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, prepare_image_inputs


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import ViTImageProcessor

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=None							, _A=True							, _A=[0.5, 0.5, 0.5]							, _A=[0.5, 0.5, 0.5]							, ):






				'''simple docstring'''







				UpperCAmelCase       		=    size if size is not None else {"height": 1_8, "width": 1_8}
				UpperCAmelCase       		=    parent
				UpperCAmelCase       		=    batch_size
				UpperCAmelCase       		=    num_channels
				UpperCAmelCase       		=    image_size
				UpperCAmelCase       		=    min_resolution
				UpperCAmelCase       		=    max_resolution
				UpperCAmelCase       		=    do_resize
				UpperCAmelCase       		=    size
				UpperCAmelCase       		=    do_normalize
				UpperCAmelCase       		=    image_mean
				UpperCAmelCase       		=    image_std







	def 					_lowercase    (   self		):






				'''simple docstring'''







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

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






				'''simple docstring'''







				UpperCAmelCase       		=    ViTImageProcessingTester(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__							, '''image_mean'''		)		)
				self.assertTrue(hasattr(UpperCamelCase__							, '''image_std'''		)		)
				self.assertTrue(hasattr(UpperCamelCase__							, '''do_normalize'''		)		)
				self.assertTrue(hasattr(UpperCamelCase__							, '''do_resize'''		)		)
				self.assertTrue(hasattr(UpperCamelCase__							, '''size'''		)		)
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    self.image_processing_class.from_dict(self.image_processor_dict		)
				self.assertEqual(image_processor.size							, {'''height''': 1_8, '''width''': 1_8}		)

				UpperCAmelCase       		=    self.image_processing_class.from_dict(self.image_processor_dict							, size=4_2		)
				self.assertEqual(image_processor.size							, {'''height''': 4_2, '''width''': 4_2}		)
	def 					_lowercase    (   self		):






				'''simple docstring'''







				pass
	def 					_lowercase    (   self		):






				'''simple docstring'''







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

				# Test not batched input
				UpperCAmelCase       		=    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
				UpperCAmelCase       		=    image_processing(UpperCamelCase__							, 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 					_lowercase    (   self		):






				'''simple docstring'''







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

				# Test not batched input
				UpperCAmelCase       		=    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
				UpperCAmelCase       		=    image_processing(UpperCamelCase__							, 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 					_lowercase    (   self		):






				'''simple docstring'''







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

				# Test not batched input
				UpperCAmelCase       		=    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
				UpperCAmelCase       		=    image_processing(UpperCamelCase__							, 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'''],
				    )							, )










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__)



