





from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__A				:    Optional[Any]       =   logging.get_logger(__name__)

class 	A_       (a_			):
	UpperCAmelCase__					=			['''pixel_values''']
	def __init__(    self							,					_A = True							,					_A = 3_2							,					_A=PILImageResampling.BILINEAR							,					_A = True							,					**_A							,					):






				'''simple docstring'''







				UpperCAmelCase   		=				do_resize
				UpperCAmelCase   		=				do_rescale
				UpperCAmelCase   		=				size_divisor
				UpperCAmelCase   		=				resample
				super().__init__(**_A		)
	def      _lowercase				(    self							,					_A							,					_A							,					_A							,					_A = None							,					**_A		):






				'''simple docstring'''







				UpperCAmelCase		,			UpperCAmelCase   		=				get_image_size(_A		)
				# Rounds the height and width down to the closest multiple of size_divisor
				UpperCAmelCase   		=				height // size_divisor * size_divisor
				UpperCAmelCase   		=				width // size_divisor * size_divisor
				UpperCAmelCase   		=				resize(_A							,					(new_h, new_w)							,					resample=_A							,					data_format=_A							,					**_A		)
				return image
	def      _lowercase				(    self							,					_A							,					_A							,					_A = None							,					**_A		):






				'''simple docstring'''







				return rescale(image=_A							,					scale=_A							,					data_format=_A							,					**_A		)







	def      _lowercase				(    self							,					_A							,					_A = None							,					_A = None							,					_A=None							,					_A = None							,					_A = None							,					_A = ChannelDimension.FIRST							,					**_A							,					):






				'''simple docstring'''







				UpperCAmelCase   		=				do_resize if do_resize is not None else self.do_resize
				UpperCAmelCase   		=				do_rescale if do_rescale is not None else self.do_rescale
				UpperCAmelCase   		=				size_divisor if size_divisor is not None else self.size_divisor
				UpperCAmelCase   		=				resample if resample is not None else self.resample

				if do_resize and size_divisor is None:
							raise ValueError('''size_divisor is required for resizing'''		)

				UpperCAmelCase   		=				make_list_of_images(_A		)

				if not valid_images(_A		):
							raise ValueError('''Invalid image(s)'''		)

				# All transformations expect numpy arrays.
				UpperCAmelCase   		=				[to_numpy_array(_A		) for img in images]

				if do_resize:
							UpperCAmelCase   		=				[self.resize(_A							,					size_divisor=_A							,					resample=_A		) for image in images]

				if do_rescale:
							UpperCAmelCase   		=				[self.rescale(_A							,					scale=1 / 2_5_5		) for image in images]

				UpperCAmelCase   		=				[to_channel_dimension_format(_A							,					_A		) for image in images]

				UpperCAmelCase   		=				{'''pixel_values''': images}
				return BatchFeature(data=_A							,					tensor_type=_A		)










from typing import TYPE_CHECKING

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


__A				:    Dict       =   {
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A				:    Union[str, Any]       =   ["RoFormerTokenizerFast"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A				:    str       =   [
				    "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:
				__A				:    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:
				__A				:    Any       =   [
				    "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

				__A				:    int       =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



