

"""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 lowerCamelCase__   (				unittest.TestCase    ):



    '''simple docstring'''




    _lowerCamelCase					=   ViTImageProcessor if is_vision_available() else None




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




    def UpperCamelCase__		( self    )    ->      Any:
          A				  =		(3, 3_2, 1_2_8)
          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(lowerCamelCase_ ,range(len(lowerCamelCase_    )    )    )    )

          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(lowerCamelCase_    ) + """\n"""    )

          A				  =		{
              """do_normalize""": False,
              """do_resize""": True,
              """image_processor_type""": """ViTImageProcessor""",
              """resample""": 3,
              """size""": {"""height""": 3_2, """width""": 1_2_8},
          }
          A				  =		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_    )    ->      str:
          return ViTImageProcessor.from_pretrained(self.tmpdirname ,**lowerCamelCase_    )




    def UpperCamelCase__		( self    )    ->      int:
          shutil.rmtree(self.tmpdirname    )




    def UpperCamelCase__		( self    )    ->      Dict:
          A				  =		np.random.randint(2_5_5 ,size=(3, 3_0, 4_0_0) ,dtype=np.uinta    )

          A				  =		Image.fromarray(np.moveaxis(lowerCamelCase_ ,0 ,-1    )    )

          return image_input




    def UpperCamelCase__		( self    )    ->      Dict:
          A				  =		self.get_tokenizer()
          A				  =		self.get_image_processor()

          A				  =		MgpstrProcessor(tokenizer=lowerCamelCase_ ,image_processor=lowerCamelCase_    )
          processor.save_pretrained(self.tmpdirname    )
          A				  =		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    )    ->      Dict:
          A				  =		self.get_tokenizer()
          A				  =		self.get_image_processor()

          A				  =		MgpstrProcessor(tokenizer=lowerCamelCase_ ,image_processor=lowerCamelCase_    )
          processor.save_pretrained(self.tmpdirname    )

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

          A				  =		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    )    ->      Any:
          A				  =		self.get_image_processor()
          A				  =		self.get_tokenizer()

          A				  =		MgpstrProcessor(tokenizer=lowerCamelCase_ ,image_processor=lowerCamelCase_    )

          A				  =		self.prepare_image_inputs()

          A				  =		image_processor(lowerCamelCase_ ,return_tensors="""np"""    )
          A				  =		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    )    ->      Union[str, Any]:
          A				  =		self.get_image_processor()
          A				  =		self.get_tokenizer()

          A				  =		MgpstrProcessor(tokenizer=lowerCamelCase_ ,image_processor=lowerCamelCase_    )

          A				  =		"""test"""

          A				  =		processor(text=lowerCamelCase_    )

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




    def UpperCamelCase__		( self    )    ->      Union[str, Any]:
          A				  =		self.get_image_processor()
          A				  =		self.get_tokenizer()

          A				  =		MgpstrProcessor(tokenizer=lowerCamelCase_ ,image_processor=lowerCamelCase_    )

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

          A				  =		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    )    ->      Any:
          A				  =		self.get_image_processor()
          A				  =		self.get_tokenizer()

          A				  =		MgpstrProcessor(tokenizer=lowerCamelCase_ ,image_processor=lowerCamelCase_    )

          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(lowerCamelCase_    )
          A				  =		tokenizer.batch_decode(lowerCamelCase_    )
          A				  =		[seq.replace(""" """ ,""""""    ) for seq in decoded_tok]

          self.assertListEqual(lowerCamelCase_ ,lowerCamelCase_    )




    def UpperCamelCase__		( self    )    ->      Dict:
          A				  =		self.get_image_processor()
          A				  =		self.get_tokenizer()

          A				  =		MgpstrProcessor(tokenizer=lowerCamelCase_ ,image_processor=lowerCamelCase_    )

          A				  =		None
          A				  =		self.prepare_image_inputs()

          A				  =		processor(text=lowerCamelCase_ ,images=lowerCamelCase_    )

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




    def UpperCamelCase__		( self    )    ->      Union[str, Any]:
          A				  =		self.get_image_processor()
          A				  =		self.get_tokenizer()

          A				  =		MgpstrProcessor(tokenizer=lowerCamelCase_ ,image_processor=lowerCamelCase_    )

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

          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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCAmelCase													={
    "configuration_mega": ["MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegaConfig", "MegaOnnxConfig"],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase													=[
      "MEGA_PRETRAINED_MODEL_ARCHIVE_LIST",
      "MegaForCausalLM",
      "MegaForMaskedLM",
      "MegaForMultipleChoice",
      "MegaForQuestionAnswering",
      "MegaForSequenceClassification",
      "MegaForTokenClassification",
      "MegaModel",
      "MegaPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mega import (
        MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
        MegaForCausalLM,
        MegaForMaskedLM,
        MegaForMultipleChoice,
        MegaForQuestionAnswering,
        MegaForSequenceClassification,
        MegaForTokenClassification,
        MegaModel,
        MegaPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase													=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




