






import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
 import torch


class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  A_						       = """char"""
  A_						       = """bpe"""
  A_						       = """wp"""




snake_case__   :						Union[str, Any]													=   (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)


class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  A_						       = ["""image_processor""", """char_tokenizer"""]
  A_						       = """ViTImageProcessor"""
  A_						       = """MgpstrTokenizer"""




  def __init__(  self		,		_UpperCAmelCase=None		,		_UpperCAmelCase=None		,		**_UpperCAmelCase			)      ->			List[Any]:
     UpperCamelCase_						=  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_		,		)
        UpperCamelCase_						=  kwargs.pop('feature_extractor'			)

     UpperCamelCase_						=  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`.'			)

     UpperCamelCase_						=  tokenizer
     UpperCamelCase_						=  AutoTokenizer.from_pretrained('gpt2'			)
     UpperCamelCase_						=  AutoTokenizer.from_pretrained('bert-base-uncased'			)

     super().__init__(a_		,		a_			)




  def __call__(  self		,		_UpperCAmelCase=None		,		_UpperCAmelCase=None		,		_UpperCAmelCase=None		,		**_UpperCAmelCase			)      ->			List[Any]:
     if images is None and text is None:
        raise ValueError('You need to specify either an `images` or `text` input to process.'			)

     if images is not None:
        UpperCamelCase_						=  self.image_processor(a_		,		return_tensors=a_		,		**a_			)
     if text is not None:
        UpperCamelCase_						=  self.char_tokenizer(a_		,		return_tensors=a_		,		**a_			)

     if text is None:
        return inputs
     elif images is None:
        return encodings
     else:
        UpperCamelCase_						=  encodings['input_ids']
        return inputs




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			int:
     UpperCamelCase_		,   UpperCamelCase_		,   UpperCamelCase_						=  sequences
     UpperCamelCase_						=  char_preds.size(0			)

     UpperCamelCase_		,   UpperCamelCase_						=  self._decode_helper(a_		,		'char'			)
     UpperCamelCase_		,   UpperCamelCase_						=  self._decode_helper(a_		,		'bpe'			)
     UpperCamelCase_		,   UpperCamelCase_						=  self._decode_helper(a_		,		'wp'			)

     UpperCamelCase_						=  []
     UpperCamelCase_						=  []
     for i in range(a_			):
        UpperCamelCase_						=  [char_scores[i], bpe_scores[i], wp_scores[i]]
        UpperCamelCase_						=  [char_strs[i], bpe_strs[i], wp_strs[i]]
        UpperCamelCase_						=  scores.index(max(a_			)			)
        final_strs.append(strs[max_score_index]			)
        final_scores.append(scores[max_score_index]			)

     UpperCamelCase_						=  {}
     UpperCamelCase_						=  final_strs
     UpperCamelCase_						=  final_scores
     UpperCamelCase_						=  char_strs
     UpperCamelCase_						=  bpe_strs
     UpperCamelCase_						=  wp_strs
     return out




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			List[str]:
     if format == DecodeType.CHARACTER:
        UpperCamelCase_						=  self.char_decode
        UpperCamelCase_						=  1
        UpperCamelCase_						=  '[s]'
     elif format == DecodeType.BPE:
        UpperCamelCase_						=  self.bpe_decode
        UpperCamelCase_						=  2
        UpperCamelCase_						=  '#'
     elif format == DecodeType.WORDPIECE:
        UpperCamelCase_						=  self.wp_decode
        UpperCamelCase_						=  102
        UpperCamelCase_						=  '[SEP]'
     else:
        raise ValueError(f"""Format {format} is not supported."""			)

     UpperCamelCase_		,   UpperCamelCase_						=  [], []
     UpperCamelCase_						=  pred_logits.size(0			)
     UpperCamelCase_						=  pred_logits.size(1			)
     UpperCamelCase_		,   UpperCamelCase_						=  pred_logits.topk(1		,		dim=-1		,		largest=a_		,		sorted=a_			)
     UpperCamelCase_						=  preds_index.view(-1		,		a_			)[:, 1:]
     UpperCamelCase_						=  decoder(a_			)
     UpperCamelCase_		,   UpperCamelCase_						=  torch.nn.functional.softmax(a_		,		dim=2			).max(dim=2			)
     UpperCamelCase_						=  preds_max_prob[:, 1:]

     for index in range(a_			):
        UpperCamelCase_						=  preds_str[index].find(a_			)
        UpperCamelCase_						=  preds_str[index][:pred_eos]
        UpperCamelCase_						=  preds_index[index].cpu().tolist()
        UpperCamelCase_						=  pred_index.index(a_			) if eos_token in pred_index else -1
        UpperCamelCase_						=  preds_max_prob[index][: pred_eos_index + 1]
        UpperCamelCase_						=  pred_max_prob.cumprod(dim=0			)[-1] if pred_max_prob.nelement() != 0 else 0.0
        dec_strs.append(a_			)
        conf_scores.append(a_			)

     return dec_strs, conf_scores




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			Any:
     UpperCamelCase_						=  [seq.replace(' '		,		''			) for seq in self.char_tokenizer.batch_decode(a_			)]
     return decode_strs




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			Optional[int]:
     return self.bpe_tokenizer.batch_decode(a_			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			Dict:
     UpperCamelCase_						=  [seq.replace(' '		,		''			) for seq in self.wp_tokenizer.batch_decode(a_			)]
     return decode_strs










import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
	from PIL import Image

	from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor


@require_tokenizers
@require_vision
class 	__snake_case   ( unittest.TestCase ):
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  tempfile.mkdtemp()

						# fmt: off
						lowerCAmelCase__		  =  ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
						# fmt: on
						lowerCAmelCase__		  =  os.path.join(self.tmpdirname    ,VOCAB_FILES_NAMES['vocab_file']						)
						with open(self.vocab_file    ,'w'    ,encoding='utf-8'						) as vocab_writer:
										vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]						)						)

						lowerCAmelCase__		  =  {
						    'do_resize': True,
						    'size': {'height': 18, 'width': 18},
						    'do_normalize': True,
						    'image_mean': [0.5, 0.5, 0.5],
						    'image_std': [0.5, 0.5, 0.5],
						}
						lowerCAmelCase__		  =  os.path.join(self.tmpdirname    ,a_						)
						with open(self.image_processor_file    ,'w'    ,encoding='utf-8'						) as fp:
										json.dump(a_    ,a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,**a_						):







						"""simple docstring"""
						return BertTokenizer.from_pretrained(self.tmpdirname    ,**a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,**a_						):







						"""simple docstring"""
						return ViTImageProcessor.from_pretrained(self.tmpdirname    ,**a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						shutil.rmtree(self.tmpdirname						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  [np.random.randint(255    ,size=(3, 30, 400)    ,dtype=np.uinta						)]

						lowerCAmelCase__		  =  [Image.fromarray(np.moveaxis(a_    ,0    ,-1						)						) for x in image_inputs]

						return image_inputs
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.get_tokenizer()
						lowerCAmelCase__		  =  self.get_image_processor()

						lowerCAmelCase__		  =  VisionTextDualEncoderProcessor(tokenizer=a_    ,image_processor=a_						)

						processor.save_pretrained(self.tmpdirname						)
						lowerCAmelCase__		  =  VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname						)

						self.assertEqual(processor.tokenizer.get_vocab()    ,tokenizer.get_vocab()						)
						self.assertIsInstance(processor.tokenizer    ,(BertTokenizer, BertTokenizerFast)						)

						self.assertEqual(processor.image_processor.to_json_string()    ,image_processor.to_json_string()						)
						self.assertIsInstance(processor.image_processor    ,a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  VisionTextDualEncoderProcessor(
						    tokenizer=self.get_tokenizer()    ,image_processor=self.get_image_processor()						)
						processor.save_pretrained(self.tmpdirname						)

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

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

						self.assertEqual(processor.tokenizer.get_vocab()    ,tokenizer_add_kwargs.get_vocab()						)
						self.assertIsInstance(processor.tokenizer    ,(BertTokenizer, BertTokenizerFast)						)

						self.assertEqual(processor.image_processor.to_json_string()    ,image_processor_add_kwargs.to_json_string()						)
						self.assertIsInstance(processor.image_processor    ,a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.get_image_processor()
						lowerCAmelCase__		  =  self.get_tokenizer()

						lowerCAmelCase__		  =  VisionTextDualEncoderProcessor(tokenizer=a_    ,image_processor=a_						)

						lowerCAmelCase__		  =  self.prepare_image_inputs()

						lowerCAmelCase__		  =  image_processor(a_    ,return_tensors='np'						)
						lowerCAmelCase__		  =  processor(images=a_    ,return_tensors='np'						)

						for key in input_feat_extract.keys():
										self.assertAlmostEqual(input_feat_extract[key].sum()    ,input_processor[key].sum()    ,delta=1e-2						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.get_image_processor()
						lowerCAmelCase__		  =  self.get_tokenizer()

						lowerCAmelCase__		  =  VisionTextDualEncoderProcessor(tokenizer=a_    ,image_processor=a_						)

						lowerCAmelCase__		  =  'lower newer'

						lowerCAmelCase__		  =  processor(text=a_						)

						lowerCAmelCase__		  =  tokenizer(a_						)

						for key in encoded_tok.keys():
										self.assertListEqual(encoded_tok[key]    ,encoded_processor[key]						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.get_image_processor()
						lowerCAmelCase__		  =  self.get_tokenizer()

						lowerCAmelCase__		  =  VisionTextDualEncoderProcessor(tokenizer=a_    ,image_processor=a_						)

						lowerCAmelCase__		  =  'lower newer'
						lowerCAmelCase__		  =  self.prepare_image_inputs()

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

						self.assertListEqual(list(inputs.keys()						)    ,['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values']						)

						# test if it raises when no input is passed
						with self.assertRaises(a_						):
										processor()
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.get_image_processor()
						lowerCAmelCase__		  =  self.get_tokenizer()

						lowerCAmelCase__		  =  VisionTextDualEncoderProcessor(tokenizer=a_    ,image_processor=a_						)

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

						lowerCAmelCase__		  =  processor.batch_decode(a_						)
						lowerCAmelCase__		  =  tokenizer.batch_decode(a_						)

						self.assertListEqual(a_    ,a_						)



		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.get_image_processor()
						lowerCAmelCase__		  =  self.get_tokenizer()

						lowerCAmelCase__		  =  VisionTextDualEncoderProcessor(tokenizer=a_    ,image_processor=a_						)

						lowerCAmelCase__		  =  'lower newer'
						lowerCAmelCase__		  =  self.prepare_image_inputs()

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

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