
"""simple docstring"""


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
		from PIL import Image

		from transformers import AlignProcessor, EfficientNetImageProcessor







@require_vision
class 					lowerCAmelCase_					(   unittest.TestCase     ):



						'''simple docstring'''


						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]   )  -> Optional[Any]:
							A     = tempfile.mkdtemp()

							A     = [
							    '[UNK]',
							    '[CLS]',
							    '[SEP]',
							    '[PAD]',
							    '[MASK]',
							    'want',
							    '##want',
							    '##ed',
							    'wa',
							    'un',
							    'runn',
							    '##ing',
							    ',',
							    'low',
							    'lowest',
							]
							A     = os.path.join(self.tmpdirname	,VOCAB_FILES_NAMES['vocab_file']   )
							with open(self.vocab_file	,'w'	,encoding='utf-8'   ) as vocab_writer:
								vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]   )   )

							A     = {
							    'do_resize': True,
							    'size': 20,
							    'do_center_crop': True,
							    'crop_size': 18,
							    'do_normalize': True,
							    'image_mean': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
							    'image_std': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
							}
							A     = os.path.join(self.tmpdirname	,A_   )
							with open(self.image_processor_file	,'w'	,encoding='utf-8'   ) as fp:
								json.dump(A_	,A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict	,**A_  :   List[str]   )  -> Dict:
							return BertTokenizer.from_pretrained(self.tmpdirname	,**A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]	,**A_  :   int   )  -> Optional[int]:
							return BertTokenizerFast.from_pretrained(self.tmpdirname	,**A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]	,**A_  :   Union[str, Any]   )  -> Union[str, Any]:
							return EfficientNetImageProcessor.from_pretrained(self.tmpdirname	,**A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]   )  -> Dict:
							shutil.rmtree(self.tmpdirname   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict   )  -> List[Any]:
							A     = [np.random.randint(255	,size=(3, 30, 400)	,dtype=np.uinta   )]
							A     = [Image.fromarray(np.moveaxis(A_	,0	,-1   )   ) for x in image_inputs]
							return image_inputs


						def     _SCREAMING_SNAKE_CASE   (			self  :   str   )  -> str:
							A     = self.get_tokenizer()
							A     = self.get_rust_tokenizer()
							A     = self.get_image_processor()

							A     = AlignProcessor(tokenizer=A_	,image_processor=A_   )
							processor_slow.save_pretrained(self.tmpdirname   )
							A     = AlignProcessor.from_pretrained(self.tmpdirname	,use_fast=A_   )

							A     = AlignProcessor(tokenizer=A_	,image_processor=A_   )
							processor_fast.save_pretrained(self.tmpdirname   )
							A     = AlignProcessor.from_pretrained(self.tmpdirname   )

							self.assertEqual(processor_slow.tokenizer.get_vocab()	,tokenizer_slow.get_vocab()   )
							self.assertEqual(processor_fast.tokenizer.get_vocab()	,tokenizer_fast.get_vocab()   )
							self.assertEqual(tokenizer_slow.get_vocab()	,tokenizer_fast.get_vocab()   )
							self.assertIsInstance(processor_slow.tokenizer	,A_   )
							self.assertIsInstance(processor_fast.tokenizer	,A_   )

							self.assertEqual(processor_slow.image_processor.to_json_string()	,image_processor.to_json_string()   )
							self.assertEqual(processor_fast.image_processor.to_json_string()	,image_processor.to_json_string()   )
							self.assertIsInstance(processor_slow.image_processor	,A_   )
							self.assertIsInstance(processor_fast.image_processor	,A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]   )  -> Union[str, Any]:
							A     = AlignProcessor(tokenizer=self.get_tokenizer()	,image_processor=self.get_image_processor()   )
							processor.save_pretrained(self.tmpdirname   )

							A     = self.get_tokenizer(bos_token='(BOS)'	,eos_token='(EOS)'   )
							A     = self.get_image_processor(do_normalize=A_	,padding_value=1.0   )

							A     = AlignProcessor.from_pretrained(
							    self.tmpdirname	,bos_token='(BOS)'	,eos_token='(EOS)'	,do_normalize=A_	,padding_value=1.0   )

							self.assertEqual(processor.tokenizer.get_vocab()	,tokenizer_add_kwargs.get_vocab()   )
							self.assertIsInstance(processor.tokenizer	,A_   )

							self.assertEqual(processor.image_processor.to_json_string()	,image_processor_add_kwargs.to_json_string()   )
							self.assertIsInstance(processor.image_processor	,A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   str   )  -> List[Any]:
							A     = self.get_image_processor()
							A     = self.get_tokenizer()

							A     = AlignProcessor(tokenizer=A_	,image_processor=A_   )

							A     = self.prepare_image_inputs()

							A     = image_processor(A_	,return_tensors='np'   )
							A     = processor(images=A_	,return_tensors='np'   )

							for key in input_image_proc.keys():
								self.assertAlmostEqual(input_image_proc[key].sum()	,input_processor[key].sum()	,delta=1e-2   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> Any:
							A     = self.get_image_processor()
							A     = self.get_tokenizer()

							A     = AlignProcessor(tokenizer=A_	,image_processor=A_   )

							A     = 'lower newer'

							A     = processor(text=A_   )

							A     = tokenizer(A_	,padding='max_length'	,max_length=64   )

							for key in encoded_tok.keys():
								self.assertListEqual(encoded_tok[key]	,encoded_processor[key]   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> str:
							A     = self.get_image_processor()
							A     = self.get_tokenizer()

							A     = AlignProcessor(tokenizer=A_	,image_processor=A_   )

							A     = 'lower newer'
							A     = self.prepare_image_inputs()

							A     = processor(text=A_	,images=A_   )

							self.assertListEqual(list(inputs.keys()   )	,['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values']   )

							# test if it raises when no input is passed
							with pytest.raises(A_   ):
								processor()


						def     _SCREAMING_SNAKE_CASE   (			self  :   Tuple   )  -> Dict:
							A     = self.get_image_processor()
							A     = self.get_tokenizer()

							A     = AlignProcessor(tokenizer=A_	,image_processor=A_   )

							A     = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							A     = processor.batch_decode(A_   )
							A     = tokenizer.batch_decode(A_   )

							self.assertListEqual(A_	,A_   )




						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]   )  -> int:
							A     = self.get_image_processor()
							A     = self.get_tokenizer()

							A     = AlignProcessor(tokenizer=A_	,image_processor=A_   )

							A     = 'lower newer'
							A     = self.prepare_image_inputs()

							A     = processor(text=A_	,images=A_   )

							self.assertListEqual(list(inputs.keys()   )	,processor.model_input_names   )

"""simple docstring"""


import os

import torch

from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version


if is_torch_version('''>=''', FSDP_PYTORCH_VERSION):
		import torch.distributed.checkpoint as dist_cp
		from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner
		from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
		from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
		from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType


_lowercase				 =					get_logger(__name__)






def    _snake_case					(		snake_case__ :  Optional[Any]						,   snake_case__ :  List[Any]						,   snake_case__ :  int						,   snake_case__ :  int						,   snake_case__ :  str=0  ):
	os.makedirs(snake_case__						,   exist_ok=snake_case__  )
	with FSDP.state_dict_type(
	    snake_case__						,   fsdp_plugin.state_dict_type						,   fsdp_plugin.state_dict_config						,   fsdp_plugin.optim_state_dict_config  ):
		A     = model.state_dict()
		if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
			A     = F'{MODEL_NAME}.bin' if model_index == 0 else F'{MODEL_NAME}_{model_index}.bin'
			A     = os.path.join(snake_case__						,   snake_case__  )
			if accelerator.process_index == 0:
				logger.info(F'Saving model to {output_model_file}'  )
				torch.save(snake_case__						,   snake_case__  )
				logger.info(F'Model saved to {output_model_file}'  )
		elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
			A     = (
			    F'{MODEL_NAME}_rank{accelerator.process_index}.bin'
			    if model_index == 0
			    else F'{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'
			)
			A     = os.path.join(snake_case__						,   snake_case__  )
			logger.info(F'Saving model to {output_model_file}'  )
			torch.save(snake_case__						,   snake_case__  )
			logger.info(F'Model saved to {output_model_file}'  )
		elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
			A     = os.path.join(snake_case__						,   F'{MODEL_NAME}_{model_index}'  )
			os.makedirs(snake_case__						,   exist_ok=snake_case__  )
			logger.info(F'Saving model to {ckpt_dir}'  )
			A     = {'model': state_dict}

			dist_cp.save_state_dict(
			    state_dict=snake_case__						,   storage_writer=dist_cp.FileSystemWriter(snake_case__  )						,   planner=DefaultSavePlanner()						,   )
			logger.info(F'Model saved to {ckpt_dir}'  )






def    _snake_case					(		snake_case__ :  int						,   snake_case__ :  List[str]						,   snake_case__ :  str						,   snake_case__ :  str						,   snake_case__ :  Any=0  ):
	accelerator.wait_for_everyone()
	with FSDP.state_dict_type(
	    snake_case__						,   fsdp_plugin.state_dict_type						,   fsdp_plugin.state_dict_config						,   fsdp_plugin.optim_state_dict_config  ):
		if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
			if type(snake_case__  ) != FSDP and accelerator.process_index != 0:
				if not fsdp_plugin.sync_module_states:
					raise ValueError(
					    'Set the `sync_module_states` flag to `True` so that model states are synced across processes when '
					    'initializing FSDP object'  )
				return
			A     = F'{MODEL_NAME}.bin' if model_index == 0 else F'{MODEL_NAME}_{model_index}.bin'
			A     = os.path.join(snake_case__						,   snake_case__  )
			logger.info(F'Loading model from {input_model_file}'  )
			A     = torch.load(snake_case__  )
			logger.info(F'Model loaded from {input_model_file}'  )
		elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
			A     = (
			    F'{MODEL_NAME}_rank{accelerator.process_index}.bin'
			    if model_index == 0
			    else F'{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'
			)
			A     = os.path.join(snake_case__						,   snake_case__  )
			logger.info(F'Loading model from {input_model_file}'  )
			A     = torch.load(snake_case__  )
			logger.info(F'Model loaded from {input_model_file}'  )
		elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
			A     = (
			    os.path.join(snake_case__						,   F'{MODEL_NAME}_{model_index}'  )
			    if F'{MODEL_NAME}' not in input_dir
			    else input_dir
			)
			logger.info(F'Loading model from {ckpt_dir}'  )
			A     = {'model': model.state_dict()}
			dist_cp.load_state_dict(
			    state_dict=snake_case__						,   storage_reader=dist_cp.FileSystemReader(snake_case__  )						,   planner=DefaultLoadPlanner()						,   )
			A     = state_dict['model']
			logger.info(F'Model loaded from {ckpt_dir}'  )
		model.load_state_dict(snake_case__  )






def    _snake_case					(		snake_case__ :  Tuple						,   snake_case__ :  Optional[int]						,   snake_case__ :  Optional[int]						,   snake_case__ :  Dict						,   snake_case__ :  List[str]						,   snake_case__ :  Any=0  ):
	os.makedirs(snake_case__						,   exist_ok=snake_case__  )
	with FSDP.state_dict_type(
	    snake_case__						,   fsdp_plugin.state_dict_type						,   fsdp_plugin.state_dict_config						,   fsdp_plugin.optim_state_dict_config  ):
		A     = FSDP.optim_state_dict(snake_case__						,   snake_case__  )
		if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
			if accelerator.process_index == 0:
				A     = (
				    F'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else F'{OPTIMIZER_NAME}_{optimizer_index}.bin'
				)
				A     = os.path.join(snake_case__						,   snake_case__  )
				logger.info(F'Saving Optimizer state to {output_optimizer_file}'  )
				torch.save(snake_case__						,   snake_case__  )
				logger.info(F'Optimizer state saved in {output_optimizer_file}'  )
		else:
			A     = os.path.join(snake_case__						,   F'{OPTIMIZER_NAME}_{optimizer_index}'  )
			os.makedirs(snake_case__						,   exist_ok=snake_case__  )
			logger.info(F'Saving Optimizer state to {ckpt_dir}'  )
			dist_cp.save_state_dict(
			    state_dict={'optimizer': optim_state}						,   storage_writer=dist_cp.FileSystemWriter(snake_case__  )						,   planner=DefaultSavePlanner()						,   )
			logger.info(F'Optimizer state saved in {ckpt_dir}'  )






def    _snake_case					(		snake_case__ :  str						,   snake_case__ :  Optional[int]						,   snake_case__ :  List[str]						,   snake_case__ :  List[str]						,   snake_case__ :  int						,   snake_case__ :  Optional[int]=0  ):
	accelerator.wait_for_everyone()
	with FSDP.state_dict_type(
	    snake_case__						,   fsdp_plugin.state_dict_type						,   fsdp_plugin.state_dict_config						,   fsdp_plugin.optim_state_dict_config  ):
		if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
			A     = None
			# below check should work but currently it isn't working (mostly opytorch issue),
			# in the meantime disabling it at the cost of excess memory usage
			# if accelerator.process_index == 0 or not fsdp_plugin.optim_state_dict_config.rank0_only:
			A     = (
			    F'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else F'{OPTIMIZER_NAME}_{optimizer_index}.bin'
			)
			A     = os.path.join(snake_case__						,   snake_case__  )
			logger.info(F'Loading Optimizer state from {input_optimizer_file}'  )
			A     = torch.load(snake_case__  )
			logger.info(F'Optimizer state loaded from {input_optimizer_file}'  )
		else:
			A     = (
			    os.path.join(snake_case__						,   F'{OPTIMIZER_NAME}_{optimizer_index}'  )
			    if F'{OPTIMIZER_NAME}' not in input_dir
			    else input_dir
			)
			logger.info(F'Loading Optimizer from {ckpt_dir}'  )
			A     = load_sharded_optimizer_state_dict(
			    model_state_dict=model.state_dict()						,   optimizer_key='optimizer'						,   storage_reader=dist_cp.FileSystemReader(snake_case__  )						,   )
			A     = optim_state['optimizer']
			logger.info(F'Optimizer loaded from {ckpt_dir}'  )
		A     = FSDP.optim_state_dict_to_load(snake_case__						,   snake_case__						,   snake_case__  )
		optimizer.load_state_dict(snake_case__  )