






"""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							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =ViTImageProcessor if is_vision_available() else None




							@property
							def  lowercase		(   self:     List[Any]    ):

												'''simple docstring'''

												return self.image_processor_tester.prepare_image_processor_dict()




							def  lowercase		(   self:     List[str]    ):

												'''simple docstring'''

												a__          =  (3, 32, 128)
												a__          =  tempfile.mkdtemp()

												# fmt: off
												a__          =  ['''[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
												a__          =  dict(zip(__A				,		range(len(__A    )    )    )    )

												a__          =  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(__A    ) + '''\n'''    )

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




							def  lowercase		(   self:     Optional[Any]				,		**__A:     str    ):

												'''simple docstring'''

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




							def  lowercase		(   self:     Optional[int]				,		**__A:     Union[str, Any]    ):

												'''simple docstring'''

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




							def  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

												shutil.rmtree(self.tmpdirname    )




							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												a__          =  np.random.randint(255				,		size=(3, 30, 400)				,		dtype=np.uinta    )

												a__          =  Image.fromarray(np.moveaxis(__A				,		0				,		-1    )    )

												return image_input




							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												a__          =  self.get_tokenizer()
												a__          =  self.get_image_processor()

												a__          =  MgpstrProcessor(tokenizer=__A				,		image_processor=__A    )
												processor.save_pretrained(self.tmpdirname    )
												a__          =  MgpstrProcessor.from_pretrained(self.tmpdirname				,		use_fast=__A    )

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

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




							def  lowercase		(   self:     List[Any]    ):

												'''simple docstring'''

												a__          =  self.get_tokenizer()
												a__          =  self.get_image_processor()

												a__          =  MgpstrProcessor(tokenizer=__A				,		image_processor=__A    )
												processor.save_pretrained(self.tmpdirname    )

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

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

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

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




							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												a__          =  self.get_image_processor()
												a__          =  self.get_tokenizer()

												a__          =  MgpstrProcessor(tokenizer=__A				,		image_processor=__A    )

												a__          =  self.prepare_image_inputs()

												a__          =  image_processor(__A				,		return_tensors='''np'''    )
												a__          =  processor(images=__A				,		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:     Dict    ):

												'''simple docstring'''

												a__          =  self.get_image_processor()
												a__          =  self.get_tokenizer()

												a__          =  MgpstrProcessor(tokenizer=__A				,		image_processor=__A    )

												a__          =  '''test'''

												a__          =  processor(text=__A    )

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




							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												a__          =  self.get_image_processor()
												a__          =  self.get_tokenizer()

												a__          =  MgpstrProcessor(tokenizer=__A				,		image_processor=__A    )

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

												a__          =  processor(text=__A				,		images=__A    )

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

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




							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												a__          =  self.get_image_processor()
												a__          =  self.get_tokenizer()

												a__          =  MgpstrProcessor(tokenizer=__A				,		image_processor=__A    )

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

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

												self.assertListEqual(__A				,		__A    )




							def  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

												a__          =  self.get_image_processor()
												a__          =  self.get_tokenizer()

												a__          =  MgpstrProcessor(tokenizer=__A				,		image_processor=__A    )

												a__          =  None
												a__          =  self.prepare_image_inputs()

												a__          =  processor(text=__A				,		images=__A    )

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





							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

												a__          =  self.get_image_processor()
												a__          =  self.get_tokenizer()

												a__          =  MgpstrProcessor(tokenizer=__A				,		image_processor=__A    )

												a__          =  torch.randn(1				,		27				,		38    )
												a__          =  torch.randn(1				,		27				,		50257    )
												a__          =  torch.randn(1				,		27				,		30522    )

												a__          =  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 ...configuration_utils import PretrainedConfig
from ...utils import logging


__a       :	List[str]             = logging.get_logger(__name__)

__a       :	int             = {
    'facebook/vit-mae-base': 'https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json',
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    ='vit_mae'




							def __init__(   self:     str				,		__A:     str=768				,		__A:     Union[str, Any]=12				,		__A:     str=12				,		__A:     List[Any]=3072				,		__A:     List[Any]="gelu"				,		__A:     Union[str, Any]=0.0				,		__A:     Tuple=0.0				,		__A:     str=0.0_2				,		__A:     Tuple=1e-12				,		__A:     Dict=224				,		__A:     List[Any]=16				,		__A:     Tuple=3				,		__A:     str=True				,		__A:     Optional[Any]=16				,		__A:     List[Any]=512				,		__A:     Optional[Any]=8				,		__A:     Dict=2048				,		__A:     str=0.7_5				,		__A:     str=False				,		**__A:     Tuple				,		):

												'''simple docstring'''

												super().__init__(**__A    )

												a__          =  hidden_size
												a__          =  num_hidden_layers
												a__          =  num_attention_heads
												a__          =  intermediate_size
												a__          =  hidden_act
												a__          =  hidden_dropout_prob
												a__          =  attention_probs_dropout_prob
												a__          =  initializer_range
												a__          =  layer_norm_eps
												a__          =  image_size
												a__          =  patch_size
												a__          =  num_channels
												a__          =  qkv_bias
												a__          =  decoder_num_attention_heads
												a__          =  decoder_hidden_size
												a__          =  decoder_num_hidden_layers
												a__          =  decoder_intermediate_size
												a__          =  mask_ratio
												a__          =  norm_pix_loss


