'''simple docstring'''




import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import CLIPImageProcessor, CLIPProcessor




@require_vision
class    a__				(       unittest.TestCase			):






     def        SCREAMING_SNAKE_CASE__       (			self		:		str   ):





         """simple docstring"""



         __lowerCamelCase          =  tempfile.mkdtemp()

         # fmt: off
         __lowerCamelCase          =  ['''l''', '''o''', '''w''', '''e''', '''r''', '''s''', '''t''', '''i''', '''d''', '''n''', '''lo''', '''l</w>''', '''w</w>''', '''r</w>''', '''t</w>''', '''low</w>''', '''er</w>''', '''lowest</w>''', '''newer</w>''', '''wider''', '''<unk>''', '''<|startoftext|>''', '''<|endoftext|>''']
         # fmt: on
         __lowerCamelCase          =  dict(zip(a  ,    range(len(a   )   )   )   )
         __lowerCamelCase          =  ['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
         __lowerCamelCase          =  {'''unk_token''': '''<unk>'''}

         __lowerCamelCase          =  os.path.join(self.tmpdirname  ,    VOCAB_FILES_NAMES['''vocab_file''']   )
         __lowerCamelCase          =  os.path.join(self.tmpdirname  ,    VOCAB_FILES_NAMES['''merges_file''']   )
         with open(self.vocab_file  ,    '''w'''  ,    encoding='''utf-8'''   ) as fp:
             fp.write(json.dumps(a   ) + '''\n'''   )
         with open(self.merges_file  ,    '''w'''  ,    encoding='''utf-8'''   ) as fp:
             fp.write('''\n'''.join(a   )   )

         __lowerCamelCase          =  {
             '''do_resize''': True,
             '''size''': 20,
             '''do_center_crop''': True,
             '''crop_size''': 18,
             '''do_normalize''': True,
             '''image_mean''': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
             '''image_std''': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
         }
         __lowerCamelCase          =  os.path.join(self.tmpdirname  ,    a   )
         with open(self.image_processor_file  ,    '''w'''  ,    encoding='''utf-8'''   ) as fp:
             json.dump(a  ,    a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		int  ,    **a		:		Optional[int]   ):





         """simple docstring"""



         return CLIPTokenizer.from_pretrained(self.tmpdirname  ,    **a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[int]  ,    **a		:		Tuple   ):





         """simple docstring"""



         return CLIPTokenizerFast.from_pretrained(self.tmpdirname  ,    **a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Any  ,    **a		:		List[Any]   ):





         """simple docstring"""



         return CLIPImageProcessor.from_pretrained(self.tmpdirname  ,    **a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[int]   ):





         """simple docstring"""



         shutil.rmtree(self.tmpdirname   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		int   ):





         """simple docstring"""



         __lowerCamelCase          =  [np.random.randint(2_55  ,    size=(3, 30, 4_00)  ,    dtype=np.uinta   )]

         __lowerCamelCase          =  [Image.fromarray(np.moveaxis(a  ,    0  ,    -1   )   ) for x in image_inputs]

         return image_inputs






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[int]   ):





         """simple docstring"""



         __lowerCamelCase          =  self.get_tokenizer()
         __lowerCamelCase          =  self.get_rust_tokenizer()
         __lowerCamelCase          =  self.get_image_processor()

         __lowerCamelCase          =  CLIPProcessor(tokenizer=a  ,    image_processor=a   )
         processor_slow.save_pretrained(self.tmpdirname   )
         __lowerCamelCase          =  CLIPProcessor.from_pretrained(self.tmpdirname  ,    use_fast=a   )

         __lowerCamelCase          =  CLIPProcessor(tokenizer=a  ,    image_processor=a   )
         processor_fast.save_pretrained(self.tmpdirname   )
         __lowerCamelCase          =  CLIPProcessor.from_pretrained(self.tmpdirname   )

         self.assertEqual(processor_slow.tokenizer.get_vocab()  ,    tokenizer_slow.get_vocab()   )
         self.assertEqual(processor_fast.tokenizer.get_vocab()  ,    tokenizer_fast.get_vocab()   )
         self.assertEqual(tokenizer_slow.get_vocab()  ,    tokenizer_fast.get_vocab()   )
         self.assertIsInstance(processor_slow.tokenizer  ,    a   )
         self.assertIsInstance(processor_fast.tokenizer  ,    a   )

         self.assertEqual(processor_slow.image_processor.to_json_string()  ,    image_processor.to_json_string()   )
         self.assertEqual(processor_fast.image_processor.to_json_string()  ,    image_processor.to_json_string()   )
         self.assertIsInstance(processor_slow.image_processor  ,    a   )
         self.assertIsInstance(processor_fast.image_processor  ,    a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Tuple   ):





         """simple docstring"""



         __lowerCamelCase          =  CLIPProcessor(tokenizer=self.get_tokenizer()  ,    image_processor=self.get_image_processor()   )
         processor.save_pretrained(self.tmpdirname   )

         __lowerCamelCase          =  self.get_tokenizer(bos_token='''(BOS)'''  ,    eos_token='''(EOS)'''   )
         __lowerCamelCase          =  self.get_image_processor(do_normalize=a  ,    padding_value=1.0   )

         __lowerCamelCase          =  CLIPProcessor.from_pretrained(
             self.tmpdirname  ,    bos_token='''(BOS)'''  ,    eos_token='''(EOS)'''  ,    do_normalize=a  ,    padding_value=1.0   )

         self.assertEqual(processor.tokenizer.get_vocab()  ,    tokenizer_add_kwargs.get_vocab()   )
         self.assertIsInstance(processor.tokenizer  ,    a   )

         self.assertEqual(processor.image_processor.to_json_string()  ,    image_processor_add_kwargs.to_json_string()   )
         self.assertIsInstance(processor.image_processor  ,    a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		str   ):





         """simple docstring"""



         __lowerCamelCase          =  self.get_image_processor()
         __lowerCamelCase          =  self.get_tokenizer()

         __lowerCamelCase          =  CLIPProcessor(tokenizer=a  ,    image_processor=a   )

         __lowerCamelCase          =  self.prepare_image_inputs()

         __lowerCamelCase          =  image_processor(a  ,    return_tensors='''np'''   )
         __lowerCamelCase          =  processor(images=a  ,    return_tensors='''np'''   )

         for key in input_image_proc.keys():
             self.assertAlmostEqual(input_image_proc[key].sum()  ,    input_processor[key].sum()  ,    delta=1e-2   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Union[str, Any]   ):





         """simple docstring"""



         __lowerCamelCase          =  self.get_image_processor()
         __lowerCamelCase          =  self.get_tokenizer()

         __lowerCamelCase          =  CLIPProcessor(tokenizer=a  ,    image_processor=a   )

         __lowerCamelCase          =  '''lower newer'''

         __lowerCamelCase          =  processor(text=a   )

         __lowerCamelCase          =  tokenizer(a   )

         for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key]  ,    encoded_processor[key]   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[Any]   ):





         """simple docstring"""



         __lowerCamelCase          =  self.get_image_processor()
         __lowerCamelCase          =  self.get_tokenizer()

         __lowerCamelCase          =  CLIPProcessor(tokenizer=a  ,    image_processor=a   )

         __lowerCamelCase          =  '''lower newer'''
         __lowerCamelCase          =  self.prepare_image_inputs()

         __lowerCamelCase          =  processor(text=a  ,    images=a   )

         self.assertListEqual(list(inputs.keys()   )  ,    ['''input_ids''', '''attention_mask''', '''pixel_values''']   )

         # test if it raises when no input is passed
         with pytest.raises(a   ):
             processor()






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[Any]   ):





         """simple docstring"""



         __lowerCamelCase          =  self.get_image_processor()
         __lowerCamelCase          =  self.get_tokenizer()

         __lowerCamelCase          =  CLIPProcessor(tokenizer=a  ,    image_processor=a   )

         __lowerCamelCase          =  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         __lowerCamelCase          =  processor.batch_decode(a   )
         __lowerCamelCase          =  tokenizer.batch_decode(a   )

         self.assertListEqual(a  ,    a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Union[str, Any]   ):





         """simple docstring"""



         __lowerCamelCase          =  self.get_image_processor()
         __lowerCamelCase          =  self.get_tokenizer()

         __lowerCamelCase          =  CLIPProcessor(tokenizer=a  ,    image_processor=a   )

         __lowerCamelCase          =  '''lower newer'''
         __lowerCamelCase          =  self.prepare_image_inputs()

         __lowerCamelCase          =  processor(text=a  ,    images=a   )

         self.assertListEqual(list(inputs.keys()   )  ,    processor.model_input_names   )

'''simple docstring'''




import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_ta import TaTokenizer
else:
  __UpperCAmelCase												=None


__UpperCAmelCase												=logging.get_logger(__name__)

__UpperCAmelCase												={"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

__UpperCAmelCase												={
    "vocab_file": {
        "t5-small": "https://huggingface.co/t5-small/resolve/main/spiece.model",
        "t5-base": "https://huggingface.co/t5-base/resolve/main/spiece.model",
        "t5-large": "https://huggingface.co/t5-large/resolve/main/spiece.model",
        "t5-3b": "https://huggingface.co/t5-3b/resolve/main/spiece.model",
        "t5-11b": "https://huggingface.co/t5-11b/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "t5-small": "https://huggingface.co/t5-small/resolve/main/tokenizer.json",
        "t5-base": "https://huggingface.co/t5-base/resolve/main/tokenizer.json",
        "t5-large": "https://huggingface.co/t5-large/resolve/main/tokenizer.json",
        "t5-3b": "https://huggingface.co/t5-3b/resolve/main/tokenizer.json",
        "t5-11b": "https://huggingface.co/t5-11b/resolve/main/tokenizer.json",
    },
}


# TODO(PVP) - this should be removed in Transformers v5
__UpperCAmelCase												={
    "t5-small": 5_1_2,
    "t5-base": 5_1_2,
    "t5-large": 5_1_2,
    "t5-3b": 5_1_2,
    "t5-11b": 5_1_2,
}




class    a__				(       UpperCAmelCase__			):
     lowerCamelCase  :					Dict							=VOCAB_FILES_NAMES
     lowerCamelCase  :					Optional[Any]							=PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase  :					Dict							=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase  :					int							=["input_ids", "attention_mask"]
     lowerCamelCase  :					Tuple							=TaTokenizer

     lowerCamelCase  :					List[int]							=[]






     def __init__(			self		:		int  ,    a		:		List[str]=None  ,    a		:		List[str]=None  ,    a		:		Dict="</s>"  ,    a		:		Optional[int]="<unk>"  ,    a		:		Any="<pad>"  ,    a		:		Optional[Any]=1_00  ,    a		:		List[Any]=None  ,    **a		:		Union[str, Any]  ,    ):





         """simple docstring"""



         if extra_ids > 0 and additional_special_tokens is None:
             __lowerCamelCase          =  [f"""<extra_id_{i}>""" for i in range(a   )]
         elif extra_ids > 0 and additional_special_tokens is not None:
             # Check that we have the right number of extra special tokens
             __lowerCamelCase          =  len(set(filter(lambda a   : bool('''extra_id_''' in str(a   )   )  ,    a   )   )   )
             if extra_tokens != extra_ids:
                 raise ValueError(
                     f"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
                     ''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
                     ''' tokens'''   )

         super().__init__(
             a  ,    tokenizer_file=a  ,    eos_token=a  ,    unk_token=a  ,    pad_token=a  ,    extra_ids=a  ,    additional_special_tokens=a  ,    **a  ,    )

         __lowerCamelCase          =  vocab_file
         __lowerCamelCase          =  False if not self.vocab_file else True
         __lowerCamelCase          =  extra_ids






     @staticmethod
     def        SCREAMING_SNAKE_CASE__       (			a		:		Optional[int]  ,    a		:		List[str]  ,    a		:		Union[str, Any]   ):





         """simple docstring"""



         if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
             __lowerCamelCase          =  TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
             if init_max_model_length is not None and init_max_model_length != max_model_length:
                 return init_max_model_length
             elif init_max_model_length is None:
                 warnings.warn(
                     '''This tokenizer was incorrectly instantiated with a model max length of'''
                     f""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
                     ''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
                     ''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
                     f""" {pretrained_model_name_or_path} automatically truncating your input to"""
                     f""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
                     f""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
                     ''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
                     ''' instantiate this tokenizer with `model_max_length` set to your preferred value.'''  ,    a  ,    )

         return max_model_length






     def        SCREAMING_SNAKE_CASE__       (			self		:		Tuple  ,    a		:		str  ,    a		:		Optional[str] = None   ):





         """simple docstring"""



         if not self.can_save_slow_tokenizer:
             raise ValueError(
                 '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
                 '''tokenizer.'''   )

         if not os.path.isdir(a   ):
             logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""   )
             return
         __lowerCamelCase          =  os.path.join(
             a  ,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )

         if os.path.abspath(self.vocab_file   ) != os.path.abspath(a   ):
             copyfile(self.vocab_file  ,    a   )
             logger.info(f"""Copy vocab file to {out_vocab_file}"""   )

         return (out_vocab_file,)






     def        SCREAMING_SNAKE_CASE__       (			self		:		int  ,    a		:		List[int]  ,    a		:		Optional[List[int]] = None   ):





         """simple docstring"""



         __lowerCamelCase          =  token_ids_a + [self.eos_token_id]
         if token_ids_a is None:
             return self.prefix_tokens + token_ids_a
         else:
             __lowerCamelCase          =  token_ids_a + [self.eos_token_id]
             return self.prefix_tokens + token_ids_a + token_ids_a






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[Any]  ,    a		:		List[int]  ,    a		:		Optional[List[int]] = None   ):





         """simple docstring"""



         __lowerCamelCase          =  [self.eos_token_id]

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






     def        SCREAMING_SNAKE_CASE__       (			self		:		Any   ):





         """simple docstring"""



         return list(
             set(filter(lambda a   : bool(re.search(R'''<extra_id_\d+>'''  ,    a   )   ) is not None  ,    self.additional_special_tokens   )   )   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Union[str, Any]   ):





         """simple docstring"""



         return [self.convert_tokens_to_ids(a   ) for token in self.get_sentinel_tokens()]
