


'''simple docstring'''






import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class  __magic_name__							(		lowerCAmelCase ,unittest.TestCase    ):
       UpperCAmelCase										=None
       UpperCAmelCase										=BloomTokenizerFast
       UpperCAmelCase										=BloomTokenizerFast
       UpperCAmelCase										=True
       UpperCAmelCase										=False
       UpperCAmelCase										="tokenizer_file"
       UpperCAmelCase										={"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}






       def       lowerCAmelCase		(      self)						->  int:




          '''simple docstring'''
          super().setUp()
          _UpperCAmelCase       :       Union[str, Any]							   =BloomTokenizerFast.from_pretrained('bigscience/tokenizer')
          tokenizer.save_pretrained(self.tmpdirname)






       def       lowerCAmelCase		(      self  ,							**snake_case)						->  int:




          '''simple docstring'''
          kwargs.update(self.special_tokens_map)
          return BloomTokenizerFast.from_pretrained(self.tmpdirname  ,							**snake_case)






       def       lowerCAmelCase		(      self)						->  Optional[Any]:




          '''simple docstring'''
          _UpperCAmelCase       :       str							   =self.get_rust_tokenizer()

          _UpperCAmelCase       :       str							   =['The quick brown fox</s>', 'jumps over the lazy dog</s>']
          _UpperCAmelCase       :       str							   =[[2_1_7_5, 2_3_7_1_4, 7_3_1_7_3, 1_4_4_2_5_2, 2], [7_7, 1_3_2_6_1_9, 3_4_7_8, 3_6_8, 1_0_9_5_8_6, 3_5_4_3_3, 2]]

          _UpperCAmelCase       :       List[Any]							   =tokenizer.batch_encode_plus(snake_case)['input_ids']
          self.assertListEqual(snake_case  ,							snake_case)

          _UpperCAmelCase       :       Tuple							   =tokenizer.batch_decode(snake_case)
          self.assertListEqual(snake_case  ,							snake_case)






       def       lowerCAmelCase		(      self  ,							snake_case=6)						->  Dict:




          '''simple docstring'''
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
                _UpperCAmelCase       :       List[str]							   =self.rust_tokenizer_class.from_pretrained(snake_case  ,							**snake_case)
                # tokenizer_r.pad_token = None # Hotfixing padding = None
                # Simple input
                _UpperCAmelCase       :       str							   ='This is a simple input'
                _UpperCAmelCase       :       Tuple							   =['This is a simple input 1', 'This is a simple input 2']
                _UpperCAmelCase       :       List[str]							   =('This is a simple input', 'This is a pair')
                _UpperCAmelCase       :       Optional[Any]							   =[
                    ('This is a simple input 1', 'This is a simple input 2'),
                    ('This is a simple pair 1', 'This is a simple pair 2'),
                ]

                # Simple input tests
                try:
                   tokenizer_r.encode(snake_case  ,							max_length=snake_case)
                   tokenizer_r.encode_plus(snake_case  ,							max_length=snake_case)

                   tokenizer_r.batch_encode_plus(snake_case  ,							max_length=snake_case)
                   tokenizer_r.encode(snake_case  ,							max_length=snake_case)
                   tokenizer_r.batch_encode_plus(snake_case  ,							max_length=snake_case)
                except ValueError:
                   self.fail('Bloom Tokenizer should be able to deal with padding')

                _UpperCAmelCase       :       Optional[Any]							   =None  # Hotfixing padding = None
                self.assertRaises(snake_case  ,							tokenizer_r.encode  ,							snake_case  ,							max_length=snake_case  ,							padding='max_length')

                # Simple input
                self.assertRaises(snake_case  ,							tokenizer_r.encode_plus  ,							snake_case  ,							max_length=snake_case  ,							padding='max_length')

                # Simple input
                self.assertRaises(
                    snake_case  ,							tokenizer_r.batch_encode_plus  ,							snake_case  ,							max_length=snake_case  ,							padding='max_length'  ,							)

                # Pair input
                self.assertRaises(snake_case  ,							tokenizer_r.encode  ,							snake_case  ,							max_length=snake_case  ,							padding='max_length')

                # Pair input
                self.assertRaises(snake_case  ,							tokenizer_r.encode_plus  ,							snake_case  ,							max_length=snake_case  ,							padding='max_length')

                # Pair input
                self.assertRaises(
                    snake_case  ,							tokenizer_r.batch_encode_plus  ,							snake_case  ,							max_length=snake_case  ,							padding='max_length'  ,							)






       def       lowerCAmelCase		(      self)						->  int:




          '''simple docstring'''
          _UpperCAmelCase       :       Union[str, Any]							   =self.get_rust_tokenizer()
          _UpperCAmelCase       :       List[str]							   =load_dataset('xnli'  ,							'all_languages'  ,							split='test'  ,							streaming=snake_case)

          _UpperCAmelCase       :       Dict							   =next(iter(snake_case))['premise']  # pick up one data
          _UpperCAmelCase       :       Optional[int]							   =list(sample_data.values())

          _UpperCAmelCase       :       Optional[Any]							   =list(map(tokenizer.encode  ,							snake_case))
          _UpperCAmelCase       :       str							   =[tokenizer.decode(snake_case  ,							clean_up_tokenization_spaces=snake_case) for x in output_tokens]
          self.assertListEqual(snake_case  ,							snake_case)






       def       lowerCAmelCase		(      self)						->  int:




          '''simple docstring'''
          # The test has to be overriden because BLOOM uses ALiBi positional embeddings that does not have
          # any sequence length constraints. This test of the parent class will fail since it relies on the
          # maximum sequence length of the positoonal embeddings.
          self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map)  ,							1)
          self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values())[0])  ,							1)





'''simple docstring'''






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re

from ..utils import cached_file


# docstyle-ignore
lowercase							='\nHuman: <<task>>\n\nAssistant: '


lowercase							='huggingface-tools/default-prompts'
lowercase							={'chat': 'chat_prompt_template.txt', 'run': 'run_prompt_template.txt'}





def lowerCamelCase__      (						__lowerCamelCase	:							Union[str, Any]	,   __lowerCamelCase	:							Union[str, Any]	,   __lowerCamelCase	:							int="run"							):







   '''simple docstring'''




   if prompt_or_repo_id is None:
      _UpperCAmelCase       :       List[str]							   =DEFAULT_PROMPTS_REPO

   # prompt is considered a repo ID when it does not contain any kind of space
   if re.search('\\s'	,   __lowerCamelCase							) is not None:
      return prompt_or_repo_id

   _UpperCAmelCase       :       Dict							   =cached_file(
       __lowerCamelCase	,   PROMPT_FILES[mode]	,   repo_type='dataset'	,   user_agent={'agent': agent_name}							)
   with open(__lowerCamelCase	,   'r'	,   encoding='utf-8'							) as f:
      return f.read()

