import concurrent.futures
import json
import os
import shutil
import tempfile
import unittest

from transformers import AutoTokenizer, PreTrainedTokenizerFast
from transformers.testing_utils import require_tokenizers

from ..test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class _a   (       __SCREAMING_SNAKE_CASE    ,							unittest.TestCase					):

	'''simple docstring'''



	lowerCamelCase_    :			str	  =       PreTrainedTokenizerFast
	lowerCamelCase_    :			Tuple	  =       False
	lowerCamelCase_    :			List[str]	  =       True
	lowerCamelCase_    :			Tuple	  =       """tokenizer_file"""

	def      __UpperCAmelCase(   self    ):
			__A							:    Any         =      False  # because we don't have pretrained_vocab_files_map
			super().setUp()
			__A							:    Tuple         =      True

			__A							:    List[str]         =      ['robot-test/dummy-tokenizer-fast', 'robot-test/dummy-tokenizer-wordlevel']
			__A							:    List[Any]         =      'SaulLu/dummy-tokenizer-bytelevel-bpe'

			# Inclusion of 2 tokenizers to test different types of models (Unigram and WordLevel for the moment)
			__A							:    Optional[Any]         =      [(PreTrainedTokenizerFast, model_path, {}) for model_path in model_paths]

			__A							:    Dict         =      PreTrainedTokenizerFast.from_pretrained(model_paths[0]    )
			tokenizer.save_pretrained(self.tmpdirname    )

	def      __UpperCAmelCase(   self    ):
			pass

	def      __UpperCAmelCase(   self    ):
			pass

	def      __UpperCAmelCase(   self    ):
			pass

	def      __UpperCAmelCase(   self    ):
			pass

	def      __UpperCAmelCase(   self    ):
			__A							:    Optional[int]         =      self.tmpdirname
			# Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
			for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
					with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"    ):
							try:
									__A							:    List[str]         =      tempfile.mkdtemp()
									__A							:    str         =      self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__							, **lowerCAmelCase__    )

									tokenizer.save_pretrained(self.tmpdirname    )
									super().test_training_new_tokenizer()
							finally:
									# Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
									# is restored
									shutil.rmtree(self.tmpdirname    )
									__A							:    Dict         =      tmpdirname_orig

	def      __UpperCAmelCase(   self    ):
			__A							:    Any         =      self.tmpdirname
			# Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
			for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
					with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"    ):
							try:
									__A							:    Optional[int]         =      tempfile.mkdtemp()
									__A							:    Dict         =      self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__							, **lowerCAmelCase__    )

									tokenizer.save_pretrained(self.tmpdirname    )
									super().test_training_new_tokenizer_with_special_tokens_change()
							finally:
									# Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
									# is restored
									shutil.rmtree(self.tmpdirname    )
									__A							:    Any         =      tmpdirname_orig







	def      __UpperCAmelCase(   self    ):
			__A							:    Any         =      self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name    )

			__A							:    List[str]         =      ('a' for _ in range(1_000    ))
			__A							:    List[str]         =      tokenizer.train_new_from_iterator(text_iterator=lowerCAmelCase__							, length=1_000							, vocab_size=50    )

			__A							:    Union[str, Any]         =      new_tokenizer.encode("a🤗"    )
			self.assertEqual(lowerCAmelCase__							, [64, 172, 253, 97, 245]    )


@require_tokenizers
class _a   (       unittest.TestCase					):

	'''simple docstring'''



	def      __UpperCAmelCase(   self    ):
			__A							:    Any         =      AutoTokenizer.from_pretrained("bert-base-cased"    )
			__A							:    Dict         =      json.loads(tokenizer._tokenizer.to_str()    )
			__A							:    List[str]         =      len(lowerCAmelCase__    )

			with tempfile.TemporaryDirectory() as tmp_dir:
					# Hack to save this in the tokenizer_config.json
					__A							:    Union[str, Any]         =      ['tokenizer.4.0.0.json']
					tokenizer.save_pretrained(lowerCAmelCase__    )
					json.dump(lowerCAmelCase__							, open(os.path.join(lowerCAmelCase__							, "tokenizer.4.0.0.json"    )							, "w"    )    )

					# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
					__A							:    str         =      AutoTokenizer.from_pretrained(lowerCAmelCase__    )
					self.assertEqual(len(lowerCAmelCase__    )							, len(lowerCAmelCase__    ) + 1    )
					__A							:    str         =      json.loads(new_tokenizer._tokenizer.to_str()    )
					self.assertIn("huggingface"							, json_tokenizer["model"]["vocab"]    )

					# Will need to be adjusted if we reach v42 and this test is still here.
					# Should pick the old tokenizer file as the version of Transformers is < 4.0.0
					shutil.move(os.path.join(lowerCAmelCase__							, "tokenizer.4.0.0.json"    )							, os.path.join(lowerCAmelCase__							, "tokenizer.42.0.0.json"    )    )
					__A							:    int         =      ['tokenizer.42.0.0.json']
					tokenizer.save_pretrained(lowerCAmelCase__    )
					__A							:    List[str]         =      AutoTokenizer.from_pretrained(lowerCAmelCase__    )
					self.assertEqual(len(lowerCAmelCase__    )							, len(lowerCAmelCase__    )    )
					__A							:    Optional[int]         =      json.loads(new_tokenizer._tokenizer.to_str()    )
					self.assertNotIn("huggingface"							, json_tokenizer["model"]["vocab"]    )







	def      __UpperCAmelCase(   self    ):
			__A							:    str         =      'hf-internal-testing/test-two-tokenizers'

			# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
			__A							:    Optional[Any]         =      AutoTokenizer.from_pretrained(lowerCAmelCase__    )
			self.assertEqual(len(lowerCAmelCase__    )							, 28_997    )
			__A							:    Tuple         =      json.loads(tokenizer._tokenizer.to_str()    )
			self.assertIn("huggingface"							, json_tokenizer["model"]["vocab"]    )

			# Testing an older version by monkey-patching the version in the module it's used.
			import transformers as old_transformers

			__A							:    List[str]         =      '3.0.0'
			__A							:    str         =      old_transformers.models.auto.AutoTokenizer.from_pretrained(lowerCAmelCase__    )
			self.assertEqual(len(lowerCAmelCase__    )							, 28_996    )
			__A							:    Tuple         =      json.loads(old_tokenizer._tokenizer.to_str()    )
			self.assertNotIn("huggingface"							, json_tokenizer["model"]["vocab"]    )


@require_tokenizers
class _a   (       unittest.TestCase					):

	'''simple docstring'''



	def      __UpperCAmelCase(   self    ):
			__A							:    Tuple         =      PreTrainedTokenizerFast.from_pretrained("robot-test/dummy-tokenizer-wordlevel"    )
			__A							:    Union[str, Any]         =      'The Matrix is a 1999 science fiction action film.'

			with concurrent.futures.ThreadPoolExecutor() as executor:
					__A							:    Tuple         =      [executor.submit(self.fetch							, lowerCAmelCase__							, lowerCAmelCase__    ) for i in range(10    )]
					__A							:    Dict         =      [future.result() for future in futures]
					self.assertEqual(lowerCAmelCase__							, [[1, 10, 0, 8, 0, 18, 0, 0, 0, 2] for i in range(10    )]    )







	def      __UpperCAmelCase(   self							, __UpperCAmelCase							, __UpperCAmelCase    ):
			return tokenizer.encode(lowerCAmelCase__							, truncation="longest_first"							, padding="longest"    )









import warnings

from ...utils import logging
from .image_processing_vilt import ViltImageProcessor


lowerCAmelCase__       :	int     				=logging.get_logger(__name__)







class      __lowercase  (__SCREAMING_SNAKE_CASE   ):





							"""simple docstring"""







							def __init__(	self       ,		*lowerCAmelCase__       ,		**lowerCAmelCase__    ):





										"""simple docstring"""




										warnings.warn(
										    'The class ViltFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
										    ' use ViltImageProcessor instead.'       ,		lowerCAmelCase__       ,		)
										super().__init__(*lowerCAmelCase__       ,		**lowerCAmelCase__    )





