'''simple docstring'''





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast






@require_vision
class 				_A (       unittest.TestCase ):




    '''simple docstring'''







    def       _UpperCamelCase    (       self )							-> int:
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       tempfile.mkdtemp()

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       BlipImageProcessor()
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       GPTaTokenizer.from_pretrained('hf-internal-testing/tiny-random-GPT2Model' )

         SCREAMING_SNAKE_CASE					:			Any    	=       BlipaProcessor(UpperCamelCase__    ,							UpperCamelCase__ )

         processor.save_pretrained(self.tmpdirname )






    def       _UpperCamelCase    (       self    ,							**lowercase__ )							-> Optional[int]:
         return AutoProcessor.from_pretrained(self.tmpdirname    ,							**UpperCamelCase__ ).tokenizer






    def       _UpperCamelCase    (       self    ,							**lowercase__ )							-> int:
         return AutoProcessor.from_pretrained(self.tmpdirname    ,							**UpperCamelCase__ ).image_processor






    def       _UpperCamelCase    (       self )							-> Tuple:
         shutil.rmtree(self.tmpdirname )






    def       _UpperCamelCase    (       self )							-> Any:
         SCREAMING_SNAKE_CASE					:			int    	=       [np.random.randint(255    ,							size=(3, 30, 400)    ,							dtype=np.uinta )]

         SCREAMING_SNAKE_CASE					:			Dict    	=       [Image.fromarray(np.moveaxis(UpperCamelCase__    ,							0    ,							-1 ) ) for x in image_inputs]

         return image_inputs






    def       _UpperCamelCase    (       self )							-> Dict:
         SCREAMING_SNAKE_CASE					:			Any    	=       BlipaProcessor(tokenizer=self.get_tokenizer()    ,							image_processor=self.get_image_processor() )
         processor.save_pretrained(self.tmpdirname )

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

         SCREAMING_SNAKE_CASE					:			int    	=       BlipaProcessor.from_pretrained(
             self.tmpdirname    ,							bos_token='(BOS)'    ,							eos_token='(EOS)'    ,							do_normalize=UpperCamelCase__    ,							padding_value=1.0 )

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

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






    def       _UpperCamelCase    (       self )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			Any    	=       self.get_image_processor()
         SCREAMING_SNAKE_CASE					:			List[str]    	=       self.get_tokenizer()

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       BlipaProcessor(tokenizer=UpperCamelCase__    ,							image_processor=UpperCamelCase__ )

         SCREAMING_SNAKE_CASE					:			Any    	=       self.prepare_image_inputs()

         SCREAMING_SNAKE_CASE					:			Dict    	=       image_processor(UpperCamelCase__    ,							return_tensors='np' )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       processor(images=UpperCamelCase__    ,							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 )							-> Any:
         SCREAMING_SNAKE_CASE					:			str    	=       self.get_image_processor()
         SCREAMING_SNAKE_CASE					:			Tuple    	=       self.get_tokenizer()

         SCREAMING_SNAKE_CASE					:			Any    	=       BlipaProcessor(tokenizer=UpperCamelCase__    ,							image_processor=UpperCamelCase__ )

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       '''lower newer'''

         SCREAMING_SNAKE_CASE					:			Tuple    	=       processor(text=UpperCamelCase__ )

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       tokenizer(UpperCamelCase__    ,							return_token_type_ids=UpperCamelCase__ )

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






    def       _UpperCamelCase    (       self )							-> str:
         SCREAMING_SNAKE_CASE					:			Any    	=       self.get_image_processor()
         SCREAMING_SNAKE_CASE					:			int    	=       self.get_tokenizer()

         SCREAMING_SNAKE_CASE					:			List[str]    	=       BlipaProcessor(tokenizer=UpperCamelCase__    ,							image_processor=UpperCamelCase__ )

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       '''lower newer'''
         SCREAMING_SNAKE_CASE					:			Tuple    	=       self.prepare_image_inputs()

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       processor(text=UpperCamelCase__    ,							images=UpperCamelCase__ )

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

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






    def       _UpperCamelCase    (       self )							-> List[Any]:
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.get_image_processor()
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.get_tokenizer()

         SCREAMING_SNAKE_CASE					:			Tuple    	=       BlipaProcessor(tokenizer=UpperCamelCase__    ,							image_processor=UpperCamelCase__ )

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       processor.batch_decode(UpperCamelCase__ )
         SCREAMING_SNAKE_CASE					:			Dict    	=       tokenizer.batch_decode(UpperCamelCase__ )

         self.assertListEqual(UpperCamelCase__    ,							UpperCamelCase__ )






    def       _UpperCamelCase    (       self )							-> Any:
         SCREAMING_SNAKE_CASE					:			Tuple    	=       self.get_image_processor()
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.get_tokenizer()

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       BlipaProcessor(tokenizer=UpperCamelCase__    ,							image_processor=UpperCamelCase__ )

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

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       processor(text=UpperCamelCase__    ,							images=UpperCamelCase__ )

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




'''simple docstring'''





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 (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   int							=						0
    snake_case__							:   bool							=						False
    snake_case__							:   float							=						3.0






class 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    def       _UpperCamelCase    (       self )							-> Tuple:
         # 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=lowercase__ ).to_kwargs()    ,							{'a': 2, 'b': True} )
         self.assertDictEqual(MockClass(a=2    ,							c=2.2_5 ).to_kwargs()    ,							{'a': 2, 'c': 2.2_5} )






    @require_cuda
    def       _UpperCamelCase    (       self )							-> Dict:
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       GradScalerKwargs(init_scale=1_024    ,							growth_factor=2 )
         AcceleratorState._reset_state()
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       Accelerator(mixed_precision='fp16'    ,							kwargs_handlers=[scaler_handler] )
         print(accelerator.use_fpaa )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       accelerator.scaler

         # Check the kwargs have been applied
         self.assertEqual(scaler._init_scale    ,							1_0_2_4.0 )
         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    ,							2_000 )
         self.assertEqual(scaler._enabled    ,							lowercase__ )






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


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

 # Check the values changed in kwargs
 _lowerCAmelCase  :Dict  				=    """"""
 _lowerCAmelCase  :Optional[int]  				=    model.bucket_bytes_cap // (1_024 * 1_024)
 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)



