


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 	__lowercase    (unittest.TestCase  ):





							"""simple docstring"""







							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     int					=					tempfile.mkdtemp()

										SCREAMING_SNAKE_CASE_		:     List[Any]					=					[
										    '[UNK]',
										    '[CLS]',
										    '[SEP]',
										    '[PAD]',
										    '[MASK]',
										    'want',
										    '##want',
										    '##ed',
										    'wa',
										    'un',
										    'runn',
										    '##ing',
										    ',',
										    'low',
										    'lowest',
										]
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					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]       )       )

										SCREAMING_SNAKE_CASE_		:     Any					=					{
										    'do_resize': True,
										    'size': 2_0,
										    'do_center_crop': True,
										    'crop_size': 1_8,
										    'do_normalize': True,
										    'image_mean': [0.48_145_466, 0.4_578_275, 0.40_821_073],
										    'image_std': [0.26_862_954, 0.26_130_258, 0.27_577_711],
										}
										SCREAMING_SNAKE_CASE_		:     List[str]					=					os.path.join(self.tmpdirname			,						lowerCAmelCase__       )
										with open(self.image_processor_file			,						'w'			,						encoding='utf-8'       ) as fp:
													json.dump(lowerCAmelCase__			,						lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self			,						**lowerCAmelCase__       ):





										"""simple docstring"""




										return BertTokenizer.from_pretrained(self.tmpdirname			,						**lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self			,						**lowerCAmelCase__       ):





										"""simple docstring"""




										return BertTokenizerFast.from_pretrained(self.tmpdirname			,						**lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self			,						**lowerCAmelCase__       ):





										"""simple docstring"""




										return EfficientNetImageProcessor.from_pretrained(self.tmpdirname			,						**lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										shutil.rmtree(self.tmpdirname       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Dict					=					[np.random.randint(2_5_5			,						size=(3, 3_0, 4_0_0)			,						dtype=np.uinta       )]
										SCREAMING_SNAKE_CASE_		:     str					=					[Image.fromarray(np.moveaxis(lowerCAmelCase__			,						0			,						-1       )       ) for x in image_inputs]
										return image_inputs






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     str					=					self.get_tokenizer()
										SCREAMING_SNAKE_CASE_		:     Dict					=					self.get_rust_tokenizer()
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					self.get_image_processor()

										SCREAMING_SNAKE_CASE_		:     List[Any]					=					AlignProcessor(tokenizer=lowerCAmelCase__			,						image_processor=lowerCAmelCase__       )
										processor_slow.save_pretrained(self.tmpdirname       )
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					AlignProcessor.from_pretrained(self.tmpdirname			,						use_fast=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     str					=					AlignProcessor(tokenizer=lowerCAmelCase__			,						image_processor=lowerCAmelCase__       )
										processor_fast.save_pretrained(self.tmpdirname       )
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					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			,						lowerCAmelCase__       )
										self.assertIsInstance(processor_fast.tokenizer			,						lowerCAmelCase__       )

										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			,						lowerCAmelCase__       )
										self.assertIsInstance(processor_fast.image_processor			,						lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					AlignProcessor(tokenizer=self.get_tokenizer()			,						image_processor=self.get_image_processor()       )
										processor.save_pretrained(self.tmpdirname       )

										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					self.get_tokenizer(bos_token='(BOS)'			,						eos_token='(EOS)'       )
										SCREAMING_SNAKE_CASE_		:     Any					=					self.get_image_processor(do_normalize=lowerCAmelCase__			,						padding_value=1.0       )

										SCREAMING_SNAKE_CASE_		:     List[str]					=					AlignProcessor.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       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					self.get_image_processor()
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					self.get_tokenizer()

										SCREAMING_SNAKE_CASE_		:     List[Any]					=					AlignProcessor(tokenizer=lowerCAmelCase__			,						image_processor=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					self.prepare_image_inputs()

										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					image_processor(lowerCAmelCase__			,						return_tensors='np'       )
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					processor(images=lowerCAmelCase__			,						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 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Any					=					self.get_image_processor()
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					self.get_tokenizer()

										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					AlignProcessor(tokenizer=lowerCAmelCase__			,						image_processor=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     int					=					'lower newer'

										SCREAMING_SNAKE_CASE_		:     Tuple					=					processor(text=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Tuple					=					tokenizer(lowerCAmelCase__			,						padding='max_length'			,						max_length=6_4       )

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






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					self.get_image_processor()
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					self.get_tokenizer()

										SCREAMING_SNAKE_CASE_		:     Any					=					AlignProcessor(tokenizer=lowerCAmelCase__			,						image_processor=lowerCAmelCase__       )

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

										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					processor(text=lowerCAmelCase__			,						images=lowerCAmelCase__       )

										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(lowerCAmelCase__       ):
													processor()






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Tuple					=					self.get_image_processor()
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					self.get_tokenizer()

										SCREAMING_SNAKE_CASE_		:     str					=					AlignProcessor(tokenizer=lowerCAmelCase__			,						image_processor=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     List[Any]					=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					processor.batch_decode(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     str					=					tokenizer.batch_decode(lowerCAmelCase__       )

										self.assertListEqual(lowerCAmelCase__			,						lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Dict					=					self.get_image_processor()
										SCREAMING_SNAKE_CASE_		:     Tuple					=					self.get_tokenizer()

										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					AlignProcessor(tokenizer=lowerCAmelCase__			,						image_processor=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Any					=					'lower newer'
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					self.prepare_image_inputs()

										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					processor(text=lowerCAmelCase__			,						images=lowerCAmelCase__       )

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













import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler




@dataclass
class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	lowerCAmelCase						=					0
	lowerCAmelCase						=					False
	lowerCAmelCase						=					3.0






class 					UpperCamelCase_ (     unittest.TestCase							):



	'''simple docstring'''




	def 				_UpperCamelCase     (			self   )			->       List[Any]:
								# If no defaults are changed, `to_kwargs` returns an empty dict.
								self.assertDictEqual(MockClass().to_kwargs()			,     {}   )
								self.assertDictEqual(MockClass(a=2   ).to_kwargs()			,     {'a': 2}   )
								self.assertDictEqual(MockClass(a=2			,     b=a   ).to_kwargs()			,     {'a': 2, 'b': True}   )
								self.assertDictEqual(MockClass(a=2			,     c=2.25   ).to_kwargs()			,     {'a': 2, 'c': 2.25}   )




	@require_cuda
	def 				_UpperCamelCase     (			self   )			->       str:
								# If no defaults are changed, `to_kwargs` returns an empty dict.
								snake_case_           =				GradScalerKwargs(init_scale=10_24			,     growth_factor=2   )
								AcceleratorState._reset_state()
								snake_case_           =				Accelerator(mixed_precision='fp16'			,     kwargs_handlers=[scaler_handler]   )
								print(accelerator.use_fpaa   )
								snake_case_           =				accelerator.scaler

								# Check the kwargs have been applied
								self.assertEqual(scaler._init_scale			,     1_024.0   )
								self.assertEqual(scaler._growth_factor			,     2.0   )

								# Check the other values are at the default
								self.assertEqual(scaler._backoff_factor			,     0.5   )
								self.assertEqual(scaler._growth_interval			,     20_00   )
								self.assertEqual(scaler._enabled			,     a   )






	@require_multi_gpu
	def 				_UpperCamelCase     (			self   )			->       Optional[Any]:
								snake_case_           =				['torchrun', F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__   )]
								execute_subprocess_async(a			,     env=os.environ.copy()   )


if __name__ == "__main__":
	lowercase    				=   DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
	lowercase    				=   Accelerator(kwargs_handlers=[ddp_scaler])
	lowercase    				=   torch.nn.Linear(100, 200)
	lowercase    				=   accelerator.prepare(model)

	# Check the values changed in kwargs
	lowercase    				=   ""
	lowercase    				=   model.bucket_bytes_cap // (1024 * 1024)
	if observed_bucket_cap_map != 15:
		error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
	if model.find_unused_parameters is not True:
		error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

	# Check the values of the defaults
	if model.dim != 0:
		error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
	if model.broadcast_buffers is not True:
		error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
	if model.gradient_as_bucket_view is not False:
		error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

	# Raise error at the end to make sure we don't stop at the first failure.
	if len(error_msg) > 0:
		raise ValueError(error_msg)





