






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  SCREAMING_SNAKE_CASE			(			lowerCAmelCase      ,		unittest.TestCase					):
     '''simple docstring'''


     UpperCamelCase_							:		Dict			=	None
     UpperCamelCase_							:		Dict			=	BloomTokenizerFast
     UpperCamelCase_							:		Tuple			=	BloomTokenizerFast
     UpperCamelCase_							:		List[Any]			=	True
     UpperCamelCase_							:		int			=	False
     UpperCamelCase_							:		List[Any]			=	'''tokenizer_file'''
     UpperCamelCase_							:		Any			=	{'''bos_token''': '''<s>''', '''eos_token''': '''</s>''', '''unk_token''': '''<unk>''', '''pad_token''': '''<pad>'''}


     def 				_A       (				self       :	Dict      ):
       super().setUp()
       SCREAMING_SNAKE_CASE       :      str							=    BloomTokenizerFast.from_pretrained("bigscience/tokenizer"      )
       tokenizer.save_pretrained(self.tmpdirname      )


     def 				_A       (				self       :	List[str]					, **UpperCAmelCase_       :	List[Any]      ):
       kwargs.update(self.special_tokens_map      )
       return BloomTokenizerFast.from_pretrained(self.tmpdirname					, **UpperCAmelCase_      )


     def 				_A       (				self       :	Union[str, Any]      ):
       SCREAMING_SNAKE_CASE       :      Any							=    self.get_rust_tokenizer()

       SCREAMING_SNAKE_CASE       :      str							=    ["The quick brown fox</s>", "jumps over the lazy dog</s>"]
       SCREAMING_SNAKE_CASE       :      str							=    [[2175, 2_3714, 7_3173, 14_4252, 2], [77, 13_2619, 3478, 368, 10_9586, 3_5433, 2]]

       SCREAMING_SNAKE_CASE       :      List[Any]							=    tokenizer.batch_encode_plus(UpperCAmelCase_      )["input_ids"]
       self.assertListEqual(UpperCAmelCase_					, UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      List[Any]							=    tokenizer.batch_decode(UpperCAmelCase_      )
       self.assertListEqual(UpperCAmelCase_					, UpperCAmelCase_      )


     def 				_A       (				self       :	Dict					, UpperCAmelCase_       :	Optional[int]=6      ):
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''      ):
           SCREAMING_SNAKE_CASE       :      List[Any]							=    self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_					, **UpperCAmelCase_      )
           # tokenizer_r.pad_token = None # Hotfixing padding = None
           # Simple input
           SCREAMING_SNAKE_CASE       :      Tuple							=    "This is a simple input"
           SCREAMING_SNAKE_CASE       :      Optional[int]							=    ["This is a simple input 1", "This is a simple input 2"]
           SCREAMING_SNAKE_CASE       :      Optional[int]							=    ("This is a simple input", "This is a pair")
           SCREAMING_SNAKE_CASE       :      List[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(UpperCAmelCase_					, max_length=UpperCAmelCase_      )
             tokenizer_r.encode_plus(UpperCAmelCase_					, max_length=UpperCAmelCase_      )

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

           SCREAMING_SNAKE_CASE       :      Optional[Any]							=    None  # Hotfixing padding = None
           self.assertRaises(UpperCAmelCase_					, tokenizer_r.encode					, UpperCAmelCase_					, max_length=UpperCAmelCase_					, padding="max_length"      )

           # Simple input
           self.assertRaises(UpperCAmelCase_					, tokenizer_r.encode_plus					, UpperCAmelCase_					, max_length=UpperCAmelCase_					, padding="max_length"      )

           # Simple input
           self.assertRaises(
               UpperCAmelCase_					, tokenizer_r.batch_encode_plus					, UpperCAmelCase_					, max_length=UpperCAmelCase_					, padding="max_length"					, )

           # Pair input
           self.assertRaises(UpperCAmelCase_					, tokenizer_r.encode					, UpperCAmelCase_					, max_length=UpperCAmelCase_					, padding="max_length"      )

           # Pair input
           self.assertRaises(UpperCAmelCase_					, tokenizer_r.encode_plus					, UpperCAmelCase_					, max_length=UpperCAmelCase_					, padding="max_length"      )

           # Pair input
           self.assertRaises(
               UpperCAmelCase_					, tokenizer_r.batch_encode_plus					, UpperCAmelCase_					, max_length=UpperCAmelCase_					, padding="max_length"					, )


     def 				_A       (				self       :	Optional[Any]      ):
       SCREAMING_SNAKE_CASE       :      Tuple							=    self.get_rust_tokenizer()
       SCREAMING_SNAKE_CASE       :      str							=    load_dataset("xnli"					, "all_languages"					, split="test"					, streaming=UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      List[Any]							=    next(iter(UpperCAmelCase_      )      )["premise"]  # pick up one data
       SCREAMING_SNAKE_CASE       :      Tuple							=    list(sample_data.values()      )

       SCREAMING_SNAKE_CASE       :      List[Any]							=    list(map(tokenizer.encode					, UpperCAmelCase_      )      )
       SCREAMING_SNAKE_CASE       :      List[Any]							=    [tokenizer.decode(UpperCAmelCase_					, clean_up_tokenization_spaces=UpperCAmelCase_      ) for x in output_tokens]
       self.assertListEqual(UpperCAmelCase_					, UpperCAmelCase_      )






     def 				_A       (				self       :	Optional[int]      ):
       # 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      )














# Copyright 2022 The HuggingFace Team and The OpenBMB 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.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


snake_case    				=		{
    """configuration_cpmant""": ["""CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CpmAntConfig"""],
    """tokenization_cpmant""": ["""CpmAntTokenizer"""],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case    				=		[
       """CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """CpmAntForCausalLM""",
       """CpmAntModel""",
       """CpmAntPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
   from .tokenization_cpmant import CpmAntTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_cpmant import (
          CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
          CpmAntForCausalLM,
          CpmAntModel,
          CpmAntPreTrainedModel,
      )


else:
   import sys

   snake_case    				=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






