

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 						lowercase     (					_lowercase     ,				unittest.TestCase				):


		"""simple docstring"""




		a__       							=	PreTrainedTokenizerFast
		a__       							=	False
		a__       							=	True
		a__       							=	'''tokenizer_file'''


		def 		A__       (					self):
			_UpperCamelCase :		Union[str, Any]								=    False  # because we don't have pretrained_vocab_files_map
			super().setUp()
			_UpperCamelCase :		Any								=    True

			_UpperCamelCase :		List[str]								=    ["robot-test/dummy-tokenizer-fast", "robot-test/dummy-tokenizer-wordlevel"]
			_UpperCamelCase :		List[str]								=    "SaulLu/dummy-tokenizer-bytelevel-bpe"

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

			_UpperCamelCase :		Union[str, Any]								=    PreTrainedTokenizerFast.from_pretrained(model_paths[0])
			tokenizer.save_pretrained(self.tmpdirname)


		def 		A__       (					self):
			pass


		def 		A__       (					self):
			pass


		def 		A__       (					self):
			pass


		def 		A__       (					self):
			pass


		def 		A__       (					self):
			_UpperCamelCase :		Optional[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:
						_UpperCamelCase :		List[str]								=    tempfile.mkdtemp()
						_UpperCamelCase :		Optional[Any]								=    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)
						_UpperCamelCase :		List[Any]								=    tmpdirname_orig


		def 		A__       (					self):
			_UpperCamelCase :		Tuple								=    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:
						_UpperCamelCase :		Optional[int]								=    tempfile.mkdtemp()
						_UpperCamelCase :		str								=    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)
						_UpperCamelCase :		int								=    tmpdirname_orig






		def 		A__       (					self):
			_UpperCamelCase :		str								=    self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name)

			_UpperCamelCase :		Dict								=    ("a" for _ in range(10_00))
			_UpperCamelCase :		Any								=    tokenizer.train_new_from_iterator(text_iterator=lowerCamelCase__						,							length=10_00						,							vocab_size=50)

			_UpperCamelCase :		Optional[Any]								=    new_tokenizer.encode('a🤗')
			self.assertEqual(lowerCamelCase__						,							[64, 1_72, 2_53, 97, 2_45])







@require_tokenizers
class 						lowercase     (					unittest.TestCase				):


		"""simple docstring"""




		def 		A__       (					self):
			_UpperCamelCase :		List[Any]								=    AutoTokenizer.from_pretrained('bert-base-cased')
			_UpperCamelCase :		Dict								=    json.loads(tokenizer._tokenizer.to_str())
			_UpperCamelCase :		Any								=    len(lowerCamelCase__)

			with tempfile.TemporaryDirectory() as tmp_dir:
				# Hack to save this in the tokenizer_config.json
				_UpperCamelCase :		List[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
				_UpperCamelCase :		Tuple								=    AutoTokenizer.from_pretrained(lowerCamelCase__)
				self.assertEqual(len(lowerCamelCase__)						,							len(lowerCamelCase__) + 1)
				_UpperCamelCase :		Union[str, Any]								=    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'))
				_UpperCamelCase :		Optional[int]								=    ["tokenizer.42.0.0.json"]
				tokenizer.save_pretrained(lowerCamelCase__)
				_UpperCamelCase :		List[str]								=    AutoTokenizer.from_pretrained(lowerCamelCase__)
				self.assertEqual(len(lowerCamelCase__)						,							len(lowerCamelCase__))
				_UpperCamelCase :		Any								=    json.loads(new_tokenizer._tokenizer.to_str())
				self.assertNotIn('huggingface'						,							json_tokenizer['model']['vocab'])






		def 		A__       (					self):
			_UpperCamelCase :		List[Any]								=    "hf-internal-testing/test-two-tokenizers"

			# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
			_UpperCamelCase :		Union[str, Any]								=    AutoTokenizer.from_pretrained(lowerCamelCase__)
			self.assertEqual(len(lowerCamelCase__)						,							2_89_97)
			_UpperCamelCase :		Optional[Any]								=    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

			_UpperCamelCase :		Any								=    "3.0.0"
			_UpperCamelCase :		Optional[Any]								=    old_transformers.models.auto.AutoTokenizer.from_pretrained(lowerCamelCase__)
			self.assertEqual(len(lowerCamelCase__)						,							2_89_96)
			_UpperCamelCase :		Any								=    json.loads(old_tokenizer._tokenizer.to_str())
			self.assertNotIn('huggingface'						,							json_tokenizer['model']['vocab'])







@require_tokenizers
class 						lowercase     (					unittest.TestCase				):


		"""simple docstring"""




		def 		A__       (					self):
			_UpperCamelCase :		str								=    PreTrainedTokenizerFast.from_pretrained('robot-test/dummy-tokenizer-wordlevel')
			_UpperCamelCase :		Any								=    "The Matrix is a 1999 science fiction action film."

			with concurrent.futures.ThreadPoolExecutor() as executor:
				_UpperCamelCase :		Dict								=    [executor.submit(self.fetch						,							lowerCamelCase__						,							lowerCamelCase__) for i in range(10)]
				_UpperCamelCase :		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 		A__       (					self						,							__snake_case						,							__snake_case):
			return tokenizer.encode(lowerCamelCase__						,							truncation='longest_first'						,							padding='longest')








"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


lowercase  		=			{
    """configuration_mobilevitv2""": [
        """MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MobileViTV2Config""",
        """MobileViTV2OnnxConfig""",
    ],
}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  		=			[
					    """MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """MobileViTV2ForImageClassification""",
					    """MobileViTV2ForSemanticSegmentation""",
					    """MobileViTV2Model""",
					    """MobileViTV2PreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_mobilevitva import (
					    MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    MobileViTVaConfig,
					    MobileViTVaOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mobilevitva import (
										    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MobileViTVaForImageClassification,
										    MobileViTVaForSemanticSegmentation,
										    MobileViTVaModel,
										    MobileViTVaPreTrainedModel,
										)

else:
					import sys

					lowercase  		=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





