






from typing import TYPE_CHECKING

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


snake_case			 =						{
    """configuration_segformer""": ["""SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SegformerConfig""", """SegformerOnnxConfig"""]
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case			 =						["""SegformerFeatureExtractor"""]
   snake_case			 =						["""SegformerImageProcessor"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case			 =						[
       """SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """SegformerDecodeHead""",
       """SegformerForImageClassification""",
       """SegformerForSemanticSegmentation""",
       """SegformerLayer""",
       """SegformerModel""",
       """SegformerPreTrainedModel""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case			 =						[
       """TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TFSegformerDecodeHead""",
       """TFSegformerForImageClassification""",
       """TFSegformerForSemanticSegmentation""",
       """TFSegformerModel""",
       """TFSegformerPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_segformer import SegformerFeatureExtractor
      from .image_processing_segformer import SegformerImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_segformer import (
          SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          SegformerDecodeHead,
          SegformerForImageClassification,
          SegformerForSemanticSegmentation,
          SegformerLayer,
          SegformerModel,
          SegformerPreTrainedModel,
      )
   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_segformer import (
          TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFSegformerDecodeHead,
          TFSegformerForImageClassification,
          TFSegformerForSemanticSegmentation,
          TFSegformerModel,
          TFSegformerPreTrainedModel,
      )

else:
   import sys

   snake_case			 =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)














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 ConvNextImageProcessor


class        SCREAMING_SNAKE_CASE	(	unittest.TestCase    ):
     '''simple docstring'''


     def __init__( self				:		Optional[Any]    ,       UpperCAmelCase_				:		Optional[int]    ,       UpperCAmelCase_				:		Any=7    ,       UpperCAmelCase_				:		Union[str, Any]=3    ,       UpperCAmelCase_				:		List[str]=18    ,       UpperCAmelCase_				:		Optional[int]=30    ,       UpperCAmelCase_				:		int=400    ,       UpperCAmelCase_				:		int=True    ,       UpperCAmelCase_				:		List[Any]=None    ,       UpperCAmelCase_				:		Tuple=0.875    ,       UpperCAmelCase_				:		str=True    ,       UpperCAmelCase_				:		Optional[Any]=[0.5, 0.5, 0.5]    ,       UpperCAmelCase_				:		List[Any]=[0.5, 0.5, 0.5]    ,       ):
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			size if size is not None else {"shortest_edge": 20}
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			parent
       SCREAMING_SNAKE_CASE				:					int		  =			batch_size
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			num_channels
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			image_size
       SCREAMING_SNAKE_CASE				:					Any		  =			min_resolution
       SCREAMING_SNAKE_CASE				:					List[str]		  =			max_resolution
       SCREAMING_SNAKE_CASE				:					int		  =			do_resize
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			size
       SCREAMING_SNAKE_CASE				:					Any		  =			crop_pct
       SCREAMING_SNAKE_CASE				:					Dict		  =			do_normalize
       SCREAMING_SNAKE_CASE				:					List[str]		  =			image_mean
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			image_std






     def  _A				( self				:		str					):
       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
           "crop_pct": self.crop_pct,
       }





@require_torch
@require_vision
class        SCREAMING_SNAKE_CASE	(	lowerCAmelCase					,						unittest.TestCase    ):
     '''simple docstring'''


     UpperCamelCase_							:						str								=		ConvNextImageProcessor if is_vision_available() else None


     def  _A				( self				:		Optional[int]					):
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			ConvNextImageProcessingTester(self					)


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


     def  _A				( self				:		str					):
       SCREAMING_SNAKE_CASE				:					str		  =			self.image_processing_class(**self.image_processor_dict					)
       self.assertTrue(hasattr(UpperCAmelCase_    ,       "do_resize"					)					)
       self.assertTrue(hasattr(UpperCAmelCase_    ,       "size"					)					)
       self.assertTrue(hasattr(UpperCAmelCase_    ,       "crop_pct"					)					)
       self.assertTrue(hasattr(UpperCAmelCase_    ,       "do_normalize"					)					)
       self.assertTrue(hasattr(UpperCAmelCase_    ,       "image_mean"					)					)
       self.assertTrue(hasattr(UpperCAmelCase_    ,       "image_std"					)					)


     def  _A				( self				:		Union[str, Any]					):
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			self.image_processing_class.from_dict(self.image_processor_dict					)
       self.assertEqual(image_processor.size    ,       {"shortest_edge": 20}					)

       SCREAMING_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  _A				( self				:		List[str]					):
       pass


     def  _A				( self				:		Optional[Any]					):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			self.image_processing_class(**self.image_processor_dict					)
       # create random PIL images
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=UpperCAmelCase_					)
       for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase_    ,       Image.Image					)

       # Test not batched input
       SCREAMING_SNAKE_CASE				:					Dict		  =			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["shortest_edge"],
               self.image_processor_tester.size["shortest_edge"],
           )    ,       )

       # Test batched
       SCREAMING_SNAKE_CASE				:					str		  =			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["shortest_edge"],
               self.image_processor_tester.size["shortest_edge"],
           )    ,       )


     def  _A				( self				:		Optional[Any]					):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE				:					List[str]		  =			self.image_processing_class(**self.image_processor_dict					)
       # create random numpy tensors
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			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
       SCREAMING_SNAKE_CASE				:					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.size["shortest_edge"],
               self.image_processor_tester.size["shortest_edge"],
           )    ,       )

       # Test batched
       SCREAMING_SNAKE_CASE				:					Dict		  =			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["shortest_edge"],
               self.image_processor_tester.size["shortest_edge"],
           )    ,       )






     def  _A				( self				:		Tuple					):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE				:					str		  =			self.image_processing_class(**self.image_processor_dict					)
       # create random PyTorch tensors
       SCREAMING_SNAKE_CASE				:					Tuple		  =			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
       SCREAMING_SNAKE_CASE				:					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.size["shortest_edge"],
               self.image_processor_tester.size["shortest_edge"],
           )    ,       )

       # Test batched
       SCREAMING_SNAKE_CASE				:					Any		  =			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["shortest_edge"],
               self.image_processor_tester.size["shortest_edge"],
           )    ,       )






