
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


__snake_case								=  logging.get_logger(__name__)






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)		->		Tuple[int, int]:
					'''simple docstring'''


					def constraint_to_multiple_of(UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_=0	,	UpperCamelCase_=None		):
										SCREAMING_SNAKE_CASE__													= round(val / multiple		) * multiple

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

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

										return x

					SCREAMING_SNAKE_CASE__													= (output_size, output_size) if isinstance(_UpperCAmelCase	,	_UpperCAmelCase		) else output_size

					SCREAMING_SNAKE_CASE__													= get_image_size(_UpperCAmelCase		)
					SCREAMING_SNAKE_CASE__													= output_size

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

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

					SCREAMING_SNAKE_CASE__													= constraint_to_multiple_of(scale_height * input_height	,	multiple=_UpperCAmelCase		)
					SCREAMING_SNAKE_CASE__													= constraint_to_multiple_of(scale_width * input_width	,	multiple=_UpperCAmelCase		)

					return (new_height, new_width)




class 						lowercase__  (						snake_case__  ):
	A__    :   str				       =["pixel_values"]
	def __init__(			self							:   List[str]       ,   UpperCAmelCase_							:   bool = True       ,   UpperCAmelCase_							:   Dict[str, int] = None       ,   UpperCAmelCase_							:   PILImageResampling = PILImageResampling.BILINEAR       ,   UpperCAmelCase_							:   bool = False       ,   UpperCAmelCase_							:   int = 1       ,   UpperCAmelCase_							:   bool = True       ,   UpperCAmelCase_							:   Union[int, float] = 1 / 255       ,   UpperCAmelCase_							:   bool = True       ,   UpperCAmelCase_							:   Optional[Union[float, List[float]]] = None       ,   UpperCAmelCase_							:   Optional[Union[float, List[float]]] = None       ,   **UpperCAmelCase_							:   Dict       ,   ):
						super().__init__(**UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= size if size is not None else {"height": 384, "width": 384}
						SCREAMING_SNAKE_CASE__													= get_size_dict(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= do_resize
						SCREAMING_SNAKE_CASE__													= size
						SCREAMING_SNAKE_CASE__													= keep_aspect_ratio
						SCREAMING_SNAKE_CASE__													= ensure_multiple_of
						SCREAMING_SNAKE_CASE__													= resample
						SCREAMING_SNAKE_CASE__													= do_rescale
						SCREAMING_SNAKE_CASE__													= rescale_factor
						SCREAMING_SNAKE_CASE__													= do_normalize
						SCREAMING_SNAKE_CASE__													= image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						SCREAMING_SNAKE_CASE__													= image_std if image_std is not None else IMAGENET_STANDARD_STD
	def      A_				(			self							:   List[str]       ,   UpperCAmelCase_							:   np.ndarray       ,   UpperCAmelCase_							:   Dict[str, int]       ,   UpperCAmelCase_							:   bool = False       ,   UpperCAmelCase_							:   int = 1       ,   UpperCAmelCase_							:   PILImageResampling = PILImageResampling.BICUBIC       ,   UpperCAmelCase_							:   Optional[Union[str, ChannelDimension]] = None       ,   **UpperCAmelCase_							:   int       ,   ):
						SCREAMING_SNAKE_CASE__													= get_size_dict(UpperCAmelCase_							)
						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()}'							)
						SCREAMING_SNAKE_CASE__													= get_resize_output_image_size(
						    UpperCAmelCase_       ,   output_size=(size['height'], size['width'])       ,   keep_aspect_ratio=UpperCAmelCase_       ,   multiple=UpperCAmelCase_       ,   )
						return resize(UpperCAmelCase_       ,   size=UpperCAmelCase_       ,   resample=UpperCAmelCase_       ,   data_format=UpperCAmelCase_       ,   **UpperCAmelCase_							)
	def      A_				(			self							:   List[Any]       ,   UpperCAmelCase_							:   np.ndarray       ,   UpperCAmelCase_							:   Union[int, float]       ,   UpperCAmelCase_							:   Optional[Union[str, ChannelDimension]] = None       ,   **UpperCAmelCase_							:   Union[str, Any]       ,   ):
						return rescale(UpperCAmelCase_       ,   scale=UpperCAmelCase_       ,   data_format=UpperCAmelCase_       ,   **UpperCAmelCase_							)
	def      A_				(			self							:   List[Any]       ,   UpperCAmelCase_							:   np.ndarray       ,   UpperCAmelCase_							:   Union[float, List[float]]       ,   UpperCAmelCase_							:   Union[float, List[float]]       ,   UpperCAmelCase_							:   Optional[Union[str, ChannelDimension]] = None       ,   **UpperCAmelCase_							:   Any       ,   ):
						return normalize(UpperCAmelCase_       ,   mean=UpperCAmelCase_       ,   std=UpperCAmelCase_       ,   data_format=UpperCAmelCase_       ,   **UpperCAmelCase_							)
	def      A_				(			self							:   List[Any]       ,   UpperCAmelCase_							:   ImageInput       ,   UpperCAmelCase_							:   bool = None       ,   UpperCAmelCase_							:   int = None       ,   UpperCAmelCase_							:   bool = None       ,   UpperCAmelCase_							:   int = None       ,   UpperCAmelCase_							:   PILImageResampling = None       ,   UpperCAmelCase_							:   bool = None       ,   UpperCAmelCase_							:   float = None       ,   UpperCAmelCase_							:   bool = None       ,   UpperCAmelCase_							:   Optional[Union[float, List[float]]] = None       ,   UpperCAmelCase_							:   Optional[Union[float, List[float]]] = None       ,   UpperCAmelCase_							:   Optional[Union[str, TensorType]] = None       ,   UpperCAmelCase_							:   ChannelDimension = ChannelDimension.FIRST       ,   **UpperCAmelCase_							:   Any       ,   ):
						SCREAMING_SNAKE_CASE__													= do_resize if do_resize is not None else self.do_resize
						SCREAMING_SNAKE_CASE__													= size if size is not None else self.size
						SCREAMING_SNAKE_CASE__													= get_size_dict(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
						SCREAMING_SNAKE_CASE__													= ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
						SCREAMING_SNAKE_CASE__													= resample if resample is not None else self.resample
						SCREAMING_SNAKE_CASE__													= do_rescale if do_rescale is not None else self.do_rescale
						SCREAMING_SNAKE_CASE__													= rescale_factor if rescale_factor is not None else self.rescale_factor
						SCREAMING_SNAKE_CASE__													= do_normalize if do_normalize is not None else self.do_normalize
						SCREAMING_SNAKE_CASE__													= image_mean if image_mean is not None else self.image_mean
						SCREAMING_SNAKE_CASE__													= image_std if image_std is not None else self.image_std

						SCREAMING_SNAKE_CASE__													= make_list_of_images(UpperCAmelCase_							)

						if not valid_images(UpperCAmelCase_							):
											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.
						SCREAMING_SNAKE_CASE__													= [to_numpy_array(UpperCAmelCase_							) for image in images]

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

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

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

						SCREAMING_SNAKE_CASE__													= [to_channel_dimension_format(UpperCAmelCase_       ,   UpperCAmelCase_							) for image in images]

						SCREAMING_SNAKE_CASE__													= {"pixel_values": images}
						return BatchFeature(data=UpperCAmelCase_       ,   tensor_type=UpperCAmelCase_							)





	def      A_				(			self							:   Optional[int]       ,   UpperCAmelCase_							:   List[str]       ,   UpperCAmelCase_							:   List[Tuple] = None							):
						SCREAMING_SNAKE_CASE__													= outputs.logits

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

											if is_torch_tensor(UpperCAmelCase_							):
																SCREAMING_SNAKE_CASE__													= target_sizes.numpy()

											SCREAMING_SNAKE_CASE__													= []

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

						return semantic_segmentation






'''simple docstring'''




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_pegasus import PegasusTokenizer
else:
  __SCREAMING_SNAKE_CASE       :     Optional[Any]     =  None


__SCREAMING_SNAKE_CASE       :     Dict     =  logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE       :     Union[str, Any]     =  """▁"""

__SCREAMING_SNAKE_CASE       :     str     =  {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

__SCREAMING_SNAKE_CASE       :     int     =  {
    """vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""},
    """tokenizer_file""": {
        """google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"""
    },
}

__SCREAMING_SNAKE_CASE       :     str     =  {
    """google/pegasus-xsum""": 512,
}

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  Optional[int]						=						VOCAB_FILES_NAMES
      __UpperCamelCase:  Dict						=						PRETRAINED_VOCAB_FILES_MAP
      __UpperCamelCase:  List[Any]						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __UpperCamelCase:  Optional[int]						=						PegasusTokenizer
      __UpperCamelCase:  Optional[Any]						=						["input_ids", "attention_mask"]




      def __init__(       self						:				Dict						,					A						:				List[str]=None						,					A						:				Union[str, Any]=None						,					A						:				Optional[int]="<pad>"						,					A						:				Tuple="</s>"						,					A						:				Union[str, Any]="<unk>"						,					A						:				Union[str, Any]="<mask_2>"						,					A						:				Dict="<mask_1>"						,					A						:				Union[str, Any]=None						,					A						:				int=103						,					**A						:				Optional[Any]						,					):
             _UpperCAmelCase     :  Dict							      =    offset

             if additional_special_tokens is not None:
                    if not isinstance(A						,					A       ):
                           raise TypeError(
                               F"""additional_special_tokens should be of type {type(A       )}, but is"""
                               F""" {type(A       )}"""       )

                    _UpperCAmelCase     :  Optional[int]							      =    (
                        ([mask_token_sent] + additional_special_tokens)
                        if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                        else additional_special_tokens
                    )
                    # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
                    additional_special_tokens_extended += [
                        F"""<unk_{i}>""" for i in range(len(A       )						,					self.offset - 1       )
                    ]

                    if len(set(A       )       ) != len(A       ):
                           raise ValueError(
                               "Please make sure that the provided additional_special_tokens do not contain an incorrectly"
                               F""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."""       )
                    _UpperCAmelCase     :  Any							      =    additional_special_tokens_extended
             else:
                    _UpperCAmelCase     :  Dict							      =    [mask_token_sent] if mask_token_sent is not None else []
                    additional_special_tokens += [F"""<unk_{i}>""" for i in range(2						,					self.offset       )]

             super().__init__(
                 A						,					tokenizer_file=A						,					pad_token=A						,					eos_token=A						,					unk_token=A						,					mask_token=A						,					mask_token_sent=A						,					offset=A						,					additional_special_tokens=A						,					**A						,					)
             _UpperCAmelCase     :  Optional[Any]							      =    vocab_file
             _UpperCAmelCase     :  Optional[Any]							      =    False if not self.vocab_file else True




      def      _A	(       self						:				List[str]						,					A						:				Optional[Any]       ):
             _UpperCAmelCase     :  Any							      =    set(self.all_special_ids       )  # call it once instead of inside list comp
             all_special_ids.remove(self.unk_token_id       )  # <unk> is only sometimes special

             if all_special_ids != set(range(len(self.additional_special_tokens       ) + 3       )       ):
                    raise ValueError(
                        "There should be 3 special tokens: mask_token, pad_token, and eos_token +"
                        F""" {len(self.additional_special_tokens       )} additional_special_tokens, but got {all_special_ids}"""       )

             return [1 if x in all_special_ids else 0 for x in seq]




      def      _A	(       self						:				str						,					A						:				List						,					A						:				Optional[List] = None						,					A						:				bool = False       ):
             if already_has_special_tokens:
                    return self._special_token_mask(A       )
             elif token_ids_a is None:
                    return self._special_token_mask(A       ) + [1]
             else:
                    return self._special_token_mask(token_ids_a + token_ids_a       ) + [1]




      def      _A	(       self						:				Optional[int]						,					A						:				Union[str, Any]						,					A						:				int=None       ):
             if token_ids_a is None:
                    return token_ids_a + [self.eos_token_id]
             # We don't expect to process pairs, but leave the pair logic for API consistency
             return token_ids_a + token_ids_a + [self.eos_token_id]





      def      _A	(       self						:				Union[str, Any]						,					A						:				str						,					A						:				Optional[str] = None       ):
             if not self.can_save_slow_tokenizer:
                    raise ValueError(
                        "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                        "tokenizer."       )

             if not os.path.isdir(A       ):
                    logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""       )
                    return
             _UpperCAmelCase     :  List[Any]							      =    os.path.join(
                 A						,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]       )

             if os.path.abspath(self.vocab_file       ) != os.path.abspath(A       ):
                    copyfile(self.vocab_file						,					A       )

             return (out_vocab_file,)

