


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

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
		import torch

if is_vision_available():
		import PIL


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






def 	__lowerCamelCase						(							lowerCamelCase__		:						np.ndarray  ,							lowerCamelCase__		:						Union[int, Iterable[int]]  ,							lowerCamelCase__		:						bool  ,							lowerCamelCase__		:						int      ):





			'''simple docstring'''




			def constraint_to_multiple_of(lowerCamelCase__		:						int  ,							lowerCamelCase__		:						List[Any]  ,							lowerCamelCase__		:						List[Any]=0  ,							lowerCamelCase__		:						Any=None      ):
						lowerCamelCase    		=  round(val / multiple      ) * multiple

						if max_val is not None and x > max_val:
									lowerCamelCase    		=  math.floor(val / multiple      ) * multiple

						if x < min_val:
									lowerCamelCase    		=  math.ceil(val / multiple      ) * multiple

						return x

			lowerCamelCase    		=  (output_size, output_size) if isinstance(lowerCamelCase__  ,							lowerCamelCase__      ) else output_size

			lowerCamelCase		,		lowerCamelCase    		=  get_image_size(lowerCamelCase__      )
			lowerCamelCase		,		lowerCamelCase    		=  output_size

			# determine new height and width
			lowerCamelCase    		=  output_height / input_height
			lowerCamelCase    		=  output_width / input_width

			if keep_aspect_ratio:
						# scale as little as possible
						if abs(1 - scale_width      ) < abs(1 - scale_height      ):
									# fit width
									lowerCamelCase    		=  scale_width
						else:
									# fit height
									lowerCamelCase    		=  scale_height

			lowerCamelCase    		=  constraint_to_multiple_of(scale_height * input_height  ,							multiple=lowerCamelCase__      )
			lowerCamelCase    		=  constraint_to_multiple_of(scale_width * input_width  ,							multiple=lowerCamelCase__      )

			return (new_height, new_width)







class        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					str			       =		["pixel_values"]







		def __init__( self		,      A = True		,      A = None		,      A = PILImageResampling.BILINEAR		,      A = False		,      A = 1		,      A = True		,      A = 1 / 2_55		,      A = True		,      A = None		,      A = None		,      **A		,      )  -> None:







					'''simple docstring'''







					super().__init__(**A	)
					lowerCamelCase    		=  size if size is not None else {"""height""": 3_84, """width""": 3_84}
					lowerCamelCase    		=  get_size_dict(A	)
					lowerCamelCase    		=  do_resize
					lowerCamelCase    		=  size
					lowerCamelCase    		=  keep_aspect_ratio
					lowerCamelCase    		=  ensure_multiple_of
					lowerCamelCase    		=  resample
					lowerCamelCase    		=  do_rescale
					lowerCamelCase    		=  rescale_factor
					lowerCamelCase    		=  do_normalize
					lowerCamelCase    		=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
					lowerCamelCase    		=  image_std if image_std is not None else IMAGENET_STANDARD_STD







		def 					__A   ( self		,      A		,      A		,      A = False		,      A = 1		,      A = PILImageResampling.BICUBIC		,      A = None		,      **A		,      )  -> np.ndarray:







					'''simple docstring'''







					lowerCamelCase    		=  get_size_dict(A	)
					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()}'	)
					lowerCamelCase    		=  get_resize_output_image_size(
					    A		,      output_size=(size["""height"""], size["""width"""])		,      keep_aspect_ratio=A		,      multiple=A		,      )
					return resize(A		,      size=A		,      resample=A		,      data_format=A		,      **A	)







		def 					__A   ( self		,      A		,      A		,      A = None		,      **A		,      )  -> int:







					'''simple docstring'''







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







		def 					__A   ( self		,      A		,      A		,      A		,      A = None		,      **A		,      )  -> np.ndarray:







					'''simple docstring'''







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







		def 					__A   ( 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		,      )  -> PIL.Image.Image:







					'''simple docstring'''







					lowerCamelCase    		=  do_resize if do_resize is not None else self.do_resize
					lowerCamelCase    		=  size if size is not None else self.size
					lowerCamelCase    		=  get_size_dict(A	)
					lowerCamelCase    		=  keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
					lowerCamelCase    		=  ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
					lowerCamelCase    		=  resample if resample is not None else self.resample
					lowerCamelCase    		=  do_rescale if do_rescale is not None else self.do_rescale
					lowerCamelCase    		=  rescale_factor if rescale_factor is not None else self.rescale_factor
					lowerCamelCase    		=  do_normalize if do_normalize is not None else self.do_normalize
					lowerCamelCase    		=  image_mean if image_mean is not None else self.image_mean
					lowerCamelCase    		=  image_std if image_std is not None else self.image_std

					lowerCamelCase    		=  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 or resample is None:
								raise ValueError("""Size and resample 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_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.
					lowerCamelCase    		=  [to_numpy_array(A	) for image in images]

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

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

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

					lowerCamelCase    		=  [to_channel_dimension_format(A		,      A	) for image in images]

					lowerCamelCase    		=  {"""pixel_values""": images}
					return BatchFeature(data=A		,      tensor_type=A	)







		def 					__A   ( self		,      A		,      A = None	)  -> Union[str, Any]:







					'''simple docstring'''







					lowerCamelCase    		=  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	):
											lowerCamelCase    		=  target_sizes.numpy()

								lowerCamelCase    		=  []

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


UpperCAmelCase   :    Tuple     =		{
    "configuration_blenderbot_small": [
        "BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotSmallConfig",
        "BlenderbotSmallOnnxConfig",
    ],
    "tokenization_blenderbot_small": ["BlenderbotSmallTokenizer"],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase   :    List[Any]     =		["BlenderbotSmallTokenizerFast"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase   :    List[str]     =		[
		    "BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "BlenderbotSmallForCausalLM",
		    "BlenderbotSmallForConditionalGeneration",
		    "BlenderbotSmallModel",
		    "BlenderbotSmallPreTrainedModel",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase   :    Any     =		[
		    "TFBlenderbotSmallForConditionalGeneration",
		    "TFBlenderbotSmallModel",
		    "TFBlenderbotSmallPreTrainedModel",
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase   :    List[Any]     =		[
		    "FlaxBlenderbotSmallForConditionalGeneration",
		    "FlaxBlenderbotSmallModel",
		    "FlaxBlenderbotSmallPreTrainedModel",
		]

if TYPE_CHECKING:
		from .configuration_blenderbot_small import (
		    BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    BlenderbotSmallConfig,
		    BlenderbotSmallOnnxConfig,
		)
		from .tokenization_blenderbot_small import BlenderbotSmallTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_blenderbot_small_fast import BlenderbotSmallTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_blenderbot_small import (
				    BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BlenderbotSmallForCausalLM,
				    BlenderbotSmallForConditionalGeneration,
				    BlenderbotSmallModel,
				    BlenderbotSmallPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_blenderbot_small import (
				    TFBlenderbotSmallForConditionalGeneration,
				    TFBlenderbotSmallModel,
				    TFBlenderbotSmallPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_blenderbot_small import (
				    FlaxBlenderbotSmallForConditionalGeneration,
				    FlaxBlenderbotSmallModel,
				    FlaxBlenderbotSmallPreTrainedModel,
				)

else:
		import sys

		UpperCAmelCase   :    List[Any]     =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

