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


lowercase__     :							List[Any]            =      get_tests_dir('''fixtures/test_sentencepiece.model''')
lowercase__     :							Dict            =      get_tests_dir('''fixtures/test_sentencepiece_bpe.model''')

lowercase__     :							Optional[int]            =      """pt""" if is_torch_available() else """tf"""




@require_sentencepiece
@require_tokenizers
class   lowercase_    (    UpperCAmelCase__ ,	unittest.TestCase		):



	"""simple docstring"""





	UpperCAmelCase_    :   Optional[int]  =							CamembertTokenizer
	UpperCAmelCase_    :   Union[str, Any]  =							CamembertTokenizerFast
	UpperCAmelCase_    :   Any  =							True
	UpperCAmelCase_    :   Dict  =							True


	def      SCREAMING_SNAKE_CASE_							(				self							)		->Tuple:
								super().setUp()

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


	def      SCREAMING_SNAKE_CASE_							(				self							)		->List[Any]:
								lowerCAmelCase      						=						"<pad>"
								lowerCAmelCase      						=						1

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


	def      SCREAMING_SNAKE_CASE_							(				self							)		->int:
								lowerCAmelCase      						=						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(lowerCamelCase__							)						,     1004							)


	def      SCREAMING_SNAKE_CASE_							(				self							)		->str:
								self.assertEqual(self.get_tokenizer().vocab_size						,     1005							)


	def      SCREAMING_SNAKE_CASE_							(				self							)		->int:
								lowerCAmelCase      						=						CamembertTokenizer(lowerCamelCase__							)
								tokenizer.save_pretrained(self.tmpdirname							)
								lowerCAmelCase      						=						CamembertTokenizerFast.from_pretrained(self.tmpdirname							)

								lowerCAmelCase      						=						"I was born in 92000, and this is falsé."

								lowerCAmelCase      						=						tokenizer.encode(lowerCamelCase__							)
								lowerCAmelCase      						=						rust_tokenizer.encode(lowerCamelCase__							)
								self.assertListEqual(lowerCamelCase__						,     lowerCamelCase__							)

								lowerCAmelCase      						=						tokenizer.encode(lowerCamelCase__						,     add_special_tokens=lowerCamelCase__							)
								lowerCAmelCase      						=						rust_tokenizer.encode(lowerCamelCase__						,     add_special_tokens=lowerCamelCase__							)
								self.assertListEqual(lowerCamelCase__						,     lowerCamelCase__							)

								# <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)
								lowerCAmelCase      						=						tokenizer.convert_ids_to_tokens(lowerCamelCase__							)
								lowerCAmelCase      						=						rust_tokenizer.tokenize(lowerCamelCase__							)
								self.assertListEqual(lowerCamelCase__						,     lowerCamelCase__							)


	def      SCREAMING_SNAKE_CASE_							(				self							)		->List[Any]:
								if not self.test_rust_tokenizer:
															return

								lowerCAmelCase      						=						self.get_tokenizer()
								lowerCAmelCase      						=						self.get_rust_tokenizer()

								lowerCAmelCase      						=						"I was born in 92000, and this is falsé."

								lowerCAmelCase      						=						tokenizer.tokenize(lowerCamelCase__							)
								lowerCAmelCase      						=						rust_tokenizer.tokenize(lowerCamelCase__							)
								self.assertListEqual(lowerCamelCase__						,     lowerCamelCase__							)

								lowerCAmelCase      						=						tokenizer.encode(lowerCamelCase__						,     add_special_tokens=lowerCamelCase__							)
								lowerCAmelCase      						=						rust_tokenizer.encode(lowerCamelCase__						,     add_special_tokens=lowerCamelCase__							)
								self.assertListEqual(lowerCamelCase__						,     lowerCamelCase__							)

								lowerCAmelCase      						=						self.get_rust_tokenizer()
								lowerCAmelCase      						=						tokenizer.encode(lowerCamelCase__							)
								lowerCAmelCase      						=						rust_tokenizer.encode(lowerCamelCase__							)
								self.assertListEqual(lowerCamelCase__						,     lowerCamelCase__							)




	@slow
	def      SCREAMING_SNAKE_CASE_							(				self							)		->Optional[int]:
								# fmt: off
								lowerCAmelCase      						=						{"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, 27575, 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, 22804, 18818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 10326, 24, 2267, 20, 416, 5072, 15612, 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.
								lowerCAmelCase      						=						[
								    "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=lowerCamelCase__						,     model_name='''camembert-base'''						,     revision='''3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'''						,     sequences=lowerCamelCase__						,     )




from collections import OrderedDict
from typing import List, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


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

a  :      Optional[Any]            =    {
    """google/efficientnet-b7""": """https://huggingface.co/google/efficientnet-b7/resolve/main/config.json""",
}

class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				Dict          =    'efficientnet'




    def __init__(    self       ,						lowerCamelCase__ = 3       ,						lowerCamelCase__ = 600       ,						lowerCamelCase__ = 2.0       ,						lowerCamelCase__ = 3.1       ,						lowerCamelCase__ = 8       ,						lowerCamelCase__ = [3, 3, 5, 3, 5, 5, 3]       ,						lowerCamelCase__ = [32, 16, 24, 40, 80, 112, 192]       ,						lowerCamelCase__ = [16, 24, 40, 80, 112, 192, 320]       ,						lowerCamelCase__ = []       ,						lowerCamelCase__ = [1, 2, 2, 2, 1, 2, 1]       ,						lowerCamelCase__ = [1, 2, 2, 3, 3, 4, 1]       ,						lowerCamelCase__ = [1, 6, 6, 6, 6, 6, 6]       ,						lowerCamelCase__ = 0.2_5       ,						lowerCamelCase__ = "swish"       ,						lowerCamelCase__ = 2_560       ,						lowerCamelCase__ = "mean"       ,						lowerCamelCase__ = 0.0_2       ,						lowerCamelCase__ = 0.0_0_1       ,						lowerCamelCase__ = 0.9_9       ,						lowerCamelCase__ = 0.5       ,						lowerCamelCase__ = 0.2       ,						**lowerCamelCase__       ,						):
       super().__init__(**lowerCamelCase__ )

       lowerCAmelCase_:							str       =	num_channels
       lowerCAmelCase_:							str       =	image_size
       lowerCAmelCase_:							int       =	width_coefficient
       lowerCAmelCase_:							Union[str, Any]       =	depth_coefficient
       lowerCAmelCase_:							int       =	depth_divisor
       lowerCAmelCase_:							List[str]       =	kernel_sizes
       lowerCAmelCase_:							Tuple       =	in_channels
       lowerCAmelCase_:							List[str]       =	out_channels
       lowerCAmelCase_:							List[str]       =	depthwise_padding
       lowerCAmelCase_:							Optional[int]       =	strides
       lowerCAmelCase_:							List[str]       =	num_block_repeats
       lowerCAmelCase_:							Any       =	expand_ratios
       lowerCAmelCase_:							List[Any]       =	squeeze_expansion_ratio
       lowerCAmelCase_:							Optional[int]       =	hidden_act
       lowerCAmelCase_:							Optional[int]       =	hidden_dim
       lowerCAmelCase_:							Dict       =	pooling_type
       lowerCAmelCase_:							Optional[Any]       =	initializer_range
       lowerCAmelCase_:							int       =	batch_norm_eps
       lowerCAmelCase_:							List[str]       =	batch_norm_momentum
       lowerCAmelCase_:							List[Any]       =	dropout_rate
       lowerCAmelCase_:							Union[str, Any]       =	drop_connect_rate
       lowerCAmelCase_:							Tuple       =	sum(lowerCamelCase__ ) * 4





class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				Optional[Any]          =    version.parse('1.11'			)




    @property
    def      _a    (    self ):
       return OrderedDict(
           [
               ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
           ] )




    @property
    def      _a    (    self ):
       return 1E-5