





'''simple docstring'''



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


_SCREAMING_SNAKE_CASE   =      logging.get_logger(__name__)






class 							__lowercase    ( lowerCAmelCase__  ):
	a      :			List[str]     =    ["pixel_values"]


	def __init__(self       ,_lowerCamelCase = True       ,_lowerCamelCase = 32       ,_lowerCamelCase=PILImageResampling.BILINEAR       ,_lowerCamelCase = True       ,**_lowerCamelCase       ,)							->							None:







				'''simple docstring'''





				__lowercase			  =							do_resize
				__lowercase			  =							do_rescale
				__lowercase			  =							size_divisor
				__lowercase			  =							resample
				super().__init__(**_lowerCamelCase	)


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase = None       ,**_lowerCamelCase	)							->							np.ndarray:







				'''simple docstring'''





				__lowercase  ,						__lowercase			  =							get_image_size(_lowerCamelCase	)
				# Rounds the height and width down to the closest multiple of size_divisor
				__lowercase			  =							height // size_divisor * size_divisor
				__lowercase			  =							width // size_divisor * size_divisor
				__lowercase			  =							resize(_lowerCamelCase       ,(new_h, new_w)       ,resample=_lowerCamelCase       ,data_format=_lowerCamelCase       ,**_lowerCamelCase	)
				return image


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase = None       ,**_lowerCamelCase	)							->							np.ndarray:







				'''simple docstring'''





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


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase = None       ,_lowerCamelCase = None       ,_lowerCamelCase=None       ,_lowerCamelCase = None       ,_lowerCamelCase = None       ,_lowerCamelCase = ChannelDimension.FIRST       ,**_lowerCamelCase       ,)							->							BatchFeature:







				'''simple docstring'''





				__lowercase			  =							do_resize if do_resize is not None else self.do_resize
				__lowercase			  =							do_rescale if do_rescale is not None else self.do_rescale
				__lowercase			  =							size_divisor if size_divisor is not None else self.size_divisor
				__lowercase			  =							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'''	)

				__lowercase			  =							make_list_of_images(_lowerCamelCase	)

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

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

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

				if do_rescale:
							__lowercase			  =							[self.rescale(_lowerCamelCase       ,scale=1 / 255	) for image in images]

				__lowercase			  =							[to_channel_dimension_format(_lowerCamelCase       ,_lowerCamelCase	) for image in images]

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












'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_SCREAMING_SNAKE_CASE   =      {
    '''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
    '''tokenization_mvp''': ['''MvpTokenizer'''],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE   =      ['''MvpTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE   =      [
	    '''MVP_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''MvpForCausalLM''',
	    '''MvpForConditionalGeneration''',
	    '''MvpForQuestionAnswering''',
	    '''MvpForSequenceClassification''',
	    '''MvpModel''',
	    '''MvpPreTrainedModel''',
	]

if TYPE_CHECKING:
	from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
	from .tokenization_mvp import MvpTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_mvp_fast import MvpTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_mvp import (
		    MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MvpForCausalLM,
		    MvpForConditionalGeneration,
		    MvpForQuestionAnswering,
		    MvpForSequenceClassification,
		    MvpModel,
		    MvpPreTrainedModel,
		)

else:
	import sys

	_SCREAMING_SNAKE_CASE   =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





