





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 ViltImageProcessor




class 			_lowerCamelCase	(      unittest.TestCase      ):



      """simple docstring"""






      def __init__(	self			,  UpperCAmelCase			,  UpperCAmelCase=7			,  UpperCAmelCase=3			,  UpperCAmelCase=18			,  UpperCAmelCase=30			,  UpperCAmelCase=400			,  UpperCAmelCase=True			,  UpperCAmelCase=None			,  UpperCAmelCase=2			,  UpperCAmelCase=True			,  UpperCAmelCase=[0.5, 0.5, 0.5]			,  UpperCAmelCase=[0.5, 0.5, 0.5]			,  )    ->				List[Any]:






        '''simple docstring'''






        __snake_case				:			Any            =    size if size is not None else {'shortest_edge': 30}
        __snake_case				:			Tuple            =    parent
        __snake_case				:			Union[str, Any]            =    batch_size
        __snake_case				:			str            =    num_channels
        __snake_case				:			List[Any]            =    image_size
        __snake_case				:			Tuple            =    min_resolution
        __snake_case				:			Optional[int]            =    max_resolution
        __snake_case				:			Optional[Any]            =    do_resize
        __snake_case				:			List[Any]            =    size
        __snake_case				:			Any            =    size_divisor
        __snake_case				:			Dict            =    do_normalize
        __snake_case				:			Dict            =    image_mean
        __snake_case				:			List[Any]            =    image_std







      def   UpperCAmelCase						(	self				)    ->				Optional[Any]:






        '''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,
            "size_divisor": self.size_divisor,
        }







      def   UpperCAmelCase						(	self			,  UpperCAmelCase			,  UpperCAmelCase=False				)    ->				Any:






        '''simple docstring'''






        if not batched:
          __snake_case				:			List[str]            =    self.size['shortest_edge']
          __snake_case				:			int            =    image_inputs[0]
          if isinstance(_A			,  Image.Image				):
            __snake_case				:			List[Any]            =    image.size
          else:
            __snake_case				:			Dict            =    image.shape[1], image.shape[2]
          __snake_case				:			Dict            =    size / min(_A			,  _A				)
          if h < w:
            __snake_case				:			str            =    size, scale * w
          else:
            __snake_case				:			str            =    scale * h, size

          __snake_case				:			Tuple            =    int((1333 / 800) * size				)
          if max(_A			,  _A				) > max_size:
            __snake_case				:			Optional[int]            =    max_size / max(_A			,  _A				)
            __snake_case				:			Any            =    newh * scale
            __snake_case				:			List[Any]            =    neww * scale

          __snake_case				:			Dict            =    int(newh + 0.5				), int(neww + 0.5				)
          __snake_case				:			Any            =    (
              newh // self.size_divisor * self.size_divisor,
              neww // self.size_divisor * self.size_divisor,
          )

        else:
          __snake_case				:			Optional[Any]            =    []
          for image in image_inputs:
            __snake_case				:			Tuple            =    self.get_expected_values([image]				)
            expected_values.append((expected_height, expected_width)				)
          __snake_case				:			List[Any]            =    max(_A			,  key=lambda UpperCAmelCase				: item[0]				)[0]
          __snake_case				:			Dict            =    max(_A			,  key=lambda UpperCAmelCase				: item[1]				)[1]

        return expected_height, expected_width




@require_torch
@require_vision
class 			_lowerCamelCase	(      snake_case__	,				unittest.TestCase      ):



      """simple docstring"""






      UpperCAmelCase_  : str				      =ViltImageProcessor if is_vision_available() else None







      def   UpperCAmelCase						(	self				)    ->				str:






        '''simple docstring'''






        __snake_case				:			Union[str, Any]            =    ViltImageProcessingTester(self				)







      @property
      def   UpperCAmelCase						(	self				)    ->				Union[str, Any]:






        '''simple docstring'''






        return self.image_processor_tester.prepare_image_processor_dict()







      def   UpperCAmelCase						(	self				)    ->				List[str]:






        '''simple docstring'''






        __snake_case				:			Optional[int]            =    self.image_processing_class(**self.image_processor_dict				)
        self.assertTrue(hasattr(_A			,  "image_mean"				)				)
        self.assertTrue(hasattr(_A			,  "image_std"				)				)
        self.assertTrue(hasattr(_A			,  "do_normalize"				)				)
        self.assertTrue(hasattr(_A			,  "do_resize"				)				)
        self.assertTrue(hasattr(_A			,  "size"				)				)
        self.assertTrue(hasattr(_A			,  "size_divisor"				)				)







      def   UpperCAmelCase						(	self				)    ->				Dict:






        '''simple docstring'''






        __snake_case				:			Tuple            =    self.image_processing_class.from_dict(self.image_processor_dict				)
        self.assertEqual(image_processor.size			,  {"shortest_edge": 30}				)

        __snake_case				:			Optional[Any]            =    self.image_processing_class.from_dict(self.image_processor_dict			,  size=42				)
        self.assertEqual(image_processor.size			,  {"shortest_edge": 42}				)







      def   UpperCAmelCase						(	self				)    ->				List[Any]:






        '''simple docstring'''






        pass







      def   UpperCAmelCase						(	self				)    ->				List[Any]:






        '''simple docstring'''






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

        # Test not batched input
        __snake_case				:			str            =    image_processing(image_inputs[0]			,  return_tensors="pt"				).pixel_values

        __snake_case				:			Tuple            =    self.image_processor_tester.get_expected_values(_A				)
        self.assertEqual(
            encoded_images.shape			,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,  )

        # Test batched
        __snake_case				:			Any            =    image_processing(_A			,  return_tensors="pt"				).pixel_values

        __snake_case				:			Optional[Any]            =    self.image_processor_tester.get_expected_values(_A			,  batched=_A				)
        self.assertEqual(
            encoded_images.shape			,  (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )			,  )







      def   UpperCAmelCase						(	self				)    ->				Any:






        '''simple docstring'''






        __snake_case				:			List[str]            =    self.image_processing_class(**self.image_processor_dict				)
        # create random numpy tensors
        __snake_case				:			int            =    prepare_image_inputs(self.image_processor_tester			,  equal_resolution=_A			,  numpify=_A				)
        for image in image_inputs:
          self.assertIsInstance(_A			,  np.ndarray				)

        # Test not batched input
        __snake_case				:			int            =    image_processing(image_inputs[0]			,  return_tensors="pt"				).pixel_values

        __snake_case				:			Any            =    self.image_processor_tester.get_expected_values(_A				)
        self.assertEqual(
            encoded_images.shape			,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,  )

        # Test batched
        __snake_case				:			List[Any]            =    image_processing(_A			,  return_tensors="pt"				).pixel_values

        __snake_case				:			int            =    self.image_processor_tester.get_expected_values(_A			,  batched=_A				)
        self.assertEqual(
            encoded_images.shape			,  (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )			,  )







      def   UpperCAmelCase						(	self				)    ->				Dict:






        '''simple docstring'''






        __snake_case				:			Optional[int]            =    self.image_processing_class(**self.image_processor_dict				)
        # create random PyTorch tensors
        __snake_case				:			Dict            =    prepare_image_inputs(self.image_processor_tester			,  equal_resolution=_A			,  torchify=_A				)
        for image in image_inputs:
          self.assertIsInstance(_A			,  torch.Tensor				)

        # Test not batched input
        __snake_case				:			str            =    image_processing(image_inputs[0]			,  return_tensors="pt"				).pixel_values

        __snake_case				:			int            =    self.image_processor_tester.get_expected_values(_A				)
        self.assertEqual(
            encoded_images.shape			,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,  )

        # Test batched
        __snake_case				:			List[Any]            =    image_processing(_A			,  return_tensors="pt"				).pixel_values

        __snake_case				:			Optional[int]            =    self.image_processor_tester.get_expected_values(_A			,  batched=_A				)
        self.assertEqual(
            encoded_images.shape			,  (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )			,  )








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_UpperCamelCase    =	{
    '''configuration_dpr''': ['''DPR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPRConfig'''],
    '''tokenization_dpr''': [
        '''DPRContextEncoderTokenizer''',
        '''DPRQuestionEncoderTokenizer''',
        '''DPRReaderOutput''',
        '''DPRReaderTokenizer''',
    ],
}


try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase    =	[
         '''DPRContextEncoderTokenizerFast''',
         '''DPRQuestionEncoderTokenizerFast''',
         '''DPRReaderTokenizerFast''',
     ]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase    =	[
         '''DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''DPRContextEncoder''',
         '''DPRPretrainedContextEncoder''',
         '''DPRPreTrainedModel''',
         '''DPRPretrainedQuestionEncoder''',
         '''DPRPretrainedReader''',
         '''DPRQuestionEncoder''',
         '''DPRReader''',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase    =	[
         '''TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''TFDPRContextEncoder''',
         '''TFDPRPretrainedContextEncoder''',
         '''TFDPRPretrainedQuestionEncoder''',
         '''TFDPRPretrainedReader''',
         '''TFDPRQuestionEncoder''',
         '''TFDPRReader''',
     ]


if TYPE_CHECKING:
     from .configuration_dpr import DPR_PRETRAINED_CONFIG_ARCHIVE_MAP, DPRConfig
     from .tokenization_dpr import (
         DPRContextEncoderTokenizer,
         DPRQuestionEncoderTokenizer,
         DPRReaderOutput,
         DPRReaderTokenizer,
     )

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_dpr_fast import (
              DPRContextEncoderTokenizerFast,
              DPRQuestionEncoderTokenizerFast,
              DPRReaderTokenizerFast,
          )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_dpr import (
              DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
              DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
              DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
              DPRContextEncoder,
              DPRPretrainedContextEncoder,
              DPRPreTrainedModel,
              DPRPretrainedQuestionEncoder,
              DPRPretrainedReader,
              DPRQuestionEncoder,
              DPRReader,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_dpr import (
              TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
              TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
              TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFDPRContextEncoder,
              TFDPRPretrainedContextEncoder,
              TFDPRPretrainedQuestionEncoder,
              TFDPRPretrainedReader,
              TFDPRQuestionEncoder,
              TFDPRReader,
          )

else:
     import sys

     _UpperCamelCase    =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

