






"""simple docstring"""
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


lowerCamelCase_       			=   logging.get_logger(__name__)

lowerCamelCase_       			=   {
    '''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     UpperCamelCase_ (__A  ):
	__magic_name__			   =       '''mobilenet_v2'''





	def __init__(   self      :				int			,			lowerCAmelCase_      :				Dict=3			,			lowerCAmelCase_      :				Any=224			,			lowerCAmelCase_      :				str=1.0			,			lowerCAmelCase_      :				List[Any]=8			,			lowerCAmelCase_      :				Optional[int]=8			,			lowerCAmelCase_      :				int=6			,			lowerCAmelCase_      :				Dict=32			,			lowerCAmelCase_      :				Dict=True			,			lowerCAmelCase_      :				List[Any]=True			,			lowerCAmelCase_      :				int="relu6"			,			lowerCAmelCase_      :				Union[str, Any]=True			,			lowerCAmelCase_      :				List[Any]=0.8			,			lowerCAmelCase_      :				List[str]=0.0_2			,			lowerCAmelCase_      :				int=0.0_0_1			,			lowerCAmelCase_      :				int=255			,			**lowerCAmelCase_      :				str			,			)		->    Optional[int]:
				super().__init__(**lowerCAmelCase_		)

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

				UpperCAmelCase_  :				Any      =      num_channels
				UpperCAmelCase_  :				List[Any]      =      image_size
				UpperCAmelCase_  :				List[Any]      =      depth_multiplier
				UpperCAmelCase_  :				Any      =      depth_divisible_by
				UpperCAmelCase_  :				int      =      min_depth
				UpperCAmelCase_  :				Union[str, Any]      =      expand_ratio
				UpperCAmelCase_  :				Optional[Any]      =      output_stride
				UpperCAmelCase_  :				Union[str, Any]      =      first_layer_is_expansion
				UpperCAmelCase_  :				Tuple      =      finegrained_output
				UpperCAmelCase_  :				str      =      hidden_act
				UpperCAmelCase_  :				Tuple      =      tf_padding
				UpperCAmelCase_  :				Any      =      classifier_dropout_prob
				UpperCAmelCase_  :				Tuple      =      initializer_range
				UpperCAmelCase_  :				Union[str, Any]      =      layer_norm_eps
				UpperCAmelCase_  :				Union[str, Any]      =      semantic_loss_ignore_index







class     UpperCamelCase_ (__A  ):
	__magic_name__			   =       version.parse('''1.11'''  )





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict		)		->    Mapping[str, Mapping[int, str]]:
				return OrderedDict([("pixel_values", {0: "batch"})]		)





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple		)		->    Mapping[str, Mapping[int, str]]:
				if self.task == "image-classification":
							return OrderedDict([("logits", {0: "batch"})]		)
				else:
							return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]		)





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]		)		->    float:
				return 1e-4











"""simple docstring"""
from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
			import numpy as np
			import tensorflow as tf

			from transformers import TFCamembertModel







@require_tf
@require_sentencepiece
@require_tokenizers
class     UpperCamelCase_ (unittest.TestCase  ):





	@slow
	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]		)		->    Optional[Any]:
				UpperCAmelCase_  :				List[Any]      =      TFCamembertModel.from_pretrained("jplu/tf-camembert-base"		)

				UpperCAmelCase_  :				Tuple      =      tf.convert_to_tensor(
				    [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]]			,			dtype=tf.intaa			,			)  # J'aime le camembert !"

				UpperCAmelCase_  :				Any      =      model(lowerCAmelCase_		)["last_hidden_state"]
				UpperCAmelCase_  :				str      =      tf.TensorShape((1, 10, 768)		)
				self.assertEqual(output.shape			,			lowerCAmelCase_		)
				# compare the actual values for a slice.
				UpperCAmelCase_  :				Tuple      =      tf.convert_to_tensor(
				    [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]			,			dtype=tf.floataa			,			)
				# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
				# camembert.eval()
				# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

				self.assertTrue(np.allclose(output[:, :3, :3].numpy()			,			expected_slice.numpy()			,			atol=1e-4		)		)



