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


SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		get_tests_dir("""fixtures/test_sentencepiece.model""")
SCREAMING_SNAKE_CASE			:						List[Any]				  =		get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")

SCREAMING_SNAKE_CASE			:						Any				  =		"""pt""" if is_torch_available() else """tf"""
@require_sentencepiece
@require_tokenizers
class  A_			(							a_      ,							unittest.TestCase			):
  _SCREAMING_SNAKE_CASE     					=      CamembertTokenizer
  _SCREAMING_SNAKE_CASE     					=      CamembertTokenizerFast
  _SCREAMING_SNAKE_CASE     					=      True
  _SCREAMING_SNAKE_CASE     					=      True

  def 		_UpperCAmelCase (		self			:   Optional[int]							):
   super().setUp()

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

  def 		_UpperCAmelCase (		self			:   Any							):
   __a							=	"<pad>"
   __a							=	1

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

  def 		_UpperCAmelCase (		self			:   Any							):
   __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(__SCREAMING_SNAKE_CASE							)  ,				10_04							)

  def 		_UpperCAmelCase (		self			:   Optional[int]							):
   self.assertEqual(self.get_tokenizer().vocab_size  ,				10_05							)

  def 		_UpperCAmelCase (		self			:   int							):
   __a							=	CamembertTokenizer(__SCREAMING_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(__SCREAMING_SNAKE_CASE							)
   __a							=	rust_tokenizer.encode(__SCREAMING_SNAKE_CASE							)
   self.assertListEqual(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

   __a							=	tokenizer.encode(__SCREAMING_SNAKE_CASE  ,				add_special_tokens=__SCREAMING_SNAKE_CASE							)
   __a							=	rust_tokenizer.encode(__SCREAMING_SNAKE_CASE  ,				add_special_tokens=__SCREAMING_SNAKE_CASE							)
   self.assertListEqual(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_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(__SCREAMING_SNAKE_CASE							)
   __a							=	rust_tokenizer.tokenize(__SCREAMING_SNAKE_CASE							)
   self.assertListEqual(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   List[Any]							):
   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(__SCREAMING_SNAKE_CASE							)
   __a							=	rust_tokenizer.tokenize(__SCREAMING_SNAKE_CASE							)
   self.assertListEqual(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

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

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

  @slow
  def 		_UpperCAmelCase (		self			:   str							):
   # fmt: off
   __a							=	{"input_ids": [[5, 54, 71_96, 2_97, 30, 23, 7_76, 18, 11, 32_15, 37_05, 82_52, 22, 31_64, 11_81, 21_16, 29, 16, 8_13, 25, 7_91, 33_14, 20, 34_46, 38, 2_75_75, 1_20, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_68, 17, 11, 90_88, 20, 15_17, 8, 2_28_04, 1_88_18, 10, 38, 6_29, 6_07, 6_07, 1_42, 19, 71_96, 8_67, 56, 1_03_26, 24, 22_67, 20, 4_16, 50_72, 1_56_12, 2_33, 7_34, 7, 23_99, 27, 16, 30_15, 16_49, 7, 24, 20, 43_38, 23_99, 27, 13, 34_00, 14, 13, 61_89, 8, 9_30, 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=__SCREAMING_SNAKE_CASE  ,				model_name="camembert-base"  ,				revision="3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"  ,				sequences=__SCREAMING_SNAKE_CASE  ,				)



from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


SCREAMING_SNAKE_CASE			:						Optional[Any]				  =		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			:						List[Any]				  =		{
    """shi-labs/nat-mini-in1k-224""": """https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json""",
    # See all Nat models at https://huggingface.co/models?filter=nat
}
class  A_			(							a_      ,							a_			):
  _SCREAMING_SNAKE_CASE     					=      """nat"""

  _SCREAMING_SNAKE_CASE     					=      {
      """num_attention_heads""": """num_heads""",
      """num_hidden_layers""": """num_layers""",
  }

  def __init__(		self			:   str  ,				__SCREAMING_SNAKE_CASE			:   Tuple=4  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=3  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=64  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=[3, 4, 6, 5]  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=[2, 4, 8, 16]  ,				__SCREAMING_SNAKE_CASE			:   int=7  ,				__SCREAMING_SNAKE_CASE			:   List[str]=3.0  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=True  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=0.0  ,				__SCREAMING_SNAKE_CASE			:   str=0.0  ,				__SCREAMING_SNAKE_CASE			:   int=0.1  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]="gelu"  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=0.02  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=1E-5  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=0.0  ,				__SCREAMING_SNAKE_CASE			:   str=None  ,				__SCREAMING_SNAKE_CASE			:   int=None  ,				**__SCREAMING_SNAKE_CASE			:   List[str]  ,				):
   super().__init__(**__SCREAMING_SNAKE_CASE							)

   __a							=	patch_size
   __a							=	num_channels
   __a							=	embed_dim
   __a							=	depths
   __a							=	len(__SCREAMING_SNAKE_CASE							)
   __a							=	num_heads
   __a							=	kernel_size
   __a							=	mlp_ratio
   __a							=	qkv_bias
   __a							=	hidden_dropout_prob
   __a							=	attention_probs_dropout_prob
   __a							=	drop_path_rate
   __a							=	hidden_act
   __a							=	layer_norm_eps
   __a							=	initializer_range
   # we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
   # this indicates the channel dimension after the last stage of the model
   __a							=	int(embed_dim * 2 ** (len(__SCREAMING_SNAKE_CASE							) - 1)							)
   __a							=	layer_scale_init_value
   __a							=	["stem"] + [f"""stage{idx}""" for idx in range(1  ,				len(__SCREAMING_SNAKE_CASE							) + 1							)]
   __a							,  __a							=	get_aligned_output_features_output_indices(
       out_features=__SCREAMING_SNAKE_CASE  ,				out_indices=__SCREAMING_SNAKE_CASE  ,				stage_names=self.stage_names							)


