"""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)

_UpperCAmelCase            =		logging.getLogger()




def     UpperCamelCase						(	):
		'''simple docstring'''




		A_		:    List[Any]         =				argparse.ArgumentParser()
		parser.add_argument('-f'     )
		A_		:    Optional[Any]         =				parser.parse_args()
		return args.f




def     UpperCamelCase						(	__lowercase						:			List[str]     ):
		'''simple docstring'''




		A_		:    List[str]         =				{}
		A_		:    List[Any]         =				os.path.join(UpperCAmelCase_						,'all_results.json'     )
		if os.path.exists(UpperCAmelCase_     ):
				with open(UpperCAmelCase_						,'r'     ) as f:
						A_		:    str         =				json.load(UpperCAmelCase_     )
		else:
				raise ValueError(f'''can\'t find {path}'''     )
		return results




def     UpperCamelCase						(	):
		'''simple docstring'''




		A_		:    Optional[Any]         =				torch.cuda.is_available() and torch_device == 'cuda'
		return is_using_cuda and is_apex_available()


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





class UpperCAmelCase						( _lowerCamelCase  ):


				'''simple docstring'''






				@classmethod
				def   lowerCAmelCase_       (						cls ):



						"""simple docstring"""





						A_		:    Optional[int]         =				tempfile.mkdtemp()
						A_		:    Optional[int]         =				os.path.join(cls.tmpdir		,     'default_config.yml' )
						write_basic_config(save_location=cls.configPath )
						A_		:    Dict         =				['accelerate', 'launch', '--config_file', cls.configPath]
				@classmethod
				def   lowerCAmelCase_       (						cls ):



						"""simple docstring"""





						shutil.rmtree(cls.tmpdir )
				@mock.patch.dict(os.environ		,     {'WANDB_MODE': 'offline'} )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





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

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

						run_command(self._launch_args + testargs )
						A_		:    Any         =				get_results(lowercase )
						self.assertGreaterEqual(result['eval_accuracy']		,     0.75 )
						self.assertTrue(os.path.exists(os.path.join(lowercase		,     'epoch_0' ) ) )
						self.assertTrue(os.path.exists(os.path.join(lowercase		,     'glue_no_trainer' ) ) )
				@mock.patch.dict(os.environ		,     {'WANDB_MODE': 'offline'} )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    List[Any]         =				self.get_auto_remove_tmp_dir()
						A_		:    Any         =				F'''
            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --block_size 128
            --per_device_train_batch_size 5
            --per_device_eval_batch_size 5
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --checkpointing_steps epoch
            --with_tracking
        '''.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 )
						A_		:    str         =				get_results(lowercase )
						self.assertLess(result['perplexity']		,     1_0_0 )
						self.assertTrue(os.path.exists(os.path.join(lowercase		,     'epoch_0' ) ) )
						self.assertTrue(os.path.exists(os.path.join(lowercase		,     'clm_no_trainer' ) ) )
				@mock.patch.dict(os.environ		,     {'WANDB_MODE': 'offline'} )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    int         =				self.get_auto_remove_tmp_dir()
						A_		:    Dict         =				F'''
            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --num_train_epochs=1
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

						run_command(self._launch_args + testargs )
						A_		:    Optional[int]         =				get_results(lowercase )
						self.assertLess(result['perplexity']		,     4_2 )
						self.assertTrue(os.path.exists(os.path.join(lowercase		,     'epoch_0' ) ) )
						self.assertTrue(os.path.exists(os.path.join(lowercase		,     'mlm_no_trainer' ) ) )
				@mock.patch.dict(os.environ		,     {'WANDB_MODE': 'offline'} )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Optional[int]         =				7 if get_gpu_count() > 1 else 2

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

						run_command(self._launch_args + testargs )
						A_		:    Tuple         =				get_results(lowercase )
						self.assertGreaterEqual(result['eval_accuracy']		,     0.75 )
						self.assertLess(result['train_loss']		,     0.5 )
						self.assertTrue(os.path.exists(os.path.join(lowercase		,     'epoch_0' ) ) )
						self.assertTrue(os.path.exists(os.path.join(lowercase		,     'ner_no_trainer' ) ) )
				@unittest.skip(reason='Fix me @muellerzr' )
				@mock.patch.dict(os.environ		,     {'WANDB_MODE': 'offline'} )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





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

						run_command(self._launch_args + testargs )
						A_		:    Tuple         =				get_results(lowercase )
						# Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
						self.assertGreaterEqual(result['eval_f1']		,     2_8 )
						self.assertGreaterEqual(result['eval_exact']		,     2_8 )
						self.assertTrue(os.path.exists(os.path.join(lowercase		,     'epoch_0' ) ) )
						self.assertTrue(os.path.exists(os.path.join(lowercase		,     'qa_no_trainer' ) ) )
				@mock.patch.dict(os.environ		,     {'WANDB_MODE': 'offline'} )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





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

						run_command(self._launch_args + testargs )
						A_		:    Tuple         =				get_results(lowercase )
						self.assertGreaterEqual(result['eval_accuracy']		,     0.8 )
						self.assertTrue(os.path.exists(os.path.join(lowercase		,     'swag_no_trainer' ) ) )
				@slow
				@mock.patch.dict(os.environ		,     {'WANDB_MODE': 'offline'} )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





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

						run_command(self._launch_args + testargs )
						A_		:    Tuple         =				get_results(lowercase )
						self.assertGreaterEqual(result['eval_rouge1']		,     1_0 )
						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(lowercase		,     'epoch_0' ) ) )
						self.assertTrue(os.path.exists(os.path.join(lowercase		,     'summarization_no_trainer' ) ) )
				@slow
				@mock.patch.dict(os.environ		,     {'WANDB_MODE': 'offline'} )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





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

						run_command(self._launch_args + testargs )
						A_		:    List[str]         =				get_results(lowercase )
						self.assertGreaterEqual(result['eval_bleu']		,     3_0 )
						self.assertTrue(os.path.exists(os.path.join(lowercase		,     'epoch_0' ) ) )
						self.assertTrue(os.path.exists(os.path.join(lowercase		,     'translation_no_trainer' ) ) )
				@slow
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    List[str]         =				logging.StreamHandler(sys.stdout )
						logger.addHandler(lowercase )

						A_		:    Optional[int]         =				self.get_auto_remove_tmp_dir()
						A_		:    str         =				F'''
            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
            --dataset_name huggingface/semantic-segmentation-test-sample
            --output_dir {tmp_dir}
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
        '''.split()

						run_command(self._launch_args + testargs )
						A_		:    int         =				get_results(lowercase )
						self.assertGreaterEqual(result['eval_overall_accuracy']		,     0.10 )







				@mock.patch.dict(os.environ		,     {'WANDB_MODE': 'offline'} )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





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

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

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







import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
	import onnxruntime as ort


_UpperCAmelCase            =		logging.get_logger(__name__)

_UpperCAmelCase            =		{
    """tensor(bool)""": np.bool_,
    """tensor(int8)""": np.inta,
    """tensor(uint8)""": np.uinta,
    """tensor(int16)""": np.intaa,
    """tensor(uint16)""": np.uintaa,
    """tensor(int32)""": np.intaa,
    """tensor(uint32)""": np.uintaa,
    """tensor(int64)""": np.intaa,
    """tensor(uint64)""": np.uintaa,
    """tensor(float16)""": np.floataa,
    """tensor(float)""": np.floataa,
    """tensor(double)""": np.floataa,
}





class UpperCAmelCase						:


				'''simple docstring'''






				def __init__(						self		,     lowercase=None		,     **lowercase ):



						"""simple docstring"""





						logger.info('`diffusers.OnnxRuntimeModel` is experimental and might change in the future.' )
						A_		:    List[Any]         =				model
						A_		:    Dict         =				kwargs.get('model_save_dir'		,     lowercase )
						A_		:    List[str]         =				kwargs.get('latest_model_name'		,     lowercase )
				def __call__(						self		,     **lowercase ):



						"""simple docstring"""





						A_		:    str         =				{k: np.array(lowercase ) for k, v in kwargs.items()}
						return self.model.run(lowercase		,     lowercase )
				@staticmethod
				def   lowerCAmelCase_       (						lowercase		,     lowercase=None		,     lowercase=None ):



						"""simple docstring"""





						if provider is None:
								logger.info('No onnxruntime provider specified, using CPUExecutionProvider' )
								A_		:    List[Any]         =				'CPUExecutionProvider'

						return ort.InferenceSession(lowercase		,     providers=[provider]		,     sess_options=lowercase )
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None		,     **lowercase ):



						"""simple docstring"""





						A_		:    str         =				file_name if file_name is not None else ONNX_WEIGHTS_NAME

						A_		:    Optional[int]         =				self.model_save_dir.joinpath(self.latest_model_name )
						A_		:    int         =				Path(lowercase ).joinpath(lowercase )
						try:
								shutil.copyfile(lowercase		,     lowercase )
						except shutil.SameFileError:
								pass

						# copy external weights (for models >2GB)
						A_		:    Optional[Any]         =				self.model_save_dir.joinpath(lowercase )
						if src_path.exists():
								A_		:    int         =				Path(lowercase ).joinpath(lowercase )
								try:
										shutil.copyfile(lowercase		,     lowercase )
								except shutil.SameFileError:
										pass
				def   lowerCAmelCase_       (						self		,     lowercase		,     **lowercase		,     ):



						"""simple docstring"""





						if os.path.isfile(lowercase ):
								logger.error(F'''Provided path ({save_directory}) should be a directory, not a file''' )
								return

						os.makedirs(lowercase		,     exist_ok=lowercase )

						# saving model weights/files
						self._save_pretrained(lowercase		,     **lowercase )
				@classmethod
				def   lowerCAmelCase_       (						cls		,     lowercase		,     lowercase = None		,     lowercase = None		,     lowercase = False		,     lowercase = None		,     lowercase = None		,     lowercase = None		,     lowercase = None		,     **lowercase		,     ):



						"""simple docstring"""





						A_		:    Any         =				file_name if file_name is not None else ONNX_WEIGHTS_NAME
						# load model from local directory
						if os.path.isdir(lowercase ):
								A_		:    Optional[int]         =				OnnxRuntimeModel.load_model(
								    os.path.join(lowercase		,     lowercase )		,     provider=lowercase		,     sess_options=lowercase )
								A_		:    Dict         =				Path(lowercase )
						# load model from hub
						else:
								# download model
								A_		:    List[str]         =				hf_hub_download(
								    repo_id=lowercase		,     filename=lowercase		,     use_auth_token=lowercase		,     revision=lowercase		,     cache_dir=lowercase		,     force_download=lowercase		,     )
								A_		:    int         =				Path(lowercase ).parent
								A_		:    Optional[Any]         =				Path(lowercase ).name
								A_		:    Any         =				OnnxRuntimeModel.load_model(lowercase		,     provider=lowercase		,     sess_options=lowercase )
						return cls(model=lowercase		,     **lowercase )







				@classmethod
				def   lowerCAmelCase_       (						cls		,     lowercase		,     lowercase = True		,     lowercase = None		,     lowercase = None		,     **lowercase		,     ):



						"""simple docstring"""





						A_		:    List[Any]         =				None
						if len(str(lowercase ).split('@' ) ) == 2:
								A_       ,      A_		:    int         =				model_id.split('@' )

						return cls._from_pretrained(
						    model_id=lowercase		,     revision=lowercase		,     cache_dir=lowercase		,     force_download=lowercase		,     use_auth_token=lowercase		,     **lowercase		,     )






