import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list



@require_torchaudio
@require_sentencepiece
class 				a							(			unittest.TestCase       ):







 """simple docstring"""






 def      UpperCAmelCase							(					self							)  ->					Optional[Any]:
        _A        =  """laion/clap-htsat-unfused"""
        _A        =  tempfile.mkdtemp()



 def      UpperCAmelCase							(					self		,				**lowerCAmelCase_							)  ->					str:
        return RobertaTokenizer.from_pretrained(self.checkpoint		,				**lowerCAmelCase_							)



 def      UpperCAmelCase							(					self		,				**lowerCAmelCase_							)  ->					Tuple:
        return ClapFeatureExtractor.from_pretrained(self.checkpoint		,				**lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					List[str]:
        shutil.rmtree(self.tmpdirname							)



 def      UpperCAmelCase							(					self							)  ->					List[Any]:
        _A        =  self.get_tokenizer()
        _A        =  self.get_feature_extractor()

        _A        =  ClapProcessor(tokenizer=lowerCAmelCase_		,				feature_extractor=lowerCAmelCase_							)

        processor.save_pretrained(self.tmpdirname							)
        _A        =  ClapProcessor.from_pretrained(self.tmpdirname							)

        self.assertEqual(processor.tokenizer.get_vocab()		,				tokenizer.get_vocab()							)
        self.assertIsInstance(processor.tokenizer		,				lowerCAmelCase_							)

        self.assertEqual(processor.feature_extractor.to_json_string()		,				feature_extractor.to_json_string()							)
        self.assertIsInstance(processor.feature_extractor		,				lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					List[Any]:
        _A        =  ClapProcessor(tokenizer=self.get_tokenizer()		,				feature_extractor=self.get_feature_extractor()							)
        processor.save_pretrained(self.tmpdirname							)

        _A        =  self.get_tokenizer(bos_token="""(BOS)"""		,				eos_token="""(EOS)"""							)
        _A        =  self.get_feature_extractor(do_normalize=lowerCAmelCase_		,				padding_value=1.0							)

        _A        =  ClapProcessor.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.feature_extractor.to_json_string()		,				feature_extractor_add_kwargs.to_json_string()							)
        self.assertIsInstance(processor.feature_extractor		,				lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					Dict:
        _A        =  self.get_feature_extractor()
        _A        =  self.get_tokenizer()

        _A        =  ClapProcessor(tokenizer=lowerCAmelCase_		,				feature_extractor=lowerCAmelCase_							)

        _A        =  floats_list((3, 10_00)							)

        _A        =  feature_extractor(lowerCAmelCase_		,				return_tensors="""np"""							)
        _A        =  processor(audios=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							)  ->					Dict:
        _A        =  self.get_feature_extractor()
        _A        =  self.get_tokenizer()

        _A        =  ClapProcessor(tokenizer=lowerCAmelCase_		,				feature_extractor=lowerCAmelCase_							)

        _A        =  """This is a test string"""

        _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_feature_extractor()
        _A        =  self.get_tokenizer()

        _A        =  ClapProcessor(tokenizer=lowerCAmelCase_		,				feature_extractor=lowerCAmelCase_							)

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

        _A        =  processor.batch_decode(lowerCAmelCase_							)
        _A        =  tokenizer.batch_decode(lowerCAmelCase_							)

        self.assertListEqual(lowerCAmelCase_		,				lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					str:
        _A        =  self.get_feature_extractor()
        _A        =  self.get_tokenizer()

        _A        =  ClapProcessor(tokenizer=lowerCAmelCase_		,				feature_extractor=lowerCAmelCase_							)

        self.assertListEqual(
            processor.model_input_names[2:]		,				feature_extractor.model_input_names		,				msg="""`processor` and `feature_extractor` model input names do not match"""		,				)





import unittest

import numpy as np

from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import DonutImageProcessor



class 				a							(			unittest.TestCase       ):







 """simple docstring"""






 def __init__(					self		,				lowerCAmelCase_		,				lowerCAmelCase_=7		,				lowerCAmelCase_=3		,				lowerCAmelCase_=18		,				lowerCAmelCase_=30		,				lowerCAmelCase_=4_00		,				lowerCAmelCase_=True		,				lowerCAmelCase_=None		,				lowerCAmelCase_=True		,				lowerCAmelCase_=False		,				lowerCAmelCase_=True		,				lowerCAmelCase_=True		,				lowerCAmelCase_=[0.5, 0.5, 0.5]		,				lowerCAmelCase_=[0.5, 0.5, 0.5]		,				)  ->					Optional[Any]:
        _A        =  parent
        _A        =  batch_size
        _A        =  num_channels
        _A        =  image_size
        _A        =  min_resolution
        _A        =  max_resolution
        _A        =  do_resize
        _A        =  size if size is not None else {"""height""": 18, """width""": 20}
        _A        =  do_thumbnail
        _A        =  do_align_axis
        _A        =  do_pad
        _A        =  do_normalize
        _A        =  image_mean
        _A        =  image_std



 def      UpperCAmelCase							(					self							)  ->					str:
        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_thumbnail": self.do_thumbnail,
            "do_align_long_axis": self.do_align_axis,
            "do_pad": self.do_pad,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
        }



@require_torch
@require_vision
class 				a							(			__lowerCAmelCase       ,			unittest.TestCase       ):







 """simple docstring"""






 lowerCamelCase	:int    			=		DonutImageProcessor if is_vision_available() else None



 def      UpperCAmelCase							(					self							)  ->					int:
        _A        =  DonutImageProcessingTester(self							)



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



 def      UpperCAmelCase							(					self							)  ->					Optional[Any]:
        _A        =  self.image_processing_class(**self.image_processor_dict							)
        self.assertTrue(hasattr(lowerCAmelCase_		,				"""do_resize"""							)							)
        self.assertTrue(hasattr(lowerCAmelCase_		,				"""size"""							)							)
        self.assertTrue(hasattr(lowerCAmelCase_		,				"""do_thumbnail"""							)							)
        self.assertTrue(hasattr(lowerCAmelCase_		,				"""do_align_long_axis"""							)							)
        self.assertTrue(hasattr(lowerCAmelCase_		,				"""do_pad"""							)							)
        self.assertTrue(hasattr(lowerCAmelCase_		,				"""do_normalize"""							)							)
        self.assertTrue(hasattr(lowerCAmelCase_		,				"""image_mean"""							)							)
        self.assertTrue(hasattr(lowerCAmelCase_		,				"""image_std"""							)							)



 def      UpperCAmelCase							(					self							)  ->					Tuple:
        _A        =  self.image_processing_class.from_dict(self.image_processor_dict							)
        self.assertEqual(image_processor.size		,				{"""height""": 18, """width""": 20}							)

        _A        =  self.image_processing_class.from_dict(self.image_processor_dict		,				size=42							)
        self.assertEqual(image_processor.size		,				{"""height""": 42, """width""": 42}							)

        # Previous config had dimensions in (width, height) order
        _A        =  self.image_processing_class.from_dict(self.image_processor_dict		,				size=(42, 84)							)
        self.assertEqual(image_processor.size		,				{"""height""": 84, """width""": 42}							)



 def      UpperCAmelCase							(					self							)  ->					Optional[int]:
        pass



 @is_flaky()
 def      UpperCAmelCase							(					self							)  ->					Optional[int]:
        # Initialize image_processing
        _A        =  self.image_processing_class(**self.image_processor_dict							)
        # create random PIL images
        _A        =  prepare_image_inputs(self.image_processor_tester		,				equal_resolution=lowerCAmelCase_							)
        for image in image_inputs:
               self.assertIsInstance(lowerCAmelCase_		,				Image.Image							)

        # Test not batched input
        _A        =  image_processing(image_inputs[0]		,				return_tensors="""pt"""							).pixel_values
        self.assertEqual(
            encoded_images.shape		,				(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )		,				)

        # Test batched
        _A        =  image_processing(lowerCAmelCase_		,				return_tensors="""pt"""							).pixel_values
        self.assertEqual(
            encoded_images.shape		,				(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )		,				)



 @is_flaky()
 def      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        # Initialize image_processing
        _A        =  self.image_processing_class(**self.image_processor_dict							)
        # create random numpy tensors
        _A        =  prepare_image_inputs(self.image_processor_tester		,				equal_resolution=lowerCAmelCase_		,				numpify=lowerCAmelCase_							)
        for image in image_inputs:
               self.assertIsInstance(lowerCAmelCase_		,				np.ndarray							)

        # Test not batched input
        _A        =  image_processing(image_inputs[0]		,				return_tensors="""pt"""							).pixel_values
        self.assertEqual(
            encoded_images.shape		,				(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )		,				)

        # Test batched
        _A        =  image_processing(lowerCAmelCase_		,				return_tensors="""pt"""							).pixel_values
        self.assertEqual(
            encoded_images.shape		,				(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )		,				)



 @is_flaky()
 def      UpperCAmelCase							(					self							)  ->					int:
        # Initialize image_processing
        _A        =  self.image_processing_class(**self.image_processor_dict							)
        # create random PyTorch tensors
        _A        =  prepare_image_inputs(self.image_processor_tester		,				equal_resolution=lowerCAmelCase_		,				torchify=lowerCAmelCase_							)
        for image in image_inputs:
               self.assertIsInstance(lowerCAmelCase_		,				torch.Tensor							)

        # Test not batched input
        _A        =  image_processing(image_inputs[0]		,				return_tensors="""pt"""							).pixel_values
        self.assertEqual(
            encoded_images.shape		,				(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )		,				)

        # Test batched
        _A        =  image_processing(lowerCAmelCase_		,				return_tensors="""pt"""							).pixel_values
        self.assertEqual(
            encoded_images.shape		,				(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )		,				)




