


import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast

@require_vision
class   lowerCamelCase						(	unittest.TestCase  ):

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Any							=   tempfile.mkdtemp()

        snake_case :			Union[str, Any]							=   CLIPImageProcessor()
        snake_case :			Dict							=   BertTokenizer.from_pretrained(
            '''hf-internal-testing/tiny-random-BertModel'''						,    model_input_names=['''input_ids''', '''attention_mask''']       )

        snake_case :			Union[str, Any]							=   GitProcessor(lowerCAmelCase__						,    lowerCAmelCase__       )

        processor.save_pretrained(self.tmpdirname       )

       def 					UpperCAmelCase					(      self						,    **a__       ):






        '''simple docstring'''






        return AutoProcessor.from_pretrained(self.tmpdirname						,    **lowerCAmelCase__       ).tokenizer

       def 					UpperCAmelCase					(      self						,    **a__       ):






        '''simple docstring'''






        return AutoProcessor.from_pretrained(self.tmpdirname						,    **lowerCAmelCase__       ).image_processor

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        shutil.rmtree(self.tmpdirname       )

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			List[str]							=   [np.random.randint(2_55						,    size=(3, 30, 4_00)						,    dtype=np.uinta       )]

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

        return image_inputs

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Optional[Any]							=   GitProcessor(tokenizer=self.get_tokenizer()						,    image_processor=self.get_image_processor()       )
        processor.save_pretrained(self.tmpdirname       )

        snake_case :			Optional[Any]							=   self.get_tokenizer(bos_token='''(BOS)'''						,    eos_token='''(EOS)'''       )
        snake_case :			Dict							=   self.get_image_processor(do_normalize=lowerCAmelCase__						,    padding_value=1.0       )

        snake_case :			int							=   GitProcessor.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       ):






        '''simple docstring'''






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

        snake_case :			Optional[int]							=   GitProcessor(tokenizer=lowerCAmelCase__						,    image_processor=lowerCAmelCase__       )

        snake_case :			Optional[Any]							=   self.prepare_image_inputs()

        snake_case :			Tuple							=   image_processor(lowerCAmelCase__						,    return_tensors='''np'''       )
        snake_case :			Tuple							=   processor(images=lowerCAmelCase__						,    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 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






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

        snake_case :			Any							=   GitProcessor(tokenizer=lowerCAmelCase__						,    image_processor=lowerCAmelCase__       )

        snake_case :			Union[str, Any]							=   """lower newer"""

        snake_case :			List[str]							=   processor(text=lowerCAmelCase__       )

        snake_case :			List[Any]							=   tokenizer(lowerCAmelCase__						,    return_token_type_ids=lowerCAmelCase__       )

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

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			List[str]							=   self.get_image_processor()
        snake_case :			Optional[int]							=   self.get_tokenizer()

        snake_case :			Optional[Any]							=   GitProcessor(tokenizer=lowerCAmelCase__						,    image_processor=lowerCAmelCase__       )

        snake_case :			Dict							=   """lower newer"""
        snake_case :			Optional[Any]							=   self.prepare_image_inputs()

        snake_case :			Any							=   processor(text=lowerCAmelCase__						,    images=lowerCAmelCase__       )

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

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

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			List[Any]							=   self.get_image_processor()
        snake_case :			Tuple							=   self.get_tokenizer()

        snake_case :			Optional[int]							=   GitProcessor(tokenizer=lowerCAmelCase__						,    image_processor=lowerCAmelCase__       )

        snake_case :			Any							=   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        snake_case :			int							=   processor.batch_decode(lowerCAmelCase__       )
        snake_case :			Dict							=   tokenizer.batch_decode(lowerCAmelCase__       )

        self.assertListEqual(lowerCAmelCase__						,    lowerCAmelCase__       )



       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






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

        snake_case :			Any							=   GitProcessor(tokenizer=lowerCAmelCase__						,    image_processor=lowerCAmelCase__       )

        snake_case :			List[Any]							=   """lower newer"""
        snake_case :			Dict							=   self.prepare_image_inputs()

        snake_case :			str							=   processor(text=lowerCAmelCase__						,    images=lowerCAmelCase__       )

        # For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
        self.assertListEqual(list(inputs.keys()       )						,    ['''input_ids''', '''attention_mask''', '''pixel_values''']       )







'''simple docstring'''

from typing import Optional

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


_lowerCamelCase :				int														= [
    """AutomaticSpeechRecognition""",
    """AudioClassification""",
    """ImageClassification""",
    """LanguageModeling""",
    """QuestionAnsweringExtractive""",
    """Summarization""",
    """TaskTemplate""",
    """TextClassification""",
]

_lowerCamelCase :				int														= get_logger(__name__)


_lowerCamelCase :				List[str]														= {
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}



def  _lowerCAmelCase				(				__a      )  ->      Optional[TaskTemplate]:
     '''simple docstring'''







     _UpperCamelCase  :List[str]       =task_template_dict.get("""task"""      )
     if task_name is None:
          logger.warning(F'''Couldn\'t find template for task \'{task_name}\'. Available templates: {list(__a      )}'''      )
          return None
     _UpperCamelCase  :List[str]       =NAMEaTEMPLATE.get(__a      )
     return template.from_dict(__a      )