


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  ):





							'''simple docstring'''






							lowercase__      :       Any     						=				ViTImageProcessor if is_vision_available() else None







							@property
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[int]:
														return self.image_processor_tester.prepare_image_processor_dict()







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[Any]:
														lowerCAmelCase__	       =     (3, 32, 1_28)
														lowerCAmelCase__	       =     tempfile.mkdtemp()

														# fmt: off
														lowerCAmelCase__	       =     ['''[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
														lowerCAmelCase__	       =     dict(zip(lowerCamelCase_ ,   range(len(lowerCamelCase_       )       )       )       )

														lowerCAmelCase__	       =     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(lowerCamelCase_       ) + '''\n'''       )

														lowerCAmelCase__	       =     {
														    '''do_normalize''': False,
														    '''do_resize''': True,
														    '''image_processor_type''': '''ViTImageProcessor''',
														    '''resample''': 3,
														    '''size''': {'''height''': 32, '''width''': 1_28},
														}
														lowerCAmelCase__	       =     os.path.join(self.tmpdirname ,   lowerCamelCase_       )
														with open(self.image_processor_file ,   '''w''' ,   encoding='''utf-8'''       ) as fp:
																					json.dump(lowerCamelCase_ ,   lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   **lowerCamelCase_       )	->							List[str]:
														return MgpstrTokenizer.from_pretrained(self.tmpdirname ,   **lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   **lowerCamelCase_       )	->							Dict:
														return ViTImageProcessor.from_pretrained(self.tmpdirname ,   **lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[Any]:
														shutil.rmtree(self.tmpdirname       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Union[str, Any]:
														lowerCAmelCase__	       =     np.random.randint(2_55 ,   size=(3, 30, 4_00) ,   dtype=np.uinta       )

														lowerCAmelCase__	       =     Image.fromarray(np.moveaxis(lowerCamelCase_ ,   0 ,   -1       )       )

														return image_input







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							int:
														lowerCAmelCase__	       =     self.get_tokenizer()
														lowerCAmelCase__	       =     self.get_image_processor()

														lowerCAmelCase__	       =     MgpstrProcessor(tokenizer=lowerCamelCase_ ,   image_processor=lowerCamelCase_       )
														processor.save_pretrained(self.tmpdirname       )
														lowerCAmelCase__	       =     MgpstrProcessor.from_pretrained(self.tmpdirname ,   use_fast=lowerCamelCase_       )

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

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







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Dict:
														lowerCAmelCase__	       =     self.get_tokenizer()
														lowerCAmelCase__	       =     self.get_image_processor()

														lowerCAmelCase__	       =     MgpstrProcessor(tokenizer=lowerCamelCase_ ,   image_processor=lowerCamelCase_       )
														processor.save_pretrained(self.tmpdirname       )

														lowerCAmelCase__	       =     self.get_tokenizer(bos_token='''(BOS)''' ,   eos_token='''(EOS)'''       )
														lowerCAmelCase__	       =     self.get_image_processor(do_normalize=lowerCamelCase_ ,   padding_value=1.0       )

														lowerCAmelCase__	       =     MgpstrProcessor.from_pretrained(
														    self.tmpdirname ,   bos_token='''(BOS)''' ,   eos_token='''(EOS)''' ,   do_normalize=lowerCamelCase_ ,   padding_value=1.0       )

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

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







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							int:
														lowerCAmelCase__	       =     self.get_image_processor()
														lowerCAmelCase__	       =     self.get_tokenizer()

														lowerCAmelCase__	       =     MgpstrProcessor(tokenizer=lowerCamelCase_ ,   image_processor=lowerCamelCase_       )

														lowerCAmelCase__	       =     self.prepare_image_inputs()

														lowerCAmelCase__	       =     image_processor(lowerCamelCase_ ,   return_tensors='''np'''       )
														lowerCAmelCase__	       =     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   __SCREAMING_SNAKE_CASE							(		self       )	->							Any:
														lowerCAmelCase__	       =     self.get_image_processor()
														lowerCAmelCase__	       =     self.get_tokenizer()

														lowerCAmelCase__	       =     MgpstrProcessor(tokenizer=lowerCamelCase_ ,   image_processor=lowerCamelCase_       )

														lowerCAmelCase__	       =     '''test'''

														lowerCAmelCase__	       =     processor(text=lowerCamelCase_       )

														lowerCAmelCase__	       =     tokenizer(lowerCamelCase_       )
														for key in encoded_tok.keys():
																					self.assertListEqual(encoded_tok[key] ,   encoded_processor[key]       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[Any]:
														lowerCAmelCase__	       =     self.get_image_processor()
														lowerCAmelCase__	       =     self.get_tokenizer()

														lowerCAmelCase__	       =     MgpstrProcessor(tokenizer=lowerCamelCase_ ,   image_processor=lowerCamelCase_       )

														lowerCAmelCase__	       =     '''test'''
														lowerCAmelCase__	       =     self.prepare_image_inputs()

														lowerCAmelCase__	       =     processor(text=lowerCamelCase_ ,   images=lowerCamelCase_       )

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

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







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Dict:
														lowerCAmelCase__	       =     self.get_image_processor()
														lowerCAmelCase__	       =     self.get_tokenizer()

														lowerCAmelCase__	       =     MgpstrProcessor(tokenizer=lowerCamelCase_ ,   image_processor=lowerCamelCase_       )

														lowerCAmelCase__	       =     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

														lowerCAmelCase__	       =     processor.char_decode(lowerCamelCase_       )
														lowerCAmelCase__	       =     tokenizer.batch_decode(lowerCamelCase_       )
														lowerCAmelCase__	       =     [seq.replace(''' ''' ,   ''''''       ) for seq in decoded_tok]

														self.assertListEqual(lowerCamelCase_ ,   lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Union[str, Any]:
														lowerCAmelCase__	       =     self.get_image_processor()
														lowerCAmelCase__	       =     self.get_tokenizer()

														lowerCAmelCase__	       =     MgpstrProcessor(tokenizer=lowerCamelCase_ ,   image_processor=lowerCamelCase_       )

														lowerCAmelCase__	       =     None
														lowerCAmelCase__	       =     self.prepare_image_inputs()

														lowerCAmelCase__	       =     processor(text=lowerCamelCase_ ,   images=lowerCamelCase_       )

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







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[Any]:
														lowerCAmelCase__	       =     self.get_image_processor()
														lowerCAmelCase__	       =     self.get_tokenizer()

														lowerCAmelCase__	       =     MgpstrProcessor(tokenizer=lowerCamelCase_ ,   image_processor=lowerCamelCase_       )

														lowerCAmelCase__	       =     torch.randn(1 ,   27 ,   38       )
														lowerCAmelCase__	       =     torch.randn(1 ,   27 ,   5_02_57       )
														lowerCAmelCase__	       =     torch.randn(1 ,   27 ,   3_05_22       )

														lowerCAmelCase__	       =     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 torch

from transformers import CamembertForMaskedLM, CamembertTokenizer


def  _snake_case       (					A			,   A			,   A			,   A=5	)						->   List[str]:
							# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
							assert masked_input.count('''<mask>'''	) == 1
							lowerCAmelCase__	       =     torch.tensor(tokenizer.encode(A			,   add_special_tokens=A	)	).unsqueeze(0	)  # Batch size 1
							lowerCAmelCase__	       =     model(A	)[0]  # The last hidden-state is the first element of the output tuple
							lowerCAmelCase__	       =     (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
							lowerCAmelCase__	       =     logits[0, masked_index, :]
							lowerCAmelCase__	       =     logits.softmax(dim=0	)
							lowerCAmelCase__       , lowerCAmelCase__	       =     prob.topk(k=A			,   dim=0	)
							lowerCAmelCase__	       =     ''' '''.join(
							    [tokenizer.convert_ids_to_tokens(indices[i].item()	) for i in range(len(A	)	)]	)
							lowerCAmelCase__	       =     tokenizer.mask_token
							lowerCAmelCase__	       =     []
							for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(''' '''	)	):
														lowerCAmelCase__	       =     predicted_token_bpe.replace('''\u2581'''			,   ''' '''	)
														if " {0}".format(A	) in masked_input:
																					topk_filled_outputs.append(
																					    (
																					        masked_input.replace(''' {0}'''.format(A	)			,   A	),
																					        values[index].item(),
																					        predicted_token,
																					    )	)
														else:
																					topk_filled_outputs.append(
																					    (
																					        masked_input.replace(A			,   A	),
																					        values[index].item(),
																					        predicted_token,
																					    )	)
							return topk_filled_outputs


__UpperCAmelCase       =							CamembertTokenizer.from_pretrained('''camembert-base''')
__UpperCAmelCase       =							CamembertForMaskedLM.from_pretrained('''camembert-base''')
model.eval()

__UpperCAmelCase       =							'''Le camembert est <mask> :)'''






print(fill_mask(masked_input, model, tokenizer, topk=3))