



'''simple docstring'''







import argparse
import json
import logging
import os
import sys
from unittest import skip
from unittest.mock import patch

import tensorflow as tf

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


SCREAMING_SNAKE_CASE__     : Optional[int]				=   [
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        """text-generation""",
        """text-classification""",
        """token-classification""",
        """language-modeling""",
        """multiple-choice""",
        """question-answering""",
        """summarization""",
        """translation""",
        """image-classification""",
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
							import run_clm
							import run_image_classification
							import run_mlm
							import run_ner
							import run_qa as run_squad
							import run_summarization
							import run_swag
							import run_text_classification
							import run_translation


logging.basicConfig(level=logging.DEBUG)

SCREAMING_SNAKE_CASE__     : Optional[int]				=   logging.getLogger()







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







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







def  SCREAMING_SNAKE_CASE__ (				):
		return bool(tf.config.list_physical_devices('GPU'     )     )


SCREAMING_SNAKE_CASE__     : Optional[int]				=   logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





class 				__lowerCAmelCase(							lowerCAmelCase__      ):






			@skip('Skipping until shape inference for to_tf_dataset PR is merged.'	)
			def 				_lowercase	(   self  :							Tuple	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Tuple										=      self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_						:List[str]										=      f'\n            run_text_classification.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --do_train\n            --do_eval\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --max_steps=10\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            '.split()

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

					with patch.object(SCREAMING_SNAKE_CASE      ,       'argv'      ,       SCREAMING_SNAKE_CASE	):
							run_text_classification.main()
							# Reset the mixed precision policy so we don't break other tests
							tf.keras.mixed_precision.set_global_policy('float32'	)
							SCREAMING_SNAKE_CASE_						:Optional[int]										=      get_results(SCREAMING_SNAKE_CASE	)
							self.assertGreaterEqual(result['eval_accuracy']      ,       0.75	)






			def 				_lowercase	(   self  :							str	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[str]										=      self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_						:str										=      f'\n            run_clm.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --block_size 128\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            '.split()

					if len(tf.config.list_physical_devices('GPU'	)	) > 1:
							# Skipping because there are not enough batches to train the model + would need a drop_last to work.
							return

					with patch.object(SCREAMING_SNAKE_CASE      ,       'argv'      ,       SCREAMING_SNAKE_CASE	):
							run_clm.main()
							SCREAMING_SNAKE_CASE_						:Any										=      get_results(SCREAMING_SNAKE_CASE	)
							self.assertLess(result['eval_perplexity']      ,       100	)






			def 				_lowercase	(   self  :							int	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Optional[int]										=      self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_						:int										=      f'\n            run_mlm.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            --max_seq_length 64\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --prediction_loss_only\n            --num_train_epochs=1\n            --learning_rate=1e-4\n        '.split()

					with patch.object(SCREAMING_SNAKE_CASE      ,       'argv'      ,       SCREAMING_SNAKE_CASE	):
							run_mlm.main()
							SCREAMING_SNAKE_CASE_						:Optional[Any]										=      get_results(SCREAMING_SNAKE_CASE	)
							self.assertLess(result['eval_perplexity']      ,       42	)






			def 				_lowercase	(   self  :							Optional[Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[str]										=      7 if get_gpu_count() > 1 else 2

					SCREAMING_SNAKE_CASE_						:Tuple										=      self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_						:str										=      f'\n            run_ner.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            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --warmup_steps=2\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        '.split()

					with patch.object(SCREAMING_SNAKE_CASE      ,       'argv'      ,       SCREAMING_SNAKE_CASE	):
							run_ner.main()
							SCREAMING_SNAKE_CASE_						:Optional[Any]										=      get_results(SCREAMING_SNAKE_CASE	)
							self.assertGreaterEqual(result['accuracy']      ,       0.75	)






			def 				_lowercase	(   self  :							Tuple	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Tuple										=      self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_						:Any										=      f'\n            run_qa.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            --overwrite_output_dir\n            --max_steps=10\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        '.split()

					with patch.object(SCREAMING_SNAKE_CASE      ,       'argv'      ,       SCREAMING_SNAKE_CASE	):
							run_squad.main()
							SCREAMING_SNAKE_CASE_						:Optional[Any]										=      get_results(SCREAMING_SNAKE_CASE	)
							self.assertGreaterEqual(result['f1']      ,       30	)
							self.assertGreaterEqual(result['exact']      ,       30	)






			def 				_lowercase	(   self  :							Union[str, Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Dict										=      self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      f'\n            run_swag.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            --overwrite_output_dir\n            --max_steps=20\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        '.split()

					with patch.object(SCREAMING_SNAKE_CASE      ,       'argv'      ,       SCREAMING_SNAKE_CASE	):
							run_swag.main()
							SCREAMING_SNAKE_CASE_						:int										=      get_results(SCREAMING_SNAKE_CASE	)
							self.assertGreaterEqual(result['val_accuracy']      ,       0.8	)






			@slow
			def 				_lowercase	(   self  :							List[Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Any										=      self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      f'\n            run_summarization.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            --overwrite_output_dir\n            --max_steps=50\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        '.split()

					with patch.object(SCREAMING_SNAKE_CASE      ,       'argv'      ,       SCREAMING_SNAKE_CASE	):
							run_summarization.main()
							SCREAMING_SNAKE_CASE_						:Dict										=      get_results(SCREAMING_SNAKE_CASE	)
							self.assertGreaterEqual(result['rouge1']      ,       10	)
							self.assertGreaterEqual(result['rouge2']      ,       2	)
							self.assertGreaterEqual(result['rougeL']      ,       7	)
							self.assertGreaterEqual(result['rougeLsum']      ,       7	)






			@slow
			def 				_lowercase	(   self  :							Optional[Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Dict										=      self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_						:List[Any]										=      f'\n            run_translation.py\n            --model_name_or_path Rocketknight1/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            --overwrite_output_dir\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --learning_rate=3e-3\n            --num_train_epochs 12\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        '.split()

					with patch.object(SCREAMING_SNAKE_CASE      ,       'argv'      ,       SCREAMING_SNAKE_CASE	):
							run_translation.main()
							SCREAMING_SNAKE_CASE_						:str										=      get_results(SCREAMING_SNAKE_CASE	)
							self.assertGreaterEqual(result['bleu']      ,       30	)






			def 				_lowercase	(   self  :							Optional[int]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[str]										=      self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_						:Any										=      f'\n            run_image_classification.py\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --model_name_or_path microsoft/resnet-18\n            --do_train\n            --do_eval\n            --learning_rate 1e-4\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --dataloader_num_workers 16\n            --num_train_epochs 2\n            --train_val_split 0.1\n            --seed 42\n            --ignore_mismatched_sizes True\n            '.split()

					with patch.object(SCREAMING_SNAKE_CASE      ,       'argv'      ,       SCREAMING_SNAKE_CASE	):
							run_image_classification.main()
							SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      get_results(SCREAMING_SNAKE_CASE	)
							self.assertGreaterEqual(result['accuracy']      ,       0.7	)


import os
import random
from pathlib import Path
from typing import List

import numpy as np
import torch
from torch.cuda.amp import GradScaler

from .utils import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    get_pretty_name,
    is_tpu_available,
    is_xpu_available,
    save,
)


if is_tpu_available(check_device=False):
							import torch_xla.core.xla_model as xm

from .logging import get_logger
from .state import PartialState


lowercase__						:Optional[Any]					    =       get_logger(__name__)



def 			UpperCamelCase       (				lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__ = None					,     ):
					'''simple docstring'''
					# Model states
					for i, state in enumerate(lowerCAmelCase__						):
										lowercase		      =		f'{MODEL_NAME}.bin' if i == 0 else f'{MODEL_NAME}_{i}.bin'
										lowercase		      =		os.path.join(lowerCAmelCase__					,     lowerCAmelCase__						)
										save(lowerCAmelCase__					,     lowerCAmelCase__						)
										logger.info(f'Model weights saved in {output_model_file}'						)
					# Optimizer states
					for i, opt in enumerate(lowerCAmelCase__						):
										lowercase		      =		opt.state_dict()
										lowercase		      =		f'{OPTIMIZER_NAME}.bin' if i == 0 else f'{OPTIMIZER_NAME}_{i}.bin'
										lowercase		      =		os.path.join(lowerCAmelCase__					,     lowerCAmelCase__						)
										save(lowerCAmelCase__					,     lowerCAmelCase__						)
										logger.info(f'Optimizer state saved in {output_optimizer_file}'						)
					# Scheduler states
					for i, scheduler in enumerate(lowerCAmelCase__						):
										lowercase		      =		scheduler.state_dict()
										lowercase		      =		f'{SCHEDULER_NAME}.bin' if i == 0 else f'{SCHEDULER_NAME}_{i}.bin'
										lowercase		      =		os.path.join(lowerCAmelCase__					,     lowerCAmelCase__						)
										save(lowerCAmelCase__					,     lowerCAmelCase__						)
										logger.info(f'Scheduler state saved in {output_scheduler_file}'						)
					# GradScaler state
					if scaler is not None:
										lowercase		      =		scaler.state_dict()
										lowercase		      =		os.path.join(lowerCAmelCase__					,     lowerCAmelCase__						)
										torch.save(lowerCAmelCase__					,     lowerCAmelCase__						)
										logger.info(f'Gradient scaler state saved in {output_scaler_file}'						)
					# Random number generator states
					lowercase		      =		{}
					lowercase		      =		f'{RNG_STATE_NAME}_{process_index}.pkl'
					lowercase		      =		random.getstate()
					lowercase		      =		np.random.get_state()
					lowercase		      =		torch.get_rng_state()
					if is_xpu_available():
										lowercase		      =		torch.xpu.get_rng_state_all()
					else:
										lowercase		      =		torch.cuda.get_rng_state_all()
					if is_tpu_available():
										lowercase		      =		xm.get_rng_state()
					lowercase		      =		os.path.join(lowerCAmelCase__					,     lowerCAmelCase__						)
					torch.save(lowerCAmelCase__					,     lowerCAmelCase__						)
					logger.info(f'Random states saved in {output_states_file}'						)
					return output_dir



def 			UpperCamelCase       (				lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__=None					,     lowerCAmelCase__=None					,     **lowerCAmelCase__					,     ):
					'''simple docstring'''
					if map_location not in [None, "cpu", "on_device"]:
										raise TypeError(
										    '''Unsupported optimizer map location passed, please choose one of `None`, `\'cpu\'`, or `\'on_device\'`'''						)
					if map_location is None:
										lowercase		      =		'''cpu'''
					elif map_location == "on_device":
										lowercase		      =		PartialState().device
					# Model states
					for i, model in enumerate(lowerCAmelCase__						):
										lowercase		      =		f'{MODEL_NAME}.bin' if i == 0 else f'{MODEL_NAME}_{i}.bin'
										lowercase		      =		os.path.join(lowerCAmelCase__					,     lowerCAmelCase__						)
										models[i].load_state_dict(torch.load(lowerCAmelCase__					,     map_location=lowerCAmelCase__						)					,     **lowerCAmelCase__						)
					logger.info('''All model weights loaded successfully'''						)

					# Optimizer states
					for i, opt in enumerate(lowerCAmelCase__						):
										lowercase		      =		f'{OPTIMIZER_NAME}.bin' if i == 0 else f'{OPTIMIZER_NAME}_{i}.bin'
										lowercase		      =		os.path.join(lowerCAmelCase__					,     lowerCAmelCase__						)
										lowercase		      =		torch.load(lowerCAmelCase__					,     map_location=lowerCAmelCase__						)
										optimizers[i].load_state_dict(lowerCAmelCase__						)
					logger.info('''All optimizer states loaded successfully'''						)

					# Scheduler states
					for i, scheduler in enumerate(lowerCAmelCase__						):
										lowercase		      =		f'{SCHEDULER_NAME}.bin' if i == 0 else f'{SCHEDULER_NAME}_{i}.bin'
										lowercase		      =		os.path.join(lowerCAmelCase__					,     lowerCAmelCase__						)
										scheduler.load_state_dict(torch.load(lowerCAmelCase__						)						)
					logger.info('''All scheduler states loaded successfully'''						)

					# GradScaler state
					if scaler is not None:
										lowercase		      =		os.path.join(lowerCAmelCase__					,     lowerCAmelCase__						)
										scaler.load_state_dict(torch.load(lowerCAmelCase__						)						)
										logger.info('''GradScaler state loaded successfully'''						)

					# Random states
					try:
										lowercase		      =		torch.load(os.path.join(lowerCAmelCase__					,     f'{RNG_STATE_NAME}_{process_index}.pkl'						)						)
										random.setstate(states['''random_state''']						)
										np.random.set_state(states['''numpy_random_seed''']						)
										torch.set_rng_state(states['''torch_manual_seed''']						)
										if is_xpu_available():
															torch.xpu.set_rng_state_all(states['''torch_xpu_manual_seed''']						)
										else:
															torch.cuda.set_rng_state_all(states['''torch_cuda_manual_seed''']						)
										if is_tpu_available():
															xm.set_rng_state(states['''xm_seed''']						)
										logger.info('''All random states loaded successfully'''						)
					except Exception:
										logger.info('''Could not load random states'''						)



def 			UpperCamelCase       (				lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__ = 0						):
					'''simple docstring'''
					lowercase		      =		Path(lowerCAmelCase__						) / f'custom_checkpoint_{index}.pkl'
					logger.info(f'Saving the state of {get_pretty_name(lowerCAmelCase__						)} to {save_location}'						)
					torch.save(obj.state_dict()					,     lowerCAmelCase__						)



def 			UpperCamelCase       (				lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__ = 0						):
					'''simple docstring'''
					lowercase		      =		f'{path}/custom_checkpoint_{index}.pkl'
					logger.info(f'Loading the state of {get_pretty_name(lowerCAmelCase__						)} from {load_location}'						)
					obj.load_state_dict(torch.load(lowerCAmelCase__					,     map_location='''cpu'''						)						)




