




import unittest

from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_rjieba
@require_tokenizers
class       a_       (     SCREAMING_SNAKE_CASE__  ,    unittest.TestCase     ):
	A    							= RoFormerTokenizer
	A    							= RoFormerTokenizerFast
	A    							= True
	A    							= True





	def 						A_(							self      )  ->					Dict:




				"""simple docstring"""



				super().setUp()





	def 						A_(							self      ,     **SCREAMING_SNAKE_CASE      )  ->					List[Any]:




				"""simple docstring"""



				return self.tokenizer_class.from_pretrained('junnyu/roformer_chinese_base'      ,     **SCREAMING_SNAKE_CASE      )





	def 						A_(							self      ,     **SCREAMING_SNAKE_CASE      )  ->					int:




				"""simple docstring"""



				return self.rust_tokenizer_class.from_pretrained('junnyu/roformer_chinese_base'      ,     **SCREAMING_SNAKE_CASE      )





	def 						A_(							self      )  ->					Optional[int]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						'永和服装饰品有限公司,今天天气非常好'
				SCREAMING_SNAKE_CASE_								=						'永和 服装 饰品 有限公司 , 今 天 天 气 非常 好'
				return input_text, output_text





	def 						A_(							self      )  ->					List[str]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.get_tokenizer()
				SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						self.get_chinese_input_output_texts()
				SCREAMING_SNAKE_CASE_								=						tokenizer.tokenize(SCREAMING_SNAKE_CASE      )

				self.assertListEqual(SCREAMING_SNAKE_CASE      ,     output_text.split()      )

				SCREAMING_SNAKE_CASE_								=						tokens + [tokenizer.unk_token]
				SCREAMING_SNAKE_CASE_								=						[2_2943, 2_1332, 3_4431, 4_5904, 117, 306, 1231, 1231, 2653, 3_3994, 1266, 100]
				self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE      )      ,     SCREAMING_SNAKE_CASE      )





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




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.get_rust_tokenizer()
				SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						self.get_chinese_input_output_texts()
				SCREAMING_SNAKE_CASE_								=						tokenizer.tokenize(SCREAMING_SNAKE_CASE      )
				self.assertListEqual(SCREAMING_SNAKE_CASE      ,     output_text.split()      )
				SCREAMING_SNAKE_CASE_								=						tokens + [tokenizer.unk_token]
				SCREAMING_SNAKE_CASE_								=						[2_2943, 2_1332, 3_4431, 4_5904, 117, 306, 1231, 1231, 2653, 3_3994, 1266, 100]
				self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE      )      ,     SCREAMING_SNAKE_CASE      )





	def 						A_(							self      )  ->					Optional[int]:




				"""simple docstring"""



				pass





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




				"""simple docstring"""



				pass





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




				"""simple docstring"""



				pass






from collections import OrderedDict
from typing import Mapping

from packaging import version

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


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

SCREAMING_SNAKE_CASE__     :     str		    =       {
    "google/mobilenet_v2_1.4_224": "https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json",
    "google/mobilenet_v2_1.0_224": "https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json",
    "google/mobilenet_v2_0.75_160": "https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json",
    "google/mobilenet_v2_0.35_96": "https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json",
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}



class       a_       (     SCREAMING_SNAKE_CASE__     ):
	A    							= '''mobilenet_v2'''





	def __init__(							self      ,     SCREAMING_SNAKE_CASE=3      ,     SCREAMING_SNAKE_CASE=224      ,     SCREAMING_SNAKE_CASE=1.0      ,     SCREAMING_SNAKE_CASE=8      ,     SCREAMING_SNAKE_CASE=8      ,     SCREAMING_SNAKE_CASE=6      ,     SCREAMING_SNAKE_CASE=32      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE="relu6"      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE=0.8      ,     SCREAMING_SNAKE_CASE=0.0_2      ,     SCREAMING_SNAKE_CASE=0.0_0_1      ,     SCREAMING_SNAKE_CASE=255      ,     **SCREAMING_SNAKE_CASE      ,     )  ->					Dict:




				"""simple docstring"""



				super().__init__(**SCREAMING_SNAKE_CASE      )

				if depth_multiplier <= 0:
							raise ValueError('depth_multiplier must be greater than zero.'      )

				SCREAMING_SNAKE_CASE_								=						num_channels
				SCREAMING_SNAKE_CASE_								=						image_size
				SCREAMING_SNAKE_CASE_								=						depth_multiplier
				SCREAMING_SNAKE_CASE_								=						depth_divisible_by
				SCREAMING_SNAKE_CASE_								=						min_depth
				SCREAMING_SNAKE_CASE_								=						expand_ratio
				SCREAMING_SNAKE_CASE_								=						output_stride
				SCREAMING_SNAKE_CASE_								=						first_layer_is_expansion
				SCREAMING_SNAKE_CASE_								=						finegrained_output
				SCREAMING_SNAKE_CASE_								=						hidden_act
				SCREAMING_SNAKE_CASE_								=						tf_padding
				SCREAMING_SNAKE_CASE_								=						classifier_dropout_prob
				SCREAMING_SNAKE_CASE_								=						initializer_range
				SCREAMING_SNAKE_CASE_								=						layer_norm_eps
				SCREAMING_SNAKE_CASE_								=						semantic_loss_ignore_index




class       a_       (     SCREAMING_SNAKE_CASE__     ):
	A    							= version.parse('''1.11'''     )





	@property
	def 						A_(							self      )  ->					Mapping[str, Mapping[int, str]]:




				"""simple docstring"""



				return OrderedDict([('pixel_values', {0: 'batch'})]      )





	@property
	def 						A_(							self      )  ->					Mapping[str, Mapping[int, str]]:




				"""simple docstring"""



				if self.task == "image-classification":
							return OrderedDict([('logits', {0: 'batch'})]      )
				else:
							return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]      )





	@property
	def 						A_(							self      )  ->					float:




				"""simple docstring"""



				return 1e-4
