


"""simple docstring"""


import unittest

import numpy as np
from datasets import load_dataset

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 BeitImageProcessor



class 	_a   (   unittest.TestCase			):
    def __init__(			self				:       Optional[Any]				,       SCREAMING_SNAKE_CASE__				:       Union[str, Any]				,       SCREAMING_SNAKE_CASE__				:       Optional[int]=7				,       SCREAMING_SNAKE_CASE__				:       Any=3				,       SCREAMING_SNAKE_CASE__				:       Union[str, Any]=18				,       SCREAMING_SNAKE_CASE__				:       Optional[int]=30				,       SCREAMING_SNAKE_CASE__				:       Optional[int]=4_00				,       SCREAMING_SNAKE_CASE__				:       Dict=True				,       SCREAMING_SNAKE_CASE__				:       int=None				,       SCREAMING_SNAKE_CASE__				:       Tuple=True				,       SCREAMING_SNAKE_CASE__				:       Optional[Any]=None				,       SCREAMING_SNAKE_CASE__				:       List[str]=True				,       SCREAMING_SNAKE_CASE__				:       str=[0.5, 0.5, 0.5]				,       SCREAMING_SNAKE_CASE__				:       Union[str, Any]=[0.5, 0.5, 0.5]				,       SCREAMING_SNAKE_CASE__				:       Tuple=False				,       ):
          lowerCamelCase__							     =		size if size is not None else {'height': 20, 'width': 20}
          lowerCamelCase__							     =		crop_size if crop_size is not None else {'height': 18, 'width': 18}
          lowerCamelCase__							     =		parent
          lowerCamelCase__							     =		batch_size
          lowerCamelCase__							     =		num_channels
          lowerCamelCase__							     =		image_size
          lowerCamelCase__							     =		min_resolution
          lowerCamelCase__							     =		max_resolution
          lowerCamelCase__							     =		do_resize
          lowerCamelCase__							     =		size
          lowerCamelCase__							     =		do_center_crop
          lowerCamelCase__							     =		crop_size
          lowerCamelCase__							     =		do_normalize
          lowerCamelCase__							     =		image_mean
          lowerCamelCase__							     =		image_std
          lowerCamelCase__							     =		do_reduce_labels


    def   _UpperCamelCase							(			self				:       List[str]					):
          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,
              "do_reduce_labels": self.do_reduce_labels,
          }





def 	snake_case    (					)->		Union[str, Any]:
      '''simple docstring'''


      lowerCamelCase__							     =		load_dataset('hf-internal-testing/fixtures_ade20k'		, split='test'				)

      lowerCamelCase__							     =		Image.open(dataset[0]['file']				)
      lowerCamelCase__							     =		Image.open(dataset[1]['file']				)

      return image, map



def 	snake_case    (					)->		List[str]:
      '''simple docstring'''


      lowerCamelCase__							     =		load_dataset('hf-internal-testing/fixtures_ade20k'		, split='test'				)

      lowerCamelCase__							     =		Image.open(ds[0]['file']				)
      lowerCamelCase__							     =		Image.open(ds[1]['file']				)
      lowerCamelCase__							     =		Image.open(ds[2]['file']				)
      lowerCamelCase__							     =		Image.open(ds[3]['file']				)

      return [imagea, imagea], [mapa, mapa]






@require_torch
@require_vision
class 	_a   (   SCREAMING_SNAKE_CASE_		,		unittest.TestCase			):
    a_     :					Dict   							=     BeitImageProcessor if is_vision_available() else None
    def   _UpperCamelCase							(			self				:       Optional[Any]					):
          lowerCamelCase__							     =		BeitImageProcessingTester(self					)
    @property
    def   _UpperCamelCase							(			self				:       List[Any]					):
          return self.image_processor_tester.prepare_image_processor_dict()
    def   _UpperCamelCase							(			self				:       Union[str, Any]					):
          lowerCamelCase__							     =		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   _UpperCamelCase							(			self				:       List[Any]					):
          lowerCamelCase__							     =		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}					)
          self.assertEqual(image_processor.do_reduce_labels				,       _lowerCAmelCase					)

          lowerCamelCase__							     =		self.image_processing_class.from_dict(
              self.image_processor_dict				,       size=42				,       crop_size=84				,       reduce_labels=_lowerCAmelCase					)
          self.assertEqual(image_processor.size				,       {'height': 42, 'width': 42}					)
          self.assertEqual(image_processor.crop_size				,       {'height': 84, 'width': 84}					)
          self.assertEqual(image_processor.do_reduce_labels				,       _lowerCAmelCase					)
    def   _UpperCamelCase							(			self				:       int					):
          pass
    def   _UpperCamelCase							(			self				:       List[str]					):
          lowerCamelCase__							     =		self.image_processing_class(**self.image_processor_dict					)
          # create random PIL images
          lowerCamelCase__							     =		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__							     =		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__							     =		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   _UpperCamelCase							(			self				:       Any					):
          lowerCamelCase__							     =		self.image_processing_class(**self.image_processor_dict					)
          # create random numpy tensors
          lowerCamelCase__							     =		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__							     =		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__							     =		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   _UpperCamelCase							(			self				:       Optional[int]					):
          lowerCamelCase__							     =		self.image_processing_class(**self.image_processor_dict					)
          # create random PyTorch tensors
          lowerCamelCase__							     =		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__							     =		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__							     =		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   _UpperCamelCase							(			self				:       str					):
          lowerCamelCase__							     =		self.image_processing_class(**self.image_processor_dict					)
          # create random PyTorch tensors
          lowerCamelCase__							     =		prepare_image_inputs(self.image_processor_tester				,       equal_resolution=_lowerCAmelCase				,       torchify=_lowerCAmelCase					)
          lowerCamelCase__							     =		[]
          for image in image_inputs:
                self.assertIsInstance(_lowerCAmelCase				,       torch.Tensor					)
                maps.append(torch.zeros(image.shape[-2:]					).long()					)

          # Test not batched input
          lowerCamelCase__							     =		image_processing(image_inputs[0]				,       maps[0]				,       return_tensors='pt'					)
          self.assertEqual(
              encoding['pixel_values'].shape				,       (
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )				,       )
          self.assertEqual(
              encoding['labels'].shape				,       (
                  1,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )				,       )
          self.assertEqual(encoding['labels'].dtype				,       torch.long					)
          self.assertTrue(encoding['labels'].min().item() >= 0					)
          self.assertTrue(encoding['labels'].max().item() <= 2_55					)

          # Test batched
          lowerCamelCase__							     =		image_processing(_lowerCAmelCase				,       _lowerCAmelCase				,       return_tensors='pt'					)
          self.assertEqual(
              encoding['pixel_values'].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'],
              )				,       )
          self.assertEqual(
              encoding['labels'].shape				,       (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )				,       )
          self.assertEqual(encoding['labels'].dtype				,       torch.long					)
          self.assertTrue(encoding['labels'].min().item() >= 0					)
          self.assertTrue(encoding['labels'].max().item() <= 2_55					)

          # Test not batched input (PIL images)
          lowerCamelCase__     ,	lowerCamelCase__							     =		prepare_semantic_single_inputs()

          lowerCamelCase__							     =		image_processing(_lowerCAmelCase				,       _lowerCAmelCase				,       return_tensors='pt'					)
          self.assertEqual(
              encoding['pixel_values'].shape				,       (
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )				,       )
          self.assertEqual(
              encoding['labels'].shape				,       (
                  1,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )				,       )
          self.assertEqual(encoding['labels'].dtype				,       torch.long					)
          self.assertTrue(encoding['labels'].min().item() >= 0					)
          self.assertTrue(encoding['labels'].max().item() <= 2_55					)

          # Test batched input (PIL images)
          lowerCamelCase__     ,	lowerCamelCase__							     =		prepare_semantic_batch_inputs()

          lowerCamelCase__							     =		image_processing(_lowerCAmelCase				,       _lowerCAmelCase				,       return_tensors='pt'					)
          self.assertEqual(
              encoding['pixel_values'].shape				,       (
                  2,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )				,       )
          self.assertEqual(
              encoding['labels'].shape				,       (
                  2,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )				,       )
          self.assertEqual(encoding['labels'].dtype				,       torch.long					)
          self.assertTrue(encoding['labels'].min().item() >= 0					)
          self.assertTrue(encoding['labels'].max().item() <= 2_55					)


    def   _UpperCamelCase							(			self				:       Union[str, Any]					):
          lowerCamelCase__							     =		self.image_processing_class(**self.image_processor_dict					)

          # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
          lowerCamelCase__     ,	lowerCamelCase__							     =		prepare_semantic_single_inputs()
          lowerCamelCase__							     =		image_processing(_lowerCAmelCase				,       _lowerCAmelCase				,       return_tensors='pt'					)
          self.assertTrue(encoding['labels'].min().item() >= 0					)
          self.assertTrue(encoding['labels'].max().item() <= 1_50					)

          lowerCamelCase__							     =		True
          lowerCamelCase__							     =		image_processing(_lowerCAmelCase				,       _lowerCAmelCase				,       return_tensors='pt'					)
          self.assertTrue(encoding['labels'].min().item() >= 0					)
          self.assertTrue(encoding['labels'].max().item() <= 2_55					)







from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE						:str			=  {'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechConfig''']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE						:List[str]			=  [
       '''UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''UniSpeechForCTC''',
       '''UniSpeechForPreTraining''',
       '''UniSpeechForSequenceClassification''',
       '''UniSpeechModel''',
       '''UniSpeechPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_unispeech import (
          UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
          UniSpeechForCTC,
          UniSpeechForPreTraining,
          UniSpeechForSequenceClassification,
          UniSpeechModel,
          UniSpeechPreTrainedModel,
      )

else:
   import sys

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




