from __future__ import annotations

from collections.abc import Callable


def SCREAMING_SNAKE_CASE (   lowercase_:		Callable[[int | float], int | float]						,  lowercase_:		int | float						,  lowercase_:		int | float						,  lowercase_:		int = 100						,  ):
       _UpperCamelCase   :				Any       =     x_start
       _UpperCamelCase   :				int       =     fnc(lowercase_ )
       _UpperCamelCase   :				str       =     0.0

       for _ in range(lowercase_ ):
              # Approximates small segments of curve as linear and solve
              # for trapezoidal area
              _UpperCamelCase   :				List[str]       =     (x_end - x_start) / steps + xa
              _UpperCamelCase   :				Dict       =     fnc(lowercase_ )
              area += abs(fxa + fxa ) * (xa - xa) / 2

              # Increment step
              _UpperCamelCase   :				str       =     xa
              _UpperCamelCase   :				Any       =     fxa
       return area


if __name__ == "__main__":


 def SCREAMING_SNAKE_CASE (   lowercase_:		int ):
        return x**3

 print('''f(x) = x^3''')
 print('''The area between the curve, x = -10, x = 10 and the x axis is:''')
 UpperCAmelCase							= 10
 while i <= 10_00_00:
  UpperCAmelCase							= trapezoidal_area(f, -5, 5, i)
  print(f'''with {i} steps: {area}''')
  i *= 10




import json
import os
import unittest

from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES, FSMTTokenizer
from transformers.testing_utils import slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


# using a different tiny model than the one used for default params defined in init to ensure proper testing
UpperCAmelCase							= '''stas/tiny-wmt19-en-ru'''




class       __lowerCAmelCase     (				a_,		unittest.TestCase  ):



 '''simple docstring'''
 _lowerCamelCase  :		str 			=       FSMTTokenizer
 _lowerCamelCase  :		Tuple 			=       False

 def     __snake_case    (	self				:						Dict     )       ->							Union[str, Any]:
        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        _UpperCamelCase   :				Dict       =     [
            'l',
            'o',
            'w',
            'e',
            'r',
            's',
            't',
            'i',
            'd',
            'n',
            'w</w>',
            'r</w>',
            't</w>',
            'lo',
            'low',
            'er</w>',
            'low</w>',
            'lowest</w>',
            'newer</w>',
            'wider</w>',
            '<unk>',
        ]
        _UpperCamelCase   :				List[str]       =     dict(zip(lowerCamelCase				,	range(len(lowerCamelCase     )     )     )     )
        _UpperCamelCase   :				List[str]       =     ['l o 123', 'lo w 1456', 'e r</w> 1789', '']

        _UpperCamelCase   :				List[str]       =     ['en', 'ru']
        _UpperCamelCase   :				int       =     {
            'langs': self.langs,
            'src_vocab_size': 1_0,
            'tgt_vocab_size': 2_0,
        }

        _UpperCamelCase   :				Tuple       =     os.path.join(self.tmpdirname				,	VOCAB_FILES_NAMES['src_vocab_file']     )
        _UpperCamelCase   :				Optional[int]       =     os.path.join(self.tmpdirname				,	VOCAB_FILES_NAMES['tgt_vocab_file']     )
        _UpperCamelCase   :				Any       =     os.path.join(self.tmpdirname				,	'tokenizer_config.json'     )
        _UpperCamelCase   :				int       =     os.path.join(self.tmpdirname				,	VOCAB_FILES_NAMES['merges_file']     )
        with open(self.src_vocab_file				,	'w'     ) as fp:
               fp.write(json.dumps(lowerCamelCase     )     )
        with open(self.tgt_vocab_file				,	'w'     ) as fp:
               fp.write(json.dumps(lowerCamelCase     )     )
        with open(self.merges_file				,	'w'     ) as fp:
               fp.write('\n'.join(lowerCamelCase     )     )
        with open(lowerCamelCase				,	'w'     ) as fp:
               fp.write(json.dumps(lowerCamelCase     )     )

 @cached_property
 def     __snake_case    (	self				:						str     )       ->							Any:
        return FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en'     )

 @cached_property
 def     __snake_case    (	self				:						Optional[int]     )       ->							Any:
        return FSMTTokenizer.from_pretrained('facebook/wmt19-en-ru'     )

 def     __snake_case    (	self				:						Optional[Any]     )       ->							Any:
        _UpperCamelCase   :				Optional[int]       =     FSMTTokenizer.from_pretrained(lowerCamelCase     )
        self.assertListEqual([tokenizer.src_lang, tokenizer.tgt_lang]				,	['en', 'ru']     )
        self.assertEqual(tokenizer.src_vocab_size				,	2_1     )
        self.assertEqual(tokenizer.tgt_vocab_size				,	2_1     )

 def     __snake_case    (	self				:						List[str]     )       ->							List[Any]:
        _UpperCamelCase   :				Union[str, Any]       =     FSMTTokenizer(self.langs				,	self.src_vocab_file				,	self.tgt_vocab_file				,	self.merges_file     )

        _UpperCamelCase   :				Optional[int]       =     'lower'
        _UpperCamelCase   :				str       =     ['low', 'er</w>']
        _UpperCamelCase   :				Optional[Any]       =     tokenizer.tokenize(lowerCamelCase     )
        self.assertListEqual(lowerCamelCase				,	lowerCamelCase     )

        _UpperCamelCase   :				Tuple       =     tokens + ['<unk>']
        _UpperCamelCase   :				int       =     [1_4, 1_5, 2_0]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase     )				,	lowerCamelCase     )

 @slow
 def     __snake_case    (	self				:						List[Any]     )       ->							Union[str, Any]:
        _UpperCamelCase   :				Dict       =     self.tokenizer_ru_en

        _UpperCamelCase   :				List[str]       =     tokenizer.encode('sequence builders'				,	add_special_tokens=lowerCamelCase     )
        _UpperCamelCase   :				Any       =     tokenizer.encode('multi-sequence build'				,	add_special_tokens=lowerCamelCase     )

        _UpperCamelCase   :				List[Any]       =     tokenizer.build_inputs_with_special_tokens(lowerCamelCase     )
        _UpperCamelCase   :				Tuple       =     tokenizer.build_inputs_with_special_tokens(lowerCamelCase				,	lowerCamelCase     )

        assert encoded_sentence == text + [2]
        assert encoded_pair == text + [2] + text_a + [2]

 @slow
 def     __snake_case    (	self				:						List[str]     )       ->							Optional[int]:
        _UpperCamelCase   :				Optional[Any]       =     self.tokenizer_en_ru
        _UpperCamelCase   :				Union[str, Any]       =     self.tokenizer_ru_en

        _UpperCamelCase   :				List[str]       =     [
            [
                'Here\'s a little song I wrote. Don\'t worry, be happy.',
                [2_4_7_0, 3_9, 1_1, 2_3_4_9, 7_2_2_2, 7_0, 5_9_7_9, 7, 8_4_5_0, 1_0_5_0, 1_3_1_6_0, 5, 2_6, 6_4_4_5, 7, 2],
            ],
            ['This is it. No more. I\'m done!', [1_3_2, 2_1, 3_7, 7, 1_4_3_4, 8_6, 7, 7_0, 6_4_7_6, 1_3_0_5, 4_2_7, 2]],
        ]

        # if data needs to be recreated or added, run:
        # import torch
        # model = torch.hub.load("pytorch/fairseq", "transformer.wmt19.en-ru", checkpoint_file="model4.pt", tokenizer="moses", bpe="fastbpe")
        # for src_text, _ in targets: print(f"""[\n"{src_text}",\n {model.encode(src_text).tolist()}\n],""")

        for src_text, tgt_input_ids in targets:
               _UpperCamelCase   :				Union[str, Any]       =     tokenizer_enc.encode(lowerCamelCase				,	return_tensors=lowerCamelCase     )
               self.assertListEqual(lowerCamelCase				,	lowerCamelCase     )

               # and decode backward, using the reversed languages model
               _UpperCamelCase   :				Optional[int]       =     tokenizer_dec.decode(lowerCamelCase				,	skip_special_tokens=lowerCamelCase     )
               self.assertEqual(lowerCamelCase				,	lowerCamelCase     )

 @slow
 def     __snake_case    (	self				:						Any     )       ->							Tuple:
        _UpperCamelCase   :				Optional[int]       =     FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en'				,	do_lower_case=lowerCamelCase     )
        _UpperCamelCase   :				str       =     tokenizer.tokenize('USA is United States of America'     )
        _UpperCamelCase   :				Optional[int]       =     ['us', 'a</w>', 'is</w>', 'un', 'i', 'ted</w>', 'st', 'ates</w>', 'of</w>', 'am', 'er', 'ica</w>']
        self.assertListEqual(lowerCamelCase				,	lowerCamelCase     )

 @unittest.skip('FSMTConfig.__init__  requires non-optional args'     )
 def     __snake_case    (	self				:						Dict     )       ->							List[str]:
        pass

 @unittest.skip('FSMTConfig.__init__  requires non-optional args'     )
 def     __snake_case    (	self				:						Dict     )       ->							int:
        pass



