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

lowerCamelCase__     :		List[Any]        = {
    """google/mobilenet_v1_1.0_224""": """https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v1_0.75_192""": """https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json""",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}



class    _snake_case (						UpperCAmelCase_				):
 __lowerCAmelCase	:  Optional[int]	   =      'mobilenet_v1'


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_=3       ,	SCREAMING_SNAKE_CASE_=2_24       ,	SCREAMING_SNAKE_CASE_=1.0       ,	SCREAMING_SNAKE_CASE_=8       ,	SCREAMING_SNAKE_CASE_="relu6"       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=0.9_9_9       ,	SCREAMING_SNAKE_CASE_=0.0_2       ,	SCREAMING_SNAKE_CASE_=0.0_0_1       ,	**SCREAMING_SNAKE_CASE_       ,	):


   '''simple docstring'''




   super().__init__(**SCREAMING_SNAKE_CASE_)

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

   lowercase__	: List[Any]     		=   num_channels
   lowercase__	: Optional[Any]     		=   image_size
   lowercase__	: Union[str, Any]     		=   depth_multiplier
   lowercase__	: Optional[Any]     		=   min_depth
   lowercase__	: Tuple     		=   hidden_act
   lowercase__	: Any     		=   tf_padding
   lowercase__	: int     		=   classifier_dropout_prob
   lowercase__	: Optional[int]     		=   initializer_range
   lowercase__	: Tuple     		=   layer_norm_eps



class    _snake_case (						UpperCAmelCase_				):
 __lowerCAmelCase	:  Tuple	   =      version.parse('1.11'				)


 @property
 def   lowercase__  (       self):


   '''simple docstring'''




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


 @property
 def   lowercase__  (       self):


   '''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   lowercase__  (       self):


   '''simple docstring'''




   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 TFXLMRobertaModel
@require_tf
@require_sentencepiece
@require_tokenizers
class 		SCREAMING_SNAKE_CASE_				(     unittest.TestCase ):
				"""simple docstring"""




				@slow
				def        __lowercase	(  self	:Union[str, Any]      ):
											__lowerCamelCase   :   List[Any]       =TFXLMRobertaModel.from_pretrained('''jplu/tf-xlm-roberta-base'''      )

											__lowerCamelCase   :   Dict       ={
											    '''input_ids''': tf.convert_to_tensor([[0, 2646, 1_0269, 83, 9_9942, 2]]    ,     dtype=tf.intaa      ),  # "My dog is cute"
											    '''attention_mask''': tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]]    ,     dtype=tf.intaa      ),
											}

											__lowerCamelCase   :   Optional[int]       =model(__lowercase      )['''last_hidden_state''']
											__lowerCamelCase   :   str       =tf.TensorShape((1, 6, 768)      )
											self.assertEqual(output.shape    ,     __lowercase      )
											# compare the actual values for a slice.
											__lowerCamelCase   :   str       =tf.convert_to_tensor(
											    [
											        [
											            [0.0681762, 0.10894451, 0.06772504],
											            [-0.06423668, 0.02366615, 0.04329344],
											            [-0.06057295, 0.09974135, -0.00070584],
											        ]
											    ]    ,     dtype=tf.floataa    ,     )

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




