
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 SegformerImageProcessor

class 				_UpperCamelCase (    unittest.TestCase     ):






			'''simple docstring'''

			def __init__(				self       :				int  ,		snake_case_       :				List[str]  ,		snake_case_       :				List[Any]=7  ,		snake_case_       :				str=3  ,		snake_case_       :				Tuple=30  ,		snake_case_       :				str=400  ,		snake_case_       :				List[str]=True  ,		snake_case_       :				Tuple=None  ,		snake_case_       :				Dict=True  ,		snake_case_       :				List[str]=[0.5, 0.5, 0.5]  ,		snake_case_       :				str=[0.5, 0.5, 0.5]  ,		snake_case_       :				List[Any]=False  ,		):
							UpperCamelCase_:							List[Any]		=  size if size is not None else {"""height""": 30, """width""": 30}
							UpperCamelCase_:							Tuple		=  parent
							UpperCamelCase_:							Optional[Any]		=  batch_size
							UpperCamelCase_:							Dict		=  num_channels
							UpperCamelCase_:							Tuple		=  min_resolution
							UpperCamelCase_:							List[Any]		=  max_resolution
							UpperCamelCase_:							Union[str, Any]		=  do_resize
							UpperCamelCase_:							Any		=  size
							UpperCamelCase_:							Tuple		=  do_normalize
							UpperCamelCase_:							Dict		=  image_mean
							UpperCamelCase_:							Union[str, Any]		=  image_std
							UpperCamelCase_:							List[str]		=  do_reduce_labels




			def 					lowerCAmelCase__		(				self       :				Any					):
							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_reduce_labels": self.do_reduce_labels,
							}







def 				A__    (  )     ->		List[Any]:
				UpperCamelCase_:							Tuple		=  load_dataset("""hf-internal-testing/fixtures_ade20k"""   ,			split="""test"""  )

				UpperCamelCase_:							List[str]		=  Image.open(dataset[0]["""file"""]  )
				UpperCamelCase_:							Union[str, Any]		=  Image.open(dataset[1]["""file"""]  )

				return image, map







def 				A__    (  )     ->		Union[str, Any]:
				UpperCamelCase_:							int		=  load_dataset("""hf-internal-testing/fixtures_ade20k"""   ,			split="""test"""  )

				UpperCamelCase_:							Optional[Any]		=  Image.open(dataset[0]["""file"""]  )
				UpperCamelCase_:							Union[str, Any]		=  Image.open(dataset[1]["""file"""]  )
				UpperCamelCase_:							int		=  Image.open(dataset[2]["""file"""]  )
				UpperCamelCase_:							str		=  Image.open(dataset[3]["""file"""]  )

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





@require_torch
@require_vision
class 				_UpperCamelCase (    _A   ,				unittest.TestCase     ):






			'''simple docstring'''

			__UpperCamelCase					:  Union[str, Any]    					=       SegformerImageProcessor if is_vision_available() else None

			def 					lowerCAmelCase__		(				self       :				Union[str, Any]					):
							UpperCamelCase_:							List[Any]		=  SegformerImageProcessingTester(self					)

			@property
			def 					lowerCAmelCase__		(				self       :				Union[str, Any]					):
							return self.image_processor_tester.prepare_image_processor_dict()

			def 					lowerCAmelCase__		(				self       :				str					):
							UpperCamelCase_:							Dict		=  self.image_processing_class(**self.image_processor_dict					)
							self.assertTrue(hasattr(snake_case_  ,		"""do_resize"""					)					)
							self.assertTrue(hasattr(snake_case_  ,		"""size"""					)					)
							self.assertTrue(hasattr(snake_case_  ,		"""do_normalize"""					)					)
							self.assertTrue(hasattr(snake_case_  ,		"""image_mean"""					)					)
							self.assertTrue(hasattr(snake_case_  ,		"""image_std"""					)					)
							self.assertTrue(hasattr(snake_case_  ,		"""do_reduce_labels"""					)					)

			def 					lowerCAmelCase__		(				self       :				int					):
							UpperCamelCase_:							int		=  self.image_processing_class.from_dict(self.image_processor_dict					)
							self.assertEqual(image_processor.size  ,		{"""height""": 30, """width""": 30}					)
							self.assertEqual(image_processor.do_reduce_labels  ,		snake_case_					)

							UpperCamelCase_:							Tuple		=  self.image_processing_class.from_dict(self.image_processor_dict  ,		size=42  ,		reduce_labels=snake_case_					)
							self.assertEqual(image_processor.size  ,		{"""height""": 42, """width""": 42}					)
							self.assertEqual(image_processor.do_reduce_labels  ,		snake_case_					)

			def 					lowerCAmelCase__		(				self       :				List[str]					):
							pass

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

							# Test not batched input
							UpperCamelCase_:							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["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )  ,		)

							# Test batched
							UpperCamelCase_:							Union[str, Any]		=  image_processing(snake_case_  ,		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["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )  ,		)

			def 					lowerCAmelCase__		(				self       :				Tuple					):
							# Initialize image_processing
							UpperCamelCase_:							Dict		=  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=snake_case_  ,		numpify=snake_case_					)
							for image in image_inputs:
											self.assertIsInstance(snake_case_  ,		np.ndarray					)

							# Test not batched input
							UpperCamelCase_:							Optional[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.size["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )  ,		)

							# Test batched
							UpperCamelCase_:							List[Any]		=  image_processing(snake_case_  ,		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["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )  ,		)

			def 					lowerCAmelCase__		(				self       :				Tuple					):
							# Initialize image_processing
							UpperCamelCase_:							Dict		=  self.image_processing_class(**self.image_processor_dict					)
							# create random PyTorch tensors
							UpperCamelCase_:							str		=  prepare_image_inputs(self.image_processor_tester  ,		equal_resolution=snake_case_  ,		torchify=snake_case_					)
							for image in image_inputs:
											self.assertIsInstance(snake_case_  ,		torch.Tensor					)

							# Test not batched input
							UpperCamelCase_:							List[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.size["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )  ,		)

							# Test batched
							UpperCamelCase_:							Dict		=  image_processing(snake_case_  ,		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["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )  ,		)

			def 					lowerCAmelCase__		(				self       :				int					):
							# Initialize image_processing
							UpperCamelCase_:							Union[str, Any]		=  self.image_processing_class(**self.image_processor_dict					)
							# create random PyTorch tensors
							UpperCamelCase_:							Dict		=  prepare_image_inputs(self.image_processor_tester  ,		equal_resolution=snake_case_  ,		torchify=snake_case_					)
							UpperCamelCase_:							Any		=  []
							for image in image_inputs:
											self.assertIsInstance(snake_case_  ,		torch.Tensor					)
											maps.append(torch.zeros(image.shape[-2:]					).long()					)

							# Test not batched input
							UpperCamelCase_:							Optional[int]		=  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.size["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )  ,		)
							self.assertEqual(
							    encoding["""labels"""].shape  ,		(
							        1,
							        self.image_processor_tester.size["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )  ,		)
							self.assertEqual(encoding["""labels"""].dtype  ,		torch.long					)
							self.assertTrue(encoding["""labels"""].min().item() >= 0					)
							self.assertTrue(encoding["""labels"""].max().item() <= 255					)

							# Test batched
							UpperCamelCase_:							str		=  image_processing(snake_case_  ,		snake_case_  ,		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.size["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )  ,		)
							self.assertEqual(
							    encoding["""labels"""].shape  ,		(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.size["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )  ,		)
							self.assertEqual(encoding["""labels"""].dtype  ,		torch.long					)
							self.assertTrue(encoding["""labels"""].min().item() >= 0					)
							self.assertTrue(encoding["""labels"""].max().item() <= 255					)

							# Test not batched input (PIL images)
							UpperCamelCase_,      UpperCamelCase_:							Tuple		=  prepare_semantic_single_inputs()

							UpperCamelCase_:							Dict		=  image_processing(snake_case_  ,		snake_case_  ,		return_tensors="""pt"""					)
							self.assertEqual(
							    encoding["""pixel_values"""].shape  ,		(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )  ,		)
							self.assertEqual(
							    encoding["""labels"""].shape  ,		(
							        1,
							        self.image_processor_tester.size["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )  ,		)
							self.assertEqual(encoding["""labels"""].dtype  ,		torch.long					)
							self.assertTrue(encoding["""labels"""].min().item() >= 0					)
							self.assertTrue(encoding["""labels"""].max().item() <= 255					)

							# Test batched input (PIL images)
							UpperCamelCase_,      UpperCamelCase_:							Dict		=  prepare_semantic_batch_inputs()

							UpperCamelCase_:							List[Any]		=  image_processing(snake_case_  ,		snake_case_  ,		return_tensors="""pt"""					)
							self.assertEqual(
							    encoding["""pixel_values"""].shape  ,		(
							        2,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )  ,		)
							self.assertEqual(
							    encoding["""labels"""].shape  ,		(
							        2,
							        self.image_processor_tester.size["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )  ,		)
							self.assertEqual(encoding["""labels"""].dtype  ,		torch.long					)
							self.assertTrue(encoding["""labels"""].min().item() >= 0					)
							self.assertTrue(encoding["""labels"""].max().item() <= 255					)




			def 					lowerCAmelCase__		(				self       :				List[str]					):
							# Initialize image_processing
							UpperCamelCase_:							Tuple		=  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
							UpperCamelCase_,      UpperCamelCase_:							int		=  prepare_semantic_single_inputs()
							UpperCamelCase_:							Any		=  image_processing(snake_case_  ,		snake_case_  ,		return_tensors="""pt"""					)
							self.assertTrue(encoding["""labels"""].min().item() >= 0					)
							self.assertTrue(encoding["""labels"""].max().item() <= 150					)

							UpperCamelCase_:							Dict		=  True
							UpperCamelCase_:							List[str]		=  image_processing(snake_case_  ,		snake_case_  ,		return_tensors="""pt"""					)
							self.assertTrue(encoding["""labels"""].min().item() >= 0					)
							self.assertTrue(encoding["""labels"""].max().item() <= 255					)








from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


_A  					= logging.get_logger(__name__)







class      lowerCamelCase		(  A_  ):
     UpperCAmelCase__				:							int           =		["input_values", "attention_mask"]





     def __init__(self      :    Tuple				,    _A      :    Optional[Any]=1				,    _A      :    Dict=1_6_0_0_0				,    _A      :    int=0.0				,    _A      :    List[str]=False				,    _A      :    Optional[int]=True				,    **_A      :    List[Any]				,    )						->						str:
         super().__init__(feature_size=_A				,    sampling_rate=_A				,    padding_value=_A				,    **_A   )
         snake_case      =					return_attention_mask
         snake_case      =					do_normalize





     @staticmethod
     def 		UpperCAmelCase(_A      :    List[np.ndarray]				,    _A      :    List[np.ndarray]				,    _A      :    float = 0.0   )						->						List[np.ndarray]:
         if attention_mask is not None:
             snake_case      =					np.array(_A				,    np.intaa   )
             snake_case      =					[]

             for vector, length in zip(_A				,    attention_mask.sum(-1   )   ):
                 snake_case      =					(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7   )
                 if length < normed_slice.shape[0]:
                     snake_case      =					padding_value

                 normed_input_values.append(_A   )
         else:
             snake_case      =					[(x - x.mean()) / np.sqrt(x.var() + 1E-7   ) for x in input_values]

         return normed_input_values





     def __call__(self      :    Optional[Any]				,    _A      :    Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]				,    _A      :    Union[bool, str, PaddingStrategy] = False				,    _A      :    Optional[int] = None				,    _A      :    bool = False				,    _A      :    Optional[int] = None				,    _A      :    Optional[bool] = None				,    _A      :    Optional[Union[str, TensorType]] = None				,    _A      :    Optional[int] = None				,    **_A      :    List[Any]				,    )						->						BatchFeature:

         if sampling_rate is not None:
             if sampling_rate != self.sampling_rate:
                 raise ValueError(
                     f'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
                     f' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'
                     f' {self.sampling_rate} and not {sampling_rate}.'   )
         else:
             logger.warning(
                 "It is strongly recommended to pass the ``sampling_rate`` argument to this function. "
                 "Failing to do so can result in silent errors that might be hard to debug."   )

         snake_case      =					isinstance(_A				,    np.ndarray   ) and len(raw_speech.shape   ) > 1
         if is_batched_numpy and len(raw_speech.shape   ) > 2:
             raise ValueError(f'Only mono-channel audio is supported for input to {self}'   )
         snake_case      =					is_batched_numpy or (
             isinstance(_A				,    (list, tuple)   ) and (isinstance(raw_speech[0]				,    (np.ndarray, tuple, list)   ))
         )

         # always return batch
         if not is_batched:
             snake_case      =					[raw_speech]

         # convert into correct format for padding
         snake_case      =					BatchFeature({"input_values": raw_speech}   )

         snake_case      =					self.pad(
             _A				,    padding=_A				,    max_length=_A				,    truncation=_A				,    pad_to_multiple_of=_A				,    return_attention_mask=_A				,    )

         # convert input values to correct format
         snake_case      =					padded_inputs["input_values"]
         if not isinstance(input_values[0]				,    np.ndarray   ):
             snake_case      =					[np.asarray(_A				,    dtype=np.floataa   ) for array in input_values]
         elif (
             not isinstance(_A				,    np.ndarray   )
             and isinstance(input_values[0]				,    np.ndarray   )
             and input_values[0].dtype is np.dtype(np.floataa   )
         ):
             snake_case      =					[array.astype(np.floataa   ) for array in input_values]
         elif isinstance(_A				,    np.ndarray   ) and input_values.dtype is np.dtype(np.floataa   ):
             snake_case      =					input_values.astype(np.floataa   )

         # convert attention_mask to correct format
         snake_case      =					padded_inputs.get("attention_mask"   )
         if attention_mask is not None:
             snake_case      =					[np.asarray(_A				,    dtype=np.intaa   ) for array in attention_mask]

         # zero-mean and unit-variance normalization
         if self.do_normalize:
             snake_case      =					(
                 attention_mask
                 if self._get_padding_strategies(_A				,    max_length=_A   ) is not PaddingStrategy.DO_NOT_PAD
                 else None
             )
             snake_case      =					self.zero_mean_unit_var_norm(
                 padded_inputs["input_values"]				,    attention_mask=_A				,    padding_value=self.padding_value   )

         if return_tensors is not None:
             snake_case      =					padded_inputs.convert_to_tensors(_A   )

         return padded_inputs

