
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import SegformerImageProcessor





class 							A__	(			unittest.TestCase):
      def __init__(   self					,						__magic_name__					,						__magic_name__=7					,						__magic_name__=3					,						__magic_name__=3_0					,						__magic_name__=4_0_0					,						__magic_name__=True					,						__magic_name__=None					,						__magic_name__=True					,						__magic_name__=[0.5, 0.5, 0.5]					,						__magic_name__=[0.5, 0.5, 0.5]					,						__magic_name__=False					,						):
       lowerCamelCase	:			str           =   size if size is not None else {"""height""": 3_0, """width""": 3_0}
       lowerCamelCase	:			List[str]           =   parent
       lowerCamelCase	:			Union[str, Any]           =   batch_size
       lowerCamelCase	:			Optional[int]           =   num_channels
       lowerCamelCase	:			List[Any]           =   min_resolution
       lowerCamelCase	:			Tuple           =   max_resolution
       lowerCamelCase	:			str           =   do_resize
       lowerCamelCase	:			int           =   size
       lowerCamelCase	:			Optional[Any]           =   do_normalize
       lowerCamelCase	:			Any           =   image_mean
       lowerCamelCase	:			str           =   image_std
       lowerCamelCase	:			str           =   do_reduce_labels






      def 		UpperCamelCase__							(   self   ):
       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_reduce_labels": self.do_reduce_labels,
       }





def 					_a      (		):
 lowerCamelCase	:			Optional[int]           =   load_dataset("""hf-internal-testing/fixtures_ade20k""",			split="""test"""							)

 lowerCamelCase	:			List[Any]           =   Image.open(dataset[0]["""file"""]							)
 lowerCamelCase	:			int           =   Image.open(dataset[1]["""file"""]							)

 return image, map





def 					_a      (		):
 lowerCamelCase	:			str           =   load_dataset("""hf-internal-testing/fixtures_ade20k""",			split="""test"""							)

 lowerCamelCase	:			List[Any]           =   Image.open(dataset[0]["""file"""]							)
 lowerCamelCase	:			Any           =   Image.open(dataset[1]["""file"""]							)
 lowerCamelCase	:			Tuple           =   Image.open(dataset[2]["""file"""]							)
 lowerCamelCase	:			int           =   Image.open(dataset[3]["""file"""]							)

 return [imagea, imagea], [mapa, mapa]





@require_torch
@require_vision
class 							A__	(			__SCREAMING_SNAKE_CASE						,     unittest.TestCase):
      _UpperCAmelCase       :					Dict					 =    SegformerImageProcessor if is_vision_available() else None
      def 		UpperCamelCase__							(   self   ):
       lowerCamelCase	:			Dict           =   SegformerImageProcessingTester(self   )
      @property
      def 		UpperCamelCase__							(   self   ):
       return self.image_processor_tester.prepare_image_processor_dict()
      def 		UpperCamelCase__							(   self   ):
       lowerCamelCase	:			Any           =   self.image_processing_class(**self.image_processor_dict   )
       self.assertTrue(hasattr(__magic_name__					,						"""do_resize"""   )   )
       self.assertTrue(hasattr(__magic_name__					,						"""size"""   )   )
       self.assertTrue(hasattr(__magic_name__					,						"""do_normalize"""   )   )
       self.assertTrue(hasattr(__magic_name__					,						"""image_mean"""   )   )
       self.assertTrue(hasattr(__magic_name__					,						"""image_std"""   )   )
       self.assertTrue(hasattr(__magic_name__					,						"""do_reduce_labels"""   )   )
      def 		UpperCamelCase__							(   self   ):
       lowerCamelCase	:			Optional[Any]           =   self.image_processing_class.from_dict(self.image_processor_dict   )
       self.assertEqual(image_processor.size					,						{"""height""": 3_0, """width""": 3_0}   )
       self.assertEqual(image_processor.do_reduce_labels					,						__magic_name__   )

       lowerCamelCase	:			str           =   self.image_processing_class.from_dict(self.image_processor_dict					,						size=4_2					,						reduce_labels=__magic_name__   )
       self.assertEqual(image_processor.size					,						{"""height""": 4_2, """width""": 4_2}   )
       self.assertEqual(image_processor.do_reduce_labels					,						__magic_name__   )
      def 		UpperCamelCase__							(   self   ):
       pass
      def 		UpperCamelCase__							(   self   ):
       # Initialize image_processing
       lowerCamelCase	:			Union[str, Any]           =   self.image_processing_class(**self.image_processor_dict   )
       # create random PIL images
       lowerCamelCase	:			List[Any]           =   prepare_image_inputs(self.image_processor_tester					,						equal_resolution=__magic_name__   )
       for image in image_inputs:
        self.assertIsInstance(__magic_name__					,						Image.Image   )

       # Test not batched input
       lowerCamelCase	:			int           =   image_processing(image_inputs[0]					,						return_tensors="""pt"""   ).pixel_values
       self.assertEqual(
           encoded_images.shape					,						(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )					,						)

       # Test batched
       lowerCamelCase	:			Any           =   image_processing(__magic_name__					,						return_tensors="""pt"""   ).pixel_values
       self.assertEqual(
           encoded_images.shape					,						(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )					,						)
      def 		UpperCamelCase__							(   self   ):
       # Initialize image_processing
       lowerCamelCase	:			Tuple           =   self.image_processing_class(**self.image_processor_dict   )
       # create random numpy tensors
       lowerCamelCase	:			List[Any]           =   prepare_image_inputs(self.image_processor_tester					,						equal_resolution=__magic_name__					,						numpify=__magic_name__   )
       for image in image_inputs:
        self.assertIsInstance(__magic_name__					,						np.ndarray   )

       # Test not batched input
       lowerCamelCase	:			List[str]           =   image_processing(image_inputs[0]					,						return_tensors="""pt"""   ).pixel_values
       self.assertEqual(
           encoded_images.shape					,						(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )					,						)

       # Test batched
       lowerCamelCase	:			Dict           =   image_processing(__magic_name__					,						return_tensors="""pt"""   ).pixel_values
       self.assertEqual(
           encoded_images.shape					,						(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )					,						)
      def 		UpperCamelCase__							(   self   ):
       # Initialize image_processing
       lowerCamelCase	:			Dict           =   self.image_processing_class(**self.image_processor_dict   )
       # create random PyTorch tensors
       lowerCamelCase	:			int           =   prepare_image_inputs(self.image_processor_tester					,						equal_resolution=__magic_name__					,						torchify=__magic_name__   )
       for image in image_inputs:
        self.assertIsInstance(__magic_name__					,						torch.Tensor   )

       # Test not batched input
       lowerCamelCase	:			str           =   image_processing(image_inputs[0]					,						return_tensors="""pt"""   ).pixel_values
       self.assertEqual(
           encoded_images.shape					,						(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )					,						)

       # Test batched
       lowerCamelCase	:			Optional[Any]           =   image_processing(__magic_name__					,						return_tensors="""pt"""   ).pixel_values
       self.assertEqual(
           encoded_images.shape					,						(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )					,						)
      def 		UpperCamelCase__							(   self   ):
       # Initialize image_processing
       lowerCamelCase	:			List[str]           =   self.image_processing_class(**self.image_processor_dict   )
       # create random PyTorch tensors
       lowerCamelCase	:			str           =   prepare_image_inputs(self.image_processor_tester					,						equal_resolution=__magic_name__					,						torchify=__magic_name__   )
       lowerCamelCase	:			Optional[int]           =   []
       for image in image_inputs:
        self.assertIsInstance(__magic_name__					,						torch.Tensor   )
        maps.append(torch.zeros(image.shape[-2:]   ).long()   )

       # Test not batched input
       lowerCamelCase	:			List[Any]           =   image_processing(image_inputs[0]					,						maps[0]					,						return_tensors="""pt"""   )
       self.assertEqual(
           encoding["""pixel_values"""].shape					,						(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )					,						)
       self.assertEqual(
           encoding["""labels"""].shape					,						(
               1,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )					,						)
       self.assertEqual(encoding["""labels"""].dtype					,						torch.long   )
       self.assertTrue(encoding["""labels"""].min().item() >= 0   )
       self.assertTrue(encoding["""labels"""].max().item() <= 2_5_5   )

       # Test batched
       lowerCamelCase	:			int           =   image_processing(__magic_name__					,						__magic_name__					,						return_tensors="""pt"""   )
       self.assertEqual(
           encoding["""pixel_values"""].shape					,						(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )					,						)
       self.assertEqual(
           encoding["""labels"""].shape					,						(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )					,						)
       self.assertEqual(encoding["""labels"""].dtype					,						torch.long   )
       self.assertTrue(encoding["""labels"""].min().item() >= 0   )
       self.assertTrue(encoding["""labels"""].max().item() <= 2_5_5   )

       # Test not batched input (PIL images)
       lowerCamelCase    ,					lowerCamelCase	:			Union[str, Any]           =   prepare_semantic_single_inputs()

       lowerCamelCase	:			int           =   image_processing(__magic_name__					,						__magic_name__					,						return_tensors="""pt"""   )
       self.assertEqual(
           encoding["""pixel_values"""].shape					,						(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )					,						)
       self.assertEqual(
           encoding["""labels"""].shape					,						(
               1,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )					,						)
       self.assertEqual(encoding["""labels"""].dtype					,						torch.long   )
       self.assertTrue(encoding["""labels"""].min().item() >= 0   )
       self.assertTrue(encoding["""labels"""].max().item() <= 2_5_5   )

       # Test batched input (PIL images)
       lowerCamelCase    ,					lowerCamelCase	:			Optional[Any]           =   prepare_semantic_batch_inputs()

       lowerCamelCase	:			Union[str, Any]           =   image_processing(__magic_name__					,						__magic_name__					,						return_tensors="""pt"""   )
       self.assertEqual(
           encoding["""pixel_values"""].shape					,						(
               2,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )					,						)
       self.assertEqual(
           encoding["""labels"""].shape					,						(
               2,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )					,						)
       self.assertEqual(encoding["""labels"""].dtype					,						torch.long   )
       self.assertTrue(encoding["""labels"""].min().item() >= 0   )
       self.assertTrue(encoding["""labels"""].max().item() <= 2_5_5   )






      def 		UpperCamelCase__							(   self   ):
       # Initialize image_processing
       lowerCamelCase	:			Tuple           =   self.image_processing_class(**self.image_processor_dict   )

       # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
       lowerCamelCase    ,					lowerCamelCase	:			Dict           =   prepare_semantic_single_inputs()
       lowerCamelCase	:			Any           =   image_processing(__magic_name__					,						__magic_name__					,						return_tensors="""pt"""   )
       self.assertTrue(encoding["""labels"""].min().item() >= 0   )
       self.assertTrue(encoding["""labels"""].max().item() <= 1_5_0   )

       lowerCamelCase	:			Optional[int]           =   True
       lowerCamelCase	:			Dict           =   image_processing(__magic_name__					,						__magic_name__					,						return_tensors="""pt"""   )
       self.assertTrue(encoding["""labels"""].min().item() >= 0   )
       self.assertTrue(encoding["""labels"""].max().item() <= 2_5_5   )






from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_lowerCamelCase           =logging.get_logger(__name__)





class 							A__	(			__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase       :					Tuple					 =    ["""input_ids""", """attention_mask"""]
      def __init__(   self					,						__magic_name__="[PAD]"					,						__magic_name__="[BOS]"					,						__magic_name__="[EOS]"					,						__magic_name__="[MASK]"					,						__magic_name__="[CLS]"					,						__magic_name__="[SEP]"					,						__magic_name__=2_0_4_8					,						**__magic_name__					,						):
       lowerCamelCase	:			List[str]           =   AddedToken(__magic_name__					,						lstrip=__magic_name__					,						rstrip=__magic_name__   ) if isinstance(__magic_name__					,						__magic_name__   ) else pad_token
       lowerCamelCase	:			Dict           =   AddedToken(__magic_name__					,						lstrip=__magic_name__					,						rstrip=__magic_name__   ) if isinstance(__magic_name__					,						__magic_name__   ) else bos_token
       lowerCamelCase	:			Any           =   AddedToken(__magic_name__					,						lstrip=__magic_name__					,						rstrip=__magic_name__   ) if isinstance(__magic_name__					,						__magic_name__   ) else eos_token
       lowerCamelCase	:			Any           =   AddedToken(__magic_name__					,						lstrip=__magic_name__					,						rstrip=__magic_name__   ) if isinstance(__magic_name__					,						__magic_name__   ) else mask_token
       lowerCamelCase	:			Any           =   AddedToken(__magic_name__					,						lstrip=__magic_name__					,						rstrip=__magic_name__   ) if isinstance(__magic_name__					,						__magic_name__   ) else cls_token
       lowerCamelCase	:			Optional[Any]           =   AddedToken(__magic_name__					,						lstrip=__magic_name__					,						rstrip=__magic_name__   ) if isinstance(__magic_name__					,						__magic_name__   ) else sep_token

       super().__init__(
           pad_token=__magic_name__					,						bos_token=__magic_name__					,						eos_token=__magic_name__					,						mask_token=__magic_name__					,						cls_token=__magic_name__					,						sep_token=__magic_name__					,						model_max_length=__magic_name__					,						**__magic_name__					,						)

       lowerCamelCase	:			List[Any]           =   2**8  # utf is 8 bits

       # define special tokens dict
       lowerCamelCase	:			Dict[str, int]           =   {
           self.pad_token: 0,
           self.bos_token: 1,
           self.eos_token: 2,
           self.mask_token: 3,
           self.cls_token: 4,
           self.sep_token: 5,
       }
       lowerCamelCase	:			Optional[int]           =   len(self.special_tokens_encoder   )
       lowerCamelCase	:			Dict[int, str]           =   {v: k for k, v in self.special_tokens_encoder.items()}
      def 		UpperCamelCase__							(   self   ):
       lowerCamelCase	:			List[str]           =   self.special_tokens_encoder.copy()
       vocab.update(self.added_tokens_encoder   )
       for i in range(self._utf_vocab_size   ):
        lowerCamelCase	:			List[Any]           =   chr(__magic_name__   )
        lowerCamelCase	:			Union[str, Any]           =   i + len(self.special_tokens_encoder   )
       return vocab
      @property
      def 		UpperCamelCase__							(   self   ):
       return self._utf_vocab_size + self._num_special_tokens
      def 		UpperCamelCase__							(   self					,						__magic_name__					,						__magic_name__ = None					,						__magic_name__ = False   ):
       if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=__magic_name__					,						token_ids_a=__magic_name__					,						already_has_special_tokens=__magic_name__   )

       # normal case: some special tokens
       if token_ids_a is None:
        return [1] + [0] * len(__magic_name__   ) + [1]
       return [1] + ([0] * len(__magic_name__   )) + [1] + ([0] * len(__magic_name__   )) + [1]
      def 		UpperCamelCase__							(   self					,						__magic_name__					,						__magic_name__ = None   ):
       if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       else:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]
      def 		UpperCamelCase__							(   self					,						__magic_name__   ):
       lowerCamelCase	:			Optional[int]           =   [chr(__magic_name__   ) for i in text.encode("""utf-8"""   )]
       return tokens
      def 		UpperCamelCase__							(   self					,						__magic_name__   ):
       if token in self.special_tokens_encoder:
        lowerCamelCase	:			List[str]           =   self.special_tokens_encoder[token]
       elif token in self.added_tokens_encoder:
        lowerCamelCase	:			List[Any]           =   self.added_tokens_encoder[token]
       elif len(__magic_name__   ) != 1:
        lowerCamelCase	:			Union[str, Any]           =   self.unk_token_id
       else:
        lowerCamelCase	:			int           =   ord(__magic_name__   ) + self._num_special_tokens
       return token_id
      def 		UpperCamelCase__							(   self					,						__magic_name__   ):
       if index in self.special_tokens_decoder:
        lowerCamelCase	:			Union[str, Any]           =   self.special_tokens_decoder[index]
       elif index in self.added_tokens_decoder:
        lowerCamelCase	:			List[str]           =   self.added_tokens_decoder[index]
       else:
        lowerCamelCase	:			Optional[int]           =   chr(index - self._num_special_tokens   )
       return token
      def 		UpperCamelCase__							(   self					,						__magic_name__   ):
       lowerCamelCase	:			List[Any]           =   b""""""
       for token in tokens:
        if token in self.special_tokens_decoder:
         lowerCamelCase	:			List[str]           =   self.special_tokens_decoder[token].encode("""utf-8"""   )
        elif token in self.added_tokens_decoder:
         lowerCamelCase	:			Optional[int]           =   self.special_tokens_decoder[token].encode("""utf-8"""   )
        elif token in self.special_tokens_encoder:
         lowerCamelCase	:			Tuple           =   token.encode("""utf-8"""   )
        elif token in self.added_tokens_encoder:
         lowerCamelCase	:			Union[str, Any]           =   token.encode("""utf-8"""   )
        else:
         lowerCamelCase	:			Dict           =   bytes([ord(__magic_name__   )]   )
        bstring += tok_string
       lowerCamelCase	:			Any           =   bstring.decode("""utf-8"""					,						errors="""replace"""   )
       return string






      def 		UpperCamelCase__							(   self					,						__magic_name__					,						__magic_name__ = None   ):
       return ()




