



import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer


__lowerCamelCase	:					int			  =					logging.get_logger(__name__)


__lowerCamelCase	:					Optional[Any]			  =					{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

# See all MVP models at https://huggingface.co/models?filter=mvp
__lowerCamelCase	:					List[str]			  =					{
    """vocab_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json""",
    },
    """added_tokens.json""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json""",
    },
    """merges_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json""",
    },
}

__lowerCamelCase	:					Union[str, Any]			  =					{
    """RUCAIBox/mvp""": 1024,
}



class  A__       (	__snake_case      ):
      _UpperCAmelCase     :Dict						   =       VOCAB_FILES_NAMES
      _UpperCAmelCase     :List[Any]						   =       PRETRAINED_VOCAB_FILES_MAP
      _UpperCAmelCase     :Any						   =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _UpperCAmelCase     :str						   =       ['input_ids', 'attention_mask']
      _UpperCAmelCase     :Optional[Any]						   =       MvpTokenizer





      def __init__(			self	,							A_=None	,							A_=None	,							A_=None	,							A_="replace"	,							A_="<s>"	,							A_="</s>"	,							A_="</s>"	,							A_="<s>"	,							A_="<unk>"	,							A_="<pad>"	,							A_="<mask>"	,							A_=False	,							A_=True	,							**A_	,							):
       '''simple docstring'''




       super().__init__(
           A_	,							A_	,							tokenizer_file=A_	,							errors=A_	,							bos_token=A_	,							eos_token=A_	,							sep_token=A_	,							cls_token=A_	,							unk_token=A_	,							pad_token=A_	,							mask_token=A_	,							add_prefix_space=A_	,							trim_offsets=A_	,							**A_	,							)

       UpperCamelCase     : Any	     =       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
       if pre_tok_state.get("add_prefix_space"	,							A_       ) != add_prefix_space:
        UpperCamelCase     : Optional[int]	     =       getattr(A_	,							pre_tok_state.pop("type"       )       )
        UpperCamelCase     : List[str]	     =       add_prefix_space
        UpperCamelCase     : Optional[int]	     =       pre_tok_class(**A_       )

       UpperCamelCase     : Optional[Any]	     =       add_prefix_space

       # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
       UpperCamelCase     : int	     =       "post_processor"
       UpperCamelCase     : Optional[int]	     =       getattr(self.backend_tokenizer	,							A_	,							A_       )
       if tokenizer_component_instance:
        UpperCamelCase     : List[str]	     =       json.loads(tokenizer_component_instance.__getstate__()       )

        # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
        if "sep" in state:
         UpperCamelCase     : Any	     =       tuple(state["sep"]       )
        if "cls" in state:
         UpperCamelCase     : Optional[int]	     =       tuple(state["cls"]       )

        UpperCamelCase     : Dict	     =       False

        if state.get("add_prefix_space"	,							A_       ) != add_prefix_space:
         UpperCamelCase     : List[Any]	     =       add_prefix_space
         UpperCamelCase     : Any	     =       True

        if state.get("trim_offsets"	,							A_       ) != trim_offsets:
         UpperCamelCase     : Union[str, Any]	     =       trim_offsets
         UpperCamelCase     : Tuple	     =       True

        if changes_to_apply:
         UpperCamelCase     : Union[str, Any]	     =       getattr(A_	,							state.pop("type"       )       )
         UpperCamelCase     : List[str]	     =       component_class(**A_       )
         setattr(self.backend_tokenizer	,							A_	,							A_       )





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       if self._mask_token is None:
        if self.verbose:
         logger.error("Using mask_token, but it is not set yet."       )
        return None
       return str(self._mask_token       )





      @mask_token.setter
      def 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : Dict	     =       AddedToken(A_	,							lstrip=A_	,							rstrip=A_       ) if isinstance(A_	,							A_       ) else value
       UpperCamelCase     : Optional[Any]	     =       value





      def 	__UpperCamelCase(			self	,							*A_	,							**A_       ):
       '''simple docstring'''




       UpperCamelCase     : str	     =       kwargs.get("is_split_into_words"	,							A_       )

       if is_split_into_words and not self.add_prefix_space:
        raise ValueError(
            F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
            "to use it with pretokenized inputs."       )

       return super()._batch_encode_plus(*A_	,							**A_       )





      def 	__UpperCamelCase(			self	,							*A_	,							**A_       ):
       '''simple docstring'''




       UpperCamelCase     : Tuple	     =       kwargs.get("is_split_into_words"	,							A_       )

       if is_split_into_words and not self.add_prefix_space:
        raise ValueError(
            F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
            "to use it with pretokenized inputs."       )

       return super()._encode_plus(*A_	,							**A_       )





      def 	__UpperCamelCase(			self	,							A_	,							A_ = None       ):
       '''simple docstring'''




       UpperCamelCase     : Union[str, Any]	     =       self._tokenizer.model.save(A_	,							name=A_       )
       return tuple(A_       )





      def 	__UpperCamelCase(			self	,							A_	,							A_=None       ):
       '''simple docstring'''




       UpperCamelCase     : Tuple	     =       [self.bos_token_id] + token_ids_a + [self.eos_token_id]
       if token_ids_a is None:
        return output

       return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





      def 	__UpperCamelCase(			self	,							A_	,							A_ = None       ):
       '''simple docstring'''




       UpperCamelCase     : Union[str, Any]	     =       [self.sep_token_id]
       UpperCamelCase     : int	     =       [self.cls_token_id]

       if token_ids_a is None:
        return len(cls + token_ids_a + sep       ) * [0]
       return len(cls + token_ids_a + sep + sep + token_ids_a + sep       ) * [0]







import unittest

import numpy as np

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 ViTImageProcessor



class  A__       (	unittest.TestCase      ):





      def __init__(			self	,							A_	,							A_=13	,							A_=3	,							A_=224	,							A_=30	,							A_=400	,							A_=True	,							A_=None	,							A_=True	,							A_=[0.5, 0.5, 0.5]	,							A_=[0.5, 0.5, 0.5]	,							):
       '''simple docstring'''




       UpperCamelCase     : Any	     =       size if size is not None else {"height": 18, "width": 18}
       UpperCamelCase     : Tuple	     =       parent
       UpperCamelCase     : Tuple	     =       batch_size
       UpperCamelCase     : Tuple	     =       num_channels
       UpperCamelCase     : str	     =       image_size
       UpperCamelCase     : Optional[int]	     =       min_resolution
       UpperCamelCase     : List[Any]	     =       max_resolution
       UpperCamelCase     : Union[str, Any]	     =       do_resize
       UpperCamelCase     : str	     =       size
       UpperCamelCase     : List[str]	     =       do_normalize
       UpperCamelCase     : Any	     =       image_mean
       UpperCamelCase     : int	     =       image_std





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




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



@require_torch
@require_vision
class  A__       (	__snake_case	,       unittest.TestCase      ):
      _UpperCAmelCase     :Tuple						   =       ViTImageProcessor if is_vision_available() else None





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : int	     =       EfficientFormerImageProcessorTester(self       )





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return self.image_proc_tester.prepare_image_processor_dict()





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Union[str, Any]	     =       self.image_processing_class(**self.image_processor_dict       )
       self.assertTrue(hasattr(A_	,							"image_mean"       )       )
       self.assertTrue(hasattr(A_	,							"image_std"       )       )
       self.assertTrue(hasattr(A_	,							"do_normalize"       )       )
       self.assertTrue(hasattr(A_	,							"do_resize"       )       )
       self.assertTrue(hasattr(A_	,							"size"       )       )





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       pass





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




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

       # Test not batched input
       UpperCamelCase     : Optional[Any]	     =       image_processor(image_inputs[0]	,							return_tensors="pt"       ).pixel_values
       self.assertEqual(
           encoded_images.shape	,							(
               1,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )	,							)

       # Test batched
       UpperCamelCase     : Tuple	     =       image_processor(A_	,							return_tensors="pt"       ).pixel_values
       self.assertEqual(
           encoded_images.shape	,							(
               self.image_proc_tester.batch_size,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )	,							)





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Optional[int]	     =       self.image_processing_class(**self.image_processor_dict       )
       # create random numpy tensors
       UpperCamelCase     : List[Any]	     =       prepare_image_inputs(self.image_proc_tester	,							equal_resolution=A_	,							numpify=A_       )
       for image in image_inputs:
        self.assertIsInstance(A_	,							np.ndarray       )

       # Test not batched input
       UpperCamelCase     : Dict	     =       image_processor(image_inputs[0]	,							return_tensors="pt"       ).pixel_values
       self.assertEqual(
           encoded_images.shape	,							(
               1,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )	,							)

       # Test batched
       UpperCamelCase     : Optional[Any]	     =       image_processor(A_	,							return_tensors="pt"       ).pixel_values
       self.assertEqual(
           encoded_images.shape	,							(
               self.image_proc_tester.batch_size,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )	,							)





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Dict	     =       self.image_processing_class(**self.image_processor_dict       )
       # create random PyTorch tensors
       UpperCamelCase     : Any	     =       prepare_image_inputs(self.image_proc_tester	,							equal_resolution=A_	,							torchify=A_       )
       for image in image_inputs:
        self.assertIsInstance(A_	,							torch.Tensor       )

       # Test not batched input
       UpperCamelCase     : Any	     =       image_processor(image_inputs[0]	,							return_tensors="pt"       ).pixel_values
       self.assertEqual(
           encoded_images.shape	,							(
               1,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )	,							)

       # Test batched
       UpperCamelCase     : Any	     =       image_processor(A_	,							return_tensors="pt"       ).pixel_values
       self.assertEqual(
           encoded_images.shape	,							(
               self.image_proc_tester.batch_size,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )	,							)
