






'''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  ):
       snake_case__    :     int   		=			ViTImageProcessor if is_vision_available() else None




       @property
       def 					a_					(					self      : List[str]					):


            """simple docstring"""







            return self.image_processor_tester.prepare_image_processor_dict()




       def 					a_					(					self      : Optional[int]					):


            """simple docstring"""







            __lowerCamelCase						:       List[str]				   =				(3, 32, 128)
            __lowerCamelCase						:       Tuple				   =				tempfile.mkdtemp()

            # fmt: off
            __lowerCamelCase						:       Optional[int]				   =				["""[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
            __lowerCamelCase						:       Dict				   =				dict(zip(UpperCamelCase_	,    range(len(UpperCamelCase_					)					)					)					)

            __lowerCamelCase						:       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(UpperCamelCase_					) + """\n"""					)

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




       def 					a_					(					self      : Optional[int]	,    **A__      : int					):


            """simple docstring"""







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




       def 					a_					(					self      : Any	,    **A__      : List[str]					):


            """simple docstring"""







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




       def 					a_					(					self      : int					):


            """simple docstring"""







            shutil.rmtree(self.tmpdirname					)




       def 					a_					(					self      : Tuple					):


            """simple docstring"""







            __lowerCamelCase						:       List[Any]				   =				np.random.randint(255	,    size=(3, 30, 400)	,    dtype=np.uinta					)

            __lowerCamelCase						:       Tuple				   =				Image.fromarray(np.moveaxis(UpperCamelCase_	,    0	,    -1					)					)

            return image_input




       def 					a_					(					self      : str					):


            """simple docstring"""







            __lowerCamelCase						:       List[Any]				   =				self.get_tokenizer()
            __lowerCamelCase						:       Optional[int]				   =				self.get_image_processor()

            __lowerCamelCase						:       Optional[Any]				   =				MgpstrProcessor(tokenizer=UpperCamelCase_	,    image_processor=UpperCamelCase_					)
            processor.save_pretrained(self.tmpdirname					)
            __lowerCamelCase						:       Dict				   =				MgpstrProcessor.from_pretrained(self.tmpdirname	,    use_fast=UpperCamelCase_					)

            self.assertEqual(processor.char_tokenizer.get_vocab()	,    tokenizer.get_vocab()					)
            self.assertIsInstance(processor.char_tokenizer	,    UpperCamelCase_					)

            self.assertEqual(processor.image_processor.to_json_string()	,    image_processor.to_json_string()					)
            self.assertIsInstance(processor.image_processor	,    UpperCamelCase_					)




       def 					a_					(					self      : List[str]					):


            """simple docstring"""







            __lowerCamelCase						:       Union[str, Any]				   =				self.get_tokenizer()
            __lowerCamelCase						:       Tuple				   =				self.get_image_processor()

            __lowerCamelCase						:       Tuple				   =				MgpstrProcessor(tokenizer=UpperCamelCase_	,    image_processor=UpperCamelCase_					)
            processor.save_pretrained(self.tmpdirname					)

            __lowerCamelCase						:       List[Any]				   =				self.get_tokenizer(bos_token="""(BOS)"""	,    eos_token="""(EOS)"""					)
            __lowerCamelCase						:       Optional[Any]				   =				self.get_image_processor(do_normalize=UpperCamelCase_	,    padding_value=1.0					)

            __lowerCamelCase						:       Optional[int]				   =				MgpstrProcessor.from_pretrained(
                self.tmpdirname	,    bos_token="""(BOS)"""	,    eos_token="""(EOS)"""	,    do_normalize=UpperCamelCase_	,    padding_value=1.0					)

            self.assertEqual(processor.char_tokenizer.get_vocab()	,    tokenizer_add_kwargs.get_vocab()					)
            self.assertIsInstance(processor.char_tokenizer	,    UpperCamelCase_					)

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




       def 					a_					(					self      : str					):


            """simple docstring"""







            __lowerCamelCase						:       Dict				   =				self.get_image_processor()
            __lowerCamelCase						:       Any				   =				self.get_tokenizer()

            __lowerCamelCase						:       Tuple				   =				MgpstrProcessor(tokenizer=UpperCamelCase_	,    image_processor=UpperCamelCase_					)

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

            __lowerCamelCase						:       int				   =				image_processor(UpperCamelCase_	,    return_tensors="""np"""					)
            __lowerCamelCase						:       Tuple				   =				processor(images=UpperCamelCase_	,    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 					a_					(					self      : Optional[int]					):


            """simple docstring"""







            __lowerCamelCase						:       Any				   =				self.get_image_processor()
            __lowerCamelCase						:       Tuple				   =				self.get_tokenizer()

            __lowerCamelCase						:       List[Any]				   =				MgpstrProcessor(tokenizer=UpperCamelCase_	,    image_processor=UpperCamelCase_					)

            __lowerCamelCase						:       str				   =				"""test"""

            __lowerCamelCase						:       str				   =				processor(text=UpperCamelCase_					)

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




       def 					a_					(					self      : Dict					):


            """simple docstring"""







            __lowerCamelCase						:       List[str]				   =				self.get_image_processor()
            __lowerCamelCase						:       str				   =				self.get_tokenizer()

            __lowerCamelCase						:       List[str]				   =				MgpstrProcessor(tokenizer=UpperCamelCase_	,    image_processor=UpperCamelCase_					)

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

            __lowerCamelCase						:       Any				   =				processor(text=UpperCamelCase_	,    images=UpperCamelCase_					)

            self.assertListEqual(list(inputs.keys()					)	,    ["""pixel_values""", """labels"""]					)

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




       def 					a_					(					self      : Union[str, Any]					):


            """simple docstring"""







            __lowerCamelCase						:       Optional[int]				   =				self.get_image_processor()
            __lowerCamelCase						:       int				   =				self.get_tokenizer()

            __lowerCamelCase						:       Any				   =				MgpstrProcessor(tokenizer=UpperCamelCase_	,    image_processor=UpperCamelCase_					)

            __lowerCamelCase						:       Dict				   =				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

            __lowerCamelCase						:       Dict				   =				processor.char_decode(UpperCamelCase_					)
            __lowerCamelCase						:       Tuple				   =				tokenizer.batch_decode(UpperCamelCase_					)
            __lowerCamelCase						:       Tuple				   =				[seq.replace(""" """	,    """"""					) for seq in decoded_tok]

            self.assertListEqual(UpperCamelCase_	,    UpperCamelCase_					)




       def 					a_					(					self      : List[str]					):


            """simple docstring"""







            __lowerCamelCase						:       int				   =				self.get_image_processor()
            __lowerCamelCase						:       Optional[int]				   =				self.get_tokenizer()

            __lowerCamelCase						:       List[str]				   =				MgpstrProcessor(tokenizer=UpperCamelCase_	,    image_processor=UpperCamelCase_					)

            __lowerCamelCase						:       Union[str, Any]				   =				None
            __lowerCamelCase						:       Optional[Any]				   =				self.prepare_image_inputs()

            __lowerCamelCase						:       Tuple				   =				processor(text=UpperCamelCase_	,    images=UpperCamelCase_					)

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




       def 					a_					(					self      : Optional[int]					):


            """simple docstring"""







            __lowerCamelCase						:       Optional[Any]				   =				self.get_image_processor()
            __lowerCamelCase						:       Dict				   =				self.get_tokenizer()

            __lowerCamelCase						:       List[str]				   =				MgpstrProcessor(tokenizer=UpperCamelCase_	,    image_processor=UpperCamelCase_					)

            __lowerCamelCase						:       str				   =				torch.randn(1	,    27	,    38					)
            __lowerCamelCase						:       List[str]				   =				torch.randn(1	,    27	,    50257					)
            __lowerCamelCase						:       List[Any]				   =				torch.randn(1	,    27	,    30522					)

            __lowerCamelCase						:       Tuple				   =				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 ..utils import DummyObject, requires_backends







class 	SCREAMING_SNAKE_CASE	(      metaclass=lowerCAmelCase_  ):
       snake_case__    :     Union[str, Any]   		=			['torch', 'torchsde']




       def __init__(					self      : int	,    *A__      : Optional[Any]	,    **A__      : Dict					):


            """simple docstring"""







            requires_backends(self	,    ["""torch""", """torchsde"""]					)




       @classmethod
       def 					a_					(					cls      : Optional[Any]	,    *A__      : str	,    **A__      : Optional[Any]					):


            """simple docstring"""







            requires_backends(cls	,    ["""torch""", """torchsde"""]					)




       @classmethod
       def 					a_					(					cls      : Union[str, Any]	,    *A__      : Any	,    **A__      : Union[str, Any]					):


            """simple docstring"""







            requires_backends(cls	,    ["""torch""", """torchsde"""]					)



