

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():
       snake_case_			  = '''pt'''
elif is_tf_available():
       snake_case_			  = '''tf'''
else:
       snake_case_			  = '''jax'''


class       SCREAMING_SNAKE_CASE__					(__snake_case		,       unittest.TestCase     ):
    __lowerCamelCase						:       Any														=     PerceiverTokenizer
    __lowerCamelCase						:       List[str]														=     False





    def       snake_case_				(      self):
         super().setUp()
         lowercase__    :   int      =       PerceiverTokenizer()
         tokenizer.save_pretrained(self.tmpdirname)





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





    def       snake_case_				(      self						,   **a):
         return self.tokenizer_class.from_pretrained(self.tmpdirname						,   **a)





    def       snake_case_				(      self						,   a						,   a=False						,   a=20						,   a=5):
         # 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.

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

         lowercase__    :   Union[str, Any]      =       list(filter(lambda a: re.match(r'^[ a-zA-Z]+$'						,   t[1])						,   a))
         lowercase__    :   List[str]      =       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:
              lowercase__    :   Union[str, Any]      =       toks[:max_length]
         if min_length is not None and len(a) < min_length and len(a) > 0:
              while len(a) < min_length:
                   lowercase__    :   List[str]      =       toks + toks
        # toks_str = [t[1] for t in toks]
         lowercase__    :   int      =       [t[0] for t in toks]

         # Ensure consistency
         lowercase__    :   List[str]      =       tokenizer.decode(a						,   clean_up_tokenization_spaces=a)
         if " " not in output_txt and len(a) > 1:
              lowercase__    :   Union[str, Any]      =       (
                  tokenizer.decode([toks_ids[0]]						,   clean_up_tokenization_spaces=a)
                  + ' '
                  + tokenizer.decode(toks_ids[1:]						,   clean_up_tokenization_spaces=a)
              )
         if with_prefix_space:
              lowercase__    :   Dict      =       ' ' + output_txt
         lowercase__    :   Tuple      =       tokenizer.encode(a						,   add_special_tokens=a)
         return output_txt, output_ids





    def       snake_case_				(      self):
         lowercase__    :   Union[str, Any]      =       self.perceiver_tokenizer
         lowercase__    :   List[Any]      =       'Unicode €.'
         lowercase__    :   Optional[int]      =       tokenizer(a)
         lowercase__    :   List[Any]      =       [4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
         self.assertEqual(encoded['input_ids']						,   a)

         # decoding
         lowercase__    :   Tuple      =       tokenizer.decode(a)
         self.assertEqual(a						,   '[CLS]Unicode €.[SEP]')

         lowercase__    :   Dict      =       tokenizer('e è é ê ë')
         lowercase__    :   Dict      =       [4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
         self.assertEqual(encoded['input_ids']						,   a)
         # decoding
         lowercase__    :   Optional[int]      =       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       snake_case_				(      self):
         lowercase__    :   List[Any]      =       self.perceiver_tokenizer
         lowercase__    :   Union[str, Any]      =       ['A long paragraph for summarization.', 'Another paragraph for summarization.']
         # fmt: off
         lowercase__    :   Union[str, Any]      =       [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
         lowercase__    :   str      =       tokenizer(a						,   padding=a						,   return_tensors=a)
         self.assertIsInstance(a						,   a)

         if FRAMEWORK != "jax":
              lowercase__    :   List[Any]      =       list(batch.input_ids.numpy()[0])
         else:
              lowercase__    :   Dict      =       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       snake_case_				(      self):
         lowercase__    :   int      =       self.perceiver_tokenizer
         lowercase__    :   Optional[Any]      =       ['A long paragraph for summarization.', 'Another paragraph for summarization.']
         lowercase__    :   int      =       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       snake_case_				(      self):
         lowercase__    :   str      =       self.perceiver_tokenizer
         lowercase__    :   List[Any]      =       [
             'Summary of the text.',
             'Another summary.',
         ]
         lowercase__    :   List[Any]      =       tokenizer(
             text_target=a						,   max_length=32						,   padding='max_length'						,   truncation=a						,   return_tensors=a)
         self.assertEqual(32						,   targets['input_ids'].shape[1])





    def       snake_case_				(      self):
         # safety check on max_len default value so we are sure the test works
         lowercase__    :   str      =       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
         lowercase__    :   Tuple      =       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
                   lowercase__    :   Tuple      =       tempfile.mkdtemp()

                   lowercase__    :   Optional[Any]      =       ' He is very happy, UNwant\u00E9d,running'
                   lowercase__    :   Optional[int]      =       tokenizer.encode(a						,   add_special_tokens=a)
                   tokenizer.save_pretrained(a)

                   lowercase__    :   str      =       tokenizer.__class__.from_pretrained(a)
                   lowercase__    :   Optional[Any]      =       after_tokenizer.encode(a						,   add_special_tokens=a)
                   self.assertListEqual(a						,   a)

                   shutil.rmtree(a)

         lowercase__    :   Optional[Any]      =       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
                   lowercase__    :   Optional[Any]      =       tempfile.mkdtemp()

                   lowercase__    :   Dict      =       ' He is very happy, UNwant\u00E9d,running'
                   tokenizer.add_tokens(['bim', 'bambam'])
                   lowercase__    :   List[str]      =       tokenizer.additional_special_tokens
                   additional_special_tokens.append('new_additional_special_token')
                   tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens})
                   lowercase__    :   int      =       tokenizer.encode(a						,   add_special_tokens=a)
                   tokenizer.save_pretrained(a)

                   lowercase__    :   Dict      =       tokenizer.__class__.from_pretrained(a)
                   lowercase__    :   str      =       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)

                   lowercase__    :   Union[str, Any]      =       tokenizer.__class__.from_pretrained(a						,   model_max_length=43)
                   self.assertEqual(tokenizer.model_max_length						,   43)

                   shutil.rmtree(a)





    def       snake_case_				(      self):
         lowercase__    :   List[str]      =       []
         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:
                        lowercase__    :   Tuple      =       json.load(a)

                   with open(os.path.join(a						,   'tokenizer_config.json')						,   encoding='utf-8') as json_file:
                        lowercase__    :   Union[str, Any]      =       json.load(a)

                   lowercase__    :   List[str]      =       [f"""<extra_id_{i}>""" for i in range(125)]

                   lowercase__    :   Dict      =       added_tokens_extra_ids + [
                       'an_additional_special_token'
                   ]
                   lowercase__    :   Tuple      =       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
                   lowercase__    :   Any      =       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
                   lowercase__    :   str      =       added_tokens_extra_ids + [AddedToken('a_new_additional_special_token'						,   lstrip=a)]
                   lowercase__    :   List[str]      =       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       snake_case_				(      self):
         lowercase__    :   Tuple      =       self.perceiver_tokenizer
         self.assertEqual(tokenizer.decode([178])						,   '�')





    def       snake_case_				(      self):
         pass





    def       snake_case_				(      self):
         pass





    def       snake_case_				(      self):
         pass





    def       snake_case_				(      self):
         pass





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

                   self.assertIsInstance(a						,   a)




import argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
       # Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
       # setup instructions, if using on-demand hardware

       # If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
       # If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
       # Throw an error if user passes both BYO and on-demand cluster args
       # Otherwise, use default values
       snake_case_			  = argparse.ArgumentParser()
       parser.add_argument('''--user''', type=str, default='''ubuntu''')
       parser.add_argument('''--host''', type=str, default='''localhost''')
       parser.add_argument('''--key_path''', type=str, default=None)
       parser.add_argument('''--instance''', type=str, default='''V100:1''')
       parser.add_argument('''--provider''', type=str, default='''cheapest''')
       parser.add_argument('''--use_spot''', type=bool, default=False)
       parser.add_argument('''--example''', type=str, default='''pytorch/text-generation/run_generation.py''')
       snake_case_  ,		snake_case_			  = parser.parse_known_args()
       if args.host != "localhost":
              if args.instance != "V100:1" or args.provider != "cheapest":
                     raise ValueError('''Cannot specify both BYO and on-demand cluster args''')
              snake_case_			  = rh.cluster(
                  name='''rh-cluster''', ips=[args.host], ssh_creds={'''ssh_user''': args.user, '''ssh_private_key''': args.key_path}
              )
       else:
              snake_case_			  = rh.cluster(
                  name='''rh-cluster''', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
              )
       snake_case_			  = args.example.rsplit('''/''', 1)[0]

       # Set up remote environment
       cluster.install_packages(['''pip:./'''])  # Installs transformers from local source
       # Note transformers is copied into the home directory on the remote machine, so we can install from there
       cluster.run([F'''pip install -r transformers/examples/{example_dir}/requirements.txt'''])
       cluster.run(['''pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117'''])

       # Run example. You can bypass the CLI wrapper and paste your own code here.
       cluster.run([F'''python transformers/examples/{args.example} {' '.join(shlex.quote(arg) for arg in unknown)}'''])

       # Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
       # from my_script... import train
       # reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
       # launch_train_gpu = rh.function(fn=train,
       #                                system=gpu,
       #                                reqs=reqs,
       #                                name='train_bert_glue')
       #
       # We can pass in arguments just like we would to a function:
       # launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
       #                  stream_logs=True)

