






"""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_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       __A		(   SCREAMING_SNAKE_CASE_			):
					UpperCAmelCase__								=				"mobilenet_v1"

					def __init__(			self  :      Tuple     ,		__snake_case  :      Dict=3     ,		__snake_case  :      str=2_2_4     ,		__snake_case  :      List[Any]=1.0     ,		__snake_case  :      Dict=8     ,		__snake_case  :      Optional[Any]="relu6"     ,		__snake_case  :      str=True     ,		__snake_case  :      str=0.999     ,		__snake_case  :      Optional[int]=0.02     ,		__snake_case  :      Optional[Any]=0.001     ,		**__snake_case  :      Dict     ,		)			->							Optional[Any]:
											super().__init__(**__snake_case					)

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

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



class       __A		(   SCREAMING_SNAKE_CASE_			):
					UpperCAmelCase__								=				version.parse("1.11"			)

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

					@property
					def   lowerCamelCase__   (			self  :      Optional[Any]					)			->							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   lowerCamelCase__   (			self  :      List[str]					)			->							float:
											return 1E-4











import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class 				_UpperCamelCase      (a_      ):
      snake_case_	=				["""image_processor""", """tokenizer"""]
      snake_case_	=				"""FlavaImageProcessor"""
      snake_case_	=				("""BertTokenizer""", """BertTokenizerFast""")


      def __init__(						self       ,    __UpperCamelCase=None       ,    __UpperCamelCase=None       ,    **__UpperCamelCase						)->						Tuple:
           __lowerCAmelCase             =       None
           if "feature_extractor" in kwargs:
                warnings.warn(
                    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                    " instead."       ,    __UpperCamelCase       ,    )
                __lowerCAmelCase             =       kwargs.pop("feature_extractor"						)

           __lowerCAmelCase             =       image_processor if image_processor is not None else feature_extractor
           if image_processor is None:
                raise ValueError("You need to specify an `image_processor`."						)
           if tokenizer is None:
                raise ValueError("You need to specify a `tokenizer`."						)

           super().__init__(__UpperCamelCase       ,    __UpperCamelCase						)
           __lowerCAmelCase             =       self.image_processor


      def __call__(						self       ,    __UpperCamelCase = None       ,    __UpperCamelCase = None       ,    __UpperCamelCase = True       ,    __UpperCamelCase = False       ,    __UpperCamelCase = False       ,    __UpperCamelCase = None       ,    __UpperCamelCase = 0       ,    __UpperCamelCase = None       ,    __UpperCamelCase = None       ,    __UpperCamelCase = None       ,    __UpperCamelCase = None       ,    __UpperCamelCase = None       ,    __UpperCamelCase = False       ,    __UpperCamelCase = False       ,    __UpperCamelCase = False       ,    __UpperCamelCase = False       ,    __UpperCamelCase = True       ,    __UpperCamelCase = None       ,    **__UpperCamelCase       ,    )->						int:

           if text is None and images is None:
                raise ValueError("You have to specify either text or images. Both cannot be none."						)

           if text is not None:
                __lowerCAmelCase             =       self.tokenizer(
                    text=__UpperCamelCase       ,    add_special_tokens=__UpperCamelCase       ,    padding=__UpperCamelCase       ,    truncation=__UpperCamelCase       ,    max_length=__UpperCamelCase       ,    stride=__UpperCamelCase       ,    pad_to_multiple_of=__UpperCamelCase       ,    return_token_type_ids=__UpperCamelCase       ,    return_attention_mask=__UpperCamelCase       ,    return_overflowing_tokens=__UpperCamelCase       ,    return_special_tokens_mask=__UpperCamelCase       ,    return_offsets_mapping=__UpperCamelCase       ,    return_length=__UpperCamelCase       ,    verbose=__UpperCamelCase       ,    return_tensors=__UpperCamelCase       ,    **__UpperCamelCase       ,    )
           if images is not None:
                __lowerCAmelCase             =       self.image_processor(
                    __UpperCamelCase       ,    return_image_mask=__UpperCamelCase       ,    return_codebook_pixels=__UpperCamelCase       ,    return_tensors=__UpperCamelCase       ,    **__UpperCamelCase       ,    )

           if text is not None and images is not None:
                encoding.update(__UpperCamelCase						)
                return encoding
           elif text is not None:
                return encoding
           else:
                return BatchEncoding(data=dict(**__UpperCamelCase						)       ,    tensor_type=__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self       ,    *__UpperCamelCase       ,    **__UpperCamelCase						)->						int:
           return self.tokenizer.batch_decode(*__UpperCamelCase       ,    **__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self       ,    *__UpperCamelCase       ,    **__UpperCamelCase						)->						Optional[int]:
           return self.tokenizer.decode(*__UpperCamelCase       ,    **__UpperCamelCase						)


      @property
      def 			__UpperCAmelCase		(						self						)->						Any:
           __lowerCAmelCase             =       self.tokenizer.model_input_names
           __lowerCAmelCase             =       self.image_processor.model_input_names
           return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)


      @property
      def 			__UpperCAmelCase		(						self						)->						Dict:
           warnings.warn(
               "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."       ,    __UpperCamelCase       ,    )
           return self.image_processor_class


      @property
      def 			__UpperCAmelCase		(						self						)->						Union[str, Any]:
           warnings.warn(
               "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."       ,    __UpperCamelCase       ,    )
           return self.image_processor






