



'''simple docstring'''

import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
 import torch


if is_vision_available():
 from PIL import Image

 from transformers import MgpstrProcessor, ViTImageProcessor


@require_torch
@require_vision
class 	a__     (				unittest.TestCase	):
 __magic_name__		:    Tuple	    =     ViTImageProcessor if is_vision_available() else None


 @property
 def 				lowercase__  (self : Dict      )     ->			Optional[Any]:

     """simple docstring"""

     return self.image_processor_tester.prepare_image_processor_dict()


 def 				lowercase__  (self : Union[str, Any]      )     ->			List[Any]:

     """simple docstring"""

     SCREAMING_SNAKE_CASE  :						List[str]      					= (3, 32, 128)
     SCREAMING_SNAKE_CASE  :						int      					= tempfile.mkdtemp()

     # fmt: off
     SCREAMING_SNAKE_CASE  :						List[Any]      					= ['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
     # fmt: on
     SCREAMING_SNAKE_CASE  :						List[Any]      					= dict(zip(__UpperCAmelCase,					range(len(__UpperCAmelCase      )      )      )      )

     SCREAMING_SNAKE_CASE  :						Optional[int]      					= os.path.join(self.tmpdirname,					VOCAB_FILES_NAMES['''vocab_file''']      )
     with open(self.vocab_file,					'''w''',					encoding='''utf-8'''      ) as fp:
         fp.write(json.dumps(__UpperCAmelCase      ) + '''\n'''      )

     SCREAMING_SNAKE_CASE  :						str      					= {
         '''do_normalize''': False,
         '''do_resize''': True,
         '''image_processor_type''': '''ViTImageProcessor''',
         '''resample''': 3,
         '''size''': {'''height''': 32, '''width''': 128},
     }
     SCREAMING_SNAKE_CASE  :						Optional[int]      					= os.path.join(self.tmpdirname,					__UpperCAmelCase      )
     with open(self.image_processor_file,					'''w''',					encoding='''utf-8'''      ) as fp:
         json.dump(__UpperCAmelCase,					__UpperCAmelCase      )


 def 				lowercase__  (self : List[Any],					**__UpperCAmelCase : Tuple      )     ->			Dict:

     """simple docstring"""

     return MgpstrTokenizer.from_pretrained(self.tmpdirname,					**__UpperCAmelCase      )


 def 				lowercase__  (self : Any,					**__UpperCAmelCase : str      )     ->			Optional[Any]:

     """simple docstring"""

     return ViTImageProcessor.from_pretrained(self.tmpdirname,					**__UpperCAmelCase      )


 def 				lowercase__  (self : int      )     ->			Optional[Any]:

     """simple docstring"""

     shutil.rmtree(self.tmpdirname      )


 def 				lowercase__  (self : Optional[Any]      )     ->			List[str]:

     """simple docstring"""

     SCREAMING_SNAKE_CASE  :						int      					= np.random.randint(255,					size=(3, 30, 400),					dtype=np.uinta      )

     SCREAMING_SNAKE_CASE  :						List[Any]      					= Image.fromarray(np.moveaxis(__UpperCAmelCase,					0,					-1      )      )

     return image_input


 def 				lowercase__  (self : str      )     ->			Any:

     """simple docstring"""

     SCREAMING_SNAKE_CASE  :						Tuple      					= self.get_tokenizer()
     SCREAMING_SNAKE_CASE  :						int      					= self.get_image_processor()

     SCREAMING_SNAKE_CASE  :						Dict      					= MgpstrProcessor(tokenizer=__UpperCAmelCase,					image_processor=__UpperCAmelCase      )
     processor.save_pretrained(self.tmpdirname      )
     SCREAMING_SNAKE_CASE  :						List[Any]      					= MgpstrProcessor.from_pretrained(self.tmpdirname,					use_fast=__UpperCAmelCase      )

     self.assertEqual(processor.char_tokenizer.get_vocab(),					tokenizer.get_vocab()      )
     self.assertIsInstance(processor.char_tokenizer,					__UpperCAmelCase      )

     self.assertEqual(processor.image_processor.to_json_string(),					image_processor.to_json_string()      )
     self.assertIsInstance(processor.image_processor,					__UpperCAmelCase      )


 def 				lowercase__  (self : str      )     ->			Tuple:

     """simple docstring"""

     SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.get_tokenizer()
     SCREAMING_SNAKE_CASE  :						List[str]      					= self.get_image_processor()

     SCREAMING_SNAKE_CASE  :						List[Any]      					= MgpstrProcessor(tokenizer=__UpperCAmelCase,					image_processor=__UpperCAmelCase      )
     processor.save_pretrained(self.tmpdirname      )

     SCREAMING_SNAKE_CASE  :						List[str]      					= self.get_tokenizer(bos_token='''(BOS)''',					eos_token='''(EOS)'''      )
     SCREAMING_SNAKE_CASE  :						Tuple      					= self.get_image_processor(do_normalize=__UpperCAmelCase,					padding_value=1.0      )

     SCREAMING_SNAKE_CASE  :						Dict      					= MgpstrProcessor.from_pretrained(
         self.tmpdirname,					bos_token='''(BOS)''',					eos_token='''(EOS)''',					do_normalize=__UpperCAmelCase,					padding_value=1.0      )

     self.assertEqual(processor.char_tokenizer.get_vocab(),					tokenizer_add_kwargs.get_vocab()      )
     self.assertIsInstance(processor.char_tokenizer,					__UpperCAmelCase      )

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


 def 				lowercase__  (self : Union[str, Any]      )     ->			List[Any]:

     """simple docstring"""

     SCREAMING_SNAKE_CASE  :						str      					= self.get_image_processor()
     SCREAMING_SNAKE_CASE  :						str      					= self.get_tokenizer()

     SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= MgpstrProcessor(tokenizer=__UpperCAmelCase,					image_processor=__UpperCAmelCase      )

     SCREAMING_SNAKE_CASE  :						str      					= self.prepare_image_inputs()

     SCREAMING_SNAKE_CASE  :						Optional[Any]      					= image_processor(__UpperCAmelCase,					return_tensors='''np'''      )
     SCREAMING_SNAKE_CASE  :						List[str]      					= processor(images=__UpperCAmelCase,					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 				lowercase__  (self : str      )     ->			Optional[int]:

     """simple docstring"""

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

     SCREAMING_SNAKE_CASE  :						Optional[Any]      					= MgpstrProcessor(tokenizer=__UpperCAmelCase,					image_processor=__UpperCAmelCase      )

     SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= '''test'''

     SCREAMING_SNAKE_CASE  :						Optional[Any]      					= processor(text=__UpperCAmelCase      )

     SCREAMING_SNAKE_CASE  :						Tuple      					= tokenizer(__UpperCAmelCase      )
     for key in encoded_tok.keys():
         self.assertListEqual(encoded_tok[key],					encoded_processor[key]      )


 def 				lowercase__  (self : str      )     ->			Any:

     """simple docstring"""

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

     SCREAMING_SNAKE_CASE  :						List[Any]      					= MgpstrProcessor(tokenizer=__UpperCAmelCase,					image_processor=__UpperCAmelCase      )

     SCREAMING_SNAKE_CASE  :						int      					= '''test'''
     SCREAMING_SNAKE_CASE  :						List[Any]      					= self.prepare_image_inputs()

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

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

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


 def 				lowercase__  (self : List[str]      )     ->			Any:

     """simple docstring"""

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

     SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= MgpstrProcessor(tokenizer=__UpperCAmelCase,					image_processor=__UpperCAmelCase      )

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

     SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= processor.char_decode(__UpperCAmelCase      )
     SCREAMING_SNAKE_CASE  :						int      					= tokenizer.batch_decode(__UpperCAmelCase      )
     SCREAMING_SNAKE_CASE  :						Optional[int]      					= [seq.replace(''' ''',					''''''      ) for seq in decoded_tok]

     self.assertListEqual(__UpperCAmelCase,					__UpperCAmelCase      )


 def 				lowercase__  (self : Tuple      )     ->			Any:

     """simple docstring"""

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

     SCREAMING_SNAKE_CASE  :						Any      					= MgpstrProcessor(tokenizer=__UpperCAmelCase,					image_processor=__UpperCAmelCase      )

     SCREAMING_SNAKE_CASE  :						Optional[Any]      					= None
     SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.prepare_image_inputs()

     SCREAMING_SNAKE_CASE  :						int      					= processor(text=__UpperCAmelCase,					images=__UpperCAmelCase      )

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






 def 				lowercase__  (self : Optional[int]      )     ->			int:

     """simple docstring"""

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

     SCREAMING_SNAKE_CASE  :						Tuple      					= MgpstrProcessor(tokenizer=__UpperCAmelCase,					image_processor=__UpperCAmelCase      )

     SCREAMING_SNAKE_CASE  :						List[Any]      					= torch.randn(1,					27,					38      )
     SCREAMING_SNAKE_CASE  :						int      					= torch.randn(1,					27,					50257      )
     SCREAMING_SNAKE_CASE  :						List[str]      					= torch.randn(1,					27,					30522      )

     SCREAMING_SNAKE_CASE  :						Dict      					= processor.batch_decode([char_input, bpe_input, wp_input]      )

     self.assertListEqual(list(results.keys()      ),					['''generated_text''', '''scores''', '''char_preds''', '''bpe_preds''', '''wp_preds''']      )









'''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 	a__     (				_lowercase	):
	__magic_name__		:    int	    =     0
	__magic_name__		:    bool	    =     False
	__magic_name__		:    float	    =     3.0



class 	a__     (				unittest.TestCase	):


	def 				lowercase__  (self : str      )     ->			Union[str, Any]:

					"""simple docstring"""

					self.assertDictEqual(MockClass().to_kwargs(),					{}      )
					self.assertDictEqual(MockClass(a=2      ).to_kwargs(),					{'''a''': 2}      )
					self.assertDictEqual(MockClass(a=2,					b=__UpperCAmelCase      ).to_kwargs(),					{'''a''': 2, '''b''': True}      )
					self.assertDictEqual(MockClass(a=2,					c=2.25      ).to_kwargs(),					{'''a''': 2, '''c''': 2.25}      )


	@require_cuda
	def 				lowercase__  (self : Dict      )     ->			str:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= GradScalerKwargs(init_scale=1024,					growth_factor=2      )
					AcceleratorState._reset_state()
					SCREAMING_SNAKE_CASE  :						Tuple      					= Accelerator(mixed_precision='''fp16''',					kwargs_handlers=[scaler_handler]      )
					print(accelerator.use_fpaa      )
					SCREAMING_SNAKE_CASE  :						str      					= accelerator.scaler

					# Check the kwargs have been applied
					self.assertEqual(scaler._init_scale,					1024.0      )
					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,					2000      )
					self.assertEqual(scaler._enabled,					__UpperCAmelCase      )






	@require_multi_gpu
	def 				lowercase__  (self : Optional[Any]      )     ->			Dict:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						str      					= ['''torchrun''', F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__      )]
					execute_subprocess_async(__UpperCAmelCase,					env=os.environ.copy()      )


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

	# Check the values changed in kwargs
	snake_case_			=     """"""
	snake_case_			=     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)




