

"""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 lowerCAmelCase__			(  unittest.TestCase		):






			'''simple docstring'''



			def __init__(						self						,   lowercase						,   lowercase=7						,   lowercase=3						,   lowercase=18						,   lowercase=30						,   lowercase=400						,   lowercase=True						,   lowercase=None						,   lowercase=True						,   lowercase=None						,   lowercase=True						,   lowercase=[0.5, 0.5, 0.5]						,   lowercase=[0.5, 0.5, 0.5]						,   lowercase=False						,   ):
							_lowerCamelCase  :    Optional[Any]					=					size if size is not None else {'height': 20, 'width': 20}
							_lowerCamelCase  :    Any					=					crop_size if crop_size is not None else {'height': 18, 'width': 18}
							_lowerCamelCase  :    List[str]					=					parent
							_lowerCamelCase  :    Tuple					=					batch_size
							_lowerCamelCase  :    Any					=					num_channels
							_lowerCamelCase  :    Union[str, Any]					=					image_size
							_lowerCamelCase  :    int					=					min_resolution
							_lowerCamelCase  :    Any					=					max_resolution
							_lowerCamelCase  :    str					=					do_resize
							_lowerCamelCase  :    List[Any]					=					size
							_lowerCamelCase  :    List[str]					=					do_center_crop
							_lowerCamelCase  :    Dict					=					crop_size
							_lowerCamelCase  :    Any					=					do_normalize
							_lowerCamelCase  :    Union[str, Any]					=					image_mean
							_lowerCamelCase  :    Dict					=					image_std
							_lowerCamelCase  :    Any					=					do_reduce_labels






			def       A_					(						self						):
							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    (       ):
				_lowerCamelCase  :    Union[str, Any]					=					load_dataset('hf-internal-testing/fixtures_ade20k'							,					split='test'	)

				_lowerCamelCase  :    str					=					Image.open(dataset[0]['file']	)
				_lowerCamelCase  :    Union[str, Any]					=					Image.open(dataset[1]['file']	)

				return image, map







def   _snake_case    (       ):
				_lowerCamelCase  :    Dict					=					load_dataset('hf-internal-testing/fixtures_ade20k'							,					split='test'	)

				_lowerCamelCase  :    Union[str, Any]					=					Image.open(ds[0]['file']	)
				_lowerCamelCase  :    Tuple					=					Image.open(ds[1]['file']	)
				_lowerCamelCase  :    Tuple					=					Image.open(ds[2]['file']	)
				_lowerCamelCase  :    Dict					=					Image.open(ds[3]['file']	)

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






@require_torch
@require_vision
class lowerCAmelCase__			(  lowercase,			unittest.TestCase		):






			'''simple docstring'''



			lowerCamelCase__   =    BeitImageProcessor if is_vision_available() else None
			def       A_					(						self						):
							_lowerCamelCase  :    List[Any]					=					BeitImageProcessingTester(self						)
			@property
			def       A_					(						self						):
							return self.image_processor_tester.prepare_image_processor_dict()
			def       A_					(						self						):
							_lowerCamelCase  :    Any					=					self.image_processing_class(**self.image_processor_dict						)
							self.assertTrue(hasattr(lowercase						,   'do_resize'						)						)
							self.assertTrue(hasattr(lowercase						,   'size'						)						)
							self.assertTrue(hasattr(lowercase						,   'do_center_crop'						)						)
							self.assertTrue(hasattr(lowercase						,   'center_crop'						)						)
							self.assertTrue(hasattr(lowercase						,   'do_normalize'						)						)
							self.assertTrue(hasattr(lowercase						,   'image_mean'						)						)
							self.assertTrue(hasattr(lowercase						,   'image_std'						)						)
			def       A_					(						self						):
							_lowerCamelCase  :    Any					=					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						,   lowercase						)

							_lowerCamelCase  :    str					=					self.image_processing_class.from_dict(
							    self.image_processor_dict						,   size=42						,   crop_size=84						,   reduce_labels=lowercase						)
							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						,   lowercase						)
			def       A_					(						self						):
							pass
			def       A_					(						self						):
							# Initialize image_processing
							_lowerCamelCase  :    Any					=					self.image_processing_class(**self.image_processor_dict						)
							# create random PIL images
							_lowerCamelCase  :    Union[str, Any]					=					prepare_image_inputs(self.image_processor_tester						,   equal_resolution=lowercase						)
							for image in image_inputs:
											self.assertIsInstance(lowercase						,   Image.Image						)

							# Test not batched input
							_lowerCamelCase  :    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.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )						,   )

							# Test batched
							_lowerCamelCase  :    List[Any]					=					image_processing(lowercase						,   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       A_					(						self						):
							# Initialize image_processing
							_lowerCamelCase  :    Any					=					self.image_processing_class(**self.image_processor_dict						)
							# create random numpy tensors
							_lowerCamelCase  :    int					=					prepare_image_inputs(self.image_processor_tester						,   equal_resolution=lowercase						,   numpify=lowercase						)
							for image in image_inputs:
											self.assertIsInstance(lowercase						,   np.ndarray						)

							# Test not batched input
							_lowerCamelCase  :    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.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )						,   )

							# Test batched
							_lowerCamelCase  :    List[str]					=					image_processing(lowercase						,   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       A_					(						self						):
							# Initialize image_processing
							_lowerCamelCase  :    Optional[Any]					=					self.image_processing_class(**self.image_processor_dict						)
							# create random PyTorch tensors
							_lowerCamelCase  :    List[Any]					=					prepare_image_inputs(self.image_processor_tester						,   equal_resolution=lowercase						,   torchify=lowercase						)
							for image in image_inputs:
											self.assertIsInstance(lowercase						,   torch.Tensor						)

							# Test not batched input
							_lowerCamelCase  :    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.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )						,   )

							# Test batched
							_lowerCamelCase  :    Optional[int]					=					image_processing(lowercase						,   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       A_					(						self						):
							# Initialize image_processing
							_lowerCamelCase  :    str					=					self.image_processing_class(**self.image_processor_dict						)
							# create random PyTorch tensors
							_lowerCamelCase  :    List[str]					=					prepare_image_inputs(self.image_processor_tester						,   equal_resolution=lowercase						,   torchify=lowercase						)
							_lowerCamelCase  :    List[str]					=					[]
							for image in image_inputs:
											self.assertIsInstance(lowercase						,   torch.Tensor						)
											maps.append(torch.zeros(image.shape[-2:]						).long()						)

							# Test not batched input
							_lowerCamelCase  :    str					=					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() <= 255						)

							# Test batched
							_lowerCamelCase  :    Tuple					=					image_processing(lowercase						,   lowercase						,   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() <= 255						)

							# Test not batched input (PIL images)
							_lowerCamelCase  :    Any					=					prepare_semantic_single_inputs()

							_lowerCamelCase  :    Tuple					=					image_processing(lowercase						,   lowercase						,   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() <= 255						)

							# Test batched input (PIL images)
							_lowerCamelCase  :    List[str]					=					prepare_semantic_batch_inputs()

							_lowerCamelCase  :    Tuple					=					image_processing(lowercase						,   lowercase						,   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() <= 255						)






			def       A_					(						self						):
							# Initialize image_processing
							_lowerCamelCase  :    str					=					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  :    Union[str, Any]					=					prepare_semantic_single_inputs()
							_lowerCamelCase  :    List[Any]					=					image_processing(lowercase						,   lowercase						,   return_tensors='pt'						)
							self.assertTrue(encoding['labels'].min().item() >= 0						)
							self.assertTrue(encoding['labels'].max().item() <= 150						)

							_lowerCamelCase  :    List[Any]					=					True
							_lowerCamelCase  :    List[Any]					=					image_processing(lowercase						,   lowercase						,   return_tensors='pt'						)
							self.assertTrue(encoding['labels'].min().item() >= 0						)
							self.assertTrue(encoding['labels'].max().item() <= 255						)





"""simple docstring"""






# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


lowercase__  		=		logging.get_logger(__name__)

lowercase__  		=		{}
lowercase__  		=		{}
lowercase__  		=		{}







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__ = None							,					):
				_lowerCamelCase  :    str					=					aliases if aliases is not None else []
				if format_type in _FORMAT_TYPES:
								logger.warning(
								    f'''Overwriting format type \'{format_type}\' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})'''	)
				_lowerCamelCase  :    str					=					formatter_cls
				for alias in set(aliases + [format_type]	):
								if alias in _FORMAT_TYPES_ALIASES:
												logger.warning(
												    f'''Overwriting format type alias \'{alias}\' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})'''	)
								_lowerCamelCase  :    Dict					=					format_type







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__ = None	):
				_lowerCamelCase  :    Dict					=					aliases if aliases is not None else []
				for alias in set(aliases + [format_type]	):
								_lowerCamelCase  :    Optional[Any]					=					unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=["""python"""])
_register_formatter(ArrowFormatter, """arrow""", aliases=["""pa""", """pyarrow"""])
_register_formatter(NumpyFormatter, """numpy""", aliases=["""np"""])
_register_formatter(PandasFormatter, """pandas""", aliases=["""pd"""])
_register_formatter(CustomFormatter, """custom""")

if config.TORCH_AVAILABLE:
			from .torch_formatter import TorchFormatter

			_register_formatter(TorchFormatter, """torch""", aliases=["""pt""", """pytorch"""])
else:
			lowercase__  		=		ValueError("""PyTorch needs to be installed to be able to return PyTorch tensors.""")
			_register_unavailable_formatter(_torch_error, """torch""", aliases=["""pt""", """pytorch"""])

if config.TF_AVAILABLE:
			from .tf_formatter import TFFormatter

			_register_formatter(TFFormatter, """tensorflow""", aliases=["""tf"""])
else:
			lowercase__  		=		ValueError("""Tensorflow needs to be installed to be able to return Tensorflow tensors.""")
			_register_unavailable_formatter(_tf_error, """tensorflow""", aliases=["""tf"""])

if config.JAX_AVAILABLE:
			from .jax_formatter import JaxFormatter

			_register_formatter(JaxFormatter, """jax""", aliases=[])
else:
			lowercase__  		=		ValueError("""JAX needs to be installed to be able to return JAX arrays.""")
			_register_unavailable_formatter(_jax_error, """jax""", aliases=[])







def   _snake_case    (       lowercase__	):
				if format_type in _FORMAT_TYPES_ALIASES:
								return _FORMAT_TYPES_ALIASES[format_type]
				else:
								return format_type







def   _snake_case    (       lowercase__							,					**lowercase__	):
				_lowerCamelCase  :    List[str]					=					get_format_type_from_alias(lowercase__	)
				if format_type in _FORMAT_TYPES:
								return _FORMAT_TYPES[format_type](**lowercase__	)
				if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
								raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
				else:
								raise ValueError(
								    f'''Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None	)}, but got \'{format_type}\''''	)