"""simple docstring"""





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 LevitImageProcessor
class 			_snake_case  (       unittest.TestCase		):






						def __init__(							self   :      str				,       UpperCAmelCase   :      str				,       UpperCAmelCase   :      Tuple=7				,       UpperCAmelCase   :      Optional[int]=3				,       UpperCAmelCase   :      Optional[int]=18				,       UpperCAmelCase   :      Any=30				,       UpperCAmelCase   :      Any=400				,       UpperCAmelCase   :      int=True				,       UpperCAmelCase   :      Union[str, Any]=None				,       UpperCAmelCase   :      Optional[int]=True				,       UpperCAmelCase   :      Optional[int]=None				,       UpperCAmelCase   :      List[str]=True				,       UpperCAmelCase   :      Optional[Any]=[0.5, 0.5, 0.5]				,       UpperCAmelCase   :      List[Any]=[0.5, 0.5, 0.5]				,       ):
													__lowerCamelCase       :		str		 =  size if size is not None else {"shortest_edge": 18}
													__lowerCamelCase       :		Union[str, Any]		 =  crop_size if crop_size is not None else {"height": 18, "width": 18}
													__lowerCamelCase       :		List[str]		 =  parent
													__lowerCamelCase       :		List[Any]		 =  batch_size
													__lowerCamelCase       :		Optional[Any]		 =  num_channels
													__lowerCamelCase       :		int		 =  image_size
													__lowerCamelCase       :		Optional[Any]		 =  min_resolution
													__lowerCamelCase       :		str		 =  max_resolution
													__lowerCamelCase       :		Tuple		 =  do_resize
													__lowerCamelCase       :		Optional[Any]		 =  size
													__lowerCamelCase       :		Any		 =  do_center_crop
													__lowerCamelCase       :		int		 =  crop_size
													__lowerCamelCase       :		List[str]		 =  do_normalize
													__lowerCamelCase       :		Any		 =  image_mean
													__lowerCamelCase       :		List[Any]		 =  image_std







						def    lowerCamelCase__  (							self   :      List[Any]     ):
													return {
													    "image_mean": self.image_mean,
													    "image_std": self.image_std,
													    "do_normalize": self.do_normalize,
													    "do_resize": self.do_resize,
													    "do_center_crop": self.do_center_crop,
													    "size": self.size,
													    "crop_size": self.crop_size,
													}

@require_torch
@require_vision
class 			_snake_case  (       __A					,			unittest.TestCase		):
						snake_case__      	=      LevitImageProcessor if is_vision_available() else None






						def    lowerCamelCase__  (							self   :      List[str]     ):
													__lowerCamelCase       :		int		 =  LevitImageProcessingTester(self     )






						@property
						def    lowerCamelCase__  (							self   :      int     ):
													return self.image_processor_tester.prepare_image_processor_dict()






						def    lowerCamelCase__  (							self   :      str     ):
													__lowerCamelCase       :		Dict		 =  self.image_processing_class(**self.image_processor_dict     )
													self.assertTrue(hasattr(lowerCAmelCase_				,       "image_mean"     )     )
													self.assertTrue(hasattr(lowerCAmelCase_				,       "image_std"     )     )
													self.assertTrue(hasattr(lowerCAmelCase_				,       "do_normalize"     )     )
													self.assertTrue(hasattr(lowerCAmelCase_				,       "do_resize"     )     )
													self.assertTrue(hasattr(lowerCAmelCase_				,       "do_center_crop"     )     )
													self.assertTrue(hasattr(lowerCAmelCase_				,       "size"     )     )






						def    lowerCamelCase__  (							self   :      List[Any]     ):
													__lowerCamelCase       :		str		 =  self.image_processing_class.from_dict(self.image_processor_dict     )
													self.assertEqual(image_processor.size				,       {"shortest_edge": 18}     )
													self.assertEqual(image_processor.crop_size				,       {"height": 18, "width": 18}     )

													__lowerCamelCase       :		List[Any]		 =  self.image_processing_class.from_dict(self.image_processor_dict				,       size=42				,       crop_size=84     )
													self.assertEqual(image_processor.size				,       {"shortest_edge": 42}     )
													self.assertEqual(image_processor.crop_size				,       {"height": 84, "width": 84}     )






						def    lowerCamelCase__  (							self   :      Optional[int]     ):
													pass






						def    lowerCamelCase__  (							self   :      Union[str, Any]     ):
													# Initialize image_processing
													__lowerCamelCase       :		Union[str, Any]		 =  self.image_processing_class(**self.image_processor_dict     )
													# create random PIL images
													__lowerCamelCase       :		Any		 =  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       :		Optional[int]		 =  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.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )				,       )

													# Test batched
													__lowerCamelCase       :		Optional[int]		 =  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.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )				,       )






						def    lowerCamelCase__  (							self   :      Any     ):
													# Initialize image_processing
													__lowerCamelCase       :		List[str]		 =  self.image_processing_class(**self.image_processor_dict     )
													# create random numpy tensors
													__lowerCamelCase       :		Tuple		 =  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
													__lowerCamelCase       :		List[str]		 =  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.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )				,       )

													# Test batched
													__lowerCamelCase       :		List[str]		 =  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.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )				,       )







						def    lowerCamelCase__  (							self   :      Union[str, Any]     ):
													# Initialize image_processing
													__lowerCamelCase       :		int		 =  self.image_processing_class(**self.image_processor_dict     )
													# create random PyTorch tensors
													__lowerCamelCase       :		Tuple		 =  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       :		int		 =  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.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )				,       )

													# Test batched
													__lowerCamelCase       :		int		 =  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.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )				,       )







"""simple docstring"""
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig


lowerCamelCase_							      = logging.get_logger(__name__)

lowerCamelCase_							      = '''T5Config'''







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''mt5'''
	__magic_name__       =							MTaConfig







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''mt5'''
	__magic_name__       =							MTaConfig







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''mt5'''
	__magic_name__       =							MTaConfig



