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 SwinaSRImageProcessor
				from transformers.image_transforms import get_image_size

class      A_	(unittest.TestCase      ):
	def __init__(   self							, _A							, _A=7							, _A=3							, _A=1_8							, _A=3_0							, _A=4_0_0							, _A=True							, _A=1 / 2_5_5							, _A=True							, _A=8							, ):






				'''simple docstring'''







				UpperCAmelCase       		=    parent
				UpperCAmelCase       		=    batch_size
				UpperCAmelCase       		=    num_channels
				UpperCAmelCase       		=    image_size
				UpperCAmelCase       		=    min_resolution
				UpperCAmelCase       		=    max_resolution
				UpperCAmelCase       		=    do_rescale
				UpperCAmelCase       		=    rescale_factor
				UpperCAmelCase       		=    do_pad
				UpperCAmelCase       		=    pad_size
	def 					_lowercase    (   self		):






				'''simple docstring'''







				return {
				    "do_rescale": self.do_rescale,
				    "rescale_factor": self.rescale_factor,
				    "do_pad": self.do_pad,
				    "pad_size": self.pad_size,
				}







	def 					_lowercase    (   self							, _A=False							, _A=False							, _A=False		):






				'''simple docstring'''







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

				if equal_resolution:
							UpperCAmelCase       		=    []
							for i in range(self.batch_size		):
										image_inputs.append(
										    np.random.randint(
										        2_5_5							, size=(self.num_channels, self.max_resolution, self.max_resolution)							, dtype=np.uinta		)		)
				else:
							UpperCAmelCase       		=    []
							for i in range(self.batch_size		):
										UpperCAmelCase		,      UpperCAmelCase       		=    np.random.choice(np.arange(self.min_resolution							, self.max_resolution		)							, 2		)
										image_inputs.append(np.random.randint(2_5_5							, size=(self.num_channels, width, height)							, dtype=np.uinta		)		)

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

				if torchify:
							UpperCAmelCase       		=    [torch.from_numpy(UpperCamelCase_		) for x in image_inputs]

				return image_inputs

@require_torch
@require_vision
class      A_	(lowercase_					,							unittest.TestCase      ):
	UpperCAmelCase__ =      SwinaSRImageProcessor if is_vision_available() else None
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    SwinaSRImageProcessingTester(self		)
	@property
	def 					_lowercase    (   self		):






				'''simple docstring'''







				return self.image_processor_tester.prepare_image_processor_dict()
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    self.image_processing_class(**self.image_processor_dict		)
				self.assertTrue(hasattr(UpperCamelCase_							, '''do_rescale'''		)		)
				self.assertTrue(hasattr(UpperCamelCase_							, '''rescale_factor'''		)		)
				self.assertTrue(hasattr(UpperCamelCase_							, '''do_pad'''		)		)
				self.assertTrue(hasattr(UpperCamelCase_							, '''pad_size'''		)		)
	def 					_lowercase    (   self		):






				'''simple docstring'''







				pass
	def 					_lowercase    (   self							, _A		):






				'''simple docstring'''







				UpperCAmelCase		,      UpperCAmelCase       		=    get_image_size(UpperCamelCase_		)
				UpperCAmelCase       		=    self.image_processor_tester.pad_size

				UpperCAmelCase       		=    (old_height // size + 1) * size - old_height
				UpperCAmelCase       		=    (old_width // size + 1) * size - old_width
				return old_height + pad_height, old_width + pad_width
	def 					_lowercase    (   self		):






				'''simple docstring'''







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

				# Test not batched input
				UpperCAmelCase       		=    image_processor(image_inputs[0]							, return_tensors='''pt'''		).pixel_values
				UpperCAmelCase		,      UpperCAmelCase       		=    self.calculate_expected_size(np.array(image_inputs[0]		)		)
				self.assertEqual(
				    encoded_images.shape							, (
				        1,
				        self.image_processor_tester.num_channels,
				        expected_height,
				        expected_width,
				    )							, )
	def 					_lowercase    (   self		):






				'''simple docstring'''







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

				# Test not batched input
				UpperCAmelCase       		=    image_processor(image_inputs[0]							, return_tensors='''pt'''		).pixel_values
				UpperCAmelCase		,      UpperCAmelCase       		=    self.calculate_expected_size(image_inputs[0]		)
				self.assertEqual(
				    encoded_images.shape							, (
				        1,
				        self.image_processor_tester.num_channels,
				        expected_height,
				        expected_width,
				    )							, )







	def 					_lowercase    (   self		):






				'''simple docstring'''







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

				# Test not batched input
				UpperCAmelCase       		=    image_processor(image_inputs[0]							, return_tensors='''pt'''		).pixel_values
				UpperCAmelCase		,      UpperCAmelCase       		=    self.calculate_expected_size(image_inputs[0]		)
				self.assertEqual(
				    encoded_images.shape							, (
				        1,
				        self.image_processor_tester.num_channels,
				        expected_height,
				        expected_width,
				    )							, )










from typing import TYPE_CHECKING

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


__A    :   Any          =       {}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A    :   Union[str, Any]          =       ["CpmTokenizer"]

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A    :   List[Any]          =       ["CpmTokenizerFast"]


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

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_cpm_fast import CpmTokenizerFast

else:
				import sys

				__A    :   str          =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



