

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						:	Tuple														=					ViTImageProcessor if is_vision_available() else None





    @property
    def 				snake_case_    (				self):
         return self.image_processor_tester.prepare_image_processor_dict()





    def 				snake_case_    (				self):
         lowercase__   :       List[str]				=	(3, 32, 128)
         lowercase__   :       Optional[int]				=	tempfile.mkdtemp()

         # fmt: off
         lowercase__   :       Tuple				=	['[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__   :       Tuple				=	dict(zip(a						,       range(len(a))))

         lowercase__   :       Optional[Any]				=	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')

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





    def 				snake_case_    (				self						,       **a):
         return MgpstrTokenizer.from_pretrained(self.tmpdirname						,       **a)





    def 				snake_case_    (				self						,       **a):
         return ViTImageProcessor.from_pretrained(self.tmpdirname						,       **a)





    def 				snake_case_    (				self):
         shutil.rmtree(self.tmpdirname)





    def 				snake_case_    (				self):
         lowercase__   :       int				=	np.random.randint(255						,       size=(3, 30, 400)						,       dtype=np.uinta)

         lowercase__   :       Tuple				=	Image.fromarray(np.moveaxis(a						,       0						,       -1))

         return image_input





    def 				snake_case_    (				self):
         lowercase__   :       Tuple				=	self.get_tokenizer()
         lowercase__   :       Optional[Any]				=	self.get_image_processor()

         lowercase__   :       Dict				=	MgpstrProcessor(tokenizer=a						,       image_processor=a)
         processor.save_pretrained(self.tmpdirname)
         lowercase__   :       int				=	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 				snake_case_    (				self):
         lowercase__   :       Optional[Any]				=	self.get_tokenizer()
         lowercase__   :       Any				=	self.get_image_processor()

         lowercase__   :       Tuple				=	MgpstrProcessor(tokenizer=a						,       image_processor=a)
         processor.save_pretrained(self.tmpdirname)

         lowercase__   :       List[Any]				=	self.get_tokenizer(bos_token='(BOS)'						,       eos_token='(EOS)')
         lowercase__   :       str				=	self.get_image_processor(do_normalize=a						,       padding_value=1.0)

         lowercase__   :       str				=	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 				snake_case_    (				self):
         lowercase__   :       Dict				=	self.get_image_processor()
         lowercase__   :       str				=	self.get_tokenizer()

         lowercase__   :       Tuple				=	MgpstrProcessor(tokenizer=a						,       image_processor=a)

         lowercase__   :       Optional[int]				=	self.prepare_image_inputs()

         lowercase__   :       str				=	image_processor(a						,       return_tensors='np')
         lowercase__   :       int				=	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 				snake_case_    (				self):
         lowercase__   :       Dict				=	self.get_image_processor()
         lowercase__   :       Any				=	self.get_tokenizer()

         lowercase__   :       Union[str, Any]				=	MgpstrProcessor(tokenizer=a						,       image_processor=a)

         lowercase__   :       Optional[Any]				=	'test'

         lowercase__   :       int				=	processor(text=a)

         lowercase__   :       Union[str, Any]				=	tokenizer(a)
         for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]						,       encoded_processor[key])





    def 				snake_case_    (				self):
         lowercase__   :       List[str]				=	self.get_image_processor()
         lowercase__   :       str				=	self.get_tokenizer()

         lowercase__   :       Optional[int]				=	MgpstrProcessor(tokenizer=a						,       image_processor=a)

         lowercase__   :       str				=	'test'
         lowercase__   :       List[str]				=	self.prepare_image_inputs()

         lowercase__   :       Union[str, Any]				=	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 				snake_case_    (				self):
         lowercase__   :       Any				=	self.get_image_processor()
         lowercase__   :       str				=	self.get_tokenizer()

         lowercase__   :       Any				=	MgpstrProcessor(tokenizer=a						,       image_processor=a)

         lowercase__   :       List[Any]				=	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

         lowercase__   :       Tuple				=	processor.char_decode(a)
         lowercase__   :       Optional[Any]				=	tokenizer.batch_decode(a)
         lowercase__   :       int				=	[seq.replace(' '						,       '') for seq in decoded_tok]

         self.assertListEqual(a						,       a)





    def 				snake_case_    (				self):
         lowercase__   :       str				=	self.get_image_processor()
         lowercase__   :       str				=	self.get_tokenizer()

         lowercase__   :       List[str]				=	MgpstrProcessor(tokenizer=a						,       image_processor=a)

         lowercase__   :       List[Any]				=	None
         lowercase__   :       Any				=	self.prepare_image_inputs()

         lowercase__   :       Optional[Any]				=	processor(text=a						,       images=a)

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





    def 				snake_case_    (				self):
         lowercase__   :       List[Any]				=	self.get_image_processor()
         lowercase__   :       Tuple				=	self.get_tokenizer()

         lowercase__   :       Tuple				=	MgpstrProcessor(tokenizer=a						,       image_processor=a)

         lowercase__   :       str				=	torch.randn(1						,       27						,       38)
         lowercase__   :       Any				=	torch.randn(1						,       27						,       5_0257)
         lowercase__   :       int				=	torch.randn(1						,       27						,       3_0522)

         lowercase__   :       str				=	processor.batch_decode([char_input, bpe_input, wp_input])

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




import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):
    __lowerCamelCase						:	Tuple														=					["""image_processor""", """tokenizer"""]
    __lowerCamelCase						:	int														=					"""ViltImageProcessor"""
    __lowerCamelCase						:	Union[str, Any]														=					("""BertTokenizer""", """BertTokenizerFast""")





    def __init__(				self						,       a=None						,       a=None						,       **a):
         lowercase__   :       Any				=	None
         if "feature_extractor" in kwargs:
              warnings.warn(
                  'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                  ' instead.'						,       a						,       )
              lowercase__   :       Union[str, Any]				=	kwargs.pop('feature_extractor')

         lowercase__   :       List[Any]				=	image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
              raise ValueError('You need to specify an `image_processor`.')
         if tokenizer is None:
              raise ValueError('You need to specify a `tokenizer`.')

         super().__init__(a						,       a)
         lowercase__   :       str				=	self.image_processor





    def __call__(				self						,       a						,       a = None						,       a = True						,       a = False						,       a = None						,       a = None						,       a = 0						,       a = None						,       a = None						,       a = None						,       a = False						,       a = False						,       a = False						,       a = False						,       a = True						,       a = None						,       **a						,       ):
         lowercase__   :       Optional[Any]				=	self.tokenizer(
             text=a						,       add_special_tokens=a						,       padding=a						,       truncation=a						,       max_length=a						,       stride=a						,       pad_to_multiple_of=a						,       return_token_type_ids=a						,       return_attention_mask=a						,       return_overflowing_tokens=a						,       return_special_tokens_mask=a						,       return_offsets_mapping=a						,       return_length=a						,       verbose=a						,       return_tensors=a						,       **a						,       )
         # add pixel_values + pixel_mask
         lowercase__   :       str				=	self.image_processor(a						,       return_tensors=a)
         encoding.update(a)

         return encoding





    def 				snake_case_    (				self						,       *a						,       **a):
         return self.tokenizer.batch_decode(*a						,       **a)





    def 				snake_case_    (				self						,       *a						,       **a):
         return self.tokenizer.decode(*a						,       **a)





    @property
    def 				snake_case_    (				self):
         lowercase__   :       int				=	self.tokenizer.model_input_names
         lowercase__   :       List[str]				=	self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))





    @property
    def 				snake_case_    (				self):
         warnings.warn(
             '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'						,       a						,       )
         return self.image_processor_class





    @property
    def 				snake_case_    (				self):
         warnings.warn(
             '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'						,       a						,       )
         return self.image_processor

