




'''simple docstring'''





import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard

class      _a       (		unittest.TestCase							):






     def        _lowercase			(				self					)							->    Any:
          _snake_case							=   {
              "model_details": {
                  "Organization": "testing",
                  "Model date": "today",
                  "Model version": "v2.1, Developed by Test Corp in 2019.",
                  "Architecture": "Convolutional Neural Network.",
              },
              "metrics": "BLEU and ROUGE-1",
              "evaluation_data": {
                  "Datasets": {"BLEU": "My-great-dataset-v1", "ROUGE-1": "My-short-dataset-v2.1"},
                  "Preprocessing": "See details on https://arxiv.org/pdf/1810.03993.pdf",
              },
              "training_data": {
                  "Dataset": "English Wikipedia dump dated 2018-12-01",
                  "Preprocessing": (
                      "Using SentencePiece vocabulary of size 52k tokens. See details on"
                      " https://arxiv.org/pdf/1810.03993.pdf"
                  ),
              },
              "quantitative_analyses": {"BLEU": 5_5.1, "ROUGE-1": 76},
          }






     def        _lowercase			(				self					)							->    Union[str, Any]:
          _snake_case							=   ModelCard.from_dict(self.inputs_dict					)
          self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,"model_details"					)					)
          self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,"intended_use"					)					)
          self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,"factors"					)					)
          self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,"metrics"					)					)
          self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,"evaluation_data"					)					)
          self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,"training_data"					)					)
          self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,"quantitative_analyses"					)					)
          self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,"ethical_considerations"					)					)
          self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,"caveats_and_recommendations"					)					)






     def        _lowercase			(				self					)							->    List[Any]:
          _snake_case							=   ModelCard.from_dict(self.inputs_dict					)
          _snake_case							=   json.loads(modelcard.to_json_string()					)
          for key, value in self.inputs_dict.items():
               self.assertEqual(obj[key]      ,_SCREAMING_SNAKE_CASE					)






     def        _lowercase			(				self					)							->    Optional[Any]:
          _snake_case							=   ModelCard.from_dict(self.inputs_dict					)

          with tempfile.TemporaryDirectory() as tmpdirname:
               _snake_case							=   os.path.join(_SCREAMING_SNAKE_CASE      ,"modelcard.json"					)
               model_card_first.to_json_file(_SCREAMING_SNAKE_CASE					)
               _snake_case							=   ModelCard.from_json_file(_SCREAMING_SNAKE_CASE					)

          self.assertEqual(model_card_second.to_dict()      ,model_card_first.to_dict()					)






     def        _lowercase			(				self					)							->    Dict:
          _snake_case							=   ModelCard.from_dict(self.inputs_dict					)

          with tempfile.TemporaryDirectory() as tmpdirname:
               model_card_first.save_pretrained(_SCREAMING_SNAKE_CASE					)
               _snake_case							=   ModelCard.from_pretrained(_SCREAMING_SNAKE_CASE					)

          self.assertEqual(model_card_second.to_dict()      ,model_card_first.to_dict()					)








'''simple docstring'''





from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_clip import CLIPTokenizer


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

UpperCamelCase_		:    str									=		{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCamelCase_		:    List[str]									=		{
    '''vocab_file''': {
        '''openai/clip-vit-base-patch32''': '''https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''openai/clip-vit-base-patch32''': '''https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''openai/clip-vit-base-patch32''': (
            '''https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json'''
        ),
    },
}

UpperCamelCase_		:    List[Any]									=		{
    '''openai/clip-vit-base-patch32''': 77,
}

class      _a       (		__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:					Any					       =		VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE_							:					List[Any]					       =		PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE_							:					Dict					       =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE_							:					int					       =		["""input_ids""", """attention_mask"""]
     SCREAMING_SNAKE_CASE_							:					Optional[Any]					       =		CLIPTokenizer






     def __init__(				self      ,_SCREAMING_SNAKE_CASE=None      ,_SCREAMING_SNAKE_CASE=None      ,_SCREAMING_SNAKE_CASE=None      ,_SCREAMING_SNAKE_CASE="<|endoftext|>"      ,_SCREAMING_SNAKE_CASE="<|startoftext|>"      ,_SCREAMING_SNAKE_CASE="<|endoftext|>"      ,_SCREAMING_SNAKE_CASE="<|endoftext|>"      ,**_SCREAMING_SNAKE_CASE      ,)							->    List[Any]:
          super().__init__(
              _SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,tokenizer_file=_SCREAMING_SNAKE_CASE      ,unk_token=_SCREAMING_SNAKE_CASE      ,bos_token=_SCREAMING_SNAKE_CASE      ,eos_token=_SCREAMING_SNAKE_CASE      ,pad_token=_SCREAMING_SNAKE_CASE      ,**_SCREAMING_SNAKE_CASE      ,)

          if not isinstance(self.backend_tokenizer.pre_tokenizer      ,pre_tokenizers.Sequence					):
               raise ValueError(
                   "The `backend_tokenizer` provided does not match the expected format. The CLIP tokenizer has been"
                   " heavily modified from transformers version 4.17.0. You need to convert the tokenizer you are using"
                   " to be compatible with this version.The easiest way to do so is"
                   " `CLIPTokenizerFast.from_pretrained(\"path_to_local_folder_or_hub_repo, from_slow=True)`. If you want"
                   " to use your existing tokenizer, you will have to revert to a version prior to 4.17.0 of"
                   " transformers."					)

          self._wrap_decode_method_backend_tokenizer()






     def        _lowercase			(				self					)							->    Tuple:
          _snake_case							=   self.backend_tokenizer.decode

          def new_decode_method(*_SCREAMING_SNAKE_CASE      ,**_SCREAMING_SNAKE_CASE					):
               _snake_case							=   orig_decode_method(*_SCREAMING_SNAKE_CASE      ,**_SCREAMING_SNAKE_CASE					)
               _snake_case							=   text.replace(self.backend_tokenizer.model.end_of_word_suffix      ," "					).strip()
               return text

          _snake_case							=   new_decode_method






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE = None					)							->    List[int]:
          _snake_case							=   [self.bos_token_id]
          _snake_case							=   [self.eos_token_id]

          if token_ids_a is None:
               return bos_token + token_ids_a + eos_token
          return bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE = None					)							->    List[int]:
          _snake_case							=   [self.bos_token_id]
          _snake_case							=   [self.eos_token_id]

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






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE = None					)							->    Tuple[str]:
          _snake_case							=   self._tokenizer.model.save(_SCREAMING_SNAKE_CASE      ,name=_SCREAMING_SNAKE_CASE					)
          return tuple(_SCREAMING_SNAKE_CASE					)


