


from typing import TYPE_CHECKING

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


_lowerCAmelCase:						Union[str, Any]   				=       {
    'configuration_roformer': ['ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoFormerConfig', 'RoFormerOnnxConfig'],
    'tokenization_roformer': ['RoFormerTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCAmelCase:						Any   				=       ['RoFormerTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCAmelCase:						Tuple   				=       [
	    'ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'RoFormerForCausalLM',
	    'RoFormerForMaskedLM',
	    'RoFormerForMultipleChoice',
	    'RoFormerForQuestionAnswering',
	    'RoFormerForSequenceClassification',
	    'RoFormerForTokenClassification',
	    'RoFormerLayer',
	    'RoFormerModel',
	    'RoFormerPreTrainedModel',
	    'load_tf_weights_in_roformer',
	]


try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCAmelCase:						Optional[int]   				=       [
	    'TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFRoFormerForCausalLM',
	    'TFRoFormerForMaskedLM',
	    'TFRoFormerForMultipleChoice',
	    'TFRoFormerForQuestionAnswering',
	    'TFRoFormerForSequenceClassification',
	    'TFRoFormerForTokenClassification',
	    'TFRoFormerLayer',
	    'TFRoFormerModel',
	    'TFRoFormerPreTrainedModel',
	]


try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCAmelCase:						int   				=       [
	    'FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'FlaxRoFormerForMaskedLM',
	    'FlaxRoFormerForMultipleChoice',
	    'FlaxRoFormerForQuestionAnswering',
	    'FlaxRoFormerForSequenceClassification',
	    'FlaxRoFormerForTokenClassification',
	    'FlaxRoFormerModel',
	    'FlaxRoFormerPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
	from .tokenization_roformer import RoFormerTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_roformer_fast import RoFormerTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_roformer import (
		    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RoFormerForCausalLM,
		    RoFormerForMaskedLM,
		    RoFormerForMultipleChoice,
		    RoFormerForQuestionAnswering,
		    RoFormerForSequenceClassification,
		    RoFormerForTokenClassification,
		    RoFormerLayer,
		    RoFormerModel,
		    RoFormerPreTrainedModel,
		    load_tf_weights_in_roformer,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_roformer import (
		    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFRoFormerForCausalLM,
		    TFRoFormerForMaskedLM,
		    TFRoFormerForMultipleChoice,
		    TFRoFormerForQuestionAnswering,
		    TFRoFormerForSequenceClassification,
		    TFRoFormerForTokenClassification,
		    TFRoFormerLayer,
		    TFRoFormerModel,
		    TFRoFormerPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_roformer import (
		    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    FlaxRoFormerForMaskedLM,
		    FlaxRoFormerForMultipleChoice,
		    FlaxRoFormerForQuestionAnswering,
		    FlaxRoFormerForSequenceClassification,
		    FlaxRoFormerForTokenClassification,
		    FlaxRoFormerModel,
		    FlaxRoFormerPreTrainedModel,
		)


else:
	import sys

	_lowerCAmelCase:						Union[str, Any]   				=       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
	import torch


_lowerCAmelCase:						List[Any]   				=       logging.get_logger(__name__)




class       lowercase_					(lowercase__   ):
				snake_case	    =['pixel_values']





				def __init__(				self       ,  lowercase_ = True       ,  lowercase_ = None       ,  lowercase_ = PILImageResampling.BILINEAR       ,  lowercase_ = True       ,  lowercase_ = None       ,  lowercase_ = True       ,  lowercase_ = 1 / 255       ,  lowercase_ = True       ,  lowercase_ = None       ,  lowercase_ = None       ,  **lowercase_       ,  )  -> None:
											super().__init__(**lowercase_)
											a__      =size if size is not None else {'shortest_edge': 256}
											a__      =get_size_dict(lowercase_       ,  default_to_square=lowercase_)
											a__      =crop_size if crop_size is not None else {'height': 224, 'width': 224}
											a__      =get_size_dict(lowercase_       ,  param_name='crop_size')
											a__      =do_resize
											a__      =size
											a__      =resample
											a__      =do_center_crop
											a__      =crop_size
											a__      =do_rescale
											a__      =rescale_factor
											a__      =do_normalize
											a__      =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											a__      =image_std if image_std is not None else IMAGENET_STANDARD_STD





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_       ,  lowercase_ = PILImageResampling.BICUBIC       ,  lowercase_ = None       ,  **lowercase_       ,  )  -> np.ndarray:
											a__      =get_size_dict(lowercase_       ,  default_to_square=lowercase_)
											if "shortest_edge" not in size:
																		raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}""")
											a__      =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       ,  lowercase_       ,  lowercase_       ,  lowercase_ = None       ,  **lowercase_       ,  )  -> np.ndarray:
											a__      =get_size_dict(lowercase_)
											if "height" not in size or "width" not in size:
																		raise ValueError(F"""The `size` parameter 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       ,  lowercase_       ,  lowercase_       ,  lowercase_ = None       ,  **lowercase_)  -> np.ndarray:
											return rescale(lowercase_       ,  scale=lowercase_       ,  data_format=lowercase_       ,  **lowercase_)





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_ = None       ,  **lowercase_       ,  )  -> np.ndarray:
											return normalize(lowercase_       ,  mean=lowercase_       ,  std=lowercase_       ,  data_format=lowercase_       ,  **lowercase_)





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_ = None       ,  lowercase_ = None       ,  lowercase_ = None       ,  lowercase_ = None       ,  lowercase_ = None       ,  lowercase_ = None       ,  lowercase_ = None       ,  lowercase_ = None       ,  lowercase_ = None       ,  lowercase_ = None       ,  lowercase_ = None       ,  lowercase_ = ChannelDimension.FIRST       ,  **lowercase_       ,  )  -> Tuple:
											a__      =do_resize if do_resize is not None else self.do_resize
											a__      =size if size is not None else self.size
											a__      =get_size_dict(lowercase_       ,  default_to_square=lowercase_)
											a__      =resample if resample is not None else self.resample
											a__      =do_center_crop if do_center_crop is not None else self.do_center_crop
											a__      =crop_size if crop_size is not None else self.crop_size
											a__      =get_size_dict(lowercase_       ,  param_name='crop_size')
											a__      =do_rescale if do_rescale is not None else self.do_rescale
											a__      =rescale_factor if rescale_factor is not None else self.rescale_factor
											a__      =do_normalize if do_normalize is not None else self.do_normalize
											a__      =image_mean if image_mean is not None else self.image_mean
											a__      =image_std if image_std is not None else self.image_std

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

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

											if do_normalize and (image_mean is None or image_std is None):
																		raise ValueError('Image mean and std must be specified if do_normalize is True.')

											# All transformations expect numpy arrays.
											a__      =[to_numpy_array(lowercase_) for image in images]

											if do_resize:
																		a__      =[self.resize(image=lowercase_       ,  size=lowercase_       ,  resample=lowercase_) for image in images]

											if do_center_crop:
																		a__      =[self.center_crop(image=lowercase_       ,  size=lowercase_) for image in images]

											if do_rescale:
																		a__      =[self.rescale(image=lowercase_       ,  scale=lowercase_) for image in images]

											if do_normalize:
																		a__      =[self.normalize(image=lowercase_       ,  mean=lowercase_       ,  std=lowercase_) for image in images]

											a__      =[to_channel_dimension_format(lowercase_       ,  lowercase_) for image in images]

											a__      ={'pixel_values': images}
											return BatchFeature(data=lowercase_       ,  tensor_type=lowercase_)





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_ = None)  -> str:
											a__      =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__      =target_sizes.numpy()

																		a__      =[]

																		for idx in range(len(lowercase_)):
																									a__      =torch.nn.functional.interpolate(
																									    logits[idx].unsqueeze(dim=0)       ,  size=target_sizes[idx]       ,  mode='bilinear'       ,  align_corners=lowercase_)
																									a__      =resized_logits[0].argmax(dim=0)
																									semantic_segmentation.append(lowercase_)
											else:
																		a__      =logits.argmax(dim=1)
																		a__      =[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]

											return semantic_segmentation

