


import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
   _A		       =						'pt'
elif is_tf_available():
   _A		       =						'tf'
else:
   _A		       =						'jax'






class     UpperCAmelCase__       (					A_   ,  unittest.TestCase		):



    """simple docstring"""






    UpperCAmelCase__						:		List[Any]      =  PerceiverTokenizer
    UpperCAmelCase__						:		Optional[Any]      =  False






    def _a					(			self    )  ->       Dict:
         super().setUp()
         __UpperCamelCase    							=PerceiverTokenizer()
         tokenizer.save_pretrained(self.tmpdirname    )






    @cached_property
    def _a					(			self    )  ->       int:
         return PerceiverTokenizer.from_pretrained('deepmind/language-perceiver'    )






    def _a					(			self					,   **A_    )  ->       PerceiverTokenizer:
         return self.tokenizer_class.from_pretrained(self.tmpdirname					,   **A_    )






    def _a					(			self					,   A_					,   A_=False					,   A_=20					,   A_=5    )  ->       Tuple[str, list]:
         # XXX The default common tokenizer tests assume that every ID is decodable on its own.
         # This assumption is invalid for Perceiver because single bytes might not be
         # valid utf-8 (byte 128 for instance).
         # Here we're overriding the smallest possible method to provide
         # a clean sequence without making the same assumption.

         __UpperCamelCase    							=[]
         for i in range(len(A_    )    ):
              try:
                   __UpperCamelCase    							=tokenizer.decode([i]					,   clean_up_tokenization_spaces=A_    )
              except UnicodeDecodeError:
                   pass
              toks.append((i, tok)    )

         __UpperCamelCase    							=list(filter(lambda A_    : re.match(r'^[ a-zA-Z]+$'					,   t[1]    )					,   A_    )    )
         __UpperCamelCase    							=list(filter(lambda A_    : [t[0]] == tokenizer.encode(t[1]					,   add_special_tokens=A_    )					,   A_    )    )
         if max_length is not None and len(A_    ) > max_length:
              __UpperCamelCase    							=toks[:max_length]
         if min_length is not None and len(A_    ) < min_length and len(A_    ) > 0:
              while len(A_    ) < min_length:
                   __UpperCamelCase    							=toks + toks
        # toks_str = [t[1] for t in toks]
         __UpperCamelCase    							=[t[0] for t in toks]

         # Ensure consistency
         __UpperCamelCase    							=tokenizer.decode(A_					,   clean_up_tokenization_spaces=A_    )
         if " " not in output_txt and len(A_    ) > 1:
              __UpperCamelCase    							=(
                  tokenizer.decode([toks_ids[0]]					,   clean_up_tokenization_spaces=A_    )
                  + ' '
                  + tokenizer.decode(toks_ids[1:]					,   clean_up_tokenization_spaces=A_    )
              )
         if with_prefix_space:
              __UpperCamelCase    							=' ' + output_txt
         __UpperCamelCase    							=tokenizer.encode(A_					,   add_special_tokens=A_    )
         return output_txt, output_ids






    def _a					(			self    )  ->       Union[str, Any]:
         __UpperCamelCase    							=self.perceiver_tokenizer
         __UpperCamelCase    							='Unicode €.'
         __UpperCamelCase    							=tokenizer(A_    )
         __UpperCamelCase    							=[4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
         self.assertEqual(encoded['input_ids']					,   A_    )

         # decoding
         __UpperCamelCase    							=tokenizer.decode(A_    )
         self.assertEqual(A_					,   '[CLS]Unicode €.[SEP]'    )

         __UpperCamelCase    							=tokenizer('e è é ê ë'    )
         __UpperCamelCase    							=[4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
         self.assertEqual(encoded['input_ids']					,   A_    )
         # decoding
         __UpperCamelCase    							=tokenizer.decode(A_    )
         self.assertEqual(A_					,   '[CLS]e è é ê ë[SEP]'    )

         # encode/decode, but with `encode` instead of `__call__`
         self.assertEqual(tokenizer.decode(tokenizer.encode('e è é ê ë'    )    )					,   '[CLS]e è é ê ë[SEP]'    )






    def _a					(			self    )  ->       Optional[Any]:
         __UpperCamelCase    							=self.perceiver_tokenizer
         __UpperCamelCase    							=['A long paragraph for summarization.', 'Another paragraph for summarization.']
         # fmt: off
         __UpperCamelCase    							=[4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0]
         # fmt: on
         __UpperCamelCase    							=tokenizer(A_					,   padding=A_					,   return_tensors=A_    )
         self.assertIsInstance(A_					,   A_    )

         if FRAMEWORK != "jax":
              __UpperCamelCase    							=list(batch.input_ids.numpy()[0]    )
         else:
              __UpperCamelCase    							=list(batch.input_ids.tolist()[0]    )

         self.assertListEqual(A_					,   A_    )

         self.assertEqual((2, 38)					,   batch.input_ids.shape    )
         self.assertEqual((2, 38)					,   batch.attention_mask.shape    )






    def _a					(			self    )  ->       Optional[int]:
         __UpperCamelCase    							=self.perceiver_tokenizer
         __UpperCamelCase    							=['A long paragraph for summarization.', 'Another paragraph for summarization.']
         __UpperCamelCase    							=tokenizer(A_					,   padding=A_					,   return_tensors=A_    )
         # check if input_ids are returned and no decoder_input_ids
         self.assertIn('input_ids'					,   A_    )
         self.assertIn('attention_mask'					,   A_    )
         self.assertNotIn('decoder_input_ids'					,   A_    )
         self.assertNotIn('decoder_attention_mask'					,   A_    )






    def _a					(			self    )  ->       Any:
         __UpperCamelCase    							=self.perceiver_tokenizer
         __UpperCamelCase    							=[
             'Summary of the text.',
             'Another summary.',
         ]
         __UpperCamelCase    							=tokenizer(
             text_target=A_					,   max_length=32					,   padding='max_length'					,   truncation=A_					,   return_tensors=A_    )
         self.assertEqual(32					,   targets['input_ids'].shape[1]    )






    def _a					(			self    )  ->       Tuple:
         # safety check on max_len default value so we are sure the test works
         __UpperCamelCase    							=self.get_tokenizers()
         for tokenizer in tokenizers:
              with self.subTest(f'{tokenizer.__class__.__name__}'    ):
                   self.assertNotEqual(tokenizer.model_max_length					,   42    )

        # Now let's start the test
         __UpperCamelCase    							=self.get_tokenizers()
         for tokenizer in tokenizers:
              with self.subTest(f'{tokenizer.__class__.__name__}'    ):
                   # Isolate this from the other tests because we save additional tokens/etc
                   __UpperCamelCase    							=tempfile.mkdtemp()

                   __UpperCamelCase    							=' He is very happy, UNwant\u00E9d,running'
                   __UpperCamelCase    							=tokenizer.encode(A_					,   add_special_tokens=A_    )
                   tokenizer.save_pretrained(A_    )

                   __UpperCamelCase    							=tokenizer.__class__.from_pretrained(A_    )
                   __UpperCamelCase    							=after_tokenizer.encode(A_					,   add_special_tokens=A_    )
                   self.assertListEqual(A_					,   A_    )

                   shutil.rmtree(A_    )

         __UpperCamelCase    							=self.get_tokenizers(model_max_length=42    )
         for tokenizer in tokenizers:
              with self.subTest(f'{tokenizer.__class__.__name__}'    ):
                   # Isolate this from the other tests because we save additional tokens/etc
                   __UpperCamelCase    							=tempfile.mkdtemp()

                   __UpperCamelCase    							=' He is very happy, UNwant\u00E9d,running'
                   tokenizer.add_tokens(['bim', 'bambam']    )
                   __UpperCamelCase    							=tokenizer.additional_special_tokens
                   additional_special_tokens.append('new_additional_special_token'    )
                   tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens}    )
                   __UpperCamelCase    							=tokenizer.encode(A_					,   add_special_tokens=A_    )
                   tokenizer.save_pretrained(A_    )

                   __UpperCamelCase    							=tokenizer.__class__.from_pretrained(A_    )
                   __UpperCamelCase    							=after_tokenizer.encode(A_					,   add_special_tokens=A_    )
                   self.assertListEqual(A_					,   A_    )
                   self.assertIn('new_additional_special_token'					,   after_tokenizer.additional_special_tokens    )
                   self.assertEqual(after_tokenizer.model_max_length					,   42    )

                   __UpperCamelCase    							=tokenizer.__class__.from_pretrained(A_					,   model_max_length=43    )
                   self.assertEqual(tokenizer.model_max_length					,   43    )

                   shutil.rmtree(A_    )






    def _a					(			self    )  ->       Any:
         __UpperCamelCase    							=[]
         if self.test_slow_tokenizer:
              tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())    )

         if self.test_rust_tokenizer:
              tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())    )

         for tokenizer_class, tokenizer_utils in tokenizer_list:
              with tempfile.TemporaryDirectory() as tmp_dir:
                   tokenizer_utils.save_pretrained(A_    )

                   with open(os.path.join(A_					,   'special_tokens_map.json'    )					,   encoding='utf-8'    ) as json_file:
                        __UpperCamelCase    							=json.load(A_    )

                   with open(os.path.join(A_					,   'tokenizer_config.json'    )					,   encoding='utf-8'    ) as json_file:
                        __UpperCamelCase    							=json.load(A_    )

                   __UpperCamelCase    							=[f'<extra_id_{i}>' for i in range(125    )]

                   __UpperCamelCase    							=added_tokens_extra_ids + [
                       'an_additional_special_token'
                   ]
                   __UpperCamelCase    							=added_tokens_extra_ids + [
                       'an_additional_special_token'
                   ]

                   with open(os.path.join(A_					,   'special_tokens_map.json'    )					,   'w'					,   encoding='utf-8'    ) as outfile:
                        json.dump(A_					,   A_    )
                   with open(os.path.join(A_					,   'tokenizer_config.json'    )					,   'w'					,   encoding='utf-8'    ) as outfile:
                        json.dump(A_					,   A_    )

                   # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
                   # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
                   # "special_tokens_map.json" files
                   __UpperCamelCase    							=tokenizer_class.from_pretrained(
                       A_					,   )
                   self.assertIn(
                       'an_additional_special_token'					,   tokenizer_without_change_in_init.additional_special_tokens    )
                   self.assertEqual(
                       ['an_additional_special_token']					,   tokenizer_without_change_in_init.convert_ids_to_tokens(
                           tokenizer_without_change_in_init.convert_tokens_to_ids(['an_additional_special_token']    )    )					,   )

                   # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                   __UpperCamelCase    							=added_tokens_extra_ids + [AddedToken('a_new_additional_special_token'					,   lstrip=A_    )]
                   __UpperCamelCase    							=tokenizer_class.from_pretrained(
                       A_					,   additional_special_tokens=A_					,   )

                   self.assertIn('a_new_additional_special_token'					,   tokenizer.additional_special_tokens    )
                   self.assertEqual(
                       ['a_new_additional_special_token']					,   tokenizer.convert_ids_to_tokens(
                           tokenizer.convert_tokens_to_ids(['a_new_additional_special_token']    )    )					,   )






    def _a					(			self    )  ->       Dict:
         __UpperCamelCase    							=self.perceiver_tokenizer
         self.assertEqual(tokenizer.decode([178]    )					,   '�'    )






    def _a					(			self    )  ->       Union[str, Any]:
         pass






    def _a					(			self    )  ->       Optional[Any]:
         pass






    def _a					(			self    )  ->       int:
         pass






    def _a					(			self    )  ->       List[Any]:
         pass






    def _a					(			self    )  ->       Optional[Any]:
         # The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
         # strings and special added tokens as tokens
         __UpperCamelCase    							=self.get_tokenizers(fast=A_					,   do_lower_case=A_    )
         for tokenizer in tokenizers:
              with self.subTest(f'{tokenizer.__class__.__name__}'    ):
                   __UpperCamelCase    							=['[CLS]', 't', 'h', 'i', 's', ' ', 'i', 's', ' ', 'a', ' ', 't', 'e', 's', 't', '[SEP]']
                   __UpperCamelCase    							=tokenizer.convert_tokens_to_string(A_    )

                   self.assertIsInstance(A_					,   A_    )









from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					str = ""				):
     __UpperCamelCase    							=url or 'https://www.imdb.com/chart/top/?ref_=nv_mv_250'
     __UpperCamelCase    							=BeautifulSoup(requests.get(SCREAMING_SNAKE_CASE__				).text			,     'html.parser'				)
     __UpperCamelCase    							=soup.find_all('td'			,     attrs='titleColumn'				)
     __UpperCamelCase    							=soup.find_all('td'			,     class_='ratingColumn imdbRating'				)
     return {
         title.a.text: float(rating.strong.text				)
         for title, rating in zip(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)
     }

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					str = "IMDb_Top_250_Movies.csv"				):
     __UpperCamelCase    							=get_imdb_top_aaa_movies()
     with open(SCREAMING_SNAKE_CASE__			,     'w'			,     newline=''				) as out_file:
          __UpperCamelCase    							=csv.writer(SCREAMING_SNAKE_CASE__				)
          writer.writerow(['Movie title', 'IMDb rating']				)
          for title, rating in movies.items():
               writer.writerow([title, rating]				)


if __name__ == "__main__":
   write_movies()





