

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 DeiTImageProcessor






class    __A				(							unittest.TestCase ):






    def __init__(	self			:    List[Any]    ,				UpperCAmelCase_			:    Any    ,				UpperCAmelCase_			:    List[Any]=7    ,				UpperCAmelCase_			:    List[str]=3    ,				UpperCAmelCase_			:    int=18    ,				UpperCAmelCase_			:    str=30    ,				UpperCAmelCase_			:    Any=400    ,				UpperCAmelCase_			:    int=True    ,				UpperCAmelCase_			:    List[str]=None    ,				UpperCAmelCase_			:    Tuple=True    ,				UpperCAmelCase_			:    List[str]=None    ,				UpperCAmelCase_			:    List[Any]=True    ,				UpperCAmelCase_			:    List[str]=[0.5, 0.5, 0.5]    ,				UpperCAmelCase_			:    List[str]=[0.5, 0.5, 0.5]    ,				):
        lowerCAmelCase				:			Optional[Any]    			=      size if size is not None else {"""height""": 20, """width""": 20}
        lowerCAmelCase				:			Optional[Any]    			=      crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

        lowerCAmelCase				:			str    			=      parent
        lowerCAmelCase				:			Union[str, Any]    			=      batch_size
        lowerCAmelCase				:			Any    			=      num_channels
        lowerCAmelCase				:			List[Any]    			=      image_size
        lowerCAmelCase				:			Union[str, Any]    			=      min_resolution
        lowerCAmelCase				:			Union[str, Any]    			=      max_resolution
        lowerCAmelCase				:			Optional[int]    			=      do_resize
        lowerCAmelCase				:			int    			=      size
        lowerCAmelCase				:			List[Any]    			=      do_center_crop
        lowerCAmelCase				:			Any    			=      crop_size
        lowerCAmelCase				:			List[Any]    			=      do_normalize
        lowerCAmelCase				:			Dict    			=      image_mean
        lowerCAmelCase				:			str    			=      image_std






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






@require_torch
@require_vision
class    __A				(							__lowerCamelCase						,						unittest.TestCase ):
    lowerCAmelCase_							:		int						=							DeiTImageProcessor if is_vision_available() else None
    lowerCAmelCase_							:		Dict						=							True






    def 	lowercase__ (	self			:    List[str]    ):
        lowerCAmelCase				:			Union[str, Any]    			=      DeiTImageProcessingTester(self    )






    @property
    def 	lowercase__ (	self			:    str    ):
        return self.image_processor_tester.prepare_image_processor_dict()






    def 	lowercase__ (	self			:    List[Any]    ):
        lowerCAmelCase				:			str    			=      self.image_processing_class(**self.image_processor_dict    )
        self.assertTrue(hasattr(_lowerCAmelCase    ,				'do_resize'    )    )
        self.assertTrue(hasattr(_lowerCAmelCase    ,				'size'    )    )
        self.assertTrue(hasattr(_lowerCAmelCase    ,				'do_center_crop'    )    )
        self.assertTrue(hasattr(_lowerCAmelCase    ,				'center_crop'    )    )
        self.assertTrue(hasattr(_lowerCAmelCase    ,				'do_normalize'    )    )
        self.assertTrue(hasattr(_lowerCAmelCase    ,				'image_mean'    )    )
        self.assertTrue(hasattr(_lowerCAmelCase    ,				'image_std'    )    )






    def 	lowercase__ (	self			:    str    ):
        lowerCAmelCase				:			Optional[int]    			=      self.image_processing_class.from_dict(self.image_processor_dict    )
        self.assertEqual(image_processor.size    ,				{'height': 20, 'width': 20}    )
        self.assertEqual(image_processor.crop_size    ,				{'height': 18, 'width': 18}    )

        lowerCAmelCase				:			Tuple    			=      self.image_processing_class.from_dict(self.image_processor_dict    ,				size=42    ,				crop_size=84    )
        self.assertEqual(image_processor.size    ,				{'height': 42, 'width': 42}    )
        self.assertEqual(image_processor.crop_size    ,				{'height': 84, 'width': 84}    )






    def 	lowercase__ (	self			:    Dict    ):
        pass






    def 	lowercase__ (	self			:    int    ):
        # Initialize image_processing
        lowerCAmelCase				:			Tuple    			=      self.image_processing_class(**self.image_processor_dict    )
        # create random PIL images
        lowerCAmelCase				:			Optional[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				:			Union[str, Any]    			=      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[Any]    			=      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 	lowercase__ (	self			:    Optional[int]    ):
        # Initialize image_processing
        lowerCAmelCase				:			Dict    			=      self.image_processing_class(**self.image_processor_dict    )
        # create random numpy tensors
        lowerCAmelCase				:			Dict    			=      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				:			Optional[Any]    			=      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 	lowercase__ (	self			:    Optional[int]    ):
        # Initialize image_processing
        lowerCAmelCase				:			Optional[int]    			=      self.image_processing_class(**self.image_processor_dict    )
        # create random PyTorch tensors
        lowerCAmelCase				:			Optional[int]    			=      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				:			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				:			Dict    			=      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'],
            )    ,				)








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A   :     List[Any]      =			{'''configuration_umt5''': ['''UMT5Config''', '''UMT5OnnxConfig''']}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A   :     Tuple      =			[
     '''UMT5EncoderModel''',
     '''UMT5ForConditionalGeneration''',
     '''UMT5ForQuestionAnswering''',
     '''UMT5Model''',
     '''UMT5PreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_umta import UMTaConfig, UMTaOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_umta import (
      UMTaEncoderModel,
      UMTaForConditionalGeneration,
      UMTaForQuestionAnswering,
      UMTaModel,
      UMTaPreTrainedModel,
  )
else:
 import sys

 __A   :     Any      =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





