
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_  :	Any 				=					logging.get_logger(__name__)


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

# See all MVP models at https://huggingface.co/models?filter=mvp
lowerCamelCase_  :	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""": 10_24,
}

class     _UpperCamelCase			(   _A					):






			'''simple docstring'''

			__UpperCamelCase :  List[Any]    =	VOCAB_FILES_NAMES
			__UpperCamelCase :  int    =	PRETRAINED_VOCAB_FILES_MAP
			__UpperCamelCase :  Any    =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__UpperCamelCase :  Any    =	["""input_ids""", """attention_mask"""]
			__UpperCamelCase :  int    =	MvpTokenizer

			def __init__(    self	:    Union[str, Any]  ,		snake_case_	:    Any=None  ,		snake_case_	:    Any=None  ,		snake_case_	:    Union[str, Any]=None  ,		snake_case_	:    Tuple="replace"  ,		snake_case_	:    Dict="<s>"  ,		snake_case_	:    Dict="</s>"  ,		snake_case_	:    Tuple="</s>"  ,		snake_case_	:    int="<s>"  ,		snake_case_	:    Tuple="<unk>"  ,		snake_case_	:    Optional[int]="<pad>"  ,		snake_case_	:    Any="<mask>"  ,		snake_case_	:    Union[str, Any]=False  ,		snake_case_	:    Optional[int]=True  ,		**snake_case_	:    Union[str, Any]  ,		):
							super().__init__(
							    snake_case_  ,		snake_case_  ,		tokenizer_file=snake_case_  ,		errors=snake_case_  ,		bos_token=snake_case_  ,		eos_token=snake_case_  ,		sep_token=snake_case_  ,		cls_token=snake_case_  ,		unk_token=snake_case_  ,		pad_token=snake_case_  ,		mask_token=snake_case_  ,		add_prefix_space=snake_case_  ,		trim_offsets=snake_case_  ,		**snake_case_  ,		)

							UpperCamelCase_:							Any		=  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
							if pre_tok_state.get("""add_prefix_space"""  ,		snake_case_ ) != add_prefix_space:
											UpperCamelCase_:							str		=  getattr(snake_case_  ,		pre_tok_state.pop("""type""" ) )
											UpperCamelCase_:							int		=  add_prefix_space
											UpperCamelCase_:							int		=  pre_tok_class(**snake_case_ )

							UpperCamelCase_:							int		=  add_prefix_space

							# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
							UpperCamelCase_:							Tuple		=  """post_processor"""
							UpperCamelCase_:							Optional[int]		=  getattr(self.backend_tokenizer  ,		snake_case_  ,		snake_case_ )
							if tokenizer_component_instance:
											UpperCamelCase_:							Optional[int]		=  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_:							Union[str, Any]		=  tuple(state["""sep"""] )
											if "cls" in state:
															UpperCamelCase_:							List[Any]		=  tuple(state["""cls"""] )

											UpperCamelCase_:							List[str]		=  False

											if state.get("""add_prefix_space"""  ,		snake_case_ ) != add_prefix_space:
															UpperCamelCase_:							str		=  add_prefix_space
															UpperCamelCase_:							Union[str, Any]		=  True

											if state.get("""trim_offsets"""  ,		snake_case_ ) != trim_offsets:
															UpperCamelCase_:							Optional[int]		=  trim_offsets
															UpperCamelCase_:							Any		=  True

											if changes_to_apply:
															UpperCamelCase_:							Optional[int]		=  getattr(snake_case_  ,		state.pop("""type""" ) )
															UpperCamelCase_:							Union[str, Any]		=  component_class(**snake_case_ )
															setattr(self.backend_tokenizer  ,		snake_case_  ,		snake_case_ )

			@property
			def  lowerCAmelCase__		(    self	:    Union[str, Any] ):
							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  lowerCAmelCase__		(    self	:    Union[str, Any]  ,		snake_case_	:    Dict ):
							UpperCamelCase_:							List[str]		=  AddedToken(snake_case_  ,		lstrip=snake_case_  ,		rstrip=snake_case_ ) if isinstance(snake_case_  ,		snake_case_ ) else value
							UpperCamelCase_:							Optional[int]		=  value

			def  lowerCAmelCase__		(    self	:    Optional[int]  ,		*snake_case_	:    Dict  ,		**snake_case_	:    Optional[int] ):
							UpperCamelCase_:							Optional[int]		=  kwargs.get("""is_split_into_words"""  ,		snake_case_ )

							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(*snake_case_  ,		**snake_case_ )

			def  lowerCAmelCase__		(    self	:    Union[str, Any]  ,		*snake_case_	:    Dict  ,		**snake_case_	:    Tuple ):
							UpperCamelCase_:							Any		=  kwargs.get("""is_split_into_words"""  ,		snake_case_ )

							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(*snake_case_  ,		**snake_case_ )

			def  lowerCAmelCase__		(    self	:    Tuple  ,		snake_case_	:    str  ,		snake_case_	:    Optional[str] = None ):
							UpperCamelCase_:							Optional[int]		=  self._tokenizer.model.save(snake_case_  ,		name=snake_case_ )
							return tuple(snake_case_ )

			def  lowerCAmelCase__		(    self	:    Tuple  ,		snake_case_	:    Union[str, Any]  ,		snake_case_	:    Any=None ):
							UpperCamelCase_:							str		=  [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  lowerCAmelCase__		(    self	:    Any  ,		snake_case_	:    List[int]  ,		snake_case_	:    Optional[List[int]] = None ):
							UpperCamelCase_:							Tuple		=  [self.sep_token_id]
							UpperCamelCase_:							List[str]		=  [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 json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import ImageGPTImageProcessor

class     _UpperCamelCase			(   unittest.TestCase					):






			'''simple docstring'''

			def __init__(    self	:    Optional[int]  ,		snake_case_	:    Dict  ,		snake_case_	:    Tuple=7  ,		snake_case_	:    Optional[Any]=3  ,		snake_case_	:    Dict=18  ,		snake_case_	:    Dict=30  ,		snake_case_	:    Union[str, Any]=400  ,		snake_case_	:    List[Any]=True  ,		snake_case_	:    Any=None  ,		snake_case_	:    List[str]=True  ,		):
							UpperCamelCase_:							Dict		=  size if size is not None else {"""height""": 18, """width""": 18}
							UpperCamelCase_:							Union[str, Any]		=  parent
							UpperCamelCase_:							Tuple		=  batch_size
							UpperCamelCase_:							List[str]		=  num_channels
							UpperCamelCase_:							Optional[int]		=  image_size
							UpperCamelCase_:							Dict		=  min_resolution
							UpperCamelCase_:							Optional[int]		=  max_resolution
							UpperCamelCase_:							str		=  do_resize
							UpperCamelCase_:							Tuple		=  size
							UpperCamelCase_:							Dict		=  do_normalize




			def  lowerCAmelCase__		(    self	:    str ):
							return {
							    # here we create 2 clusters for the sake of simplicity
							    "clusters": np.asarray(
							        [
							            [0.8866_4436_3403_3203, 0.6618_8293_6954_4983, 0.3891_7464_0178_6804],
							            [-0.6042_5591_4688_1104, -0.0_2295_0088_6052_8469, 0.5423_7973_6900_3296],
							        ] ),
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_normalize": self.do_normalize,
							}




@require_torch
@require_vision
class     _UpperCamelCase			(   _A       ,    unittest.TestCase					):






			'''simple docstring'''

			__UpperCamelCase :  Optional[int]    =	ImageGPTImageProcessor if is_vision_available() else None

			def  lowerCAmelCase__		(    self	:    List[str] ):
							UpperCamelCase_:							Any		=  ImageGPTImageProcessingTester(self )

			@property
			def  lowerCAmelCase__		(    self	:    Optional[int] ):
							return self.image_processor_tester.prepare_image_processor_dict()

			def  lowerCAmelCase__		(    self	:    Optional[int] ):
							UpperCamelCase_:							List[Any]		=  self.image_processing_class(**self.image_processor_dict )
							self.assertTrue(hasattr(snake_case_  ,		"""clusters""" ) )
							self.assertTrue(hasattr(snake_case_  ,		"""do_resize""" ) )
							self.assertTrue(hasattr(snake_case_  ,		"""size""" ) )
							self.assertTrue(hasattr(snake_case_  ,		"""do_normalize""" ) )

			def  lowerCAmelCase__		(    self	:    int ):
							UpperCamelCase_:							List[Any]		=  self.image_processing_class.from_dict(self.image_processor_dict )
							self.assertEqual(image_processor.size  ,		{"""height""": 18, """width""": 18} )

							UpperCamelCase_:							Tuple		=  self.image_processing_class.from_dict(self.image_processor_dict  ,		size=42 )
							self.assertEqual(image_processor.size  ,		{"""height""": 42, """width""": 42} )

			def  lowerCAmelCase__		(    self	:    Optional[Any] ):
							UpperCamelCase_:							int		=  self.image_processing_class(**self.image_processor_dict )
							UpperCamelCase_:							Optional[int]		=  json.loads(image_processor.to_json_string() )
							for key, value in self.image_processor_dict.items():
											if key == "clusters":
															self.assertTrue(np.array_equal(snake_case_  ,		obj[key] ) )
											else:
															self.assertEqual(obj[key]  ,		snake_case_ )

			def  lowerCAmelCase__		(    self	:    List[Any] ):
							UpperCamelCase_:							Dict		=  self.image_processing_class(**self.image_processor_dict )

							with tempfile.TemporaryDirectory() as tmpdirname:
											UpperCamelCase_:							int		=  os.path.join(snake_case_  ,		"""image_processor.json""" )
											image_processor_first.to_json_file(snake_case_ )
											UpperCamelCase_:							Any		=  self.image_processing_class.from_json_file(snake_case_ ).to_dict()

							UpperCamelCase_:							str		=  image_processor_first.to_dict()
							for key, value in image_processor_first.items():
											if key == "clusters":
															self.assertTrue(np.array_equal(snake_case_  ,		image_processor_second[key] ) )
											else:
															self.assertEqual(image_processor_first[key]  ,		snake_case_ )

			def  lowerCAmelCase__		(    self	:    Union[str, Any] ):
							UpperCamelCase_:							Optional[Any]		=  self.image_processing_class(**self.image_processor_dict )

							with tempfile.TemporaryDirectory() as tmpdirname:
											image_processor_first.save_pretrained(snake_case_ )
											UpperCamelCase_:							Optional[int]		=  self.image_processing_class.from_pretrained(snake_case_ ).to_dict()

							UpperCamelCase_:							Union[str, Any]		=  image_processor_first.to_dict()
							for key, value in image_processor_first.items():
											if key == "clusters":
															self.assertTrue(np.array_equal(snake_case_  ,		image_processor_second[key] ) )
											else:
															self.assertEqual(image_processor_first[key]  ,		snake_case_ )




			@unittest.skip("""ImageGPT requires clusters at initialization""" )
			def  lowerCAmelCase__		(    self	:    List[Any] ):
							pass







def     A__   (  )					->		Optional[int]:
				UpperCamelCase_:							Optional[int]		=  load_dataset("""hf-internal-testing/fixtures_image_utils"""       ,      split="""test"""  )

				UpperCamelCase_:							Tuple		=  Image.open(dataset[4]["""file"""]  )
				UpperCamelCase_:							Union[str, Any]		=  Image.open(dataset[5]["""file"""]  )

				UpperCamelCase_:							List[str]		=  [imagea, imagea]

				return images





@require_vision
@require_torch
class     _UpperCamelCase			(   unittest.TestCase					):






			'''simple docstring'''

			@slow
			def  lowerCAmelCase__		(    self	:    List[str] ):
							UpperCamelCase_:							List[Any]		=  ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small""" )

							UpperCamelCase_:							List[str]		=  prepare_images()

							# test non-batched
							UpperCamelCase_:							List[str]		=  image_processing(images[0]  ,		return_tensors="""pt""" )

							self.assertIsInstance(encoding.input_ids  ,		torch.LongTensor )
							self.assertEqual(encoding.input_ids.shape  ,		(1, 1024) )

							UpperCamelCase_:							Union[str, Any]		=  [306, 191, 191]
							self.assertEqual(encoding.input_ids[0, :3].tolist()  ,		snake_case_ )

							# test batched
							UpperCamelCase_:							Optional[int]		=  image_processing(snake_case_  ,		return_tensors="""pt""" )

							self.assertIsInstance(encoding.input_ids  ,		torch.LongTensor )
							self.assertEqual(encoding.input_ids.shape  ,		(2, 1024) )

							UpperCamelCase_:							str		=  [303, 13, 13]
							self.assertEqual(encoding.input_ids[1, -3:].tolist()  ,		snake_case_ )






