


"""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"""



   _a   :       Optional[Any]									=    ViTImageProcessor if is_vision_available() else None


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


   def 					UpperCAmelCase__(						self						)      ->							str:
    lowercase__       :       Union[str, Any]				=						(3, 32, 128)
    lowercase__       :       Any				=						tempfile.mkdtemp()

    # fmt: off
    lowercase__       :       Union[str, Any]				=						["""[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__       :       Any				=						dict(zip(lowerCamelCase__ ,     range(len(lowerCamelCase__						)						)						)						)

    lowercase__       :       List[str]				=						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"""						)

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


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


   def 					UpperCAmelCase__(						self ,     **lowerCamelCase__						)      ->							List[Any]:
    return ViTImageProcessor.from_pretrained(self.tmpdirname ,     **lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							Any:
    shutil.rmtree(self.tmpdirname						)


   def 					UpperCAmelCase__(						self						)      ->							str:
    lowercase__       :       List[Any]				=						np.random.randint(255 ,     size=(3, 30, 400) ,     dtype=np.uinta						)

    lowercase__       :       List[Any]				=						Image.fromarray(np.moveaxis(lowerCamelCase__ ,     0 ,     -1						)						)

    return image_input


   def 					UpperCAmelCase__(						self						)      ->							Tuple:
    lowercase__       :       Optional[Any]				=						self.get_tokenizer()
    lowercase__       :       Dict				=						self.get_image_processor()

    lowercase__       :       List[Any]				=						MgpstrProcessor(tokenizer=lowerCamelCase__ ,     image_processor=lowerCamelCase__						)
    processor.save_pretrained(self.tmpdirname						)
    lowercase__       :       str				=						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 					UpperCAmelCase__(						self						)      ->							int:
    lowercase__       :       Tuple				=						self.get_tokenizer()
    lowercase__       :       List[Any]				=						self.get_image_processor()

    lowercase__       :       int				=						MgpstrProcessor(tokenizer=lowerCamelCase__ ,     image_processor=lowerCamelCase__						)
    processor.save_pretrained(self.tmpdirname						)

    lowercase__       :       str				=						self.get_tokenizer(bos_token="""(BOS)""" ,     eos_token="""(EOS)"""						)
    lowercase__       :       int				=						self.get_image_processor(do_normalize=lowerCamelCase__ ,     padding_value=1.0						)

    lowercase__       :       Optional[int]				=						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 					UpperCAmelCase__(						self						)      ->							List[Any]:
    lowercase__       :       Union[str, Any]				=						self.get_image_processor()
    lowercase__       :       List[Any]				=						self.get_tokenizer()

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

    lowercase__       :       Union[str, Any]				=						self.prepare_image_inputs()

    lowercase__       :       List[Any]				=						image_processor(lowerCamelCase__ ,     return_tensors="""np"""						)
    lowercase__       :       Optional[int]				=						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 					UpperCAmelCase__(						self						)      ->							str:
    lowercase__       :       Tuple				=						self.get_image_processor()
    lowercase__       :       str				=						self.get_tokenizer()

    lowercase__       :       List[Any]				=						MgpstrProcessor(tokenizer=lowerCamelCase__ ,     image_processor=lowerCamelCase__						)

    lowercase__       :       Optional[Any]				=						"""test"""

    lowercase__       :       Any				=						processor(text=lowerCamelCase__						)

    lowercase__       :       List[Any]				=						tokenizer(lowerCamelCase__						)
    for key in encoded_tok.keys():
     self.assertListEqual(encoded_tok[key] ,     encoded_processor[key]						)


   def 					UpperCAmelCase__(						self						)      ->							Optional[Any]:
    lowercase__       :       Optional[int]				=						self.get_image_processor()
    lowercase__       :       Optional[Any]				=						self.get_tokenizer()

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

    lowercase__       :       Optional[Any]				=						"""test"""
    lowercase__       :       List[str]				=						self.prepare_image_inputs()

    lowercase__       :       List[str]				=						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 					UpperCAmelCase__(						self						)      ->							int:
    lowercase__       :       Optional[int]				=						self.get_image_processor()
    lowercase__       :       Optional[Any]				=						self.get_tokenizer()

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

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

    lowercase__       :       List[str]				=						processor.char_decode(lowerCamelCase__						)
    lowercase__       :       Union[str, Any]				=						tokenizer.batch_decode(lowerCamelCase__						)
    lowercase__       :       str				=						[seq.replace(""" """ ,     """"""						) for seq in decoded_tok]

    self.assertListEqual(lowerCamelCase__ ,     lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							Tuple:
    lowercase__       :       Union[str, Any]				=						self.get_image_processor()
    lowercase__       :       List[Any]				=						self.get_tokenizer()

    lowercase__       :       Optional[Any]				=						MgpstrProcessor(tokenizer=lowerCamelCase__ ,     image_processor=lowerCamelCase__						)

    lowercase__       :       Dict				=						None
    lowercase__       :       Any				=						self.prepare_image_inputs()

    lowercase__       :       Dict				=						processor(text=lowerCamelCase__ ,     images=lowerCamelCase__						)

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







   def 					UpperCAmelCase__(						self						)      ->							Dict:
    lowercase__       :       Any				=						self.get_image_processor()
    lowercase__       :       Optional[Any]				=						self.get_tokenizer()

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

    lowercase__       :       Union[str, Any]				=						torch.randn(1 ,     27 ,     38						)
    lowercase__       :       Optional[Any]				=						torch.randn(1 ,     27 ,     5_0257						)
    lowercase__       :       Union[str, Any]				=						torch.randn(1 ,     27 ,     3_0522						)

    lowercase__       :       Optional[int]				=						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 json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

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


if is_vision_available():
   from PIL import Image

   from transformers import AlignProcessor, EfficientNetImageProcessor

@require_vision
class 				_SCREAMING_SNAKE_CASE				(							unittest.TestCase  ):
   """simple docstring"""



   def 					UpperCAmelCase__(						self						)      ->							Tuple:
    lowercase__       :       str				=						tempfile.mkdtemp()

    lowercase__       :       List[Any]				=						[
        """[UNK]""",
        """[CLS]""",
        """[SEP]""",
        """[PAD]""",
        """[MASK]""",
        """want""",
        """##want""",
        """##ed""",
        """wa""",
        """un""",
        """runn""",
        """##ing""",
        """,""",
        """low""",
        """lowest""",
    ]
    lowercase__       :       str				=						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]						)						)

    lowercase__       :       Optional[Any]				=						{
        """do_resize""": True,
        """size""": 20,
        """do_center_crop""": True,
        """crop_size""": 18,
        """do_normalize""": True,
        """image_mean""": [0.4814_5466, 0.457_8275, 0.4082_1073],
        """image_std""": [0.2686_2954, 0.2613_0258, 0.2757_7711],
    }
    lowercase__       :       Any				=						os.path.join(self.tmpdirname ,     lowerCamelCase__						)
    with open(self.image_processor_file ,     """w""" ,     encoding="""utf-8"""						) as fp:
     json.dump(lowerCamelCase__ ,     lowerCamelCase__						)


   def 					UpperCAmelCase__(						self ,     **lowerCamelCase__						)      ->							str:
    return BertTokenizer.from_pretrained(self.tmpdirname ,     **lowerCamelCase__						)


   def 					UpperCAmelCase__(						self ,     **lowerCamelCase__						)      ->							str:
    return BertTokenizerFast.from_pretrained(self.tmpdirname ,     **lowerCamelCase__						)


   def 					UpperCAmelCase__(						self ,     **lowerCamelCase__						)      ->							List[Any]:
    return EfficientNetImageProcessor.from_pretrained(self.tmpdirname ,     **lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							Any:
    shutil.rmtree(self.tmpdirname						)


   def 					UpperCAmelCase__(						self						)      ->							Optional[int]:
    lowercase__       :       Tuple				=						[np.random.randint(255 ,     size=(3, 30, 400) ,     dtype=np.uinta						)]
    lowercase__       :       Any				=						[Image.fromarray(np.moveaxis(lowerCamelCase__ ,     0 ,     -1						)						) for x in image_inputs]
    return image_inputs


   def 					UpperCAmelCase__(						self						)      ->							int:
    lowercase__       :       Union[str, Any]				=						self.get_tokenizer()
    lowercase__       :       Union[str, Any]				=						self.get_rust_tokenizer()
    lowercase__       :       Any				=						self.get_image_processor()

    lowercase__       :       Union[str, Any]				=						AlignProcessor(tokenizer=lowerCamelCase__ ,     image_processor=lowerCamelCase__						)
    processor_slow.save_pretrained(self.tmpdirname						)
    lowercase__       :       Tuple				=						AlignProcessor.from_pretrained(self.tmpdirname ,     use_fast=lowerCamelCase__						)

    lowercase__       :       List[Any]				=						AlignProcessor(tokenizer=lowerCamelCase__ ,     image_processor=lowerCamelCase__						)
    processor_fast.save_pretrained(self.tmpdirname						)
    lowercase__       :       str				=						AlignProcessor.from_pretrained(self.tmpdirname						)

    self.assertEqual(processor_slow.tokenizer.get_vocab() ,     tokenizer_slow.get_vocab()						)
    self.assertEqual(processor_fast.tokenizer.get_vocab() ,     tokenizer_fast.get_vocab()						)
    self.assertEqual(tokenizer_slow.get_vocab() ,     tokenizer_fast.get_vocab()						)
    self.assertIsInstance(processor_slow.tokenizer ,     lowerCamelCase__						)
    self.assertIsInstance(processor_fast.tokenizer ,     lowerCamelCase__						)

    self.assertEqual(processor_slow.image_processor.to_json_string() ,     image_processor.to_json_string()						)
    self.assertEqual(processor_fast.image_processor.to_json_string() ,     image_processor.to_json_string()						)
    self.assertIsInstance(processor_slow.image_processor ,     lowerCamelCase__						)
    self.assertIsInstance(processor_fast.image_processor ,     lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							int:
    lowercase__       :       Optional[int]				=						AlignProcessor(tokenizer=self.get_tokenizer() ,     image_processor=self.get_image_processor()						)
    processor.save_pretrained(self.tmpdirname						)

    lowercase__       :       Dict				=						self.get_tokenizer(bos_token="""(BOS)""" ,     eos_token="""(EOS)"""						)
    lowercase__       :       Any				=						self.get_image_processor(do_normalize=lowerCamelCase__ ,     padding_value=1.0						)

    lowercase__       :       Union[str, Any]				=						AlignProcessor.from_pretrained(
        self.tmpdirname ,     bos_token="""(BOS)""" ,     eos_token="""(EOS)""" ,     do_normalize=lowerCamelCase__ ,     padding_value=1.0						)

    self.assertEqual(processor.tokenizer.get_vocab() ,     tokenizer_add_kwargs.get_vocab()						)
    self.assertIsInstance(processor.tokenizer ,     lowerCamelCase__						)

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


   def 					UpperCAmelCase__(						self						)      ->							Tuple:
    lowercase__       :       Optional[int]				=						self.get_image_processor()
    lowercase__       :       Dict				=						self.get_tokenizer()

    lowercase__       :       Tuple				=						AlignProcessor(tokenizer=lowerCamelCase__ ,     image_processor=lowerCamelCase__						)

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

    lowercase__       :       Tuple				=						image_processor(lowerCamelCase__ ,     return_tensors="""np"""						)
    lowercase__       :       str				=						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 					UpperCAmelCase__(						self						)      ->							List[str]:
    lowercase__       :       Union[str, Any]				=						self.get_image_processor()
    lowercase__       :       Tuple				=						self.get_tokenizer()

    lowercase__       :       List[str]				=						AlignProcessor(tokenizer=lowerCamelCase__ ,     image_processor=lowerCamelCase__						)

    lowercase__       :       Any				=						"""lower newer"""

    lowercase__       :       List[Any]				=						processor(text=lowerCamelCase__						)

    lowercase__       :       Tuple				=						tokenizer(lowerCamelCase__ ,     padding="""max_length""" ,     max_length=64						)

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


   def 					UpperCAmelCase__(						self						)      ->							str:
    lowercase__       :       List[Any]				=						self.get_image_processor()
    lowercase__       :       Any				=						self.get_tokenizer()

    lowercase__       :       Union[str, Any]				=						AlignProcessor(tokenizer=lowerCamelCase__ ,     image_processor=lowerCamelCase__						)

    lowercase__       :       List[Any]				=						"""lower newer"""
    lowercase__       :       List[str]				=						self.prepare_image_inputs()

    lowercase__       :       List[Any]				=						processor(text=lowerCamelCase__ ,     images=lowerCamelCase__						)

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

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


   def 					UpperCAmelCase__(						self						)      ->							Union[str, Any]:
    lowercase__       :       int				=						self.get_image_processor()
    lowercase__       :       Union[str, Any]				=						self.get_tokenizer()

    lowercase__       :       Tuple				=						AlignProcessor(tokenizer=lowerCamelCase__ ,     image_processor=lowerCamelCase__						)

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

    lowercase__       :       Tuple				=						processor.batch_decode(lowerCamelCase__						)
    lowercase__       :       Optional[Any]				=						tokenizer.batch_decode(lowerCamelCase__						)

    self.assertListEqual(lowerCamelCase__ ,     lowerCamelCase__						)







   def 					UpperCAmelCase__(						self						)      ->							List[Any]:
    lowercase__       :       List[str]				=						self.get_image_processor()
    lowercase__       :       Optional[Any]				=						self.get_tokenizer()

    lowercase__       :       Optional[int]				=						AlignProcessor(tokenizer=lowerCamelCase__ ,     image_processor=lowerCamelCase__						)

    lowercase__       :       str				=						"""lower newer"""
    lowercase__       :       List[Any]				=						self.prepare_image_inputs()

    lowercase__       :       int				=						processor(text=lowerCamelCase__ ,     images=lowerCamelCase__						)

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