
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_   ( _a				, unittest.TestCase						):
			a  : Optional[Any]				      =       PreTrainedTokenizerFast
			a  : str				      =       False
			a  : Optional[Any]				      =       True
			a  : Optional[Any]				      =       '''tokenizer_file'''






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				False  # because we don't have pretrained_vocab_files_map
									super().setUp()
									_lowercase									=				True

									_lowercase									=				["""robot-test/dummy-tokenizer-fast""", """robot-test/dummy-tokenizer-wordlevel"""]
									_lowercase									=				"""SaulLu/dummy-tokenizer-bytelevel-bpe"""

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

									_lowercase									=				PreTrainedTokenizerFast.from_pretrained(model_paths[0]     )
									tokenizer.save_pretrained(self.tmpdirname     )






			def       UpperCamelCase_							(			self     ):
									# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
									# model
									pass






			def       UpperCamelCase_							(			self     ):
									# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
									# model
									pass






			def       UpperCamelCase_							(			self     ):
									# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
									# model
									pass






			def       UpperCamelCase_							(			self     ):
									# PreTrainedTokenizerFast doesn't have tokenizer_file in its signature
									pass






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				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:
																											_lowercase									=				tempfile.mkdtemp()
																											_lowercase									=				self.rust_tokenizer_class.from_pretrained(__UpperCamelCase							, **__UpperCamelCase     )

																											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     )
																											_lowercase									=				tmpdirname_orig






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				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:
																											_lowercase									=				tempfile.mkdtemp()
																											_lowercase									=				self.rust_tokenizer_class.from_pretrained(__UpperCamelCase							, **__UpperCamelCase     )

																											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     )
																											_lowercase									=				tmpdirname_orig






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name     )

									_lowercase									=				("""a""" for _ in range(1_000     ))
									_lowercase									=				tokenizer.train_new_from_iterator(text_iterator=__UpperCamelCase							, length=1_000							, vocab_size=50     )

									_lowercase									=				new_tokenizer.encode("""a🤗"""     )
									self.assertEqual(__UpperCamelCase							, [64, 172, 253, 97, 245]     )







@require_tokenizers
class       a_   ( unittest.TestCase						):






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				AutoTokenizer.from_pretrained("""bert-base-cased"""     )
									_lowercase									=				json.loads(tokenizer._tokenizer.to_str()     )
									_lowercase									=				len(__UpperCamelCase     )

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

															# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
															_lowercase									=				AutoTokenizer.from_pretrained(__UpperCamelCase     )
															self.assertEqual(len(__UpperCamelCase     )							, len(__UpperCamelCase     ) + 1     )
															_lowercase									=				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(__UpperCamelCase							, """tokenizer.4.0.0.json"""     )							, os.path.join(__UpperCamelCase							, """tokenizer.42.0.0.json"""     )     )
															_lowercase									=				["""tokenizer.42.0.0.json"""]
															tokenizer.save_pretrained(__UpperCamelCase     )
															_lowercase									=				AutoTokenizer.from_pretrained(__UpperCamelCase     )
															self.assertEqual(len(__UpperCamelCase     )							, len(__UpperCamelCase     )     )
															_lowercase									=				json.loads(new_tokenizer._tokenizer.to_str()     )
															self.assertNotIn("""huggingface"""							, json_tokenizer["""model"""]["""vocab"""]     )






			def       UpperCamelCase_							(			self     ):
									# This repo has two tokenizer files, one for v4.0.0 and above with an added token, one for versions lower.
									_lowercase									=				"""hf-internal-testing/test-two-tokenizers"""

									# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
									_lowercase									=				AutoTokenizer.from_pretrained(__UpperCamelCase     )
									self.assertEqual(len(__UpperCamelCase     )							, 28_997     )
									_lowercase									=				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

									_lowercase									=				"""3.0.0"""
									_lowercase									=				old_transformers.models.auto.AutoTokenizer.from_pretrained(__UpperCamelCase     )
									self.assertEqual(len(__UpperCamelCase     )							, 28_996     )
									_lowercase									=				json.loads(old_tokenizer._tokenizer.to_str()     )
									self.assertNotIn("""huggingface"""							, json_tokenizer["""model"""]["""vocab"""]     )







@require_tokenizers
class       a_   ( unittest.TestCase						):






			def       UpperCamelCase_							(			self     ):
									# See https://github.com/huggingface/transformers/pull/12550
									# and https://github.com/huggingface/tokenizers/issues/537
									_lowercase									=				PreTrainedTokenizerFast.from_pretrained("""robot-test/dummy-tokenizer-wordlevel"""     )
									_lowercase									=				"""The Matrix is a 1999 science fiction action film."""

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






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

from typing import TYPE_CHECKING

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


A		:   int         =    {'''configuration_vilt''': ['''VILT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViltConfig''']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A		:   int         =    ['''ViltFeatureExtractor''']
		A		:   List[Any]         =    ['''ViltImageProcessor''']
		A		:   Dict         =    ['''ViltProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A		:   Tuple         =    [
		    '''VILT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''ViltForImageAndTextRetrieval''',
		    '''ViltForImagesAndTextClassification''',
		    '''ViltForTokenClassification''',
		    '''ViltForMaskedLM''',
		    '''ViltForQuestionAnswering''',
		    '''ViltLayer''',
		    '''ViltModel''',
		    '''ViltPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_vilt import VILT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViltConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_vilt import ViltFeatureExtractor
				from .image_processing_vilt import ViltImageProcessor
				from .processing_vilt import ViltProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_vilt import (
				    VILT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ViltForImageAndTextRetrieval,
				    ViltForImagesAndTextClassification,
				    ViltForMaskedLM,
				    ViltForQuestionAnswering,
				    ViltForTokenClassification,
				    ViltLayer,
				    ViltModel,
				    ViltPreTrainedModel,
				)


else:
		import sys

		A		:   Dict         =    _LazyModule(__name__, globals()['''__file__'''], _import_structure)