
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__:    Optional[Any]      = logging.getLogger()



def   __SCREAMING_SNAKE_CASE (		)		-> Union[str, Any]:
		SCREAMING_SNAKE_CASE_					:	Optional[Any]		  =			argparse.ArgumentParser()
		parser.add_argument('-f' )
		SCREAMING_SNAKE_CASE_					:	int		  =			parser.parse_args()
		return args.f



def   __SCREAMING_SNAKE_CASE (		SCREAMING_SNAKE_CASE )		-> int:
		SCREAMING_SNAKE_CASE_					:	List[Any]		  =			{}
		SCREAMING_SNAKE_CASE_					:	Tuple		  =			os.path.join(UpperCamelCase__ ,    'all_results.json' )
		if os.path.exists(UpperCamelCase__ ):
				with open(UpperCamelCase__ ,    'r' ) as f:
						SCREAMING_SNAKE_CASE_					:	Dict		  =			json.load(UpperCamelCase__ )
		else:
				raise ValueError(f'can\'t find {path}' )
		return results



def   __SCREAMING_SNAKE_CASE (		)		-> int:
		SCREAMING_SNAKE_CASE_					:	str		  =			torch.cuda.is_available() and torch_device == 'cuda'
		return is_using_cuda and is_apex_available()


lowerCAmelCase__:    int      = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)

class 							snake_case_ (				lowercase_    ):





			@classmethod
			def 						__A						(		cls      ):
					# Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
					SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			tempfile.mkdtemp()
					SCREAMING_SNAKE_CASE_					:	List[str]		  =			os.path.join(cls.tmpdir      ,						'default_config.yml'      )
					write_basic_config(save_location=cls.configPath      )
					SCREAMING_SNAKE_CASE_					:	Optional[int]		  =			['accelerate', 'launch', '--config_file', cls.configPath]





			@classmethod
			def 						__A						(		cls      ):
					shutil.rmtree(cls.tmpdir      )





			@mock.patch.dict(os.environ      ,						{'WANDB_MODE': 'offline'}      )
			def 						__A						(		self      ):
					SCREAMING_SNAKE_CASE_					:	int		  =			self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_					:	List[str]		  =			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      )
					SCREAMING_SNAKE_CASE_					:	Tuple		  =			get_results(__lowerCAmelCase      )
					self.assertGreaterEqual(result['eval_accuracy']      ,						0.75      )
					self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase      ,						'epoch_0'      )      )      )
					self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase      ,						'glue_no_trainer'      )      )      )





			@mock.patch.dict(os.environ      ,						{'WANDB_MODE': 'offline'}      )
			def 						__A						(		self      ):
					SCREAMING_SNAKE_CASE_					:	Optional[Any]		  =			self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_					:	int		  =			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      )
					SCREAMING_SNAKE_CASE_					:	Optional[int]		  =			get_results(__lowerCAmelCase      )
					self.assertLess(result['perplexity']      ,						100      )
					self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase      ,						'epoch_0'      )      )      )
					self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase      ,						'clm_no_trainer'      )      )      )





			@mock.patch.dict(os.environ      ,						{'WANDB_MODE': 'offline'}      )
			def 						__A						(		self      ):
					SCREAMING_SNAKE_CASE_					:	Tuple		  =			self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_					:	Optional[int]		  =			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      )
					SCREAMING_SNAKE_CASE_					:	Optional[int]		  =			get_results(__lowerCAmelCase      )
					self.assertLess(result['perplexity']      ,						42      )
					self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase      ,						'epoch_0'      )      )      )
					self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase      ,						'mlm_no_trainer'      )      )      )





			@mock.patch.dict(os.environ      ,						{'WANDB_MODE': 'offline'}      )
			def 						__A						(		self      ):
					# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
					SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			7 if get_gpu_count() > 1 else 2

					SCREAMING_SNAKE_CASE_					:	List[str]		  =			self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_					:	Tuple		  =			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      )
					SCREAMING_SNAKE_CASE_					:	Dict		  =			get_results(__lowerCAmelCase      )
					self.assertGreaterEqual(result['eval_accuracy']      ,						0.75      )
					self.assertLess(result['train_loss']      ,						0.5      )
					self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase      ,						'epoch_0'      )      )      )
					self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase      ,						'ner_no_trainer'      )      )      )





			@unittest.skip(reason='Fix me @muellerzr'      )
			@mock.patch.dict(os.environ      ,						{'WANDB_MODE': 'offline'}      )
			def 						__A						(		self      ):
					SCREAMING_SNAKE_CASE_					:	Any		  =			self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_					:	List[str]		  =			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      )
					SCREAMING_SNAKE_CASE_					:	str		  =			get_results(__lowerCAmelCase      )
					# 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(__lowerCAmelCase      ,						'epoch_0'      )      )      )
					self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase      ,						'qa_no_trainer'      )      )      )





			@mock.patch.dict(os.environ      ,						{'WANDB_MODE': 'offline'}      )
			def 						__A						(		self      ):
					SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			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      )
					SCREAMING_SNAKE_CASE_					:	List[Any]		  =			get_results(__lowerCAmelCase      )
					self.assertGreaterEqual(result['eval_accuracy']      ,						0.8      )
					self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase      ,						'swag_no_trainer'      )      )      )





			@slow
			@mock.patch.dict(os.environ      ,						{'WANDB_MODE': 'offline'}      )
			def 						__A						(		self      ):
					SCREAMING_SNAKE_CASE_					:	Tuple		  =			self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			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      )
					SCREAMING_SNAKE_CASE_					:	Tuple		  =			get_results(__lowerCAmelCase      )
					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(__lowerCAmelCase      ,						'epoch_0'      )      )      )
					self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase      ,						'summarization_no_trainer'      )      )      )





			@slow
			@mock.patch.dict(os.environ      ,						{'WANDB_MODE': 'offline'}      )
			def 						__A						(		self      ):
					SCREAMING_SNAKE_CASE_					:	Optional[Any]		  =			self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			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      )
					SCREAMING_SNAKE_CASE_					:	Any		  =			get_results(__lowerCAmelCase      )
					self.assertGreaterEqual(result['eval_bleu']      ,						30      )
					self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase      ,						'epoch_0'      )      )      )
					self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase      ,						'translation_no_trainer'      )      )      )





			@slow
			def 						__A						(		self      ):
					SCREAMING_SNAKE_CASE_					:	Optional[Any]		  =			logging.StreamHandler(sys.stdout      )
					logger.addHandler(__lowerCAmelCase      )

					SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_					:	int		  =			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      )
					SCREAMING_SNAKE_CASE_					:	List[str]		  =			get_results(__lowerCAmelCase      )
					self.assertGreaterEqual(result['eval_overall_accuracy']      ,						0.10      )






			@mock.patch.dict(os.environ      ,						{'WANDB_MODE': 'offline'}      )
			def 						__A						(		self      ):
					SCREAMING_SNAKE_CASE_					:	List[str]		  =			self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_					:	Optional[Any]		  =			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      )
					SCREAMING_SNAKE_CASE_					:	List[str]		  =			get_results(__lowerCAmelCase      )
					# The base model scores a 25%
					self.assertGreaterEqual(result['eval_accuracy']      ,						0.6      )
					self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase      ,						'step_1'      )      )      )
					self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase      ,						'image_classification_no_trainer'      )      )      )









from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_UpperCAmelCase						:  Union[str, Any]									=			logging.get_logger(__name__)

_UpperCAmelCase						:  Dict									=			{
    """microsoft/resnet-50""": """https://huggingface.co/microsoft/resnet-50/blob/main/config.json""",
}







class 					lowercase							(			lowercase_      ,    lowercase_     ):
				__SCREAMING_SNAKE_CASE   :					List[str]			     =     '''resnet'''
				__SCREAMING_SNAKE_CASE   :					Dict			     =     ['''basic''', '''bottleneck''']




				def __init__(   self ,  snake_case=3 ,  snake_case=64 ,  snake_case=[256, 512, 1024, 2048] ,  snake_case=[3, 4, 6, 3] ,  snake_case="bottleneck" ,  snake_case="relu" ,  snake_case=False ,  snake_case=None ,  snake_case=None ,  **snake_case ,  ):
											super().__init__(**snake_case			)
											if layer_type not in self.layer_types:
																		raise ValueError(F'''layer_type={layer_type} is not one of {",".join(self.layer_types			)}'''			)
											snake_case_     							=       num_channels
											snake_case_     							=       embedding_size
											snake_case_     							=       hidden_sizes
											snake_case_     							=       depths
											snake_case_     							=       layer_type
											snake_case_     							=       hidden_act
											snake_case_     							=       downsample_in_first_stage
											snake_case_     							=       ['stem'] + [F'''stage{idx}''' for idx in range(1 ,  len(snake_case			) + 1			)]
											snake_case_							,       snake_case_     							=       get_aligned_output_features_output_indices(
											    out_features=snake_case ,  out_indices=snake_case ,  stage_names=self.stage_names			)







class 					lowercase							(			lowercase_     ):
				__SCREAMING_SNAKE_CASE   :					List[Any]			     =     version.parse('''1.11'''     )




				@property
				def        a				(   self			):
											return OrderedDict(
											    [
											        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
											    ]			)




				@property
				def        a				(   self			):
											return 1e-3




