


from typing import List, Optional, Union

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






class   __lowercase				(  __snake_case  ):
    UpperCamelCase								=       ['''image_processor''', '''tokenizer''']
    UpperCamelCase								=       '''BlipImageProcessor'''
    UpperCamelCase								=       ('''BertTokenizer''', '''BertTokenizerFast''')





    def __init__(		self						:					List[Any]					,       __lowerCamelCase						:					int					,       __lowerCamelCase						:					Tuple       )    ->   Any:






       """simple docstring"""







       UpperCAmelCase							=     False
       super().__init__(__UpperCamelCase					,       __UpperCamelCase       )
       UpperCAmelCase							=     self.image_processor





    def __call__(		self						:					Tuple					,       __lowerCamelCase						:					ImageInput = None					,       __lowerCamelCase						:					Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None					,       __lowerCamelCase						:					bool = True					,       __lowerCamelCase						:					Union[bool, str, PaddingStrategy] = False					,       __lowerCamelCase						:					Union[bool, str, TruncationStrategy] = None					,       __lowerCamelCase						:					Optional[int] = None					,       __lowerCamelCase						:					int = 0					,       __lowerCamelCase						:					Optional[int] = None					,       __lowerCamelCase						:					Optional[bool] = None					,       __lowerCamelCase						:					bool = False					,       __lowerCamelCase						:					bool = False					,       __lowerCamelCase						:					bool = False					,       __lowerCamelCase						:					bool = False					,       __lowerCamelCase						:					bool = False					,       __lowerCamelCase						:					bool = True					,       __lowerCamelCase						:					Optional[Union[str, TensorType]] = None					,       **__lowerCamelCase						:					str					,       )    ->   int:






       """simple docstring"""







       if images is None and text is None:
          raise ValueError("""You have to specify either images or text."""       )

       # Get only text
       if images is None:
          UpperCAmelCase							=     self.tokenizer
          UpperCAmelCase							=     self.tokenizer(
              text=__UpperCamelCase					,       add_special_tokens=__UpperCamelCase					,       padding=__UpperCamelCase					,       truncation=__UpperCamelCase					,       max_length=__UpperCamelCase					,       stride=__UpperCamelCase					,       pad_to_multiple_of=__UpperCamelCase					,       return_attention_mask=__UpperCamelCase					,       return_overflowing_tokens=__UpperCamelCase					,       return_special_tokens_mask=__UpperCamelCase					,       return_offsets_mapping=__UpperCamelCase					,       return_token_type_ids=__UpperCamelCase					,       return_length=__UpperCamelCase					,       verbose=__UpperCamelCase					,       return_tensors=__UpperCamelCase					,       **__UpperCamelCase					,       )
          return text_encoding

       # add pixel_values
       UpperCAmelCase							=     self.image_processor(__UpperCamelCase					,       return_tensors=__UpperCamelCase       )

       if text is not None:
          UpperCAmelCase							=     self.tokenizer(
              text=__UpperCamelCase					,       add_special_tokens=__UpperCamelCase					,       padding=__UpperCamelCase					,       truncation=__UpperCamelCase					,       max_length=__UpperCamelCase					,       stride=__UpperCamelCase					,       pad_to_multiple_of=__UpperCamelCase					,       return_attention_mask=__UpperCamelCase					,       return_overflowing_tokens=__UpperCamelCase					,       return_special_tokens_mask=__UpperCamelCase					,       return_offsets_mapping=__UpperCamelCase					,       return_token_type_ids=__UpperCamelCase					,       return_length=__UpperCamelCase					,       verbose=__UpperCamelCase					,       return_tensors=__UpperCamelCase					,       **__UpperCamelCase					,       )
       else:
          UpperCAmelCase							=     None

       if text_encoding is not None:
          encoding_image_processor.update(__UpperCamelCase       )

       return encoding_image_processor





    def        _lowercase				(		self						:					int					,       *__lowerCamelCase						:					List[str]					,       **__lowerCamelCase						:					List[str]       )    ->   Any:






       """simple docstring"""







       return self.tokenizer.batch_decode(*__UpperCamelCase					,       **__UpperCamelCase       )





    def        _lowercase				(		self						:					Dict					,       *__lowerCamelCase						:					Optional[Any]					,       **__lowerCamelCase						:					Dict       )    ->   Union[str, Any]:






       """simple docstring"""







       return self.tokenizer.decode(*__UpperCamelCase					,       **__UpperCamelCase       )





    @property
    def        _lowercase				(		self						:					Optional[int]       )    ->   Any:






       """simple docstring"""







       UpperCAmelCase							=     self.tokenizer.model_input_names
       UpperCAmelCase							=     self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )







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   __lowercase				(  unittest.TestCase  ):
      UpperCamelCase								=       ViTImageProcessor if is_vision_available() else None





      @property
      def        _lowercase				(		self						:					Tuple       )    ->   int:






         """simple docstring"""







         return self.image_processor_tester.prepare_image_processor_dict()





      def        _lowercase				(		self						:					str       )    ->   Union[str, Any]:






         """simple docstring"""







         UpperCAmelCase							=     (3, 3_2, 1_2_8)
         UpperCAmelCase							=     tempfile.mkdtemp()

         # fmt: off
         UpperCAmelCase							=     ["""[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
         UpperCAmelCase							=     dict(zip(__lowerCamelCase					,       range(len(__lowerCamelCase       )       )       )       )

         UpperCAmelCase							=     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"""       )

         UpperCAmelCase							=     {
             """do_normalize""": False,
             """do_resize""": True,
             """image_processor_type""": """ViTImageProcessor""",
             """resample""": 3,
             """size""": {"""height""": 3_2, """width""": 1_2_8},
         }
         UpperCAmelCase							=     os.path.join(self.tmpdirname					,       __lowerCamelCase       )
         with open(self.image_processor_file					,       """w"""					,       encoding="""utf-8"""       ) as fp:
            json.dump(__lowerCamelCase					,       __lowerCamelCase       )





      def        _lowercase				(		self						:					Optional[Any]					,       **__lowerCamelCase						:					Union[str, Any]       )    ->   int:






         """simple docstring"""







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





      def        _lowercase				(		self						:					int					,       **__lowerCamelCase						:					Optional[int]       )    ->   Optional[int]:






         """simple docstring"""







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





      def        _lowercase				(		self						:					Optional[Any]       )    ->   Dict:






         """simple docstring"""







         shutil.rmtree(self.tmpdirname       )





      def        _lowercase				(		self						:					Any       )    ->   str:






         """simple docstring"""







         UpperCAmelCase							=     np.random.randint(2_5_5					,       size=(3, 3_0, 4_0_0)					,       dtype=np.uinta       )

         UpperCAmelCase							=     Image.fromarray(np.moveaxis(__lowerCamelCase					,       0					,       -1       )       )

         return image_input





      def        _lowercase				(		self						:					Tuple       )    ->   Tuple:






         """simple docstring"""







         UpperCAmelCase							=     self.get_tokenizer()
         UpperCAmelCase							=     self.get_image_processor()

         UpperCAmelCase							=     MgpstrProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )
         processor.save_pretrained(self.tmpdirname       )
         UpperCAmelCase							=     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        _lowercase				(		self						:					int       )    ->   Optional[Any]:






         """simple docstring"""







         UpperCAmelCase							=     self.get_tokenizer()
         UpperCAmelCase							=     self.get_image_processor()

         UpperCAmelCase							=     MgpstrProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )
         processor.save_pretrained(self.tmpdirname       )

         UpperCAmelCase							=     self.get_tokenizer(bos_token="""(BOS)"""					,       eos_token="""(EOS)"""       )
         UpperCAmelCase							=     self.get_image_processor(do_normalize=__lowerCamelCase					,       padding_value=1.0       )

         UpperCAmelCase							=     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        _lowercase				(		self						:					int       )    ->   Any:






         """simple docstring"""







         UpperCAmelCase							=     self.get_image_processor()
         UpperCAmelCase							=     self.get_tokenizer()

         UpperCAmelCase							=     MgpstrProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )

         UpperCAmelCase							=     self.prepare_image_inputs()

         UpperCAmelCase							=     image_processor(__lowerCamelCase					,       return_tensors="""np"""       )
         UpperCAmelCase							=     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        _lowercase				(		self						:					str       )    ->   int:






         """simple docstring"""







         UpperCAmelCase							=     self.get_image_processor()
         UpperCAmelCase							=     self.get_tokenizer()

         UpperCAmelCase							=     MgpstrProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )

         UpperCAmelCase							=     """test"""

         UpperCAmelCase							=     processor(text=__lowerCamelCase       )

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





      def        _lowercase				(		self						:					Optional[int]       )    ->   Dict:






         """simple docstring"""







         UpperCAmelCase							=     self.get_image_processor()
         UpperCAmelCase							=     self.get_tokenizer()

         UpperCAmelCase							=     MgpstrProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )

         UpperCAmelCase							=     """test"""
         UpperCAmelCase							=     self.prepare_image_inputs()

         UpperCAmelCase							=     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        _lowercase				(		self						:					str       )    ->   Tuple:






         """simple docstring"""







         UpperCAmelCase							=     self.get_image_processor()
         UpperCAmelCase							=     self.get_tokenizer()

         UpperCAmelCase							=     MgpstrProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )

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

         UpperCAmelCase							=     processor.char_decode(__lowerCamelCase       )
         UpperCAmelCase							=     tokenizer.batch_decode(__lowerCamelCase       )
         UpperCAmelCase							=     [seq.replace(""" """					,       """"""       ) for seq in decoded_tok]

         self.assertListEqual(__lowerCamelCase					,       __lowerCamelCase       )





      def        _lowercase				(		self						:					Optional[Any]       )    ->   Tuple:






         """simple docstring"""







         UpperCAmelCase							=     self.get_image_processor()
         UpperCAmelCase							=     self.get_tokenizer()

         UpperCAmelCase							=     MgpstrProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )

         UpperCAmelCase							=     None
         UpperCAmelCase							=     self.prepare_image_inputs()

         UpperCAmelCase							=     processor(text=__lowerCamelCase					,       images=__lowerCamelCase       )

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





      def        _lowercase				(		self						:					Dict       )    ->   Optional[Any]:






         """simple docstring"""







         UpperCAmelCase							=     self.get_image_processor()
         UpperCAmelCase							=     self.get_tokenizer()

         UpperCAmelCase							=     MgpstrProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )

         UpperCAmelCase							=     torch.randn(1					,       2_7					,       3_8       )
         UpperCAmelCase							=     torch.randn(1					,       2_7					,       5_0_2_5_7       )
         UpperCAmelCase							=     torch.randn(1					,       2_7					,       3_0_5_2_2       )

         UpperCAmelCase							=     processor.batch_decode([char_input, bpe_input, wp_input]       )

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



