

import unittest

from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate


def        __lowerCAmelCase    (       _UpperCamelCase		:  Any	)		->							Tuple:
 '''simple docstring'''


 return x + 2




class      UpperCamelCase (     unittest.TestCase   ):

    def UpperCamelCase			(			self :      Optional[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			'x = 3'
     SCREAMING_SNAKE_CASE								=			{}
     SCREAMING_SNAKE_CASE								=			evaluate(snake_case__							,    {}							,    state=snake_case__    )
     assert result == 3
     self.assertDictEqual(snake_case__							,    {'x': 3}    )

     SCREAMING_SNAKE_CASE								=			'x = y'
     SCREAMING_SNAKE_CASE								=			{'y': 5}
     SCREAMING_SNAKE_CASE								=			evaluate(snake_case__							,    {}							,    state=snake_case__    )
     # evaluate returns the value of the last assignment.
     assert result == 5
     self.assertDictEqual(snake_case__							,    {'x': 5, 'y': 5}    )

    def UpperCamelCase			(			self :      List[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			'y = add_two(x)'
     SCREAMING_SNAKE_CASE								=			{'x': 3}
     SCREAMING_SNAKE_CASE								=			evaluate(snake_case__							,    {'add_two': add_two}							,    state=snake_case__    )
     assert result == 5
     self.assertDictEqual(snake_case__							,    {'x': 3, 'y': 5}    )

     # Won't work without the tool
     with CaptureStdout() as out:
      SCREAMING_SNAKE_CASE								=			evaluate(snake_case__							,    {}							,    state=snake_case__    )
     assert result is None
     assert "tried to execute add_two" in out.out

    def UpperCamelCase			(			self :      List[str]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			'x = 3'
     SCREAMING_SNAKE_CASE								=			{}
     SCREAMING_SNAKE_CASE								=			evaluate(snake_case__							,    {}							,    state=snake_case__    )
     assert result == 3
     self.assertDictEqual(snake_case__							,    {'x': 3}    )

    def UpperCamelCase			(			self :      List[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			'test_dict = {\'x\': x, \'y\': add_two(x)}'
     SCREAMING_SNAKE_CASE								=			{'x': 3}
     SCREAMING_SNAKE_CASE								=			evaluate(snake_case__							,    {'add_two': add_two}							,    state=snake_case__    )
     self.assertDictEqual(snake_case__							,    {'x': 3, 'y': 5}    )
     self.assertDictEqual(snake_case__							,    {'x': 3, 'test_dict': {'x': 3, 'y': 5}}    )

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

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			'x = 3\ny = 5'
     SCREAMING_SNAKE_CASE								=			{}
     SCREAMING_SNAKE_CASE								=			evaluate(snake_case__							,    {}							,    state=snake_case__    )
     # evaluate returns the value of the last assignment.
     assert result == 5
     self.assertDictEqual(snake_case__							,    {'x': 3, 'y': 5}    )

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

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			'text = f\'This is x: {x}.\''
     SCREAMING_SNAKE_CASE								=			{'x': 3}
     SCREAMING_SNAKE_CASE								=			evaluate(snake_case__							,    {}							,    state=snake_case__    )
     # evaluate returns the value of the last assignment.
     assert result == "This is x: 3."
     self.assertDictEqual(snake_case__							,    {'x': 3, 'text': 'This is x: 3.'}    )

    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			'if x <= 3:\n    y = 2\nelse:\n    y = 5'
     SCREAMING_SNAKE_CASE								=			{'x': 3}
     SCREAMING_SNAKE_CASE								=			evaluate(snake_case__							,    {}							,    state=snake_case__    )
     # evaluate returns the value of the last assignment.
     assert result == 2
     self.assertDictEqual(snake_case__							,    {'x': 3, 'y': 2}    )

     SCREAMING_SNAKE_CASE								=			{'x': 8}
     SCREAMING_SNAKE_CASE								=			evaluate(snake_case__							,    {}							,    state=snake_case__    )
     # evaluate returns the value of the last assignment.
     assert result == 5
     self.assertDictEqual(snake_case__							,    {'x': 8, 'y': 5}    )

    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			'test_list = [x, add_two(x)]'
     SCREAMING_SNAKE_CASE								=			{'x': 3}
     SCREAMING_SNAKE_CASE								=			evaluate(snake_case__							,    {'add_two': add_two}							,    state=snake_case__    )
     self.assertListEqual(snake_case__							,    [3, 5]    )
     self.assertDictEqual(snake_case__							,    {'x': 3, 'test_list': [3, 5]}    )

    def UpperCamelCase			(			self :      Optional[int]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			'y = x'
     SCREAMING_SNAKE_CASE								=			{'x': 3}
     SCREAMING_SNAKE_CASE								=			evaluate(snake_case__							,    {}							,    state=snake_case__    )
     assert result == 3
     self.assertDictEqual(snake_case__							,    {'x': 3, 'y': 3}    )

    def UpperCamelCase			(			self :      Optional[int]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			'test_list = [x, add_two(x)]\ntest_list[1]'
     SCREAMING_SNAKE_CASE								=			{'x': 3}
     SCREAMING_SNAKE_CASE								=			evaluate(snake_case__							,    {'add_two': add_two}							,    state=snake_case__    )
     assert result == 5
     self.assertDictEqual(snake_case__							,    {'x': 3, 'test_list': [3, 5]}    )

     SCREAMING_SNAKE_CASE								=			'test_dict = {\'x\': x, \'y\': add_two(x)}\ntest_dict[\'y\']'
     SCREAMING_SNAKE_CASE								=			{'x': 3}
     SCREAMING_SNAKE_CASE								=			evaluate(snake_case__							,    {'add_two': add_two}							,    state=snake_case__    )
     assert result == 5
     self.assertDictEqual(snake_case__							,    {'x': 3, 'test_dict': {'x': 3, 'y': 5}}    )




    def UpperCamelCase			(			self :      str    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			'x = 0\nfor i in range(3):\n    x = i'
     SCREAMING_SNAKE_CASE								=			{}
     SCREAMING_SNAKE_CASE								=			evaluate(snake_case__							,    {'range': range}							,    state=snake_case__    )
     assert result == 2
     self.assertDictEqual(snake_case__							,    {'x': 2, 'i': 2}    )




import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


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


a_    :						Union[str, Any]    		=      {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

a_    :						Any    		=      {
    "vocab_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json",
    },
    "merges_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json",
    },
}

a_    :						List[Any]    		=      {
    "allenai/led-base-16384": 1_6384,
}




class      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):
    __UpperCamelCase         =VOCAB_FILES_NAMES
    __UpperCamelCase         =PRETRAINED_VOCAB_FILES_MAP
    __UpperCamelCase         =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __UpperCamelCase         =LEDTokenizer
    __UpperCamelCase         =["input_ids", "attention_mask"]

    def __init__(			self :      Tuple							,    snake_case__ :      List[Any]=None							,    snake_case__ :      List[str]=None							,    snake_case__ :      List[str]=None							,    snake_case__ :      Dict="replace"							,    snake_case__ :      Tuple="<s>"							,    snake_case__ :      Optional[Any]="</s>"							,    snake_case__ :      int="</s>"							,    snake_case__ :      Dict="<s>"							,    snake_case__ :      Union[str, Any]="<unk>"							,    snake_case__ :      Optional[int]="<pad>"							,    snake_case__ :      List[str]="<mask>"							,    snake_case__ :      List[Any]=False							,    snake_case__ :      int=True							,    **snake_case__ :      Dict							,    ):

     """simple docstring"""






     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__							,    )

     SCREAMING_SNAKE_CASE								=			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
     if pre_tok_state.get('add_prefix_space'							,    snake_case__    ) != add_prefix_space:
      SCREAMING_SNAKE_CASE								=			getattr(snake_case__							,    pre_tok_state.pop('type'    )    )
      SCREAMING_SNAKE_CASE								=			add_prefix_space
      SCREAMING_SNAKE_CASE								=			pre_tok_class(**snake_case__    )

     SCREAMING_SNAKE_CASE								=			add_prefix_space

     # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
     SCREAMING_SNAKE_CASE								=			'post_processor'
     SCREAMING_SNAKE_CASE								=			getattr(self.backend_tokenizer							,    snake_case__							,    snake_case__    )
     if tokenizer_component_instance:
      SCREAMING_SNAKE_CASE								=			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:
       SCREAMING_SNAKE_CASE								=			tuple(state['sep']    )
      if "cls" in state:
       SCREAMING_SNAKE_CASE								=			tuple(state['cls']    )

      SCREAMING_SNAKE_CASE								=			False

      if state.get('add_prefix_space'							,    snake_case__    ) != add_prefix_space:
       SCREAMING_SNAKE_CASE								=			add_prefix_space
       SCREAMING_SNAKE_CASE								=			True

      if state.get('trim_offsets'							,    snake_case__    ) != trim_offsets:
       SCREAMING_SNAKE_CASE								=			trim_offsets
       SCREAMING_SNAKE_CASE								=			True

      if changes_to_apply:
       SCREAMING_SNAKE_CASE								=			getattr(snake_case__							,    state.pop('type'    )    )
       SCREAMING_SNAKE_CASE								=			component_class(**snake_case__    )
       setattr(self.backend_tokenizer							,    snake_case__							,    snake_case__    )

    @property
    # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
    def UpperCamelCase			(			self :      Optional[Any]    ):

     """simple docstring"""






     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 UpperCamelCase			(			self :      List[Any]							,    snake_case__ :      Any    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			AddedToken(snake_case__							,    lstrip=snake_case__							,    rstrip=snake_case__    ) if isinstance(snake_case__							,    snake_case__    ) else value
     SCREAMING_SNAKE_CASE								=			value

    def UpperCamelCase			(			self :      Dict							,    *snake_case__ :      Optional[Any]							,    **snake_case__ :      Tuple    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			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 UpperCamelCase			(			self :      List[str]							,    *snake_case__ :      List[Any]							,    **snake_case__ :      Optional[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			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 UpperCamelCase			(			self :      Union[str, Any]							,    snake_case__ :      str							,    snake_case__ :      Optional[str] = None    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self._tokenizer.model.save(snake_case__							,    name=snake_case__    )
     return tuple(snake_case__    )

    def UpperCamelCase			(			self :      List[str]							,    snake_case__ :      int							,    snake_case__ :      Tuple=None    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			[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 UpperCamelCase			(			self :      Optional[int]							,    snake_case__ :      List[int]							,    snake_case__ :      Optional[List[int]] = None    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			[self.sep_token_id]
     SCREAMING_SNAKE_CASE								=			[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]




    def UpperCamelCase			(			self :      Optional[Any]							,    snake_case__ :      Union[Dict[str, EncodedInput], BatchEncoding]							,    snake_case__ :      Optional[int] = None							,    snake_case__ :      PaddingStrategy = PaddingStrategy.DO_NOT_PAD							,    snake_case__ :      Optional[int] = None							,    snake_case__ :      Optional[bool] = None							,    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			super()._pad(
         encoded_inputs=snake_case__							,    max_length=snake_case__							,    padding_strategy=snake_case__							,    pad_to_multiple_of=snake_case__							,    return_attention_mask=snake_case__							,    )

     # Load from model defaults
     if return_attention_mask is None:
      SCREAMING_SNAKE_CASE								=			'attention_mask' in self.model_input_names

     if return_attention_mask and "global_attention_mask" in encoded_inputs:
      SCREAMING_SNAKE_CASE								=			encoded_inputs[self.model_input_names[0]]
      # `global_attention_mask` need to have the same length as other (sequential) inputs.
      SCREAMING_SNAKE_CASE								=			len(encoded_inputs['global_attention_mask']    ) != len(snake_case__    )

      if needs_to_be_padded:
       SCREAMING_SNAKE_CASE								=			len(snake_case__    ) - len(encoded_inputs['global_attention_mask']    )

       if self.padding_side == "right":
        # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
        SCREAMING_SNAKE_CASE								=			(
            encoded_inputs['global_attention_mask'] + [-1] * difference
        )
       elif self.padding_side == "left":
        SCREAMING_SNAKE_CASE								=			[-1] * difference + encoded_inputs[
            'global_attention_mask'
        ]
       else:
        raise ValueError('Invalid padding strategy:' + str(self.padding_side    )    )

     return encoded_inputs

