





import unittest

import numpy as np
import requests

from transformers.testing_utils import 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

   from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
   _a						=   False

if is_vision_available():
   from PIL import Image

   from transformers import PixaStructImageProcessor






class       __A					(					unittest.TestCase				):

   '''simple docstring'''







   def __init__(					self ,						__lowerCAmelCase ,						__lowerCAmelCase=7 ,						__lowerCAmelCase=3 ,						__lowerCAmelCase=1_8 ,						__lowerCAmelCase=3_0 ,						__lowerCAmelCase=4_0_0 ,						__lowerCAmelCase=None ,						__lowerCAmelCase=True ,						__lowerCAmelCase=True ,						__lowerCAmelCase=None ,						):



    '''simple docstring'''







    lowerCamelCase__              =	size if size is not None else {'''height''': 2_0, '''width''': 2_0}
    lowerCamelCase__              =	parent
    lowerCamelCase__              =	batch_size
    lowerCamelCase__              =	num_channels
    lowerCamelCase__              =	image_size
    lowerCamelCase__              =	min_resolution
    lowerCamelCase__              =	max_resolution
    lowerCamelCase__              =	size
    lowerCamelCase__              =	do_normalize
    lowerCamelCase__              =	do_convert_rgb
    lowerCamelCase__              =	[5_1_2, 1_0_2_4, 2_0_4_8, 4_0_9_6]
    lowerCamelCase__              =	patch_size if patch_size is not None else {'''height''': 1_6, '''width''': 1_6}





   def 	__lowerCamelCase       (					self							):



    '''simple docstring'''







    return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}





   def 	__lowerCamelCase       (					self							):



    '''simple docstring'''







    lowerCamelCase__              =	'''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'''
    lowerCamelCase__              =	Image.open(requests.get(__lowerCAmelCase ,						stream=__lowerCAmelCase							).raw							).convert('''RGB'''							)
    return raw_image






@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11    ,       reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""    ,       )
@require_torch
@require_vision
class       __A					(					lowerCAmelCase    ,       unittest.TestCase				):

   '''simple docstring'''







   lowerCAmelCase_							  =		PixaStructImageProcessor if is_vision_available() else None





   def 	__lowerCamelCase       (					self							):



    '''simple docstring'''







    lowerCamelCase__              =	PixaStructImageProcessingTester(self							)





   @property
   def 	__lowerCamelCase       (					self							):



    '''simple docstring'''







    return self.image_processor_tester.prepare_image_processor_dict()





   def 	__lowerCamelCase       (					self							):



    '''simple docstring'''







    lowerCamelCase__              =	self.image_processing_class(**self.image_processor_dict							)
    self.assertTrue(hasattr(__lowerCAmelCase ,						'''do_normalize'''							)							)
    self.assertTrue(hasattr(__lowerCAmelCase ,						'''do_convert_rgb'''							)							)





   def 	__lowerCamelCase       (					self							):



    '''simple docstring'''







    lowerCamelCase__              =	self.image_processor_tester.prepare_dummy_image()

    lowerCamelCase__              =	self.image_processing_class(**self.image_processor_dict							)
    lowerCamelCase__              =	2_0_4_8

    lowerCamelCase__              =	image_processor(__lowerCAmelCase ,						return_tensors='''pt''' ,						max_patches=__lowerCAmelCase							)
    self.assertTrue(torch.allclose(inputs.flattened_patches.mean() ,						torch.tensor(0.0606							) ,						atol=1E-3 ,						rtol=1E-3							)							)





   def 	__lowerCamelCase       (					self							):



    '''simple docstring'''







    lowerCamelCase__              =	self.image_processing_class(**self.image_processor_dict							)
    # create random PIL images
    lowerCamelCase__              =	prepare_image_inputs(self.image_processor_tester ,						equal_resolution=__lowerCAmelCase							)
    for image in image_inputs:
     self.assertIsInstance(__lowerCAmelCase ,						Image.Image							)

    # Test not batched input
    lowerCamelCase__              =	(
        (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
        * self.image_processor_tester.num_channels
    ) + 2

    for max_patch in self.image_processor_tester.max_patches:
     # Test not batched input
     lowerCamelCase__              =	image_processor(
         image_inputs[0] ,						return_tensors='''pt''' ,						max_patches=__lowerCAmelCase							).flattened_patches
     self.assertEqual(
         encoded_images.shape ,						(1, max_patch, expected_hidden_dim) ,						)

     # Test batched
     lowerCamelCase__              =	image_processor(
         __lowerCAmelCase ,						return_tensors='''pt''' ,						max_patches=__lowerCAmelCase							).flattened_patches
     self.assertEqual(
         encoded_images.shape ,						(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) ,						)





   def 	__lowerCamelCase       (					self							):



    '''simple docstring'''







    lowerCamelCase__              =	self.image_processing_class(**self.image_processor_dict							)
    # create random PIL images
    lowerCamelCase__              =	prepare_image_inputs(self.image_processor_tester ,						equal_resolution=__lowerCAmelCase							)
    for image in image_inputs:
     self.assertIsInstance(__lowerCAmelCase ,						Image.Image							)

    # Test not batched input
    lowerCamelCase__              =	(
        (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
        * self.image_processor_tester.num_channels
    ) + 2

    lowerCamelCase__              =	True

    for max_patch in self.image_processor_tester.max_patches:
     # Test not batched input
     with self.assertRaises(__lowerCAmelCase							):
      lowerCamelCase__              =	image_processor(
          image_inputs[0] ,						return_tensors='''pt''' ,						max_patches=__lowerCAmelCase							).flattened_patches

     lowerCamelCase__              =	'''Hello'''

     lowerCamelCase__              =	image_processor(
         image_inputs[0] ,						return_tensors='''pt''' ,						max_patches=__lowerCAmelCase ,						header_text=__lowerCAmelCase							).flattened_patches
     self.assertEqual(
         encoded_images.shape ,						(1, max_patch, expected_hidden_dim) ,						)

     # Test batched
     lowerCamelCase__              =	image_processor(
         __lowerCAmelCase ,						return_tensors='''pt''' ,						max_patches=__lowerCAmelCase ,						header_text=__lowerCAmelCase							).flattened_patches
     self.assertEqual(
         encoded_images.shape ,						(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) ,						)





   def 	__lowerCamelCase       (					self							):



    '''simple docstring'''







    lowerCamelCase__              =	self.image_processing_class(**self.image_processor_dict							)
    # create random numpy tensors
    lowerCamelCase__              =	prepare_image_inputs(self.image_processor_tester ,						equal_resolution=__lowerCAmelCase ,						numpify=__lowerCAmelCase							)
    for image in image_inputs:
     self.assertIsInstance(__lowerCAmelCase ,						np.ndarray							)

    lowerCamelCase__              =	(
        (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
        * self.image_processor_tester.num_channels
    ) + 2

    for max_patch in self.image_processor_tester.max_patches:
     # Test not batched input
     lowerCamelCase__              =	image_processor(
         image_inputs[0] ,						return_tensors='''pt''' ,						max_patches=__lowerCAmelCase							).flattened_patches
     self.assertEqual(
         encoded_images.shape ,						(1, max_patch, expected_hidden_dim) ,						)

     # Test batched
     lowerCamelCase__              =	image_processor(
         __lowerCAmelCase ,						return_tensors='''pt''' ,						max_patches=__lowerCAmelCase							).flattened_patches
     self.assertEqual(
         encoded_images.shape ,						(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) ,						)





   def 	__lowerCamelCase       (					self							):



    '''simple docstring'''







    lowerCamelCase__              =	self.image_processing_class(**self.image_processor_dict							)
    # create random PyTorch tensors
    lowerCamelCase__              =	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
    lowerCamelCase__              =	(
        (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
        * self.image_processor_tester.num_channels
    ) + 2

    for max_patch in self.image_processor_tester.max_patches:
     # Test not batched input
     lowerCamelCase__              =	image_processor(
         image_inputs[0] ,						return_tensors='''pt''' ,						max_patches=__lowerCAmelCase							).flattened_patches
     self.assertEqual(
         encoded_images.shape ,						(1, max_patch, expected_hidden_dim) ,						)

     # Test batched
     lowerCamelCase__              =	image_processor(
         __lowerCAmelCase ,						return_tensors='''pt''' ,						max_patches=__lowerCAmelCase							).flattened_patches
     self.assertEqual(
         encoded_images.shape ,						(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) ,						)






@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11    ,       reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""    ,       )
@require_torch
@require_vision
class       __A					(					lowerCAmelCase    ,       unittest.TestCase				):

   '''simple docstring'''







   lowerCAmelCase_							  =		PixaStructImageProcessor if is_vision_available() else None





   def 	__lowerCamelCase       (					self							):



    '''simple docstring'''







    lowerCamelCase__              =	PixaStructImageProcessingTester(self ,						num_channels=4							)
    lowerCamelCase__              =	3





   @property
   def 	__lowerCamelCase       (					self							):



    '''simple docstring'''







    return self.image_processor_tester.prepare_image_processor_dict()





   def 	__lowerCamelCase       (					self							):



    '''simple docstring'''







    lowerCamelCase__              =	self.image_processing_class(**self.image_processor_dict							)
    self.assertTrue(hasattr(__lowerCAmelCase ,						'''do_normalize'''							)							)
    self.assertTrue(hasattr(__lowerCAmelCase ,						'''do_convert_rgb'''							)							)





   def 	__lowerCamelCase       (					self							):



    '''simple docstring'''







    lowerCamelCase__              =	self.image_processing_class(**self.image_processor_dict							)
    # create random PIL images
    lowerCamelCase__              =	prepare_image_inputs(self.image_processor_tester ,						equal_resolution=__lowerCAmelCase							)
    for image in image_inputs:
     self.assertIsInstance(__lowerCAmelCase ,						Image.Image							)

    # Test not batched input
    lowerCamelCase__              =	(
        (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
        * (self.image_processor_tester.num_channels - 1)
    ) + 2

    for max_patch in self.image_processor_tester.max_patches:
     # Test not batched input
     lowerCamelCase__              =	image_processor(
         image_inputs[0] ,						return_tensors='''pt''' ,						max_patches=__lowerCAmelCase							).flattened_patches
     self.assertEqual(
         encoded_images.shape ,						(1, max_patch, expected_hidden_dim) ,						)

     # Test batched
     lowerCamelCase__              =	image_processor(
         __lowerCAmelCase ,						return_tensors='''pt''' ,						max_patches=__lowerCAmelCase							).flattened_patches
     self.assertEqual(
         encoded_images.shape ,						(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) ,						)










from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class       __A					(					lowerCAmelCase				):

			'''simple docstring'''







			lowerCAmelCase_							  =		"""ClapFeatureExtractor"""
			lowerCAmelCase_							  =		("""RobertaTokenizer""", """RobertaTokenizerFast""")





			def __init__(					self ,						__lowerCAmelCase ,						__lowerCAmelCase							):



				'''simple docstring'''







				super().__init__(__lowerCAmelCase ,						__lowerCAmelCase							)





			def __call__(					self ,						__lowerCAmelCase=None ,						__lowerCAmelCase=None ,						__lowerCAmelCase=None ,						**__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__              =	kwargs.pop('''sampling_rate''' ,						__lowerCAmelCase							)

				if text is None and audios is None:
					raise ValueError('''You have to specify either text or audios. Both cannot be none.'''							)

				if text is not None:
					lowerCamelCase__              =	self.tokenizer(__lowerCAmelCase ,						return_tensors=__lowerCAmelCase ,						**__lowerCAmelCase							)

				if audios is not None:
					lowerCamelCase__              =	self.feature_extractor(
					    __lowerCAmelCase ,						sampling_rate=__lowerCAmelCase ,						return_tensors=__lowerCAmelCase ,						**__lowerCAmelCase							)

				if text is not None and audios is not None:
					lowerCamelCase__              =	audio_features.input_features
					return encoding
				elif text is not None:
					return encoding
				else:
					return BatchEncoding(data=dict(**__lowerCAmelCase							) ,						tensor_type=__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self ,						*__lowerCAmelCase ,						**__lowerCAmelCase							):



				'''simple docstring'''







				return self.tokenizer.batch_decode(*__lowerCAmelCase ,						**__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self ,						*__lowerCAmelCase ,						**__lowerCAmelCase							):



				'''simple docstring'''







				return self.tokenizer.decode(*__lowerCAmelCase ,						**__lowerCAmelCase							)





			@property
			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	self.tokenizer.model_input_names
				lowerCamelCase__              =	self.feature_extractor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names							)							)



