

import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCAmelCase					:			Optional[Any]  				=			get_tests_dir("fixtures/test_sentencepiece.model")
_lowerCAmelCase					:			Optional[int]  				=			get_tests_dir("fixtures/test_sentencepiece_bpe.model")

_lowerCAmelCase					:			int  				=			"pt" if is_torch_available() else "tf"






@require_sentencepiece
@require_tokenizers
class 	__magic_name__    (			lowerCAmelCase_  ,    unittest.TestCase      ):
			SCREAMING_SNAKE_CASE							   =						CamembertTokenizer
			SCREAMING_SNAKE_CASE							   =						CamembertTokenizerFast
			SCREAMING_SNAKE_CASE							   =						True
			SCREAMING_SNAKE_CASE							   =						True





			def 		__magic_name__    (		self					)   -> Optional[Any]:




						'''simple docstring'''
						super().setUp()

						# We have a SentencePiece fixture for testing
						__a        =CamembertTokenizer(__snake_case					)
						tokenizer.save_pretrained(self.tmpdirname					)





			def 		__magic_name__    (		self					)   -> Union[str, Any]:




						'''simple docstring'''
						__a        ='<pad>'
						__a        =1

						self.assertEqual(self.get_tokenizer()._convert_token_to_id(__snake_case					)					,  __snake_case					)
						self.assertEqual(self.get_tokenizer()._convert_id_to_token(__snake_case					)					,  __snake_case					)





			def 		__magic_name__    (		self					)   -> Union[str, Any]:




						'''simple docstring'''
						__a        =list(self.get_tokenizer().get_vocab().keys()					)

						self.assertEqual(vocab_keys[0]					,  '<s>NOTUSED'					)
						self.assertEqual(vocab_keys[1]					,  '<pad>'					)
						self.assertEqual(vocab_keys[-1]					,  '<mask>'					)
						self.assertEqual(len(__snake_case					)					,  1004					)





			def 		__magic_name__    (		self					)   -> Any:




						'''simple docstring'''
						self.assertEqual(self.get_tokenizer().vocab_size					,  1005					)





			def 		__magic_name__    (		self					)   -> Dict:




						'''simple docstring'''
						__a        =CamembertTokenizer(__snake_case					)
						tokenizer.save_pretrained(self.tmpdirname					)
						__a        =CamembertTokenizerFast.from_pretrained(self.tmpdirname					)

						__a        ='I was born in 92000, and this is falsé.'

						__a        =tokenizer.encode(__snake_case					)
						__a        =rust_tokenizer.encode(__snake_case					)
						self.assertListEqual(__snake_case					,  __snake_case					)

						__a        =tokenizer.encode(__snake_case					,  add_special_tokens=__snake_case					)
						__a        =rust_tokenizer.encode(__snake_case					,  add_special_tokens=__snake_case					)
						self.assertListEqual(__snake_case					,  __snake_case					)

						# <unk> tokens are not the same for `rust` than for `slow`.
						# Because spm gives back raw token instead of `unk` in EncodeAsPieces
						# tokens = tokenizer.tokenize(sequence)
						__a        =tokenizer.convert_ids_to_tokens(__snake_case					)
						__a        =rust_tokenizer.tokenize(__snake_case					)
						self.assertListEqual(__snake_case					,  __snake_case					)





			def 		__magic_name__    (		self					)   -> Any:




						'''simple docstring'''
						if not self.test_rust_tokenizer:
									return

						__a        =self.get_tokenizer()
						__a        =self.get_rust_tokenizer()

						__a        ='I was born in 92000, and this is falsé.'

						__a        =tokenizer.tokenize(__snake_case					)
						__a        =rust_tokenizer.tokenize(__snake_case					)
						self.assertListEqual(__snake_case					,  __snake_case					)

						__a        =tokenizer.encode(__snake_case					,  add_special_tokens=__snake_case					)
						__a        =rust_tokenizer.encode(__snake_case					,  add_special_tokens=__snake_case					)
						self.assertListEqual(__snake_case					,  __snake_case					)

						__a        =self.get_rust_tokenizer()
						__a        =tokenizer.encode(__snake_case					)
						__a        =rust_tokenizer.encode(__snake_case					)
						self.assertListEqual(__snake_case					,  __snake_case					)





			@slow
			def 		__magic_name__    (		self					)   -> str:




						'''simple docstring'''
						# fmt: off
						__a        ={'input_ids': [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 2_7575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 2_2804, 1_8818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 1_0326, 24, 2267, 20, 416, 5072, 1_5612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 9, 6]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
						# fmt: on

						# camembert is a french model. So we also use french texts.
						__a        =[
						    'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
						    'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
						    'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
						    'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
						    'telles que la traduction et la synthèse de texte.',
						]

						self.tokenizer_integration_test_util(
						    expected_encoding=__snake_case					,  model_name='camembert-base'					,  revision='3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'					,  sequences=__snake_case					,  )









import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCAmelCase					:			Optional[int]  				=			logging.get_logger(__name__)

_lowerCAmelCase					:			List[str]  				=			{
    "microsoft/git-base": "https://huggingface.co/microsoft/git-base/resolve/main/config.json",
}






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						'git_vision_model'





			def __init__(		self					,  __snake_case=768					,  __snake_case=3072					,  __snake_case=12					,  __snake_case=12					,  __snake_case=3					,  __snake_case=224					,  __snake_case=16					,  __snake_case="quick_gelu"					,  __snake_case=1e-5					,  __snake_case=0.0					,  __snake_case=0.02					,  **__snake_case					,  )   -> int:




						'''simple docstring'''
						super().__init__(**__snake_case					)

						__a        =hidden_size
						__a        =intermediate_size
						__a        =num_hidden_layers
						__a        =num_attention_heads
						__a        =num_channels
						__a        =patch_size
						__a        =image_size
						__a        =initializer_range
						__a        =attention_dropout
						__a        =layer_norm_eps
						__a        =hidden_act





			@classmethod
			def 		__magic_name__    (		cls					,  __snake_case					,  **__snake_case					)   -> "PretrainedConfig":




						'''simple docstring'''
						cls._set_token_in_kwargs(__snake_case					)

						__a      ,    __a        =cls.get_config_dict(__snake_case					,  **__snake_case					)

						# get the vision config dict if we are loading from GITConfig
						if config_dict.get('model_type'					) == "git":
									__a        =config_dict['vision_config']

						if "model_type" in config_dict and hasattr(cls					,  'model_type'					) and config_dict["model_type"] != cls.model_type:
									logger.warning(
									    f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
									    f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'					)

						return cls.from_dict(__snake_case					,  **__snake_case					)






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						'git'





			def __init__(		self					,  __snake_case=None					,  __snake_case=3_0522					,  __snake_case=768					,  __snake_case=6					,  __snake_case=12					,  __snake_case=3072					,  __snake_case="gelu"					,  __snake_case=0.1					,  __snake_case=0.1					,  __snake_case=1024					,  __snake_case=0.02					,  __snake_case=1e-12					,  __snake_case=0					,  __snake_case="absolute"					,  __snake_case=True					,  __snake_case=False					,  __snake_case=101					,  __snake_case=102					,  __snake_case=None					,  **__snake_case					,  )   -> Optional[int]:




						'''simple docstring'''
						super().__init__(bos_token_id=__snake_case					,  eos_token_id=__snake_case					,  pad_token_id=__snake_case					,  **__snake_case					)

						if vision_config is None:
									__a        ={}
									logger.info('vision_config is None. initializing the GitVisionConfig with default values.'					)

						__a        =GitVisionConfig(**__snake_case					)
						__a        =vocab_size
						__a        =hidden_size
						__a        =num_hidden_layers
						__a        =num_attention_heads
						__a        =hidden_act
						__a        =intermediate_size
						__a        =hidden_dropout_prob
						__a        =attention_probs_dropout_prob
						__a        =max_position_embeddings
						__a        =initializer_range
						__a        =layer_norm_eps
						__a        =position_embedding_type
						__a        =use_cache
						__a        =tie_word_embeddings
						__a        =num_image_with_embedding

						__a        =bos_token_id
						__a        =eos_token_id





			def 		__magic_name__    (		self					)   -> Optional[Any]:




						'''simple docstring'''
						__a        =copy.deepcopy(self.__dict__					)
						__a        =self.vision_config.to_dict()
						__a        =self.__class__.model_type
						return output






