , size=42     , crop_size=84			)
       self.assertEqual(image_processor.size     , {"shortest_edge": 42}			)
       self.assertEqual(image_processor.crop_size     , {"height": 84, "width": 84}			)





   def      _snake_case		(       self					:			List[str]			):
       pass





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

       # Test not batched input
       SCREAMING_SNAKE_CASE										=   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
       SCREAMING_SNAKE_CASE										=   image_processing(__lowerCamelCase     , 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      _snake_case		(       self					:			Optional[int]			):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE										=   self.image_processing_class(**self.image_processor_dict			)
       # create random numpy tensors
       SCREAMING_SNAKE_CASE										=   prepare_image_inputs(self.image_processor_tester     , equal_resolution=__lowerCamelCase     , numpify=__lowerCamelCase			)
       for image in image_inputs:
           self.assertIsInstance(__lowerCamelCase     , np.ndarray			)

       # Test not batched input
       SCREAMING_SNAKE_CASE										=   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
       SCREAMING_SNAKE_CASE										=   image_processing(__lowerCamelCase     , 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      _snake_case		(       self					:			str			):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE										=   self.image_processing_class(**self.image_processor_dict			)
       # create random PyTorch tensors
       SCREAMING_SNAKE_CASE										=   prepare_image_inputs(self.image_processor_tester     , equal_resolution=__lowerCamelCase     , torchify=__lowerCamelCase			)
       for image in image_inputs:
           self.assertIsInstance(__lowerCamelCase     , torch.Tensor			)

       # Test not batched input
       SCREAMING_SNAKE_CASE										=   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
       SCREAMING_SNAKE_CASE										=   image_processing(__lowerCamelCase     , 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"],
           )     , )

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)

lowerCamelCase__						=				logging.getLogger()


def 				__A()		->					str:

		"""simple docstring"""






		_UpperCamelCase         =						argparse.ArgumentParser()
		parser.add_argument("""-f"""			)
		_UpperCamelCase         =						parser.parse_args()
		return args.f


def 				__A(lowerCAmelCase			)		->					Any:

		"""simple docstring"""






		_UpperCamelCase         =						{}
		_UpperCamelCase         =						os.path.join(lowerCAmelCase    ,	"""all_results.json"""			)
		if os.path.exists(lowerCAmelCase			):
				with open(lowerCAmelCase    ,	"""r"""			) as f:
						_UpperCamelCase         =						json.load(lowerCAmelCase			)
		else:
				raise ValueError(F'can\'t find {path}'			)
		return results


def 				__A()		->					Tuple:

		"""simple docstring"""






		_UpperCamelCase         =						torch.cuda.is_available() and torch_device == """cuda"""
		return is_using_cuda and is_apex_available()


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







class 							lowerCAmelCase__   ( __lowercase						):

						@classmethod
						def 			A_					(      cls					)       ->   Tuple:


								'''simple docstring'''

								_UpperCamelCase         =						tempfile.mkdtemp()
								_UpperCamelCase         =						os.path.join(cls.tmpdir							,				"""default_config.yml"""					)
								write_basic_config(save_location=cls.configPath					)
								_UpperCamelCase         =						["""accelerate""", """launch""", """--config_file""", cls.configPath]

						@classmethod
						def 			A_					(      cls					)       ->   str:


								'''simple docstring'''

								shutil.rmtree(cls.tmpdir					)

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


								'''simple docstring'''

								_UpperCamelCase         =						self.get_auto_remove_tmp_dir()
								_UpperCamelCase         =						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					)
								_UpperCamelCase         =						get_results(a					)
								self.assertGreaterEqual(result["""eval_accuracy"""]							,				0.75					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""epoch_0"""					)					)					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""glue_no_trainer"""					)					)					)

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


								'''simple docstring'''

								_UpperCamelCase         =						self.get_auto_remove_tmp_dir()
								_UpperCamelCase         =						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					)
								_UpperCamelCase         =						get_results(a					)
								self.assertLess(result["""perplexity"""]							,				1_00					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""epoch_0"""					)					)					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""clm_no_trainer"""					)					)					)

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


								'''simple docstring'''

								_UpperCamelCase         =						self.get_auto_remove_tmp_dir()
								_UpperCamelCase         =						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					)
								_UpperCamelCase         =						get_results(a					)
								self.assertLess(result["""perplexity"""]							,				42					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""epoch_0"""					)					)					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""mlm_no_trainer"""					)					)					)

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


								'''simple docstring'''

								_UpperCamelCase         =						7 if get_gpu_count() > 1 else 2

								_UpperCamelCase         =						self.get_auto_remove_tmp_dir()
								_UpperCamelCase         =						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					)
								_UpperCamelCase         =						get_results(a					)
								self.assertGreaterEqual(result["""eval_accuracy"""]							,				0.75					)
								self.assertLess(result["""train_loss"""]							,				0.5					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""epoch_0"""					)					)					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""ner_no_trainer"""					)					)					)

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


								'''simple docstring'''

								_UpperCamelCase         =						self.get_auto_remove_tmp_dir()
								_UpperCamelCase         =						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					)
								_UpperCamelCase         =						get_results(a					)
								# 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(a							,				"""epoch_0"""					)					)					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""qa_no_trainer"""					)					)					)

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


								'''simple docstring'''

								_UpperCamelCase         =						self.get_auto_remove_tmp_dir()
								_UpperCamelCase         =						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					)
								_UpperCamelCase         =						get_results(a					)
								self.assertGreaterEqual(result["""eval_accuracy"""]							,				0.8					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""swag_no_trainer"""					)					)					)

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


								'''simple docstring'''

								_UpperCamelCase         =						self.get_auto_remove_tmp_dir()
								_UpperCamelCase         =						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					)
								_UpperCamelCase         =						get_results(a					)
								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(a							,				"""epoch_0"""					)					)					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""summarization_no_trainer"""					)					)					)

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


								'''simple docstring'''

								_UpperCamelCase         =						self.get_auto_remove_tmp_dir()
								_UpperCamelCase         =						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					)
								_UpperCamelCase         =						get_results(a					)
								self.assertGreaterEqual(result["""eval_bleu"""]							,				30					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""epoch_0"""					)					)					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""translation_no_trainer"""					)					)					)

						@slow
						def 			A_					(      self					)       ->   Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase         =						logging.StreamHandler(sys.stdout					)
								logger.addHandler(a					)

								_UpperCamelCase         =						self.get_auto_remove_tmp_dir()
								_UpperCamelCase         =						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					)
								_UpperCamelCase         =						get_results(a					)
								self.assertGreaterEqual(result["""eval_overall_accuracy"""]							,				0.10					)







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


								'''simple docstring'''

								_UpperCamelCase         =						self.get_auto_remove_tmp_dir()
								_UpperCamelCase         =						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					)
								_UpperCamelCase         =						get_results(a					)
								# The base model scores a 25%
								self.assertGreaterEqual(result["""eval_accuracy"""]							,				0.6					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""step_1"""					)					)					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""image_classification_no_trainer"""					)					)					)



