



'''simple docstring'''
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
					from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
					snake_case_				:			str						 =						False

if is_vision_available():
					from PIL import Image

					from transformers import (
					    AutoProcessor,
					    PixaStructImageProcessor,
					    PixaStructProcessor,
					    PreTrainedTokenizerFast,
					    TaTokenizer,
					)






@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11		,      reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""		,      )
@require_vision
@require_torch
class 							lowercase__			(							unittest.TestCase     ):





	def 			UpperCamelCase_						(				self      :       int				):







							'''simple docstring'''
							_UpperCamelCase      :	Any				       =     tempfile.mkdtemp()

							_UpperCamelCase      :	Any				       =     PixaStructImageProcessor()
							_UpperCamelCase      :	Optional[int]				       =     TaTokenizer.from_pretrained('t5-small'				)

							_UpperCamelCase      :	List[Any]				       =     PixaStructProcessor(lowerCamelCase__				,lowerCamelCase__				)

							processor.save_pretrained(self.tmpdirname				)





	def 			UpperCamelCase_						(				self      :       Tuple				,**lowerCamelCase__      :       List[Any]				):







							'''simple docstring'''
							return AutoProcessor.from_pretrained(self.tmpdirname				,**lowerCamelCase__				).tokenizer





	def 			UpperCamelCase_						(				self      :       str				,**lowerCamelCase__      :       Tuple				):







							'''simple docstring'''
							return AutoProcessor.from_pretrained(self.tmpdirname				,**lowerCamelCase__				).image_processor





	def 			UpperCamelCase_						(				self      :       Dict				):







							'''simple docstring'''
							shutil.rmtree(self.tmpdirname				)





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







							'''simple docstring'''
							_UpperCamelCase      :	int				       =     [np.random.randint(255				,size=(3, 30, 400)				,dtype=np.uinta				)]

							_UpperCamelCase      :	Any				       =     [Image.fromarray(np.moveaxis(lowerCamelCase__				,0				,-1				)				) for x in image_inputs]

							return image_inputs





	def 			UpperCamelCase_						(				self      :       str				):







							'''simple docstring'''
							_UpperCamelCase      :	Dict				       =     PixaStructProcessor(tokenizer=self.get_tokenizer()				,image_processor=self.get_image_processor()				)
							processor.save_pretrained(self.tmpdirname				)

							_UpperCamelCase      :	Optional[Any]				       =     self.get_tokenizer(bos_token='(BOS)'				,eos_token='(EOS)'				)
							_UpperCamelCase      :	Union[str, Any]				       =     self.get_image_processor(do_normalize=lowerCamelCase__				,padding_value=1.0				)

							_UpperCamelCase      :	Any				       =     PixaStructProcessor.from_pretrained(
							    self.tmpdirname				,bos_token='(BOS)'				,eos_token='(EOS)'				,do_normalize=lowerCamelCase__				,padding_value=1.0				)

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

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





	def 			UpperCamelCase_						(				self      :       Dict				):







							'''simple docstring'''
							_UpperCamelCase      :	Optional[int]				       =     self.get_image_processor()
							_UpperCamelCase      :	Any				       =     self.get_tokenizer()

							_UpperCamelCase      :	int				       =     PixaStructProcessor(tokenizer=lowerCamelCase__				,image_processor=lowerCamelCase__				)

							_UpperCamelCase      :	Union[str, Any]				       =     self.prepare_image_inputs()

							_UpperCamelCase      :	List[Any]				       =     image_processor(lowerCamelCase__				,return_tensors='np'				)
							_UpperCamelCase      :	Optional[int]				       =     processor(images=lowerCamelCase__				,return_tensors='np'				)

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





	def 			UpperCamelCase_						(				self      :       Optional[Any]				):







							'''simple docstring'''
							_UpperCamelCase      :	str				       =     self.get_image_processor()
							_UpperCamelCase      :	str				       =     self.get_tokenizer()

							_UpperCamelCase      :	Optional[Any]				       =     PixaStructProcessor(tokenizer=lowerCamelCase__				,image_processor=lowerCamelCase__				)

							_UpperCamelCase      :	int				       =     'lower newer'

							_UpperCamelCase      :	List[Any]				       =     processor(text=lowerCamelCase__				)

							_UpperCamelCase      :	Any				       =     tokenizer(lowerCamelCase__				,return_token_type_ids=lowerCamelCase__				,add_special_tokens=lowerCamelCase__				)

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





	def 			UpperCamelCase_						(				self      :       List[Any]				):







							'''simple docstring'''
							_UpperCamelCase      :	List[str]				       =     self.get_image_processor()
							_UpperCamelCase      :	Optional[Any]				       =     self.get_tokenizer()

							_UpperCamelCase      :	str				       =     PixaStructProcessor(tokenizer=lowerCamelCase__				,image_processor=lowerCamelCase__				)

							_UpperCamelCase      :	Tuple				       =     'lower newer'
							_UpperCamelCase      :	List[str]				       =     self.prepare_image_inputs()

							_UpperCamelCase      :	Optional[int]				       =     processor(text=lowerCamelCase__				,images=lowerCamelCase__				)

							self.assertListEqual(
							    list(inputs.keys()				)				,['flattened_patches', 'attention_mask', 'decoder_attention_mask', 'decoder_input_ids']				)

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





	def 			UpperCamelCase_						(				self      :       Optional[Any]				):







							'''simple docstring'''
							_UpperCamelCase      :	Union[str, Any]				       =     self.get_image_processor()
							_UpperCamelCase      :	Union[str, Any]				       =     self.get_tokenizer()

							_UpperCamelCase      :	str				       =     PixaStructProcessor(tokenizer=lowerCamelCase__				,image_processor=lowerCamelCase__				)

							_UpperCamelCase      :	str				       =     'lower newer'
							_UpperCamelCase      :	List[str]				       =     self.prepare_image_inputs()

							_UpperCamelCase      :	Union[str, Any]				       =     processor(text=lowerCamelCase__				,images=lowerCamelCase__				)

							_UpperCamelCase      :	Optional[int]				       =     [512, 1024, 2048, 4096]
							_UpperCamelCase      :	Union[str, Any]				       =     [770, 770, 770, 770]
							# with text
							for i, max_patch in enumerate(lowerCamelCase__				):
													_UpperCamelCase      :	str				       =     processor(text=lowerCamelCase__				,images=lowerCamelCase__				,max_patches=lowerCamelCase__				)
													self.assertEqual(inputs['flattened_patches'][0].shape[0]				,lowerCamelCase__				)
													self.assertEqual(inputs['flattened_patches'][0].shape[1]				,expected_hidden_size[i]				)

							# without text input
							for i, max_patch in enumerate(lowerCamelCase__				):
													_UpperCamelCase      :	List[str]				       =     processor(images=lowerCamelCase__				,max_patches=lowerCamelCase__				)
													self.assertEqual(inputs['flattened_patches'][0].shape[0]				,lowerCamelCase__				)
													self.assertEqual(inputs['flattened_patches'][0].shape[1]				,expected_hidden_size[i]				)





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







							'''simple docstring'''
							_UpperCamelCase      :	Tuple				       =     self.get_image_processor()
							_UpperCamelCase      :	Tuple				       =     self.get_tokenizer()

							_UpperCamelCase      :	str				       =     PixaStructProcessor(tokenizer=lowerCamelCase__				,image_processor=lowerCamelCase__				)

							_UpperCamelCase      :	Union[str, Any]				       =     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							_UpperCamelCase      :	Optional[int]				       =     processor.batch_decode(lowerCamelCase__				)
							_UpperCamelCase      :	Optional[int]				       =     tokenizer.batch_decode(lowerCamelCase__				)

							self.assertListEqual(lowerCamelCase__				,lowerCamelCase__				)





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







							'''simple docstring'''
							_UpperCamelCase      :	Dict				       =     self.get_image_processor()
							_UpperCamelCase      :	Any				       =     self.get_tokenizer()

							_UpperCamelCase      :	Tuple				       =     PixaStructProcessor(tokenizer=lowerCamelCase__				,image_processor=lowerCamelCase__				)

							_UpperCamelCase      :	Optional[int]				       =     'lower newer'
							_UpperCamelCase      :	Tuple				       =     self.prepare_image_inputs()

							_UpperCamelCase      :	int				       =     processor(text=lowerCamelCase__				,images=lowerCamelCase__				)

							# For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
							self.assertListEqual(
							    list(inputs.keys()				)				,['flattened_patches', 'attention_mask', 'decoder_attention_mask', 'decoder_input_ids']				)

							_UpperCamelCase      :	Optional[int]				       =     processor(text=lowerCamelCase__				)

							# For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
							self.assertListEqual(list(inputs.keys()				)				,['input_ids', 'attention_mask']				)







'''simple docstring'''
import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment






def A__					(  *UpperCAmelCase_							):
						_UpperCamelCase      :	str				       =     torch.cuda.device_count()
						print(f'Device count: {num_processes}'							)
						with patch_environment(
						    world_size=UpperCAmelCase_						,						master_addr='127.0.01'						,						master_port='29500'						,						mixed_precision=args[1].mixed_precision							):
												_UpperCamelCase      :	Optional[int]				       =     PrepareForLaunch(UpperCAmelCase_						,						distributed_type='MULTI_GPU'							)
												torch.multiprocessing.start_processes(UpperCAmelCase_						,						args=UpperCAmelCase_						,						nprocs=UpperCAmelCase_						,						start_method='spawn'							)


if __name__ == "__main__":
					# Refer to https://runhouse-docs.readthedocs-hosted.com/en/main/rh_primitives/cluster.html#hardware-setup
					# for cloud access setup instructions (if using on-demand hardware), and for API specifications.

					# on-demand GPU
					# gpu = rh.cluster(name='rh-cluster', instance_type='V100:1', provider='cheapest', use_spot=False)  # single GPU
					snake_case_				:			Optional[Any]						 =						rh.cluster(name='rh-cluster', instance_type='V100:4', provider='cheapest', use_spot=False)  # multi GPU
					gpu.up_if_not()

					# on-prem GPU
					# gpu = rh.cluster(
					#           ips=["ip_addr"], ssh_creds={ssh_user:"<username>", ssh_private_key:"<key_path>"}, name="rh-cluster"
					#       )

					# Set up remote function
					snake_case_				:			List[Any]						 =						[
					    'pip:./',
					    'transformers',
					    'datasets',
					    'evaluate',
					    'tqdm',
					    'scipy',
					    'scikit-learn',
					    'tensorboard',
					    'torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117',
					]
					snake_case_				:			Union[str, Any]						 =						rh.function(fn=launch_train, system=gpu, reqs=reqs, name='train_bert_glue')

					# Define train args/config, run train function
					snake_case_				:			List[str]						 =						argparse.Namespace(cpu=False, mixed_precision='fp16')
					snake_case_				:			List[str]						 =						{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
					launch_train_gpu(config, train_args, stream_logs=True)

					# Alternatively, we can just run as instructed in the README (but only because there's already a wrapper CLI):
					# gpu.install_packages(reqs)
					# gpu.run(['accelerate launch --multi_gpu accelerate/examples/nlp_example.py'])


