



import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class 				lowerCAmelCase_		(  a__       ,    unittest.TestCase							):
       UpperCAmelCase__				:					Any        =  MgpstrTokenizer
       UpperCAmelCase__				:					int        =  False
       UpperCAmelCase__				:					Dict        =  {}
       UpperCAmelCase__				:					str        =  False
       def 					snake_case_       (			self       )     ->		Any:
          super().setUp()

          # fmt: off
          UpperCamelCase    :  Union[str, Any]							=	["[GO]", "[s]", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z"]
          # fmt: on
          UpperCamelCase    :  List[str]							=	dict(zip(__a,			range(len(__a       )       )       )       )

          UpperCamelCase    :  Optional[int]							=	os.path.join(self.tmpdirname,			VOCAB_FILES_NAMES['vocab_file']       )
          with open(self.vocab_file,			'w',			encoding='utf-8'       ) as fp:
             fp.write(json.dumps(__a       ) + '\n'       )
       def 					snake_case_       (			self,			**SCREAMING_SNAKE_CASE_       )     ->		List[Any]:
          return MgpstrTokenizer.from_pretrained(self.tmpdirname,			**__a       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		Optional[Any]:
          UpperCamelCase    :  str							=	"tester"
          UpperCamelCase    :  List[str]							=	"tester"
          return input_text, output_text
       @unittest.skip('MGP-STR always lower cases letters.'       )
       def 					snake_case_       (			self       )     ->		Any:
          pass
       def 					snake_case_       (			self       )     ->		str:
          UpperCamelCase    :  List[str]							=	self.get_tokenizers(do_lower_case=__a       )
          for tokenizer in tokenizers:
             with self.subTest(F"""{tokenizer.__class__.__name__}"""       ):
                UpperCamelCase    :  str							=	"[SPECIAL_TOKEN]"

                tokenizer.add_special_tokens({'cls_token': special_token}       )
                UpperCamelCase    :  List[str]							=	tokenizer.encode([special_token],			add_special_tokens=__a       )
                self.assertEqual(len(__a       ),			1       )

                UpperCamelCase    :  Optional[Any]							=	tokenizer.decode(__a,			skip_special_tokens=__a       )
                self.assertTrue(special_token not in decoded       )
       def 					snake_case_       (			self       )     ->		int:
          UpperCamelCase    :  List[Any]							=	self.get_tokenizers()
          for tokenizer in tokenizers:
             with self.subTest(F"""{tokenizer.__class__.__name__}"""       ):
                UpperCamelCase    :  Union[str, Any]							=	self.get_input_output_texts(__a       )

                UpperCamelCase    :  int							=	tokenizer.tokenize(__a       )
                UpperCamelCase    :  Dict							=	tokenizer.convert_tokens_to_ids(__a       )
                UpperCamelCase    :  Dict							=	tokenizer.encode(__a,			add_special_tokens=__a       )
                self.assertListEqual(__a,			__a       )

                UpperCamelCase    :  Dict							=	tokenizer.convert_ids_to_tokens(__a       )
                self.assertNotEqual(len(__a       ),			0       )
                UpperCamelCase    :  List[str]							=	tokenizer.decode(__a       )
                self.assertIsInstance(__a,			__a       )

                self.assertEqual(text_a.replace(' ',			''       ),			__a       )
       @unittest.skip('MGP-STR tokenizer only handles one sequence.'       )
       def 					snake_case_       (			self       )     ->		Optional[int]:
          pass






       @unittest.skip('inputs cannot be pretokenized in MgpstrTokenizer'       )
       def 					snake_case_       (			self       )     ->		Union[str, Any]:
          pass








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 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							def constraint_to_multiple_of(_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase=0				, _lowerCamelCase=None  ):
														_lowerCAmelCase     :							Tuple    =    round(val / multiple  ) * multiple

														if max_val is not None and x > max_val:
																					_lowerCAmelCase     :							Optional[int]    =    math.floor(val / multiple  ) * multiple

														if x < min_val:
																					_lowerCAmelCase     :							List[str]    =    math.ceil(val / multiple  ) * multiple

														return x

							_lowerCAmelCase     :							Union[str, Any]    =    (output_size, output_size) if isinstance(_lowerCamelCase				, _lowerCamelCase  ) else output_size

							_lowerCAmelCase   ,    _lowerCAmelCase     :							Optional[Any]    =    get_image_size(_lowerCamelCase  )
							_lowerCAmelCase   ,    _lowerCAmelCase     :							Any    =    output_size

							# determine new height and width
							_lowerCAmelCase     :							List[Any]    =    output_height / input_height
							_lowerCAmelCase     :							Any    =    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     :							Union[str, Any]    =    scale_width
														else:
																					# fit height
																					_lowerCAmelCase     :							Union[str, Any]    =    scale_height

							_lowerCAmelCase     :							List[str]    =    constraint_to_multiple_of(scale_height * input_height				, multiple=_lowerCamelCase  )
							_lowerCAmelCase     :							Dict    =    constraint_to_multiple_of(scale_width * input_width				, multiple=_lowerCamelCase  )

							return (new_height, new_width)



class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		['pixel_values']







							def __init__(   self,			__a = True,			__a = None,			__a = PILImageResampling.BILINEAR,			__a = False,			__a = 1,			__a = True,			__a = 1 / 255,			__a = True,			__a = None,			__a = None,			**__a,			):

														'''simple docstring'''

														super().__init__(**__a)
														_lowerCAmelCase     :							Any    =    size if size is not None else {"height": 384, "width": 384}
														_lowerCAmelCase     :							Optional[int]    =    get_size_dict(__a)
														_lowerCAmelCase     :							Optional[Any]    =    do_resize
														_lowerCAmelCase     :							Dict    =    size
														_lowerCAmelCase     :							Any    =    keep_aspect_ratio
														_lowerCAmelCase     :							str    =    ensure_multiple_of
														_lowerCAmelCase     :							str    =    resample
														_lowerCAmelCase     :							Dict    =    do_rescale
														_lowerCAmelCase     :							Optional[int]    =    rescale_factor
														_lowerCAmelCase     :							Dict    =    do_normalize
														_lowerCAmelCase     :							Dict    =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
														_lowerCAmelCase     :							int    =    image_std if image_std is not None else IMAGENET_STANDARD_STD







							def    snake_case__      (   self,			__a,			__a,			__a = False,			__a = 1,			__a = PILImageResampling.BICUBIC,			__a = None,			**__a,			):

														'''simple docstring'''

														_lowerCAmelCase     :							List[Any]    =    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     :							List[Any]    =    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    snake_case__      (   self,			__a,			__a,			__a = None,			**__a,			):

														'''simple docstring'''

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







							def    snake_case__      (   self,			__a,			__a,			__a,			__a = None,			**__a,			):

														'''simple docstring'''

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







							def    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'''

														_lowerCAmelCase     :							int    =    do_resize if do_resize is not None else self.do_resize
														_lowerCAmelCase     :							List[Any]    =    size if size is not None else self.size
														_lowerCAmelCase     :							str    =    get_size_dict(__a)
														_lowerCAmelCase     :							Dict    =    keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
														_lowerCAmelCase     :							Any    =    ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
														_lowerCAmelCase     :							int    =    resample if resample is not None else self.resample
														_lowerCAmelCase     :							Union[str, Any]    =    do_rescale if do_rescale is not None else self.do_rescale
														_lowerCAmelCase     :							Tuple    =    rescale_factor if rescale_factor is not None else self.rescale_factor
														_lowerCAmelCase     :							List[str]    =    do_normalize if do_normalize is not None else self.do_normalize
														_lowerCAmelCase     :							Dict    =    image_mean if image_mean is not None else self.image_mean
														_lowerCAmelCase     :							List[str]    =    image_std if image_std is not None else self.image_std

														_lowerCAmelCase     :							Optional[Any]    =    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     :							List[Any]    =    [to_numpy_array(__a) for image in images]

														if do_resize:
																					_lowerCAmelCase     :							Any    =    [self.resize(image=__a,			size=__a,			resample=__a) for image in images]

														if do_rescale:
																					_lowerCAmelCase     :							List[str]    =    [self.rescale(image=__a,			scale=__a) for image in images]

														if do_normalize:
																					_lowerCAmelCase     :							Dict    =    [self.normalize(image=__a,			mean=__a,			std=__a) for image in images]

														_lowerCAmelCase     :							List[str]    =    [to_channel_dimension_format(__a,			__a) for image in images]

														_lowerCAmelCase     :							Optional[Any]    =    {"pixel_values": images}
														return BatchFeature(data=__a,			tensor_type=__a)







							def    snake_case__      (   self,			__a,			__a = None):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    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     :							List[Any]    =    target_sizes.numpy()

																					_lowerCAmelCase     :							Dict    =    []

																					for idx in range(len(__a)):
																												_lowerCAmelCase     :							int    =    torch.nn.functional.interpolate(
																												    logits[idx].unsqueeze(dim=0),			size=target_sizes[idx],			mode="bilinear",			align_corners=__a)
																												_lowerCAmelCase     :							int    =    resized_logits[0].argmax(dim=0)
																												semantic_segmentation.append(__a)
														else:
																					_lowerCAmelCase     :							Dict    =    logits.argmax(dim=1)
																					_lowerCAmelCase     :							str    =    [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]

														return semantic_segmentation
