





'''simple docstring'''




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():
  lowercase__   :				str      =   '''pt'''
elif is_tf_available():
  lowercase__   :				Optional[Any]      =   '''tf'''
else:
  lowercase__   :				Optional[Any]      =   '''jax'''


class 			SCREAMING_SNAKE_CASE   (a__ ,		unittest.TestCase							):
     lowerCAmelCase		      =	PerceiverTokenizer
     lowerCAmelCase		      =	False


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          super().setUp()
          __A						:			str								=	PerceiverTokenizer()
          tokenizer.save_pretrained(self.tmpdirname)


     @cached_property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return PerceiverTokenizer.from_pretrained('deepmind/language-perceiver')


     def 							SCREAMING_SNAKE_CASE  (						self						,     **_UpperCAmelCase):

          '''simple docstring'''






          return self.tokenizer_class.from_pretrained(self.tmpdirname						,     **_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase=False						,     _UpperCAmelCase=20						,     _UpperCAmelCase=5):

          '''simple docstring'''






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

          __A						:			Tuple								=	list(filter(lambda _UpperCAmelCase: re.match(R'^[ a-zA-Z]+$'						,     t[1])						,     _UpperCAmelCase))
          __A						:			Tuple								=	list(filter(lambda _UpperCAmelCase: [t[0]] == tokenizer.encode(t[1]						,     add_special_tokens=_UpperCAmelCase)						,     _UpperCAmelCase))
          if max_length is not None and len(_UpperCAmelCase) > max_length:
               __A						:			Any								=	toks[:max_length]
          if min_length is not None and len(_UpperCAmelCase) < min_length and len(_UpperCAmelCase) > 0:
               while len(_UpperCAmelCase) < min_length:
                    __A						:			Dict								=	toks + toks
        # toks_str = [t[1] for t in toks]
          __A						:			Optional[int]								=	[t[0] for t in toks]

          # Ensure consistency
          __A						:			Optional[Any]								=	tokenizer.decode(_UpperCAmelCase						,     clean_up_tokenization_spaces=_UpperCAmelCase)
          if " " not in output_txt and len(_UpperCAmelCase) > 1:
               __A						:			List[Any]								=	(
                   tokenizer.decode([toks_ids[0]]						,     clean_up_tokenization_spaces=_UpperCAmelCase)
                   + ' '
                   + tokenizer.decode(toks_ids[1:]						,     clean_up_tokenization_spaces=_UpperCAmelCase)
               )
          if with_prefix_space:
               __A						:			str								=	' ' + output_txt
          __A						:			int								=	tokenizer.encode(_UpperCAmelCase						,     add_special_tokens=_UpperCAmelCase)
          return output_txt, output_ids


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			int								=	self.perceiver_tokenizer
          __A						:			int								=	'Unicode €.'
          __A						:			Any								=	tokenizer(_UpperCAmelCase)
          __A						:			Optional[Any]								=	[4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
          self.assertEqual(encoded['input_ids']						,     _UpperCAmelCase)

          # decoding
          __A						:			Union[str, Any]								=	tokenizer.decode(_UpperCAmelCase)
          self.assertEqual(_UpperCAmelCase						,     '[CLS]Unicode €.[SEP]')

          __A						:			Optional[int]								=	tokenizer('e è é ê ë')
          __A						:			List[str]								=	[4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
          self.assertEqual(encoded['input_ids']						,     _UpperCAmelCase)
          # decoding
          __A						:			List[Any]								=	tokenizer.decode(_UpperCAmelCase)
          self.assertEqual(_UpperCAmelCase						,     '[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 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Any								=	self.perceiver_tokenizer
          __A						:			Optional[int]								=	['A long paragraph for summarization.', 'Another paragraph for summarization.']
          # fmt: off
          __A						:			Optional[int]								=	[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
          __A						:			int								=	tokenizer(_UpperCAmelCase						,     padding=_UpperCAmelCase						,     return_tensors=_UpperCAmelCase)
          self.assertIsInstance(_UpperCAmelCase						,     _UpperCAmelCase)

          if FRAMEWORK != "jax":
               __A						:			str								=	list(batch.input_ids.numpy()[0])
          else:
               __A						:			Union[str, Any]								=	list(batch.input_ids.tolist()[0])

          self.assertListEqual(_UpperCAmelCase						,     _UpperCAmelCase)

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


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			str								=	self.perceiver_tokenizer
          __A						:			List[str]								=	['A long paragraph for summarization.', 'Another paragraph for summarization.']
          __A						:			int								=	tokenizer(_UpperCAmelCase						,     padding=_UpperCAmelCase						,     return_tensors=_UpperCAmelCase)
          # check if input_ids are returned and no decoder_input_ids
          self.assertIn('input_ids'						,     _UpperCAmelCase)
          self.assertIn('attention_mask'						,     _UpperCAmelCase)
          self.assertNotIn('decoder_input_ids'						,     _UpperCAmelCase)
          self.assertNotIn('decoder_attention_mask'						,     _UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Optional[Any]								=	self.perceiver_tokenizer
          __A						:			Optional[int]								=	[
              'Summary of the text.',
              'Another summary.',
          ]
          __A						:			str								=	tokenizer(
              text_target=_UpperCAmelCase						,     max_length=32						,     padding='max_length'						,     truncation=_UpperCAmelCase						,     return_tensors=_UpperCAmelCase)
          self.assertEqual(32						,     targets['input_ids'].shape[1])


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			int								=	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
          __A						:			Any								=	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
                    __A						:			Any								=	tempfile.mkdtemp()

                    __A						:			Any								=	' He is very happy, UNwant\u00E9d,running'
                    __A						:			Union[str, Any]								=	tokenizer.encode(_UpperCAmelCase						,     add_special_tokens=_UpperCAmelCase)
                    tokenizer.save_pretrained(_UpperCAmelCase)

                    __A						:			int								=	tokenizer.__class__.from_pretrained(_UpperCAmelCase)
                    __A						:			Optional[int]								=	after_tokenizer.encode(_UpperCAmelCase						,     add_special_tokens=_UpperCAmelCase)
                    self.assertListEqual(_UpperCAmelCase						,     _UpperCAmelCase)

                    shutil.rmtree(_UpperCAmelCase)

          __A						:			List[str]								=	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
                    __A						:			str								=	tempfile.mkdtemp()

                    __A						:			List[Any]								=	' He is very happy, UNwant\u00E9d,running'
                    tokenizer.add_tokens(['bim', 'bambam'])
                    __A						:			Optional[Any]								=	tokenizer.additional_special_tokens
                    additional_special_tokens.append('new_additional_special_token')
                    tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens})
                    __A						:			Dict								=	tokenizer.encode(_UpperCAmelCase						,     add_special_tokens=_UpperCAmelCase)
                    tokenizer.save_pretrained(_UpperCAmelCase)

                    __A						:			Tuple								=	tokenizer.__class__.from_pretrained(_UpperCAmelCase)
                    __A						:			Dict								=	after_tokenizer.encode(_UpperCAmelCase						,     add_special_tokens=_UpperCAmelCase)
                    self.assertListEqual(_UpperCAmelCase						,     _UpperCAmelCase)
                    self.assertIn('new_additional_special_token'						,     after_tokenizer.additional_special_tokens)
                    self.assertEqual(after_tokenizer.model_max_length						,     42)

                    __A						:			int								=	tokenizer.__class__.from_pretrained(_UpperCAmelCase						,     model_max_length=43)
                    self.assertEqual(tokenizer.model_max_length						,     43)

                    shutil.rmtree(_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Any								=	[]
          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(_UpperCAmelCase)

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

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

                    __A						:			Dict								=	[F'<extra_id_{i}>' for i in range(125)]

                    __A						:			Tuple								=	added_tokens_extra_ids + [
                        'an_additional_special_token'
                    ]
                    __A						:			Optional[Any]								=	added_tokens_extra_ids + [
                        'an_additional_special_token'
                    ]

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

                    # 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
                    __A						:			Dict								=	tokenizer_class.from_pretrained(
                        _UpperCAmelCase						,     )
                    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
                    __A						:			int								=	added_tokens_extra_ids + [AddedToken('a_new_additional_special_token'						,     lstrip=_UpperCAmelCase)]
                    __A						:			Any								=	tokenizer_class.from_pretrained(
                        _UpperCAmelCase						,     additional_special_tokens=_UpperCAmelCase						,     )

                    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 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Union[str, Any]								=	self.perceiver_tokenizer
          self.assertEqual(tokenizer.decode([178])						,     '�')


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          pass


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          pass


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          pass


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          pass


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Dict								=	self.get_tokenizers(fast=_UpperCAmelCase						,     do_lower_case=_UpperCAmelCase)
          for tokenizer in tokenizers:
               with self.subTest(F'{tokenizer.__class__.__name__}'):
                    __A						:			int								=	['[CLS]', 't', 'h', 'i', 's', ' ', 'i', 's', ' ', 'a', ' ', 't', 'e', 's', 't', '[SEP]']
                    __A						:			Dict								=	tokenizer.convert_tokens_to_string(_UpperCAmelCase)

                    self.assertIsInstance(_UpperCAmelCase						,     _UpperCAmelCase)






'''simple docstring'''




from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
lowercase__   :				List[Any]      =   HfArgumentParser(InitializationArguments)
lowercase__   :				List[Any]      =   parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
lowercase__   :				Union[str, Any]      =   AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
lowercase__   :				List[str]      =   {
    '''vocab_size''': len(tokenizer),
    '''scale_attn_by_inverse_layer_idx''': True,
    '''reorder_and_upcast_attn''': True,
}

# Load model config (GPT-2 large in this case)
lowercase__   :				Optional[int]      =   AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
lowercase__   :				Optional[int]      =   AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)