


"""simple docstring"""



import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
  import onnxruntime as ort







class _lowerCAmelCase				(      snake_case_    ,							unittest.TestCase	):
   __UpperCAmelCase :       str							  =  '''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''







   def  lowerCamelCase		(	self						,	UpperCamelCase__=0						)	->		Any:

       '''simple docstring'''




       snake_case    :      List[str]      					=    floats_tensor((1, 3, 128, 128)						,	rng=random.Random(UpperCamelCase__						)						)
       snake_case    :      str      					=    np.random.RandomState(UpperCamelCase__						)
       snake_case    :      Union[str, Any]      					=    {
           "prompt": "A painting of a squirrel eating a burger",
           "image": image,
           "generator": generator,
           "num_inference_steps": 3,
           "strength": 0.75,
           "guidance_scale": 7.5,
           "output_type": "numpy",
       }
       return inputs







   def  lowerCamelCase		(	self						)	->		str:

       '''simple docstring'''




       snake_case    :      List[str]      					=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,	provider="CPUExecutionProvider"						)
       pipe.set_progress_bar_config(disable=UpperCamelCase__						)

       snake_case    :      Union[str, Any]      					=    self.get_dummy_inputs()
       snake_case    :      List[Any]      					=    pipe(**UpperCamelCase__						).images
       snake_case    :      Optional[Any]      					=    image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 128, 128, 3)
       snake_case    :      Tuple      					=    np.array([0.69643, 0.58484, 0.50314, 0.58760, 0.55368, 0.59643, 0.51529, 0.41217, 0.49087]						)
       assert np.abs(image_slice - expected_slice						).max() < 1e-1







   def  lowerCamelCase		(	self						)	->		List[Any]:

       '''simple docstring'''




       snake_case    :      int      					=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,	provider="CPUExecutionProvider"						)
       snake_case    :      str      					=    PNDMScheduler.from_config(pipe.scheduler.config						,	skip_prk_steps=UpperCamelCase__						)
       pipe.set_progress_bar_config(disable=UpperCamelCase__						)

       snake_case    :      str      					=    self.get_dummy_inputs()
       snake_case    :      str      					=    pipe(**UpperCamelCase__						).images
       snake_case    :      Any      					=    image[0, -3:, -3:, -1]

       assert image.shape == (1, 128, 128, 3)
       snake_case    :      int      					=    np.array([0.61737, 0.54642, 0.53183, 0.54465, 0.52742, 0.60525, 0.49969, 0.40655, 0.48154]						)

       assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-1







   def  lowerCamelCase		(	self						)	->		List[Any]:

       '''simple docstring'''




       snake_case    :      str      					=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,	provider="CPUExecutionProvider"						)
       snake_case    :      Dict      					=    LMSDiscreteScheduler.from_config(pipe.scheduler.config						)
       pipe.set_progress_bar_config(disable=UpperCamelCase__						)

       # warmup pass to apply optimizations
       snake_case    :      Dict      					=    pipe(**self.get_dummy_inputs()						)

       snake_case    :      Optional[Any]      					=    self.get_dummy_inputs()
       snake_case    :      Dict      					=    pipe(**UpperCamelCase__						).images
       snake_case    :      List[str]      					=    image[0, -3:, -3:, -1]

       assert image.shape == (1, 128, 128, 3)
       snake_case    :      Union[str, Any]      					=    np.array([0.52761, 0.59977, 0.49033, 0.49619, 0.54282, 0.50311, 0.47600, 0.40918, 0.45203]						)

       assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-1







   def  lowerCamelCase		(	self						)	->		Union[str, Any]:

       '''simple docstring'''




       snake_case    :      str      					=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,	provider="CPUExecutionProvider"						)
       snake_case    :      List[Any]      					=    EulerDiscreteScheduler.from_config(pipe.scheduler.config						)
       pipe.set_progress_bar_config(disable=UpperCamelCase__						)

       snake_case    :      List[str]      					=    self.get_dummy_inputs()
       snake_case    :      Dict      					=    pipe(**UpperCamelCase__						).images
       snake_case    :      Any      					=    image[0, -3:, -3:, -1]

       assert image.shape == (1, 128, 128, 3)
       snake_case    :      int      					=    np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304]						)

       assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-1







   def  lowerCamelCase		(	self						)	->		List[Any]:

       '''simple docstring'''




       snake_case    :      List[str]      					=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,	provider="CPUExecutionProvider"						)
       snake_case    :      Dict      					=    EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config						)
       pipe.set_progress_bar_config(disable=UpperCamelCase__						)

       snake_case    :      Tuple      					=    self.get_dummy_inputs()
       snake_case    :      Optional[int]      					=    pipe(**UpperCamelCase__						).images
       snake_case    :      Optional[int]      					=    image[0, -3:, -3:, -1]

       assert image.shape == (1, 128, 128, 3)
       snake_case    :      Optional[int]      					=    np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304]						)

       assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-1







   def  lowerCamelCase		(	self						)	->		str:

       '''simple docstring'''




       snake_case    :      Union[str, Any]      					=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,	provider="CPUExecutionProvider"						)
       snake_case    :      str      					=    DPMSolverMultistepScheduler.from_config(pipe.scheduler.config						)
       pipe.set_progress_bar_config(disable=UpperCamelCase__						)

       snake_case    :      Tuple      					=    self.get_dummy_inputs()
       snake_case    :      Any      					=    pipe(**UpperCamelCase__						).images
       snake_case    :      str      					=    image[0, -3:, -3:, -1]

       assert image.shape == (1, 128, 128, 3)
       snake_case    :      Dict      					=    np.array([0.65331, 0.58277, 0.48204, 0.56059, 0.53665, 0.56235, 0.50969, 0.40009, 0.46552]						)

       assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-1







@nightly
@require_onnxruntime
@require_torch_gpu
class _lowerCAmelCase				(      unittest.TestCase	):







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

       '''simple docstring'''




       return (
           "CUDAExecutionProvider",
           {
               "gpu_mem_limit": "15000000000",  # 15GB
               "arena_extend_strategy": "kSameAsRequested",
           },
       )







   @property
   def  lowerCamelCase		(	self						)	->		List[Any]:

       '''simple docstring'''




       snake_case    :      List[Any]      					=    ort.SessionOptions()
       snake_case    :      int      					=    False
       return options







   def  lowerCamelCase		(	self						)	->		Union[str, Any]:

       '''simple docstring'''




       snake_case    :      List[Any]      					=    load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/img2img/sketch-mountains-input.jpg"						)
       snake_case    :      Union[str, Any]      					=    init_image.resize((768, 512)						)
       # using the PNDM scheduler by default
       snake_case    :      Optional[Any]      					=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(
           "CompVis/stable-diffusion-v1-4"						,	revision="onnx"						,	safety_checker=UpperCamelCase__						,	feature_extractor=UpperCamelCase__						,	provider=self.gpu_provider						,	sess_options=self.gpu_options						,	)
       pipe.set_progress_bar_config(disable=UpperCamelCase__						)

       snake_case    :      List[str]      					=    "A fantasy landscape, trending on artstation"

       snake_case    :      Optional[Any]      					=    np.random.RandomState(0						)
       snake_case    :      List[Any]      					=    pipe(
           prompt=UpperCamelCase__						,	image=UpperCamelCase__						,	strength=0.75						,	guidance_scale=7.5						,	num_inference_steps=10						,	generator=UpperCamelCase__						,	output_type="np"						,	)
       snake_case    :      Optional[Any]      					=    output.images
       snake_case    :      List[str]      					=    images[0, 255:258, 383:386, -1]

       assert images.shape == (1, 512, 768, 3)
       snake_case    :      Any      					=    np.array([0.4909, 0.5059, 0.5372, 0.4623, 0.4876, 0.5049, 0.4820, 0.4956, 0.5019]						)
       # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

       assert np.abs(image_slice.flatten() - expected_slice						).max() < 2e-2







   def  lowerCamelCase		(	self						)	->		Tuple:

       '''simple docstring'''




       snake_case    :      Tuple      					=    load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/img2img/sketch-mountains-input.jpg"						)
       snake_case    :      Optional[Any]      					=    init_image.resize((768, 512)						)
       snake_case    :      Tuple      					=    LMSDiscreteScheduler.from_pretrained(
           "runwayml/stable-diffusion-v1-5"						,	subfolder="scheduler"						,	revision="onnx"						)
       snake_case    :      str      					=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(
           "runwayml/stable-diffusion-v1-5"						,	revision="onnx"						,	scheduler=UpperCamelCase__						,	safety_checker=UpperCamelCase__						,	feature_extractor=UpperCamelCase__						,	provider=self.gpu_provider						,	sess_options=self.gpu_options						,	)
       pipe.set_progress_bar_config(disable=UpperCamelCase__						)

       snake_case    :      str      					=    "A fantasy landscape, trending on artstation"

       snake_case    :      str      					=    np.random.RandomState(0						)
       snake_case    :      Tuple      					=    pipe(
           prompt=UpperCamelCase__						,	image=UpperCamelCase__						,	strength=0.75						,	guidance_scale=7.5						,	num_inference_steps=20						,	generator=UpperCamelCase__						,	output_type="np"						,	)
       snake_case    :      Optional[Any]      					=    output.images
       snake_case    :      Union[str, Any]      					=    images[0, 255:258, 383:386, -1]

       assert images.shape == (1, 512, 768, 3)
       snake_case    :      str      					=    np.array([0.8043, 0.926, 0.9581, 0.8119, 0.8954, 0.913, 0.7209, 0.7463, 0.7431]						)
       # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

       assert np.abs(image_slice.flatten() - expected_slice						).max() < 2e-2









"""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"						)						)						)





