


"""simple docstring"""
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


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



class 			_UpperCAmelCase (							__snake_case				):






		'''simple docstring'''

		lowerCamelCase__				=['pixel_values']



		def __init__(self				,       a_ = True				,       a_ = None				,       a_ = PILImageResampling.BILINEAR				,       a_ = True				,       a_ = None				,       a_ = True				,       a_ = 1 / 2_55				,       a_ = True				,       a_ = None				,       a_ = None				,       **a_				,       ):





			'''simple docstring'''

			super().__init__(**a_					)
			__snake_case	:						Any						=				size if size is not None else {'''shortest_edge''': 2_56}
			__snake_case	:						Tuple						=				get_size_dict(a_				,       default_to_square=a_					)
			__snake_case	:						Dict						=				crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
			__snake_case	:						Union[str, Any]						=				get_size_dict(a_				,       param_name='''crop_size'''					)
			__snake_case	:						Optional[int]						=				do_resize
			__snake_case	:						Tuple						=				size
			__snake_case	:						Optional[int]						=				resample
			__snake_case	:						str						=				do_center_crop
			__snake_case	:						str						=				crop_size
			__snake_case	:						Tuple						=				do_rescale
			__snake_case	:						Tuple						=				rescale_factor
			__snake_case	:						Dict						=				do_normalize
			__snake_case	:						List[Any]						=				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
			__snake_case	:						Union[str, Any]						=				image_std if image_std is not None else IMAGENET_STANDARD_STD



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_				,       a_ = PILImageResampling.BICUBIC				,       a_ = None				,       **a_				,       ):





			'''simple docstring'''

			__snake_case	:						Dict						=				get_size_dict(a_				,       default_to_square=a_					)
			if "shortest_edge" not in size:
				raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""					)
			__snake_case	:						Tuple						=				get_resize_output_image_size(a_				,       size=size['''shortest_edge''']				,       default_to_square=a_					)
			return resize(a_				,       size=a_				,       resample=a_				,       data_format=a_				,       **a_					)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_				,       a_ = None				,       **a_				,       ):





			'''simple docstring'''

			__snake_case	:						List[Any]						=				get_size_dict(a_					)
			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(a_				,       size=(size['''height'''], size['''width'''])				,       data_format=a_				,       **a_					)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_				,       a_ = None				,       **a_					):





			'''simple docstring'''

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



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_				,       a_				,       a_ = None				,       **a_				,       ):





			'''simple docstring'''

			return normalize(a_				,       mean=a_				,       std=a_				,       data_format=a_				,       **a_					)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_ = None				,       a_ = None				,       a_ = None				,       a_ = None				,       a_ = None				,       a_ = None				,       a_ = None				,       a_ = None				,       a_ = None				,       a_ = None				,       a_ = None				,       a_ = ChannelDimension.FIRST				,       **a_				,       ):





			'''simple docstring'''

			__snake_case	:						int						=				do_resize if do_resize is not None else self.do_resize
			__snake_case	:						Optional[Any]						=				size if size is not None else self.size
			__snake_case	:						Optional[Any]						=				get_size_dict(a_				,       default_to_square=a_					)
			__snake_case	:						str						=				resample if resample is not None else self.resample
			__snake_case	:						Dict						=				do_center_crop if do_center_crop is not None else self.do_center_crop
			__snake_case	:						Optional[int]						=				crop_size if crop_size is not None else self.crop_size
			__snake_case	:						List[Any]						=				get_size_dict(a_				,       param_name='''crop_size'''					)
			__snake_case	:						int						=				do_rescale if do_rescale is not None else self.do_rescale
			__snake_case	:						Optional[int]						=				rescale_factor if rescale_factor is not None else self.rescale_factor
			__snake_case	:						List[str]						=				do_normalize if do_normalize is not None else self.do_normalize
			__snake_case	:						Dict						=				image_mean if image_mean is not None else self.image_mean
			__snake_case	:						Tuple						=				image_std if image_std is not None else self.image_std

			__snake_case	:						Dict						=				make_list_of_images(a_					)

			if not valid_images(a_					):
				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.
			__snake_case	:						List[str]						=				[to_numpy_array(a_					) for image in images]

			if do_resize:
				__snake_case	:						List[Any]						=				[self.resize(image=a_				,       size=a_				,       resample=a_					) for image in images]

			if do_center_crop:
				__snake_case	:						Dict						=				[self.center_crop(image=a_				,       size=a_					) for image in images]

			if do_rescale:
				__snake_case	:						List[Any]						=				[self.rescale(image=a_				,       scale=a_					) for image in images]

			if do_normalize:
				__snake_case	:						Any						=				[self.normalize(image=a_				,       mean=a_				,       std=a_					) for image in images]

			__snake_case	:						Tuple						=				[to_channel_dimension_format(a_				,       a_					) for image in images]

			__snake_case	:						str						=				{'''pixel_values''': images}
			return BatchFeature(data=a_				,       tensor_type=a_					)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_ = None					):





			'''simple docstring'''

			__snake_case	:						Tuple						=				outputs.logits

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

				if is_torch_tensor(a_					):
					__snake_case	:						Optional[Any]						=				target_sizes.numpy()

				__snake_case	:						Dict						=				[]

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

			return semantic_segmentation













from typing import TYPE_CHECKING

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


_a      	=  {'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OPTConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_a      	=  [
		    '''OPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''OPTForCausalLM''',
		    '''OPTModel''',
		    '''OPTPreTrainedModel''',
		    '''OPTForSequenceClassification''',
		    '''OPTForQuestionAnswering''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_a      	=  ['''TFOPTForCausalLM''', '''TFOPTModel''', '''TFOPTPreTrainedModel''']

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_a      	=  [
		    '''FlaxOPTForCausalLM''',
		    '''FlaxOPTModel''',
		    '''FlaxOPTPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_opt import (
				    OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    OPTForCausalLM,
				    OPTForQuestionAnswering,
				    OPTForSequenceClassification,
				    OPTModel,
				    OPTPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
		import sys

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