



"""simple docstring"""




import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor






class      UpperCAmelCase_  (    unittest.TestCase						):



      def __init__(					self					:     List[Any]					,     A					:     Optional[int]					,     A					:     bool = True					,     A					:     Dict[str, int] = None					,     A					:     int = 3_2					,     A					:     bool = True					,     A					:     Union[int, float] = 1 / 2_5_5					,     A					:     bool = True					,     A					:     bool = True					,     A					:     Optional[Union[float, List[float]]] = [0.48_145_466, 0.4_578_275, 0.40_821_073]					,     A					:     Optional[Union[float, List[float]]] = [0.26_862_954, 0.26_130_258, 0.27_577_711]					,     A					:     bool = True					,     A					:     Optional[Any]=7					,     A					:     str=3_0					,     A					:     List[str]=4_0_0					,     A					:     int=3					,     ):
           _UpperCAmelCase      :     int        =   parent
           _UpperCAmelCase      :     Dict        =   do_resize
           _UpperCAmelCase      :     List[str]        =   size if size is not None else {"shortest_edge": 2_8_8}
           _UpperCAmelCase      :     int        =   size_divisor
           _UpperCAmelCase      :     List[Any]        =   do_rescale
           _UpperCAmelCase      :     Any        =   rescale_factor
           _UpperCAmelCase      :     Tuple        =   do_normalize
           _UpperCAmelCase      :     str        =   do_center_crop
           _UpperCAmelCase      :     List[str]        =   image_mean
           _UpperCAmelCase      :     List[Any]        =   image_std
           _UpperCAmelCase      :     List[str]        =   do_pad
           _UpperCAmelCase      :     List[Any]        =   batch_size
           _UpperCAmelCase      :     Union[str, Any]        =   num_channels
           _UpperCAmelCase      :     Optional[int]        =   min_resolution
           _UpperCAmelCase      :     Union[str, Any]        =   max_resolution



      def 						snake_case_ (					self					:     int						):
           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 						snake_case_ (					self					:     Tuple					,     A					:     Tuple					,     A					:     List[Any]=False						):
           if not batched:
                _UpperCAmelCase      :     List[str]        =   self.size["shortest_edge"]
                _UpperCAmelCase      :     Optional[int]        =   image_inputs[0]
                if isinstance(A					,     Image.Image						):
                     _UpperCAmelCase    ,	_UpperCAmelCase      :     List[Any]        =   image.size
                else:
                     _UpperCAmelCase    ,	_UpperCAmelCase      :     Tuple        =   image.shape[1], image.shape[2]
                _UpperCAmelCase      :     int        =   size / min(A					,     A						)
                if h < w:
                     _UpperCAmelCase    ,	_UpperCAmelCase      :     str        =   size, scale * w
                else:
                     _UpperCAmelCase    ,	_UpperCAmelCase      :     int        =   scale * h, size

                _UpperCAmelCase      :     List[Any]        =   int((1_3_3_3 / 8_0_0) * size						)
                if max(A					,     A						) > max_size:
                     _UpperCAmelCase      :     Optional[Any]        =   max_size / max(A					,     A						)
                     _UpperCAmelCase      :     Optional[Any]        =   newh * scale
                     _UpperCAmelCase      :     Tuple        =   neww * scale

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

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

           return expected_height, expected_width







@require_torch
@require_vision
class      UpperCAmelCase_  (    _UpperCamelCase	,      unittest.TestCase						):
      __SCREAMING_SNAKE_CASE						:       Union[str, Any]								=			BridgeTowerImageProcessor if is_vision_available() else None



      def 						snake_case_ (					self					:     Optional[Any]						):
           _UpperCAmelCase      :     Tuple        =   BridgeTowerImageProcessingTester(self						)



      @property
      def 						snake_case_ (					self					:     Tuple						):
           return self.image_processor_tester.prepare_image_processor_dict()



      def 						snake_case_ (					self					:     int						):
           _UpperCAmelCase      :     Tuple        =   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 						snake_case_ (					self					:     List[Any]						):
           pass



      def 						snake_case_ (					self					:     List[Any]						):
           # Initialize image processor
           _UpperCAmelCase      :     str        =   self.image_processing_class(**self.image_processor_dict						)
           # create random PIL images
           _UpperCAmelCase      :     List[str]        =   prepare_image_inputs(self.image_processor_tester					,     equal_resolution=A						)
           for image in image_inputs:
                self.assertIsInstance(A					,     Image.Image						)

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

           _UpperCAmelCase    ,	_UpperCAmelCase      :     str        =   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
           _UpperCAmelCase      :     Optional[Any]        =   image_processing(A					,     return_tensors="pt"						).pixel_values

           _UpperCAmelCase    ,	_UpperCAmelCase      :     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 						snake_case_ (					self					:     Dict						):
           # Initialize image processor
           _UpperCAmelCase      :     Optional[Any]        =   self.image_processing_class(**self.image_processor_dict						)
           # create random numpy tensors
           _UpperCAmelCase      :     Union[str, Any]        =   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
           _UpperCAmelCase      :     Dict        =   image_processing(image_inputs[0]					,     return_tensors="pt"						).pixel_values

           _UpperCAmelCase    ,	_UpperCAmelCase      :     List[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
           _UpperCAmelCase      :     List[Any]        =   image_processing(A					,     return_tensors="pt"						).pixel_values

           _UpperCAmelCase    ,	_UpperCAmelCase      :     str        =   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 						snake_case_ (					self					:     Dict						):
           # Initialize image processor
           _UpperCAmelCase      :     str        =   self.image_processing_class(**self.image_processor_dict						)
           # create random PyTorch tensors
           _UpperCAmelCase      :     Optional[int]        =   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
           _UpperCAmelCase      :     Union[str, Any]        =   image_processing(image_inputs[0]					,     return_tensors="pt"						).pixel_values

           _UpperCAmelCase    ,	_UpperCAmelCase      :     Union[str, 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
           _UpperCAmelCase      :     int        =   image_processing(A					,     return_tensors="pt"						).pixel_values

           _UpperCAmelCase    ,	_UpperCAmelCase      :     Dict        =   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,
               )					,     )





"""simple docstring"""




from typing import TYPE_CHECKING

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


_lowerCAmelCase    : Union[str, Any]							=							{
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase    : Any							=							["RoFormerTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase    : Any							=							[
     "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "RoFormerForCausalLM",
     "RoFormerForMaskedLM",
     "RoFormerForMultipleChoice",
     "RoFormerForQuestionAnswering",
     "RoFormerForSequenceClassification",
     "RoFormerForTokenClassification",
     "RoFormerLayer",
     "RoFormerModel",
     "RoFormerPreTrainedModel",
     "load_tf_weights_in_roformer",
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase    : Optional[int]							=							[
     "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFRoFormerForCausalLM",
     "TFRoFormerForMaskedLM",
     "TFRoFormerForMultipleChoice",
     "TFRoFormerForQuestionAnswering",
     "TFRoFormerForSequenceClassification",
     "TFRoFormerForTokenClassification",
     "TFRoFormerLayer",
     "TFRoFormerModel",
     "TFRoFormerPreTrainedModel",
 ]


try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase    : List[str]							=							[
     "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "FlaxRoFormerForMaskedLM",
     "FlaxRoFormerForMultipleChoice",
     "FlaxRoFormerForQuestionAnswering",
     "FlaxRoFormerForSequenceClassification",
     "FlaxRoFormerForTokenClassification",
     "FlaxRoFormerModel",
     "FlaxRoFormerPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
 from .tokenization_roformer import RoFormerTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_roformer_fast import RoFormerTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_roformer import (
      ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      RoFormerForCausalLM,
      RoFormerForMaskedLM,
      RoFormerForMultipleChoice,
      RoFormerForQuestionAnswering,
      RoFormerForSequenceClassification,
      RoFormerForTokenClassification,
      RoFormerLayer,
      RoFormerModel,
      RoFormerPreTrainedModel,
      load_tf_weights_in_roformer,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_roformer import (
      TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFRoFormerForCausalLM,
      TFRoFormerForMaskedLM,
      TFRoFormerForMultipleChoice,
      TFRoFormerForQuestionAnswering,
      TFRoFormerForSequenceClassification,
      TFRoFormerForTokenClassification,
      TFRoFormerLayer,
      TFRoFormerModel,
      TFRoFormerPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_roformer import (
      FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      FlaxRoFormerForMaskedLM,
      FlaxRoFormerForMultipleChoice,
      FlaxRoFormerForQuestionAnswering,
      FlaxRoFormerForSequenceClassification,
      FlaxRoFormerForTokenClassification,
      FlaxRoFormerModel,
      FlaxRoFormerPreTrainedModel,
  )


else:
 import sys

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