

'''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 		__SCREAMING_SNAKE_CASE       ( unittest.TestCase			):
				lowerCamelCase_							=						ViTImageProcessor if is_vision_available() else None







				@property
				def    lowerCamelCase_  (					self    :		Any      ):
										'''simple docstring'''
										return self.image_processor_tester.prepare_image_processor_dict()







				def    lowerCamelCase_  (					self    :		Union[str, Any]      ):
										'''simple docstring'''
										lowercase   :       Optional[int]								=(3, 32, 128)
										lowercase   :       Union[str, Any]								=tempfile.mkdtemp()

										# fmt: off
										lowercase   :       int								=['''[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
										lowercase   :       Dict								=dict(zip(UpperCAmelCase__		,				range(len(UpperCAmelCase__      )      )      )      )

										lowercase   :       List[str]								=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'''      )

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







				def    lowerCamelCase_  (					self    :		int		,				**UpperCAmelCase__    :		Any      ):
										'''simple docstring'''
										return MgpstrTokenizer.from_pretrained(self.tmpdirname		,				**UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		List[str]		,				**UpperCAmelCase__    :		Tuple      ):
										'''simple docstring'''
										return ViTImageProcessor.from_pretrained(self.tmpdirname		,				**UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Dict      ):
										'''simple docstring'''
										shutil.rmtree(self.tmpdirname      )







				def    lowerCamelCase_  (					self    :		Dict      ):
										'''simple docstring'''
										lowercase   :       List[Any]								=np.random.randint(255		,				size=(3, 30, 400)		,				dtype=np.uinta      )

										lowercase   :       str								=Image.fromarray(np.moveaxis(UpperCAmelCase__		,				0		,				-1      )      )

										return image_input







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

										lowercase   :       str								=MgpstrProcessor(tokenizer=UpperCAmelCase__		,				image_processor=UpperCAmelCase__      )
										processor.save_pretrained(self.tmpdirname      )
										lowercase   :       List[str]								=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    lowerCamelCase_  (					self    :		int      ):
										'''simple docstring'''
										lowercase   :       Optional[Any]								=self.get_tokenizer()
										lowercase   :       List[Any]								=self.get_image_processor()

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

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

										lowercase   :       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    lowerCamelCase_  (					self    :		str      ):
										'''simple docstring'''
										lowercase   :       str								=self.get_image_processor()
										lowercase   :       Tuple								=self.get_tokenizer()

										lowercase   :       int								=MgpstrProcessor(tokenizer=UpperCAmelCase__		,				image_processor=UpperCAmelCase__      )

										lowercase   :       str								=self.prepare_image_inputs()

										lowercase   :       Dict								=image_processor(UpperCAmelCase__		,				return_tensors='''np'''      )
										lowercase   :       Union[str, Any]								=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    lowerCamelCase_  (					self    :		Optional[int]      ):
										'''simple docstring'''
										lowercase   :       List[str]								=self.get_image_processor()
										lowercase   :       List[str]								=self.get_tokenizer()

										lowercase   :       Tuple								=MgpstrProcessor(tokenizer=UpperCAmelCase__		,				image_processor=UpperCAmelCase__      )

										lowercase   :       Dict								='''test'''

										lowercase   :       str								=processor(text=UpperCAmelCase__      )

										lowercase   :       Dict								=tokenizer(UpperCAmelCase__      )
										for key in encoded_tok.keys():
																self.assertListEqual(encoded_tok[key]		,				encoded_processor[key]      )







				def    lowerCamelCase_  (					self    :		Any      ):
										'''simple docstring'''
										lowercase   :       Optional[int]								=self.get_image_processor()
										lowercase   :       str								=self.get_tokenizer()

										lowercase   :       Dict								=MgpstrProcessor(tokenizer=UpperCAmelCase__		,				image_processor=UpperCAmelCase__      )

										lowercase   :       List[str]								='''test'''
										lowercase   :       Optional[Any]								=self.prepare_image_inputs()

										lowercase   :       Optional[int]								=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    lowerCamelCase_  (					self    :		List[Any]      ):
										'''simple docstring'''
										lowercase   :       Dict								=self.get_image_processor()
										lowercase   :       str								=self.get_tokenizer()

										lowercase   :       Optional[int]								=MgpstrProcessor(tokenizer=UpperCAmelCase__		,				image_processor=UpperCAmelCase__      )

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

										lowercase   :       List[str]								=processor.char_decode(UpperCAmelCase__      )
										lowercase   :       Tuple								=tokenizer.batch_decode(UpperCAmelCase__      )
										lowercase   :       Dict								=[seq.replace(''' '''		,				''''''      ) for seq in decoded_tok]

										self.assertListEqual(UpperCAmelCase__		,				UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Dict      ):
										'''simple docstring'''
										lowercase   :       List[str]								=self.get_image_processor()
										lowercase   :       Union[str, Any]								=self.get_tokenizer()

										lowercase   :       Dict								=MgpstrProcessor(tokenizer=UpperCAmelCase__		,				image_processor=UpperCAmelCase__      )

										lowercase   :       str								=None
										lowercase   :       Optional[Any]								=self.prepare_image_inputs()

										lowercase   :       str								=processor(text=UpperCAmelCase__		,				images=UpperCAmelCase__      )

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







				def    lowerCamelCase_  (					self    :		Union[str, Any]      ):
										'''simple docstring'''
										lowercase   :       str								=self.get_image_processor()
										lowercase   :       List[str]								=self.get_tokenizer()

										lowercase   :       str								=MgpstrProcessor(tokenizer=UpperCAmelCase__		,				image_processor=UpperCAmelCase__      )

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

										lowercase   :       List[Any]								=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'''





from datetime import datetime

import matplotlib.pyplot as plt
import torch






def 							_lowerCAmelCase (      __magic_name__	:    Dict	)							->     Dict:
						for param in module.parameters():
												lowercase   :       List[str]								=False






def 							_lowerCAmelCase (      )							->     List[str]:
						lowercase   :       Dict								='''cuda''' if torch.cuda.is_available() else '''cpu'''
						if torch.backends.mps.is_available() and torch.backends.mps.is_built():
												lowercase   :       Optional[int]								='''mps'''
						if device == "mps":
												print(
												    '''WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'''
												    ''' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'''
												    ''' with generations.'''	)
						return device






def 							_lowerCAmelCase (      __magic_name__	:    Union[str, Any]	)							->     str:
						lowercase   :       Optional[int]								=plt.imshow(__magic_name__	)
						fig.axes.get_xaxis().set_visible(__magic_name__	)
						fig.axes.get_yaxis().set_visible(__magic_name__	)
						plt.show()







def 							_lowerCAmelCase (      )							->     List[Any]:
						lowercase   :       Any								=datetime.now()
						lowercase   :       Dict								=current_time.strftime('''%H:%M:%S'''	)
						return timestamp





