import unittest

import numpy as np

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


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import BlipImageProcessor
class 							a_					(		unittest.TestCase		):






 '''simple docstring'''






 def __init__(     self   :    Optional[Any]     ,       lowercase__   :    str     ,       lowercase__   :    str=7     ,       lowercase__   :    Optional[Any]=3     ,       lowercase__   :    str=18     ,       lowercase__   :    Optional[int]=30     ,       lowercase__   :    Optional[Any]=400     ,       lowercase__   :    Tuple=True     ,       lowercase__   :    Optional[int]=None     ,       lowercase__   :    Tuple=True     ,       lowercase__   :    Optional[int]=False     ,       lowercase__   :    int=[0.48_145_466, 0.4_578_275, 0.40_821_073]     ,       lowercase__   :    Dict=[0.26_862_954, 0.26_130_258, 0.27_577_711]     ,       lowercase__   :    List[Any]=True     ,       ):







        '''simple docstring'''



        lowerCAmelCase__	      =   size if size is not None else {'height': 20, 'width': 20}
        lowerCAmelCase__	      =   parent
        lowerCAmelCase__	      =   batch_size
        lowerCAmelCase__	      =   num_channels
        lowerCAmelCase__	      =   image_size
        lowerCAmelCase__	      =   min_resolution
        lowerCAmelCase__	      =   max_resolution
        lowerCAmelCase__	      =   do_resize
        lowerCAmelCase__	      =   size
        lowerCAmelCase__	      =   do_normalize
        lowerCAmelCase__	      =   image_mean
        lowerCAmelCase__	      =   image_std
        lowerCAmelCase__	      =   do_pad
        lowerCAmelCase__	      =   do_convert_rgb




 def 						__snake_case		(     self   :    Optional[int]):







        '''simple docstring'''



        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_convert_rgb": self.do_convert_rgb,
            "do_pad": self.do_pad,
        }




 def 						__snake_case		(     self   :    int     ,       lowercase__   :    Any=False     ,       lowercase__   :    Any=False     ,       lowercase__   :    int=False):







        '''simple docstring'''



        assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

        if equal_resolution:
               lowerCAmelCase__	      =   []
               for i in range(self.batch_size):
                      image_inputs.append(
                          np.random.randint(
                              255     ,       size=(self.num_channels, self.max_resolution, self.max_resolution)     ,       dtype=np.uinta))
        else:
               lowerCAmelCase__	      =   []
               for i in range(self.batch_size):
                      lowerCAmelCase__	      =   np.random.choice(np.arange(self.min_resolution     ,       self.max_resolution)     ,       2)
                      image_inputs.append(np.random.randint(255     ,       size=(self.num_channels, width, height)     ,       dtype=np.uinta))

        if not numpify and not torchify:
               # PIL expects the channel dimension as last dimension
               lowerCAmelCase__	      =   [Image.fromarray(np.moveaxis(snake_case__     ,       0     ,       -1)) for x in image_inputs]

        if torchify:
               lowerCAmelCase__	      =   [torch.from_numpy(snake_case__) for x in image_inputs]

        return image_inputs






@require_torch
@require_vision
class 							a_					(		SCREAMING_SNAKE_CASE			,	unittest.TestCase		):






 '''simple docstring'''






 UpperCAmelCase_      					=   BlipImageProcessor if is_vision_available() else None




 def 						__snake_case		(     self   :    Any):







        '''simple docstring'''



        lowerCAmelCase__	      =   BlipImageProcessingTester(self)




 @property
 def 						__snake_case		(     self   :    Union[str, Any]):







        '''simple docstring'''



        return self.image_processor_tester.prepare_image_processor_dict()




 def 						__snake_case		(     self   :    Any):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.image_processing_class(**self.image_processor_dict)
        self.assertTrue(hasattr(snake_case__     ,       'do_resize'))
        self.assertTrue(hasattr(snake_case__     ,       'size'))
        self.assertTrue(hasattr(snake_case__     ,       'do_normalize'))
        self.assertTrue(hasattr(snake_case__     ,       'image_mean'))
        self.assertTrue(hasattr(snake_case__     ,       'image_std'))
        self.assertTrue(hasattr(snake_case__     ,       'do_convert_rgb'))




 def 						__snake_case		(     self   :    Optional[int]):







        '''simple docstring'''



        pass




 def 						__snake_case		(     self   :    List[str]):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.image_processing_class(**self.image_processor_dict)
        # create random PIL images
        lowerCAmelCase__	      =   self.image_processor_tester.prepare_inputs(equal_resolution=snake_case__)
        for image in image_inputs:
               self.assertIsInstance(snake_case__     ,       Image.Image)

        # Test not batched input
        lowerCAmelCase__	      =   image_processor(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
        lowerCAmelCase__	      =   image_processor(snake_case__     ,       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'],
            )     ,       )




 def 						__snake_case		(     self   :    int):







        '''simple docstring'''



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

        # Test not batched input
        lowerCAmelCase__	      =   image_processor(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
        lowerCAmelCase__	      =   image_processor(snake_case__     ,       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'],
            )     ,       )




 def 						__snake_case		(     self   :    List[Any]):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.image_processing_class(**self.image_processor_dict)
        # create random PyTorch tensors
        lowerCAmelCase__	      =   self.image_processor_tester.prepare_inputs(equal_resolution=snake_case__     ,       torchify=snake_case__)
        for image in image_inputs:
               self.assertIsInstance(snake_case__     ,       torch.Tensor)

        # Test not batched input
        lowerCAmelCase__	      =   image_processor(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
        lowerCAmelCase__	      =   image_processor(snake_case__     ,       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'],
            )     ,       )






@require_torch
@require_vision
class 							a_					(		SCREAMING_SNAKE_CASE			,	unittest.TestCase		):






 '''simple docstring'''






 UpperCAmelCase_      					=   BlipImageProcessor if is_vision_available() else None




 def 						__snake_case		(     self   :    Tuple):







        '''simple docstring'''



        lowerCAmelCase__	      =   BlipImageProcessingTester(self     ,       num_channels=4)
        lowerCAmelCase__	      =   3




 @property
 def 						__snake_case		(     self   :    Optional[int]):







        '''simple docstring'''



        return self.image_processor_tester.prepare_image_processor_dict()




 def 						__snake_case		(     self   :    Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.image_processing_class(**self.image_processor_dict)
        self.assertTrue(hasattr(snake_case__     ,       'do_resize'))
        self.assertTrue(hasattr(snake_case__     ,       'size'))
        self.assertTrue(hasattr(snake_case__     ,       'do_normalize'))
        self.assertTrue(hasattr(snake_case__     ,       'image_mean'))
        self.assertTrue(hasattr(snake_case__     ,       'image_std'))
        self.assertTrue(hasattr(snake_case__     ,       'do_convert_rgb'))




 def 						__snake_case		(     self   :    Optional[int]):







        '''simple docstring'''



        pass




 def 						__snake_case		(     self   :    int):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.image_processing_class(**self.image_processor_dict)
        # create random PIL images
        lowerCAmelCase__	      =   self.image_processor_tester.prepare_inputs(equal_resolution=snake_case__)
        for image in image_inputs:
               self.assertIsInstance(snake_case__     ,       Image.Image)

        # Test not batched input
        lowerCAmelCase__	      =   image_processor(image_inputs[0]     ,       return_tensors='pt').pixel_values
        self.assertEqual(
            encoded_images.shape     ,       (
                1,
                self.expected_encoded_image_num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )     ,       )

        # Test batched
        lowerCAmelCase__	      =   image_processor(snake_case__     ,       return_tensors='pt').pixel_values
        self.assertEqual(
            encoded_images.shape     ,       (
                self.image_processor_tester.batch_size,
                self.expected_encoded_image_num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )     ,       )




'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


_lowerCAmelCase	:		Dict		  =						{}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCAmelCase	:		Union[str, Any]		  =						['BarthezTokenizer']

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCAmelCase	:		Union[str, Any]		  =						['BarthezTokenizerFast']


if TYPE_CHECKING:
				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_barthez import BarthezTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_barthez_fast import BarthezTokenizerFast

else:
				import sys

				_lowerCAmelCase	:		List[str]		  =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
