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

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
					import PIL

if is_torch_available():
					import torch


UpperCamelCase_						=						logging.get_logger(__name__)

class      _SCREAMING_SNAKE_CASE				(		snake_case__				):
				lowerCamelCase_	  =  ["""pixel_values"""]





				def __init__(		self			:		List[Any]       ,   snake_case_			:		bool = True       ,   snake_case_			:		Dict[str, int] = None       ,   snake_case_			:		PILImageResampling = PILImageResampling.BILINEAR       ,   snake_case_			:		bool = True       ,   snake_case_			:		Union[int, float] = 1 / 255       ,   snake_case_			:		bool = True       ,   snake_case_			:		Dict[str, int] = None       ,   snake_case_			:		bool = True       ,   **snake_case_			:		Tuple       ,   ):


								"""simple docstring"""

								super().__init__(**__lowercase				)
								A  :	Optional[Any]				=					size if size is not None else {'''shortest_edge''': 224}
								A  :	Optional[Any]				=					get_size_dict(__lowercase       ,   default_to_square=__lowercase				)
								A  :	Dict				=					crop_size if crop_size is not None else {'''height''': 256, '''width''': 256}
								A  :	Optional[Any]				=					get_size_dict(__lowercase       ,   param_name='''crop_size'''				)

								A  :	Union[str, Any]				=					do_resize
								A  :	Any				=					size
								A  :	Dict				=					resample
								A  :	List[Any]				=					do_rescale
								A  :	int				=					rescale_factor
								A  :	Dict				=					do_center_crop
								A  :	Optional[Any]				=					crop_size
								A  :	Optional[Any]				=					do_flip_channel_order





				def 							_UpperCAmelCase    (		self			:		Union[str, Any]       ,   snake_case_			:		np.ndarray       ,   snake_case_			:		Dict[str, int]       ,   snake_case_			:		PILImageResampling = PIL.Image.BILINEAR       ,   snake_case_			:		Optional[Union[str, ChannelDimension]] = None       ,   **snake_case_			:		Optional[int]       ,   ):


								"""simple docstring"""

								A  :	Optional[int]				=					get_size_dict(__lowercase       ,   default_to_square=__lowercase				)
								if "shortest_edge" not in size:
												raise ValueError(f"""The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}"""				)
								A  :	int				=					get_resize_output_image_size(__lowercase       ,   size=size['''shortest_edge''']       ,   default_to_square=__lowercase				)
								return resize(__lowercase       ,   size=__lowercase       ,   resample=__lowercase       ,   data_format=__lowercase       ,   **__lowercase				)





				def 							_UpperCAmelCase    (		self			:		int       ,   snake_case_			:		np.ndarray       ,   snake_case_			:		Dict[str, int]       ,   snake_case_			:		Optional[Union[str, ChannelDimension]] = None       ,   **snake_case_			:		Any       ,   ):


								"""simple docstring"""

								A  :	List[Any]				=					get_size_dict(__lowercase				)
								if "height" not in size or "width" not in size:
												raise ValueError(f"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""				)
								return center_crop(__lowercase       ,   size=(size['''height'''], size['''width'''])       ,   data_format=__lowercase       ,   **__lowercase				)





				def 							_UpperCAmelCase    (		self			:		Optional[int]       ,   snake_case_			:		np.ndarray       ,   snake_case_			:		Union[int, float]       ,   snake_case_			:		Optional[Union[str, ChannelDimension]] = None       ,   **snake_case_			:		Tuple       ,   ):


								"""simple docstring"""

								return rescale(__lowercase       ,   scale=__lowercase       ,   data_format=__lowercase       ,   **__lowercase				)





				def 							_UpperCAmelCase    (		self			:		int       ,   snake_case_			:		np.ndarray       ,   snake_case_			:		Optional[Union[str, ChannelDimension]] = None				):


								"""simple docstring"""

								return flip_channel_order(__lowercase       ,   data_format=__lowercase				)





				def 							_UpperCAmelCase    (		self			:		Dict       ,   snake_case_			:		ImageInput       ,   snake_case_			:		bool = None       ,   snake_case_			:		Dict[str, int] = None       ,   snake_case_			:		PILImageResampling = None       ,   snake_case_			:		bool = None       ,   snake_case_			:		float = None       ,   snake_case_			:		bool = None       ,   snake_case_			:		Dict[str, int] = None       ,   snake_case_			:		bool = None       ,   snake_case_			:		Optional[Union[str, TensorType]] = None       ,   snake_case_			:		ChannelDimension = ChannelDimension.FIRST       ,   **snake_case_			:		str       ,   ):


								"""simple docstring"""

								A  :	Union[str, Any]				=					do_resize if do_resize is not None else self.do_resize
								A  :	Optional[int]				=					resample if resample is not None else self.resample
								A  :	Union[str, Any]				=					do_rescale if do_rescale is not None else self.do_rescale
								A  :	Optional[int]				=					rescale_factor if rescale_factor is not None else self.rescale_factor
								A  :	Optional[Any]				=					do_center_crop if do_center_crop is not None else self.do_center_crop
								A  :	List[str]				=					(
								    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
								)

								A  :	Optional[int]				=					size if size is not None else self.size
								A  :	Optional[int]				=					get_size_dict(__lowercase       ,   default_to_square=__lowercase				)
								A  :	List[Any]				=					crop_size if crop_size is not None else self.crop_size
								A  :	Any				=					get_size_dict(__lowercase       ,   param_name='''crop_size'''				)

								A  :	Any				=					make_list_of_images(__lowercase				)

								if not valid_images(__lowercase				):
												raise ValueError(
												    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
												    '''torch.Tensor, tf.Tensor or jax.ndarray.'''				)

								if do_resize and size is None:
												raise ValueError('''Size must be specified if do_resize is True.'''				)

								if do_rescale and rescale_factor is None:
												raise ValueError('''Rescale factor must be specified if do_rescale is True.'''				)

								if do_center_crop and crop_size is None:
												raise ValueError('''Crop size must be specified if do_center_crop is True.'''				)

								# All transformations expect numpy arrays.
								A  :	List[Any]				=					[to_numpy_array(__lowercase				) for image in images]

								if do_resize:
												A  :	Tuple				=					[self.resize(image=__lowercase       ,   size=__lowercase       ,   resample=__lowercase				) for image in images]

								if do_center_crop:
												A  :	Tuple				=					[self.center_crop(image=__lowercase       ,   size=__lowercase				) for image in images]

								if do_rescale:
												A  :	str				=					[self.rescale(image=__lowercase       ,   scale=__lowercase				) for image in images]

								# the pretrained checkpoints assume images are BGR, not RGB
								if do_flip_channel_order:
												A  :	Any				=					[self.flip_channel_order(image=__lowercase				) for image in images]

								A  :	List[str]				=					[to_channel_dimension_format(__lowercase       ,   __lowercase				) for image in images]

								A  :	int				=					{'''pixel_values''': images}
								return BatchFeature(data=__lowercase       ,   tensor_type=__lowercase				)






				def 							_UpperCAmelCase    (		self			:		int       ,   snake_case_			:		str       ,   snake_case_			:		List[Tuple] = None				):


								"""simple docstring"""

								A  :	str				=					outputs.logits

								# Resize logits and compute semantic segmentation maps
								if target_sizes is not None:
												if len(__lowercase				) != len(__lowercase				):
																raise ValueError(
																    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''				)

												if is_torch_tensor(__lowercase				):
																A  :	Tuple				=					target_sizes.numpy()

												A  :	List[str]				=					[]

												for idx in range(len(__lowercase				)				):
																A  :	Optional[int]				=					torch.nn.functional.interpolate(
																    logits[idx].unsqueeze(dim=0				)       ,   size=target_sizes[idx]       ,   mode='''bilinear'''       ,   align_corners=__lowercase				)
																A  :	Tuple				=					resized_logits[0].argmax(dim=0				)
																semantic_segmentation.append(__lowercase				)
								else:
												A  :	Optional[Any]				=					logits.argmax(dim=1				)
												A  :	str				=					[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]				)]

								return semantic_segmentation






"""simple docstring"""





from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
							import numpy as np
							import tensorflow as tf

							from transformers import TFXLMRobertaModel
@require_tf
@require_sentencepiece
@require_tokenizers
class 		SCREAMING_SNAKE_CASE_				(     unittest.TestCase ):
				"""simple docstring"""




				@slow
				def        __lowercase	(  self	:Union[str, Any]      ):
											__lowerCamelCase   :   List[Any]       =TFXLMRobertaModel.from_pretrained('''jplu/tf-xlm-roberta-base'''      )

											__lowerCamelCase   :   Dict       ={
											    '''input_ids''': tf.convert_to_tensor([[0, 2646, 1_0269, 83, 9_9942, 2]]    ,     dtype=tf.intaa      ),  # "My dog is cute"
											    '''attention_mask''': tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]]    ,     dtype=tf.intaa      ),
											}

											__lowerCamelCase   :   Optional[int]       =model(__lowercase      )['''last_hidden_state''']
											__lowerCamelCase   :   str       =tf.TensorShape((1, 6, 768)      )
											self.assertEqual(output.shape    ,     __lowercase      )
											# compare the actual values for a slice.
											__lowerCamelCase   :   str       =tf.convert_to_tensor(
											    [
											        [
											            [0.0681762, 0.10894451, 0.06772504],
											            [-0.06423668, 0.02366615, 0.04329344],
											            [-0.06057295, 0.09974135, -0.00070584],
											        ]
											    ]    ,     dtype=tf.floataa    ,     )

											self.assertTrue(np.allclose(output[:, :3, :3].numpy()    ,     expected_slice.numpy()    ,     atol=1e-4      )      )




