

"""simple docstring"""


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 BeitImageProcessor

class    __a							(unittest.TestCase):






     '''simple docstring'''

     def __init__(  self		,			_a		,			_a=7		,			_a=3		,			_a=18		,			_a=30		,			_a=400		,			_a=True		,			_a=None		,			_a=True		,			_a=None		,			_a=True		,			_a=[0.5, 0.5, 0.5]		,			_a=[0.5, 0.5, 0.5]		,			_a=False		,			)     ->					str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__      :					int											=		size if size is not None else {'''height''': 20, '''width''': 20}
           SCREAMING_SNAKE_CASE__      :					Dict											=		crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
           SCREAMING_SNAKE_CASE__      :					List[Any]											=		parent
           SCREAMING_SNAKE_CASE__      :					Dict											=		batch_size
           SCREAMING_SNAKE_CASE__      :					Any											=		num_channels
           SCREAMING_SNAKE_CASE__      :					List[str]											=		image_size
           SCREAMING_SNAKE_CASE__      :					Any											=		min_resolution
           SCREAMING_SNAKE_CASE__      :					Dict											=		max_resolution
           SCREAMING_SNAKE_CASE__      :					List[str]											=		do_resize
           SCREAMING_SNAKE_CASE__      :					Any											=		size
           SCREAMING_SNAKE_CASE__      :					Any											=		do_center_crop
           SCREAMING_SNAKE_CASE__      :					Union[str, Any]											=		crop_size
           SCREAMING_SNAKE_CASE__      :					int											=		do_normalize
           SCREAMING_SNAKE_CASE__      :					Optional[Any]											=		image_mean
           SCREAMING_SNAKE_CASE__      :					Optional[int]											=		image_std
           SCREAMING_SNAKE_CASE__      :					int											=		do_reduce_labels





     def     _a	(  self     )     ->					Dict:


           """simple docstring"""


           return {
               "do_resize": self.do_resize,
               "size": self.size,
               "do_center_crop": self.do_center_crop,
               "crop_size": self.crop_size,
               "do_normalize": self.do_normalize,
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_reduce_labels": self.do_reduce_labels,
           }





def        _lowercase    (			)				-> List[Any]:
      SCREAMING_SNAKE_CASE__      :					Tuple											=		load_dataset("""hf-internal-testing/fixtures_ade20k"""     ,    split="""test"""							)

      SCREAMING_SNAKE_CASE__      :					Tuple											=		Image.open(dataset[0]["""file"""]							)
      SCREAMING_SNAKE_CASE__      :					str											=		Image.open(dataset[1]["""file"""]							)

      return image, map




def        _lowercase    (			)				-> Union[str, Any]:
      SCREAMING_SNAKE_CASE__      :					Tuple											=		load_dataset("""hf-internal-testing/fixtures_ade20k"""     ,    split="""test"""							)

      SCREAMING_SNAKE_CASE__      :					Optional[int]											=		Image.open(ds[0]["""file"""]							)
      SCREAMING_SNAKE_CASE__      :					List[Any]											=		Image.open(ds[1]["""file"""]							)
      SCREAMING_SNAKE_CASE__      :					Any											=		Image.open(ds[2]["""file"""]							)
      SCREAMING_SNAKE_CASE__      :					Optional[int]											=		Image.open(ds[3]["""file"""]							)

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



@require_torch
@require_vision
class    __a							(UpperCAmelCase__      ,		unittest.TestCase):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE  :Union[str, Any]				=       BeitImageProcessor if is_vision_available() else None

     def     _a	(  self     )     ->					List[str]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__      :					List[str]											=		BeitImageProcessingTester(self     )

     @property
     def     _a	(  self     )     ->					List[str]:


           """simple docstring"""


           return self.image_processor_tester.prepare_image_processor_dict()

     def     _a	(  self     )     ->					Optional[int]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__      :					str											=		self.image_processing_class(**self.image_processor_dict     )
           self.assertTrue(hasattr(lowercase_		,			"""do_resize"""     )     )
           self.assertTrue(hasattr(lowercase_		,			"""size"""     )     )
           self.assertTrue(hasattr(lowercase_		,			"""do_center_crop"""     )     )
           self.assertTrue(hasattr(lowercase_		,			"""center_crop"""     )     )
           self.assertTrue(hasattr(lowercase_		,			"""do_normalize"""     )     )
           self.assertTrue(hasattr(lowercase_		,			"""image_mean"""     )     )
           self.assertTrue(hasattr(lowercase_		,			"""image_std"""     )     )

     def     _a	(  self     )     ->					Tuple:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__      :					Dict											=		self.image_processing_class.from_dict(self.image_processor_dict     )
           self.assertEqual(image_processor.size		,			{"""height""": 20, """width""": 20}     )
           self.assertEqual(image_processor.crop_size		,			{"""height""": 18, """width""": 18}     )
           self.assertEqual(image_processor.do_reduce_labels		,			lowercase_     )

           SCREAMING_SNAKE_CASE__      :					Dict											=		self.image_processing_class.from_dict(
               self.image_processor_dict		,			size=42		,			crop_size=84		,			reduce_labels=lowercase_     )
           self.assertEqual(image_processor.size		,			{"""height""": 42, """width""": 42}     )
           self.assertEqual(image_processor.crop_size		,			{"""height""": 84, """width""": 84}     )
           self.assertEqual(image_processor.do_reduce_labels		,			lowercase_     )

     def     _a	(  self     )     ->					List[Any]:


           """simple docstring"""


           pass

     def     _a	(  self     )     ->					Dict:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__      :					Union[str, Any]											=		self.image_processing_class(**self.image_processor_dict     )
           # create random PIL images
           SCREAMING_SNAKE_CASE__      :					Any											=		prepare_image_inputs(self.image_processor_tester		,			equal_resolution=lowercase_     )
           for image in image_inputs:
                 self.assertIsInstance(lowercase_		,			Image.Image     )

           # Test not batched input
           SCREAMING_SNAKE_CASE__      :					Dict											=		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.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )		,			)

           # Test batched
           SCREAMING_SNAKE_CASE__      :					Union[str, Any]											=		image_processing(lowercase_		,			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.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )		,			)

     def     _a	(  self     )     ->					Dict:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__      :					List[str]											=		self.image_processing_class(**self.image_processor_dict     )
           # create random numpy tensors
           SCREAMING_SNAKE_CASE__      :					Optional[int]											=		prepare_image_inputs(self.image_processor_tester		,			equal_resolution=lowercase_		,			numpify=lowercase_     )
           for image in image_inputs:
                 self.assertIsInstance(lowercase_		,			np.ndarray     )

           # Test not batched input
           SCREAMING_SNAKE_CASE__      :					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.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )		,			)

           # Test batched
           SCREAMING_SNAKE_CASE__      :					Optional[int]											=		image_processing(lowercase_		,			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.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )		,			)

     def     _a	(  self     )     ->					int:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__      :					Optional[int]											=		self.image_processing_class(**self.image_processor_dict     )
           # create random PyTorch tensors
           SCREAMING_SNAKE_CASE__      :					List[Any]											=		prepare_image_inputs(self.image_processor_tester		,			equal_resolution=lowercase_		,			torchify=lowercase_     )
           for image in image_inputs:
                 self.assertIsInstance(lowercase_		,			torch.Tensor     )

           # Test not batched input
           SCREAMING_SNAKE_CASE__      :					Union[str, Any]											=		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.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )		,			)

           # Test batched
           SCREAMING_SNAKE_CASE__      :					List[Any]											=		image_processing(lowercase_		,			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.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )		,			)

     def     _a	(  self     )     ->					Tuple:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__      :					Union[str, Any]											=		self.image_processing_class(**self.image_processor_dict     )
           # create random PyTorch tensors
           SCREAMING_SNAKE_CASE__      :					Tuple											=		prepare_image_inputs(self.image_processor_tester		,			equal_resolution=lowercase_		,			torchify=lowercase_     )
           SCREAMING_SNAKE_CASE__      :					Optional[Any]											=		[]
           for image in image_inputs:
                 self.assertIsInstance(lowercase_		,			torch.Tensor     )
                 maps.append(torch.zeros(image.shape[-2:]     ).long()     )

           # Test not batched input
           SCREAMING_SNAKE_CASE__      :					List[str]											=		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.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )		,			)
           self.assertEqual(
               encoding["""labels"""].shape		,			(
                   1,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )		,			)
           self.assertEqual(encoding["""labels"""].dtype		,			torch.long     )
           self.assertTrue(encoding["""labels"""].min().item() >= 0     )
           self.assertTrue(encoding["""labels"""].max().item() <= 255     )

           # Test batched
           SCREAMING_SNAKE_CASE__      :					List[Any]											=		image_processing(lowercase_		,			lowercase_		,			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.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )		,			)
           self.assertEqual(
               encoding["""labels"""].shape		,			(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )		,			)
           self.assertEqual(encoding["""labels"""].dtype		,			torch.long     )
           self.assertTrue(encoding["""labels"""].min().item() >= 0     )
           self.assertTrue(encoding["""labels"""].max().item() <= 255     )

           # Test not batched input (PIL images)
           SCREAMING_SNAKE_CASE__      :					Any											=		prepare_semantic_single_inputs()

           SCREAMING_SNAKE_CASE__      :					List[Any]											=		image_processing(lowercase_		,			lowercase_		,			return_tensors="""pt"""     )
           self.assertEqual(
               encoding["""pixel_values"""].shape		,			(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )		,			)
           self.assertEqual(
               encoding["""labels"""].shape		,			(
                   1,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )		,			)
           self.assertEqual(encoding["""labels"""].dtype		,			torch.long     )
           self.assertTrue(encoding["""labels"""].min().item() >= 0     )
           self.assertTrue(encoding["""labels"""].max().item() <= 255     )

           # Test batched input (PIL images)
           SCREAMING_SNAKE_CASE__      :					int											=		prepare_semantic_batch_inputs()

           SCREAMING_SNAKE_CASE__      :					Tuple											=		image_processing(lowercase_		,			lowercase_		,			return_tensors="""pt"""     )
           self.assertEqual(
               encoding["""pixel_values"""].shape		,			(
                   2,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )		,			)
           self.assertEqual(
               encoding["""labels"""].shape		,			(
                   2,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )		,			)
           self.assertEqual(encoding["""labels"""].dtype		,			torch.long     )
           self.assertTrue(encoding["""labels"""].min().item() >= 0     )
           self.assertTrue(encoding["""labels"""].max().item() <= 255     )





     def     _a	(  self     )     ->					Dict:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__      :					Dict											=		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
           SCREAMING_SNAKE_CASE__      :					Optional[int]											=		prepare_semantic_single_inputs()
           SCREAMING_SNAKE_CASE__      :					Any											=		image_processing(lowercase_		,			lowercase_		,			return_tensors="""pt"""     )
           self.assertTrue(encoding["""labels"""].min().item() >= 0     )
           self.assertTrue(encoding["""labels"""].max().item() <= 150     )

           SCREAMING_SNAKE_CASE__      :					Tuple											=		True
           SCREAMING_SNAKE_CASE__      :					Union[str, Any]											=		image_processing(lowercase_		,			lowercase_		,			return_tensors="""pt"""     )
           self.assertTrue(encoding["""labels"""].min().item() >= 0     )
           self.assertTrue(encoding["""labels"""].max().item() <= 255     )












"""simple docstring"""






import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation


UpperCAmelCase_   : Optional[int]   			= logging.get_logger(__name__)

UpperCAmelCase_   : Dict   			= {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

UpperCAmelCase_   : Dict   			= {
    """vocab_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/vocab.json""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/vocab.json""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/vocab.json""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/vocab.json""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/merges.txt""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/merges.txt""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/merges.txt""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/merges.txt""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/tokenizer.json""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/tokenizer.json""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/tokenizer.json""",
    },
}

UpperCAmelCase_   : List[str]   			= {
    """gpt2""": 1024,
    """gpt2-medium""": 1024,
    """gpt2-large""": 1024,
    """gpt2-xl""": 1024,
    """distilgpt2""": 1024,
}




class 							lowerCAmelCase__				(			UpperCAmelCase__ ):






      '''simple docstring'''

      __UpperCamelCase	 =  VOCAB_FILES_NAMES
      __UpperCamelCase	 =  PRETRAINED_VOCAB_FILES_MAP
      __UpperCamelCase	 =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __UpperCamelCase	 =  ["input_ids", "attention_mask"]
      __UpperCamelCase	 =  GPTaTokenizer
      def __init__(						self     :  Optional[int]						,	lowercase_     :  int=None						,	lowercase_     :  List[str]=None						,	lowercase_     :  Union[str, Any]=None						,	lowercase_     :  Tuple="<|endoftext|>"						,	lowercase_     :  str="<|endoftext|>"						,	lowercase_     :  Dict="<|endoftext|>"						,	lowercase_     :  Tuple=False						,	**lowercase_     :  Optional[int]						,	):




          '''simple docstring'''


          super().__init__(
              lowercase_						,	lowercase_						,	tokenizer_file=lowercase_						,	unk_token=lowercase_						,	bos_token=lowercase_						,	eos_token=lowercase_						,	add_prefix_space=lowercase_						,	**lowercase_						,	)

          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		kwargs.pop('''add_bos_token'''						,	lowercase_)

          SCREAMING_SNAKE_CASE_      :  Dict    						=		json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
          if pre_tok_state.get('''add_prefix_space'''						,	lowercase_) != add_prefix_space:
              SCREAMING_SNAKE_CASE_      :  int    						=		getattr(lowercase_						,	pre_tok_state.pop('''type'''))
              SCREAMING_SNAKE_CASE_      :  str    						=		add_prefix_space
              SCREAMING_SNAKE_CASE_      :  Dict    						=		pre_tok_class(**lowercase_)

          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		add_prefix_space
      def _SCREAMING_SNAKE_CASE (						self     :  str						,	*lowercase_     :  List[Any]						,	**lowercase_     :  Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		kwargs.get('''is_split_into_words'''						,	lowercase_)
          assert self.add_prefix_space or not is_split_into_words, (
              F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
              "to use it with pretokenized inputs."
          )

          return super()._batch_encode_plus(*lowercase_						,	**lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]						,	*lowercase_     :  List[str]						,	**lowercase_     :  List[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[str]    						=		kwargs.get('''is_split_into_words'''						,	lowercase_)

          assert self.add_prefix_space or not is_split_into_words, (
              F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
              "to use it with pretokenized inputs."
          )

          return super()._encode_plus(*lowercase_						,	**lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  int						,	lowercase_     :  str						,	lowercase_     :  Optional[str] = None):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[str]    						=		self._tokenizer.model.save(lowercase_						,	name=lowercase_)
          return tuple(lowercase_)



      def _SCREAMING_SNAKE_CASE (						self     :  int						,	lowercase_     :  "Conversation"):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Dict    						=		[]
          for is_user, text in conversation.iter_texts():
              input_ids.extend(self.encode(lowercase_						,	add_special_tokens=lowercase_) + [self.eos_token_id])

          if len(lowercase_) > self.model_max_length:
              SCREAMING_SNAKE_CASE_      :  Any    						=		input_ids[-self.model_max_length :]
          return input_ids


